JCO Precision Oncology

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: Circulating tumour DNA (ctDNA) liquid biopsy is now established across oncology for early cancer detection, minimal residual disease surveillance, and treatment monitoring. Detection thresholds for all current ctDNA assays are derived empirically through receiver operating characteristic analysis on training cohorts - a statistically valid but theoretically uninformed approach that does not specify the minimum detectable tumour fraction given assay technical characteristics, nor identify when increasing sequencing depth ceases to provide additional clinical information. Methods: We model ctDNA detection as a binary hypothesis testing problem with Binomial-distributed mutant allele counts against a sequencing error noise floor. The Neyman-Pearson lemma is applied to derive the uniformly most powerful detector and the minimum detectable tumour fraction in closed form. The sequencing assay is modelled as a binary symmetric channel and Shannon channel capacity is calculated. Empirical validation uses n=61 data points extracted from five published peer-reviewed analytical validation studies across five independent institutions in the US and EU (2018 - 2025): Yu et al. 2022, Stetson et al. 2018, Frydendahl et al. 2023, Northcott et al. 2024, and Cheng et al. 2025. Results: The minimum detectable tumour fraction is derived in closed form as f_min approximately equal to (z_alpha + z_beta) multiplied by the square root of (epsilon divided by N), where N is sequencing depth, epsilon is the platform error rate, and z_alpha, z_beta are standard normal quantiles at the specified false positive and false negative rates. Shannon channel capacity is C = 1 minus H(epsilon) bits per read, where H(epsilon) is binary entropy. Empirical validation yields 84.3% agreement for single-locus assays. Discordance for multi-locus tumour-informed assays (NeXT Personal, duplex WGS) is consistent with the single-locus model scope and identifies the principal theoretical extension required. Conclusions: This framework provides the first formal Neyman-Pearson optimality proof for ctDNA detection, a closed-form detection limit, and a platform-independent efficiency metric for NHS and regulatory standardisation. Keywords: circulating tumour DNA; liquid biopsy; Neyman-Pearson detection; Shannon channel capacity; sequencing depth; limit of detection; minimal residual disease; signal detection theory

Abstract Introduction: MET tyrosine kinase (TKI) therapy has improved outcomes in patients with non-small cell lung cancer (NSCLC) harboring MET alterations. However, primary and acquired resistance ultimately limits durability of response. This study evaluated the safety and efficacy of the MET inhibitor capmatinib with the MEK inhibitor trametinib in patients with metastatic MET-driven NSCLC who had progressed on prior treatment with at least one MET inhibitor. Methods: A multicenter phase I study evaluated capmatinib in combination with trametinib in patients with advanced stage NSCLC harboring activating MET alterations and prior exposure to at least one MET TKI. A 3+3 dose-escalation design was employed to assess safety and tolerability of the combination. Results: Three patients (n = 3) were enrolled in the study and completed a median of 3 cycles of therapy. Dose-limiting toxicities, including rash, edema, and nausea, necessitated dose reductions in the first two patients and initiation of the third patient at a lower dose level. Ultimately, all patients discontinued therapy due to treatment-related adverse events. The study was terminated early due to poor accrual and TRAEs. No radiographic objective responses were observed. Conclusions: In this phase I trial, capmatinib plus trametinib was associated with significant treatment-related adverse events and treatment was discontinued in all participants. Based on these findings, further investigation of this combination of MET and MEK inhibitors is not recommended.

