The multicancer early detection (MCED) test has the potential to enhance current cancer-screening methods. We evaluated a new MCED test that analyzes plasma cell-free DNA using genetic- and fragmentomics-based features from whole-genome sequencing. The present study included an internal validation cohort of 3,021 patients with cancer and 3,370 noncancer controls, and an independent cohort of 677 patients with cancer and 687 noncancer individuals. The results demonstrated an overall sensitivity of 87.4%, specificity of 97.8% and tissue-of-origin prediction accuracy of 82.4% in the independent validation cohort. Preliminary results from a prospective study of 3,724 asymptomatic participants showed a sensitivity of 53.5% (predominantly early stage cancers) and specificity of 98.1%. These findings indicate that the MCED test has strong potential to improve early cancer detection and support clinical decision-making.

Guideline-recommended screening programs exist for only a few single-cancer types, and these cancers represent less than one-half of all new cancer cases diagnosed each year in the U.S. In addition, these "single-cancer" standard of care (SoC) screening tests vary in accuracy, adherence, and effectiveness, though all are generally understood to lead to reductions in cancer-related mortality. Recent advances in high-throughput technologies and machine learning have facilitated the development of blood-based multi-cancer early detection (MCED) tests. The opportunity for early detection of multiple cancers with a single blood test holds promise in addressing the current unmet need in cancer screening. By complementing existing SoC screening, MCED tests have the potential to detect a wide range of cancers at earlier stages when patients are asymptomatic, enabling more effective treatment options and improved cancer outcomes. MCED tests are positioned to be utilized as a complementary screening tool to improve screening adherence at the population level, to broaden screening availability for individuals who are not adherent with SoC screening programs, as well as for those who may harbor cancers that do not have SoC testing available. Published work to date has primarily focused on test performance relating to sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). MCED tests will require approval through the pre-market approval pathway from the United States Food and Drug Administration. Additional studies will be needed to demonstrate clinical utility (i.e., improvements in health outcomes) and establish optimal implementation strategies, (i.e., testing intervals), follow-up and logistics of shared decision making. Here, we propose core attributes of MCED testing for which clinical data are needed to ideally position MCED testing for widespread use in clinical practice.

Approximately two-thirds of patients with colorectal cancer (CRC) undergo resection with curative intent; however, 30% to 50% of these patients experience recurrence. The concentration of cell-free DNA (cfDNA) before and after surgery may be related to the prognosis of patients with CRC, but there is limited information regarding cfDNA levels at the time of surgery. Here, we analyzed surgical cfDNA release using plasma samples from 30 colorectal cancer patients at three key points during surgery: preoperative (immediately before surgery), intraoperative (during surgery), and postoperative (at the end of surgery). Automated electrophoresis was used to analyze cfDNA concentrations and fragment sizes, which were then correlated with clinical variables. Our findings indicate a significant increase in cfDNA release during and after surgery (2.8- and 2.2-fold higher respectively, p < 0.01). Characteristic fragments of cfDNA (<400 bp) predominated at all surgical stages; however, the release of genomic material (>400 bp) was also observed. We found that cfDNA concentration increases during and after surgery in patients over 60 years old (2.9-fold higher intraoperatively than preoperatively and 2.3 folds higher postoperatively than preoperatively, p < 0.01); in patients with comorbidities (3.0-fold higher intraoperatively and 2.3-fold higher postoperatively, p < 0.01); and in patients with CEA levels >5 ng/mL (3.1-fold higher intraoperatively and 1.3-fold higher postoperatively, p < 0.01). Interestingly, cfDNA release during surgery is significantly higher in patients with adverse clinical characteristics. Patients bearing locally advanced tumors or metastasis had a 3.1-fold increase in cfDNA release intraoperatively and 2.4-fold increase postoperatively, p < 0.01. cfDNA concentration also increases intraoperatively in patients with a high score of tumor buds (2.6 folds higher, p < 0.02), patients with perineural invasion (3.4-fold higher, p < 0.02) and in patients with lymphovascular invasion (3.1-fold higher, p < 0.05). Furthermore, we observed that cfDNA concentration may rise in correlation with the duration of the surgery, highlighting its potential as a marker of surgical quality. Taken together, our results suggest that in addition to physiological age, comorbidities and unfavorable clinical traits, intense surgical manipulation from the tumor's extent, may result in greater tissue damage and elevated cfDNA release.

The analysis of circulating tumour DNA (ctDNA) through minimally invasive liquid biopsies is promising for early multi-cancer detection and monitoring minimal residual disease. Most existing methods focus on targeted deep sequencing, but few integrate multiple data modalities. Here, we develop a methodology for ctDNA detection using deep (80x) whole-genome TET-Assisted Pyridine Borane Sequencing (TAPS), a less destructive approach than bisulphite sequencing, which permits the simultaneous analysis of genomic and methylomic data. We conduct a diagnostic accuracy study across multiple cancer types in symptomatic patients, achieving 94.9% sensitivity and 88.8% specificity. Matched tumour biopsies are used for validation, not for guiding the analysis, imitating an early detection scenario. Furthermore, in silico validation demonstrates strong discrimination (86% AUC) at ctDNA fractions as low as 0.7%. Additionally, we successfully track tumour burden and ctDNA shedding from precancerous lesions post-treatment without requiring matched tumour biopsies. This pipeline is ready for further clinical evaluation to extend cancer screening and improve patient triage and monitoring.

