European Journal of Cancer

Background: Immune checkpoint inhibitors have transformed cancer treatment, yet a large number of patients fail to respond. Identifying molecular characteristics that predict response before treatment initiation remains an unmet need. Towards that end, this study presents a large-scale integrative analysis of existing single-cell and bulk tissue datasets, aimed at identifying predictive features while providing insights into their cellular origin and potential function within the tumor microenvironment. Methods: A stepwise analysis was performed using single-cell RNA-sequencing data from 60 melanoma patients at baseline, separated into discovery (n=41) and validation (n=19) sets. An integrated bulk transcriptomics dataset (n=128) from melanoma patients and a bladder cancer dataset (n=298) were used for further validation. Results: Integrative analysis of melanoma single-cell datasets revealed that responders exhibit distinct molecular profiles across multiple cell types compared to non-responders. Notably, these included downregulation of the TNFR superfamily and other immunosuppressive genes (TNFRSF18, TNFRSF9, TNFRSF4, LGALS1, BATF, IL12RB2, LINGO1, DUSP4, SDC4, VCAM1) in T-cells. By investigating the findings from the immune cell populations in the bulk tumor context, 13 transcripts were found to be consistently associated with response across all cohorts. These were differentially expressed in T-cells (SELL, EPB41, CD96, UHFR2, LINGO1, LGALS1), B-cells (ALDH5A1), NK cells (PLEC, PDGFRB) and Monocytes (TLR10, ST6GAL1, IKZF1, MPRIP). A predictive model based on these features effectively discriminated responders from non-responders in melanoma (AUC=0.73). The model maintained significant predictive power in an independent bladder cancer dataset (IMvigor210; AUC=0.64). Of high clinical relevance, it demonstrated enhanced performance in identifying responders among patients with low tumor mutational burden (AUC=0.75). Conclusion: Our study reveals pre-treatment molecular features related to immune-cancer crosstalk that are associated with response to immunotherapy. A 13-gene model demonstrates potential added clinical value in stratifying responders, particularly in patients with low tumor mutational burden, meriting further validation.

Accurate risk stratification of pigmented skin lesions is critical for early melanoma detection and for reducing unnecessary excisions. Artificial intelligence (AI) is increasingly applied to dermoscopic image analysis, but its diagnostic performance relative to standard dermoscopy in real-world clinical settings remains uncertain. To address this gap, we conducted a systematic review and meta-analysis of prospective clinical studies directly comparing AI alone, dermoscopy, and AI-assisted clinicians for malignancy risk assessment of pigmented skin lesions. We systematically searched PubMed, Embase, Web of Science, and Cochrane Library from inception to January 2026. Ten studies with 17 diagnostic arms (10 dermoscopy arms, 6 AI-alone arms, and 1 AI-assisted clinician arm) were included. Pooled sensitivity and specificity were 0.773 (95% CI, 0.648-0.863) and 0.793 (95% CI, 0.673-0.877) for dermoscopy, and 0.757 (95% CI, 0.428-0.928) and 0.859 (95% CI, 0.619-0.958) for standalone AI. Summary ROC curves showed overlapping performance, indicating that autonomous AI is broadly comparable to dermoscopy but does not demonstrate a consistent advantage. Heterogeneity in AI performance was driven almost entirely by threshold effects rather than by differences in inherent model capacity. AI-assisted clinicians showed promising results (sensitivity 1.000, specificity 0.837) in a single study, but more evidence is needed. Our findings suggest that, at present, AI should be viewed as a complementary decision-support tool rather than a replacement for dermoscopic evaluation. The study provides valuable evidence for clinicians, guideline developers, and researchers working on AI integration into melanoma diagnostic pathways.

Distant melanoma metastasis at the time of diagnosis is uncommon, but has major implications for patient prognosis and treatment selection. However, few tools can reliably predict the risk of distant metastasis at initial presentation. Here, we developed and evaluated machine learning models to predict distant melanoma metastasis using routinely captured clinicopathologic and demographic variables across all histologic subtypes. Using the National Cancer Institute Surveillance, Epidemiology, and End Results (SEER) program from 2010-2022, we identified adults aged 20 to 90 years with melanoma as the first and only primary malignancy (n=51,285). Explainable Boosting Machine achieved a strong balance of discrimination and precision (AUROC = 0.947, AUPRC = 0.610, Precision = 0.793, Brier = 0.015). At 90% sensitivity, specificity was 0.843 with consistent performance across cross-validation folds. Clinicopathologic variables, including T stage, Breslow thickness, ulceration, and mitotic activity, contributed the largest share of predictive signal across descriptive, regression-based, and SHAP analyses, with smaller contributions from demographic factors. Decision curve analysis supported clinical utility, showing a net reduction of 88.3 per 100 patients and a standardized net benefit of 0.541. This model could be used to identify patients at sufficiently elevated risk to justify staging PET/CT despite otherwise localized clinical presentation. Cost-consequence analysis further showed that imaging true- and false-positive patients at 85% to 95% sensitivity threshold nearly doubled downstream imaging cost. We deployed the final model as an online calculator to support exploration of individualized risk estimates (https://melanoma-calculator.streamlit.app/).