Background Emerging evidence suggests irreversible electroporation (IRE) with standard-of-care (SOC) chemo-therapy may improve survival in patients with Stage 3 pancreatic ductal adenocarcinoma (PDAC) when compared to SOC alone. This study evaluates the overall survival (OS) and progression-free survival (PFS) of Stage 3 PDAC patients treated with SOC plus IRE with the NanoKnife System versus SOC alone. Methods This prospective, multicenter study included two cohorts from the DIRECT registry: an IRE cohort from sites offering IRE as part of clinical care, and a comparator SOC cohort of prospectively enrolled and contemporaneous retrospective patients. Enrollment spanned 08/05/2019 to 02/05/2023, with follow-up through at least 24 months, death, or loss to follow-up. Included were 137 patients (99 IRE; 38 SOC), aged [&ge;]18 years with Stage 3 PDAC and no progression after three months of SOC therapy. Results Median (interquartile range) time from diagnosis to enrollment was 8 (6-10) months for IRE and 4 (3-6) for SOC (p<0.0001). Median OS and PSF from enrollment were 18 (95% confidence interval [CI]: 15-24) months and 9 (95% CI: 7-12) months for IRE, and 10 (95% CI: 8-14) months and 6 (5-8) months for SOC, respectively (p<0.0001 and p=0.009). Adverse events occurred in 80% (79/99) of IRE patients and 95% (36/38) of SOC patients; 29% (29/99) of the IRE cohort experiencing an IRE-related adverse event. Conclusions IRE was associated with improved OS versus SOC alone and may be an effective consolidative treatment for Stage 3 PDAC after three months of induction chemotherapy.

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.

Melanoma remains one of the most treatment-refractory malignancies due to immune evasion, high mutational burden, and profound tumor heterogeneity. Although immune checkpoint inhibitors have transformed frontline management, a substantial proportion of patients develop resistance or experience relapse, underscoring the need for alternative and complementary immunotherapeutic strategies. Tumor-infiltrating lymphocyte (TIL) therapy and engineered viral vector-based immunotherapies represent mechanistically distinct yet clinically promising approaches for advanced melanoma. This systematic review and Bayesian meta-analysis evaluated the comparative efficacy of TIL therapy and engineered viral vector immunotherapies in advanced melanoma. A structured search of PubMed, Embase, Scopus, and Web of Science (2015-2025) identified 13 eligible studies, including four randomized controlled trials and nine prospective single-arm studies, reporting objective response rate (ORR), progression-free survival (PFS), overall survival (OS), and treatment-related adverse events. Eight studies met criteria for inclusion in the Bayesian quantitative synthesis of ORR outcomes. Risk of bias and certainty of evidence were assessed using Cochrane and GRADE frameworks. TIL therapy demonstrated substantial standalone efficacy, particularly in PD-1-refractory populations, with reported ORRs reaching 49%, median PFS of 7.2 months, and OS extending to 25.8 months. Viral vector-based therapies, including talimogene laherparepvec (T-VEC) and RP1, showed more modest monotherapy activity but demonstrated improved responses when combined with immune checkpoint inhibitors. Among the studies included in the Bayesian quantitative synthesis, the pooled ORR estimate was 37.8% (95% highest density interval [HDI]: 30.6%-45.3%). Sensitivity analysis excluding the small-sample Cui et al. (2022) study yielded a similar pooled estimate of 38.3% (95% HDI: 30.4%-46.2%). Exploratory meta-regression supported the overall robustness of the findings. Certainty of evidence for ORR was moderate, whereas survival and safety outcomes were downgraded due to heterogeneity, sparse reporting, and inconsistent endpoint definitions. Collectively, these findings support complementary rather than competing roles for TIL and engineered viral vector immunotherapies within evolving melanoma treatment paradigms. The results further highlight the potential importance of biomarker-guided sequencing strategies, including viral immune priming followed by adoptive cellular therapy, as a framework for optimizing personalized immunotherapy in both refractory and earlier-line melanoma settings.

Background: Population-relevant BRCA1/BRCA2 data from Bangladesh are scarce, creating challenges for hereditary breast and ovarian cancer variant interpretation, counseling, and follow-up testing. We examined a clinically referred Bangladeshi cohort to characterize assay-derived BRCA1/BRCA2 short variants, sequencing-depth performance, and copy-number findings in a conservative pilot framework. Methods: Twenty-three de-identified blood-derived DNA samples were assessed using a targeted BRCA1/BRCA2 next-generation sequencing workflow. Downstream analysis used assay-generated short-variant, coverage, and CNV outputs, with coordinates reported on hg19/GRCh37. Short variants were evaluated from high-confidence PASS/VCC-H calls, and CNV review incorporated both target-region and amplicon-level copy-number patterns. Results: After removal of four low-VAF review observations, the primary germline-compatible dataset comprised 304 short-variant observations representing 34 unique variants. Both BRCA1 and BRCA2 contributed comparable variant burdens, while the overall profile was mainly composed of missense and synonymous changes. Six sample-specific heterozygous BRCA1 truncating candidates were observed, including five frameshift variants and one stop-gain variant. Protein-level mapping placed these events across the central-to-C-terminal portion of BRCA1. Sequencing depth was consistently high across the targeted regions, with all 4,255 amplicon-sample measurements exceeding 280x and 99.91% reaching at least 500x. Copy-number analysis highlighted one candidate BRCA1 multi-exon deletion-like event involving exons 15-20 in BCSIR-BRCA-21, with unresolved partial exon 14 involvement. Conclusions: This study provides an initial Bangladesh-focused targeted BRCA1/BRCA2 dataset and identifies candidate short-variant and CNV findings for validation. These findings should be interpreted as analytical candidates only and require confirmatory testing and expert clinical curation before any clinical application. The cohort is referral-enriched and should not be used to infer population prevalence.