Cell-free DNA (cfDNA) technology has allowed for cerebrospinal fluid (CSF), a previously underutilized biofluid, to be analyzed in new ways. The interrogation of CSF-derived cfDNA is giving rise to novel molecular insights, particularly in pediatric central nervous system (CNS) tumors, where invasive tumor tissue acquisition may be challenging. Contemporary disease monitoring is currently restricted to radiographic surveillance by magnetic resonance imaging and CSF cytology to directly detect abnormal cells and cell clusters. Alternatively, cfDNA is often present in the CSF from pediatric patients with both malignant and nonmalignant CNS tumors and can be accessed by minimally invasive lumbar puncture and other CSF-liberating procedures, offering a promising alternative for longitudinal molecular disease analysis and surveillance.

This review explores the use of low-pass whole genome sequencing (LP-WGS) to analyze cfDNA from the CSF of pediatric patients with CNS tumors. This platform is uniquely poised for the detection of tumors harboring copy number variants, which are prevalent in this population. The utility and sensitivity of LP-WGS as a clinical tool is explored and discussed in the context of alternative CSF liquid biopsy interrogation modalities, including nanopore sequencing and methylation array.

Analysis of CSF-derived cfDNA by LP-WGS has broad diagnostic, prognostic, and clinical implications for pediatric patients with CNS tumors. Careful interpretation of LP-WGS results may aid in therapeutic targeting of pediatric CNS tumors and may provide insight into tumor heterogeneity and evolution over time, without the need for invasive and potentially risky tissue sampling.

General population cancer screening in the United Kingdom is limited to selected cancers. Blood-based multi-cancer early detection tests aim to detect potential cancer signals from multiple cancers in the blood. The use of a multi-cancer early detection test for population screening requires a high specificity and a reasonable sensitivity to detect early-stage disease so that the benefits of earlier diagnosis and treatment can be realised.

To undertake a systematic literature review of the clinical effectiveness evidence on blood-based multi-cancer early detection tests for screening.

Comprehensive searches of electronic databases (including MEDLINE and EMBASE) and trial registers were undertaken in September 2023 to identify published and unpublished studies of multi-cancer early detection tests. Test manufacturer websites and reference lists of included studies and pertinent reviews were checked for additional studies. The target population was individuals aged 50-79 years without clinical suspicion of cancer. Outcomes of interest included test accuracy, number and proportion of cancers detected (by site and stage), time to diagnostic resolution, mortality, potential harms, health-related quality of life, acceptability and satisfaction. The risk of bias was assessed using the quality assessment of diagnostic accuracy studies-2 checklist. Results were summarised using narrative synthesis. Stakeholders contributed to protocol development, report drafting and interpretation of review findings.

Over 8000 records were identified. Thirty-six studies met the inclusion criteria: 1 ongoing randomised controlled trial, 13 completed cohort studies, 17 completed case-control studies and 5 ongoing cohort or case-control studies. Individual tests claimed to detect from 3 to over 50 different types of cancer. Diagnostic accuracy of currently available multi-cancer early detection tests varied substantially: Galleri® (GRAIL, Menlo Park, CA, USA) sensitivity 20.8-66.3%, specificity 98.4-99.5% (three studies); CancerSEEK (Exact Sciences, Madison, WI, USA) sensitivity 27.1-62.3%, specificity 98.9- 99.1% (two studies); SPOT-MAS™ (Gene Solutions, Ho Chi Minh City, Vietnam) sensitivity 72.4-100%, specificity 97.0-99.9% (two studies); Trucheck™ (Datar Cancer Genetics, Bayreuth, Germany) sensitivity 90.0%, specificity 96.4% (one study); Cancer Differentiation Analysis (AnPac Bio, Shanghai, China) sensitivity 40.0%, specificity 97.6% (one study). AICS® (AminoIndex Cancer Screening; Ajinomoto, Tokyo, Japan) screens for individual cancers separately, so no overall test performance statistics are available. Where reported, sensitivity was lower for detecting earlier-stage cancers (stages I-II) compared with later-stage cancers (stages III-IV). Studies of seven other multi-cancer early detection tests at an unclear stage of development were also summarised.

Study selection was complex; it was often difficult to determine the stage of development of multi-cancer early detection tests. The evidence was limited; there were no completed randomised controlled trials and most included studies had a high overall risk of bias, primarily owing to limited follow-up of participants with negative test results. Only one study of Galleri recruited asymptomatic individuals aged over 50 in the United States of America; however, study results may not be representative of the United Kingdom's general screening population. No meaningful results were reported relating to patient-relevant outcomes, such as mortality, potential harms, health-related quality of life, acceptability or satisfaction.