Importance Prognostic tools beyond staging are needed to guide treatment and counseling in head and neck squamous cell carcinoma (HNSCC). Objective To develop and externally validate a machine learning model predicting survival in advanced HNSCC using routinely collected clinical and biomarker data. Design, Setting, and Participants Retrospective, multi-institutional cohort study including 2,385 patients with stage III-IV HNSCC diagnosed from 2012-2022 in the University of California Health Data Warehouse (UCHDW). Patients were randomly split into training (n = 1,908) and test (n = 477) sets. Partial external validation used 7,749 patients from the Surveillance, Epidemiology, and End Results (SEER) registry (2010-2020). Exposures Demographic, tumor, treatment, comorbidity, and biomarker variables recorded at or before diagnosis. Main Outcomes and Measures The primary outcome was all-cause mortality within 70 months. Cox proportional hazards models included all predictors. Discrimination was assessed with Harrell's concordance index (C-index), calibration with predicted vs observed survival, and stratification with Kaplan-Meier curves. A Random Survival Forest (RSF) was trained for benchmarking and interpretability using Shapley Additive exPlanations (SHAP). Results Among 2,385 patients in UCHDW (median age, 63 years; 29.0% mortality), the Cox model achieved a C-index of 0.735 in the internal test set. Risk quartiles showed clear separation on Kaplan-Meier curves (log-rank p < 0.0001). In the SEER cohort (n = 7,749), where only demographic, staging, subsite, and treatment variables were available, the reduced Cox model achieved a C-index of 0.688, with calibration showing modest underestimation of survival in high-risk groups. Age, T stage, Charlson Comorbidity Index, neutrophil-to-lymphocyte ratio, and platelet count were among the strongest predictors, while surgery was associated with improved survival. The RSF achieved a C-index of 0.758 internally, with SHAP highlighting nonlinear effects of albumin, BMI, and inflammatory markers. Conclusions and Relevance A machine learning model using routine clinical and biomarker data demonstrated good prognostic performance in advanced HNSCC, with partial external validation. Such approaches may support individualized survival estimates, risk stratification, and treatment discussions, but broader validation is required before clinical adoption.

Purpose: The prognostic role of tumor-infiltrating lymphocytes in luminal breast cancer remains uncertain, partly because density-based metrics do not capture spatial interactions between immune cell subsets. We developed a density-independent spatial metric quantifying macrophage-T cell proximity and assessed its prognostic value. Experimental Design: Using multiplex immunohistochemistry across three breast cancer cohorts (exploratory, n = 17; discovery, n = 687; validation, n = 305), we measured nearest-neighbor distances from T cells to M1-like and M2-like macrophages, benchmarked against a randomly subsampled total macrophage pool. We defined the Macrophage Spatial Polarity Index (MSPI) as the difference between M2-to-T cell and M1-to-T cell affinity scores, where higher values reflect an M2-dominated spatial phenotype. Cox regression was used to assess associations with distant disease-free survival (discovery) and overall survival (validation). Results: M2-like macrophages preferentially localized near T cells, independent of cell density. Higher MSPI was associated with shorter survival in luminal cancers (discovery: HR = 1.45, p < 0.001), with the strongest effect in young women with early-stage disease (HR = 2.16, p < 0.0001). MSPI remained independently prognostic after adjustment for stage, systemic treatment, and diagnosis period (HR = 2.31, 95% CI 1.73-3.09, p < 0.0001) and was non-significant in HER2-positive and triple-negative subtypes. Validation in an independent ER-positive cohort confirmed the finding (HR = 1.30, p = 0.004). Pooled analysis yielded HR = 2.13 (95% CI 1.68-2.70, p = 3.45 x 10-10). Conclusions: MSPI is a robust prognostic biomarker in luminal breast cancer, particularly in young women with early-stage disease, warranting further validation for risk stratification and therapeutic guidance.

