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Jaber F, El-Serag HB. HES V2.0 surpasses GALAD for HCC detection: a review of multi-dimensional biomarker scores and studies. Hepat Oncol 2025; 12:2494446. [PMID: 40308043 PMCID: PMC12051611 DOI: 10.1080/20450923.2025.2494446] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2024] [Accepted: 04/14/2025] [Indexed: 05/02/2025] Open
Abstract
This was a narrative review of select studies published through September of 2024. We review the shift toward multi-dimensional scores such as HCC early detection screening (HES), GALAD, ASAP, and mt-HBT represents a significant advancement in biomarker research for hepatocellular carcinoma (HCC) detection. Unlike single biomarker approaches, these scores integrate various clinical and biochemical factors to enhance predictive accuracy by reflecting different complementary aspects of disease progression and HCC oncogenesis. Proper testing and validation of biomarker scores in phase 3 biomarker studies is essential before wide use can be recommended. We also review the comparative performance of biomarker scores in phase 3 studies. The new version of HES (HES V2.0) which includes AFP, AFP L3, DCP, and changes in their levels the past one year, if available, in addition to age, platelets, albumin, ALT and underlying liver disease etiology outperforms GALAD in detecting early-stage HCC with overall 6.7% higher sensitivity, and ASAP with 13.4%-18.0% higher sensitivity, both at fixed 90% specificity. HES V2.0 is a leading candidate biomarker score for prospective testing in clinical studies of early HCC detection.
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Affiliation(s)
- Fouad Jaber
- Department of Internal Medicine, Section of Gastroenterology and Hepatology, Baylor College of Medicine, Houston, Texas, USA
| | - Hashem B. El-Serag
- Department of Internal Medicine, Section of Gastroenterology and Hepatology, Baylor College of Medicine, Houston, Texas, USA
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2
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Han X, Zhang B. Comparison of the value of transvaginal ultrasonography and MRI in the diagnosis of cesarean scar pregnancy: a meta-analysis. J Matern Fetal Neonatal Med 2025; 38:2445661. [PMID: 39762030 DOI: 10.1080/14767058.2024.2445661] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2024] [Revised: 12/13/2024] [Accepted: 12/16/2024] [Indexed: 01/11/2025]
Abstract
OBJECTIVE To compare the diagnostic value of transvaginal ultrasound (TVS) and magnetic resonance imaging (MRI) in cesarean scar pregnancy (CSP) by a method of meta-analysis. METHODS Studies on TVS and MRI for CSP were collected from PubMed, Cochrane Library, Embase, Web of Science, China National Knowledge Infrastructure (CNKI), Wanfang data, and Chinese Scientific Journal Database (VIP database) until April 1, 2024. Stata 15.0 software was used for data analysis. Mann-Whitney U-test was applied to compare the diagnostic efficiency of the TVS and MRI groups. RESULTS Nine articles with 713 subjects were involved in this review. The pooled sensitivity (0.96, 95%CI: 0.94-0.97), specificity (0.90, 95%CI: 0.84-0.94), and DOR (197.28, 95%CI: 99.71-390.31) in the MRI group were higher than those (Sensitivity = 0.83, 95%CI: 0.77-0.87; Specificity= 0.74, 95%CI: 0.63-0.83; DOR = 13.66, 95%CI: 7.84-23.79) in the TVS group. The positive likelihood ratio and negative likelihood ratio of the MRI group were 9.56 (95%CI: 8.82-15.72) and 0.05 (95%CI: 0.03-0.07), while those of the TVS group were 3.21 (95%CI:2.18-4.74) and 0.24 (95%CI: 0.18-0.31), respectively. In the MRI and TVS groups, the area under the curve (AUC) of the summary receiver operating characteristic was 0.9497 and 0.86, respectively. The results of Mann-Whitney U-tests of the two groups showed significant differences in the pooled sensitivity (Z= -3.311, p < 0.001), specificity (Z= -2.123, p = 0.034), and DOR (Z= -3.272, p = 0.001). CONCLUSION Both MRI and TVS can effectively diagnose CSP. However, compared with TVS, MRI has better diagnostic accuracy for CSP, with higher sensitivity and specificity. Considering the good diagnostic accuracy of ultrasound, patients with ultrasound suspicion of CSP should be sent to a reference center where MRI can express its full diagnostic potential regarding depth, topography of invasion and myometral residue, which is useful for subsequent management.
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Affiliation(s)
- Xiao Han
- Department of Ultrasound, the First Affiliated Hospital of Hainan Medical College, Haikou, Hainan, China
| | - Boyang Zhang
- Department of Ultrasound, the First Affiliated Hospital of Hainan Medical College, Haikou, Hainan, China
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Earl J. A plasma-based multimetabolite biomarker for early detection of pancreatic cancer. Lancet Gastroenterol Hepatol 2025:S2468-1253(25)00089-5. [PMID: 40388949 DOI: 10.1016/s2468-1253(25)00089-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/06/2025] [Accepted: 03/06/2025] [Indexed: 05/21/2025]
Affiliation(s)
- Julie Earl
- Biomarkers and Personalized Approach to Cancer (BIOPAC) Group, Ramón y Cajal Health Research Institute (IRYCIS), 28034 Madrid, Spain.
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Liou WL, Tan SY, Yamada H, Krishnamoorthy T, Chang JPE, Yeo CP, Tan CK. Performance of the GALAD Model in an Asian Cohort Undergoing Hepatocellular Carcinoma Surveillance: A Prospective Cohort Study. J Gastroenterol Hepatol 2025. [PMID: 40346978 DOI: 10.1111/jgh.16997] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/18/2025] [Revised: 04/05/2025] [Accepted: 04/28/2025] [Indexed: 05/12/2025]
Abstract
BACKGROUND AND AIM Current hepatocellular carcinoma (HCC) surveillance strategy has its limitations, consequently delaying early detection. The GALAD model has been validated in retrospective studies, with two published cut-off values yielding different sensitivities for HCCs of different etiologies. We evaluated the performance of GALAD model in HCC surveillance and determined the ideal cut-off value for our cohort. METHODS Patients undergoing 6-monthly HCC surveillance in Singapore General Hospital were recruited between December 2017-October 2018. Study serum specimens were prospectively collected and retrospectively tested using the μTASWako alpha-fetoprotein (AFP), AFP-L3, and protein induced by vitamin K antagonism-II (PIVKA-II) kits. GALAD score was calculated and compared with individual biomarkers using area under the curve (AUC) analysis. Published GALAD cut-offs of -0.63 and -1.95 were compared for their performance in HCC detection. RESULTS There were 207 patients (median age 59 years, 55.1% males). Hepatitis B was the commonest etiology (72.9%). By February 2023, with a median follow-up of 48.9 months, 20 patients had developed HCC. Eight patients developed HCC within 1 year from specimen collection. For HCC developing within 1 year, GALAD model detected HCC with an AUC of 0.84, greater than AFP (AUC 0.77), AFP-L3 (AUC 0.60), and PIVKA-II (AUC 0.67). GALAD at cut-off -1.95 achieved sensitivity and specificity of 75% and 92.5% for HCCs detected within 1 year, superior to cut-off -0.63 (sensitivity 12.5%, specificity 100%). CONCLUSION In this prospective study of HCC surveillance, the GALAD model performed better than individual biomarkers. The cut-off of -1.95 was more useful in our predominantly chronic hepatitis B cohort.
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Affiliation(s)
- Wei-Lun Liou
- Department of Gastroenterology and Hepatology, Singapore General Hospital, Singapore
| | - Si-Yu Tan
- Department of Clinical Pathology, Singapore General Hospital, Singapore
| | - Hiroyuki Yamada
- Medical Systems Business Division, FUJIFILM Corporation, Osaka, Japan
| | | | - Jason Pik-Eu Chang
- Department of Gastroenterology and Hepatology, Singapore General Hospital, Singapore
| | - Chin-Pin Yeo
- Department of Clinical Pathology, Singapore General Hospital, Singapore
| | - Chee-Kiat Tan
- Department of Gastroenterology and Hepatology, Singapore General Hospital, Singapore
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Woodhouse P, Jackson L, Kammer MN, Godfrey CM, Antic S, Zou Y, Meyers P, Gawel SH, Maldonado F, Grogan EL, Davis GJ, Deppen SA. Optimizing Biomarker Models for Biologically Heterogeneous Cancers: A Nested Model Approach for Lung Cancer. Cancer Epidemiol Biomarkers Prev 2025; 34:788-794. [PMID: 40072122 DOI: 10.1158/1055-9965.epi-24-0523] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2024] [Revised: 07/09/2024] [Accepted: 02/11/2025] [Indexed: 05/03/2025] Open
Abstract
BACKGROUND The heterogeneous biology of cancer subtypes, especially in lung cancer, poses significant challenges for biomarker development. Standard model building techniques often fall short in accurately incorporating various histologic subtypes because of their diverse biological characteristics. This study explores a nested biomarker model to address this issue, aiming to improve lung cancer early detection. METHODS The study included 337 patients from two clinical sites. Blood biomarkers were analyzed and various statistical methods employed to develop a nested model. This model was designed to account for the biological heterogeneity across histologic subtypes, compared against traditional logistic regression models. RESULTS The patient cohort included a range of malignant and benign nodules and included different cancer subtypes reflecting lung cancer heterogeneity. The nested model had comparable performance overall with the Mayo Clinic model and a standard logistic regression model with an AUC of 77.6 (95% confidence interval, 72.2-83.0) in training and 77.3 (95% confidence interval, 65.8-88.9) in testing. The nested subtype versus benign model had the best performance in the training set overall and had a particular advantage for small cell subtype prediction. CONCLUSIONS This study highlights the challenges cancer heterogeneity present for biomarker development and the potential for nested biomarker models to improve early cancer detection. Validation of this approach in larger cohorts is essential to prove its predictive benefit in biologically diverse cancers. IMPACT This work addresses the challenge of biological heterogeneity in biomarker development. A nested modeling approach may assist in developing more effective multicancer early detection strategies.
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Affiliation(s)
| | | | | | | | - Sanja Antic
- Vanderbilt University Medical Center, Nashville, Tennessee
| | - Yong Zou
- Vanderbilt University Medical Center, Nashville, Tennessee
| | - Patrick Meyers
- Vanderbilt University Medical Center, Nashville, Tennessee
| | | | | | - Eric L Grogan
- Vanderbilt University Medical Center, Nashville, Tennessee
- Tennessee Valley Healthcare System, Nashville, Tennessee
| | | | - Stephen A Deppen
- Vanderbilt University Medical Center, Nashville, Tennessee
- Tennessee Valley Healthcare System, Nashville, Tennessee
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Wang L, Li N, Zheng Y, Huang Q, Cui G, Cheng X, He Y, Niu Y, Sun Y, Wang X, Luo H, Liu P, Tan J, Huang B, Li L, Ma P, Li D, Li Y, Li J, Yu Z, Ren Z, Yuan Y. The tongue coating microbiome is perturbed in atrial fibrillation and partly normalized after catheter ablation. Front Microbiol 2025; 16:1508089. [PMID: 40371119 PMCID: PMC12075123 DOI: 10.3389/fmicb.2025.1508089] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2024] [Accepted: 04/14/2025] [Indexed: 05/16/2025] Open
Abstract
Background There is accumulating evidence linking the microbiome and cardiovascular diseases. Nevertheless, no existing studies have been conducted on atrial fibrillation (AF) and the oral microbiome. Materials and methods We collected and sequenced 245 AF tongue-coating samples and 26 AF samples after catheter ablation from Zhengzhou and Guangshan, China. We characterized tongue coating microbiome, constructed microbial classifiers in the discovery cohort, and verified their diagnostic potential in a cross-regional cohort. Results Tongue coating microbial richness and diversity were significantly increased in the AF group compared to the control group, indicating increased bacterial colonization. The classifiers based on four optimal tongue coating microbial markers achieved good diagnostic efficiency in AF cohorts, with area under the curve (AUC) of 99.10 and 98.62% in the discovery and validation cohorts, respectively, and 97.97% in the cross-regional cohort. Paroxysmal AF and persistent AF shared similar taxonomic features, but some specific differential bacteria acted in the AF progression. Moreover, the outcomes revealed that catheter ablation contributed to rehabilitating oral bacterial disorders. Conclusion This was the first cross-sectional and longitudinal research of oral microbiome in AF patients and the alternations after catheter ablation, which offers promising new perspectives for AF clinical diagnosis and management.
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Affiliation(s)
- Ling Wang
- Department of Clinical Laboratory, Henan Provincial Chest Hospital, Zhengzhou University, Zhengzhou, Henan, China
| | - Na Li
- Department of Infectious Diseases, State Key Laboratory of Antiviral Drugs, Pingyuan Laboratory, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yuchen Zheng
- Affiliated Henan Cardiovascular Hospital, Southern Medical University (Zhengzhou Seventh People's Hospital), Zhengzhou, Henan, China
| | - Qiong Huang
- Department of Cardiovascular Medicine, Henan Provincial Chest Hospital, Zhengzhou University, Zhengzhou, Henan, China
| | - Guangying Cui
- Department of Infectious Diseases, State Key Laboratory of Antiviral Drugs, Pingyuan Laboratory, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Xiaoshuai Cheng
- Department of Cardiovascular Medicine, Henan Provincial Chest Hospital, Zhengzhou University, Zhengzhou, Henan, China
| | - Yu He
- Department of Cardiovascular Medicine, Henan Provincial Chest Hospital, Zhengzhou University, Zhengzhou, Henan, China
| | - Yifei Niu
- Department of Cardiovascular Medicine, Henan Provincial Chest Hospital, Zhengzhou University, Zhengzhou, Henan, China
| | - Yumei Sun
- Department of Cardiovascular Medicine, Henan Provincial Chest Hospital, Zhengzhou University, Zhengzhou, Henan, China
| | - Xiaoming Wang
- Department of Cardiovascular Medicine, Henan Provincial Chest Hospital, Zhengzhou University, Zhengzhou, Henan, China
| | - Hong Luo
- Department of General Surgery, Guangshan County People’s Hospital, Xinyang, Henan, China
| | - Pengfei Liu
- Department of Cardiovascular Medicine, Guangshan County People’s Hospital, Xinyang, Henan, China
| | - Junjie Tan
- Department of Clinical Laboratory, Guangshan County People’s Hospital, Xinyang, Henan, China
| | - Bingsen Huang
- Department of Cardiovascular Medicine, Henan Provincial Chest Hospital, Zhengzhou University, Zhengzhou, Henan, China
| | - Li Li
- Department of Cardiovascular Medicine, Henan Provincial Chest Hospital, Zhengzhou University, Zhengzhou, Henan, China
| | - Peiyao Ma
- Department of Cardiovascular Medicine, Henan Provincial Chest Hospital, Zhengzhou University, Zhengzhou, Henan, China
| | - Dandan Li
- Department of Cardiovascular Medicine, Henan Provincial Chest Hospital, Zhengzhou University, Zhengzhou, Henan, China
| | - Yanyan Li
- Department of Clinical Laboratory, Henan Provincial Chest Hospital, Zhengzhou University, Zhengzhou, Henan, China
| | - Jing Li
- Department of Clinical Laboratory, Henan Provincial Chest Hospital, Zhengzhou University, Zhengzhou, Henan, China
| | - Zujiang Yu
- Department of Infectious Diseases, State Key Laboratory of Antiviral Drugs, Pingyuan Laboratory, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Zhigang Ren
- Department of Infectious Diseases, State Key Laboratory of Antiviral Drugs, Pingyuan Laboratory, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yiqiang Yuan
- Department of Cardiovascular Medicine, Henan Provincial Chest Hospital, Zhengzhou University, Zhengzhou, Henan, China
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Millward SW, Wei P, Piwnica-Worms D, Gammon ST. Can the Discovery of High-Impact Diagnostics Be Improved by Matching the Sampling Rate of Clinical Diagnostics to the Frequency Domain of Diagnostic Information? Cancers (Basel) 2025; 17:1387. [PMID: 40361314 PMCID: PMC12071022 DOI: 10.3390/cancers17091387] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2024] [Revised: 03/28/2025] [Accepted: 04/15/2025] [Indexed: 05/15/2025] Open
Abstract
Over the past 30 years, academic and industrial research investigators have developed molecular reporters to visualize cell death in complex biological systems. In parallel, clinical researchers, chemists, biochemists, and molecular biologists have endeavored to translate these molecular tools into clinical imaging agents. Despite these efforts, there are no clinically approved imaging methodologies with which to image cell death consistently and quantitatively. One reason may reside in the intrinsic mismatch between the sampling frequency of translational molecular imaging and the biochemical kinetics that define cell death. Beyond cell death imaging, many active research programs are now attempting to create translational diagnostic pharmaceuticals to image immunological, fibrotic, amyloidotic, and metabolic pathways. Each of these pathways is defined by a unique set of biochemical rate constants, some of which are associated with key predictive pathways. Exhaustively sampling all permutations of pathways and kinetic constants would seem to be an intractable strategy for target identification and validation. Sampling theory, if applied to these pathways, could accelerate the translation of high-impact diagnostics through prioritization of pathways for either AI enhanced diagnostic imaging or AI-enhanced wearable devices. In this perspective, we identify the Nyquist sampling rate as a key criterion for evaluating the optimal application for novel diagnostics. Sampling theory states that to fully characterize a band-limited, stationary, temporal data set, the signal must be sampled at more than twice the rate of the fastest frequency in the signal or, for diagnostics, the discriminatory signal. Through the study of the medical imaging process chain, Nyquist sampling rates of 0.25 day-1 and, more likely, slower than 0.02 day-1 were determined to provide high quality information. By prioritizing low-frequency predictive processes, or "state changes,", imaging researchers may improve the "hit rate" of research programs by appropriately matching the rate of change in diagnostic and predictive information with the limiting sampling rate of medical imaging. Critically, however, high-frequency diagnostic information (and therefore high-frequency biological processes) need not be ignored; these processes are simply better interrogated through continuous monitoring, e.g., by wearable devices combined with machine learning or artificial intelligence.
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Affiliation(s)
- Steven W. Millward
- Department of Cancer Systems Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; (S.W.M.); (D.P.-W.)
| | - Peng Wei
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA;
| | - David Piwnica-Worms
- Department of Cancer Systems Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; (S.W.M.); (D.P.-W.)
| | - Seth T. Gammon
- Department of Cancer Systems Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; (S.W.M.); (D.P.-W.)
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Berndt JD, Duffy FJ, D'Ascenzo MD, Miller LR, Qi Y, Whitney GA, Danziger SA, Vachani A, Massion PP, Deppen SA, Lipshutz RJ, Aitchison JD, Smith JJ. A multivariate cell-based assay for blood-based diagnostics enhances lung cancer risk stratification. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2025:2024.11.04.24316585. [PMID: 40313309 PMCID: PMC12045427 DOI: 10.1101/2024.11.04.24316585] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2025]
Abstract
The indicator cell assay platform (iCAP) is a tool for blood-based diagnostics that addresses the low signal-to-noise ratio of blood biomarkers by using cells as biosensors. The assay exposes small volumes of patient serum to standardized cells in culture and classifies disease by machine learning analysis of the gene expression readout from the cells. We developed the lung cancer iCAP (LC-iCAP) as a rule-out test for nodule management in computed tomography (CT)-based lung-cancer screening. We performed analytical optimization, rigorous reproducibility testing, and assessed performance in a study with prospective-specimen-collection, retrospective-blinded-evaluation (PRoBE) design. LC-iCAP achieved an AUC of 0.64 (95% CI, 0.51-0.76) on the ROC curve in validation. Post-validation integration of the assay readout with CT-based features showed improved clinical utility compared to the Mayo Clinic model, with 90% sensitivity, 64% specificity, and 95% negative predictive value at 25% prevalence. The lung-cancer specific readout was enriched for hypoxia-responsive genes and was reproducible across different indicator cell lineages. This is the first validation study of an iCAP and the first application for early cancer detection. The LC-iCAP uses immortalized cells, is scalable and cost-effective and has a multivariate readout. This study supports its potential as a next-generation multivalent platform for precision medicine applications in multi-cancer screening and drug development. Key Points We developed the LC-iCAP, novel approach for liquid biopsies that uses cultured cells as biosensors. The cells detect cancer signals in serum and transduce them into standardized gene expression profiles, which are analyzed by machine learning for disease classification. The assay is inexpensive and scalable and has a multivariate readout with potential utility for precision medicine and multi-cancer early detection.A LC-iCAP-based lung cancer risk classifier demonstrated improved specificity compared to existing tests, suggesting meaningful clinical utility for managing indeterminate pulmonary nodules.We identified a lung-cancer specific transcriptional response to hypoxia in the assay readout, implicating HIF1A and HIF2A activity in the response consistent with known lung cancer biology and highlighting the platform's mechanistic relevance.Standardized controls and validation studies demonstrated assay reproducibility, lineage stability, and detection of technical errors-supporting the platform's readiness for clinical deployment.
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Hagn-Meincke R, Novovic S, Hadi A, Jensen AB, Drewes AM, Krarup H, Frøkjær JB, Park WG, Jørgensen PL, Møller HJ, Deleuran BW, Olesen SS. Circulating Biomarkers of Macrophage Activation in Different Stages of Chronic Pancreatitis: A Pilot Study. Pancreas 2025; 54:e331-e339. [PMID: 39626186 DOI: 10.1097/mpa.0000000000002443] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/11/2024] [Accepted: 11/11/2024] [Indexed: 04/24/2025]
Abstract
OBJECTIVES Activation of type M2 macrophages has been implicated in the pathogenesis of chronic pancreatitis (CP). In a clinical pilot study, we investigated blood-based markers of macrophage activation at different stages of CP. MATERIALS AND METHODS We performed a cross-sectional analysis of prospectively collected plasma samples from healthy controls and patients with suspected or definitive CP according to the M-ANNHEIM criteria. Plasma concentrations of soluble CD163 (sCD163), soluble CD206 (sCD206), and monocyte chemoattractant protein-1 (MCP-1) were analyzed using enzyme-linked immunosorbent assays. Group and pairwise comparisons of analytes were performed using regression models and area under the receiver operating curves (AUC-ROC). RESULTS In total, 73 subjects with CP (28 suspected CP and 45 definitive CP) and 40 controls were included. Compared to controls, the median plasma concentrations of sCD163 ( P = 0.019) and sCD206 ( P = 0.033) were elevated in patients with definitive CP. sCD206 was also elevated in patients with definitive CP ( P = 0.042) compared to suspected CP. ROC analysis revealed the optimal sCD163 cutpoint to distinguish definitive CP from controls was 1.84 mg/mL (AUC-ROC 0.65; 95% confidence interval [CI], 0.54-0.77). The optimal sCD206 cutpoint to distinguish definitive CP from controls was 0.24 mg/mL (AUC-ROC 0.66; 95% CI, 0.54-0.78). MCP-1 concentrations showed no differences across subgroups. CONCLUSION Our study demonstrates that subjects with definitive CP, sampled during a clinically quiescent phase, exhibited increased levels of sCD163 and sCD206. This indicates the presence of activated M2 macrophages in patients with CP at advanced, but not early, clinical stages.
