RNA methylation is a vital epigenetic modification that regulates gene expression by influencing RNA processes such as transcription, degradation, translation, and transport. Aberrant methylation, including modifications like m6A, m5C, m1A, m7G, and m3C, is closely linked to tumorigenesis and progression. Liquid biopsy, a non-invasive technique analyzing tumor markers in body fluids, offers significant potential for early diagnosis and dynamic monitoring. In this context, RNA methylation, due to its tumor-specific properties, is emerging as a valuable marker. However, significant challenges remain in its clinical application. This review explores the roles of RNA methylation in cancer, recent advances in detection technologies, and its potential as a liquid biopsy marker in tumor management. It highlights its promising applications in cancer diagnosis, prognosis, and personalized treatment in the era of precision oncology.

Gastric cancer (GC), one of the most prevalent malignancies worldwide, is distinguished by extensive genetic and phenotypic heterogeneity, posing persistent challenges to conventional diagnostic and therapeutic strategies. The significant global burden of GC highlights an urgent need to unravel its complex underlying mechanisms, discover novel diagnostic and prognostic biomarkers, and develop more effective therapeutic interventions. In this context, this review comprehensively examines the transformative roles of cutting-edge technologies, including radiomics, pathomics, genomics, transcriptomics, epigenomics, proteomics, and metabolomics, in advancing precision diagnosis and treatment for GC. Multiomics data analysis not only deepens our understanding of GC pathogenesis and molecular subtypes but also identifies promising biomarkers, facilitating the creation of tailored therapeutic approaches. Additionally, integrating multiomics approaches holds immense potential for elucidating drug resistance mechanisms, predicting patient outcomes, and uncovering novel therapeutic targets, thereby laying a robust foundation for precision medicine in the comprehensive management of GC.

Estrogen receptor-positive (ER+) breast carcinoma represents a significant portion of breast cancer cases and is characterized by the presence of estrogen receptors that promote tumor growth upon estrogen binding. ER + breast cancer progression involves hormonal influences, interactions within the tumor microenvironment, and genetic mutations that may lead to treatment resistance. Successful therapeutic options include hormonal therapies, particularly aromatase inhibitors (AIs), which aim to block the effects of estrogen or reduce its synthesis. With higher efficacy than tamoxifen, AIs such as anastrozole, letrozole, and exemestane have become widely employed in adjuvant and first-line treatments for advanced breast cancer. AIs function by inhibiting the enzyme aromatase, which converts androgens into estrogens in the peripheral tissues. Because too much estrogen might promote tumor growth, this decrease in estrogen levels is essential for treating ER+ malignancies. To provide a comprehensive overview of AIs in the treatment of ER+ breast cancer, this study examined the pharmacokinetics, clinical uses, mechanisms of action, and problems with treatment resistance. To maximize therapeutic approaches and enhance patient outcomes in the treatment of ER breast cancer, it is imperative to understand these characteristics.

Breast cancer is highly prevalent, with an increasing trend in women globally. Although the survival of breast cancer is relatively high, the recurrence rate is also high, demanding effective predictive solutions to breast cancer prognosis among post-operative patients. So far, Artificial intelligence algorithms integrated with various clinical data have demonstrated potential predictive capability regarding breast cancer recurrence.

This study aims to specifically conduct a predictive analysis of one-year recurrence of breast cancer by comparing and analyzing different machine learning and deep learning algorithms trained by structural prognostic data.

This retrospective study was carried out using one database, including 1156 post-operative breast cancer data from 30 January 2020 to 30 December 2022, in three clinical centers in Tehran City. The inclusion criteria were patients who had undergone at least one surgery, had at least one year of medical records, and did not have other conditions. The patients who were diagnosed with malignant BC and had undergone adjuvant therapies without surgery were excluded from the study. Twenty-three prognostic factors were utilized to train algorithms to establish prediction models for the one-year recurrence of breast cancer. The data were analyzed using univariate and adjusted correlation-based methods and chosen machine learning and deep learning algorithms. The discrimination, calibration, and clinical utility were leveraged to assess the algorithms' performance efficiency. The SHapley Additive exPlanations plot was generated to identify the prominent prognostic factors affecting the one-year recurrence of breast cancer.

Totally, 445 relapsed and 711 non-relapsed cases were utilized in this study. Our empirical study showed that the random forest with a positive predictive value of 0.96, negative predictive value of 0.92, sensitivity of 0.92, specificity of 0.96, accuracy of 0.94, F-score of 0.94, area under the receiver operator characteristics curve of 0.919 was the best-performing model for predicting the breast cancer recurrence. As the analysis of SHapley Additive exPlanations indicated, the tumor grade, HER-2, and the number of lymph nodes involved were more significant predictors.

The current study demonstrated the potential predictive power of the random forest for early predicting tumors among breast cancer patients who have undergone surgery and its utility in enhancing decision-making in clinical environments. It is crucial in promoting the prognosis, more effectively choosing therapies, augmenting post-operative breast cancer patients' survival, and controlling the limited healthcare resources.

Not applicable.

Targeted therapies have pervasively enhanced clinical protocols and significantly improved survival and quality of life of cancer patients. Mostly grounded on small molecules and antibodies targeting deregulated mechanisms in cancer cells, precision oncology approaches are limited to a few tumor types because of the paucity of clinically actionable targets. Here, we report a comparative analysis of the cation channel transient receptor potential melastatin 8 (TRPM8; also known as transient receptor potential cation channel subfamily M member 8) in lung, breast, colorectal, and prostate cancers. Our findings reveal high levels of channel expression in cores of all four carcinomas, irrespective of reduced expression of its RNA. Importantly, cancer cell lines that represent the various tumor types consistently show that sub-lethal chemotherapy dosages combined with the TRPM8 agonist D-3263 have a synergistic lethal effect. In addition, administration of D-3263 increases the cytotoxicity of 5-FU/Oxaliplatin in patient-derived colorectal cancer organoids, depending on the levels of TRPM8. Overall, our study strengthens the candidacy of TRPM8 as a molecular target for precision oncology approaches and paves the way for the design of basket trials for its clinical testing in TRPM8-high tumors.