All currently available multi-cancer early-detection tests reported high specificity (> 96%). Sensitivity was highly variable and influenced by study design, population, reference standard test used and length of follow-up.

Further research should report patient-relevant outcomes and consider patient and service impacts.

This study is registered as PROSPERO CRD42023467901.

This award was funded by the National Institute for Health and Care Research (NIHR) Health Technology Assessment programme (NIHR award ref: NIHR161758) and is published in full in Health Technology Assessment; Vol. 29, No. 2. See the NIHR Funding and Awards website for further award information.

Lung cancer is one of the most prevalent cancers worldwide, yet only approximately 16% of patients are diagnosed in early stage, highlighting the urgent need for novel, highly accurate detection models. In our study, patients with suspected lung cancer or lung disease, as identified through radiographic imaging, along with healthy individuals, were consecutively recruited from Beijing Chest Hospital. Circulating free DNA (cfDNA) was extracted from plasma samples, and low-depth whole-genome sequencing was performed to identify fragmentomic features for model construction. A total of 265 participants were prospectively enrolled, comprising 124 lung cancer patients and 141 noncancer individuals. The model we developed was based on four cfDNA fragmentation characteristics, including 20-bp breakpoint nucleotides motif, fragmentation size coverage, fragmentation size distribution, and copy number variation. In an independent test cohort, the model achieved an area under the curve (AUC) of 0.861 (95% CI: 0.781-0.942) and demonstrated a sensitivity of 70% (95% CI: 53.5%-83.4%) at a specificity of 89.4% (95% CI: 76.9%-96.5%). Notably, the model was also effective in detecting early-stage cancer, with an AUC of 0.808 (95% CI: 0.69-0.925). In summary, our lung cancer detection model shows strong screening capabilities by leveraging four cfDNA fragmentation characteristics, exhibiting robust performance in early cancer diagnosis.

Fragmentomic features of circulating cell free mitochondrial DNA (ccf-mtDNA) including fragmentation profile, 5' end base preference and motif diversity are poorly understood. Here, we generated ccf-mtDNA sequencing data of 1607 plasma samples using capture-based next generation sequencing. We firstly found that fragmentomic features of ccf-mtDNA were remarkably different from those of circulating cell free nuclear DNA. Furthermore, region-specific fragmentomic features of ccf-mtDNA were observed, which was associated with protein binding, base composition and special structure of mitochondrial DNA. When comparing to non-cancer controls, six types of cancer patients exhibited aberrant fragmentomic features. Then, cancer detection models were built based on the fragmentomic features. Both internal and external validation cohorts demonstrated the excellent capacity of our model in distinguishing cancer patients from non-cancer control, with all area under curve higher than 0.9322. The overall accuracy of tissue-of-origin was 89.24% and 87.92% for six cancer types in two validation cohort, respectively. Altogether, our study comprehensively describes cancer-specific fragmentomic features of ccf-mtDNA and provides a proof-of-principle for the ccf-mtDNA fragmentomics-based multi-cancer detection and tissue-of-origin classification.

Clinical genomic profiling of cell-free nucleic acids (e.g. cell-free DNA or cfDNA) from blood and other body fluids has ushered in a new era in non-invasive diagnostics and treatment monitoring strategies for health conditions and diseases such as cancer. Genomic analysis of cfDNAs not only identifies disease-associated mutations, but emerging findings suggest that structural, topological, and fragmentation characteristics of cfDNAs reveal crucial information about the location of source tissues, their epigenomes, and other clinically relevant characteristics, leading to the burgeoning field of fragmentomics. The field has seen rapid developments in computational and genomics methodologies for conducting large-scale studies on health conditions and diseases - that have led to fundamental, mechanistic discoveries as well as translational applications. Several recent studies have shown the clinical utilities of the cfDNA fragmentomics technique which has the potential to be effective for early disease diagnosis, determining treatment outcomes, and risk-free continuous patient monitoring in a non-invasive manner. In this article, we outline recent developments in computational genomic methodologies and analysis strategies, as well as the emerging insights from cfNA fragmentomics. We conclude by highlighting the current challenges and opportunities.