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Affiliation(s)
| | - Srdan Novovic
- Departments of Gastroenterology and Gastrointestinal Surgery and
| | - Amer Hadi
- Departments of Gastroenterology and Gastrointestinal Surgery and
| | | | | | - Henrik Krarup
- Department of Clinical Medicine, Aalborg University, and Section of Molecular Diagnostics and
| | | | - Walter G Park
- Division of Gastroenterology and Hepatology, Department of Medicine, Stanford University School of Medicine, Stanford, CA
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10
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Fujiwara N, Lopez C, Marsh TL, Raman I, Marquez CA, Paul S, Mishra SK, Kubota N, Katz C, Kanzaki H, Gonzalez M, Quirk L, Deodhar S, Selvakumar P, Raj P, Parikh ND, Roberts LR, Schwartz ME, Nguyen MH, Befeler AS, Page-Lester S, Srivastava S, Feng Z, Reddy KR, Khaderi S, Asrani SK, Kanwal F, El-Serag HB, Marrero JA, Singal AG, Hoshida Y. Phase 3 Validation of PAaM for Hepatocellular Carcinoma Risk Stratification in Cirrhosis. Gastroenterology 2025; 168:556-567.e7. [PMID: 39521255 PMCID: PMC7617545 DOI: 10.1053/j.gastro.2024.10.035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/04/2024] [Revised: 10/08/2024] [Accepted: 10/21/2024] [Indexed: 11/16/2024]
Abstract
BACKGROUND & AIMS Hepatocellular carcinoma (HCC) risk stratification is an urgent unmet need for cost-effective HCC screening and early detection in patients with cirrhosis to improve poor HCC prognosis. METHODS Molecular (prognostic liver secretome signature with α-fetoprotein) and clinical (aMAP [age, male sex, albumin-bilirubin, and platelets] score) variable-based scores were integrated into PAaM (prognostic liver secretome signature with α-fetoprotein plus age, male sex, albumin-bilirubin, and platelets), which was subsequently validated in 2 phase 3 biomarker validation studies: the statewide Texas HCC Consortium and nationwide HCC Early Detection Strategy prospective cohorts, following the prospective specimen collection, retrospective blinded evaluation design. The associations between baseline PAaM and incident HCC were assessed using Fine-Gray regression, with overall death and liver transplantation as competing events. RESULTS Of 2156 patients with cirrhosis in the Texas HCC Consortium, PAaM identified 404 (19%) high-risk, 903 (42%) intermediate-risk, and 849 (39%) low-risk patients with annual HCC incidence rates of 5.3%, 2.7%, and 0.6%, respectively. Compared with low-risk patients, high- and intermediate-risk groups had sub-distribution hazard ratios for incident HCC of 7.51 (95% CI, 4.42-12.8) and 4.20 (95% CI, 2.52-7.01), respectively. Of 1328 patients with cirrhosis in the HCC early detection strategy, PAaM identified 201 high-risk (15%), 540 intermediate-risk (41%), and 587 low-risk (44%) patients, with annual HCC incidence rates of 6.2%, 1.8%, and 0.8%, respectively. High- and intermediate-risk groups were associated with sub-distribution hazard ratios for incident HCC of 6.54 (95% CI, 3.85-11.1) and 1.77 (95% CI, 1.02-3.08), respectively. Subgroup analysis showed robust risk stratification across HCC etiologies, including metabolic dysfunction-associated steatotic liver disease and cured hepatitis C infection. CONCLUSIONS PAaM enables accurate HCC risk stratification in patients with cirrhosis from contemporary etiologies.
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Affiliation(s)
- Naoto Fujiwara
- Division of Digestive and Liver Diseases, Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, Texas; Department of Gastroenterology and Hepatology, Graduate School of Medicine, Mie University, Mie, Japan
| | - Camden Lopez
- Biostatistics Program, Public Health Science Division, Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - Tracey L Marsh
- Biostatistics Program, Public Health Science Division, Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - Indu Raman
- BioCenter, University of Texas Southwestern Medical Center, Dallas, Texas
| | - Cesia A Marquez
- Division of Digestive and Liver Diseases, Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, Texas
| | - Subhojit Paul
- Division of Digestive and Liver Diseases, Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, Texas
| | - Sumit K Mishra
- Division of Digestive and Liver Diseases, Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, Texas
| | - Naoto Kubota
- Division of Digestive and Liver Diseases, Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, Texas
| | - Courtney Katz
- Division of Digestive and Liver Diseases, Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, Texas
| | - Hiroaki Kanzaki
- Division of Digestive and Liver Diseases, Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, Texas
| | - Michael Gonzalez
- Division of Digestive and Liver Diseases, Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, Texas
| | - Lisa Quirk
- Division of Digestive and Liver Diseases, Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, Texas
| | - Sneha Deodhar
- Division of Digestive and Liver Diseases, Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, Texas
| | | | - Prithvi Raj
- BioCenter, University of Texas Southwestern Medical Center, Dallas, Texas
| | - Neehar D Parikh
- Division of Gastroenterology and Hepatology, University of Michigan, Ann Arbor, Michigan
| | - Lewis R Roberts
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, Minnesota
| | - Myron E Schwartz
- Department of Surgery, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Mindie H Nguyen
- Division of Gastroenterology and Hepatology, Stanford University Medical Center, Palo Alto, California
| | - Alex S Befeler
- Division of Gastroenterology and Hepatology, Saint Louis University School of Medicine, St Louis, Missouri
| | - Stephanie Page-Lester
- Biostatistics Program, Public Health Science Division, Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - Sudhir Srivastava
- Cancer Biomarker Research Group, Division of Cancer Prevention, National Cancer Institute, National Institutes of Health, Rockville, Maryland
| | - Ziding Feng
- Biostatistics Program, Public Health Science Division, Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - K Rajender Reddy
- Division of Gastroenterology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Saira Khaderi
- Department of Medicine, Baylor College of Medicine, Houston, Texas
| | - Sumeet K Asrani
- Baylor University Medical Center, Baylor Scott and White, Dallas, Texas
| | - Fasiha Kanwal
- Department of Medicine, Baylor College of Medicine, Houston, Texas
| | | | - Jorge A Marrero
- Division of Gastroenterology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Amit G Singal
- Division of Digestive and Liver Diseases, Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, Texas.
| | - Yujin Hoshida
- Division of Digestive and Liver Diseases, Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, Texas.
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11
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Qin Y, Zhao J, Wang L, Yang X, Wang J, Li S, Chen Y, Guo J, Wang F, Luo K. Decrease in Escherichia-Shigella in the gut microbiota of ESKD patients undergoing maintenance hemodialysis. BMC Nephrol 2025; 26:98. [PMID: 40001029 PMCID: PMC11852509 DOI: 10.1186/s12882-025-03988-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Accepted: 01/28/2025] [Indexed: 02/27/2025] Open
Abstract
BACKGROUND Gut dysbiosis is thought to be involved in the pathogenesis and progression of chronic kidney disease and end-stage kidney disease (ESKD). However, differences in the composition and function of gut microbiota in hemodialysis patients are not consistently concluded. METHODS A total of 20 patients receiving maintenance hemodialysis (MHD) treatment at the Blood Purification Center of Bethune International Peace Hospital from March 2021 to December 2022 were included based on the inclusion criteria. Additionally, 20 healthy volunteers matched for age, gender, and body mass index were recruited from the Health Examination Center as the healthy control (HC) group. The structure of the gut microbiota community in the study subjects was analyzed using second-generation high-throughput sequencing technology based on 16S rRNA and amplicon sequence variants (ASV) analysis. RESULTS There were significant differences in gut microbial communities between the two groups. At the genus level, significant differences were found in 19 genera. Among them, Escherichia-Shigella, Lachnospira, Parasutterella, [Ruminococcus]-torques-group, Butyricicoccus, and Streptococcus were significantly decreased, while Phascolarctobacterium, Ruminococcaceae-UBA1819, Erysipelotrichaceae-UCG-003, Flavonifractor, and Erysipelatoclostridium were significantly increased in MHD patients. In particular, the abnormal decrease in the abundance of p-Proteobacteria.c-Gammaproteobacteria.o-Enterobacterales.f-Enterobacteriaceae.g-Escherichia-Shigella might be a significant characteristic of gut microbiota in MHD patients. CONCLUSION The decreased abundance of Escherichia-Shigella is a signature gut microbiota alteration in patients with ESKD undergoing MHD, and Escherichia-Shigella may represent a key bacterial group warranting exploration in the field of hemodialysis. The dysbiosis of gut microbiota holds promise as a therapeutic target and biomarker for the diagnosis and treatment of MHD.
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Affiliation(s)
- Yunlong Qin
- Department of Nephrology, Bethune International Peace Hospital, No.398 Zhongshan West Road, Shijiazhuang, Hebei, 050051, China
| | - Jin Zhao
- Department of Nephrology, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Lihui Wang
- Department of Nephrology, Bethune International Peace Hospital, No.398 Zhongshan West Road, Shijiazhuang, Hebei, 050051, China
| | - Xinjun Yang
- Department of Nephrology, Bethune International Peace Hospital, No.398 Zhongshan West Road, Shijiazhuang, Hebei, 050051, China
| | - Jinghua Wang
- Department of Nephrology, Bethune International Peace Hospital, No.398 Zhongshan West Road, Shijiazhuang, Hebei, 050051, China
| | - Shaojian Li
- Department of Nutrition, Bethune International Peace Hospital, Shijiazhuang, China
| | - Yunshuang Chen
- Department of Nephrology, Bethune International Peace Hospital, No.398 Zhongshan West Road, Shijiazhuang, Hebei, 050051, China
| | - Jiaming Guo
- Department of Nephrology, Bethune International Peace Hospital, No.398 Zhongshan West Road, Shijiazhuang, Hebei, 050051, China
| | - Fang Wang
- Department of Nephrology, Bethune International Peace Hospital, No.398 Zhongshan West Road, Shijiazhuang, Hebei, 050051, China
| | - Kaifa Luo
- Department of Nephrology, Bethune International Peace Hospital, No.398 Zhongshan West Road, Shijiazhuang, Hebei, 050051, China.
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12
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May CJ, Ford NP, Welsh GI, Saleem MA. Biomarkers to predict or measure steroid resistance in idiopathic nephrotic syndrome: A systematic review. PLoS One 2025; 20:e0312232. [PMID: 39946431 PMCID: PMC11824968 DOI: 10.1371/journal.pone.0312232] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Accepted: 10/02/2024] [Indexed: 02/16/2025] Open
Abstract
In this systematic review we have sought to summarise the current knowledge concerning biomarkers that can distinguish between steroid-resistant nephrotic syndrome and steroid-sensitive nephrotic syndrome. Additionally, we aim to select biomarkers that have the best evidence-base and should be prioritised for further research. Pub med and web of science databases were searched using "steroid resistant nephrotic syndrome AND biomarker". Papers published between 01/01/2012 and 10/05/2022 were included. Papers that did not compare steroid resistant and steroid sensitive nephrotic syndrome, did not report sensitivity/specificity or area under curve and reviews/letters were excluded. The selected papers were then assessed for bias using the QUADAS-2 tool. The source of the biomarker, cut off, sensitivity/specificity, area under curve and sample size were all extracted. Quality assessment was performed using the BIOCROSS tool. 17 studies were included, comprising 15 case-control studies and 2 cross-sectional studies. Given the rarity of nephrotic syndrome and difficulty in recruiting large cohorts, case-control studies were accepted despite their limitations. We present a range of candidate biomarkers along with scores relating to the quality of the original publications and the risk of bias to inform future investigations. None of the selected papers stated whether the authors were blinded to the patient's disease when assessing the index test in the cohort. Highlighting a key problem in the field that needs to be addressed. These candidate biomarkers must now be tested with much larger sample sizes. Using new biobanks such as the one built by the NURTuRE-INS team will be very helpful in this regard.
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Affiliation(s)
- Carl J. May
- Bristol Renal, University of Bristol, Bristol, United Kingdom
| | | | - Gavin I. Welsh
- Bristol Renal, University of Bristol, Bristol, United Kingdom
| | - Moin A. Saleem
- Bristol Renal, University of Bristol, Bristol, United Kingdom
- Bristol Royal Hospital for Children, Bristol, United Kingdom
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13
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Cao Y, Meng L, Wang Y, Zhao S, Zheng Y, Ran R, Du J, Wu H, Han J, Xu Z, Lu Y, Liu L, Chen L, Wang J, Li Y, Zhai Y, Sun Z, Cao Z. Large-scale prospective serum metabolomic profiling reveals candidate predictive biomarkers for suspected preeclampsia patients. Sci Rep 2025; 15:4807. [PMID: 39922859 PMCID: PMC11807192 DOI: 10.1038/s41598-025-87905-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2024] [Accepted: 01/22/2025] [Indexed: 02/10/2025] Open
Abstract
Preeclampsia (PE) is a serious pregnancy complication that contributes to maternal and perinatal morbidity and mortality. Understanding its pathogenesis and revealing predictive biomarkers are essential for guiding treatment decisions. In order to explore the global changes of serum metabolites in PE patients and identify potential predictive biomarkers for suspected PE patients (pregnant women who had already shown PE-related symptoms in the middle to late stages of pregnancy, but were not yet confirmatively diagnosed as PE.), a large-scale serum metabolomic analysis was conducted in this study with a prospective cohort of 328 suspected PE patients in the middle or late pregnancy stages, as well as a retrospective cohort of 30 healthy pregnant women and 30 PE patients. Using liquid chromatography mass spectrometry (LC - MS), serum metabolomic profiling revealed that the development of PE was closely associated with disturbed amino acid metabolism. Moreover, a panel of seven predictive biomarkers including 2-methyl-3-hydroxy-5-formylpyridine-4-carboxylate, gamma-glutamyl-leucine, 2-hydroxyvaleric acid, LysoPC(16:1(9Z)/0:0), PC(DiMe(13,5)/MonoMe(13,5)), ADP-D-glycero-beta-D-manno-heptose and phenylalanyl-tryptophan were identified for PE development by performing multiple statistical analysis and LASSO regression analysis. The combination of these biomarkers showed promise in the prediction of PE development for suspected PE patients, with an AUC of 0.753 and 0.885 for the discovery and validation cohorts, respectively. These findings highlight the potential of large-scale prospective metabolomic studies combined with machine learning algorithms in identifying key biomarkers for predicting PE development, while retrospective metabolomics studies provide insights into the pathogenesis of PE.
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Affiliation(s)
- Yan Cao
- Department of Laboratory Medicine, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing Maternal and Child Health Care Hospital, 251 Yaojiayuan Road, Beijing, 100026, China
- Center of Clinical Mass Spectrometry, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing Maternal and Child Health Care Hospital, Beijing, China
| | - Lanlan Meng
- Department of Laboratory Medicine, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing Maternal and Child Health Care Hospital, 251 Yaojiayuan Road, Beijing, 100026, China
- Center of Clinical Mass Spectrometry, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing Maternal and Child Health Care Hospital, Beijing, China
| | - Yifei Wang
- Department of Obstetrics and Gynecology, Shanghai Tenth People's Hospital, Tongji University, Shanghai, China
| | - Shenglong Zhao
- Department of Obstetrics, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing Maternal and Child Health Care Hospital, Beijing, China
| | - Yuanyuan Zheng
- Department of Obstetrics, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing Maternal and Child Health Care Hospital, Beijing, China
| | - Rui Ran
- Biotree Metabolomics Technology Research Center, Shanghai, China
| | - Jie Du
- Biotree Metabolomics Technology Research Center, Shanghai, China
| | - Hongqiang Wu
- Biotree Metabolomics Technology Research Center, Shanghai, China
| | - Jiaqi Han
- Department of Laboratory Medicine, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing Maternal and Child Health Care Hospital, 251 Yaojiayuan Road, Beijing, 100026, China
- Center of Clinical Mass Spectrometry, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing Maternal and Child Health Care Hospital, Beijing, China
| | - Zhengwen Xu
- Department of Laboratory Medicine, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing Maternal and Child Health Care Hospital, 251 Yaojiayuan Road, Beijing, 100026, China
- Center of Clinical Mass Spectrometry, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing Maternal and Child Health Care Hospital, Beijing, China
| | - Yifan Lu
- Department of Laboratory Medicine, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing Maternal and Child Health Care Hospital, 251 Yaojiayuan Road, Beijing, 100026, China
- Center of Clinical Mass Spectrometry, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing Maternal and Child Health Care Hospital, Beijing, China
| | - Lin Liu
- Department of Laboratory Medicine, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing Maternal and Child Health Care Hospital, 251 Yaojiayuan Road, Beijing, 100026, China
- Center of Clinical Mass Spectrometry, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing Maternal and Child Health Care Hospital, Beijing, China
| | - Lu Chen
- Department of Laboratory Medicine, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing Maternal and Child Health Care Hospital, 251 Yaojiayuan Road, Beijing, 100026, China
- Center of Clinical Mass Spectrometry, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing Maternal and Child Health Care Hospital, Beijing, China
| | - Jing Wang
- Department of Laboratory Medicine, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing Maternal and Child Health Care Hospital, 251 Yaojiayuan Road, Beijing, 100026, China
- Center of Clinical Mass Spectrometry, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing Maternal and Child Health Care Hospital, Beijing, China
| | - Youran Li
- Department of Laboratory Medicine, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing Maternal and Child Health Care Hospital, 251 Yaojiayuan Road, Beijing, 100026, China
- Center of Clinical Mass Spectrometry, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing Maternal and Child Health Care Hospital, Beijing, China
| | - Yanhong Zhai
- Department of Laboratory Medicine, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing Maternal and Child Health Care Hospital, 251 Yaojiayuan Road, Beijing, 100026, China.
- Center of Clinical Mass Spectrometry, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing Maternal and Child Health Care Hospital, Beijing, China.
| | - Zhi Sun
- Department of Pharmacy, The First Affiliated Hospital of Zhengzhou University, No.1 Jianshe East Road, Zhengzhou, 450052, Henan, China.
| | - Zheng Cao
- Department of Laboratory Medicine, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing Maternal and Child Health Care Hospital, 251 Yaojiayuan Road, Beijing, 100026, China.
- Center of Clinical Mass Spectrometry, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing Maternal and Child Health Care Hospital, Beijing, China.
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14
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El-Serag HB, Jin Q, Tayob N, Salem E, Luster M, Alsarraj A, Khaderi S, Singal AG, Marrero JA, Asrani SK, Kanwal F. HES V2.0 outperforms GALAD for detection of HCC: A phase 3 biomarker study in the United States. Hepatology 2025; 81:465-475. [PMID: 38899967 PMCID: PMC11655698 DOI: 10.1097/hep.0000000000000953] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Accepted: 05/20/2024] [Indexed: 06/21/2024]
Abstract
BACKGROUND AND AIMS The original hepatocellular carcinoma early detection screening (HES) score, which combines alpha-fetoprotein (AFP) with age, alanine aminotransferase, and platelets, has better performance than AFP alone for early HCC detection. We have developed HES V2.0 by adding AFP-L3 and des-gamma-carboxy prothrombin to the score and compared its performance to GALAD and ASAP scores among patients with cirrhosis. APPROACH AND RESULTS We conducted a prospective-specimen collection, retrospective-blinded-evaluation phase 3 biomarker cohort study in patients with cirrhosis enrolled in imaging and AFP surveillance. True-positive rate (TPR)/sensitivity and false-positive rate for any or early HCC were calculated for GALAD, ASAP, and HES V2.0 scores within 6, 12, and 24 months of HCC diagnosis. We calculated the AUROC curve and estimated TPR based on an optimal threshold at a fixed false-positive rate of 10%. We analyzed 2331 patients, of whom 125 developed HCC (71% in the early stages). For any HCC, HES V2.0 had higher TPR than GALAD overall (+7.2%), at 6 months (+3.6%), at 12 months (+7.2%), and 24 months (+13.0%) before HCC diagnosis. HES V2.0 had higher TPR than ASAP for all time points (+5.9% to +12.0%). For early HCC, HES V2.0 had higher sensitivity/TPR than GALAD overall (+6.7%), at 12 months (+6.3%), and 24 months (+14.6%) but not at 6 months (+0.0%) and higher than ASAP for all time points (+13.4% to +18.0%). CONCLUSIONS In a prospective cohort study, HES V2.0 had a significantly higher performance for identifying new HCC, including early stage, than GALAD or ASAP.
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Affiliation(s)
- Hashem B. El-Serag
- Section of Gastroenterology and Hepatology, Department of Medicine, Michael E. DeBakey VA Medical Center and Baylor College of Medicine, Houston, Texas, USA
- Section of Health Services Research, Michael E. DeBakey VA Medical Center and Baylor College of Medicine, Houston, Texas, USA
| | - Qingchun Jin
- Department of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
| | - Nabihah Tayob
- Department of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
| | - Emad Salem
- Section of Gastroenterology and Hepatology, Department of Medicine, Michael E. DeBakey VA Medical Center and Baylor College of Medicine, Houston, Texas, USA
| | - Michelle Luster
- Section of Gastroenterology and Hepatology, Department of Medicine, Michael E. DeBakey VA Medical Center and Baylor College of Medicine, Houston, Texas, USA
| | - Abeer Alsarraj
- Section of Health Services Research, Michael E. DeBakey VA Medical Center and Baylor College of Medicine, Houston, Texas, USA
| | - Saira Khaderi
- Section of Gastroenterology and Hepatology, Department of Medicine, Michael E. DeBakey VA Medical Center and Baylor College of Medicine, Houston, Texas, USA
| | - Amit G. Singal
- Department of Medicine, UT Southwestern Medical Center, Dallas, Texas, USA
| | - Jorge A. Marrero
- Department of Medicine, Division of Gastroenterology and Hepatology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Sumeet K. Asrani
- Department of Medicine, Baylor University Medical Center, Dallas, Texas, USA
| | - Fasiha Kanwal
- Section of Gastroenterology and Hepatology, Department of Medicine, Michael E. DeBakey VA Medical Center and Baylor College of Medicine, Houston, Texas, USA
- Section of Health Services Research, Michael E. DeBakey VA Medical Center and Baylor College of Medicine, Houston, Texas, USA
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15
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Goodman MT, Lombardi C, Torrens A, Bresee C, Saloman JL, Li L, Yang Y, Fisher WE, Fogel EL, Forsmark CE, Conwell DL, Hart PA, Park WG, Topazian M, Vege SS, Van Den Eeden SK, Bellin MD, Andersen DK, Serrano J, Yadav D, Pandol SJ, Piomelli D, on behalf of the Consortium for the Study of Chronic Pancreatitis, Diabetes, and Pancreatic Cancer (CPDPC). Association of Serum Endocannabinoid Levels with Pancreatitis and Pancreatitis-Related Pain. Cannabis Cannabinoid Res 2025; 10:60-70. [PMID: 39291350 PMCID: PMC11947650 DOI: 10.1089/can.2024.0079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/19/2024] Open
Abstract
Background and Aims: This investigation examined the association of pancreatitis and pancreatitis-related pain with serum levels of two endocannabinoid molecules such as anandamide (AEA) and 2-arachidonoylglycerol (2-AG) and two paracannabinoid molecules such as oleoylethanolamide (OEA) and palmitoylethanolamide (PEA). Methods: A case-control study was conducted within the Prospective Evaluation of Chronic Pancreatitis for Epidemiological and Translational Studies, including participants with no pancreas disease (N = 56), chronic abdominal pain of suspected pancreatic origin or indeterminate chronic pancreatitis (CP) (N = 22), acute pancreatitis (N = 33), recurrent acute pancreatitis (N = 57), and definite CP (N = 63). Results: Circulating AEA concentrations were higher in women than in men (p = 0.0499), and PEA concentrations were higher in obese participants than those who were underweight/normal or overweight (p = 0.003). Asymptomatic controls with no pancreatic disease had significantly (p = 0.03) lower concentrations of AEA compared with all disease groups combined. The highest concentrations of AEA were observed in participants with acute pancreatitis, followed by those with recurrent acute pancreatitis, chronic abdominal pain/indeterminant CP, and definite CP. Participants with pancreatitis reporting abdominal pain in the past year had significantly (p = 0.04) higher concentrations of AEA compared with asymptomatic controls. Levels of 2-AG were significantly lower (p = 0.02) among participants reporting abdominal pain in the past week, and pain intensity was inversely associated with concentrations of 2-AG and OEA. Conclusions: Endocannabinoid levels may be associated with stage of pancreatitis, perhaps through activation of the CB1 receptor. Validation of our findings would support the investigation of novel therapeutics, including cannabinoid receptor-1 antagonists, in this patient population.