Treatment of early breast cancer is currently experiencing a rapid evolution because of important insight into tumor subtypes and continuous development and improvement of novel therapeutics. Historically considered non-immunogenic, breast cancer has seen a paradigm shift with increased understanding of immune microenvironment, which have revealed extensive heterogeneity in tumor-associated inflammation. Notably, the more aggressive breast cancer subtypes, including triple-negative and HER2-positive, have exhibited favorable responses to combined chemo-immunotherapy protocols. Neoadjuvant therapy has emerged as the standard of care for these tumors, with pathological complete response used as a surrogate endpoint for long-term clinical outcomes and coincidently expediting new drug approval. The neoadjuvant setting affords a unique opportunity for in vivo treatment response evaluation and effects on the tumor microenvironment. In this review, the predictive and prognostic value of the tumor immune microenvironment before, during, and after treatment across various therapeutic regimens, tailored to distinct breast cancer subtypes, is carefully examined.

Timely intervention and improved prognosis for breast cancer patients rely on early metastasis risk detection and accurate treatment predictions. This study introduces an advanced multimodal MRI and AI-driven 3D deep learning model, termed the 3D-MMR-model, designed to predict recurrence risk in non-metastatic breast cancer patients. We conducted a multicenter study involving 1199 non-metastatic breast cancer patients from four institutions in China, with comprehensive MRI and clinical data retrospectively collected. Our model employed multimodal-data fusion, utilizing contrast-enhanced T1-weighted imaging (T1 + C) and T2-weighted imaging (T2WI) volumes, processed through a modified 3D-UNet for tumor segmentation and a DenseNet121-based architecture for disease-free survival (DFS) prediction. Additionally, we performed RNA-seq analysis to delve further into the relationship between concentrated hotspots within the tumor region and the tumor microenvironment. The 3D-MR-model demonstrated superior predictive performance, with time-dependent ROC analysis yielding AUC values of 0.90, 0.89, and 0.88 for 2-, 3-, and 4-year DFS predictions, respectively, in the training cohort. External validation cohorts corroborated these findings, highlighting the model's robustness across diverse clinical settings. Integration of clinicopathological features further enhanced the model's accuracy, with a multimodal approach significantly improving risk stratification and decision-making in clinical practice. Visualization techniques provided insights into the decision-making process, correlating predictions with tumor microenvironment characteristics. In summary, the 3D-MMR-model represents a significant advancement in breast cancer prognosis, combining cutting-edge AI technology with multimodal imaging to deliver precise and clinically relevant predictions of recurrence risk. This innovative approach holds promise for enhancing patient outcomes and guiding individualized treatment plans in breast cancer care.

Accurately predicting pathological complete response (pCR) before neoadjuvant chemotherapy (NAC) is crucial for patients with breast cancer. In this study, we developed a multimodal integrated fully automated pipeline system (MIFAPS) in forecasting pCR to NAC, using a multicenter and prospective dataset of 1004 patients with locally advanced breast cancer, incorporating pretreatment magnetic resonance imaging, whole slide image, and clinical risk factors. The results demonstrated that MIFAPS offered a favorable predictive performance in both the pooled external test set [area under the curve (AUC) = 0.882] and the prospective test set (AUC = 0.909). In addition, MIFAPS significantly outperformed single-modality models (P < 0.05). Furthermore, the high deep learning scores were associated with immune-related pathways and the promotion of antitumor cells in the microenvironment during biological basis exploration. Overall, our study demonstrates a promising approach for improving the prediction of pCR to NAC in patients with breast cancer through the integration of multimodal data.

Alzheimer's disease (AD) is a complex disease with unknown etiology and pathogenesis. We described a combined analysis of murine proteomics and microbiomics to find potential therapeutic targets of different doses of xanthohumol (Xn), with the goal of providing a biological basis for the treatment of early AD. Xn improved the spatial learning and memory ability of APP/PS1 mice; this was associated with an increased number of newborn neurons in the subgranular zone (SGZ) and dentate gyrus (DG) and a decreased inflammatory response. 108 proteins were significantly changed after 0.5 mg/kg Xn treatment while only 72 proteins changed by 5 mg/kg Xn. Eight significant microbiota were modulated by different doses of Xn at line discriminant analysis (LDA) score 3.0, but only three of which were regulated by 0.5 mg/kg Xn at LDA score 4.0. In addition, Xn treatment could significantly regulate the pathways of neurodegeneration- multiple diseases in the hippocampus and the penicillin and cephalosporin biosynthesis and atrazine degradation pathways in the gut. Interestingly, Nefl protein validated by correlation analysis was found in the common signaling pathway. 0.5 mg/kg Xn was able to reverse the correlation between hippocampal proteins and gut microbiota. Xn treatment significantly improved cognitive function in AD transgenic mice. Different doses of Xn caused significant differences in protein expression and flora composition and abundance, suggesting that the doses of Xn should be selected with caution, and low dose may be better in the prevention of AD.