Importance: Enfortumab vedotin (EV) is an antibody-drug conjugate approved for the treatment of locally advanced or metastatic urothelial cancer (la/mUC). Cutaneous adverse events (cAEs) are common during EV therapy, with prior studies suggesting an association between EV-related cAEs and improved survival; however, there is insufficient data to delineate the survival benefit of EV-induced cAEs from those associated with concurrent immune checkpoint inhibitors (ICIs). Objective: This study aims to evaluate the association of EV-induced cAEs and survival, and to characterize the timing and morphology of EV-induced cAEs. Design: We conducted a multi-institutional retrospective study of patients with la/mUC treated with EV between 2020 and 2025. Setting: Multicenter academic referral center. Participants: A total of 449 EV-treated patients were included. Patient characteristics were extracted manually, and likelihood scoring was used to attribute cAEs to either EV or other etiologies. Exposure: EV treatment. Main Outcomes and Measures: We estimated progression-free (PFS) and overall (OS) survival using Kaplan-Meier method. Multivariable time-varying and landmark Cox regression models were used to evaluate associations between EV-induced cAE and survival. Sensitivity analyses were performed at landmarks from 15 to 105 days. Results: Of 449 patients, 206 (45.9%) developed a cAE; 39 (18.9%) were high-grade and 127 (61.7%) were attributed to EV. The most common cAEs were pruritus (41.3%), unspecified and desquamating dermatitis (37.3%), and morbilliform dermatitis (27.7%). Across all treatment groups, survival was longer in patients with EV-induced cAEs. Developing an EV-induced cAE was protective across all examined landmark times, with hazard ratio (HR) 0.60 (95% CI: 0.43-0.82, p<0.001) for PFS and HR 0.46 (95% CI: 0.31-0.67, p<0.001) for OS at primary landmark time of 30 days. Early-onset EV-induced cAEs were protective at all landmark times and high-grade EV-induced cAEs were not associated with worse survival. Conclusions and Relevance: EV-induced cAEs were independently associated with improved PFS and OS in patients with la/mUC, even after accounting for immortal time bias and ICI exposure. Distinguishing EV-induced cAEs from other etiologies in timeline and morphology may help guide oncology and dermatology management.

Background. The European Society for Medical Oncology (ESMO) Scale for Clinical Actionability of molecular Targets (ESCAT) ranks genomic alterations by the evidence supporting the predictive value of the molecular target for response to targeted therapies. No openly available, systematically curated set of standard care biomarkers mapped to the ESCAT framework exists to support clinical decision-making or harmonize biomarker interpretation. Methods. We mapped all OncoKBTM Level 1 biomarkers to ESCAT tiers using evidence cited by OncoKBTM, excluding abstract-only data. Eight board-certified oncologists and hematologists independently assigned ESCAT tiers, with discrepancies resolved through structured consensus meetings. Recurring evidence scenarios that did not correspond to any existing ESCAT tier informed a set of a priori defined modifications, which were subsequently applied to biomarkers that could not be classified using native ESCAT criteria. Results. Of 188 OncoKBTM Level 1 biomarkers, 16 were excluded due to abstract-only evidence. Using native ESCAT criteria, 51% of the remaining biomarkers were classified as Tier 1, 3% Tier 2, 18% Tier 3, 6% Tier X and 22% could not be assigned to any tier. Applying the modified ESCAT criteria resolved all previously unclassifiable biomarkers and increased Tier 1 assignments to 73%. Inter-rater reliability (Krippendorffs alpha) was moderate (0.586) and 62% of classifications required consensus discussions. Comparison with ESCAT tiers reported in ESMO Clinical Practice Guidelines showed improved concordance when using the modified criteria. Conclusions. The native ESCAT criteria are highly stringent, resulting in many FDA-recognized, clinically validated biomarkers that are currently assigned level 1 by OncoKBTM not mapping to any existing tier. Our predefined modifications improved alignment with OncoKBTM Level 1 designations and with published ESMO clinical practice guidelines. The mapped set of standard care biomarkers are provided on the OncoKBTM website, offering a practical resource that harmonizes ESCAT tiers of evidence with a widely adopted levels of evidence schema.

Background Background breast features are frequently noted in pathology reports alongside invasive breast cancer but rarely factor into prognosis or treatment decisions. Their relationship to tumor characteristics and patient outcomes remains incompletely characterised. Methods We conducted a retrospective cohort study of 7,603 patients with Stage I-III invasive breast cancer (diagnosed 1991-2022, age <80 years) from the Joint Breast Cancer Registry in Singapore. Natural language processing (NLP) was applied to 9,754 free-text pathology reports to extract co-existing background breast features, with accuracy validated by dual-reviewer assessment of 200 reports. Unsupervised hierarchical clustering grouped extracted features into three categories. Associations with tumor characteristics were assessed by multinomial logistic regression, and ten-year overall survival by Cox proportional hazards models (median follow-up 9.6 years; 620 deaths). Results Here we show that NLP-based extraction of background breast features from routine pathology reports achieves an accuracy of over 90% across features. Lobular neoplasia and benign proliferative changes are associated with less aggressive tumor characteristics, whereas early neoplastic and papillary lesions are more prevalent in HER2-enriched and luminal B tumor subtypes. Benign proliferative changes are associated with better survival in age- and year-adjusted models (hazard ratio 0.91, 95% CI 0.86-0.97), but this association is attenuated after adjustment for stage and subtype. Conclusions NLP-enabled extraction of background breast features from pathology text is feasible at scale. These features reflect tumor biology but do not independently add prognostic information beyond established clinical variables.