Biopsy, including tissue and liquid biopsy, offers comprehensive and real-time physiological and pathological information for disease detection, diagnosis, and monitoring. Fluorescent probes are frequently selected to obtain adequate information on pathological processes in a rapid and minimally invasive manner based on their advantages for biopsy. However, conventional fluorescent probes have been found to show aggregation-caused quenching (ACQ) properties, impeding greater progresses in this area. Since the discovery of aggregation-induced emission luminogen (AIEgen) have promoted rapid advancements in molecular bionanomaterials owing to their unique properties, including high quantum yield (QY) and signal-to-noise ratio (SNR), etc. This review seeks to present the latest advances in AIEgen-based biofluorescent probes for biopsy in real or artificial samples, and also the key properties of these AIE probes. This review is divided into: (i) tissue biopsy based on smart AIEgens, (ii) blood sample biopsy based on smart AIEgens, (iii) urine sample biopsy based on smart AIEgens, (iv) saliva sample biopsy based on smart AIEgens, (v) biopsy of other liquid samples based on smart AIEgens, and (vi) perspectives and conclusion. This review could provide additional guidance to motivate interest and bolster more innovative ideas for further exploring the applications of various smart AIEgens in precision medicine.

Ovarian cancer (OC) is a major cause of cancer mortality in women worldwide. Due to the occult onset of OC, its nonspecific clinical symptoms in the early phase, and a lack of effective early diagnostic tools, most OC patients are diagnosed at an advanced stage. In this study, shallow whole-genome sequencing was utilized to characterize fragmentomics features of circulating tumor DNA (ctDNA) in OC patients. By applying a machine learning model, multiclass fragmentomics data achieved a mean area under the curve (AUC) of 0.97 (95% CI 0.962-0.976) for diagnosing OC. OC scores derived from this model strongly correlated with the disease stage. Further comparative analysis of OC scores illustrated that the fragmentomics-based technology provided additional clinical benefits over the traditional serum biomarkers cancer antigen 125 (CA125) and the Risk of Ovarian Malignancy Algorithm (ROMA) index. In conclusion, fragmentomics features in ctDNA are potential biomarkers for the accurate diagnosis of OC.

Colorectal cancer is frequently diagnosed in advanced stages, highlighting the need for developing approaches for early detection. Liquid biopsy using cell-free DNA (cfDNA) fragmentomics is a promising approach, but the clinical application is hindered by complexity and cost. This study aimed to develop an integrated model using cfDNA fragmentomics for accurate, cost-effective early-stage colorectal cancer detection. Plasma cfDNA was extracted and sequenced from a training cohort of 360 participants, including 176 patients with colorectal cancer and 184 healthy controls. An ensemble stacked model comprising five machine learning models was employed to distinguish patients with colorectal cancer from healthy controls using five cfDNA fragmentomic features. The model was validated in an independent cohort of 236 participants (117 patients with colorectal cancer and 119 controls) and a prospective cohort of 242 participants (129 patients with colorectal cancer and 113 controls). The ensemble stacked model showed remarkable discriminatory power between patients with colorectal cancer and controls, outperforming all base models and achieving a high area under the receiver operating characteristic curve of 0.986 in the validation cohort. It reached 94.88% sensitivity and 98% specificity for detecting colorectal cancer in the validation cohort, with sensitivity increasing as the cancer progressed. The model also demonstrated consistently high accuracy in within-run and between-run tests and across various conditions in healthy individuals. In the prospective cohort, it achieved 91.47% sensitivity and 95.58% specificity. This integrated model capitalizes on the multiplex nature of cfDNA fragmentomics to achieve high sensitivity and robustness, offering significant promise for early colorectal cancer detection and broad patient benefit.  Significance: The development of a minimally invasive, efficient approach for early colorectal cancer detection using advanced machine learning to analyze cfDNA fragment patterns could expedite diagnosis and improve treatment outcomes for patients. See related commentary by Rolfo and Russo, p. 3128.

At present, cancer is still an important factor threatening human health. Colorectal cancer (CRC) is one of the top three most common cancers worldwide and one of the deadliest malignancies in humans. The latest data showed that CRC incidence and mortality rank third and second, respectively, among global malignancies. Early and accurate diagnosis is crucial to reduce the morbidity, mortality and improve survival of patients with CRC, but the current early diagnostic methods have limitations. The effectiveness and compliance of diagnostic methods have a certain impact on whether people choose screening. In this editorial, we explore strategies for the early diagnosis of CRC, including stool-based, blood-based, direct visualization, and imaging examinations.

Current radiotherapy guidelines rely heavily on imaging-based monitoring. Liquid biopsy monitoring promises to complement imaging by providing frequent systemic information about the tumor. In particular, cell-free DNA (cfDNA) sequencing offers a tumor-agnostic approach, which lends itself to monitoring heterogeneous cohorts of cancer patients.

We collected plasma cfDNA from oligometastatic patients (OMD) and head-and-neck cancer patients (SCCHN) at six time points before, during, and after radiotherapy, and compared them to the plasma samples of healthy and polymetastatic volunteers. We performed low-pass (on average 7x) whole-genome sequencing on 93 plasma cfDNA samples and correlated copy number alterations and fragment length distributions to clinical and imaging findings.

We observed copy number alterations in 4/7 polymetastatic cancer patients, 1/7 OMD and 1/7 SCCHN patients, these patients' imaging showed progression following radiotherapy. Using unsupervised learning, we identified cancer-specific fragment length features that showed a strong correlation with copy number-based tumor fraction estimates. In 4/4 HPV-positive SCCHN patient samples, we detected viral DNA that enabled the monitoring of very low tumor fraction samples.