Endometriosis is a chronic disease that jeopardises the quality of life of about 10% of women. The aim of this study was to investigate the expression, function, regulatory mechanism, and relationship with immune cell infiltration of PNMA2 in endometriosis. This study investigates the potential involvement and regulatory mechanisms of PNMA2 in the development of endometriosis through the integration of public data, machine learning, clinical sample transcriptome sequencing, and in vitro cell experiments. Cytological in vitro experiments were conducted to validate the impact of PNMA2 on the modulation of proliferation, migration, apoptosis, and autophagy in 12z cells. Rescue experiments were performed based on the autophagy activator (RAPA) to clarify the regulatory details of PNMA2, apoptosis, and autophagy. The Ciberort algorithm was employed to discern the association between PNMA2 gene expression and more than 20 distinct immune cell infiltration types in endometriosis. The expression of PNMA2 exhibited a notable increase in individuals with endometriosis. Knockdown of PNMA2 inhibited the proliferation and migration of 12z cells. Knockdown of PNMA2 could directly promote apoptosis and could also inhibit autophagy and indirectly promote apoptosis. PNMA2 displayed associations with immune cell infiltration and immunomodulation. The present study demonstrated that the up-regulation of PNMA2 was associated with malignant growth, anti-apoptosis, and immunoregulation of human endometriotic cells. Therefore, PNMA2 may serve as a new diagnostic biomarker and a promising therapeutic target.

Nongenetic adaptive resistance to chemotherapy, driven by transcriptional rewiring, is emerging as a significant mechanism in tumor survival. In this study we combined longitudinal transcriptomics with temporal pattern analysis to investigate patient-specific mechanisms underlying acquired resistance in breast cancer. Matched tumor biopsies (pretreatment, posttreatment, and adjacent normal) were collected from breast cancer patients who received neoadjuvant chemotherapy. Transcriptomes were analyzed by longitudinal gene-pattern classification to track patient-specific gene expression alterations that occur during treatment. Our findings reveal that resistance-associated genes were already dysregulated in primary tumors, suggesting the presence of a preexisting drug-tolerant state. While each patient displayed unique resistance-associated gene rewiring, these alterations converged into a limited number of dysregulated functional modules. Notably, patients receiving the same treatment exhibited distinct rewiring of genes and pathways, revealing parallel, individualized routes to resistance. In conclusion, we propose that tumor cells survive chemotherapy by sustaining or amplifying a preexisting drug-tolerant state that circumvents drug action. We suggest that individualized "chemoresistome maps" could identify cancer vulnerabilities and inform personalized therapeutic strategies to overcome or prevent resistance.

Artificial intelligence (AI) is rapidly transforming the fields of pathology and oncology, offering novel opportunities for advancing diagnosis, prognosis, and treatment of cancer.

Through a systematic review-based approach, the representatives from the European Society for Medical Oncology (ESMO) Precision Oncology Working Group (POWG) and international experts identified studies in pathology and oncology that applied AI-based algorithms for tumour diagnosis, molecular biomarker detection, and cancer prognosis assessment. These findings were synthesised to provide a comprehensive overview of current AI applications and future directions in cancer pathology.

The integration of AI tools in digital pathology is markedly improving the accuracy and efficiency of image analysis, allowing for automated tumour detection and classification, identification of prognostic molecular biomarkers, and prediction of treatment response and patient outcomes. Several barriers for the adoption of AI in clinical workflows, such as data availability, explainability, and regulatory considerations, still persist. There are currently no prognostic or predictive AI-based biomarkers supported by level IA or IB evidence. The ongoing advancements in AI algorithms, particularly foundation models, generalist models and transformer-based deep learning, offer immense promise for the future of cancer research and care. AI is also facilitating the integration of multi-omics data, leading to more precise patient stratification and personalised treatment strategies.

The application of AI in pathology is poised to not only enhance the accuracy and efficiency of cancer diagnosis and prognosis but also facilitate the development of personalised treatment strategies. Although barriers to implementation remain, ongoing research and development in this field coupled with addressing ethical and regulatory considerations will likely lead to a future where AI plays an integral role in cancer management and precision medicine. The continued evolution and adoption of AI in pathology and oncology are anticipated to reshape the landscape of cancer care, heralding a new era of precision medicine and improved patient outcomes.

Venous tumour thrombus (VTT), where the primary tumour invades the renal vein and inferior vena cava, affects 10-15% of renal cell carcinoma (RCC) patients. Curative surgery for VTT is high-risk, but neoadjuvant therapy may improve outcomes. The NAXIVA trial demonstrated a 35% VTT response rate after 8 weeks of neoadjuvant axitinib, a VEGFR-directed therapy. However, understanding non-response is critical for better treatment. Here we show that response to axitinib in this setting is characterised by a distinct and predictable set of features. We conduct a multiparametric investigation of samples collected during NAXIVA using digital pathology, flow cytometry, plasma cytokine profiling and RNA sequencing. Responders have higher baseline microvessel density and increased induction of VEGF-A and PlGF during treatment. A multi-modal machine learning model integrating features predict response with an AUC of 0.868, improving to 0.945 when using features from week 3. Key predictive features include plasma CCL17 and IL-12. These findings may guide future treatment strategies for VTT, improving the clinical management of this challenging scenario.

Homologous recombination deficiency (HRD) leads to genomic instability, and patients with HRD can benefit from HRD-targeting therapies. Previous studies have primarily focused on identifying HRD biomarkers using data from a single technology. Here we integrated features from different genomic data types, including total copy number (CN), allele-specific copy number (ASCN) and single nucleotide variants (SNV). Using a semi-supervised method, we developed HRD classifiers from 1404 breast tumours across two datasets based on their BRCA1/2 status, demonstrating improved HRD identification when aggregating different data types. Notably, HRD-positive tumours in ER-negative disease showed improved survival post-adjuvant chemotherapy, while HRD status strongly correlated with neoadjuvant treatment response. Furthermore, our analysis of cell lines highlighted a sensitivity to PARP inhibitors, particularly rucaparib, among predicted HRD-positive lines. Exploring somatic mutations outside BRCA1/2, we confirmed variants in several genes associated with HRD. Our method for HRD classification can adapt to different data types or resolutions and can be used in various scenarios to help refine patient selection for HRD-targeting therapies that might lead to better clinical outcomes.