Background: The development of personalised mRNA cancer vaccines holds considerable promise for oncology, yet a significant translational gap persists between neoantigen identification and the selection of therapeutically impactful targets. Current approaches predominantly prioritise human leukocyte antigen (HLA) binding affinity and immunogenicity, often overlooking the systems-level biological context of the target. This can inadvertently favour immunogenic but biologically peripheral peptides that exert limited influence on tumour signalling networks, thereby constraining vaccine efficacy. Furthermore, mRNA therapeutics must satisfy additional design requirements, including favourable codon usage and favourable secondary-structure stability, which directly affect in vivo translation and half-life. A unified computational framework that integrates neoantigen discovery with network biology is therefore critically needed. Results: Here, we present PimRNA, a Priority index (Pi)-centric computational medicine framework that bridges this gap by unifying neoantigen identification, mRNA sequence optimisation, and gene interaction network analysis. First, high-confidence tumour-specific HLA class I and II neoantigenic peptides are identified from paired tumour-normal genomic and tumour transcriptomic data using NeoDisc. Second, the coding sequences of these peptides are optimised for stability and translational efficiency with LinearDesign, yielding a core set of neoantigen-encoding mRNAs. Third, a random walk with restart algorithm is applied to a knowledgebase of gene interactions to identify peripheral genes exhibiting significant network connectivity to core genes, generating a gene-predictor matrix in which each gene is assigned an affinity score reflecting its network proximity to immunogenic neoantigens. These scores are consolidated into a single, unified priority rating (0-5) for each gene, followed by subnetwork analysis that reveals therapeutically relevant gene modules. Application of PimRNA to breast cancer and melanoma datasets demonstrates that it successfully selects high-confidence immunogenic neoantigen candidates embedded within biologically meaningful tumour-specific networks. Conclusion: PimRNA provides a systems biology foundation for mRNA vaccine design, moving beyond isolated immunogenicity to prioritise targets that are both highly presented and central to tumour-relevant biological networks. This framework offers a generalisable strategy for the rational discovery and prioritisation of mRNA therapeutics, significantly advancing the field of computational medicine towards personalised cancer vaccines.

Purpose: Chemotherapy-induced peripheral neuropathy (CIPN) affects many people receiving taxane treatment for breast cancer. Symptom trajectories vary, with some recovering, and others experiencing persistent, or delayed worsening (coasting) symptoms. The prevalence and predictors of these trajectories remain unclear. This study identified the prevalence and biopsychosocial predictors of CIPN persistence, improvement, and coasting within three months post-treatment. Methods: This secondary analysis included participants treated with taxanes for stage I-III breast cancer who completed the Functional Assessment of Cancer Therapy/Gynecologic Oncology Group-Neurotoxicity-4 (FACT/GOG-NTX-4) at baseline, post-chemotherapy, and three months later. A minimally important difference (MID) from baseline on the FACT/GOG-NTX-4 defined persistence, improvement, coasting, and no MID-CIPN (below the MID threshold at each assessment) trajectories. Baseline assessments included self-reported pain/well-being, sensory, balance, and lower limb physical functioning measures, and sociodemographic and treatment data were collected. Results: Among 102 participants (51.57{+/-}11.24 years), persistence occurred in 34.3%, improvement in 25.5%, coasting in 6.9%, and no MID-CIPN in 33.3%. Compared to no MID-CIPN, older age (OR=1.120; 95%CI: 1.026-1.222), higher expected pain (OR=1.630; 95%CI: 1.082-2.456), and cold hyperalgesia at the foot (OR=1.130; 95%CI: 1.018-1.254) predicted persistence. Lower fatigue predicted improvement (OR=0.904; 95%CI: 0.845-0.968). No predictors were identified for coasting. Conclusion: CIPN trajectories are heterogeneous. Age and pre-treatment pain expectations, cold hyperalgesia, and fatigue differentiate patients with persistent CIPN and those likely to improve from those with no CIPN. Implications for Cancer Survivors: Early identification of individuals at risk for persistent neurotoxicity may support risk stratification and guide targeted supportive care strategies.

High grade serous ovarian cancer patients initially respond to platinum-based chemotherapy, but usually relapse within two years and ultimately develop therapy resistance. Management of response and effective clinical decisions are currently based on unspecific biomarkers and limited imaging techniques, illustrating the clear clinical need for reliable predictors of response. In this work, we evaluated the performance of patient-derived organoids generated from ascitic fluid and functionally tested in parallel to the patients clinical course, in the prediction of treatment response, and guiding clinical decision-making in a patient-specific manner. Ascites derived organoids reliably recapitulated the histological and molecular features of a paradigmatic HGSOC patient with an apparent dissociated response, and demonstrated chemoresistance months before laparoscopy confirmed persistent inoperable disease with poor pathological response. Drug screening identified alternative therapeutic options, while multi-omics provided additional insights into the tumor-specific biological features, to assist in the personalized clinical management in ovarian cancer.