Objective Accurate prognostication of recurrence risk in HR+/HER2- early breast cancer is central for therapeutic decision-making, including identifying patients who may safely avoid adjuvant systemic therapy. However, the performance of existing prognostic tools remains insufficient for effective clinical stratification, motivating the development of artificial intelligence (AI)-based methods to improve risk stratification. Methods Ataraxis Breast CTX (ATX) is a multi-modal AI test that integrates H&E-stained whole-slide images with clinicopathologic features to predict risk of recurrence for individual patients. This study aims to validate ATX in an external dataset enriched for clinically low-risk patients from Dordrecht, the Netherlands. ATX scores were generated for 892 women diagnosed with early HR+/HER2- breast cancer. Of the 892 patients, 299 did not receive adjuvant systemic therapy. The discriminative performance of ATX was assessed using C-index and its stratification ability was evaluated by log-rank tests comparing Kaplan-Meier survival curves across risk groups. Results ATX achieved a C-index of 0.71 and a 5-year time-dependent AUC of 0.71, demonstrating strong discrimination in predicting recurrence-free survival (RFS). Among 299 patients who received no adjuvant therapy, ATX achieved a C-index and time-dependent AUC of 0.78 and 0.81 respectively, suggesting ATX retains prognostic information in the absence of systemic therapy. ATX scores were used to stratify patients into risk groups using a pre-specified threshold, where 656 (74%) were classified as ATX low-risk and 236 (26%) were classified as high-risk. Notably, untreated and treated ATX low-risk patients had comparable 5-year RFS (untreated: 5-year RFS = 96%, 95% CI = 92-97%; treated: 5-year RFS = 96%, 95% CI = 93-97%) with near identical 10-year RFS (86%, 95% CI = 83-92% for both), suggesting ATX low-risk status may identify a subgroup with favorable prognosis independent of treatment exposure. Conclusion ATX provides robust prognostic stratification in an external cohort of clinically low-risk HR+/HER2- early breast cancer and identifies a subgroup of patients who did not receive systemic therapy with favorable observed outcomes. These results support prospective validation of ATX as a decision-support tool for adjuvant therapy de-escalation in HR+/HER2- early breast cancer.