Radiogenomic research provides a deeper understanding of breast cancer biology by investigating the correlations between imaging phenotypes and genetic data. However, current radiogenomic research primarily focuses on the correlation between imaging phenotypes and single-genomic data (e.g., gene expression data), overlooking the potential of multi-genomics data to unveil more nuances in cancer characterization. To this end, we propose a multiview nonnegative matrix factorization (MVNMF) method for the radio-multigenomic analysis that identifies dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) features associated with multi-genomics data, including DNA copy number alterations, mutations, and mRNAs, each of which is independently predictive of cancer outcomes. MVNMF incorporates subspace learning and multiview regularization into a unified model to simultaneously select features and explore correlations. Subspace learning is utilized to identify representative radiomic features crucial for tumor analysis, while multiview regularization enables the learning of the correlation between the identified radiomic features and multi-genomics data. Experimental results showed that, for overall survival prediction in breast cancer, MVNMF classified patients into two distinct groups characterized by significant differences in survival (p = 0.0012). Furthermore, it achieved better performance with a C-index of 0.698 compared to the method without considering any genomics data (C-index = 0.528). MVNMF is an effective framework for identifying radiomic features linked to multi-genomics data, which improves its predictive power and provides a better understanding of the biological mechanisms underlying observed phenotypes. MVNMF offers a novel framework for prognostic prediction in breast cancer, with the potential to catalyze further radiogenomic/radio-multigenomic studies.

Acute Myeloid Leukemia (AML) is a complex and heterogeneous disease identified by severe clinical progression, fast cellular proliferation, and often high mortality rates. Incorporating diverse longitudinal information on patients' medical histories is essential for developing effective disease predictive models applicable to both research and clinical settings. Here, we present a robust methodology for discovering dynamic predictive models to elucidate AML disease progression dynamics from a novel longitudinal multimodal clinical dataset of patients diagnosed with AML. The clinical dataset was analyzed to reveal the main clinical, genetic, and treatment features modulating disease progression. To discover mathematical models-including interactions, parameters, and nodes-predictive of AML progression, we present an explainable machine learning algorithm based on high-performance evolutionary computation. The results demonstrate that the predictive methodology could accurately estimate the clinical dynamics of AML progression in terms of blast percentages for both training and novel patients. This study demonstrates that the developed explainable machine learning approach can successfully predict AML progression by leveraging the heterogeneous and longitudinal dynamics of patients' clinical data. More importantly, this methodology shows significant potential for application in modeling the progression dynamics of other acute diseases, providing a flexible and adaptable framework for advancing clinical and translational research.

Breast cancer is one of the most common malignancies among women worldwide. Patients who do not achieve a pathological complete response (pCR) or a clinical complete response (cCR) post-neoadjuvant chemotherapy (NAC) typically have a worse prognosis compared to those who do achieve these responses.

This study aimed to develop and validate a random survival forest (RSF) model to predict survival risk in patients with breast cancer who do not achieve a pCR or cCR post-NAC.

We analyzed patients with no pCR/cCR post-NAC treated at the First Affiliated Hospital of Chongqing Medical University from January 2019 to 2023, with external validation in Duke University and Surveillance, Epidemiology, and End Results (SEER) cohorts. RSF and Cox regression models were compared using the time-dependent area under the curve (AUC), the concordance index (C-index), and risk stratification.

The study cohort included 306 patients with breast cancer, with most aged 40-60 years (204/306, 66.7%). The majority had invasive ductal carcinoma (290/306, 94.8%), with estrogen receptor (ER)+ (182/306, 59.5%), progesterone receptor (PR)- (179/306, 58.5%), and human epidermal growth factor receptor 2 (HER2)+ (94/306, 30.7%) profiles. Most patients presented with T2 (185/306, 60.5%), N1 (142/306, 46.4%), and M0 (295/306, 96.4%) staging (TNM meaning "tumor, node, metastasis"), with 17.6% (54/306) experiencing disease progression during a median follow-up of 25.9 months (IQR 17.2-36.3). External validation using Duke (N=94) and SEER (N=2760) cohorts confirmed consistent patterns in age (40-60 years: 59/94, 63%, vs 1480/2760, 53.6%), HER2+ rates (26/94, 28%, vs 935/2760, 33.9%), and invasive ductal carcinoma prevalence (89/94, 95%, vs 2506/2760, 90.8%). In the internal cohort, the RSF achieved significantly higher time-dependent AUCs compared to Cox regression at 1-year (0.811 vs 0.763), 3-year (0.834 vs 0.783), and 5-year (0.810 vs 0.771) intervals (overall C-index: 0.803, 95% CI 0.747-0.859, vs 0.736, 95% CI 0.673-0.799). External validation confirmed robust generalizability: the Duke cohort showed 1-, 3-, and 5-year AUCs of 0.912, 0.803, and 0.776, respectively, while the SEER cohort maintained consistent performance with AUCs of 0.771, 0.729, and 0.702, respectively. Risk stratification using the RSF identified 25.8% (79/306) high-risk patients and a significantly reduced survival time (P<.001). Notably, the RSF maintained improved net benefits across decision thresholds in decision curve analysis (DCA); similar results were observed in external studies. The RSF model also showed promising performance across different molecular subtypes in all datasets. Based on the RSF predicted scores, patients were stratified into high- and low-risk groups, with notably poorer survival outcomes observed in the high-risk group compared to the low-risk group.

The RSF model, based solely on clinicopathological variables, provides a promising tool for identifying high-risk patients with breast cancer post-NAC. This approach may facilitate personalized treatment strategies and improve patient management in clinical practice.

This study conducted a novel systematic bibliometric and visualization analysis of global literature on immunotherapy for endometrial cancer (EC) to explore dynamic trends, research hotspots, and emerging topics, providing valuable references for future research.