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Affiliation(s)
- Marc T. Goodman
- Prevention and Control Program, Cancer Center, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Christina Lombardi
- Prevention and Control Program, Cancer Center, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Alexa Torrens
- Department and Anatomy and Neurobiology, University of California, Irvine, California, USA
| | - Catherine Bresee
- Department of Biostatistics and Bioinformatics Research Center, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Jami L. Saloman
- Center for Pain Research, Division of Gastroenterology, Hepatology and Nutrition, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - Liang Li
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Yunlong Yang
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - William E. Fisher
- Michael E. DeBakey Department of Surgery, Baylor College of Medicine, Houston, Texas, USA
| | - Evan L. Fogel
- Division of Gastroenterology and Hepatology, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Christopher E. Forsmark
- Division of Gastroenterology, Hepatology, and Nutrition, University of Florida, Gainesville, Florida, USA
| | - Darwin L. Conwell
- Department of Internal Medicine, University of Kentucky College of Medicine, Lexington, Kentucky, USA
| | - Phil A. Hart
- Division of Gastroenterology, Hepatology, & Nutrition, The Ohio State University Wexner Medical Center, Columbus, Ohio, USA
| | - Walter G. Park
- Department of Medicine, Division of Gastroenterology and Hepatology, Stanford University Medical Center, Stanford, California, USA
| | | | - Santhi S. Vege
- Department of Internal Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | | | - Melena D. Bellin
- Division of Pediatric Endocrinology, Department of Pediatrics, University of Minnesota Medical School, Minneapolis, Minnesota, USA
| | - Dana K. Andersen
- Division of Digestive Diseases and Nutrition, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Maryland, USA
| | - Jose Serrano
- Division of Digestive Diseases and Nutrition, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Maryland, USA
| | - Dhiraj Yadav
- Department of Medicine Division of Gastroenterology, Hepatology & Nutrition University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - Stephen J. Pandol
- Division of Gastroenterology and Hepatology, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Daniele Piomelli
- Department and Anatomy and Neurobiology, University of California, Irvine, California, USA
- Department of Biological Chemistry, University of California, Irvine, California, USA
- Department of Pharmaceutical Sciences, University of California, Irvine, California, USA
| | - on behalf of the Consortium for the Study of Chronic Pancreatitis, Diabetes, and Pancreatic Cancer (CPDPC)
- Prevention and Control Program, Cancer Center, Cedars-Sinai Medical Center, Los Angeles, California, USA
- Department and Anatomy and Neurobiology, University of California, Irvine, California, USA
- Department of Biostatistics and Bioinformatics Research Center, Cedars-Sinai Medical Center, Los Angeles, California, USA
- Center for Pain Research, Division of Gastroenterology, Hepatology and Nutrition, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
- Michael E. DeBakey Department of Surgery, Baylor College of Medicine, Houston, Texas, USA
- Division of Gastroenterology and Hepatology, Indiana University School of Medicine, Indianapolis, Indiana, USA
- Division of Gastroenterology, Hepatology, and Nutrition, University of Florida, Gainesville, Florida, USA
- Department of Internal Medicine, University of Kentucky College of Medicine, Lexington, Kentucky, USA
- Division of Gastroenterology, Hepatology, & Nutrition, The Ohio State University Wexner Medical Center, Columbus, Ohio, USA
- Department of Medicine, Division of Gastroenterology and Hepatology, Stanford University Medical Center, Stanford, California, USA
- Mayo Clinic, Rochester, Minnesota, USA
- Department of Internal Medicine, Mayo Clinic, Rochester, Minnesota, USA
- Division of Research, Kaiser Permanente Northern California, Oakland, California, USA
- Division of Pediatric Endocrinology, Department of Pediatrics, University of Minnesota Medical School, Minneapolis, Minnesota, USA
- Division of Digestive Diseases and Nutrition, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Maryland, USA
- Department of Medicine Division of Gastroenterology, Hepatology & Nutrition University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
- Division of Gastroenterology and Hepatology, Cedars-Sinai Medical Center, Los Angeles, California, USA
- Department of Biological Chemistry, University of California, Irvine, California, USA
- Department of Pharmaceutical Sciences, University of California, Irvine, California, USA
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16
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Ma Q, Huang CX, He JW, Zeng X, Qu YL, Xiang HX, Zhong Y, Lei M, Zheng RY, Xiao JJ, Jiang YL, Tan SY, Xiao P, Zhuang X, You LT, Fu X, Ren YF, Zheng C, You FM. Oral microbiota as a biomarker for predicting the risk of malignancy in indeterminate pulmonary nodules: a prospective multicenter study. Int J Surg 2025; 111:2055-2071. [PMID: 39728732 DOI: 10.1097/js9.0000000000002152] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2024] [Accepted: 11/07/2024] [Indexed: 12/28/2024]
Abstract
BACKGROUND Determining the benign or malignant status of indeterminate pulmonary nodules (IPN) with intermediate malignancy risk is a significant clinical challenge. Oral microbiota-lung cancer (LC) interactions have qualified oral microbiota as a promising non-invasive predictive biomarker in IPN. MATERIALS AND METHODS Prospectively collected saliva, throat swabs, and tongue coating samples from 1040 IPN patients and 70 healthy controls across three hospitals. Following up, the IPNs were diagnosed as benign (BPN) or malignant pulmonary nodules (MPN). Through 16S rRNA sequencing, bioinformatics analysis, fluorescence in situ hybridization (FISH), and seven machine learning algorithms (support vector machine, logistic regression, naïve Bayes, multi-layer perceptron, random forest, gradient-boosting decision tree, and LightGBM), we revealed the oral microbiota characteristics at different stages of HC-BPN-MPN, identified the sample types with the highest predictive potential, constructed and evaluated the optimal MPN prediction model for predictive efficacy, and determined microbial biomarkers. Additionally, based on the SHAP algorithm interpretation of the ML model's output, we have developed a visualized IPN risk prediction system on the web. RESULTS Saliva, tongue coating, and throat swab microbiotas exhibit site-specific characteristics, with saliva microbiota being the optimal sample type for disease prediction. The saliva-LightGBM model demonstrated the best predictive performance (AUC = 0.887, 95%CI: 0.865-0.918), and identified Actinomyces, Rothia, Streptococcus, Prevotella, Porphyromonas , and Veillonella as biomarkers for predicting MPN. FISH was used to confirm the presence of a microbiota within tumors, and external data from a LC cohort, along with three non-IPN disease cohorts, were employed to validate the specificity of the microbial biomarkers. Notably, coabundance analysis of the ecological network revealed that microbial biomarkers exhibit richer interspecies connections within the MPN, which may contribute to the pathogenesis of MPN. CONCLUSION This study presents a new predictive strategy for the clinic to determine MPNs from BPNs, which aids in the surgical decision-making for IPN.
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Affiliation(s)
- Qiong Ma
- Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan Province, China
| | - Chun-Xia Huang
- Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan Province, China
| | - Jia-Wei He
- Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan Province, China
| | - Xiao Zeng
- Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan Province, China
| | - Yu-Li Qu
- College of Artificial Intelligence, Xi'an Jiaotong University, Xian, Shanxi Province, China
| | - Hong-Xia Xiang
- Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan Province, China
| | - Yang Zhong
- Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan Province, China
| | - Mao Lei
- Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan Province, China
| | - Ru-Yi Zheng
- Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan Province, China
| | - Jun-Jie Xiao
- Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan Province, China
| | - Yu-Ling Jiang
- Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan Province, China
| | - Shi-Yan Tan
- Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan Province, China
| | - Ping Xiao
- Department of Thoracic Surgery, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, Sichuan Province, China
| | - Xiang Zhuang
- Department of Thoracic Surgery, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, Sichuan Province, China
| | - Li-Ting You
- Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan Province, China
- Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China
| | - Xi Fu
- Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan Province, China
- TCM Regulating Metabolic Diseases Key Laboratory of Sichuan Province, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan Province, China
| | - Yi-Feng Ren
- Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan Province, China
- TCM Regulating Metabolic Diseases Key Laboratory of Sichuan Province, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan Province, China
| | - Chuan Zheng
- Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan Province, China
- TCM Regulating Metabolic Diseases Key Laboratory of Sichuan Province, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan Province, China
| | - Feng-Ming You
- Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan Province, China
- TCM Regulating Metabolic Diseases Key Laboratory of Sichuan Province, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan Province, China
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Marsh TL, Parikh ND, Roberts LR, Schwartz ME, Nguyen MH, Befeler A, Page-Lester S, Tayob N, Srivastava S, Rinaudo JA, Singal AG, Reddy KR, Marrero JA. A Phase 3 Biomarker Validation of GALAD for the Detection of Hepatocellular Carcinoma in Cirrhosis. Gastroenterology 2025; 168:316-326.e6. [PMID: 39293548 PMCID: PMC12120501 DOI: 10.1053/j.gastro.2024.09.008] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/12/2024] [Revised: 08/12/2024] [Accepted: 09/02/2024] [Indexed: 09/20/2024]
Abstract
BACKGROUND & AIMS Better surveillance tests for hepatocellular carcinoma (HCC) are needed. The GALAD score (gender, age, α-fetoprotein [AFP] L3, AFP, and des-γ carboxyprothrombin) has been shown to have excellent sensitivity and specificity for HCC in phase 2 studies. We performed a phase 3 biomarker validation study to compare GALAD with AFP in detecting HCC. METHODS This is a prospective study of patients with cirrhosis enrolled at 7 centers. Surveillance for HCC was performed every 6 months at each site, and HCC diagnosis was confirmed per American Association for the Study of Liver Diseases guidelines. Blood for biomarker research was obtained at each follow-up visit and stored in a biorepository. Measurements of AFP, AFP-L3, and des-γ carboxyprothrombin) were performed in a FujiFilm laboratory by staff blinded to clinical data. The performance of GALAD in detecting HCC was retrospectively evaluated within 12 months before the clinical diagnosis. All analyses were conducted by an unblinded statistician in the Early Detection Research Network data management and coordinating center. RESULTS A total of 1,558 patients with cirrhosis were enrolled and followed for a median of 2.2 years. A total of 109 patients developed HCC (76 very early or early stage), with an annual incident rate of 2.4%. The areas under the curve for AFP and GALAD within 12 months before HCC were 0.66 and 0.78 (P < .001), respectively. Using a cutoff for GALAD of -1.36, the specificity was 82%, and the sensitivity at 12 months before HCC diagnosis was 62%. For comparison, performance of AFP at 82% specificity showed 41% sensitivity at 12 months before HCC diagnosis (P = .001). CONCLUSIONS GALAD score, compared to AFP, improves the detection of HCC within 12 months before the actual diagnosis.
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Affiliation(s)
- Tracey L Marsh
- Biostatistics Program, Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - Neehar D Parikh
- Division of Gastroenterology and Hepatology, University of Michigan, Ann Arbor, Michigan
| | - Lewis R Roberts
- Division of Gastroenterology and Hepatology, Mayo Clinic College of Medicine and Science, Rochester, Minnesota
| | - Myron E Schwartz
- Recanati/Miller Transplant Institute, Mount Sinai Medical Center, New York, New York
| | - Mindie H Nguyen
- Division of Gastroenterology and Hepatology and Department of Epidemiology and Population Health, Stanford University, Palo Alto, California
| | - Alex Befeler
- Division of Gastroenterology, Saint Louis University, St. Louis, Missouri
| | - Stephanie Page-Lester
- Biostatistics Program, Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - Nabihah Tayob
- Department of Data Science, Dana Farber Cancer Institute, Boston, Massachusetts
| | - Sudhir Srivastava
- Cancer Biomarkers Research Group, Division of Cancer Prevention, National Cancer Institute, Bethesda, Maryland
| | - Jo Ann Rinaudo
- Cancer Biomarkers Research Group, Division of Cancer Prevention, National Cancer Institute, Bethesda, Maryland
| | - Amit G Singal
- Division of Digestive and Liver Disease, University of Texas Southwestern, Dallas, Texas
| | - K Rajender Reddy
- Division of Gastroenterology and Hepatology, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Jorge A Marrero
- Division of Gastroenterology and Hepatology, University of Pennsylvania, Philadelphia, Pennsylvania.
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Emanuelle Pereira Santos V, Luiz de França Neto P, Eda de Oliveira Isídio B, Henrique Bezerra Fontes P, Andrêssa de Moura I, Isabel Santos Cruz B, Máyra Gois de Sousa M, Luana Dos Santos D, de França São Marcos B, Sousa de Pinho S, Mendonça Alves Bandeira B, Loureiro Leão S, de Almeida Lima T, da Conceição Viana Invenção M, Rosa Sales Leal L, Cristofer Flores Espinoza B, Silva de Macêdo L, do Nascimento Carvalho M, Jéssica Duarte Silva A, Carlos de Freitas A. An overview about biomarkers in breast cancer: Insights into the diagnostic and prognostic significance. Clin Chim Acta 2025; 567:120030. [PMID: 39515632 DOI: 10.1016/j.cca.2024.120030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2024] [Revised: 11/01/2024] [Accepted: 11/04/2024] [Indexed: 11/16/2024]
Abstract
Breast cancer (BC) is one of the most significant neoplasms globally due to its high incidence and mortality, particularly among females. As a highly heterogeneous pathology, biomarkers are essential for characterizing specific tumors. Currently, several biological processes are well-described in the context of this neoplasm, such as alterations in BRCA1/2, HER, and pathways involving estrogen and progesterone hormone receptors. These studies have enabled the use of these findings as more precise methods for diagnosis, prognosis, and treatment. However, beyond patients who do not exhibit these classic markers, some individuals within the same risk group respond differently to treatment. Therefore, the search for biological markers that can improve diagnosis, aid in stratification, or serve as therapeutic targets is continuous and urgent. Genetic signatures have led to molecular tests currently used in clinical practice, though certain limitations persist. Understanding genetic and epigenetic mechanisms facilitates the identification of potential biomarkers. Biomarker targets must undergo experimental and clinical trials on samples of significant size before reaching clinical utility. In this review, we compile the classical markers and describe the potential use of other markers associated with the biological processes of this neoplasm.
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Affiliation(s)
- Vanessa Emanuelle Pereira Santos
- Laboratory of Molecular Studies and Experimental Therapy, Department of Genetics, Federal University of Pernambuco - Av. Prof. Moraes Rego, 1235. Cidade Universitária Recife, Pernambuco CEP: 50670-901, Brazil.
| | - Pedro Luiz de França Neto
- Laboratory of Molecular Studies and Experimental Therapy, Department of Genetics, Federal University of Pernambuco - Av. Prof. Moraes Rego, 1235. Cidade Universitária Recife, Pernambuco CEP: 50670-901, Brazil.
| | - Beatriz Eda de Oliveira Isídio
- Laboratory of Molecular Studies and Experimental Therapy, Department of Genetics, Federal University of Pernambuco - Av. Prof. Moraes Rego, 1235. Cidade Universitária Recife, Pernambuco CEP: 50670-901, Brazil.
| | - Pedro Henrique Bezerra Fontes
- Laboratory of Molecular Studies and Experimental Therapy, Department of Genetics, Federal University of Pernambuco - Av. Prof. Moraes Rego, 1235. Cidade Universitária Recife, Pernambuco CEP: 50670-901, Brazil.
| | - Ingrid Andrêssa de Moura
- Laboratory of Molecular Studies and Experimental Therapy, Department of Genetics, Federal University of Pernambuco - Av. Prof. Moraes Rego, 1235. Cidade Universitária Recife, Pernambuco CEP: 50670-901, Brazil.
| | - Bruna Isabel Santos Cruz
- Laboratory of Molecular Studies and Experimental Therapy, Department of Genetics, Federal University of Pernambuco - Av. Prof. Moraes Rego, 1235. Cidade Universitária Recife, Pernambuco CEP: 50670-901, Brazil.
| | - Mylenna Máyra Gois de Sousa
- Laboratory of Molecular Studies and Experimental Therapy, Department of Genetics, Federal University of Pernambuco - Av. Prof. Moraes Rego, 1235. Cidade Universitária Recife, Pernambuco CEP: 50670-901, Brazil.
| | - Daffany Luana Dos Santos
- Laboratory of Molecular Studies and Experimental Therapy, Department of Genetics, Federal University of Pernambuco - Av. Prof. Moraes Rego, 1235. Cidade Universitária Recife, Pernambuco CEP: 50670-901, Brazil.
| | - Bianca de França São Marcos
- Laboratory of Molecular Studies and Experimental Therapy, Department of Genetics, Federal University of Pernambuco - Av. Prof. Moraes Rego, 1235. Cidade Universitária Recife, Pernambuco CEP: 50670-901, Brazil.
| | - Samara Sousa de Pinho
- Laboratory of Molecular Studies and Experimental Therapy, Department of Genetics, Federal University of Pernambuco - Av. Prof. Moraes Rego, 1235. Cidade Universitária Recife, Pernambuco CEP: 50670-901, Brazil.
| | - Beatriz Mendonça Alves Bandeira
- Laboratory of Molecular Studies and Experimental Therapy, Department of Genetics, Federal University of Pernambuco - Av. Prof. Moraes Rego, 1235. Cidade Universitária Recife, Pernambuco CEP: 50670-901, Brazil.
| | - Stephanie Loureiro Leão
- Laboratory of Molecular Studies and Experimental Therapy, Department of Genetics, Federal University of Pernambuco - Av. Prof. Moraes Rego, 1235. Cidade Universitária Recife, Pernambuco CEP: 50670-901, Brazil.
| | - Thainá de Almeida Lima
- Laboratory of Molecular Studies and Experimental Therapy, Department of Genetics, Federal University of Pernambuco - Av. Prof. Moraes Rego, 1235. Cidade Universitária Recife, Pernambuco CEP: 50670-901, Brazil.
| | - Maria da Conceição Viana Invenção
- Laboratory of Molecular Studies and Experimental Therapy, Department of Genetics, Federal University of Pernambuco - Av. Prof. Moraes Rego, 1235. Cidade Universitária Recife, Pernambuco CEP: 50670-901, Brazil.
| | - Lígia Rosa Sales Leal
- Laboratory of Molecular Studies and Experimental Therapy, Department of Genetics, Federal University of Pernambuco - Av. Prof. Moraes Rego, 1235. Cidade Universitária Recife, Pernambuco CEP: 50670-901, Brazil.
| | - Benigno Cristofer Flores Espinoza
- Laboratory of Molecular Studies and Experimental Therapy, Department of Genetics, Federal University of Pernambuco - Av. Prof. Moraes Rego, 1235. Cidade Universitária Recife, Pernambuco CEP: 50670-901, Brazil.
| | - Larissa Silva de Macêdo
- Laboratory of Molecular Studies and Experimental Therapy, Department of Genetics, Federal University of Pernambuco - Av. Prof. Moraes Rego, 1235. Cidade Universitária Recife, Pernambuco CEP: 50670-901, Brazil.
| | - Matheus do Nascimento Carvalho
- Laboratory of Molecular Studies and Experimental Therapy, Department of Genetics, Federal University of Pernambuco - Av. Prof. Moraes Rego, 1235. Cidade Universitária Recife, Pernambuco CEP: 50670-901, Brazil.
| | - Anna Jéssica Duarte Silva
- Laboratory of Molecular Studies and Experimental Therapy, Department of Genetics, Federal University of Pernambuco - Av. Prof. Moraes Rego, 1235. Cidade Universitária Recife, Pernambuco CEP: 50670-901, Brazil.
| | - Antonio Carlos de Freitas
- Laboratory of Molecular Studies and Experimental Therapy, Department of Genetics, Federal University of Pernambuco - Av. Prof. Moraes Rego, 1235. Cidade Universitária Recife, Pernambuco CEP: 50670-901, Brazil.
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Szternel Ł, Sobucki B, Wieprzycka L, Krintus M, Panteghini M. Golgi protein 73 in liver fibrosis. Clin Chim Acta 2025; 565:119999. [PMID: 39401651 DOI: 10.1016/j.cca.2024.119999] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2024] [Revised: 10/10/2024] [Accepted: 10/10/2024] [Indexed: 10/17/2024]
Abstract
Golgi protein 73 (GP73) is implicated in key pathogenic processes, particularly those related to inflammation and fibrogenesis. In the last years, its measurement has emerged as a promising biomarker for detection of liver fibrosis (LF), a common consequence of chronic liver disease that can progress to cirrhosis and eventually hepatocellular carcinoma. GP73 concentrations in blood appear significantly increased in LF patients, correlating with disease severity, making this biomarker a possible non-invasive alternative for detecting and monitoring this condition regardless of etiology. Understanding the molecular mechanisms involving GP73 expression could also lead to new therapeutic strategies aimed at modulating its synthesis or function to prevent or reverse LF. Despite its clinical potential, GP73 as a LF biomarker faces several challenges. The lack of demonstrated comparability among different assays as well as the lack of knowledge of individual variability can make difficult the result interpretation. Further research is therefore needed focusing on robust clinical validation of GP73 as a LF biomarker. Addressing analytical, biological, and clinical limitations will be critical to exploiting its potential for improving detection and monitoring of advanced LF.
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Affiliation(s)
- Łukasz Szternel
- Department of Laboratory Medicine, Nicolaus Copernicus University in Torun, Collegium Medicum in Bydgoszcz, Poland
| | - Bartłomiej Sobucki
- Department of Laboratory Medicine, Nicolaus Copernicus University in Torun, Collegium Medicum in Bydgoszcz, Poland
| | - Laura Wieprzycka
- Department of Laboratory Medicine, Nicolaus Copernicus University in Torun, Collegium Medicum in Bydgoszcz, Poland
| | - Magdalena Krintus
- Department of Laboratory Medicine, Nicolaus Copernicus University in Torun, Collegium Medicum in Bydgoszcz, Poland.
| | - Mauro Panteghini
- Department of Laboratory Medicine, Nicolaus Copernicus University in Torun, Collegium Medicum in Bydgoszcz, Poland
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20
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Katsivelos N, Spyrou N, Weber D, Vasova I, Ayuk F, Choe H, Hogan W, DeFilipp Z, Qayed M, Etra AM, Sandhu K, Kraus S, Olson T, Hexner E, Aguayo-Hiraldo P, Reshef R, Ullrich E, Schechter T, Kitko C, Chanswangphuwana C, Merli P, Akahoshi Y, Baez J, Eng G, Beheshti R, Kowalyk S, Morales G, Louloudis IE, Young R, Holler E, Nakamura R, Ferrara JLM, Levine JE. Serial Clinical and Biomarker Monitoring during Graft-Versus-Host Disease Treatment Identifies Distinct Risk Strata Including an Ultra-Low Risk Group. Transplant Cell Ther 2025; 31:10.e1-10.e9. [PMID: 39667999 PMCID: PMC11735289 DOI: 10.1016/j.jtct.2024.10.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2024] [Accepted: 10/21/2024] [Indexed: 12/14/2024]
Abstract
BACKGROUND The standard treatment for acute graft-vs-host disease (GVHD), a common complication following allogeneic hematopoietic cell transplant, remains prolonged courses of high dose corticosteroids. Previous attempts to decrease corticosteroid exposure during GVHD therapy failed because physicians lack the tools necessary to safely reduce and shorten therapy and fear loss of GVHD control in responding patients. Prior studies have shown that a serum biomarker risk score, the MAGIC algorithm probability (MAP), provided prognostic value within groups with similar clinical severity and that patients with GVHD that is Minnesota standard risk by clinical symptoms and who have a low MAP at the start of corticosteroid treatment represent a low risk group with good outcomes. OBJECTIVE This study tested the hypothesis that serial monitoring of GVHD symptoms and the MAP score in patients with low risk GVHD could provide further risk stratification, and would identify a subset with exceptionally low rates of failure with standard treatment, which we term ultra-low risk (ULR) GVHD who might benefit from reduced corticosteroid treatment. STUDY DESIGN Weekly monitoring of clinical symptoms and MAPs from initiation to day 14 of treatment was used to further divide 450 patients with low risk GVHD into groups with different outcomes, such as overall response rates at day 28 and non-relapse mortality at six-months. RESULTS 310/450 low risk patients (69%) who achieved clinical response by day 14 and had low MAPs at days 7 and 14 constituted an ultra-low risk (ULR) group. that experienced a significantly higher overall response rate at day 28 (93% vs 50%, p<0.001) that was sustained to day 56 (84% vs 45%, p<0.001) and significantly lower six-month NRM (4% vs 13%, p<0.001) compared to the non-ULR patients. Patients who achieved clinical response by day 14 but who developed a high MAP during monitoring (n=20) experienced six-fold higher six-month NRM than the ULR group (25% vs 4%, p<0.001). Among 120 patients who did not achieve a clinical response by day 14, the overwhelming majority (n=112) who maintained low MAPs at both days 7 and 14 of treatment experienced six-fold lower NRM at six months compared to patients with a high MAP at either time point (8% vs 50%, p<0.001). The majority of deaths within the ULR group were due to infections in patients with complete and sustained control of GVHD symptoms while the majority of deaths in the non-ULR group were due to poorly controlled GVHD. CONCLUSIONS Serial monitoring during treatment can identify a large subset of patients by day 14 who achieve excellent GVHD control but remain at risk for treatment complications with standard treatment and who might be suitable candidates for testing abbreviated corticosteroid courses.