Background: Androgen receptor pathway inhibitors (ARPIs) have transformed the treatment of high risk and metastatic prostate cancer, though are associated with increased cardiovascular risk. Glucagon-like peptide-1 receptor agonists (GLP-1 RAs) have been shown to reduce cardiovascular events in non-cancer populations, but their role in patients receiving ARPIs is unclear. Methods: Retrospective analysis of 120 men with PC treated with ARPIs between 2015-2025 with any GLP-1 RA exposure. The time of GLP-1 RA use was categorized relative to ARPI initiation (pre- vs post-ARPI). Cumulative incidences for major adverse cardiac events (MACE) any grade 2 or greater cardiac common terminology criteria for adverse events (CTCAE) were estimated. Fine-Gray regressions were performed (non-cardiac death as a competing risk). Results: The median follow-up was 2.3 years (interquartile range [IQR] 1.3-3.7). The median age was 72 years (IQR 66-78). Atherosclerotic cardiovascular disease (ASCVD) was present in 45.0% (n=54). Overall, 55.0% (n=66) initiated GLP-1 RA therapy prior to ARPI and 45.0% (n=54) after ARPI initiation, with a median duration of GLP-1 RA use of 4.0 years (IQR, 2.3-7.0) and 1.3 years (IQR, 0.6-2.1), respectively. Four patients experienced MACE, including three coronary revascularizations and one ischemic stroke. 25 patients experienced at least one grade 2 or greater cardiac event, most commonly arrhythmia (n=20) and thromboembolic disease (n=11). The 2-year cumulative incidence of MACE and grade [&ge;]2 cardiac events was 1.7% and 16.1%, respectively. Adjusting for pre-existing cardiovascular risk, GLP-1 RA duration, and pre-ARPI androgen deprivation therapy use, GLP-1RA use prior to ARPI initiation (vs. after ARPI start) was associated with reduced risk of grade [&ge;]2 cardiac events (subdistribution hazard ratio 0.26, 95% CI 0.08-0.91; p=0.036). Conclusion: GLP-1 RA use prior to ARPI initiation was associated with reduced risk of cardiac events, suggesting that earlier metabolic optimization may influence cardiovascular outcomes. These hypothesis-generating findings support investigation of early GLP-1 RA initiation as a potential cardiovascular risk mitigation strategy during ARPI therapy.

Background Endometrial cancer exhibits marked molecular and immune heterogeneity that is only partially explained by established genomic biomarkers. We investigated whether T cell receptor (TCR) repertoire architecture captures complementary dimensions of antitumor immunity beyond conventional molecular classification. Methods Paired tumor and peripheral blood samples from eight patients with molecularly characterized endometrial cancer underwent TCR repertoire profiling. Diversity, clonality, and tumor blood overlap metrics were integrated with genomic variables, including tumor mutational burden (TMB), genomic instability metric (GIM), and POLE status. Principal component analysis and correlation analyses were used to identify major dimensions of repertoire organization. Composite Immune Focusing and Immune Sharing Scores were derived to summarize dominant repertoire patterns. Results The first two principal components explained 70.1% of total repertoire variance and revealed substantial heterogeneity independent of histological subtype. TMB was strongly associated with reduced repertoire diversity and increased clonal dominance, resulting in a robust association with the Immune Focusing Score ({rho} = 0.88, p = 0.004). POLE mutated tumors occupied the extreme end of this focusing continuum. In contrast, genomic instability was associated with increased tumor blood repertoire overlap and preserved diversity, reflected by a strong correlation between GIM and the Immune Sharing Score ({rho} = 0.76, p = 0.027). The two immune scores showed minimal correlation with each other ({rho} = -0.24, p = 0.57), indicating that they capture largely independent aspects of immune organization. Conclusion Integrative analysis of TCR repertoire architecture and tumor genomics identifies distinct immunogenomic states in endometrial cancer that are not fully captured by conventional molecular classification. If validated in larger cohorts, immune focusing and immune sharing metrics may provide complementary biomarkers for patient stratification and immunotherapy-oriented precision oncology

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: 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.