Articles and reviews on EC immunotherapy published between 2012 and August 2024 were retrieved from the Web of Science Core Collection (WoSCC). Bibliometric tools, CiteSpace and VOSviewer, were used to analyze clustering patterns and research dynamics.

A total of 861 articles were contributed by 5,331 authors from 1,392 institutions across 58 countries or regions, involving 1,823 keywords. China demonstrated outstanding performance in this field, contributing over 40% of the total publications and ranking first in publication volume. However, the total citation counts for publications from China lags that of the United States, highlighting the latter's leading position and areas for further improvement in China's research efforts. The University of Texas Medical Anderson Cancer Center and Nanjing Medical University were the two institutions with the highest number of publications. In terms of authorship, research teams led by Bosse, Tjalling, and Creutzberg, Carien L made significant contributions to advancing the field. Among individual publications, the work by Talhouk et al. achieved the highest average annual citation count of 70.88, demonstrating its profound impact. In terms of journals, Gynecologic Oncology emerged as a pivotal academic platform, publishing numerous articles and achieving the highest co-citation frequency. Additionally, Frontiers in Oncology, Frontiers in Immunology, and Frontiers in Genetics have become some of the most active and rapidly developing journals in recent years. Research hotspots are concentrated on themes such as the "Tumor Immune Microenvironment", "Immune Checkpoint Inhibitors", and "Targeted Therapy". Recent trends and frontier research focus on the combined application of immune checkpoint inhibitors with other therapies, research on the application of nanotechnology in immunotherapy, and the integration of artificial intelligence to enhance precision medicine. Additionally, efforts are increasingly directed toward advancing various immunotherapy strategies from basic research to clinical applications.

This comprehensive analysis reveals rapid advancements and significant potential in EC immunotherapy. Strengthening international collaboration and addressing barriers in the translation of research to clinical practice will drive further progress in this promising field.

We developed a practical framework to construct digital twins for predicting and optimizing triple-negative breast cancer (TNBC) response to neoadjuvant chemotherapy (NAC). This study employed 105 TNBC patients from the ARTEMIS trial (NCT02276443, registered on 10/21/2014) who received Adriamycin/Cytoxan (A/C)-Taxol (T). Digital twins were established by calibrating a biology-based mathematical model to patient-specific MRI data, which accurately predicted pathological complete response (pCR) with an AUC of 0.82. We then used each patient's twin to theoretically optimize outcome by identifying their optimal A/C-T schedule from 128 options. The patient-specifically optimized treatment yielded a significant improvement in pCR rate of 20.95-24.76%. Retrospective validation was conducted by virtually treating the twins with AC-T schedules from historical trials and obtaining identical observations on outcomes: bi-weekly A/C-T outperforms tri-weekly A/C-T, and weekly/bi-weekly T outperforms tri-weekly T. This proof-of-principle study demonstrates that our digital twin framework provides a practical methodology to identify patient-specific TNBC treatment schedules.

Over the last decade, in line with the goals of precision medicine to offer individualized patient care, various single-cell technologies measuring gene and proteomic expression in various tissues have rapidly advanced to study health and disease at the single cell level. Precisely understanding cell composition, position within tissues, signaling pathways, and communication can reveal insights into disease mechanisms and systemic changes during development, pregnancy, and gynecologic disorders across the lifespan. Single-cell technologies dissect the complex cellular compositions of reproductive tract tissues, providing insights into mechanisms behind reproductive tract dysfunction which impact wellness and quality of life. These technologies aim to understand basic tissue and organ functions and, clinically, to develop novel diagnostics, early disease biomarkers, and cell-targeted therapies for currently suboptimally-treated disorders. Increasingly, they are applied to pregnancy and pregnancy disorders, gynecologic malignancies, and uterine and ovarian physiology and aging, which are discussed in more detail in manuscripts in this special issue of AJOG. Here, we review recent applications of single-cell technologies to the study of gynecologic disorders and systemic biological adaptations during fetal development, pregnancy, and across a woman's lifespan. We discuss sequencing- and proteomic-based single-cell methods, as well as spatial transcriptomics and high-dimensional proteomic imaging, describing each technology's mechanism, workflow, quality control, and highlighting specific benefits, drawbacks, and utility in the context of reproductive medicine. We consider analytical methods for the high-dimensional single-cell data generated, highlighting statistical constraints and recent computational techniques for downstream clinical translation. Overall, current and evolving single-cell "looking glasses", or perspectives, have the potential to transform fundamental understanding of women's health and reproductive disorders and alter the trajectory of clinical practice and patient outcomes in the future.

The use of biomarkers in breast cancer has significantly improved patient outcomes through targeted therapies, such as hormone therapy anti-Her2 therapy and CDK4/6 or PARP inhibitors. However, existing knowledge does not fully encompass the diverse nature of breast cancer, particularly in triple-negative tumors. The integration of multi-omics and multimodal data has the potential to provide new insights into biological processes, to improve breast cancer patient stratification, enhance prognosis and response prediction, and identify new biomarkers. This review presents a comprehensive overview of the state-of-the-art multimodal (including molecular and image) data integration algorithms developed and with applicability to breast cancer stratification, prognosis, or biomarker identification. We examined the primary challenges and opportunities of these multimodal data integration algorithms, including their advantages, limitations, and critical considerations for future research. We aimed to describe models that are not only academically and preclinically relevant, but also applicable to clinical settings.

