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Importance In a phase 2 randomized clinical trial, high-dose vitamin D 3 added to standard treatment improved progression-free survival (PFS) compared with standard-dose vitamin D 3 in patients with metastatic colorectal cancer (mCRC). Objective To determine if high-dose vitamin D 3 added to standard chemotherapy improves outcomes in patients with previously untreated mCRC. Design, Setting, and Participants Double-blind phase 3 randomized clinical trial enrolling 455 patients with previously untreated mCRC, conducted in the US through the National Clinical Trials Network from October 2019 to December 2022 (database freeze: July 15, 2024). Interventions mFOLFOX6 (modified FOLFOX6 [5-fluorouracil, leucovorin, oxaliplatin]) or FOLFIRI (5-fluorouracil, leucovorin, irinotecan) plus bevacizumab every 2 weeks with either high-dose vitamin D 3 (8000 IU daily × 14 days as loading dose followed by 4000 IU daily) or standard-dose vitamin D 3 (400 IU daily) until disease progression, intolerable toxicity, or withdrawal of consent. Main Outcomes and Measures The primary end point was PFS assessed by the unstratified log-rank test. Secondary end points included objective response rate, overall survival, and toxicity. Prespecified subgroup analyses of PFS were performed according to known prognostic factors. Results Among 455 randomized patients (median age, 59 years; 181 [40%] female) with median follow-up 20 months, the median PFS for high-dose vitamin D 3 (n = 228) was 11.8 months (95% CI, 10.3-13.3) vs 10.3 months (95% CI, 9.4-12.2) for standard-dose vitamin D 3 (n = 227) (1-sided log-rank P = .25). There were no significant differences in objective response rate between high-dose and standard-dose vitamin D 3 (51% [95% CI, 44%-58%] vs 44% [95% CI, 37%-50%], respectively; P = .12), or in overall survival (median, 25.6 vs 27.0 months; 1-sided log-rank P = .66). There were no clinically meaningful differences in the most common grade 3 or greater adverse events between the high- and standard-dose groups, including neutropenia (n = 67 [32%] vs n = 62 [30%]) and hypertension (n = 42 [20%] vs n = 49 [23%]) or in incidence of vitamin D–associated toxicities. Conclusions and Relevance Among patients with previously untreated mCRC, addition of high-dose vitamin D 3 , vs standard-dose vitamin D 3 , to standard chemotherapy plus bevacizumab did not improve PFS. Trial Registration ClinicalTrials.gov Identifier: NCT04094688