Our results indicate that an elevated tumor fraction is associated with tumor aggressiveness and systemic tumor spread. This information may be used to adapt treatment strategies. Further, we show that by detecting specific sequences such as viral DNA, the sensitivity of detecting cancer from cell-free DNA sequencing data can be greatly increased.

Circulating tumor DNA (ctDNA) analysis has been applied in cancer diagnostics including lung cancer. Specifically for the early detection purpose, various modalities of ctDNA analysis have demonstrated their potentials. Such analyses have showed diverse performance across different studies.

We performed a systematic review of original studies published before 1 January 2023. Studies that evaluated ctDNA alone and in combination with other biomarkers for early detection of lung cancer were included.

The systematic review analysis included 56 original studies that were aimed for early detection of lung cancer. There were 39 studies for lung cancer only and 17 for pan-cancer early detection. Cancer and control cases included were heterogenous across studies. Different molecular features of ctDNA have been evaluated, including 7 studies on cell-free DNA concentration, 17 on mutation, 29 on methylation, 5 on hydroxymethylation and 8 on fragmentation patterns. Among these 56 studies, 17 have utilised different combinations of the above-mentioned ctDNA features and/or circulation protein markers. For all the modalities, lower sensitivities were reported for the detection of early-stage cancer.

The systematic review suggested the clinical utility of ctDNA analysis for early detection of lung cancer, alone or in combination with other biomarkers. Future validation with standardised testing protocols would help integration into clinical care.

Early screening using low-dose computed tomography (LDCT) can reduce mortality caused by non-small-cell lung cancer. However, ∼25% of the 'suspicious' pulmonary nodules identified by LDCT are later confirmed benign through resection surgery, adding to patients' discomfort and the burden on the healthcare system. In this study, we aim to develop a noninvasive liquid biopsy assay for distinguishing pulmonary malignancy from benign yet 'suspicious' lung nodules using cell-free DNA (cfDNA) fragmentomics profiling.

An independent training cohort consisting of 193 patients with malignant nodules and 44 patients with benign nodules was used to construct a machine learning model. Base models using four different fragmentomics profiles were optimized using an automated machine learning approach before being stacked into the final predictive model. An independent validation cohort, including 96 malignant nodules and 22 benign nodules, and an external test cohort, including 58 malignant nodules and 41 benign nodules, were used to assess the performance of the stacked ensemble model.

Our machine learning models demonstrated excellent performance in detecting patients with malignant nodules. The area under the curves reached 0.857 and 0.860 in the independent validation cohort and the external test cohort, respectively. The validation cohort achieved an excellent specificity (68.2%) at the targeted 90% sensitivity (89.6%). An equivalently good performance was observed while applying the cut-off to the external cohort, which reached a specificity of 63.4% at 89.7% sensitivity. A subgroup analysis for the independent validation cohort showed that the sensitivities for detecting various subgroups of nodule size (<1 cm: 91.7%; 1-3 cm: 88.1%; >3 cm: 100%; unknown: 100%) and smoking history (yes: 88.2%; no: 89.9%) all remained high among the lung cancer group.

Our cfDNA fragmentomics assay can provide a noninvasive approach to distinguishing malignant nodules from radiographically suspicious but pathologically benign ones, amending LDCT false positives.

Plasma cell-free DNA (cfDNA) fragmentomics has demonstrated significant differentiation power between cancer patients and healthy individuals, but little is known in pancreatic and biliary tract cancers. The aim of this study is to characterize the cfDNA fragmentomics in biliopancreatic cancers and develop an accurate method for cancer detection.

One hundred forty-seven patients with biliopancreatic cancers and 71 non-cancer volunteers were enrolled, including 55 patients with cholangiocarcinoma, 30 with gallbladder cancer, and 62 with pancreatic cancer. Low-coverage whole-genome sequencing (median coverage: 2.9 ×) was performed on plasma cfDNA. Three cfDNA fragmentomic features, including fragment size, end motif and nucleosome footprint, were subjected to construct a stacked machine learning model for cancer detection. Integration of carbohydrate antigen 19-9 (CA19-9) was explored to improve model performance.

The stacked model presented robust performance for cancer detection (area under curve (AUC) of 0.978 in the training cohort, and AUC of 0.941 in the validation cohort), and remained consistent even when using extremely low-coverage sequencing depth of 0.5 × (AUC: 0.905). Besides, our method could also help differentiate biliopancreatic cancer subtypes. By integrating the stacked model and CA19-9 to generate the final detection model, a high accuracy in distinguishing biliopancreatic cancers from non-cancer samples with an AUC of 0.995 was achieved.