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Affiliation(s)
- Nikolaos Katsivelos
- The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Nikolaos Spyrou
- The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Daniela Weber
- Department of Hematology and Oncology, Internal Medicine III, University of Regensburg, Regensburg, Germany
| | - Ingrid Vasova
- Department of Internal Medicine 5, Hematology and Oncology, Friedrich-Alexander-Universität Erlangen-Nürnberg and University Hospital Erlangen, Erlangen, Germany
| | - Francis Ayuk
- Department of Stem Cell Transplantation, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Hannah Choe
- Blood and Marrow Transplantation Program, Ohio State University, Columbus, Ohio
| | - William Hogan
- Division of Hematology, Mayo Clinic, Rochester, Minnesota
| | - Zachariah DeFilipp
- Hematopoietic Cell Transplant and Cellular Therapy Program, Massachusetts General Hospital, Boston, Massachusetts
| | - Muna Qayed
- Emory University School of Medicine, Atlanta, Georgia
| | - Aaron M Etra
- The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, New York
| | | | - Sabrina Kraus
- Department of Internal Medicine II, University Hospital of Würzburg, Würzburg, Germany
| | - Tim Olson
- Division of Oncology, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania; Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Elizabeth Hexner
- Department of Medicine and Abramson Cancer Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Paibel Aguayo-Hiraldo
- Cancer and Blood Disease Institute, Children's Hospital Los Angeles, University of Southern California, Los Angeles, California
| | - Ran Reshef
- Blood and Marrow Transplantation Program and Columbia Center for Translational Immunology, Columbia University Irving Medical Center, New York, New York
| | - Evelyn Ullrich
- Department of Pediatrics, Experimental Immunology and Cell Therapy, Goethe University Frankfurt, Frankfurt am Main, Germany
| | - Tal Schechter
- Division of Hematology/Oncology/BMT, The Hospital for Sick Children, University of Toronto, Toronto, Ontario, Canada
| | - Carrie Kitko
- Pediatric Stem Cell Transplant Program, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Chantiya Chanswangphuwana
- Division of Hematology and Center of Excellence in Translational Hematology, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Bangkok, Thailand
| | - Pietro Merli
- Department of Pediatric Hematology/Oncology and of Cell and Gene Therapy, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy
| | - Yu Akahoshi
- The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Janna Baez
- The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Gilbert Eng
- The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Rahnuma Beheshti
- The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Steven Kowalyk
- The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, New York
| | - George Morales
- The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, New York
| | | | - Rachel Young
- The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Ernst Holler
- Department of Hematology and Oncology, Internal Medicine III, University of Regensburg, Regensburg, Germany
| | | | - James L M Ferrara
- The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, New York
| | - John E Levine
- The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, New York.
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Molina-Pinelo S, Ferrer Sánchez I, Najarro P, Paz-Ares L, Fernández L, Castelló N, Richart López LA, Rodríguez Gambarte JD, Sanz García M, Salinas A, Suárez R, Romero-Romero B, Martín-Juan J, Viñuela ME, Butler RG, de Pedro N. Telomere-based risk models for the early diagnosis of lung cancer. Heliyon 2024; 10:e41040. [PMID: 39759351 PMCID: PMC11696659 DOI: 10.1016/j.heliyon.2024.e41040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Revised: 12/04/2024] [Accepted: 12/05/2024] [Indexed: 01/07/2025] Open
Abstract
Background The objective of this study was to evaluate the use of telomere length measurements as diagnostic biomarkers during early screening for lung cancer in high-risk patients. Methods This was a prospective study of patients undergoing lung cancer diagnosis at two Spanish hospitals between April 2017 and January 2020. Telomeres from peripheral blood lymphocytes were analysed by Telomere Analysis Technology, which is based in high-throughput quantitative fluorescent in situ hybridization. Analytical predictive models were developed using Random Forest from the dataset of telomere-associated variables (TAV). Receiver Operating Characteristic curves were used to characterize model performance. Findings From 233 patients undergoing lung cancer diagnosis, 106 patients aged 55-75 with lung cancer or lung cancer and COPD were selected. A control group (N = 453) included individuals of similar age with COPD or healthy. Telomere analysis showed that patients in the cancer cohort had a higher proportion of short telomeres compared to the control cohort. A TAV-based predictive model assuming a prevalence of 5 % of lung cancer among screened subjects showed an AUC of 0.98 %, a positive predictive value of 0.60 (95 % CI, 0.49-0.70) and a negative predictive value of 0.99 (95 % CI, 0.98-0.99) for prediction of lung cancer. Interpretation The results of this study suggest that TAV analysis in peripheral lymphocytes can be considered a useful diagnostic tool during screening for lung cancer in high-risk patients. TAV-based models could improve the predictive power of current initial diagnostic pathways, but further work is needed to integrate them into routine clinical evaluation. Funding Life Length SL.
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Affiliation(s)
- Sonia Molina-Pinelo
- Department de Medical Oncology, University Hospital Virgen del Rocío, Sevilla, Spain
- Institute of Biomedicine de Seville (CSIC), University of Seville, Seville, Spain
- Spanish Center for Biomedical Research Network in Oncology (CIBERONC), Madrid, Spain
| | - Irene Ferrer Sánchez
- Spanish Center for Biomedical Research Network in Oncology (CIBERONC), Madrid, Spain
- H12O-CNIO Lung Cancer Clinical Research Unit, Health Research Institute Hospital 12 de Octubre (i+12)/Spanish National Cancer Research Center (CNIO), Madrid, Spain
| | | | - Luis Paz-Ares
- Spanish Center for Biomedical Research Network in Oncology (CIBERONC), Madrid, Spain
- Department of Medical Oncology, Hospital Universitario 12 de octubre, Madrid, Spain
| | | | | | | | | | - Máximo Sanz García
- Department of Anaesthesiology, Hospital Universitario Príncipe de Asturias, Alcalá de Henares, Spain
| | - Ana Salinas
- Department de Medical Oncology, University Hospital Virgen del Rocío, Sevilla, Spain
- Institute of Biomedicine de Seville (CSIC), University of Seville, Seville, Spain
| | - Rocío Suárez
- Spanish Center for Biomedical Research Network in Oncology (CIBERONC), Madrid, Spain
- H12O-CNIO Lung Cancer Clinical Research Unit, Health Research Institute Hospital 12 de Octubre (i+12)/Spanish National Cancer Research Center (CNIO), Madrid, Spain
| | | | - José Martín-Juan
- Department of Pneumology, University Hospital Virgen del Rocio, Seville, Spain
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22
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Lemoine É, Neves Briard J, Rioux B, Gharbi O, Podbielski R, Nauche B, Toffa D, Keezer M, Lesage F, Nguyen DK, Bou Assi E. Computer-assisted analysis of routine EEG to identify hidden biomarkers of epilepsy: A systematic review. Comput Struct Biotechnol J 2024; 24:66-86. [PMID: 38204455 PMCID: PMC10776381 DOI: 10.1016/j.csbj.2023.12.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Revised: 12/05/2023] [Accepted: 12/05/2023] [Indexed: 01/12/2024] Open
Abstract
Background Computational analysis of routine electroencephalogram (rEEG) could improve the accuracy of epilepsy diagnosis. We aim to systematically assess the diagnostic performances of computed biomarkers for epilepsy in individuals undergoing rEEG. Methods We searched MEDLINE, EMBASE, EBM reviews, IEEE Explore and the grey literature for studies published between January 1961 and December 2022. We included studies reporting a computational method to diagnose epilepsy based on rEEG without relying on the identification of interictal epileptiform discharges or seizures. Diagnosis of epilepsy as per a treating physician was the reference standard. We assessed the risk of bias using an adapted QUADAS-2 tool. Results We screened 10 166 studies, and 37 were included. The sample size ranged from 8 to 192 (mean=54). The computed biomarkers were based on linear (43%), non-linear (27%), connectivity (38%), and convolutional neural networks (10%) models. The risk of bias was high or unclear in all studies, more commonly from spectrum effect and data leakage. Diagnostic accuracy ranged between 64% and 100%. We observed high methodological heterogeneity, preventing pooling of accuracy measures. Conclusion The current literature provides insufficient evidence to reliably assess the diagnostic yield of computational analysis of rEEG. Significance We provide guidelines regarding patient selection, reference standard, algorithms, and performance validation.
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Affiliation(s)
- Émile Lemoine
- Department of Neurosciences, University of Montreal, Canada
- Institute of biomedical engineering, Polytechnique Montreal, Canada
- University of Montreal Hospital Center’s Research Center, Canada
| | - Joel Neves Briard
- Department of Neurosciences, University of Montreal, Canada
- University of Montreal Hospital Center’s Research Center, Canada
| | - Bastien Rioux
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | - Oumayma Gharbi
- Department of Neurosciences, University of Montreal, Canada
- University of Montreal Hospital Center’s Research Center, Canada
| | | | - Bénédicte Nauche
- University of Montreal Hospital Center’s Research Center, Canada
| | - Denahin Toffa
- Department of Neurosciences, University of Montreal, Canada
- University of Montreal Hospital Center’s Research Center, Canada
| | - Mark Keezer
- Department of Neurosciences, University of Montreal, Canada
- School of Public Health, University of Montreal, Canada
- Stichting Epilepsie Instellingen Nederland (SEIN), Heemstede, the Netherlands
| | - Frédéric Lesage
- Institute of biomedical engineering, Polytechnique Montreal, Canada
| | - Dang K. Nguyen
- Department of Neurosciences, University of Montreal, Canada
- University of Montreal Hospital Center’s Research Center, Canada
| | - Elie Bou Assi
- Department of Neurosciences, University of Montreal, Canada
- University of Montreal Hospital Center’s Research Center, Canada
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23
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Smith LM, Mahoney DW, Bamlet WR, Yu F, Liu S, Goggins MG, Darabi S, Majumder S, Wang QL, Coté GA, Demeure MJ, Zhang Z, Srivastava S, Chawla A, Izmirlian G, Olson JE, Wolpin BM, Genkinger JM, Zaret KS, Brand R, Koay EJ, Oberg AL. Early detection of pancreatic cancer: Study design and analytical considerations in biomarker discovery and early phase validation studies. Pancreatology 2024; 24:1265-1279. [PMID: 39516175 PMCID: PMC11780679 DOI: 10.1016/j.pan.2024.10.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/15/2024] [Revised: 10/05/2024] [Accepted: 10/27/2024] [Indexed: 11/16/2024]
Abstract
OBJECTIVES Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal disease that is challenging to detect at an early stage. Biomarkers are needed that can detect PDAC early in the course of disease when interventions lead to the best outcomes. We highlight study design and statistical considerations that inform pancreatic cancer early detection biomarker evaluation. METHODS We describe experimental design strategies in this setting useful for streamlining biomarker evaluation at each Early Detection Research Network (EDRN) phase of biomarker development. We break the early EDRN phases into sub-phases, proposing objectives, study design strategies, and biomarker performance benchmarks. RESULTS The goal of early detection in populations at high-risk of PDAC is described. Evaluating biomarker behavior in patients under surveillance without disease can winnow candidate biomarkers. Potential resources for biomarker validation studies are described. CONCLUSIONS Multisite and multidisciplinary collaboration can facilitate study design strategies in this lethal but low incidence disease and streamline the path from biomarker discovery to clinical use. Improvements in analytical and experimental design methods could help accelerate biomarker evaluation through the phases of biomarker development.
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Affiliation(s)
- Lynette M Smith
- Department of Biostatistics, University of Nebraska Medical Center, Omaha, NE, USA
| | - Douglas W Mahoney
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - William R Bamlet
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Fang Yu
- Department of Biostatistics, University of Nebraska Medical Center, Omaha, NE, USA
| | - Suyu Liu
- Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Michael G Goggins
- Departments of Pathology and Oncology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Sourat Darabi
- Hoag Family Cancer Institute, Newport Beach, CA, USA
| | - Shounak Majumder
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN, USA
| | - Qiao-Li Wang
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | - Gregory A Coté
- Department of Medicine, Oregon Health & Science University, Portland, OR, USA
| | | | - Zhen Zhang
- Departments of Pathology and Oncology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | | | - Akhil Chawla
- Department of Surgery, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | | | - Janet E Olson
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Brian M Wolpin
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | - Jeanine M Genkinger
- Department of Epidemiology, Mailman School of Public Health, Columbia University, NY, NY, USA
| | - Kenneth S Zaret
- Department of Cell and Developmental Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Randall Brand
- Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Eugene J Koay
- Department of Gastrointestinal Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Ann L Oberg
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA.
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24
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Rios CAO, Qayed M, Etra AM, Reshef R, Newcomb R, Yuhasz N, Hexner EO, Aguayo-Hiraldo P, Merli P, Hogan WJ, Weber D, Kitko CL, Ayuk F, Eder M, Grupp SA, Kraus S, Sandhu K, Ullrich E, Vasova I, Wölfl M, Baez J, Beheshti R, Eng G, Gleich S, Katsivelos N, Kowalyk S, Louloudis IE, Morales G, Spyrou N, Young R, Nakamura R, Levine JE, Ferrara JLM, Akahoshi Y. Differences in Acute Graft-Versus-Host Disease (GVHD) Severity and Its Outcomes Between Black and White Patients. Transplant Cell Ther 2024; 30:1061.e1-1061.e10. [PMID: 39222793 PMCID: PMC11540730 DOI: 10.1016/j.jtct.2024.08.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2024] [Revised: 07/31/2024] [Accepted: 08/23/2024] [Indexed: 09/04/2024]
Abstract
Acute graft-versus-host disease (GVHD) is a significant complication following hematopoietic stem cell transplantation (HCT). Although recent advancements in GVHD prophylaxis have resulted in successful HCT across HLA barriers and expanded access to HCT for racial minorities, less is known about how race affects the severity and outcomes of acute GVHD. This study examines differences in the clinical course of acute GVHD and the prognostic value of GVHD biomarkers for Black and White recipients. We conducted a retrospective analysis of patients in the Mount Sinai Acute GVHD International Consortium (MAGIC) database who underwent HCT between 2014 and 2021 to describe the difference in clinical course of acute GVHD and significance of GVHD biomarkers between Black and White recipients. We used propensity score matching to generate a 1:3 matched cohort of 234 Black patients and 702 White patients with similar baseline characteristics. In the first year after HCT Black patients experienced a higher cumulative incidence of grade III-IV acute GVHD (17% versus 12%, P = 0.050), higher nonrelapse mortality (NRM; 18% versus 12%, P = .009), and lower overall survival that trended toward statistical significance (73% versus 79%, P = .071) compared to White patients. The difference in NRM in the first year was even greater among Black patients who developed GVHD than White patients (24% versus 14%, P = .041). The distribution of low, intermediate, and high MAGIC biomarker scores at the time of treatment was similar across racial groups (P = .847), however, Black patients with high biomarker scores experienced significantly worse NRM than White patients (71% versus 32%, P = .010). Our data indicate that Black patients are at a higher risk of NRM following HCT, primarily from a higher incidence of severe GVHD. Serum biomarkers at treatment initiation can stratify patients for risk of NRM across races, however Black patients with high biomarker scores had a significantly greater NRM risk. These results suggest a need for strategies that mitigate the higher risk for poor GVHD outcomes among Black patients.
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Affiliation(s)
- Carlos A Ortega Rios
- The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Muna Qayed
- Emory University School of Medicine, Atlanta, Georgia
| | - Aaron M Etra
- The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Ran Reshef
- Blood and Marrow Transplantation Program and Columbia Center for Translational Immunology, Columbia University Irving Medical Center, New York, New York
| | - Richard Newcomb
- Hematopoietic Cell Transplant and Cellular Therapy Program, Massachusetts General Hospital, Boston, Massachusetts
| | - Nicholas Yuhasz
- Division of Hematology, Blood and Marrow Transplantation Program, The Ohio State University Comprehensive Cancer Center, Columbus, Ohio
| | - Elizabeth O Hexner
- Department of Medicine and Abramson Cancer Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Paibel Aguayo-Hiraldo
- Cancer and Blood Disease Institute, Children's Hospital Los Angeles, University of Southern California, Los Angeles, California
| | - Pietro Merli
- Department of Pediatric Hematology/Oncology and of Cell and Gene Therapy, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy
| | | | - Daniela Weber
- Department of Hematology and Oncology, Internal Medicine III, University of Regensburg, Regensburg, Germany
| | - Carrie L Kitko
- Pediatric Stem Cell Transplant Program, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Francis Ayuk
- Department of Stem Cell Transplantation, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Matthias Eder
- Department of Hematology, Hemostasis, Oncology and Stem Cell Transplantation, Hannover Medical School, Hannover, Germany
| | - Stephan A Grupp
- Division of Oncology, Department of Pediatrics, Perelman School of Medicine, Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Sabrina Kraus
- Department of Internal Medicine II, University Hospital of Würzburg, Würzburg, Germany
| | | | - Evelyn Ullrich
- Department of Pediatrics, Experimental Immunology and Cell Therapy, Goethe University Frankfurt, Frankfurt, Germany
| | - Ingrid Vasova
- Department of Internal Medicine 5, Hematology and Oncology, Friedrich-Alexander-Universität Erlangen-Nürnberg and University Hospital Erlangen, Erlangen, Germany
| | - Matthias Wölfl
- Pediatric Blood and Marrow Transplantation Program, Children's Hospital, University Hospital of Würzburg, Würzburg, Germany
| | - Janna Baez
- The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Rahnuma Beheshti
- The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Gilbert Eng
- The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Sigrun Gleich
- Department of Pediatrics, Experimental Immunology and Cell Therapy, Goethe University Frankfurt, Frankfurt, Germany
| | - Nikolaos Katsivelos
- The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Steven Kowalyk
- The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, New York
| | | | - George Morales
- The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Nikolaos Spyrou
- The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Rachel Young
- The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, New York
| | | | - John E Levine
- The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, New York
| | - James L M Ferrara
- The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Yu Akahoshi
- The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, New York.
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25
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El-Serag HB, Thrift AP, Duong H, Ning J, Khaderi S, Singal AG, Asrani SK, Marrero JA, Powell H, Rizwan K, Najjar O, Amos CI, Luster M, Al-Sarraj A, Salem E, Scheurer ME, Chhatwal J, Kaochar S, Kanwal F. Serum levels of total bile acids are associated with an increased risk of HCC in patients with cirrhosis. Hepatol Commun 2024; 8:e0545. [PMID: 39652379 PMCID: PMC11469875 DOI: 10.1097/hc9.0000000000000545] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/24/2024] [Accepted: 08/05/2024] [Indexed: 12/13/2024] Open
Abstract
BACKGROUND Previous studies have reported higher circulating bile acid levels in patients with HCC compared to healthy controls. However, the association between prediagnostic bile acid levels and HCC risk among patients with cirrhosis is unclear. METHODS We measured total BA (TBA) concentration in serum samples collected from a prospective cohort of patients with cirrhosis who were followed until the development of HCC, death, or last study date. Competing risk proportional hazard-adjusted models were used to estimate the association between tertiles of serum TBA levels and the risk of developing HCC. We quantified the incremental predictive value of serum bile acid when added to a previously validated clinical model. RESULTS We analyzed data from 940 patients with cirrhosis, of whom 68 patients progressed to HCC during 3406 person-years of follow-up. Higher baseline serum TBA level was significantly associated with an increased risk of developing HCC with an adjusted HR of 3.69 (95% CI = 1.85-7.37) for the highest versus lowest tertile. TBA levels significantly increased predictive ability for progression to HCC at 2 years of follow-up; the c statistic increased from 0.74 to 0.80 (p < 0.001). There was evidence for a significant interaction between TBA level and hepatitis C (p = 0.04). CONCLUSIONS In a large prospective cohort study, the prediagnostic serum level of TBAs was associated with a significant increase in the risk of developing HCC among patients with multi-etiology cirrhosis. The TBA-associated risk was additive to that of established demographic and clinical predictors.
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Affiliation(s)
- Hashem B. El-Serag
- Section of Gastroenterology and Hepatology, Department of Medicine, Baylor College of Medicine, Houston, Texas, USA
- Section of Health Services Research, Department of Medicine, Baylor College of Medicine, Houston, Texas, USA
- VA HSR&D Center for Innovations in Quality, Effectiveness, and Safety (IQuESt), Michael E. DeBakey Veterans Affairs Medical Center, Houston, Texas, USA
| | - Aaron P. Thrift
- Section of Epidemiology and Population Sciences, Department of Medicine, Baylor College of Medicine, Houston, Texas, USA
- Department of Medicine, Dan L. Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, Texas, USA
| | - Hao Duong
- VA HSR&D Center for Innovations in Quality, Effectiveness, and Safety (IQuESt), Michael E. DeBakey Veterans Affairs Medical Center, Houston, Texas, USA
| | - Jing Ning
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Saira Khaderi
- Section of Gastroenterology and Hepatology, Department of Medicine, Baylor College of Medicine, Houston, Texas, USA
| | - Amit G. Singal
- Department of Internal Medicine, UT Southwestern Medical Center, Dallas, Texas, USA
| | - Sumeet K. Asrani
- Department of Medicine, Baylor University Medical Center, Baylor Scott and White, Dallas, Texas, USA
| | - Jorge A. Marrero
- Division of Gastroenterology and Hepatology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Hannah Powell
- Section of Hematology and Oncology, Department of Medicine, Baylor College of Medicine, Houston, Texas, USA
| | - Kinza Rizwan
- Section of Hematology and Oncology, Department of Medicine, Baylor College of Medicine, Houston, Texas, USA
| | - Omar Najjar
- Section of Hematology and Oncology, Department of Medicine, Baylor College of Medicine, Houston, Texas, USA
| | - Christopher I. Amos
- Section of Epidemiology and Population Sciences, Department of Medicine, Baylor College of Medicine, Houston, Texas, USA
| | - Michelle Luster
- Section of Gastroenterology and Hepatology, Department of Medicine, Baylor College of Medicine, Houston, Texas, USA
| | - Abeer Al-Sarraj
- Section of Gastroenterology and Hepatology, Department of Medicine, Baylor College of Medicine, Houston, Texas, USA
- Section of Health Services Research, Department of Medicine, Baylor College of Medicine, Houston, Texas, USA
- VA HSR&D Center for Innovations in Quality, Effectiveness, and Safety (IQuESt), Michael E. DeBakey Veterans Affairs Medical Center, Houston, Texas, USA
| | - Emad Salem
- Section of Gastroenterology and Hepatology, Department of Medicine, Baylor College of Medicine, Houston, Texas, USA
- Section of Health Services Research, Department of Medicine, Baylor College of Medicine, Houston, Texas, USA
| | - Michael E. Scheurer
- Department of Medicine, Dan L. Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, Texas, USA
- Department of Pediatrics, Baylor College of Medicine and Texas Children’s Hospital, Houston, Texas, USA
| | - Jagpreet Chhatwal
- Department of Radiology, Institute for Technology Assessment, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Salma Kaochar
- Section of Epidemiology and Population Sciences, Department of Medicine, Baylor College of Medicine, Houston, Texas, USA
- Department of Medicine, Dan L. Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, Texas, USA
| | - Fasiha Kanwal
- Section of Gastroenterology and Hepatology, Department of Medicine, Baylor College of Medicine, Houston, Texas, USA
- Section of Health Services Research, Department of Medicine, Baylor College of Medicine, Houston, Texas, USA
- VA HSR&D Center for Innovations in Quality, Effectiveness, and Safety (IQuESt), Michael E. DeBakey Veterans Affairs Medical Center, Houston, Texas, USA
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26
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Liss MA, Zeltser N, Zheng Y, Lopez C, Liu M, Patel Y, Yamaguchi TN, Eng SE, Tian M, Semmes OJ, Lin DW, Brooks JD, Wei JT, Klein EA, Tewari AK, Mosquera JM, Khani F, Robinson BD, Aasad M, Troyer DA, Kagan J, Sanda MG, Thompson IM, Boutros PC, Leach RJ. Upgrading of Grade Group 1 Prostate Cancer at Prostatectomy: Germline Risk Factors in a Prospective Cohort. Cancer Epidemiol Biomarkers Prev 2024; 33:1500-1511. [PMID: 39158404 PMCID: PMC11528207 DOI: 10.1158/1055-9965.epi-24-0326] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Revised: 05/21/2024] [Accepted: 08/14/2024] [Indexed: 08/20/2024] Open
Abstract
BACKGROUND Localized prostate tumors show significant spatial heterogeneity, with regions of high-grade disease adjacent to lower grade disease. Consequently, prostate cancer biopsies are prone to sampling bias, potentially leading to underestimation of tumor grade. To study the clinical, epidemiologic, and molecular hallmarks of this phenomenon, we conducted a prospective study of grade upgrading: differences in detected prostate cancer grade between biopsy and surgery. METHODS We established a prospective, multi-institutional cohort of men with grade group 1 (GG1) prostate cancer on biopsy who underwent radical prostatectomy. Upgrading was defined as detection of GG2+ in the resected tumor. Germline DNA from 192 subjects was subjected to whole-genome sequencing to quantify ancestry, pathogenic variants in DNA damage response genes, and polygenic risk. RESULTS Of 285 men, 67% upgraded at surgery. PSA density and percent of cancer in pre-prostatectomy positive biopsy cores were significantly associated with upgrading. No assessed genetic risk factor was predictive of upgrading, including polygenic risk scores for prostate cancer diagnosis. CONCLUSIONS In a cohort of patients with low-grade prostate cancer, a majority upgraded at radical prostatectomy. PSA density and percent of cancer in pre-prostatectomy positive biopsy cores portended the presence of higher-grade disease, while germline genetics was not informative in this setting. Patients with low-risk prostate cancer, but elevated PSA density or percent cancer in positive biopsy cores, may benefit from repeat biopsy, additional imaging or other approaches to complement active surveillance. IMPACT Further risk stratification of patients with low-risk prostate cancer may provide useful context for active surveillance decision-making.