Background Circulating tumor DNA (ctDNA) has rapidly evolved from a research tool to a clinically applicable biomarker in colorectal cancer (CRC), transforming the assessment of minimal residual disease (MRD) and recurrence risk. The growing availability of commercial assays has complicated test selection and interpretation, particularly for gastroenterologists who increasingly encounter ctDNA data in postoperative and surveillance settings. Methods We conducted a narrative review of the literature using PubMed, Embase, and Cochrane databases from January 2015 through March 2026, prioritizing randomized controlled trials, prospective cohort studies, and clinical practice guidelines relevant to ctDNA applications in CRC. The search employed predefined keyword combinations applied independently to each database. Two authors (A.S. and S.M.) independently screened titles and abstracts against the prespecified inclusion criteria, with disagreements resolved by discussion. Results Tumor-informed assays that analyze patient-specific mutations exhibit high specificity for MRD detection, with serial sampling sensitivity reaching 88% compared to 59% for tumor-agnostic approaches, based on a 2026 systematic review and diagnostic accuracy meta-analysis that pooled data across multiple platforms and settings; the authors note substantial study-level heterogeneity in platform design and patient populations, and results should be interpreted accordingly, but require tissue access and extended processing times. Tumor-agnostic tests are more rapid and accessible, incorporating genomic and epigenomic signals, though they exhibit site-specific sensitivity variations ranging from 100% for liver metastases to 40% for peritoneal recurrence and remain vulnerable to biological confounders such as clonal hematopoiesis (CH). Importantly, ctDNA interpretation is subject to additional biological limitations including tumor shedding variability by anatomical site and disease burden, the timing of sample collection relative to surgery or chemotherapy, platform-specific detection thresholds, and pre-analytical variables such as hemolysis and delayed plasma processing. Randomized evidence supports ctDNA-guided de-escalation of adjuvant therapy in stage II colon cancer, with 5-year follow-up from the DYNAMIC trial demonstrating comparable recurrence-free survival (88% vs. 87%) and overall survival (93.8% vs. 93.3%) despite reduced chemotherapy use. In stage III disease, ctDNA is strongly prognostic, but chemotherapy intensification based solely on molecular positivity has not improved outcomes. This review additionally addresses underexplored areas including ctDNA in rectal cancer and neoadjuvant therapy, the influence of microsatellite instability (MSI) status on assay performance, and disparities in ctDNA access across racial, socioeconomic, and geographic populations. Conclusions For gastroenterologists encountering ctDNA results in postoperative and surveillance settings, three principles should guide practice (i): match assay selection to clinical intent - tumor-informed platforms for MRD-directed adjuvant decisions, tumor-agnostic platforms when tissue is unavailable (ii); interpret results within biological and technical context, accounting for platform-specific sensitivity limitations, biological confounders, and quantitative VAF trajectory rather than binary thresholds; and (iii) integrate ctDNA findings into multidisciplinary pathways - de-escalation is supported by high-quality randomized evidence in stage II disease (Grade A; Level I), while chemotherapy escalation based solely on ctDNA positivity in stage III disease is not supported outside clinical trials (Grade C; Level I). Standardization of assay performance benchmarks and equitable access to testing remain critical unmet needs.

ABSTRACT The recent inclusion of the Merlin clinicopathologic–gene expression profile (CP‐GEP) assay in the National Comprehensive Cancer Network (NCCN) Melanoma Guidelines represents an important milestone in the clinical integration of molecular testing for cutaneous melanoma. Unlike earlier melanoma gene expression profile (GEP) assays, which focused primarily on prognostic risk stratification, CP‐GEP was specifically developed to predict sentinel lymph node (SLN) metastasis risk by identifying early metastatic competence within primary melanomas, with additional prognostic utility. This review discusses the biological evidence supporting CP‐GEP and examines how unbiased transcriptomic discovery converged with established insights from cancer cell biology, integrin signaling, focal adhesions, and extracellular matrix (ECM) remodeling to shape its conceptual framework. We further discuss how CP‐GEP captures a transformation‐associated biological state centered on an integrin‐ and TGF‐β–dependent signaling axis ( ITGB3 , TGFBR1 ) that promotes pericellular proteolysis and ECM remodeling ( PLAT , SERPINE2 , LOXL4 ), inflammatory and angiogenic signaling ( CXCL8 , GDF15 ), and melanocytic lineage identity ( MLANA ). Collectively, these genes identify a dissemination‐competent phenotype that is detectable within primary tumors before clinically apparent metastasis. Overall, the biological framework supporting CP‐GEP reinforces the concept that altered adhesion signaling and ECM remodeling are central drivers of early melanoma metastasis and represent clinically actionable biomarkers for individualized melanoma management. image

