European Journal of Cancer

Abstract: Background: More than half of metastatic melanomas arise from patients initially diagnosed with early-stage melanoma. Objective biomarkers are needed to better identify high-risk patients. Objective: To evaluate the prognostic value of multiple histopathological characteristics in predicting distant metastasis risk, in early-stage melanoma. Methods: Using data from discovery set (n=442) and a population-based validation cohort (n=306, sampled from 5,815 patients) of the Dutch Early-Stage Melanoma (D-ESMEL) study, we investigated 14 histopathological characteristics of melanoma and their tumor micro-environment (TME) in an unprecedented integration, by expert pathologist scoring and automated quantitative measurements derived from a validated automated segmentation. Results: Increased immune infiltrates (40% in cases vs. 50% in controls) were associated with lower risk of metastasis. Automated immune cell density was predictive in both the discovery set and the validation cohort, outperforming the manual pathological tumor infiltrating lymphocytes. The remaining histopathological features, including mitotic activity, did not retain independent value after controlling for current staging variables. Limitations: TME evaluation in standard Hematoxylin-Eosin slides. Conclusion: TME reaction is an important determinant of melanoma progression. The automated quantification of immune cell density appears to be a biomarker for distant metastasis risk. Further investigation into specific immune cell subtypes is required to facilitate clinical integration.

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.

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.

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.

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.

Malignant pleural mesothelioma (MPM) is a rare malignancy characterised by extensive structural genomic alterations and a low burden of recurrent single nucleotide variants. However, the full spectrum and functional impact of structural variation (SVs) remain incompletely understood because short-read sequencing has limitation ability to resolve complex genomic rearrangements. Here, we performed integrated short-read and long-read whole-genome sequencing on tumour-normal pairs from three MPM patients, together with RNA sequencing and nanopore-derived promoter methylation profiling. Long-reads sequencing substantially improved SV detection, identifying 61-156 novel SVs per sample, including complex rearrangements and breakpoint-resolved events affecting cancer-associated genes. Complex SV clusters consistent with chromoplexy and chromothripsis were observed and frequently involved oncogenes. Integration with transcriptomics data showed that several SVs-affected genes, including WEE1 and GPC6, exhibited increased expression independent of gene dosage. Promoter methylation analysis revealed a conserved bimodal methylation landscape across tumours and a significant inverse relationship with gene expression. SV-associated genes showed coordinated promoter hypermethylation and transcriptional activation, suggesting that SVs may influence gene regulation through epigenetic mechanisms. Survival analysis using the TCGA-MESO cohort further showed that elevated expression of WEE1 and GPC6 was associated with poorer overall survival. Together, these findings highlight the value of long-read sequencing for uncovering functionally and clinically relevant structural variation in MPM.

Background: Store-and-forward teledermatology commonly relies on several patient-submitted photographs of the same concern, but most dermatology artificial intelligence models classify single images independently. Objective: To develop and internally validate a case-level diagnostic-support model that aggregates multiple patient-submitted photographs for common dermatologic conditions. Methods: We conducted a retrospective diagnostic-modeling study using the Skin Condition Image Network, a public dataset of deidentified self-taken dermatology images from US adults. We curated 2,336 cases comprising 5,041 images across 10 common inflammatory, allergic, and infectious conditions. Cases were split at the submission level into training, validation, and held-out test sets. Frozen general-purpose and dermatology-specific encoders were compared with image-level classifiers and a gated-attention multiple instance learning model that generated one case-level output from 1-3 images. Results: The strongest image-level baseline, dermatology-specific embeddings with random forest classification, achieved macro/micro ROC-AUCs of 0.797/0.854. Case-level aggregation improved discrimination, with dermatology-specific embeddings plus multiple instance learning achieving mean macro/micro ROC-AUCs of 0.819/0.863 across repeated stratified experiments. The locked final model achieved macro/micro ROC-AUCs of 0.800/0.849 on the held-out test set. Balanced-threshold sensitivity/specificity examples were 0.702/0.688 for eczema and 0.818/0.826 for urticaria. Limitations: Internal validation used a 10-condition subset from a US volunteer dataset; external validation, calibration, subgroup performance analysis, and prospective workflow studies are required. Conclusion: Modeling the teledermatology submission as a multi-image case better reflects asynchronous dermatology workflow than single-image classification. The model is preliminary clinician-facing support for structured review and triage, not autonomous diagnosis.

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.

Circulating tumor DNA (ctDNA) from blood plasma has emerged as a promising biomarker for noninvasive profiling of tumor mutational landscapes and disease monitoring across cancers. In this study, we developed a targeted next-generation sequencing approach to explore the role of ctDNA for comprehensive tumor genotyping, early response prediction, and characterization of clonal heterogeneity in patients with advanced and rare cancers treated within molecular tumor boards. We applied our technology to 157 plasma specimens from 57 patients at distinct disease milestones and detected tumor variants in 96% of baseline samples, with 65% of them harboring actionable aberrations. Longitudinal monitoring of baseline mutations in on-treatment plasma revealed that ctDNA dynamics were significantly associated with clinical outcomes and enabled early prediction of disease progression. Finally, we observed substantial clonal heterogeneity over time, identifying emerging mutations in all analyzed plasma samples obtained at progression, including potentially targetable variants for subsequent personalized therapies.