Our model demonstrated ultrasensitivity of plasma cfDNA fragementomics in detecting biliopancreatic cancers, fulfilling the unmet accuracy of widely-used serum biomarker CA19-9, and provided an affordable way for accurate noninvasive biliopancreatic cancer screening in clinical practice.

Cancer continues to pose a considerable challenge to global health. In the search for innovative strategies to combat this complex enemy, the concept of universal cancer screening has emerged as a promising avenue for early detection and prevention. In contrast to targeted approaches that focus on specific populations or high-risk individuals, universal screening seeks to cast a wide net to detect incipient malignancies in different demographic groups. This paradigm shift in cancer care underscores the importance of comprehensive screening programs that go beyond conventional boundaries. As our understanding of the complex molecular and genetic basis of cancer deepens, the need to develop comprehensive screening methods becomes increasingly apparent. In this article, we look at the rationale and potential benefits of universal cancer screening.

Cancer screening and early detection greatly increase the chances of successful treatment. However, most cancer types lack effective early screening biomarkers. In recent years, natural language processing (NLP)-based text-mining methods have proven effective in searching the scientific literature and identifying promising associations between potential biomarkers and disease, but unfortunately few are widely used.

In this study, we used an NLP-enabled text-mining system, MarkerGenie, to identify potential stool bacterial markers for early detection and screening of colorectal cancer. After filtering markers based on text-mining results, we validated bacterial markers using multiplex digital droplet polymerase chain reaction (ddPCR). Classifiers were built based on ddPCR results, and sensitivity, specificity, and area under the curve (AUC) were used to evaluate the performance.

A total of 7 of the 14 bacterial markers showed significantly increased abundance in the stools of colorectal cancer patients. A five-bacteria classifier for colorectal cancer diagnosis was built, and achieved an AUC of 0.852, with a sensitivity of 0.692 and specificity of 0.935. When combined with the fecal immunochemical test (FIT), our classifier achieved an AUC of 0.959 and increased the sensitivity of FIT (0.929 vs. 0.872) at a specificity of 0.900.

Our study provides a valuable case example of the use of NLP-based marker mining for biomarker identification.

Patients benefit considerably from early detection of cancer. Existing single-cancer tests have various limitations, which could be effectively addressed by circulating cell-free DNA (cfDNA)-based multi-cancer early detection (MCED). With sensitive detection and accurate localization of multiple cancer types at a very low and fixed false-positive rate (FPR), MCED has great potential to revolutionize early cancer detection. Herein, we review state-of-the-art approaches for cfDNA-based MCED and their limitations and discuss both technical and clinical challenges in the development and application of MCED tests. Given the constant improvements in technology and understanding of cancer biology, we propose that a cfDNA-based targeted sequencing assay that integrates multimodal features should be optimized for MCED.

Mitochondrial genome abnormalities can lead to mitochondrial dysfunction, which in turn affects cellular biology and is closely associated with the development of various diseases. The demand for mitochondrial DNA (mtDNA) sequencing has been increasing, and Illumina and MGI are two commonly used sequencing platforms for capture-based mtDNA sequencing. However, there is currently no systematic comparison of mtDNA sequencing performance between these two platforms. To address this gap, we compared the performance of capture-based mtDNA sequencing between Illumina's NovaSeq 6000 and MGI's DNBSEQ-T7 using tissue, peripheral blood mononuclear cell (PBMC), formalin-fixed paraffin-embedded (FFPE) tissue, plasma, and urine samples.

Our analysis indicated a high degree of consistency between the two platforms in terms of sequencing quality, GC content, and coverage. In terms of data output, DNBSEQ-T7 showed higher rates of clean data and duplication compared to NovaSeq 6000. Conversely, the amount of mtDNA data obtained by per gigabyte sequencing data was significantly lower in DNBSEQ-T7 compared to NovaSeq 6000. In terms of detection mtDNA copy number, both platforms exhibited good consistency in all sample types. When it comes to detection of mtDNA mutations in tissue, FFPE, and PBMC samples, the two platforms also showed good consistency. However, when detecting mtDNA mutations in plasma and urine samples, significant differenceof themutation number detected was observed between the two platforms. For mtDNA sequencing of plasma and urine samples, a wider range of DNA fragment size distribution was found in NovaSeq 6000 when compared to DNBSEQ-T7. Additionally, two platforms exhibited different characteristics of mtDNA fragment end preference.

In summary, the two platforms generally showed good consistency in capture-based mtDNA sequencing. However, it is necessary to consider the data preferences generated by two sequencing platforms when plasma and urine samples were analyzed.

Fragmentomics, the investigation of fragmentation patterns of cell-free DNA (cfDNA), has emerged as a promising strategy for the early detection of multiple cancers in the field of liquid biopsy. However, the clinical application of this approach has been hindered by a limited understanding of cfDNA biology. Furthermore, the prevalence of hematopoietic cell-derived cfDNA in plasma complicates the in vivo investigation of tissue-specific cfDNA other than that of hematopoietic origin. While conventional two-dimensional cell lines have contributed to research on cfDNA biology, their limited representation of in vivo tissue contexts underscores the need for more robust models. In this study, we propose three-dimensional organoids as a novel in vitro model for studying cfDNA biology, focusing on multifaceted fragmentomic analyses.