Accurate estimation of Alzheimer’s disease (AD) severity from structural magnetic resonance imaging (MRI) remains difficult, as disease-associated anatomical alterations are often subtle and publicly available datasets are typically too small to support robust deep learning model training. This feasibility study sought to determine how much Alzheimer’s disease spectrum-related information could be extracted from a small structural MRI cohort using a deliberately lightweight two-dimensional convolutional neural network (2D CNN), and whether transfer learning improves model performance. This study was intended as a methodological proof of concept rather than the development of a clinically deployable diagnostic tool. Structural scans and Clinical Dementia Rating (CDR) labels from the OASIS-1 dataset were filtered to 214 subjects: 124 cognitively normal (CN), 65 with mild cognitive impairment (MCI; CDR = 0.5), and 25 with AD-level impairment (CDR ≥ 1). A compact 2D CNN trained from scratch and a transfer learning model (frozen ImageNet MobileNetV2 features) were evaluated on four binary tasks (CN vs. AD, MCI vs. AD, CN vs. MCI, and CN vs. any impairment) under identical pre-processing and subject-level repeated 5-fold cross-validation (10 repeats), with the decision threshold tuned only on an inner split. Discrimination was summarized by ROC-AUC with 95% confidence intervals (CIs), permutation tests against chance, and per-task sensitivity and specificity. The from-scratch CNN recovered only a broad normal-versus-impaired signal (CN vs. any impairment AUC 0.59) and was at chance on adjacent-stage tasks (MCI vs. AD 0.41; CN vs. MCI 0.51). Transfer learning improved every task: CN vs. AD AUC 0.745 (95% CI 0.730–0.763), CN vs. any impairment 0.642, CN vs. MCI 0.601, and MCI vs. AD 0.599. On an independent OASIS-2 cohort, the transfer learning CN vs. AD model retained AUC 0.748. In this small-data regime, transfer learning recovers substantially more Alzheimer-spectrum signals than a from-scratch CNN, but performance remains modest because it is bounded by CDR-based, non-biomarker-confirmed labels, suggesting the model separates CDR-defined cognitive-status groups rather than detecting AD pathology.

Long-established prostate cancer cell lines provide limited insight into how contemporary early prostate cancer evolves into drug-resistant and neuroendocrine-like refractory disease. Patient-derived three-dimensional (3D) ex vivo models may better preserve this transition. Here, we found that 8 of 10 magnetic resonance imaging-guided biopsy specimens from patients with early-stage prostate cancer generated sustained 3D tumor spheroid cultures. After 12 weeks of enzalutamide selection, only one patient-derived culture acquired a resistant phenotype with treatment-emergent neuroendocrine prostate cancer (t-NEPC)-like features, including increased chromogranin A (CgA) and synaptophysin (SYP); reduced androgen receptor (AR), prostate-specific antigen (PSA), and prostate-specific membrane antigen (PSMA); and conversion from compact spheroids into irregular resistant aggregates. During this transition, WD repeat and SOCS box-containing protein 1 (WSB1) increased, whereas PSMA progressively decreased. WSB1 silencing restored PSMA, AR, and PSA expression and reduced neuroendocrine-associated features. A similar WSB1 dependency was observed in enzalutamide-resistant LNCaP cells, the castration-resistant prostate cancer model 22Rv1, and the neuroendocrine/small-cell prostate cancer model NCI-H660. Mechanistically, WSB1 functioned as a SOCS box-dependent E3 ubiquitin ligase adaptor that promoted PSMA ubiquitination and degradation. SOCS box deletion or T380A mutation impaired this process, while Aurora kinase A (AURKA) inhibition reduced WSB1-dependent PSMA ubiquitination. WSB1 depletion, AURKA inhibition with alisertib, and combined AURKA inhibition with EZH2 suppression reduced resistant aggregate growth and increased apoptosis-associated markers in patient-derived enzalutamide-resistant neuroendocrine-like spheroids and related models. These findings nominate the AURKA–WSB1–PSMA axis as a therapeutic vulnerability in refractory prostate cancer.

Breast cancer is the most common cancer in women, causing more than 600,000 deaths every year. The human microbiome is increasingly recognized as a key regulator of cancer initiation, progression, and therapeutic response. Postbiotics—defined as non-viable microbial cells and/or their structural components and metabolites that confer health benefits—are emerging as promising and safer alternatives to live probiotics in oncology. This review provides a comprehensive mechanistic overview of the potential role of postbiotics against cancer, with a specific focus on breast cancer. Preclinical evidence demonstrates that selected postbiotics exert dose- and time-dependent anticancer effects against multiple breast cancer subtypes by modulating key oncogenic pathways (such as PI3K/AKT and NF-κB) and inducing epigenetic regulation through histone deacetylase inhibition. Beyond direct effects on tumor cell proliferation and apoptosis, postbiotics influence the breast cancer microenvironment by reshaping cytokine networks, suppressing pro-metastatic inflammation, and enhancing antitumor immune responses through the activation of NK cells and T cells. We also provide emerging links between microbiome composition, estrobolome activity, and breast cancer subtype-specific biology. Despite these encouraging findings, the clinical translation of postbiotics in oncology remains limited. Currently, only one registered clinical trial investigates postbiotics in the oncology setting (melanoma), and no clinical trials have specifically evaluated postbiotics in breast cancer patients. This highlights a substantial translational gap between preclinical evidence and clinical application. Accordingly, well-designed, tumor-specific clinical trials are urgently needed to validate the safety, efficacy, and therapeutic potential of postbiotics as novel strategies in personalized breast cancer management.