This study constructed a predictive model for occurrence of radiation esophagitis during breast-cancer radiotherapy. 308 breast-cancer patients were analyzed. Lasso regression identified crucial variables that were further integrated into a radiation esophagitis risk score, which was used to segregate patients into high- and low-risk groups. A nomogram model was designed for clinical applicability. Training and validations were performed to assess robustness and generalizability of proposed models, employing C-index, AUCs, calibration curves, and decision curves. SHAP algorithm was used for model interpretation, offering insights into the major contributory factors. Seven significant variables were identified by Lasso regression. C-indexes of nomograms of individual clinical variables and risk score were 0.795 and 0.784, respectively, exhibiting strong predictive ability. In internal validation, AUCs for risk score, nomogram, and logistic models were 0.784, 0.795, and 0.812, respectively. Calibration curves showed a close fit between predicted and observed outcomes across models. Decision curve analysis indicated logistic model's superior clinical utility when the risk threshold was above 0.2. SHAP interpretation emphasized radiation dose, pruritus, molecular type, and hepatic dysfunction as top contributory factors for radiation esophagitis. Models based on interpretable machine learning offer an intuitive tool to assess risk of radiation esophagitis in breast-cancer radiotherapy.

Breast cancer (BC) remains a significant threat to women's health worldwide. The oncology field had an exponential growth in the abundance of medical images, clinical information, and genomic data. With its continuous advancement and refinement, artificial intelligence (AI) has demonstrated exceptional capabilities in processing intricate multidimensional BC-related data. AI has proven advantageous in various facets of BC management, encompassing efficient screening and diagnosis, precise prognosis assessment, and personalized treatment planning. However, the implementation of AI into precision medicine and clinical practice presents ongoing challenges that necessitate enhanced regulation, transparency, fairness, and integration of multiple clinical pathways. In this review, we provide a comprehensive overview of the current research related to AI in BC, highlighting its extensive applications throughout the whole BC cycle management and its potential for innovative impact. Furthermore, this article emphasizes the significance of constructing patient-oriented AI algorithms. Additionally, we explore the opportunities and potential research directions within this burgeoning field.

Background: Telomeres and cellular senescence are critical biological processes implicated in cancer development and progression, including breast cancer, through their influence on genomic stability and modulation of the tumor microenvironment. Methods: This study integrated bulk RNA sequencing and single-cell RNA sequencing (scRNA-seq) data to establish a gene signature associated with telomere maintenance and cellular senescence for prognostic prediction in breast cancer. Telomere-related genes (TEGs) and senescence-associated genes were curated from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. A comprehensive machine learning framework incorporating 101 algorithmic combinations across 10 survival modeling approaches, including random survival forests and ridge regression, was employed to develop a robust prognostic model. Results: A set of 19 key telomere- and senescence-related genes was identified as the optimal prognostic signature. The model demonstrated strong predictive accuracy and was successfully validated in multiple independent cohorts. Functional enrichment analyses indicated significant associations with immune responses and aging-related pathways. Single-cell transcriptomic analysis revealed marked cellular heterogeneity, identifying distinct subpopulations (fibroblasts and immune cells) with divergent risk scores and biological pathway activity. Additionally, pseudo-time trajectory analysis and intercellular communication mapping provided insights into the dynamic evolution of the tumor microenvironment. Immunohistochemical (IHC) validation using data from the Human Protein Atlas confirmed differential protein expression between normal and tumor tissues for several of the selected genes, reinforcing their biological relevance and clinical utility. Conclusions: This study presents a novel 19-gene telomere- and senescence-associated signature with strong prognostic value in breast cancer. These findings enhance our understanding of tumor heterogeneity and may inform precision oncology approaches and future therapeutic strategies.

The tumor boundary of breast cancer represents a highly heterogeneous region. In this area, the interactions between malignant and non-malignant cells influence tumor progression, immune evasion, and drug resistance. However, the spatial transcriptional profile of the tumor boundary and its role in the prognosis and treatment response of breast cancer remain unclear.

Utilizing the Cottrazm algorithm, we reconstructed the intricate boundaries and identified differentially expressed genes (DEGs) associated with these regions. Cell-cell co-positioning analysis was conducted using SpaCET, which revealed key interactions between tumor-associated macrophage (TAMs) and cancer-associated fibroblasts (CAFs). Additionally, Lasso regression analysis was employed to develop a malignant body signature (MBS), which was subsequently validated using the TCGA dataset for prognosis prediction and treatment response assessment.

Our research indicates that the tumor boundary is characterized by a rich reconstruction of the extracellular matrix (ECM), immunomodulatory regulation, and the epithelial-to-mesenchymal transition (EMT), underscoring its significance in tumor progression. Spatial colocalization analysis reveals a significant interaction between CAFs and M2-like tumor-associated macrophage (TAM), which contributes to immune exclusion and drug resistance. The MBS score effectively stratifies patients into high-risk groups, with survival outcomes for patients exhibiting high MBS scores being significantly poorer. Furthermore, drug sensitivity analysis demonstrates that high-MB tumors had poor response to chemotherapy strategies, highlighting the role of the tumor boundary in modulating therapeutic efficacy.

Collectively, we investigate the spatial transcription group and bulk data to elucidate the characteristics of tumor boundary molecules in breast cancer. The CAF-M2 phenotype emerges as a critical determinant of immunosuppression and drug resistance, suggesting that targeting this interaction may improve treatment responses. Furthermore, the MBS serves as a novel prognostic tool and offers potential strategies for guiding personalized treatment approaches in breast cancer.

Racial disparities in the clinical outcomes of triple-negative breast cancer (TNBC) have been well-documented, but the underlying biological mechanisms remain poorly understood. To investigate these disparities, we employed a multi-omic approach integrating imaging mass cytometry and spatial transcriptomics to characterize the tumor microenvironment (TME) in self-identified Black American (BA) and White American (WA) TNBC patients. Our analysis revealed that the TME in BA patients is marked by a network of endothelial cells, macrophages, and mesenchymal-like cells, which correlates with reduced patient survival. In contrast, the WA TNBC microenvironment is enriched in T-cells and neutrophils, indicative of T-cell exhaustion and suppressed immune responses. Ligand-receptor and pathway analyses further demonstrated that BA TNBC tumors exhibit a relatively "immune-cold" profile, while WA TNBC tumors display features of an "inflamed" TME, suggesting the evolution of a unique immunosuppressive mechanism. These findings provide insight into racially distinct tumor-promoting and immunosuppressive microenvironments, which may contribute to the observed differences in clinical outcomes among BA and WA TNBC patients.