We established nine patient-derived organoid lines from normal lung airway, normal gastric, and gastric cancer tissues. We then extracted cfDNA from the culture medium of these organoids in both proliferative and apoptotic states. Using whole-genome sequencing data from cfDNA, we analyzed various fragmentomic features, including fragment size, footprints, end motifs, and repeat types at the end. The distribution of cfDNA fragment sizes in organoids, especially in apoptosis samples, was similar to that found in plasma, implying occupancy by mononucleosomes. The footprints determined by sequencing depth exhibited distinct patterns depending on fragment sizes, reflecting occupancy by a variety of DNA-binding proteins. Notably, we discovered that short fragments (< 118 bp) were exclusively enriched in the proliferative state and exhibited distinct fragmentomic profiles, characterized by 3 bp palindromic end motifs and specific repeats.

In conclusion, our results highlight the utility of in vitro organoid models as a valuable tool for studying cfDNA biology and its associated fragmentation patterns. This, in turn, will pave the way for further enhancements in noninvasive cancer detection methodologies based on fragmentomics.

Lung cancer is a leading cause of death in patients with cancer. Early diagnosis is crucial to improve the prognosis of patients with lung cancer. Plasma circulating cell-free DNA (cfDNA) contains comprehensive genetic and epigenetic information from tissues throughout the body, suggesting that early detection of lung cancer can be done non-invasively, conveniently, and cost-effectively using high-sensitivity techniques such as sequencing.

In this review, we summarize the latest technological innovations, coupled with next-generation sequencing (NGS), regarding genomic alterations, methylation, and fragmentomic features of cfDNA for the early detection of lung cancer, as well as their clinical advances. Additionally, we discuss the suitability of study designs for diagnostic accuracy evaluation for different target populations and clinical questions.

Currently, cfDNA-based early screening and diagnosis of lung cancer faces many challenges, such as unsatisfactory performance, lack of quality control standards, and poor repeatability. However, the progress of several large prospective studies employing epigenetic features has shown promising predictive performance, which has inspired cfDNA sequencing for future clinical applications. Furthermore, the development of multi-omics markers for lung cancer, including genome-wide methylation and fragmentomics, is expected to play an increasingly important role in the future.

Malignancies involving the central nervous system present unique challenges for diagnosis and monitoring due to the difficulties and risks of direct biopsies and the low specificity and/or sensitivity of other techniques for assessment. In recent years, liquid biopsy of the cerebrospinal fluid (CSF) has emerged as a convenient alternative that combines minimal invasiveness with the ability to detect disease-defining or therapeutically actionable genetic alterations from circulating tumor DNA (ctDNA). Since CSF can be obtained by lumbar puncture, or an established ventricular access device at multiple time points, ctDNA analysis enables initial molecular characterization and longitudinal monitoring throughout a patient's disease course, promoting optimization of treatment regimens. This review outlines some of the key aspects of ctDNA from CSF as a highly suitable approach for clinical assessment, the benefits and drawbacks, testing methods, as well as potential future advancements in this field. We anticipate wider adoption of this practice as technologies and pipelines improve and envisage significant improvements for cancer care.

Currently, approximately 30%-55% of the patients with non-small cell lung cancer (NSCLC) develop recurrence due to minimal residual disease (MRD) after receiving surgical resection of the tumor. This study aims to develop an ultrasensitive and affordable fragmentomic assay for MRD detection in patients with NSCLC. A total of 87 patients with NSCLC, who received curative surgical resections (23 patients relapsed during follow-up), enrolled in this study. A total of 163 plasma samples, collected at 7 days and 6 months postsurgical, were used for both whole-genome sequencing (WGS) and targeted sequencing. WGS-based cell-free DNA (cfDNA) fragment profile was used to fit regularized Cox regression models, and leave-one-out cross-validation was further used to evaluate models' performance. The models showed excellent performances in detecting patients with a high risk of recurrence. At 7 days postsurgical, the high-risk patients detected by our model showed an increased risk of 4.6 times, while the risk increased to 8.3 times at 6 months postsurgical. These fragmentomics determined higher risk compared with the targeted sequencing-based circulating mutations both at 7 days and 6 months postsurgical. The overall sensitivity for detecting patients with recurrence reached 78.3% while using both fragmentomics and mutation results from 7 days and 6 months postsurgical, which increased from the 43.5% sensitivity by using only the circulating mutations. The fragmentomics showed great sensitivity in predicting patient recurrence compared with the traditional circulating mutation, especially after the surgery for early-stage NSCLC, therefore exhibiting great potential to guide adjuvant therapeutics.