Introduction: With recent approvals of multiple targeted therapies for triple-negative breast cancer (TNBC), including antibody-drug conjugates and immunotherapy in biomarker-selected populations, it is critical to define the temporal evolution of cell-surface target expression from early-stage to metastatic disease, the co-expression patterns across these markers, and optimal quantification methodologies. Here we report biomarker expression profiles measured by multi-omics and pathology-based platforms in patients with TNBC using a large cohort of matched longitudinal tumor samples. Methods: Patients who underwent neoadjuvant chemotherapy (NAC) for stage I-III TNBC or were diagnosed with any stage TNBC and developed metastatic recurrence were retrospectively identified from an institutional database and prospective research metastatic biopsy protocol. Tumor samples from diagnosis (DX), residual disease (RD) post-NAC (if applicable), and metastasis/recurrence (MR) were collected. Quantification of HER2, TROP2, and PD-L1 expression was performed by immunohistochemistry (IHC), whole-exome sequencing, transcriptome sequencing, and targeted mass spectrometry (MS). For HER2, TROP2, and stromal tumor-infiltrating lymphocytes (sTILs), both manual pathologist assessment and computational pathology quantification were obtained. HER2 status was categorized as HER2-0 or HER2-low by local (L-IHC) and central (C-IHC) review, TROP2 status was defined as low (H-score <100), medium (H-score 100-200) or high (H-score >200), and PD-L1 as low (tumor area positivity, TAP <5%) or high (TAP [&ge;]5%). Pathologist-assessed sTILs were classified as low (<10%), medium ([&ge;]10% and <40%) or high ([&ge;]40%). Biomarkers were compared between primary (DX/RD) and MR, and between pre- vs post-NAC (DX-RD) samples. Correlations between markers, quantification methods, inferred PAM50 subtype, and clinical variables of interest were evaluated. Results: A total of 359 samples from 110 patients with TNBC with data available from at least one platform were included in the analysis. HER2-low prevalence at DX, RD, and MR was: 51% (50/98), 40% (21/53), and 27% (16/60); TROP2 high/medium was 90% (47/52), 91% (42/46), and 88% (28/32); PD-L1-high was 51%, 50%, and 38% (9/24); and sTILs-high/medium was 88% (59/67), 80% (40/50), and 49% (17/35), respectively. While TROP2-high/medium vs low remained stable over time, HER2 IHC and sTILs significantly decreased from DX/RD to MR samples, both at the cohort-level (HER2, p=0.0081; sTILs, p=4.6x10e-5) and longitudinal patient-level (HER2, p=0.030; sTILs, p=0.0077), with a similar decreasing trend for PD-L1 that did not reach statistical significance. HER2 concordance (0 vs low) between L-IHC and C-IHC was 78% (91/116). ERBB2, TACSTD2 and CD274 mRNA expression were significantly correlated with IHC protein levels, though only TACSTD2 had limited overlap in distribution of gene expression between high/medium vs low groups. Strong correlation between protein membrane staining intensity from computational pathology, protein expression measured by MS, and pathologist-assed IHC was observed across all biomarkers tested by each method. In comparisons between biomarkers, pathologist-assessed PD-L1 IHC and sTILs were significantly correlated (p=0.0001); 94% (51/54) of PD-L1-high tumors were classified as sTILs high/medium. PAM50 subtype was not significantly correlated with time point or biomarker status, although there was a trend toward more HER2-enriched tumors in HER2-low (20%, 5/25) vs HER2-0 (6%, 3/52) (p=0.086). Across biomarkers and clinical variables, an association between age and sTILs was observed (p=0.038, FDR=0.42) due to a decrease in sTILs high/medium tumors with age, primarily driven by post-treatment (RD/MR) but not DX samples. Conclusions: Multi-platform and multi-omics profiling in this large unique cohort of longitudinal TNBC samples revealed distinct patterns of expression and dynamic changes of key biomarkers of interest for targeted therapies. Given variability with manual IHC scoring, improved methods for quantification of expression may help optimize treatment selection in an individualized manner.