Purpose: Adjuvant endocrine therapy (AET) substantially reduces recurrence and mortality in hormone receptor-positive breast cancer but requires sustained daily adherence over 5 to 10 years. Approximately one-third of patients fall short of recommended adherence in the first year alone, largely due to distinct combinations of attitudes, barriers, and circumstances. Existing studies have catalogued individual risk factors but lack the scale and breadth to characterize how these factors co-occur within patients, or to distinguish behavioral drivers from confounding by clinical and demographic context. We sought to characterize the behavioral and social heterogeneity underlying AET adherence in a national real-world cohort. Moving beyond population-average risk factors, we identify the distinct patient profiles, and the differing drivers within them, that any effective adherence strategy must address. Methods: We conducted a retrospective cohort study of US women with invasive breast cancer diagnosed between 2016 and 2025, linking two large-scale, individual-level datasets through privacy-preserving tokenization: Surgo Health's BehavioralPulse, which provides modeled individual-level behavioral and attitudinal risk scores together with consumer sociodemographic attributes, and longitudinal medical and pharmacy claims from a claims data provider. Eligible patients underwent 1 to 2 breast surgeries, initiated oral AET (tamoxifen or aromatase inhibitors), and maintained continuous insurance coverage for 365 days following therapy initiation. The primary outcome was adherence, defined as medication possession ratio (MPR) [&ge;]80% in the first year. Mixed-effects logistic regression with a random intercept for ZIP3 estimated adjusted associations across behavioral, sociodemographic, and clinical predictors. To characterize how behavioral factors co-occur within patients, we identified the most prevalent configurations of the statistically significant behavioral predictors and estimated their relative association with adherence, holding clinical and demographic factors constant. Results: The final cohort included 401,450 women, of whom 280,595 (69.9%) achieved MPR [&ge;]80%. Several behavioral factors were independently associated with adherence after adjustment for clinical and demographic covariates, including comfort following medication instructions (aOR, 1.15; 95% CI, 1.06-1.24), geographic proximity to breast oncologists (aOR, 1.17; 95% CI, 1.04-1.32), tangible instrumental social support (aOR, 1.06; 95% CI, 1.00-1.13), religiosity (aOR, 1.04; 95% CI, 1.01-1.08), concern about sexual side effects (aOR, 0.96; 95% CI, 0.93-0.99), and cost-related access barriers (aOR, 0.97; 95% CI, 0.95-1.00). The 10 most common configurations of significant behavioral predictors accounted for over 70% of the cohort, with the two most prevalent representing more than 40% of patients. The most common profile, defined by an absence of behavioral barriers and the presence of social support, was associated with a positive behavioral contribution to adherence propensity (behavioral linear predictor OR = 1.18; 95% CI: 1.04-1.36) comparable in magnitude to several established clinical predictors. Compared against this referent profile, six of the nine remaining profiles had lower adherence, with relative odds ranging from approximately 0.92 (95% CI: 0.89-0.95) to 0.97 (95% CI: 0.94-0.99). One profile, similar to the reference but including high trust in doctors, was associated with higher adherence odds (1.04, 95% CI: 1.01-1.07). These profiles arose from substantively different underlying combinations of factors: segments dominated by cost barriers, by side-effect concerns, or by limited social support produced comparable overall adherence risk but through distinct pathways. Conclusion: In this national cohort, nearly one-third of women did not achieve recommended first-year adherence to AET. The pathways to non-adherence were heterogeneous, structured into recurring behavioral profiles rather than randomly distributed across patients. This heterogeneity is clinically meaningful: patients with similar adherence risk may benefit from substantially different forms of support, ranging from financial navigation to side-effect management to social support resources. Surfacing this structure required linking individual-level behavioral data to large-scale claims data, offering a practical foundation for optimal design of patient-centered adherence interventions that are tailored to the specific configurations of barriers patients actually face.

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.

Importance: Hereditary breast and ovarian cancer (HBOC) variant carriers benefit from risk-reducing interventions, but only if identified. The extent to which carriers are clinically recognized, and whether recognition is equitable across diverse populations, is poorly characterized in a single large U.S. cohort. Objective: To estimate P/LP HBOC carrier prevalence across genetic ancestry groups, quantify documented clinical genetic testing among carriers, and evaluate ancestry and socioeconomic disparities in testing. Design, Setting, and Participants: Cross-sectional analysis of the All of Us Research Program Controlled Tier (Curated Data Repository v8/C2024Q3R9), comprising participants with short-read whole genome sequencing and linked electronic health record (EHR) and survey data. Carriers were ascertained from research genomic data independent of clinical testing. Exposures: Genetically inferred ancestry (African [AFR], Admixed American [AMR], East Asian [EAS], European [EUR], Middle Eastern [MID], South Asian [SAS]); self-reported household income and educational attainment. Main Outcomes and Measures: (1) Carrier prevalence with Wilson 95% CIs; (2) documented clinical genetic testing (procedure codes) among carriers; (3) adjusted odds of documented testing among women, by ancestry, before and after socioeconomic adjustment, using multivariable logistic regression. Results: Among 414,830 participants, P/LP HBOC carrier prevalence was 1.42% (95% CI, 1.38-1.45) overall and similar across ancestry groups (AFR 1.24%, AMR 1.32%, EAS 1.19%, EUR 1.52%, MID 1.68%, SAS 1.33%; overlapping CIs). Among 250,071 women in the testing analysis, documented clinical genetic testing was rare: only 74 of 5,878 carriers overall (1.3%) and 59 of 3,572 European-ancestry carriers (1.7%) had a documented test, with counts below reportable thresholds in all other ancestry groups. African-ancestry women had lower adjusted odds of documented testing than European-ancestry women (Model 1 adjusted odds ratio [aOR], 0.32; 95% CI, 0.27-0.39), an association that attenuated but persisted after adjustment for income and education (Model 2 aOR, 0.48; 95% CI, 0.40-0.58; P < 0.001); Admixed American women also had reduced adjusted odds (aOR, 0.71; 95% CI, 0.61-0.84). Lower income and lower education were independently and dose-dependently associated with lower testing odds (income <$25,000 aOR, 0.46; high-school education aOR, 0.54). Conclusions and Relevance: High-risk HBOC variant carriers are present across all ancestry groups at similar frequencies, yet documented clinical genetic testing was disparate in the different ancestry groups. African-ancestry women experience a testing gap that is not fully explained by socioeconomic position, implicating structural barriers in access and referral. Population-level strategies that decouple carrier identification from current referral pathways may be required to close this gap.