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Affiliation(s)
- Michael A. Liss
- Department of Urology, University of Texas Health San Antonio, San Antonio, Texas
| | - Nicole Zeltser
- Department of Human Genetics, University of California Los Angeles, Los Angeles, California
- Jonsson Comprehensive Cancer Center, University of California Los Angeles, Los Angeles, California
| | - Yingye Zheng
- Department of Biostatistics, Fred Hutchinson Cancer Center, Seattle, Washington
| | - Camden Lopez
- Department of Biostatistics, Fred Hutchinson Cancer Center, Seattle, Washington
| | - Menghan Liu
- Department of Biostatistics, Fred Hutchinson Cancer Center, Seattle, Washington
| | - Yash Patel
- Jonsson Comprehensive Cancer Center, University of California Los Angeles, Los Angeles, California
- Institute of Precision Health, University of California Los Angeles, Los Angeles, California
| | - Takafumi N. Yamaguchi
- Department of Human Genetics, University of California Los Angeles, Los Angeles, California
- Jonsson Comprehensive Cancer Center, University of California Los Angeles, Los Angeles, California
- Institute of Precision Health, University of California Los Angeles, Los Angeles, California
| | - Stefan E. Eng
- Jonsson Comprehensive Cancer Center, University of California Los Angeles, Los Angeles, California
| | - Mao Tian
- Department of Human Genetics, University of California Los Angeles, Los Angeles, California
- Jonsson Comprehensive Cancer Center, University of California Los Angeles, Los Angeles, California
- Institute of Precision Health, University of California Los Angeles, Los Angeles, California
| | - Oliver J. Semmes
- Department of Microbiology and Molecular Cell Biology, Leroy T. Canoles Jr. Cancer Research Center, Eastern Virginia Medical School, Norfolk, Virginia
| | - Daniel W. Lin
- Division of Public Health Sciences, Department of Urology, Fred Hutchinson Cancer Center, University of Washington, Seattle, Washington
| | - James D. Brooks
- Department of Urology, Stanford University, Palo Alto, California
| | - John T. Wei
- Department of Urology, University of Michigan, Ann Arbor, Michigan
| | - Eric A. Klein
- Glickman Urological and Kidney Institute, Cleveland Clinic Lerner College of Medicine, Cleveland, Ohio
| | - Ashutosh K. Tewari
- Department of Urology, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Juan Miguel Mosquera
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, New York
| | - Francesca Khani
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, New York
| | - Brian D. Robinson
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, New York
| | - Muhammad Aasad
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, New York
| | - Dean A. Troyer
- Department of Microbiology and Molecular Cell Biology, Leroy T. Canoles Jr. Cancer Research Center, Eastern Virginia Medical School, Norfolk, Virginia
- Department of Pathology, University of Texas Health San Antonio, San Antonio, Texas
| | - Jacob Kagan
- Division of Cancer Prevention, National Cancer Institute, Bethesda, Maryland
| | | | - Ian M. Thompson
- The Children’s Hospital of San Antonio Foundation and Christus Health, San Antonio, Texas
| | - Paul C. Boutros
- Department of Human Genetics, University of California Los Angeles, Los Angeles, California
- Jonsson Comprehensive Cancer Center, University of California Los Angeles, Los Angeles, California
- Institute of Precision Health, University of California Los Angeles, Los Angeles, California
- Department of Urology, University of California Los Angeles, Los Angeles, California
| | - Robin J. Leach
- Department of Cell Systems and Anatomy, University of Texas Health San Antonio, San Antonio, Texas
- Department of Pediatrics, University of Texas Health San Antonio, San Antonio, Texas
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27
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Nguyen NT, Pennello GA. DxGoals: A Software Tool for Determining and Analyzing Clinically Meaningful Classification Accuracy Goals for Diagnostic Tests. J Appl Lab Med 2024; 9:952-962. [PMID: 39225456 DOI: 10.1093/jalm/jfae054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Accepted: 05/01/2024] [Indexed: 09/04/2024]
Abstract
BACKGROUND To evaluate diagnostic tests for low prevalence conditions, classification accuracy metrics such as sensitivity, specificity, and positive likelihood ratio (PLR) and negative likelihood ratio (NLR) are advantageous because they are prevalence-independent and thus estimable in studies enriched for the condition. However, classification accuracy goals are often chosen without a clear understanding of whether they are clinically meaningful. Pennello (2021) proposed a risk stratification framework for determining classification accuracy goals. A software application is needed to determine the goals and provide data analysis. METHODS We introduce DxGoals, a freely available, R-Shiny software application for determining, visualizing, and analyzing classification accuracy goals for diagnostic tests. Given prevalence p for the target condition and specification that a test's positive and negative predictive values PPVand NPV=1-cNPV should satisfy PPV>PPV* and cNPV RESULTS We illustrate DxGoals on tests for penicillin allergy, ovarian cancer, and cervical cancer. The inputs cNPV*,p, and PPV* were informed by clinical management guidelines. CONCLUSIONS DxGoals facilitates determination, visualization, and analysis of clinically meaningful standalone and comparative classification accuracy goals. It is a potentially useful tool for diagnostic test evaluation.
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Affiliation(s)
- Ngoc-Ty Nguyen
- U.S. Food and Drug Administration, Center for Biologics Evaluation and Research, Silver Spring, MD, United States
| | - Gene A Pennello
- U.S. Food and Drug Administration, Center for Devices and Radiological Health, Silver Spring, MD, United States
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28
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Zheng Y, Wagner PD, Singal AG, Hanash SM, Srivastava S, Huang Y, Zhao YQ, Chari ST, Marquez G, Etizioni R, Marsh TL, Feng Z. Designing Rigorous and Efficient Clinical Utility Studies for Early Detection Biomarkers. Cancer Epidemiol Biomarkers Prev 2024; 33:1150-1157. [PMID: 39223980 PMCID: PMC11534000 DOI: 10.1158/1055-9965.epi-23-1594] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Revised: 03/11/2024] [Accepted: 06/07/2024] [Indexed: 09/04/2024] Open
Abstract
Before implementing a biomarker in routine clinical care, it must demonstrate clinical utility by leading to clinical actions that positively affect patient-relevant outcomes. Randomly controlled early detection utility trials, especially those targeting mortality endpoint, are challenging due to their high costs and prolonged duration. Special design considerations are required to determine the clinical utility of early detection assays. This commentary reports on discussions among the National Cancer Institute's Early Detection Research Network investigators, outlining the recommended process for carrying out single-organ biomarker-driven clinical utility studies. We present the early detection utility studies in the context of phased biomarker development. We describe aspects of the studies related to the features of biomarker tests, the clinical context of endpoints, the performance criteria for later phase evaluation, and study size. We discuss novel adaptive design approaches for improving the efficiency and practicality of clinical utility trials. We recommend using multiple strategies, including adopting real-world evidence, emulated trials, and mathematical modeling to circumvent the challenges in conducting early detection utility trials.
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Affiliation(s)
- Yingye Zheng
- Biostatistics Program, Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA
| | - Paul D. Wagner
- Division of Cancer Prevention, National Cancer Institute, Rockville, MD
| | - Amit G. Singal
- Division of Digestive and Liver Diseases, UT Southwestern Medical Center, Dallas TX
| | - Samir M Hanash
- Department of Clinical Cancer Prevention, McCombs Institute for Cancer Detection and Treatment at MD Anderson Cancer Center, Houston, TX
| | - Sudhir Srivastava
- Division of Cancer Prevention, National Cancer Institute, Rockville, MD
| | - Ying Huang
- Biostatstics, Bioinformatics and Epidemiology Program, Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA
| | - Ying-Qi Zhao
- Biostatistics Program, Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA
| | - Suresh T Chari
- Department of Gastroenterology, Hepatology & Nutrition, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Guillermo Marquez
- Division of Cancer Prevention, National Cancer Institute, Rockville, MD
| | - Ruth Etizioni
- Biostatistics Program, Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA
| | - Tracey L Marsh
- Biostatistics Program, Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA
| | - Ziding Feng
- Biostatistics Program, Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA
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29
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Barnhart KT, Bollig KJ, Senapati S, Takacs P, Robins JC, Haisenleder DJ, Beer LA, Savaris RF, Koelper NC, Speicher DW, Chittams J, Bao J, Wen Z, Feng Y, Kim M, Mumford S, Shen L, Gimotty P. Multiplexed serum biomarkers to discriminate nonviable and ectopic pregnancy. Fertil Steril 2024; 122:482-493. [PMID: 38677710 DOI: 10.1016/j.fertnstert.2024.04.028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Revised: 04/18/2024] [Accepted: 04/18/2024] [Indexed: 04/29/2024]
Abstract
OBJECTIVE To evaluate combinations of candidate biomarkers to develop a multiplexed prediction model for identifying the viability and location of an early pregnancy. In this study, we assessed 24 biomarkers with multiple machine learning-based methodologies to assess if multiplexed biomarkers may improve the diagnosis of normal and abnormal early pregnancies. DESIGN A nested case-control design evaluated the predictive ability and discrimination of biomarkers in patients at risk of early pregnancy failure in the first trimester to classify viability and location. SETTING Three university hospitals. PATIENTS A total of 218 individuals with pain and/or bleeding in early pregnancy: 75 had an ongoing intrauterine gestation; 68 had ectopic pregnancies (EPs); and 75 had miscarriages. INTERVENTIONS Serum levels of 24 biomarkers were assessed in the same patients. Multiple machine learning-based methodologies to evaluate combinations of these top candidates to develop a multiplexed prediction model for the identification of a nonviable pregnancy (ongoing intrauterine pregnancy vs. miscarriage or EP) and an EP (EP vs. ongoing intrauterine pregnancy or miscarriage). MAIN OUTCOME MEASURES The predicted classification using each model was compared with the actual diagnosis, and sensitivity, specificity, positive predictive value, negative predictive value, conclusive classification, and accuracy were calculated. RESULTS Models using classification regression tree analysis using 3 (pregnancy-specific beta-1-glycoprotein 3 [PSG3], chorionic gonadotropin-alpha subunit, and pregnancy-associated plasma protein-A) biomarkers were able to predict a maximum sensitivity of 93.3% and a maximum specificity of 98.6%. The model with the highest accuracy was 97.4% (with 70.2% receiving classification). Models using an overlapping group of 3 (soluble fms-like tyrosine kinase-1, PSG3, and tissue factor pathway inhibitor 2) biomarkers achieved a maximum sensitivity of 98.5% and a maximum specificity of 95.3%. The model with the highest accuracy was 94.4% (with 65.6% receiving classification). When the models were used simultaneously, the conclusive classification increased to 72.7% with an accuracy of 95.9%. The predictive ability of the biomarkers in the random forest produced similar test characteristics when using 11 predictive biomarkers. CONCLUSION We have demonstrated a pool of biomarkers from divergent biological pathways that can be used to classify individuals with potential early pregnancy loss. The biomarkers choriogonadotropin alpha, pregnancy-associated plasma protein-A, and PSG3 can be used to predict viability, and soluble fms-like tyrosine kinase-1, tissue factor pathway inhibitor 2, and PSG3 can be used to predict pregnancy location.
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Affiliation(s)
- Kurt T Barnhart
- Department of Obstetrics and Gynecology, University of Pennsylvania, Philadelphia, Pennsylvania.
| | - Kassie J Bollig
- Department of Obstetrics and Gynecology, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Suneeta Senapati
- Department of Obstetrics and Gynecology, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Peter Takacs
- Department of Obstetrics and Gynecology, Eastern Virginia Medical School, Norfolk, Virginia
| | - Jared C Robins
- Department of Obstetrics and Gynecology, Northwestern University, Chicago, Illinois
| | - Daniel J Haisenleder
- Department of Internal Medicine and the Center for Research in Reproduction, University of Virginia, Charlottesville, Virginia
| | - Lynn A Beer
- Center for Systems & Computational Biology, The Wistar Institute, Philadelphia, Pennsylvania
| | - Ricardo F Savaris
- Department of Gynecology and Obstetrics, School of Medicine, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | - Nathanael C Koelper
- Department of Obstetrics and Gynecology, University of Pennsylvania, Philadelphia, Pennsylvania
| | - David W Speicher
- Center for Systems & Computational Biology, The Wistar Institute, Philadelphia, Pennsylvania
| | - Jesse Chittams
- Biostatistics Consulting Unit, University of Pennsylvania School of Nursing, Philadelphia, Pennsylvania
| | - Jingxuan Bao
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania
| | - Zixuan Wen
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania
| | - Yanbo Feng
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania
| | - Mansu Kim
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania
| | - Sunni Mumford
- Department of Obstetrics and Gynecology, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania
| | - Li Shen
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania
| | - Phyllis Gimotty
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania
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30
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Kumada T, Toyoda H, Ogawa S, Gotoh T, Yoshida Y, Yamahira M, Hirooka M, Koizumi Y, Hiasa Y, Tamai T, Kuromatsu R, Matsuzaki T, Suehiro T, Kamada Y, Sumida Y, Tanaka J, Shimizu M. Diagnostic performance of shear wave measurement in the detection of hepatic fibrosis: A multicenter prospective study. Hepatol Res 2024; 54:851-858. [PMID: 38349813 DOI: 10.1111/hepr.14026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/07/2024] [Revised: 01/27/2024] [Accepted: 01/29/2024] [Indexed: 02/15/2024]
Abstract
AIM This study aimed to establish the shear wave measurement (SWM) cut-off value for each fibrosis stage using magnetic resonance (MR) elastography values as a reference standard. METHODS We prospectively analyzed 594 patients with chronic liver disease who underwent SWM and MR elastography. Correlation coefficients (were analyzed, and the diagnostic value was evaluated by the area under the receiver operating characteristic curve. Liver stiffness was categorized by MR elastography as F0 (<2.61 kPa), F1 (≥2.61 kPa, <2.97 kPa, any fibrosis), F2 (≥2.97 kPa, <3.62 kPa, significant fibrosis), F3 (≥3.62 kPa, <4.62 kPa, advanced fibrosis), or F4 (≥4.62 kPa, cirrhosis). RESULTS The median SWM values increased significantly with increasing fibrosis stage (p < 0.001). The correlation coefficient between SWM and MR elastography values was 0.793 (95% confidence interval 0.761-0.821). The correlation coefficients between SWM and MR elastography values significantly decreased with increasing body mass index and skin-capsular distance; skin-capsular distance values were associated with significant differences in sensitivity, specificity, accuracy, or positive predictive value, whereas body mass index values were not. The best cut-off values for any fibrosis, significant fibrosis, advanced fibrosis, and cirrhosis were 6.18, 7.09, 8.05, and 10.89 kPa, respectively. CONCLUSIONS This multicenter study in a large number of patients established SWM cut-off values for different degrees of fibrosis in chronic liver diseases using MR elastography as a reference standard. It is expected that these cut-off values will be applied to liver diseases in the future.
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Affiliation(s)
- Takashi Kumada
- Department of Nursing, Faculty of Nursing, Gifu Kyoritsu University, Ogaki, Japan
| | - Hidenori Toyoda
- Department of Gastroenterology and Hepatology, Ogaki Municipal Hospital, Ogaki, Japan
| | - Sadanobu Ogawa
- Department of Imaging Diagnosis, Ogaki Municipal Hospital, Ogaki, Japan
| | - Tatsuya Gotoh
- Department of Imaging Diagnosis, Ogaki Municipal Hospital, Ogaki, Japan
| | - Yuichi Yoshida
- Department of Gastroenterology and Hepatology, Suita Municipal Hospital, Suita, Japan
| | - Masahiro Yamahira
- Department of Clinical Laboratory Medicine, Suita Municipal Hospital, Suita, Japan
| | - Masashi Hirooka
- Department of Gastroenterology and Metabology, Ehime University Graduate School of Medicine, Toon, Ehime, Japan
| | - Yohei Koizumi
- Department of Gastroenterology and Metabology, Ehime University Graduate School of Medicine, Toon, Ehime, Japan
| | - Yoichi Hiasa
- Department of Gastroenterology and Metabology, Ehime University Graduate School of Medicine, Toon, Ehime, Japan
| | - Tsutomu Tamai
- Department of Gastroenterology, Kagoshima City Hospital, Kagoshima, Japan
| | - Ryoko Kuromatsu
- Division of Gastroenterology, Department of Medicine, Kurume University School of Medicine, Kurume, Japan
| | | | - Tomoyuki Suehiro
- Clinical Research Center, National Hospital Organization Nagasaki Medical Center, Omura, Japan
| | - Yoshihiro Kamada
- Department of Advanced Metabolic Hepatology, Osaka University Graduate School of Medicine, Suita, Japan
| | - Yoshio Sumida
- Graduate School of Healthcare Management, International University of Health and Welfare, Minatoku, Tokyo, Japan
| | - Junko Tanaka
- Department of Epidemiology, Infectious Disease Control and Prevention, Hiroshima University Institute of Biomedical and Health Sciences, Hiroshima, Japan
| | - Masahito Shimizu
- Department of Gastroenterology/Internal Medicine, Graduate School of Medicine, Gifu University, Gifu, Japan
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31
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González Á, López-Borrego S, Sandúa A, Vales-Gomez M, Alegre E. Extracellular vesicles in cancer: challenges and opportunities for clinical laboratories. Crit Rev Clin Lab Sci 2024; 61:435-457. [PMID: 38361287 DOI: 10.1080/10408363.2024.2309935] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Revised: 01/03/2024] [Accepted: 01/22/2024] [Indexed: 02/17/2024]
Abstract
Extracellular vesicles (EVs) are nano-sized particles secreted by most cells. They transport different types of biomolecules (nucleic acids, proteins, and lipids) characteristic of their tissue or cellular origin that can mediate long-distance intercellular communication. In the case of cancer, EVs participate in tumor progression by modifying the tumor microenvironment, favoring immune tolerance and metastasis development. Consequently, EVs have great potential in liquid biopsy for cancer diagnosis, prognosis and follow-up. In addition, EVs could have a role in cancer treatment as a targeted drug delivery system. The intense research in the EV field has resulted in hundreds of patents and the creation of biomedical companies. However, methodological issues and heterogeneity in EV composition have hampered the advancement of EV validation trials and the development of EV-based diagnostic and therapeutic products. Consequently, only a few EV biomarkers have moved from research to clinical laboratories, such as the ExoDx Prostate IntelliScore (EPI) test, a CLIA/FDA-approved EV prostate cancer diagnostic test. In addition, the number of large-scale multicenter studies that would clearly define biomarker performance is limited. In this review, we will critically describe the different types of EVs, the methods for their enrichment and characterization, and their biological role in cancer. Then, we will specially focus on the parameters to be considered for the translation of EV biology to the clinic laboratory, the advances already made in the field of EVs related to cancer diagnosis and treatment, and the issues still pending to be solved before EVs could be used as a routine tool in oncology.
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Affiliation(s)
- Álvaro González
- Service of Biochemistry, Clínica Universidad de Navarra, Pamplona, Spain
- IdiSNA, Navarra Institute for Health Research, Pamplona, Spain
| | - Silvia López-Borrego
- Department of Immunology and Oncology, National Centre for Biotechnology, Spanish National Research Council, Madrid, Spain
| | - Amaia Sandúa
- Service of Biochemistry, Clínica Universidad de Navarra, Pamplona, Spain
| | - Mar Vales-Gomez
- Department of Immunology and Oncology, National Centre for Biotechnology, Spanish National Research Council, Madrid, Spain
| | - Estibaliz Alegre
- Service of Biochemistry, Clínica Universidad de Navarra, Pamplona, Spain
- IdiSNA, Navarra Institute for Health Research, Pamplona, Spain
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32
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Akahoshi Y, Spyrou N, Weber D, Aguayo-Hiraldo P, Ayuk F, Chanswangphuwana C, Choe HK, Eder M, Etra AM, Grupp SA, Hexner EO, Hogan WJ, Kitko CL, Kraus S, Al Malki MM, Merli P, Qayed M, Reshef R, Schechter T, Ullrich E, Vasova I, Wölfl M, Zeiser R, Baez J, Beheshti R, Eng G, Gleich S, Katsivelos N, Kowalyk S, Morales G, Young R, Chen YB, Nakamura R, Levine JE, Ferrara JLM. Novel MAGIC composite scores using both clinical symptoms and biomarkers best predict treatment outcomes of acute GVHD. Blood 2024; 144:1010-1021. [PMID: 38968143 PMCID: PMC11830971 DOI: 10.1182/blood.2024025106] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2024] [Revised: 06/18/2024] [Accepted: 06/20/2024] [Indexed: 07/07/2024] Open
Abstract
ABSTRACT Acute graft-versus-host disease (GVHD) grading systems that use only clinical symptoms at treatment initiation such as the Minnesota risk identify standard and high-risk categories but lack a low-risk category suitable to minimize immunosuppressive strategies. We developed a new grading system that includes a low-risk stratum based on clinical symptoms alone and determined whether the incorporation of biomarkers would improve the model's prognostic accuracy. We randomly divided 1863 patients in the Mount Sinai Acute GVHD International Consortium (MAGIC) who were treated for GVHD into training and validation cohorts. Patients in the training cohort were divided into 14 groups based on similarity of clinical symptoms and similar nonrelapse mortality (NRM); we used a classification and regression tree (CART) algorithm to create three Manhattan risk groups that produced a significantly higher area under the receiver operating characteristic curve (AUC) for 6-month NRM than the Minnesota risk classification (0.69 vs 0.64, P = .009) in the validation cohort. We integrated serum GVHD biomarker scores with Manhattan risk using patients with available serum samples and again used a CART algorithm to establish 3 MAGIC composite scores that significantly improved prediction of NRM compared to Manhattan risk (AUC, 0.76 vs 0.70, P = .010). Each increase in MAGIC composite score also corresponded to a significant decrease in day 28 treatment response (80% vs 63% vs 30%, P < .001). We conclude that the MAGIC composite score more accurately predicts response to therapy and long-term outcomes than systems based on clinical symptoms alone and may help guide clinical decisions and trial design.