PurposeTo develop and validate a multimodal recurrence-risk model integrating histology, genomic testing, and clinical variables. MethodsWe developed AI-Path, a whole-slide image biomarker for recurrence prediction trained in CALGB 9344, and validated it in three independent cohorts: TAILORx, a multi-site Chicago cohort, and the MDX-BRCA cohort. We then integrated AI-Path with Oncotype DX Recurrence Score (RS), tumor size, and nodal status into a Cox model, PathClinRS, fit using 60% of cases from TAILORx, with the remaining 40% held out for validation. The primary end point was distant recurrence-free interval. Performance was assessed using Harrells concordance index (C-index) and Kaplan-Meier analyses. ResultsA total of 12,418 patients were included. In TAILORx, AI-Path outperformed RS for distant recurrence (C-index, 0.682 vs 0.647; P = .038), driven by superior prediction of late recurrence (0.656 vs 0.567; P < .001). In node-negative disease, PathClinRS outperformed RSClin in the TAILORx fitting (0.72 vs 0.70; P = .016) and validation sets (0.74 vs 0.70; P = .004). In node-positive disease, PathClinRS outperformed RSClinN+ in Chicago (0.94 vs 0.74; P < .001) and MDX-BRCA (0.71 vs 0.66; P = .004) cohorts. Compared with NATALEE eligibility, PathClinRS identified nearly twice as many high-risk node-negative patients while maintaining a comparable 10-year distant recurrence risk (16.7% vs 16.6% per NATALEE eligibility in TAILORx fitting; 21.0% vs 19.4% in TAILORx validation). PathClinRS identified 68% of intermediate risk premenopausal patients as low-risk with no evidence of chemotherapy benefit, compared to only 36% identified as low risk by standard clinicopathologic criteria. ConclusionDigital histopathology provides prognostic information complementary to genomic assays and has the potential to personalize therapy beyond existing clinicogenomic tools.

Background: CD8+ tumor-infiltrating lymphocytes (TILs) are established prognostic markers in colorectal cancer, yet the clinical significance of CD103+CD8+ tissue-resident memory-like (TRM-like) T cells in locally advanced rectal cancer (LARC) after neoadjuvant chemoradiotherapy (NACRT) remains unknown. Methods: We quantified CD8+ and CD103+CD8+ T-cell densities in stromal and intratumoral compartments of post-NACRT resection specimens from 40 LARC patients using Cu-Cyto, a deep learning-based imaging cytometry platform. Associations with survival, pathological response, and adjuvant chemotherapy (AC) were examined. Treatment-induced T-cell dynamics were assessed in paired pretreatment biopsies and post-NACRT resections (n = 9). Results: High stromal CD103+CD8+ density independently predicted better 5-year RFS (67.4% vs. 12.1%, p < 0.001) and OS (80.0% vs. 26.6%, p = 0.016); intratumoral density showed no prognostic significance. Pathological response correlated with stromal CD8+ but not CD103+CD8+ density. Paired analysis revealed a selective non-expansion of the CD103+ subset: stromal CD8+ T cells increased significantly after NACRT while CD103+CD8+ density remained unchanged. AC may preferentially benefit patients with low stromal CD103+CD8+ density. Conclusions: Stromal CD103+CD8+ T-cell density is a robust independent prognostic biomarker in rectal cancer after NACRT that appears to reflect pre-existing rather than treatment-induced immunity. Given its stability across NACRT, pretreatment biopsy assessment may provide equivalent prognostic information, with potential implications for patient stratification before treatment initiation.

Importance: Non-medullary thyroid cancer (NMTC) and melanoma are associated with inherited long telomeres due to germline pathogenic/likely pathogenic variants (PV/LPV) in POT1, TINF2, and ACD resulting in long-telomere syndrome (LTS) and they commonly have somatic TERT promoter mutations. The genetic relationship between these variants and their clinical associations are defined incompletely and may inform clinical practice. Objective: To test the hypothesis that germline LTS-associated PV/LPV are exclusive from functional somatic TERT variants and assess clinical/genetic associations. Design: Retrospective observational cohort study with/without germline LTS variants, that have somatic sequencing and pathology data. Setting: Participants were enrolled through 18 cancer centers participating in the Oncology Research Information Exchange Network (ORIEN). Participants: 995 adults with NMTC and 993 with melanoma between 2013 and 2025. All adult patients at an ORIEN center were offered enrollment Exposures: All patients with NMTC or melanoma are included. There are no required exposures. Main Outcomes and Measures: The presence/absence of a germline or somatic long-telomere variant; secondary outcomes are associations with tumor stage, telomerase expression, and oncogenes. Results: Germline and somatic variants in POT1/TINF2/ACD, somatic TERT promoter variants, TERT fusions, oncogenes, and telomerase mRNA expression were evaluated in 995 NMTC and 993 melanoma patients. In NMTC, 13 (1.5%) had a germline LTS variant while 0/12 with tumor sequencing had somatic TERT promoter variants/fusions. In melanoma, 7 (0.7%) had a LTS variant; 0/2 with tumor sequencing had a TERT promoter variant/ fusion. Meta-analysis including NMTC and melanoma in the current study, a recent thyroid cancer study, and thyroid TCGA, germline LTS-associated PV/LPV and somatic TERT variants/fusions were mutually exclusive (p=0.036). High telomerase mRNA levels were associated with TERT promoter variants/fusions (p<4e-11) and larger NMTC/distant metastases (p=0.016), but not germline LTS variants. NMTCs with somatic TERT promoter variants/fusions had higher tumor mutation burden (p<0.02) versus tumors from patients with a germline LTS variant. TERT promoter mutant variant allele frequency was lower in smaller and non-metastatic vs larger/metastatic NMTC. Conclusion and Relevance: Germline LTS-associated variants appear to be exclusive from somatic TERT promoter variants/fusions but are not associated with aggressive NMTC, suggesting common roles in tumorigenesis but different biological impacts.