The circulating tumor DNA mutation-based approach shows limited performance in MRD detection, especially for landmark MRD detection at an early-stage cancer after surgery. Here, we describe a cfDNA fragmentomics-based method in MRD detection of resectable NSCLC using WGS, and the cfDNA fragmentomics showed a great sensitivity in predicting prognosis.

Lung cancer detection and monitoring are hampered by a lack of sensitive biomarkers, which results in diagnosis at late stages and difficulty in tracking response to treatment. Recent developments have established liquid biopsies as promising non-invasive methods for detecting biomarkers in lung cancer patients. With concurrent advances in high-throughput sequencing technologies and bioinformatics tools, new approaches for biomarker discovery have emerged. In this article, we survey established and emerging biomarker discovery methods using nucleic acid materials derived from bodily fluids in the context of lung cancer. We introduce nucleic acid biomarkers extracted from liquid biopsies and outline biological sources and methods of isolation. We discuss next-generation sequencing (NGS) platforms commonly used to identify novel biomarkers and describe how these have been applied to liquid biopsy. We highlight emerging biomarker discovery methods, including applications of long-read sequencing, fragmentomics, whole-genome amplification methods for single-cell analysis, and whole-genome methylation assays. Finally, we discuss advanced bioinformatics tools, describing methods for processing NGS data, as well as recently developed software tailored for liquid biopsy biomarker detection, which holds promise for early diagnosis of lung cancer.

Cancer remains a leading cause of death worldwide, partly owing to late detection which entails limited and often ineffective therapeutic options. Most cancers lack validated screening procedures, and the ones available disclose several drawbacks, leading to low patient compliance and unnecessary workups, adding up the costs to healthcare systems. Hence, there is a great need for innovative, accurate, and minimally invasive tools for early cancer detection. In recent years, multi-cancer early detection (MCED) tests emerged as a promising screening tool, combining molecular analysis of tumor-related markers present in body fluids with artificial intelligence to simultaneously detect a variety of cancers and further discriminate the underlying cancer type. Herein, we aim to provide a highlight of the variety of strategies currently under development concerning MCED, as well as the major factors which are preventing clinical implementation. Although MCED tests depict great potential for clinical application, large-scale clinical validation studies are still lacking.

The increasing global incidence of cancer demands innovative cancer detection modalities. The current population-based early cancer detection approaches focus on several major types of cancers (breast, prostate, cervical, lung and colon) at their early stages, however, they generally do not target high-risk individuals at precancerous stages.

Some cancers, such as pancreatic cancer, are challenging to detect in their early stages. Therefore, there is a pressing need for improved, accessible, noninvasive, and cost-effective early detection methods. Harnessing cell-free-based biomarker-driven strategies paves a new era of precision diagnosis for multicancer early detection. The majority of these tests are in the early stages and expensive, but these approaches are expected to become cost sensitive in the near future.

This review provides an overview of early cancer detection strategies, highlighting the methods, challenges, and issues to be addressed to revolutionize and improve global early cancer detection.

Cell-free DNA molecules are released into the plasma via apoptotic or necrotic events and active release mechanisms, which carry the genetic and epigenetic information of its origin tissues. However, cfDNA is the mixture of various cell fragments, and the efficient enrichment of cfDNA fragments with diagnostic value remains a great challenge for application in the clinical setting. Evidence from recent years shows that cfDNA fragmentomics' characteristics differ in normal and diseased individuals without the need to distinguish the source of the cfDNA fragments, which makes it a promising novel biomarker. Moreover, cfDNA fragmentomics can identify tissue origins by inferring epigenetic information. Thus, further insights into the fragmentomics of plasma cfDNA shed light on the origin and fragmentation mechanisms of cfDNA during physiological and pathological processes in diseases and enhance our ability to take the advantage of plasma cfDNA as a molecular diagnostic tool. In this review, we focus on the cfDNA fragment characteristics and its potential application, such as fragment length, end motifs, jagged ends, preferred end coordinates, as well as nucleosome footprints, open chromatin region, and gene expression inferred by the cfDNA fragmentation pattern across the genome. Furthermore, we summarize the methods for deducing the tissue of origin by cfDNA fragmentomics.

Analysis of cell-free DNA (cfDNA) in the blood has shown promise for monitoring a variety of biological processes. Plasma cfDNA is a mixture comprising DNA molecules released from various bodily tissues, mediated by characteristic DNA fragmentations occurring during cell death. Fragmentation of cfDNA is non-random and contains tissue-of-origin information, which has been demonstrated in circulating fetal, tumoral, and transplanted organ-derived cfDNA molecules. Many studies have elucidated a plurality of fragmentomic markers for noninvasive prenatal, cancer, and organ transplantation assessment, such as fragment sizes, fragment ends, end motifs, and nucleosome footprints. Recently, researchers have further revealed the large population of previously unidentified long cfDNA molecules (kilobases in size) in the plasma DNA pool. This review focuses on the emerging biological properties of cfDNA, together with a discussion on its potential clinical implications.