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Affiliation(s)
- Yu Akahoshi
- Division of Hematology/Medical Oncology, The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Nikolaos Spyrou
- Division of Hematology/Medical Oncology, The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Daniela Weber
- Department of Hematology and Oncology, Internal Medicine III, University of Regensburg, Regensburg, Germany
| | - Paibel Aguayo-Hiraldo
- Division of Bone Marrow Transplantation, Children's Hospital Los Angeles, University of Southern California, Los Angeles, CA
| | - Francis Ayuk
- Department of Stem Cell Transplantation, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Chantiya Chanswangphuwana
- Division of Hematology and Center of Excellence in Translational Hematology, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Bangkok, Thailand
| | - Hannah K. Choe
- Division of Hematology, Blood and Marrow Transplant Program, The Ohio State University Comprehensive Cancer Center, Columbus, OH
| | - Matthias Eder
- Department of Hematology, Hemostasis, Oncology and Stem Cell Transplantation, Hannover Medical School, Hannover, Germany
| | - Aaron M. Etra
- Division of Hematology/Medical Oncology, The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Stephan A. Grupp
- Division of Oncology, Center for Childhood Cancer Research and Cancer Immunotherapy Program, Children's Hospital of Philadelphia, Philadelphia, PA
- Department of Pediatrics, Abramson Cancer Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Elizabeth O. Hexner
- Department of Medicine and Abramson Cancer Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | | | - Carrie L. Kitko
- Pediatric Hematology/Oncology Division, Vanderbilt University Medical Center, Nashville, TN
| | - Sabrina Kraus
- Department of Internal Medicine II, University Hospital of Würzburg, Würzburg, Germany
| | | | - Pietro Merli
- Department of Pediatric Hematology and Oncology, Bambino Gesù Children's Hospital, Istituto di Ricovero e Cura a Carattere Scientifico, Rome, Italy
| | - Muna Qayed
- Department of Pediatrics, Emory University School of Medicine, Atlanta, GA
| | - Ran Reshef
- Division of Hematology/Oncology and Columbia Center for Translational Immunology, Columbia University Irving Medical Center, New York, NY
| | - Tal Schechter
- Division of Hematology/Oncology/BMT, The Hospital for Sick Children, University of Toronto, Toronto, ON, Canada
| | - Evelyn Ullrich
- Department of Pediatrics, Experimental Immunology and Cell Therapy, Goethe University Frankfurt, Frankfurt, Germany
| | - Ingrid Vasova
- Department of Internal Medicine 5, Hematology and Oncology, Friedrich-Alexander-Universität Erlangen-Nürnberg and University Hospital Erlangen, Erlangen, Germany
| | - Matthias Wölfl
- Department of Pediatrics, University Hospital of Würzburg, Würzburg, Germany
| | - Robert Zeiser
- Department of Medicine I, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Janna Baez
- Division of Hematology/Medical Oncology, The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Rahnuma Beheshti
- Division of Hematology/Medical Oncology, The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Gilbert Eng
- Division of Hematology/Medical Oncology, The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Sigrun Gleich
- Department of Hematology and Oncology, Internal Medicine III, University of Regensburg, Regensburg, Germany
| | - Nikolaos Katsivelos
- Division of Hematology/Medical Oncology, The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Steven Kowalyk
- Division of Hematology/Medical Oncology, The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY
| | - George Morales
- Division of Hematology/Medical Oncology, The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Rachel Young
- Division of Hematology/Medical Oncology, The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Yi-Bin Chen
- Hematopoietic Cell Transplant and Cellular Therapy Program, Massachusetts General Hospital, Boston, MA
| | | | - John E. Levine
- Division of Hematology/Medical Oncology, The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY
| | - James L. M. Ferrara
- Division of Hematology/Medical Oncology, The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY
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33
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Brentnall TA. Familial pancreatic cancer: a long fruitful journey. Fam Cancer 2024; 23:217-220. [PMID: 38436765 DOI: 10.1007/s10689-024-00364-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Accepted: 02/05/2024] [Indexed: 03/05/2024]
Abstract
In the early years of my GI fellowship, a healthy 40-year-old man came to my clinic and announced that he was going to die of pancreatic cancer. His brothers, father and uncles had all died of the disease; he felt his fate was inescapable. I asked whether his family members had seen doctors or had any tests. His answer was yes to both. Even so, doctors could not diagnose the pancreatic cancer at early stages. CT scans were always negative. I thought to myself, in order to help this patient-CT scans may not be reliable for early detection. Perhaps other methods of imaging the pancreas might be of more benefit. This patient opened a door that led to a 30-year journey of trying to detect pancreatic cancer at earlier stages when it is curable.
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Affiliation(s)
- Teresa A Brentnall
- Department of Medicine, University of Washington, PO Box 356424, 1959 NE Pacific, Seattle, WA, USA.
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34
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DeFilipp Z, Kim HT, Spyrou N, Katsivelos N, Kowalyk S, Eng G, Kasikis S, Beheshti R, Baez J, Akahoshi Y, Ayuk F, Choe H, Etra A, Grupp SA, Hexner EO, Hogan WJ, Kitko CL, Qayed M, Reshef R, Vasova I, Zeiser R, Young R, Holler E, Ferrara JLM, Nakamura R, Levine JE, Chen YB. The MAGIC algorithm probability predicts treatment response and long-term outcomes to second-line therapy for acute GVHD. Blood Adv 2024; 8:3488-3496. [PMID: 38640197 PMCID: PMC11260849 DOI: 10.1182/bloodadvances.2024012561] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Revised: 04/02/2024] [Accepted: 04/02/2024] [Indexed: 04/21/2024] Open
Abstract
ABSTRACT The significance of biomarkers in second-line treatment for acute graft-versus-host disease (GVHD) has not been well characterized. We analyzed clinical data and serum samples at the initiation of second-line systemic treatment of acute GVHD from 167 patients from 17 centers of the Mount Sinai Acute GVHD International Consortium (MAGIC) between 2016 and 2021. Sixty-two patients received ruxolitinib-based therapy, whereas 102 received other systemic agents. In agreement with prospective trials, ruxolitinib resulted in a higher day 28 (D28) overall response Frate than nonruxolitinib therapies (55% vs 31%, P = .003) and patients who received ruxolitinib had significantly lower nonrelapse mortality (NRM) than those who received nonruxolitinib therapies (point estimates at 2-year: 35% vs 61%, P = .002). Biomarker analyses demonstrated that the benefit from ruxolitinib was observed only in patients with low MAGIC algorithm probabilities (MAPs) at the start of second-line treatment. Among patients with a low MAP, those who received ruxolitinib experienced significantly lower NRM than those who received nonruxolitinib therapies (point estimates at 2-year: 12% vs 41%, P = .016). However, patients with high MAP experienced high NRM regardless of treatment with ruxolitinib or nonruxolitinib therapies (point estimates at 2-year: 67% vs 80%, P = .65). A landmark analysis demonstrated that the relationship between the D28 response and NRM largely depends on the MAP level at the initiation of second-line therapy. In conclusion, MAP measured at second-line systemic treatment for acute GVHD predicts treatment response and NRM. The outcomes of patients with high MAP are poor regardless of treatment choice, and ruxolitinib appears to primarily benefit patients with low MAP.
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Affiliation(s)
- Zachariah DeFilipp
- Hematopoietic Cell Transplant and Cellular Therapy Program, Massachusetts General Hospital, Boston, MA
| | - Haesook T. Kim
- Department of Data Science, Dana-Farber Cancer Institute and Harvard T.H. Chan School of Public Health, Boston, MA
| | - Nikolaos Spyrou
- The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Nikolaos Katsivelos
- The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Steven Kowalyk
- The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Gilbert Eng
- The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Stelios Kasikis
- The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Rahnuma Beheshti
- The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Janna Baez
- The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Yu Akahoshi
- The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Francis Ayuk
- Department of Stem Cell Transplantation, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Hannah Choe
- Division of Hematology, James Cancer Center, The Ohio State University, Columbus, OH
| | - Aaron Etra
- The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Stephan A. Grupp
- Division of Oncology, Children’s Hospital of Philadelphia, Philadelphia, PA
| | - Elizabeth O. Hexner
- Blood and Marrow Transplantation Program, Abramson Cancer Center, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA
| | | | - Carrie L. Kitko
- Pediatric Stem Cell Transplant Program, Vanderbilt University Medical Center, Nashville, TN
| | - Muna Qayed
- Aflac Cancer and Blood Disorders Center, Emory University, Atlanta, GA
| | - Ran Reshef
- Blood and Marrow Transplantation Program, Columbia University Medical Center, New York, NY
| | - Ingrid Vasova
- Department of Internal Medicine 5, Hematology/Oncology, University Hospital Erlangen, Erlangen, Germany
| | - Robert Zeiser
- Department of Medicine I - Medical Centre, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Rachel Young
- The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Ernst Holler
- Department of Hematology and Oncology, Internal Medicine III, University of Regensburg, Regensburg, Germany
| | - James L. M. Ferrara
- The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Ryotaro Nakamura
- Hematology/Hematopoietic Cell Transplant, City of Hope National Medical Center, Duarte, CA
| | - John E. Levine
- The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Yi-Bin Chen
- Hematopoietic Cell Transplant and Cellular Therapy Program, Massachusetts General Hospital, Boston, MA
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Ogawa S, Kumada T, Gotoh T, Niwa F, Toyoda H, Tanaka J, Shimizu M. A comparative study of hepatic steatosis using two different qualitative ultrasound techniques measured based on magnetic resonance imaging-derived proton density fat fraction. Hepatol Res 2024; 54:638-654. [PMID: 38294946 DOI: 10.1111/hepr.14019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Revised: 01/06/2024] [Accepted: 01/16/2024] [Indexed: 02/02/2024]
Abstract
AIM This study aimed to evaluate the diagnostic performance of attenuation measurement (ATT; dual-frequency method) and improved algorithm of ATT (iATT; reference method) for the assessment of hepatic steatosis using magnetic resonance imaging (MRI)-derived proton density fat fraction (PDFF) as the reference standard. METHODS We prospectively analyzed 427 patients with chronic liver disease who underwent ATT, iATT, or MRI-derived PDFF. Correlation coefficients were analyzed, and diagnostic values were evaluated by area under the receiver operating characteristic curve (AUROC). The steatosis grade was categorized as S0 (<5.2%), S1 (≥5.2%, <11.3%), S2 (≥11.3%, <17.1%), and S3 (≥17.1%) according to MRI-derived PDFF values. RESULTS The median ATT and iATT values were 0.61 dB/cm/MHz (interquartile range 0.55-0.67 dB/cm/MHz) and 0.66 dB/cm/MHz (interquartile range 0.57-0.77 dB/cm/MHz). ATT and iATT values increased significantly as the steatosis grade increased in the order S0, S1, S2, and S3 (p < 0.001). The correlation coefficients between ATT or iATT values and MRI-derived PDFF values were 0.533 (95% confidence interval [CI] 0.477-0.610) and 0.803 (95% CI 0.766-0.834), with a significant difference between them (p < 0.001). For the detection of hepatic steatosis of ≥S1, ≥S2, and ≥S3, iATT yielded AUROCs of 0.926 (95% CI 0.901-0.951), 0.913 (95% CI 0.885-0.941), and 0.902 (95% CI 0.869-0.935), with significantly higher AUROC values than for ATT (p < 0.001, p < 0.001, p = 0.001). CONCLUSION iATT showed excellent diagnostic performance for hepatic steatosis, and was strongly correlated with MRI-derived PDFF, with AUROCs of ≥0.900.
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Affiliation(s)
- Sadanobu Ogawa
- Department of Imaging Diagnosis, Ogaki Municipal Hospital, Ogaki, Gifu, Japan
| | - Takashi Kumada
- Department of Nursing, Faculty of Nursing, Gifu Kyoritsu University, Ogaki, Gifu, Japan
| | - Tatsuya Gotoh
- Department of Imaging Diagnosis, Ogaki Municipal Hospital, Ogaki, Gifu, Japan
| | - Fumihiko Niwa
- Department of Imaging Diagnosis, Ogaki Municipal Hospital, Ogaki, Gifu, Japan
| | - Hidenori Toyoda
- Department of Gastroenterology and Hepatology, Ogaki Municipal Hospital, Ogaki, Gifu, Japan
| | - Junko Tanaka
- Department of Epidemiology, Infectious Disease Control, and Prevention, Hiroshima University Institute of Biomedical and Health Sciences, Hiroshima, Japan
| | - Masahito Shimizu
- Department of Gastroenterology/Internal Medicine, Graduate School of Medicine, Gifu University, Gifu, Japan
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Etra A, El Jurdi N, Katsivelos N, Kwon D, Gergoudis S, Morales G, Spyrou N, Kowalyk S, Aguayo-Hiraldo P, Akahoshi Y, Ayuk F, Baez J, Betts BC, Chanswangphuwana C, Chen YB, Choe H, DeFilipp Z, Gleich S, Hexner E, Hogan WJ, Holler E, Kitko CL, Kraus S, Al Malki M, MacMillan M, Pawarode A, Quagliarella F, Qayed M, Reshef R, Schechter T, Vasova I, Weisdorf D, Wölfl M, Young R, Nakamura R, Ferrara JLM, Levine JE, Holtan S. Amphiregulin, ST2, and REG3α biomarker risk algorithms as predictors of nonrelapse mortality in patients with acute GVHD. Blood Adv 2024; 8:3284-3292. [PMID: 38640195 PMCID: PMC11226972 DOI: 10.1182/bloodadvances.2023011049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Revised: 03/29/2024] [Accepted: 03/29/2024] [Indexed: 04/21/2024] Open
Abstract
ABSTRACT Graft-versus-host disease (GVHD) is a major cause of nonrelapse mortality (NRM) after allogeneic hematopoietic cell transplantation. Algorithms containing either the gastrointestinal (GI) GVHD biomarker amphiregulin (AREG) or a combination of 2 GI GVHD biomarkers (suppressor of tumorigenicity-2 [ST2] + regenerating family member 3 alpha [REG3α]) when measured at GVHD diagnosis are validated predictors of NRM risk but have never been assessed in the same patients using identical statistical methods. We measured the serum concentrations of ST2, REG3α, and AREG by enzyme-linked immunosorbent assay at the time of GVHD diagnosis in 715 patients divided by the date of transplantation into training (2004-2015) and validation (2015-2017) cohorts. The training cohort (n = 341) was used to develop algorithms for predicting the probability of 12-month NRM that contained all possible combinations of 1 to 3 biomarkers and a threshold corresponding to the concordance probability was used to stratify patients for the risk of NRM. Algorithms were compared with each other based on several metrics, including the area under the receiver operating characteristics curve, proportion of patients correctly classified, sensitivity, and specificity using only the validation cohort (n = 374). All algorithms were strong discriminators of 12-month NRM, whether or not patients were systemically treated (n = 321). An algorithm containing only ST2 + REG3α had the highest area under the receiver operating characteristics curve (0.757), correctly classified the most patients (75%), and more accurately risk-stratified those who developed Minnesota standard-risk GVHD and for patients who received posttransplant cyclophosphamide-based prophylaxis. An algorithm containing only AREG more accurately risk-stratified patients with Minnesota high-risk GVHD. Combining ST2, REG3α, and AREG into a single algorithm did not improve performance.
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Affiliation(s)
- Aaron Etra
- The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Najla El Jurdi
- Hematology, Oncology and Transplant, University of Minnesota, Minneapolis, MN
| | - Nikolaos Katsivelos
- The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Deukwoo Kwon
- Department of Population Health Science and Policy, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Stephanie Gergoudis
- The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY
| | - George Morales
- The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Nikolaos Spyrou
- The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Steven Kowalyk
- The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Paibel Aguayo-Hiraldo
- Division of Hematology, Oncology, and Blood and Marrow Transplantation, Children's Hospital Los Angeles, Los Angeles, CA
| | - Yu Akahoshi
- The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Francis Ayuk
- Department of Stem Cell Transplantation, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Janna Baez
- The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Brian C. Betts
- Hematology, Oncology and Transplant, University of Minnesota, Minneapolis, MN
| | | | - Yi-Bin Chen
- Hematopoietic Cell Transplant and Cellular Therapy Program, Massachusetts General Hospital, Boston, MA
| | - Hannah Choe
- Division of Hematology, James Cancer Center, The Ohio State University, Columbus, OH
| | - Zachariah DeFilipp
- Hematopoietic Cell Transplant and Cellular Therapy Program, Massachusetts General Hospital, Boston, MA
| | - Sigrun Gleich
- Department of Hematology and Oncology, Internal Medicine III, University of Regensburg, Regensburg, Germany
| | - Elizabeth Hexner
- Blood and Marrow Transplantation Program, Abramson Cancer Center, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA
| | | | - Ernst Holler
- Department of Hematology and Oncology, Internal Medicine III, University of Regensburg, Regensburg, Germany
| | - Carrie L. Kitko
- Pediatric Stem Cell Transplant Program, Vanderbilt University Medical Center, Nashville, TN
| | - Sabrina Kraus
- Department of Internal Medicine II, University Hospital of Würzburg, Würzburg, Germany
| | - Monzr Al Malki
- Hematology/Hematopoietic Cell Transplant, City of Hope National Medical Center, Duarte, CA
| | - Margaret MacMillan
- Hematology, Oncology and Transplant, University of Minnesota, Minneapolis, MN
| | - Attaphol Pawarode
- Blood and Marrow Transplantation Program, University of Michigan, Ann Arbor, MI
| | | | - Muna Qayed
- Aflac Cancer and Blood Disorders Center, Emory University, Atlanta, GA
| | - Ran Reshef
- Blood and Marrow Transplantation Program, Columbia University Medical Center, New York, NY
| | - Tal Schechter
- Division of Hematology/Oncology/BMT, The Hospital for Sick Children, University of Toronto, Toronto, ON, Canada
| | - Ingrid Vasova
- Med. Klinik III/Poliklinik, Universitatsklinik Erlangen, Erlangen, Germany
| | - Daniel Weisdorf
- Hematology, Oncology and Transplant, University of Minnesota, Minneapolis, MN
| | - Matthias Wölfl
- Pediatric Blood and Marrow Transplantation Program, Children’s Hospital, University of Würzburg, Würzburg, Germany
| | - Rachel Young
- The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Ryotaro Nakamura
- Hematology/Hematopoietic Cell Transplant, City of Hope National Medical Center, Duarte, CA
| | - James L. M. Ferrara
- The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY
| | - John E. Levine
- The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Shernan Holtan
- Hematology, Oncology and Transplant, University of Minnesota, Minneapolis, MN
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Yen SC, Wu CC, Tseng YJ, Li CH, Chen KF. Using time-course as an essential factor to accurately predict sepsis-associated mortality among patients with suspected sepsis. Biomed J 2024; 47:100632. [PMID: 37467969 PMCID: PMC11332986 DOI: 10.1016/j.bj.2023.100632] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Revised: 06/20/2023] [Accepted: 07/13/2023] [Indexed: 07/21/2023] Open
Abstract
BACKGROUND Biomarker dynamics in different time-courses might be the primary reason why a static measurement of a single biomarker cannot accurately predict sepsis outcomes. Therefore, we conducted this prospective hospital-based cohort study to simultaneously evaluate the performance of several conventional and novel biomarkers of sepsis in predicting sepsis-associated mortality on different days of illness among patients with suspected sepsis. METHODS We evaluated the performance of 15 novel biomarkers including angiopoietin-2, pentraxin 3, sTREM-1, ICAM-1, VCAM-1, sCD14 and 163, E-selectin, P-selectin, TNF-alpha, interferon-gamma, CD64, IL-6, 8, and 10, along with few conventional markers for predicting sepsis-associated mortality. Patients were grouped into quartiles according to the number of days since symptom onset. Receiver operating characteristic curve (ROC) analysis was used to evaluate the biomarker performance. RESULTS From 2014 to 2017, 1483 patients were enrolled, of which 78% fulfilled the systemic inflammatory response syndrome criteria, 62% fulfilled the sepsis-3 criteria, 32% had septic shock, and 3.3% developed sepsis-associated mortality. IL-6, pentraxin 3, sCD163, and the blood gas profile demonstrated better performance in the early days of illness, both before and after adjusting for potential confounders (adjusted area under ROC curve [AUROC]:0.81-0.88). Notably, the Sequential Organ Failure Assessment (SOFA) score was relatively consistent throughout the course of illness (adjusted AUROC:0.70-0.91). CONCLUSION IL-6, pentraxin 3, sCD163, and the blood gas profile showed excellent predictive accuracy in the early days of illness. The SOFA score was consistently predictive of sepsis-associated mortality throughout the course of illness, with an acceptable performance.
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Affiliation(s)
- Shih-Chieh Yen
- Clinical Informatics and Medical Statistics Research Center, Chang Gung University, Taoyuan, Taiwan
| | - Chin-Chieh Wu
- Clinical Informatics and Medical Statistics Research Center, Chang Gung University, Taoyuan, Taiwan
| | - Yi-Ju Tseng
- Department of Computer Science, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
| | - Chih-Huang Li
- Department of Emergency Medicine, Chang Gung Memorial Hospital, Taipei, Taiwan
| | - Kuan-Fu Chen
- Clinical Informatics and Medical Statistics Research Center, Chang Gung University, Taoyuan, Taiwan; Department of Emergency Medicine, Chang Gung Memorial Hospital at Keelung, Keelung, Taiwan.
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38
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Qayed M, Kapoor U, Gillespie S, Westbrook A, Aguayo-Hiraldo P, Ayuk FA, Aziz M, Baez J, Choe H, DeFilipp Z, Etra A, Grupp SA, Hexner E, Holler E, Hogan WJ, Kowalyk S, Merli P, Morales G, Nakamura R, Pulsipher MA, Schechter T, Shah J, Spyrou N, Srinagesh HK, Wölfl M, Yanik G, Young R, Kitko CL, Ferrara JL, Levine JE. A Validated Risk Stratification That Incorporates MAGIC Biomarkers Predicts Long-Term Outcomes in Pediatric Patients with Acute GVHD. Transplant Cell Ther 2024; 30:603.e1-603.e11. [PMID: 38548227 PMCID: PMC11139591 DOI: 10.1016/j.jtct.2024.03.022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Revised: 03/11/2024] [Accepted: 03/20/2024] [Indexed: 04/10/2024]
Abstract
Acute graft versus host disease (GVHD) is a common and serious complication of allogeneic hematopoietic cell transplantation (HCT) in children but overall clinical grade at onset only modestly predicts response to treatment and survival outcomes. Two tools to assess risk at initiation of treatment were recently developed. The Minnesota risk system stratifies children for risk of nonrelapse mortality (NRM) according to the pattern of GVHD target organ severity. The Mount Sinai Acute GVHD International Consortium (MAGIC) algorithm of 2 serum biomarkers (ST2 and REG3α) predicts NRM in adult patients but has not been validated in a pediatric population. We aimed to develop and validate a system that stratifies children at the onset of GVHD for risk of 6-month NRM. We determined the MAGIC algorithm probabilities (MAPs) and Minnesota risk for a multicenter cohort of 315 pediatric patients who developed GVHD requiring treatment with systemic corticosteroids. MAPs created 3 risk groups with distinct outcomes at the start of treatment and were more accurate than Minnesota risk stratification for prediction of NRM (area under the receiver operating curve (AUC), .79 versus .62, P = .001). A novel model that combined Minnesota risk and biomarker scores created from a training cohort was more accurate than either biomarkers or clinical systems in a validation cohort (AUC .87) and stratified patients into 2 groups with highly different 6-month NRM (5% versus 38%, P < .001). In summary, we validated the MAP as a prognostic biomarker in pediatric patients with GVHD, and a novel risk stratification that combines Minnesota risk and biomarker risk performed best. Biomarker-based risk stratification can be used in clinical trials to develop more tailored approaches for children who require treatment for GVHD.