Background: Non-invasive diagnosis, reliable recurrence surveillance remain critical unmet needs in gliomas. Glioma induces profound systemic immune alterations despite its anatomical confinement to the central nervous system. Circulating immune cells, particularly monocytes, are key mediators of tumor-host crosstalk and may retain tumor-induced transcriptional imprints. However, their potential clinical utility as blood-based biomarkers for detection and monitoring, remain largely unexplored. Methods and findings: In this study, we performed integrated single-cell RNA sequencing of blood immune cells and demonstrated that circulating CD14+ monocytes are significantly expanded in glioma patients, exhibiting features of differentiation arrest and increased transcriptional plasticity. These cells harbor glioma-specific molecular signatures distinct from those observed in healthy controls and patients with other tumors. Leveraging these findings, we developed an ensemble machine learning diagnostic model based on transcriptomic profiles of circulating CD14+ monocytes (training cohort, n=107), which achieved a mean area under the receiver operating characteristic curve (AUC) of 0.971 during cross-validation. In an independent cohort of 567 participants, the model maintained high diagnostic accuracy, yielding an AUC of 0.877 for distinguishing glioma from controls and other tumors. And it achieved a recurrence detection AUC of 0.969 in 51 postoperative samples. Moreover, in a prospective follow-up study involving 30 glioma patients, lower model-derived scores of postoperation were significantly associated with prolonged progression-free survival (log-rank test, P=0.043), supporting its prognostic utility. Conclusion: We demonstrate circulating CD14+ monocytes undergo glioma-specific transcriptional reprogramming, generating systemic tumor-associated signal captured via transcriptomic profiling. This blood-based diagnostic model provides non-invasive, scalable approach for glioma detection, recurrence surveillance, outcome prediction.

Background Cancer treatment response is highly variable, even among patients with the same tumor type and treatment. Exceptional responders (ERs), who are individuals who experience unusually favorable outcomes, provide critical insights into the biological factors driving treatment success. While prior studies have highlighted the role of somatic changes, the contribution of germline rare variants remains underexplored. This study aimed to uncover the genetic underpinnings of exceptional responses by identifying rare, non-silent and predicted deleterious germline mutations enriched among ERs compared to typical cancer patients. Methods The Network of Enigmatic Exceptional Responders (NEER) project collected clinical and germline whole-genome sequencing (WGS) data from 53 ERs. After quality control procedures and ancestry background checks, 51 ERs were left for final analysis. While non-silent mutations were identified based on allele frequencies and mutation types, multiple pathogenicity predictors were applied for predicted deleterious variants. These were compared to a harmonized and comparable subset from the Pan-Cancer Analysis of Whole Genomes (PCAWG) cohort (n=414) using Fisher's exact tests. Kaplan-Meier survival analysis applied to evaluate prognostic associations in PCAWG patients. Additionally, Fisher's exact tests were conducted stratified by cancer type and treatment regimen to identify potential associations between rare germline variants and therapeutic responses. Results Variants in immune-related genes such as CCL26 and GPRC5D were prevalent, suggesting enhanced immune regulation among ERs. Fourteen genes with non-silent and eight with predicted deleterious mutations showed significantly different frequencies between NEER and PCAWG cohorts (FDR < 0.05). IRX3 emerged as a protective gene enriched in ERs, whereas OR6B2 was associated with poor survival in PCAWG lung cancer patients. Moreover, rare non-silent germline variants in drug target genes were enriched among ERs treated with cisplatin and doxorubicin, implicating altered DNA repair and drug-binding mechanisms in their remarkable outcomes. Conclusions This study reveals a distinctive germline mutation landscape in exceptional cancer responders, marked by immune-related and drug-target-associated variants that may enhance therapy response and prolong survival. The findings highlight potential novel prognostic biomarkers, such as IRX3 and OR6B2, providing a foundation for developing personalized cancer treatments informed by rare genetic variation.