Background: Endometrial cancer incidence and mortality are increasing, particularly among Black women and for aggressive subtypes. Allostatic load (AL), a composite measure of physiologic dysregulation across metabolic, cardiovascular, and immune systems, varies by racial category and tumor subtype in other cancers. Endometrial cancer is strongly associated with obesity, and it is unknown whether AL scores maintain sufficient heterogeneity to evaluate differences across subgroups or with clinical outcomes. Objective: To describe the performance of AL scoring in endometrial cancer patients and examine associations with tumor characteristics (grade/histology) and survival outcomes. Methods: We evaluated AL among 398 participants newly diagnosed with endometrial cancer. AL score was calculated by assigning 1 point for each ''high-risk'' value (by clinical reference range or distribution-based) for 15 biologic variables for vital signs, anthropometrics, blood-based biomarkers, and medical comorbidities. Results: Distribution-based thresholds for variables were used to preserve heterogeneity in this obesity-dominant context. Overall, 68.7% of Black women had high AL compared to White (56.7%), Hispanic (56.7%), and other race (32.3%) women. Decision tree analyses revealed grade-dependent associations between AL and survival. For women with low-grade tumors, higher AL was associated with poorer overall survival. For high-grade tumors, intermediate AL ([&ge;]4, <8) were associated with shortest overall survival. Black women with low-grade disease experienced shorter progression-free survival regardless of AL. Conclusions: AL scoring maintains heterogeneity despite high obesity prevalence in endometrial cancer. Varying relationships between AL and survival by tumor grade and ethnoracial group suggest cumulative physiologic burden and social/structural factors may jointly shape endometrial cancer disparities.

Background and ObjectivesIntravesical gemcitabine/docetaxel (Gem/Doce) is an effective therapy for Bacillus Calmette- Guerin (BCG)-unresponsive non-muscle-invasive bladder cancer (NMIBC), achieving 50% complete responses at 2 years. However, the genomic determinants underlying response and resistance to Gem/Doce remain poorly defined. Our objective was to define the mutational landscape of BCG-unresponsive NMIBC and nominate genomic features associated with response or resistance Gem/Doce. MethodsPatients with BCG-unresponsive NMIBC treated with Gem/Doce were classified as responders (recurrence-free survival [RFS] >12 months) or non-responders (RFS <12 months). Whole-exome sequencing was performed on tumors prior to Gem/Doce treatment (n=23). Single nucleotide variants were identified and annotated using a Cancer Genome Analysis pipeline. Copy number alterations were inferred with ABSOLUTE, and clonal architecture was reconstructed using PhylogicNDT. Key Findings and LimitationsResponders demonstrated significantly prolonged time to high-grade recurrence (3.5 vs 42 months, p<0.001) and cystectomy compared with non-responders (9.5 months vs not reached; p<0.001). Non-responders exhibited higher tumor mutational burden (13.66 vs 8.71; p=0.02) and more frequent whole-genome doubling (2/2 non-responders vs 0/1 responders; p=0.33). Phylogenetic analyses revealed clonal BAP1 and subclonal BRCA2 mutations in responders, whereas non-responders harbored clonal FGFR3 mutations. Limitations include small sample size and retrospective design. Conclusions and Clinical ImplicationsDistinct genomic features underlie differential response to Gem/Doce in BCG-unresponsive NMIBC. In responders, alterations in DNA repair pathways (e.g., BRCA2) may sensitize tumors to chemotherapy, while non-responders with FGFR3 mutations may benefit from alternative targeted strategies. These findings warrant validation in larger cohorts and support the development of biomarker-driven clinical trials. Patient summaryIn this report we analyzed bladder tumors and found that some tumors respond well to treatment because they have defects in repairing DNA, making them more vulnerable to chemotherapy. In contrast, tumors that do not respond to chemotherapy harbor different genetic changes that help them survive and grow. These findings may help physicians choose more effective and personalized treatments in the future.