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Affiliation(s)
- Muna Qayed
- Emory University School of Medicine, Atlanta, GA
- Aflac Cancer and Blood Disorders Center, Children’s Healthcare of Atlanta, Atlanta, GA
| | - Urvi Kapoor
- The Tisch Cancer Institute and Division of Hematology / Medical Oncology, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Scott Gillespie
- Pediatric Biostatistics Core, Department of Pediatrics, Emory University, Atlanta, GA
| | - Adrianna Westbrook
- Pediatric Biostatistics Core, Department of Pediatrics, Emory University, Atlanta, GA
| | - Paibel Aguayo-Hiraldo
- Division of Hematology, Oncology, and BMT, Children’s Hospital Los Angeles, Los Angeles, CA
| | - Francis A. Ayuk
- Department of Stem Cell Transplantation, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Mina Aziz
- The Tisch Cancer Institute and Division of Hematology / Medical Oncology, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Janna Baez
- The Tisch Cancer Institute and Division of Hematology / Medical Oncology, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Hannah Choe
- Ohio State University Wexner Medical Center, Columbus, OH
| | - Zachariah DeFilipp
- Hematopoietic Cell Transplant and Cellular Therapy Program, Massachusetts General Hospital, Boston, MA
| | - Aaron Etra
- The Tisch Cancer Institute and Division of Hematology / Medical Oncology, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Stephan A. Grupp
- Division of Oncology, Children’s Hospital of Philadelphia, Philadelphia, PA
| | - Elizabeth Hexner
- Blood and Marrow Transplantation Program, Abramson Cancer Center and the Division of Hematology and Oncology, Hospital of the University of Pennsylvania, Philadelphia, PA
| | - Ernst Holler
- Department of Hematology and Oncology, Internal Medicine III, University of Regensburg, Regensburg, Germany
| | | | - Steven Kowalyk
- The Tisch Cancer Institute and Division of Hematology / Medical Oncology, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Pietro Merli
- Ospedale Pediatrico Bambino Gesú, IRCCS, Rome, Italy
| | - George Morales
- The Tisch Cancer Institute and Division of Hematology / Medical Oncology, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Ryotaro Nakamura
- Hematology/Hematopoietic Cell Transplant, City of Hope National Medical Center, Duarte, CA
| | - Michael A. Pulsipher
- Division of Hematology, Oncology, and BMT, Children’s Hospital Los Angeles, Los Angeles, CA
- Division of Hematology and Oncology, Intermountain Primary Children’s Hospital, Huntsman Cancer Institute at the Spencer Fox Eccles School of Medicine at the University of Utah, Salt Lake City, UT
| | - Tal Schechter
- Division of Hematology / Oncology / BMT, The Hospital for Sick Children, Toronto, ON, Canada
| | - Jay Shah
- The Tisch Cancer Institute and Division of Hematology / Medical Oncology, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Nikolaos Spyrou
- The Tisch Cancer Institute and Division of Hematology / Medical Oncology, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Hrishikesh K. Srinagesh
- The Tisch Cancer Institute and Division of Hematology / Medical Oncology, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Matthias Wölfl
- Pediatric Blood and Marrow Transplantation Program, Children’s Hospital, University of Würzburg, Würzburg, Germany
| | - Gregory Yanik
- Pediatric Blood and Marrow Transplant Program, University of Michigan, Ann Arbor, MI
| | - Rachel Young
- The Tisch Cancer Institute and Division of Hematology / Medical Oncology, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Carrie L. Kitko
- Pediatric Blood and Marrow Transplant Program, Vanderbilt University Medical Center, Nashville, TN
| | - James L.M. Ferrara
- The Tisch Cancer Institute and Division of Hematology / Medical Oncology, Icahn School of Medicine at Mount Sinai, New York, NY
| | - John E. Levine
- The Tisch Cancer Institute and Division of Hematology / Medical Oncology, Icahn School of Medicine at Mount Sinai, New York, NY
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Yadav D, Conwell DL, Pandol SJ, Steen H, Feng Z, Li L. Diagnostic and Prognostic Biomarkers of Chronic Pancreatitis: A Conceptual Framework Based on the PRoBE Design. Gastroenterology 2024; 166:957-962.e3. [PMID: 38423226 PMCID: PMC11102843 DOI: 10.1053/j.gastro.2024.02.030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Revised: 02/20/2024] [Accepted: 02/22/2024] [Indexed: 03/02/2024]
Affiliation(s)
- Dhiraj Yadav
- Division of Gastroenterology, Hepatology and Nutrition, Department of Medicine, University of Pittsburgh Medical Center, Pittsburgh, PA
| | | | - Stephen J. Pandol
- Division of Digestive and Liver Diseases, Cedars-Sinai Medical Center, Los Angeles, CA
| | - Hanno Steen
- Department of Pathology, Boston Children’s Hospital, Harvard Medical School, Boston, MA
| | - Ziding Feng
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA
| | - Liang Li
- Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, TX
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40
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Hagn-Meincke R, Yadav D, Andersen DK, Vege SS, Fogel EL, Serrano J, Bellin MD, Topazian MD, Conwell DL, Li L, Van Den Eeden SK, Drewes AM, Pandol SJ, Forsmark CE, Fisher WE, Hart PA, Olesen SS, Park WG. Circulating immune signatures in chronic pancreatitis with and without preceding acute pancreatitis: A pilot study. Pancreatology 2024; 24:384-393. [PMID: 38461145 PMCID: PMC11023786 DOI: 10.1016/j.pan.2024.02.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Revised: 02/05/2024] [Accepted: 02/21/2024] [Indexed: 03/11/2024]
Abstract
OBJECTIVE To investigate profiles of circulating immune signatures in healthy controls and chronic pancreatitis patients (CP) with and without a preceding history of acute pancreatitis (AP). METHODS We performed a phase 1, cross-sectional analysis of prospectively collected serum samples from the PROspective Evaluation of Chronic Pancreatitis for EpidEmiologic and Translation StuDies (PROCEED) study. All samples were collected during a clinically quiescent phase. CP subjects were categorized into two subgroups based on preceding episode(s) of AP. Healthy controls were included for comparison. Blinded samples were analyzed using an 80-plex Luminex assay of cytokines, chemokines, and adhesion molecules. Group and pairwise comparisons of analytes were performed between the subgroups. RESULTS In total, 133 patients with CP (111 with AP and 22 without AP) and 50 healthy controls were included. Among the 80 analytes studied, CP patients with a history of AP had significantly higher serum levels of pro-inflammatory cytokines (interleukin (IL)-6, IL-8, IL-1 receptor antagonist, IL-15) and chemokines (Cutaneous T-Cell Attracting Chemokine (CTACK), Monokine induced Gamma Interferon (MIG), Macrophage-derived Chemokine (MDC), Monocyte Chemoattractant Protein-1 (MCP-1)) compared to CP without preceding AP and controls. In contrast, CP patients without AP had immune profiles characterized by low systemic inflammation and downregulation of anti-inflammatory mediators, including IL-10. CONCLUSION CP patients with a preceding history of AP have signs of systemic inflammatory activity even during a clinically quiescent phase. In contrast, CP patients without a history of AP have low systemic inflammatory activity. These findings suggest the presence of two immunologically diverse subtypes of CP.
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Affiliation(s)
- Rasmus Hagn-Meincke
- Centre for Pancreatic Diseases and Mech-Sense, Department of Gastroenterology & Hepatology, Aalborg University Hospital, Aalborg, Denmark; Division of Gastroenterology and Hepatology, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Dhiraj Yadav
- Division of Gastroenterology, Hepatology and Nutrition, University of Pittsburgh, Pittsburgh, PA, USA
| | - Dana K Andersen
- Division of Digestive Diseases and Nutrition, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Santhi Swaroop Vege
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN, USA
| | - Evan L Fogel
- Division of Gastroenterology and Hepatology, Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Jose Serrano
- Division of Digestive Diseases and Nutrition, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Melena D Bellin
- Division of Pediatric Endocrinology, University of Minnesota, Minnesota, MN, USA
| | - Mark D Topazian
- Division of Digestive Diseases and Nutrition, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Darwin L Conwell
- Department of Medicine, University of Kentucky, Lexington, KY, USA
| | - Liang Li
- Department of Biostatistics, MD Anderson Cancer Center, Houston, TX, USA
| | | | - Asbjørn M Drewes
- Centre for Pancreatic Diseases and Mech-Sense, Department of Gastroenterology & Hepatology, Aalborg University Hospital, Aalborg, Denmark
| | - Stephen J Pandol
- Division of Digestive and Liver Diseases, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Chris E Forsmark
- Division of Gastroenterology, Hepatology, and Nutrition. University of Florida, Gainesville, FL, USA
| | - William E Fisher
- Division of General Surgery, Baylor College of Medicine, Houston, TX, USA
| | - Phil A Hart
- Division of Gastroenterology, Hepatology, and Nutrition, The Ohio State University Wexner Medical Center, Columbus, OH, USA
| | - Søren S Olesen
- Centre for Pancreatic Diseases and Mech-Sense, Department of Gastroenterology & Hepatology, Aalborg University Hospital, Aalborg, Denmark
| | - Walter G Park
- Division of Gastroenterology and Hepatology, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA.
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41
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Akahoshi Y, Spyrou N, Hoepting M, Aguayo-Hiraldo P, Ayuk F, Chanswangphuwana C, Choe HK, Eder M, Etra AM, Grupp SA, Hexner EO, Hogan WJ, Kitko CL, Kraus S, Al Malki MM, Merli P, Qayed M, Reshef R, Schechter T, Ullrich E, Vasova I, Wölfl M, Zeiser R, Baez J, Beheshti R, Eng G, Gleich S, Kasikis S, Katsivelos N, Kowalyk S, Morales G, Young R, DeFilipp Z, Ferrara JLM, Levine JE, Nakamura R. Flares of acute graft-versus-host disease: a Mount Sinai Acute GVHD International Consortium analysis. Blood Adv 2024; 8:2047-2057. [PMID: 38324721 PMCID: PMC11103178 DOI: 10.1182/bloodadvances.2023012091] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Revised: 01/19/2024] [Accepted: 01/19/2024] [Indexed: 02/09/2024] Open
Abstract
ABSTRACT The absence of a standardized definition for graft-versus-host disease (GVHD) flares and data on its clinical course are significant concerns. We retrospectively evaluated 968 patients across 23 Mount Sinai Acute GVHD International Consortium (MAGIC) transplant centers who achieved complete response (CR) or very good partial response (VGPR) within 4 weeks of treatment. The cumulative incidence of flares within 6 months was 22%, and flares were associated with a higher risk of nonrelapse mortality (NRM; adjusted hazard ratio [aHR], 4.84; 95% confidence interval [CI], 3.19-7.36; P < .001). Flares were more severe (grades 3/4, 41% vs 16%; P < .001) and had more frequent lower gastrointestinal (LGI) involvement (55% vs 32%; P < .001) than the initial GVHD. At CR/VGPR, elevated MAGIC biomarkers predicted the future occurrence of a flare, along with its severity and LGI involvement. In multivariate analyses, higher Ann Arbor (AA) biomarker scores at CR/VGPR were significant risk factors for flares (AA2 vs AA1: aHR, 1.81 [95% CI, 1.32-2.48; P = .001]; AA3 vs AA1: aHR, 3.14 [95% CI, 1.98-4.98; P < .001]), as were early response to initial treatment (aHR, 1.84; 95% CI, 1.21-2.80; P = .004) and HLA-mismatched unrelated donor (aHR, 1.74; 95% CI, 1.00-3.02; P = .049). MAGIC biomarkers also stratified the risk of NRM both at CR/VGPR and at the time of flare. We conclude that GVHD flares are common and carry a significant mortality risk. The occurrence of future flares can be predicted by serum biomarkers that may serve to guide adjustment and discontinuation of immunosuppression.
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Affiliation(s)
- Yu Akahoshi
- The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY
- Division of Hematology, Jichi Medical University Saitama Medical Center, Saitama, Japan
| | - Nikolaos Spyrou
- The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Matthias Hoepting
- Department of Hematology and Oncology, Internal Medicine III, University of Regensburg, Regensburg, Germany
| | - Paibel Aguayo-Hiraldo
- Cancer and Blood Disease Institute, Children's Hospital Los Angeles, University of Southern California, Los Angeles, CA
| | - Francis Ayuk
- Department of Stem Cell Transplantation, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Chantiya Chanswangphuwana
- Division of Hematology and Center of Excellence in Translational Hematology, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Bangkok, Thailand
| | - Hannah K. Choe
- Blood and Marrow Transplantation Program, The Ohio State University, Columbus, OH
| | - Matthias Eder
- Department of Hematology, Hemostasis, Oncology and Stem Cell Transplantation, Hannover Medical School, Hannover, Germany
| | - Aaron M. Etra
- The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Stephan A. Grupp
- Division of Oncology, Children’s Hospital of Philadelphia, Philadelphia, PA
- Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Elizabeth O. Hexner
- Department of Medicine and Abramson Cancer Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | | | - Carrie L. Kitko
- Pediatric Stem Cell Transplant Program, Vanderbilt University Medical Center, Nashville, TN
| | - Sabrina Kraus
- Department of Internal Medicine II, University Hospital of Würzburg, Würzburg, Germany
| | - Monzr M. Al Malki
- Department of Hematology/Hematopoietic Cell Transplantation, City of Hope, Duarte, CA
| | - Pietro Merli
- Department of Pediatric Hematology/Oncology and of Cell and Gene Therapy, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy
| | - Muna Qayed
- Division of Pediatric Hematology/Oncology and Bone Marrow Transplantation, Aflac Cancer and Blood Disorders Center, Emory University and Children's Healthcare of Atlanta, Atlanta, GA
| | - Ran Reshef
- Blood and Marrow Transplantation Program and Columbia Center for Translational Immunology, Columbia University Irving Medical Center, New York, NY
| | - Tal Schechter
- Division of Hematology/Oncology/BMT, The Hospital for Sick Children, University of Toronto, Toronto, ON, Canada
| | - Evelyn Ullrich
- Department of Pediatrics, Experimental Immunology and Cell Therapy, Goethe University Frankfurt, Frankfurt, Germany
| | - Ingrid Vasova
- Department of Internal Medicine 5, Hematology and Oncology, Friedrich-Alexander-Universität Erlangen-Nürnberg and University Hospital Erlangen, Erlangen, Germany
| | - Matthias Wölfl
- Pediatric Blood and Marrow Transplantation Program, Children's Hospital, University Hospital of Würzburg, Würzburg, Germany
| | - Robert Zeiser
- Department of Medicine I, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Janna Baez
- The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Rahnuma Beheshti
- The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Gilbert Eng
- The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Sigrun Gleich
- Department of Hematology and Oncology, Internal Medicine III, University of Regensburg, Regensburg, Germany
| | - Stelios Kasikis
- The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Nikolaos Katsivelos
- The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Steven Kowalyk
- The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY
| | - George Morales
- The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Rachel Young
- The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Zachariah DeFilipp
- Hematopoietic Cell Transplant and Cellular Therapy Program, Massachusetts General Hospital, Boston, MA
| | - James L. M. Ferrara
- The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY
| | - John E. Levine
- The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Ryotaro Nakamura
- Department of Hematology/Hematopoietic Cell Transplantation, City of Hope, Duarte, CA
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Gumpper-Fedus K, Chasser K, Pita-Grisanti V, Torok M, Pfau T, Mace TA, Cole RM, Belury MA, Culp S, Hart PA, Krishna SG, Lara LF, Ramsey ML, Fisher W, Fogel EL, Forsmark CE, Li L, Pandol S, Park WG, Serrano J, Van Den Eeden SK, Vege SS, Yadav D, Conwell DL, Cruz-Monserrate Z. Systemic Neutrophil Gelatinase-Associated Lipocalin Alterations in Chronic Pancreatitis: A Multicenter, Cross-Sectional Study. Clin Transl Gastroenterol 2024; 15:e00686. [PMID: 38284831 PMCID: PMC11042777 DOI: 10.14309/ctg.0000000000000686] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Accepted: 01/19/2024] [Indexed: 01/30/2024] Open
Abstract
INTRODUCTION Chronic pancreatitis (CP) is a progressive fibroinflammatory disorder lacking therapies and biomarkers. Neutrophil gelatinase-associated lipocalin (NGAL) is a proinflammatory cytokine elevated during inflammation that binds fatty acids (FAs) such as linoleic acid. We hypothesized that systemic NGAL could serve as a biomarker for CP and, with FAs, provide insights into inflammatory and metabolic alterations. METHODS NGAL was measured by immunoassay, and FA composition was measured by gas chromatography in plasma (n = 171) from a multicenter study, including controls (n = 50), acute and recurrent acute pancreatitis (AP/RAP) (n = 71), and CP (n = 50). Peripheral blood mononuclear cells (PBMCs) from controls (n = 16), AP/RAP (n = 17), and CP (n = 15) were measured by cytometry by time-of-flight. RESULTS Plasma NGAL was elevated in subjects with CP compared with controls (area under the curve [AUC] = 0.777) or AP/RAP (AUC = 0.754) in univariate and multivariate analyses with sex, age, body mass index, and smoking (control AUC = 0.874; AP/RAP AUC = 0.819). NGAL was elevated in CP and diabetes compared with CP without diabetes ( P < 0.001). NGAL + PBMC populations distinguished CP from controls (AUC = 0.950) or AP/RAP (AUC = 0.941). Linoleic acid was lower, whereas dihomo-γ-linolenic and adrenic acids were elevated in CP ( P < 0.05). Linoleic acid was elevated in CP with diabetes compared with CP subjects without diabetes ( P = 0.0471). DISCUSSION Elevated plasma NGAL and differences in NGAL + PBMCs indicate an immune response shift that may serve as biomarkers of CP. The potential interaction of FAs and NGAL levels provide insights into the metabolic pathophysiology and improve diagnostic classification of CP.
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Affiliation(s)
- Kristyn Gumpper-Fedus
- Division of Gastroenterology, Hepatology, and Nutrition, The Ohio State University Wexner Medical Center, Columbus, Ohio, USA
- The James Comprehensive Cancer Center, The Ohio State University Wexner Medical Center, Columbus, Ohio, USA
| | - Kaylin Chasser
- Division of Gastroenterology, Hepatology, and Nutrition, The Ohio State University Wexner Medical Center, Columbus, Ohio, USA
- The James Comprehensive Cancer Center, The Ohio State University Wexner Medical Center, Columbus, Ohio, USA
| | - Valentina Pita-Grisanti
- Division of Gastroenterology, Hepatology, and Nutrition, The Ohio State University Wexner Medical Center, Columbus, Ohio, USA
- The James Comprehensive Cancer Center, The Ohio State University Wexner Medical Center, Columbus, Ohio, USA
- The Ohio State University Interdisciplinary Nutrition Program, The Ohio State University, Columbus, Ohio, USA
| | - Molly Torok
- Division of Gastroenterology, Hepatology, and Nutrition, The Ohio State University Wexner Medical Center, Columbus, Ohio, USA
- The James Comprehensive Cancer Center, The Ohio State University Wexner Medical Center, Columbus, Ohio, USA
| | - Timothy Pfau
- Division of Gastroenterology, Hepatology, and Nutrition, The Ohio State University Wexner Medical Center, Columbus, Ohio, USA
- The James Comprehensive Cancer Center, The Ohio State University Wexner Medical Center, Columbus, Ohio, USA
| | - Thomas A. Mace
- Division of Gastroenterology, Hepatology, and Nutrition, The Ohio State University Wexner Medical Center, Columbus, Ohio, USA
- The James Comprehensive Cancer Center, The Ohio State University Wexner Medical Center, Columbus, Ohio, USA
| | - Rachel M. Cole
- Department of Food Science and Technology, College of Food, Agriculture, and Environmental Sciences, The Ohio State University Columbus, Ohio, USA
| | - Martha A. Belury
- Department of Food Science and Technology, College of Food, Agriculture, and Environmental Sciences, The Ohio State University Columbus, Ohio, USA
| | - Stacey Culp
- Department of Biomedical Informatics, The Ohio State University Wexner Medical Center, Columbus, Ohio, USA
| | - Phil A. Hart
- Division of Gastroenterology, Hepatology, and Nutrition, The Ohio State University Wexner Medical Center, Columbus, Ohio, USA
- The James Comprehensive Cancer Center, The Ohio State University Wexner Medical Center, Columbus, Ohio, USA
| | - Somashekar G. Krishna
- Division of Gastroenterology, Hepatology, and Nutrition, The Ohio State University Wexner Medical Center, Columbus, Ohio, USA
- The James Comprehensive Cancer Center, The Ohio State University Wexner Medical Center, Columbus, Ohio, USA
| | - Luis F. Lara
- Division of Gastroenterology, Hepatology, and Nutrition, The Ohio State University Wexner Medical Center, Columbus, Ohio, USA
| | - Mitchell L. Ramsey
- Division of Gastroenterology, Hepatology, and Nutrition, The Ohio State University Wexner Medical Center, Columbus, Ohio, USA
| | - William Fisher
- Division of General Surgery, Baylor College of Medicine, Houston, Texas, USA
| | - Evan L. Fogel
- Department of Medicine, Indiana University, Indianapolis, Indiana, USA
| | - Chris E. Forsmark
- Division of Gastroenterology, Hepatology, and Nutrition, University of Florida, Gainesville, Florida, USA
| | - Liang Li
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Stephen Pandol
- Division of Digestive and Liver Diseases, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Walter G. Park
- Division of Gastroenterology & Hepatology, Stanford University School of Medicine, Stanford, California, USA
| | - Jose Serrano
- Division of Digestive Diseases and Nutrition, National Institutes of Diabetes and Digestive and Kidney Diseases, Bethesda, Maryland, USA
| | | | - Santhi Swaroop Vege
- Department of Gastroenterology and Hepatology, The Mayo Clinic, Rochester, Minnesota, USA
| | - Dhiraj Yadav
- Division of Gastroenterology, Hepatology & Nutrition, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania, USA
| | - Darwin L. Conwell
- Department of Internal Medicine, University of Kentucky College of Medicine, Lexington, Kentucky, USA
| | - Zobeida Cruz-Monserrate
- Division of Gastroenterology, Hepatology, and Nutrition, The Ohio State University Wexner Medical Center, Columbus, Ohio, USA
- The James Comprehensive Cancer Center, The Ohio State University Wexner Medical Center, Columbus, Ohio, USA
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Spyrou N, Akahoshi Y, Kowalyk S, Morales G, Beheshti R, Aguayo-Hiraldo P, Al Malki MM, Ayuk F, Bader P, Baez J, Capellini A, Choe H, DeFilipp Z, Eder M, Eng G, Etra A, Gleich S, Grupp SA, Hexner E, Hoepting M, Hogan WJ, Kasikis S, Katsivelos N, Khan A, Kitko CL, Kraus S, Kwon D, Merli P, Portelli J, Qayed M, Reshef R, Schechter T, Vasova I, Wölfl M, Wudhikarn K, Young R, Holler E, Chen YB, Nakamura R, Levine JE, Ferrara JLM. A Day 14 Endpoint for Acute GVHD Clinical Trials. Transplant Cell Ther 2024; 30:421-432. [PMID: 38320730 PMCID: PMC11009039 DOI: 10.1016/j.jtct.2024.01.079] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Revised: 01/15/2024] [Accepted: 01/25/2024] [Indexed: 02/19/2024]
Abstract
The overall response rate (ORR) 28 days after treatment has been adopted as the primary endpoint for clinical trials of acute graft versus host disease (GVHD). However, physicians often need to modify immunosuppression earlier than day (D) 28, and non-relapse mortality (NRM) does not always correlate with ORR at D28. We studied 1144 patients that received systemic treatment for GVHD in the Mount Sinai Acute GVHD International Consortium (MAGIC) and divided them into a training set (n=764) and a validation set (n=380). We used a recursive partitioning algorithm to create a Mount Sinai model that classifies patients into favorable or unfavorable groups that predicted 12 month NRM according to overall GVHD grade at both onset and D14. In the Mount Sinai model grade II GVHD at D14 was unfavorable for grade III/IV GVHD at onset and predicted NRM as well as the D28 standard response model. The MAGIC algorithm probability (MAP) is a validated score that combines the serum concentrations of suppression of tumorigenicity 2 (ST2) and regenerating islet-derived 3-alpha (REG3α) to predict NRM. Inclusion of the D14 MAP biomarker score with the D14 Mount Sinai model created three distinct groups (good, intermediate, poor) with strikingly different NRM (8%, 35%, 76% respectively). This D14 MAGIC model displayed better AUC, sensitivity, positive and negative predictive value, and net benefit in decision curve analysis compared to the D28 standard response model. We conclude that this D14 MAGIC model could be useful in therapeutic decisions and may offer an improved endpoint for clinical trials of acute GVHD treatment.