ABSTRACT Background Higher time in sedentary behavior and insufficient physical activity are very common in bipolar disorder (BD). Whereas physical activity and exercise offer significant benefits across mental health, brain health, and heart health in BD. The International Society for Bipolar Disorders Nutrition and Exercise Task Force (NExT) synthesized evidence to evaluate the impacts of sedentary behavior, physical activity, and exercise on illness course, physical health, and treatment outcomes. Methods A best‐evidence synthesis was conducted using a question‐and‐answer format. We searched PsycINFO, SPORTDiscus, Embase, PubMed, and Web of Science from inception to March 1st, 2026, supplemented by reference lists and existing guidelines. Search terms included combinations of physical activity, exercise, sedentary behavior, and bipolar disorder‐related terms. Results Observational studies show that individuals with BD exhibit significantly higher sedentary behavior and lower physical activity compared to the general population, with notable discrepancies between self‐reported and objective measurements. Evidence on physical activity's protective role against BD onset is mixed, with some prospective and Mendelian randomization studies suggesting reduced risk, while others report bidirectional or null associations. Preliminary findings from clinical trials indicate that exercise improves overall functioning, reduces mood episode frequency and hospitalizations, and alleviates depressive symptoms, with one pilot study showing an 82% antidepressant response rate. Exercise also shows potential to reduce anxiety and improve sleep, though robust data are limited. Conclusions Despite limited high‐quality evidence, physical activity and exercise demonstrate promising physical and mental health benefits for individuals with BD. Future research should prioritize multicenter randomized controlled trials, enhanced assessment tools, and accessible, tailored programs to integrate physical activity into routine BD management.

Abstract Background While the radiation dose to left anterior descending (LAD) artery is associated with overall survival (OS) in NSCLC patients, manual segmentation is labor‐intensive. Several auto‐segmentation models were developed on average 4DCT with promising geometric agreement, yet their feasibility in clinical outcome analysis on lung CT remains unclear. Purpose This study evaluates whether automated LAD segmentation can reproduce, at the group level, the established dose‐survival association in a multi‐institutional clinical trial dataset. Methods Three deep learning‐based auto‐segmentation (DLAS) models for automatic segmentation of the LAD coronary artery were applied to 460 patients from the NRG Oncology/RTOG 0617 dataset. Kaplan–Meier curves and Cox proportional hazard ratio (HR) were calculated using each model's segmentation. The LAD V15 Gy was used to stratify patients into high‐and low‐risk groups, and the cut‐off values were determined by minimizing mean absolute error (MAE) between DLAS‐derived and manual OS curves. Results All three models demonstrated significantly different OS curves between the high risk and low risk groups ( p < 0.05). The HR was 1.40 (95% CI: 1.02–1.93; p = 0.0381; MAE = 7.1%), 1.32 (95% CI: 1.03–1.68; p = 0.0258; MAE = 9.6%), and 1.37 (95% CI: 1.03–1.84; p = 0.0331; MAE = 7.9%) for the three models using a common V15 Gy ≥ 10% cut‐off. After optimizing the cut‐off, one model aligned well with what was derived from manual segmentation (MAE = 4.3%). Conclusion Among the evaluated models, one DL model showed the strongest performance of survival groups separation and achieved group‐level overall survival stratification comparable to that previously reported using manual contours in the multi‐institutional cohort. Further validation with paired manual contours and pre‐specified cutoffs is warranted.