Patient-reported outcomes (PROs) capture the patient voice and have been associated with improved clinical outcomes in oncology, but their prognostic and predictive value remains underutilized due to challenges in interpreting these highly variable and noisy PRO data. Here, we developed a quantitative modeling framework integrating nonlinear mixed-effects (NLME) and item response theory (IRT) to characterize symptom-level PRO trajectories and transform them into clinically actionable predictors. Using longitudinal PRO data from 589 patients with metastatic cancers in the PRO-TECT trial, we modeled 332,920 symptom responses to estimate patient-specific PRO trajectory parameters while accounting for variability and noise. IRT-NLME modeling captured heterogeneous symptom-level PRO dynamics and is more informative than modeling with composite PRO scores. PRO trajectory parameters were strongly associated with overall survival, acute care utilization, and treatment modifications. Machine learning models leveraging these parameters achieved robust prediction of survival (AUC-ROC 0.80) and retained prognostic performance using the first 30 - 180 days of PRO observations, with AUCs of 0.69-0.78. Similar predictive performance was observed for hospitalization (AUC 0.75), emergency department visit (AUC 0.65), treatment discontinuation (AUC 0.71), and dose reduction (AUC 0.67). These findings demonstrate that longitudinal PRO trajectories can serve as early, patient-centered biomarkers of clinical risk. By converting complex symptom data into interpretable and predictive metrics, this quantitative framework provides a practical pathway to integrate the patient voice into clinical decision-making and advance precision oncology. ClinicalTrials.gov registration: NCT03249090

Purpose: Beyond estrogen receptor (ER) positivity, no genomic biomarker reliably identifies ER+ breast cancer patients who derive differential benefit from endocrine therapy (ET). We performed an unbiased genomic screen to discover genes predicting ET response and characterized the top candidate across clinical settings, treatment modalities, and an independent validation cohort. Experimental Design: We screened 240 genes in 1,197 metastatic ET-treated patients from the MSK-CHORD clinical genomics database using Cox proportional hazards regression with false discovery rate (FDR) correction. The top candidate, core-binding factor subunit beta (CBFB), was characterized across four cohorts defined by disease setting (metastatic/adjuvant) and treatment (ET/chemotherapy), with multivariable adjustment, gene-by-treatment interaction testing, left-truncation sensitivity analysis for guarantee-time bias, and external validation in METABRIC (N = 1,499 ER+). Results: CBFB mutations (prevalence, ~5%) were the only gene associated with improved time to progression (TTP). In metastatic ET patients, CBFB-mutated tumors (n = 80) demonstrated significantly longer TTP (hazard ratio [HR], 0.44; 95% CI, 0.29-0.67; P = .0002, FDR q = .010) with no chemotherapy benefit (HR, 1.16; P = .65). The gene-by-treatment interaction was significant (HR, 0.37; P = .009). Effects were robust to multivariable adjustment (HR, 0.46-0.50), independent of histology, and preserved under left-truncated Cox regression (HR, 0.38). In the adjuvant setting, CBFB mutations predicted improved recurrence-free survival (HR, 0.52; 95% CI, 0.31-0.85; P = .010), with no effect under chemotherapy. In METABRIC, CBFB mutations predicted improved ER+ overall survival (HR, 0.52; P = 9.3e-5). Conclusions: CBFB mutations identify ~5% of ER+ breast cancers with exceptional ET benefit. As CBFB is included on all major cancer gene panels, this biomarker requires no additional testing infrastructure for clinical implementation.

BACKGROUND Erythema nodosum leprosum (ENL) is a severe inflammatory complication of leprosy associated with disability, morbidity and mortality. Impairment of health-related quality of life (HRQoL) in ENL has been reported using the Dermatology Life Quality Index (DLQI) and the 36-Item Short Form Health Survey (SF-36), the latter validated in people affected by leprosy. Understanding the correlation between these measures is important to determine whether the shorter dermatology-specific DLQI provides a valid and practical measure of HRQoL in ENL. OBJECTIVES To examine the relationship between DLQI and SF-36 scores in individuals with ENL using data from the Methotrexate and Prednisolone study in ENL (MaPs in ENL). METHODS A post-hoc analysis of prospectively collected HRQoL data from the trial sites in India, Indonesia, and Nepal of the MaPs in ENL multicentre randomised clinical trial was performed. HRQoL was assessed using the DLQI and SF-36 at enrolment and at weeks 24, 48 and 60. Associations between DLQI and SF-36 physical (PCS) and mental (MCS) component summary scores were evaluated using correlation analyses and multivariable linear regression at enrolment, and linear mixed-effects models during follow-up adjusted for age, sex, recruiting centre and enrolment SF-36 scores. RESULTS A total of 383 paired HRQoL assessments from 129 participants were analysed. At enrolment, HRQoL impairment was substantial (median DLQI 19, IQR 15-21; mean PCS 30.3 + - 7.3; mean MCS 33.3 + - 8.4). DLQI scores improved markedly during follow-up. Across all timepoints, DLQI was strongly inversely correlated with PCS and MCS (both p<0.001). In adjusted analyses, higher DLQI scores were consistently associated with lower PCS and MCS. At enrolment, each 1-point increase in DLQI was associated with a 0.66-point reduction in PCS and a 0.51-point reduction in MCS (both p<0.001). These associations remained strong during follow-up, with no evidence that they varied over time. CONCLUSIONS DLQI scores were strongly and consistently associated with SF-36 physical and mental health scores. These findings support the use of the DLQI as a practical patient reported outcome measure to assess the HRQoL associated with ENL and its change following treatment.