This study identifies distinct tumor microenvironment (TME) profiles in Black and White American TNBC patients, providing new insights into the biological mechanisms driving outcome disparities. Our findings highlight the role of the tumor-endothelial-macrophage niche in these disparities, offering a potential therapeutic target for race-inclusive strategies aimed at improving clinical outcomes. By revealing racial differences in treatment response profiles, this work underscores the necessity for tailored therapies in TNBC. These insights lay the groundwork for the development of inclusive, precision-driven treatment approaches that may help mitigate racial disparities and enhance patient outcomes.

Machine learning (ML), a major branch of artificial intelligence, has not only demonstrated the potential to significantly improve numerous sectors of healthcare but has also made significant contributions to the field of solid organ transplantation. ML provides revolutionary opportunities in areas such as donor-recipient matching, post-transplant monitoring, and patient care by automatically analyzing large amounts of data, identifying patterns, and forecasting outcomes.

To conduct a comprehensive bibliometric analysis of publications on the use of ML in transplantation to understand current research trends and their implications.

On July 18, a thorough search strategy was used with the Web of Science database. ML and transplantation-related keywords were utilized. With the aid of the VOS viewer application, the identified articles were subjected to bibliometric variable analysis in order to determine publication counts, citation counts, contributing countries, and institutions, among other factors.

Of the 529 articles that were first identified, 427 were deemed relevant for bibliometric analysis. A surge in publications was observed over the last four years, especially after 2018, signifying growing interest in this area. With 209 publications, the United States emerged as the top contributor. Notably, the "Journal of Heart and Lung Transplantation" and the "American Journal of Transplantation" emerged as the leading journals, publishing the highest number of relevant articles. Frequent keyword searches revealed that patient survival, mortality, outcomes, allocation, and risk assessment were significant themes of focus.

The growing body of pertinent publications highlights ML's growing presence in the field of solid organ transplantation. This bibliometric analysis highlights the growing importance of ML in transplant research and highlights its exciting potential to change medical practices and enhance patient outcomes. Encouraging collaboration between significant contributors can potentially fast-track advancements in this interdisciplinary domain.

Oncolytic viruses (OVs) are promising for cancer treatment as they specifically replicate in tumor cells. However, the systemic delivery of OVs still faces the challenges of poor tumor targeting, short circulation periods, and limited lytic efficacy. Herein, an OV-concealed targeting nanoagonist (OV-MnO2/HE) was prepared to enhance the delivery of OVs to triple-negative breast cancer (TNBC) via intravenous administration. Decomposable MnO2 biomineral shells covered the surface antigens of OVs to prevent their clearance after systemic administration. The targeting materials of HA-EGCG (HE) enhanced intratumoral accumulation via active targeting. After entering tumors, OV-MnO2/HE readily released Mn2+ and OVs, which could enhance the number of CD4+/CD8+ T cells and maturation dendritic cells (DCs) due to the synergetic effect of the STING pathway and OVs, thereby activating the immune response, resulting in the significant inhibition of TNBC growth. This work highlights the potential of the STING agonist in enhancing the antitumor efficacy of OVs and provides a potent platform for TNBC therapy.

There are still many controversies about the factors influencing maxillary sinus cysts and their clinical management. This study aims to construct a prediction model of maxillary sinus cyst and explore its clustering pattern by cone beam computerized tomography (CBCT) technique and machine learning (ML) method to provide a theoretical basis for the prevention and clinical management of maxillary sinus cyst.

In this study, 6000 CBCT images of maxillary sinus from 3093 patients were evaluated to document the possible influencing factors of maxillary sinus cysts, including gender, age, odontogenic factors, and anatomical factors. First, the characteristic variables were screened by multiple statistical methods, and ML methods were applied to construct a prediction model for maxillary sinus cysts. Second, the model was interpreted based on the SHapley Additive exPlanations (SHAP) values, and the risk of maxillary sinus cysts was predicted by generating a web page calculator. Finally, the K-mean clustering algorithm further identified risk factors for maxillary sinus cysts.

By comparing the various metrics in the training and test sets of multiple ML models, eXtreme Gradient Boosting (XGBoost) is the best model. The average area under curve (AUC) values of the XGBoost model in the training, validation, and test sets, respectively, are 0.939, 0.923, and 0.921, which indicates its excellent classification and discrimination ability. The cluster analysis model further categorized maxillary sinus cysts into high-risk and low-risk groups, with apical lesions, severe periodontitis, and age ≥ 53 as high-risk factors for maxillary sinus cysts.

These findings provide valuable insights into the etiology and risk stratification of maxillary sinus cysts, offering a theoretical basis for their prevention and clinical management. The integration of CBCT imaging and ML techniques holds the potential for prevention and personalized treatment strategies of maxillary sinus cysts.