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Affiliation(s)
- Nikolaos Spyrou
- The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Yu Akahoshi
- The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Steven Kowalyk
- The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY
| | - George Morales
- The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Rahnuma Beheshti
- The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Paibel Aguayo-Hiraldo
- Division of Hematology, Oncology and Blood and Marrow Transplantation, Children's Hospital of Los Angeles, Los Angeles, CA
| | - Monzr M Al Malki
- Hematology/Hematopoietic Cell Transplant, City of Hope National Medical Center, Duarte, CA
| | - Francis Ayuk
- Department of Stem Cell Transplantation, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Peter Bader
- Division for Stem Cell Transplantation and Immunology, Department for Children and Adolescents, University Hospital, Goethe University, Frankfurt, Germany
| | - Janna Baez
- The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Alexandra Capellini
- The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Hannah Choe
- Division of Hematology, The James Comprehensive Cancer Center, The Ohio State University, Columbus, OH
| | - Zachariah DeFilipp
- Hematopoietic Cell Transplant and Cellular Therapy Program, Massachusetts General Hospital, Boston, MA
| | - Matthias Eder
- Department of Hematology, Hemostasis, Oncology, and Stem Cell Transplantation, Hannover Medical School, Hannover, Germany
| | - Gilbert Eng
- The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Aaron Etra
- The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Sigrun Gleich
- Department of Hematology and Oncology, Internal Medicine III, University of Regensburg, Regensburg, Germany
| | - Stephan A Grupp
- Division of Oncology, Children's Hospital of Philadelphia, and Perelman School of Medicine, Philadelphia, PA
| | - Elizabeth Hexner
- Blood and Marrow Transplantation Program, Abramson Cancer Center, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA
| | - Matthias Hoepting
- Department of Hematology and Oncology, Internal Medicine III, University of Regensburg, Regensburg, Germany
| | | | - Stelios Kasikis
- The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Nikolaos Katsivelos
- The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Alina Khan
- The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Carrie L Kitko
- Pediatric Stem Cell Transplant Program, Vanderbilt University Medical Center, Nashville TN
| | - Sabrina Kraus
- Department of Internal Medicine II, University Hospital Würzburg, Würzburg, Germany
| | - Deukwoo Kwon
- The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Pietro Merli
- Department of Hematology/Oncology, Cell and Gene Therapy, Bambino Gesù Children's Hospital, Rome, Italy
| | - Joseph Portelli
- The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Muna Qayed
- Aflac Cancer and Blood Disorders Center, Children's Healthcare of Atlanta, Atlanta, GA
| | - Ran Reshef
- Blood and Marrow Transplantation Program, Columbia University Medical Center, New York, NY
| | - Tal Schechter
- Division of Haematology/Oncology, The Hospital for Sick Children, Toronto, Canada
| | - Ingrid Vasova
- Dept. of Internal Medicine 5, Hematology/Oncology, University Hospital Erlangen, Erlangen, Germany
| | - Matthias Wölfl
- Pediatric Blood and Marrow Transplantation Program, Children's Hospital, University of Würzburg, Würzburg, Germany
| | - Kitsada Wudhikarn
- Department of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Bangkok, Thailand
| | - Rachel Young
- The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Ernst Holler
- Department of Hematology and Oncology, Internal Medicine III, University of Regensburg, Regensburg, Germany
| | - Yi-Bin Chen
- Hematopoietic Cell Transplant and Cellular Therapy Program, Massachusetts General Hospital, Boston, MA
| | - Ryotaro Nakamura
- Hematology/Hematopoietic Cell Transplant, City of Hope National Medical Center, Duarte, CA
| | - John E Levine
- The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY
| | - James L M Ferrara
- The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY.
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Sandulache VC, Kirby RP, Lai SY. Moving from conventional to adaptive risk stratification for oropharyngeal cancer. Front Oncol 2024; 14:1287010. [PMID: 38549938 PMCID: PMC10972883 DOI: 10.3389/fonc.2024.1287010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Accepted: 02/20/2024] [Indexed: 06/30/2024] Open
Abstract
Oropharyngeal cancer (OPC) poses a complex therapeutic dilemma for patients and oncologists alike, made worse by the epidemic increase in new cases associated with the oncogenic human papillomavirus (HPV). In a counterintuitive manner, the very thing which gives patients hope, the high response rate of HPV-associated OPC to conventional chemo-radiation strategies, has become one of the biggest challenges for the field as a whole. It has now become clear that for ~30-40% of patients, treatment intensity could be reduced without losing therapeutic efficacy, yet substantially diminishing the acute and lifelong morbidity resulting from conventional chemotherapy and radiation. At the same time, conventional approaches to de-escalation at a population (selected or unselected) level are hampered by a simple fact: we lack patient-specific information from individual tumors that can predict responsiveness. This results in a problematic tradeoff between the deleterious impact of de-escalation on patients with aggressive, treatment-refractory disease and the beneficial reduction in treatment-related morbidity for patients with treatment-responsive disease. True precision oncology approaches require a constant, iterative interrogation of solid tumors prior to and especially during cancer treatment in order to tailor treatment intensity to tumor biology. Whereas this approach can be deployed in hematologic diseases with some success, our ability to extend it to solid cancers with regional metastasis has been extremely limited in the curative intent setting. New developments in metabolic imaging and quantitative interrogation of circulating DNA, tumor exosomes and whole circulating tumor cells, however, provide renewed opportunities to adapt and individualize even conventional chemo-radiation strategies to diseases with highly variable biology such as OPC. In this review, we discuss opportunities to deploy developing technologies in the context of institutional and cooperative group clinical trials over the coming decade.
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Affiliation(s)
- Vlad C Sandulache
- Bobby R. Alford Department of Otolaryngology- Head and Neck Surgery, Baylor College of Medicine, Houston, TX, United States
- Ear Nose and Throat Section (ENT), Operative Care Line, Michael E. DeBakey Veterans Affairs Medical Center, Houston, TX, United States
- Center for Translational Research on Inflammatory Diseases, Michael E. DeBakey Veterans Affairs Medical Center, Houston, TX, United States
| | - R Parker Kirby
- Bobby R. Alford Department of Otolaryngology- Head and Neck Surgery, Baylor College of Medicine, Houston, TX, United States
| | - Stephen Y Lai
- Department of Head and Neck Surgery, Division of Surgery, University of Texas MD Anderson Cancer Center, Houston, TX, United States
- Department of Molecular and Cellular Oncology, Division of Surgery, University of Texas MD Anderson Cancer Center, Houston, TX, United States
- Department of Radiation Oncology, Division of Surgery, University of Texas MD Anderson Cancer Center, Houston, TX, United States
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Bresalier RS. Next-Generation Screening for Colorectal Cancer, an Incremental Approach to a Global Disease. Cancer Prev Res (Phila) 2024; 17:93-95. [PMID: 38437584 DOI: 10.1158/1940-6207.capr-24-0004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Revised: 01/09/2024] [Accepted: 01/11/2024] [Indexed: 03/06/2024]
Abstract
New screening tests for early detection of colorectal cancer and its precursors are rapidly emerging with the focus on noninvasive tests which can be used in both structured opportunistic and population-based organized screening programs. Novel technologies are identifying new combinations of promising markers. Conducting large prospective clinical trials of efficacy requires very large numbers of subjects constituting intended-use populations. These trials are often preceded by studies using smaller numbers of "convenience" samples to derive panels of relevant markers and algorithms to combine them and define what constitutes a positive test. The article by Gagrat and colleagues in this issue reports results from one such study designed to yield a "next-generation" multitargeted (mt-sDNA) stool test. This report exemplifies the advantages and limitations of this approach. See related article by Gagrat et al., p. 119.
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Affiliation(s)
- Robert S Bresalier
- Department of Gastroenterology, Hepatology and Nutrition, The University of Texas MD Anderson Cancer Center, Houston, Texas
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Huang Q, Liu Z, Yu Y, Rong Z, Wang P, Wang S, Wu H, Yan X, Cho WC, Mu T, Li J, Zhao J, Qiu M, Hou Y, Li X. Prediction of response to neoadjuvant chemo-immunotherapy in patients with esophageal squamous cell carcinoma by a rapid breath test. Br J Cancer 2024; 130:694-700. [PMID: 38177659 PMCID: PMC10876947 DOI: 10.1038/s41416-023-02547-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Revised: 12/06/2023] [Accepted: 12/08/2023] [Indexed: 01/06/2024] Open
Abstract
BACKGROUND Neoadjuvant chemo-immunotherapy combination has shown remarkable advances in the management of esophageal squamous cell carcinoma (ESCC). However, the identification of a reliable biomarker for predicting the response to this chemo-immunotherapy regimen remains elusive. While computed tomography (CT) is widely utilized for response evaluation, its inherent limitations in terms of accuracy are well recognized. Therefore, in this study, we present a novel technique to predict the response of ESCC patients before receiving chemo-immunotherapy by testing volatile organic compounds (VOCs) in exhaled breath. METHODS This study employed a prospective-specimen-collection, retrospective-blinded-evaluation design. Patients' baseline breath samples were collected and analyzed using high-pressure photon ionization time-of-flight mass spectrometry (HPPI-TOFMS). Subsequently, patients were categorized as responders or non-responders based on the evaluation of therapeutic response using pathology (for patients who underwent surgery) or CT images (for patients who did not receive surgery). RESULTS A total of 133 patients were included in this study, with 91 responders who achieved either a complete response (CR) or a partial response (PR), and 42 non-responders who had stable disease (SD) or progressive disease (PD). Among 83 participants who underwent both evaluations with CT and pathology, the paired t-test revealed significant differences between the two methods (p < 0.05). For the breath test prediction model using breath test data from all participants, the validation set demonstrated mean area under the curve (AUC) of 0.86 ± 0.06. For 83 patients with pathological reports, the breath test achieved mean AUC of 0.845 ± 0.123. CONCLUSIONS Since CT has inherent weakness in hollow organ assessment and no other ideal biomarker has been found, our study provided a noninvasive, feasible, and inexpensive tool that could precisely predict ESCC patients' response to neoadjuvant chemo-immunotherapy combination using breath test based on HPPI-TOFMS.
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Affiliation(s)
- Qi Huang
- Department of Thoracic Surgery, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450003, China
| | - Zheng Liu
- Department of Thoracic Surgery, Peking University People's Hospital, Beijing, 100044, China
- Thoracic Oncology Institute, Peking University People's Hospital, Beijing, 100044, China
| | - Yipei Yu
- Department of Biostatistics, School of Public Health, Peking University, Beijing, 100191, China
| | - Zhiwei Rong
- Department of Biostatistics, School of Public Health, Peking University, Beijing, 100191, China
| | - Peiyu Wang
- Department of Thoracic Surgery, Peking University People's Hospital, Beijing, 100044, China
- Thoracic Oncology Institute, Peking University People's Hospital, Beijing, 100044, China
| | - Shaodong Wang
- Department of Thoracic Surgery, Peking University People's Hospital, Beijing, 100044, China
| | - Hao Wu
- Department of Thoracic Surgery, Shenzhen Second People's Hospital, The First Affiliated Hospital of Shenzhen University, Shenzhen, 518000, China
| | - Xiang Yan
- Department of Thoracic Surgery, Peking University People's Hospital, Beijing, 100044, China
| | - William C Cho
- Department of Clinical Oncology, Queen Elizabeth Hospital, Hong Kong SAR, China
| | - Teng Mu
- Department of Thoracic Surgery, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450003, China
| | - Jilun Li
- Department of Thoracic Surgery, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450003, China
| | - Jia Zhao
- Department of Thoracic Surgery, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450003, China
| | - Mantang Qiu
- Department of Thoracic Surgery, Peking University People's Hospital, Beijing, 100044, China.
- Thoracic Oncology Institute, Peking University People's Hospital, Beijing, 100044, China.
- Breax Laboratory, PCAB Research Center of Breath and Metabolism, Beijing, 100074, China.
| | - Yan Hou
- Department of Biostatistics, School of Public Health, Peking University, Beijing, 100191, China.
| | - Xiangnan Li
- Department of Thoracic Surgery, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450003, China.
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El-Serag H, Kanwal F, Ning J, Powell H, Khaderi S, Singal AG, Asrani S, Marrero JA, Amos CI, Thrift AP, Luster M, Alsarraj A, Olivares L, Skapura D, Deng J, Salem E, Najjar O, Yu X, Duong H, Scheurer ME, Ballantyne CM, Kaochar S. Serum biomarker signature is predictive of the risk of hepatocellular cancer in patients with cirrhosis. Gut 2024; 73:gutjnl-2024-332034. [PMID: 38365278 PMCID: PMC11327383 DOI: 10.1136/gutjnl-2024-332034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Accepted: 01/30/2024] [Indexed: 02/18/2024]
Abstract
BACKGROUND Inflammatory and metabolic biomarkers have been associated with hepatocellular cancer (HCC) risk in phases I and II biomarker studies. We developed and internally validated a robust metabolic biomarker panel predictive of HCC in a longitudinal phase III study. METHODS We used data and banked serum from a prospective cohort of 2266 adult patients with cirrhosis who were followed until the development of HCC (n=126). We custom designed a FirePlex immunoassay to measure baseline serum levels of 39 biomarkers and established a set of biomarkers with the highest discriminatory ability for HCC. We performed bootstrapping to evaluate the predictive performance using C-index and time-dependent area under the receiver operating characteristic curve (AUROC). We quantified the incremental predictive value of the biomarker panel when added to previously validated clinical models. RESULTS We identified a nine-biomarker panel (P9) with a C-index of 0.67 (95% CI 0.66 to 0.67), including insulin growth factor-1, interleukin-10, transforming growth factor β1, adipsin, fetuin-A, interleukin-1 β, macrophage stimulating protein α chain, serum amyloid A and TNF-α. Adding P9 to our clinical model with 10 factors including AFP improved AUROC at 1 and 2 years by 4.8% and 2.7%, respectively. Adding P9 to aMAP score improved AUROC at 1 and 2 years by 14.2% and 7.6%, respectively. Adding AFP L-3 or DCP did not change the predictive ability of the P9 model. CONCLUSIONS We identified a panel of nine serum biomarkers that is independently associated with developing HCC in cirrhosis and that improved the predictive ability of risk stratification models containing clinical factors.
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Affiliation(s)
- Hashem El-Serag
- Gastroenterology and Hepatology, Baylor College of Medicine, Houston, Texas, USA
- Houston VA Health Services Research & Development Center of Excellence, Michael E. DeBakey Veterans Affairs Medical Center, Houston, Texas, USA
| | | | - Jing Ning
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Hannah Powell
- Department of Medicine, Baylor College of Medicine, Houston, Texas, USA
| | | | - Amit G Singal
- Internal Medicine, University of Texas Southwestern, Dallas, Texas, USA
| | - Sumeet Asrani
- Baylor University Medical Center at Dallas, Dallas, Texas, USA
| | | | - Christopher I Amos
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, Texas, USA
| | - Aaron P Thrift
- Medicine, Baylor College of Medicine, Houston, Texas, USA
| | | | - Abeer Alsarraj
- Gastroenterology and Hepatology, Michael DeBakey Veterans Affairs Medical Ctr and Houston Ctr for Quality of Care & Utilization Studies, Houston, Texas, USA
| | | | - Darlene Skapura
- Department of Medicine, Baylor College of Medicine, Houston, Texas, USA
| | - Jenny Deng
- Department of Medicine, Baylor College of Medicine, Houston, Texas, USA
| | - Emad Salem
- Baylor College of Medicine, Houston, Texas, USA
| | - Omar Najjar
- Department of Medicine, Baylor College of Medicine, Houston, Texas, USA
| | - Xian Yu
- Baylor College of Medicine, Houston, Texas, USA
| | - Hao Duong
- Baylor College of Medicine, Houston, Texas, USA
| | - Michael E Scheurer
- Department of Pediatrics, Baylor College of Medicine, Houston, Texas, USA
| | | | - Salma Kaochar
- Baylor College of Medicine, Houston, Texas, USA
- Department of Medicine, Baylor College of Medicine, Houston, Texas, USA
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Furuya H, Sakatani T, Tanaka S, Murakami K, Waldron RT, Hogrefe W, Rosser CJ. Bladder cancer risk stratification with the Oncuria 10-plex bead-based urinalysis assay using three different Luminex xMAP instrumentation platforms. J Transl Med 2024; 22:8. [PMID: 38167321 PMCID: PMC10763405 DOI: 10.1186/s12967-023-04811-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2023] [Accepted: 12/16/2023] [Indexed: 01/05/2024] Open
Abstract
BACKGROUND No single marker of bladder cancer (BC) exists in urine samples with sufficient accuracy for disease diagnosis and treatment monitoring. The multiplex Oncuria BC assay noninvasively quantifies the concentration of 10 protein analytes in voided urine samples to quickly generate a unique molecular profile with proven BC diagnostic and treatment-tracking utility. Test adoption by diagnostic and research laboratories mandates reliably reproducible assay performance across a variety of instrumentation platforms used in different laboratories. METHODS We compared the performance of the clinically validated Oncuria BC multiplex immunoassay when data output was generated on three different analyzer systems. Voided urine samples from 36 subjects (18 with BC and 18 Controls) were reacted with Oncuria test reagents in three 96-well microtiter plates on Day 1, and consecutively evaluated on the LED/image-based MagPix, and laser/flow-based Luminex 200 and FlexMap 3D (all xMAP instruments from Luminex Corp., Austin, TX) on Day 2. The BC assay uses magnetic bead-based fluorescence technology (xMAP, Multi-analyte profiling; Luminex) to simultaneously quantify 10 protein analytes in urine specimens [i.e., angiogenin (ANG), apolipoprotein E (ApoE), carbonic anhydrase IX (CA9), CXCL8/interleukin-8 (IL-8), matrix metalloproteinase-9 (MMP-9), matrix metalloproteinase-10 (MMP-10), serpin A1/alpha-1 anti-trypsin (A1AT), serpin E1/plasminogen activator inhibitor-1 (PAI-1), CD138/syndecan-1 (SDC1), and vascular endothelial growth factor-A (VEGF-A)]. All three analyzers quantify fluorescence signals generated by the Oncuria assay. RESULTS All three platforms categorized all 10 analytes in identical samples at nearly identical concentrations, with variance across systems typically < 5%. While the most contemporary instrument, the FlexMap 3D, output higher raw fluorescence values than the two comparator systems, standard curve slopes and analyte concentrations determined in urine samples were concordant across all three units. Forty-four percent of BC samples registered ≥ 1 analyte above the highest standard concentration, i.e., A1AT (n = 7/18), IL-8 (n = 5), and/or ANG (n = 2), while only one control sample registered an analyte (A1AT) above the highest standard concentration. CONCLUSION Multiplex BC assays generate detailed molecular signatures useful for identifying BC, predicting treatment responsiveness, and tracking disease progression and recurrence. The similar performance of the Oncuria assay across three different analyzer systems supports test adaptation by clinical and research laboratories using existing xMAP platforms. TRIAL REGISTRATION This study was registered at ClinicalTrials.gov as NCT04564781, NCT03193528, NCT03193541, and NCT03193515.
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Affiliation(s)
- Hideki Furuya
- Cedars‑Sinai Medical Center, Samuel Oschin Comprehensive Cancer Institute, 110 N. George Burns Rd, Davis 2025, Los Angeles, CA, 90048, USA.
- Department of Biomedical Sciences, Cedars‑Sinai Medical Center, Los Angeles, CA, USA.
| | - Toru Sakatani
- Cedars‑Sinai Medical Center, Samuel Oschin Comprehensive Cancer Institute, 110 N. George Burns Rd, Davis 2025, Los Angeles, CA, 90048, USA
- Department of Urology, Cedars‑Sinai Medical Center, Los Angeles, CA, USA
| | - Sunao Tanaka
- Cedars‑Sinai Medical Center, Samuel Oschin Comprehensive Cancer Institute, 110 N. George Burns Rd, Davis 2025, Los Angeles, CA, 90048, USA
| | - Kaoru Murakami
- Cedars‑Sinai Medical Center, Samuel Oschin Comprehensive Cancer Institute, 110 N. George Burns Rd, Davis 2025, Los Angeles, CA, 90048, USA
- Department of Urology, Cedars‑Sinai Medical Center, Los Angeles, CA, USA
| | - Richard T Waldron
- Department of Medicine, Cedars‑Sinai Medical Center, Los Angeles, CA, USA
| | | | - Charles J Rosser
- Cedars‑Sinai Medical Center, Samuel Oschin Comprehensive Cancer Institute, 110 N. George Burns Rd, Davis 2025, Los Angeles, CA, 90048, USA
- Department of Urology, Cedars‑Sinai Medical Center, Los Angeles, CA, USA
- Nonagen Bioscience Corp., Los Angeles, CA, USA
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Adeoye J, Su YX. Artificial intelligence in salivary biomarker discovery and validation for oral diseases. Oral Dis 2024; 30:23-37. [PMID: 37335832 DOI: 10.1111/odi.14641] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Revised: 05/19/2023] [Accepted: 05/28/2023] [Indexed: 06/21/2023]
Abstract
Salivary biomarkers can improve the efficacy, efficiency, and timeliness of oral and maxillofacial disease diagnosis and monitoring. Oral and maxillofacial conditions in which salivary biomarkers have been utilized for disease-related outcomes include periodontal diseases, dental caries, oral cancer, temporomandibular joint dysfunction, and salivary gland diseases. However, given the equivocal accuracy of salivary biomarkers during validation, incorporating contemporary analytical techniques for biomarker selection and operationalization from the abundant multi-omics data available may help improve biomarker performance. Artificial intelligence represents one such advanced approach that may optimize the potential of salivary biomarkers to diagnose and manage oral and maxillofacial diseases. Therefore, this review summarized the role and current application of techniques based on artificial intelligence for salivary biomarker discovery and validation in oral and maxillofacial diseases.
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Affiliation(s)
- John Adeoye
- Division of Oral and Maxillofacial Surgery, Faculty of Dentistry, University of Hong Kong, Hong Kong SAR, China
| | - Yu-Xiong Su
- Division of Oral and Maxillofacial Surgery, Faculty of Dentistry, University of Hong Kong, Hong Kong SAR, China
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Dong X, Zheng Y, Lin DW, Newcomb L, Zhao YQ. Constructing time-invariant dynamic surveillance rules for optimal monitoring schedules. Biometrics 2023; 79:3895-3906. [PMID: 37479875 PMCID: PMC10866138 DOI: 10.1111/biom.13911] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Accepted: 05/22/2023] [Indexed: 07/23/2023]
Abstract
Dynamic surveillance rules (DSRs) are sequential surveillance decision rules informing monitoring schedules in clinical practice, which can adapt over time according to a patient's evolving characteristics. In many clinical applications, it is desirable to identify and implement optimal time-invariant DSRs, where the parameters indexing the decision rules are shared across different decision points. We propose a new criterion for DSRs that accounts for benefit-cost tradeoff during the course of disease surveillance. We develop two methods to estimate the time-invariant DSRs optimizing the proposed criterion, and establish asymptotic properties for the estimated parameters of biomarkers indexing the DSRs. The first approach estimates the optimal decision rules for each individual at every stage via regression modeling, and then estimates the time-invariant DSRs via a classification procedure with the estimated time-varying decision rules as the response. The second approach proceeds by optimizing a relaxation of the empirical objective, where a surrogate function is utilized to facilitate computation. Extensive simulation studies are conducted to demonstrate the superior performances of the proposed methods. The methods are further applied to the Canary Prostate Active Surveillance Study (PASS).
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Affiliation(s)
| | - Yingye Zheng
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington, U.S.A
| | - Daniel W. Lin
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington, U.S.A
- Department of Urology, University of Washington, Seattle, Washington, U.S.A
| | - Lisa Newcomb
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington, U.S.A
- Department of Urology, University of Washington, Seattle, Washington, U.S.A
| | - Ying-Qi Zhao
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington, U.S.A
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