PURPOSE: To develop and validate an artificial intelligence-enabled platform that converts unstructured cancer trial eligibility criteria into structured queries and quantifies trial eligibility across advanced/metastatic cancer trials. METHODS: We downloaded actively recruiting US interventional treatment trials for advanced/metastatic breast cancer, colon cancer, and non-small cell lung cancer from ClinicalTrials.gov. Medical oncologists created 24 synthetic patient vignettes. A large language model converted trial eligibility criteria into Structured Query Language (SQL) code and patient information into structured records, enabling automated matching. Cancer details and treatment history were considered, but not laboratory results or comorbidities. Validation included physician editing of generated eligibility code for 30 trials, and blinded physician eligibility assessment for five trials. We then evaluated how age, ECOG performance status, sex, and ZIP code affected the number of eligible trials. RESULTS: Of 833 candidate trials, 746 met inclusion criteria. In physician review of 30 trials, edits to generated SQL did not change any of 720 trial-patient eligibility determinations for 24 synthetic patients. In blinded validation across 120 trial-patient pairs, automated matching achieved 97% accuracy. Across synthetic patients, eligible trials ranged from 31 to 258 when there were no geographic restrictions. Eligibility decreased markedly with worse performance status and with geographic restriction (both p<0.001). Later-phase, randomized, and molecularly selective trials had fewer eligible patients. CONCLUSION: AI-based structuring of trial eligibility criteria can support accurate, scalable measurement of potential cancer trial eligibility. In this demonstration, performance status, geography, and age were major determinants of eligibility across the active metastatic trial landscape.

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.

Background: Reirradiation (reRT) is increasingly offered following progression in diffuse intrinsic pontine glioma (DIPG) and diffuse midline glioma (DMG), though optimal patient selection remains a challenge. This study evaluated clinical outcomes after reRT in a contemporary cohort of patients with DIPG/DMG. Methods: Patients <26 years old with DMG/DIPG treated with radiation therapy between 2011-2025 were retrospectively reviewed. Primary endpoints included overall survival (OS2) and progression-free survival (PFS2), measured from first progression, and change in neurologic symptoms after reRT. Survival was estimated using Kaplan Meier methods, with Cox proportional hazards modeling for prognostic factors. Results: Fifty eight patients were included; 37 (63.8%) underwent reRT. Tumors were predominantly pontine (74.1%). ReRT was associated with improvement in motor function (51.4% vs. 9.5%, p=0.002), cranial nerve function (29.7% vs. 4.8%, p=0.044), and gait ataxia (35.1% vs. 9.5%, p=0.059). Median OS2 and PFS2 were improved with reRT (OS2: 9.67 vs. 2.57 months, p<0.001; PFS2: 5.63 vs. 1.57 months, p<0.001). OS2 was independently associated with reRT (HR 0.27, p<0.0001), pontine location (HR 2.94, p=0.004), and steroid use at progression (HR 4.12, p=0.001). PFS2 was independently associated with reRT (HR 0.23, p < .0001) and distant pattern of failure (HR 2.83, p=.037). Among reRT patients, non-pontine location was associated with improved OS2 (p=0.02), and local failure was associated with improved PFS2 (p=0.003). Conclusion: ReRT was associated with neurologic improvement and prolonged survival. Patients with non-pontine tumors or local-only failure might derive the greatest benefit. Prospective studies are warranted to define optimal dose/fractionation and refine patient selection.