Integrating and analyzing multiple omics datasets, such as genomics, environmental influences, and imaging endophenotypes, has yielded an abundance of candidate biomarkers. However, translating such findings into beneficial clinical knowledge for disease prediction remains challenging. This becomes even more challenging when studying interpretable high-order feature interactions such as gene-environment interaction (G$\times $E) to understand the etiology. To fill this gap, we draw on the idea of mutual-assistance (MA) learning and accordingly propose a fresh and powerful scheme, referred to as mutual-assistance causal biomarker discovery and stable disease prediction approach (MA-CBxDP). Specifically, we design an interpretable bi-directional mapping framework, integrated with a causal feature interaction module, to extract co-expression patterns across different modalities and identify trustworthy biomarkers including G$\times $E. A cooperative prediction module is further incorporated to ensure accurate diagnosis and identification of causal effects for pathogenesis. Importantly, biomarker discovery and disease prediction can mutually reinforce each other, helping to provide novel insights into chronic diseases. Furthermore, in light of the large computational burden incurred by the high-dimensional interactions, we devise a rapid strategy and extend it to a more practical but challenging chromosome-wide setting. We conduct extensive experiments on two databases under three tasks, i.e. multimodal correlation, disease diagnosis, and trait prediction. MA-CBxDP establishes new state-of-the-art results in predicting clinical scores and disease status classification, while maintaining exceptional interpretability, verifying its flexibility and versatility in practical applications.

Cancer is an umbrella term that includes a wide spectrum of disease severity, from those that are malignant, metastatic, and aggressive to benign lesions with very low potential for progression or death. The ability to prognosticate patient outcomes would facilitate management of various malignancies: patients whose cancer is likely to advance quickly would receive necessary treatment that is commensurate with the predicted biology of the disease. Former prognostic models based on clinical variables (age, gender, cancer stage, tumor grade, etc.), though helpful, cannot account for genetic differences, molecular etiology, tumor heterogeneity, and important host biological mechanisms. Therefore, recent prognostic models have shifted toward the integration of complementary information available in both molecular data and clinical variables to better predict patient outcomes: vital status (overall survival), metastasis (metastasis-free survival), and recurrence (progression-free survival). In this article, we review 20 survival prediction approaches that integrate multi-omics and clinical data to predict patient outcomes. We discuss their strategies for modeling survival time (continuous and discrete), the incorporation of molecular measurements and clinical variables into risk models (clinical and multi-omics data), how to cope with censored patient records, the effectiveness of data integration techniques, prediction methodologies, model validation, and assessment metrics. The goal is to inform life scientists of available resources, and to provide a complete review of important building blocks in survival prediction. At the same time, we thoroughly describe the pros and cons of each methodology, and discuss in depth the outstanding challenges that need to be addressed in future method development.

The complementary information found in different modalities of patient data can aid in more accurate modelling of a patient's disease state and a better understanding of the underlying biological processes of a disease. However, the analysis of multi-modal, multi-omics data presents many challenges. In this work, we compare the performance of a variety of ensemble machine learning (ML) algorithms that are capable of late integration of multi-class data from different modalities. The ensemble methods and their variations tested were (i) a voting ensemble, with hard and soft vote, (ii) a meta learner, and (iii) a multi-modal AdaBoost model using hard vote, soft vote, and meta learner to integrate the modalities on each boosting round, the PB-MVBoost model and a novel application of a mixture of expert's model. These were compared to simple concatenation. We examine these methods using data from an in-house study on hepatocellular carcinoma, plus validation datasets on studies from breast cancer and irritable bowel disease. We develop models that achieve an area under the receiver operating curve of up to 0.85 and find that two boosted methods, PB-MVBoost and AdaBoost with soft vote were the best performing models. We also examine the stability of features selected and the size of the clinical signature. Our work shows that integrating complementary omics and data modalities with effective ensemble ML models enhances accuracy in multi-class clinical outcome predictions and produces more stable predictive features than individual modalities or simple concatenation. We provide recommendations for the integration of multi-modal multi-class data.

The Oncotype DX® Recurrence Score (RS) is an assay for hormone receptor-positive early breast cancer with extensively validated predictive and prognostic value. However, its cost and lag time have limited global adoption, and previous attempts to estimate it using clinicopathologic variables have had limited success. To address this, we assembled 6172 cases across three institutions and developed Orpheus, a multimodal deep learning tool to infer the RS from H&E whole-slide images. Our model identifies TAILORx high-risk cases (RS > 25) with an area under the curve (AUC) of 0.89, compared to a leading clinicopathologic nomogram with 0.73. Furthermore, in patients with RS ≤ 25, Orpheus ascertains risk of metastatic recurrence more accurately than the RS itself (0.75 vs 0.49 mean time-dependent AUC). These findings have the potential to guide adjuvant therapy for high-risk cases and tailor surveillance for patients at elevated metastatic recurrence risk.

Predicting whether a patient with cancer will benefit from immune checkpoint inhibitors (ICIs) without resorting to advanced genomic or immunologic assays is an important clinical need. To address this, we developed and evaluated SCORPIO, a machine learning system that utilizes routine blood tests (complete blood count and comprehensive metabolic profile) alongside clinical characteristics from 9,745 ICI-treated patients across 21 cancer types. SCORPIO was trained on data from 1,628 patients across 17 cancer types from Memorial Sloan Kettering Cancer Center. In two internal test sets comprising 2,511 patients across 19 cancer types, SCORPIO achieved median time-dependent area under the receiver operating characteristic curve (AUC(t)) values of 0.763 and 0.759 for predicting overall survival at 6, 12, 18, 24 and 30 months, outperforming tumor mutational burden (TMB), which showed median AUC(t) values of 0.503 and 0.543. Additionally, SCORPIO demonstrated superior predictive performance for predicting clinical benefit (tumor response or prolonged stability), with AUC values of 0.714 and 0.641, compared to TMB (AUC = 0.546 and 0.573). External validation was performed using 10 global phase 3 trials (4,447 patients across 6 cancer types) and a real-world cohort from the Mount Sinai Health System (1,159 patients across 18 cancer types). In these external cohorts, SCORPIO maintained robust performance in predicting ICI outcomes, surpassing programmed death-ligand 1 immunostaining. These findings underscore SCORPIO's reliability and adaptability, highlighting its potential to predict patient outcomes with ICI therapy across diverse cancer types and healthcare settings.