The retinal nerve fiber layer, composed of axon bundles converging toward the optic nerve, is a key biomarker for diagnosing and monitoring glaucoma and other neurodegenerative diseases. High-resolution en face imaging of individual nerve fiber bundles offers morphological information beyond what conventional optical coherence tomography provides, yet clinical integration remains limited by the lack of automated analysis tools and normative data. Here, we imaged 14 healthy volunteers using time-domain full-field optical coherence tomography and adaptive optics scanning laser ophthalmoscopy, and developed automated pipelines to quantify bundle width, trajectory, tortuosity, and orientation. Bundles were on average 25% wider at shallower retinal depths, width measurements were consistent across imaging modalities, and estimated axon count per bundle decreased significantly with age. Global trajectory analysis revealed systematic deviations of high resolution data from existing mathematical models, particularly in the temporal sector, leading us to propose two refined trajectory models. These normative results provide a foundation for high resolution biomarkers for use in investigations of retinal neurodegeneration.

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.

Abstract Background: Kaposi sarcoma (KS) is the most common cancer among men in several Eastern African countries, yet treatment monitoring relies on imprecise, time-consuming ruler-based measurements defined by the AIDS Clinical Trial Group (ACTG). This method suffers from inter-observer variability, fails to capture lesion height or true geometric area, and performs poorly on dark skin. SkinScan3D (SS3D) is a portable, low-cost, AI-enabled 3D imaging device that provides objective measurements of KS skin lesion area, height, volume, and color. The Precision Imaging to Evaluate Kaposi Sarcoma (PRIME-KS) study evaluates whether SS3D provides more reproducible and accurate lesion measurements than the standard method, and validates its integration into routine clinical workflows in Kenya and Uganda. Methods: PRIME-KS is a multicountry prospective mixed-methods study with two clinical objectives. Objective 1 is a cross-sectional diagnostic accuracy study comparing SS3D with ruler-based measurement in 50 adults with KS (150 lesions) across sites in Kenya and Uganda. Two clinicians independently measure three lesions per participant using both methods. The primary outcomes are concordance correlation coefficient (CCC) for inter-rater reproducibility, and co-efficient of determination for accuracy. Objective 2 is a non-randomized before-and-after pilot study in 100 patients at three sites, evaluating device usability, acceptability, appropriateness, and feasibility using validated instruments, along with time-and-motion studies and activity-based micro-costing. Prior to these clinical objectives, a formative study used focus group discussions, discrete choice experiments, and human-centered design workshops to refine the SS3D device and protocols with end-user input. Discussion: PRIME-KS will provide the first rigorous evaluation of a 3D imaging device for monitoring KS treatment response in routine clinical settings. If SS3D demonstrates superior reproducibility and clinical utility, it could reduce unnecessary chemotherapy exposure and associated toxicities by enabling earlier, more objective assessment of treatment response. Trial registration: ClinicalTrials.gov NCT06898203, registered 27 March 2025. Pan African Clinical Trials Registry PACTR202603523439856. Keywords Kaposi sarcoma, SkinScan3D, 3D imaging, treatment monitoring, diagnostic accuracy, implementation science, usability, human-centered design, Kenya, Uganda

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.

Background: Despite epidemiological interest in aspirin's chemopreventive potential against glioma, the underlying multi-layered molecular mechanisms -- spanning COX-2/PGE2 signaling, iron metabolism, ferroptosis, epigenetic regulation, and the NEO1/hepcidin regulatory axis -- have not been systematically characterized at the multi-omics level. Methods: We conducted an integrative multi-omics analysis leveraging TCGA-GBM (n=172) and TCGA-LGG (n=534) transcriptomes, CPTAC GBM proteomics (n=99), TCGA HM450K DNA methylation data (GBM n=140, LGG n=516), GEO aspirin perturbation datasets, IEU OpenGWAS summary statistics, and independent single-cell RNA-seq data (GSE131928, 28 GBM patients). Eight analytical tracks were executed: (1) COX-2/PGE2 pathway profiling, (2) BBB tight junction characterization, (3) GEO-derived aspirin response signature projection, (4) gut-brain axis evaluation, (5) Mendelian randomization (MR) using PTGS2 cis-SNPs, (6) iron metabolism and ferroptosis pathway analysis, (7) NEO1/HFE2/BMP6/HAMP regulatory axis characterization with multi-omics validation, and (8) single-cell transcriptomic validation across GBM malignant cell states. Results: Transcriptomic analysis revealed profound reprogramming of the NEO1/hepcidin iron regulatory axis in GBM: HAMP (hepcidin) was massively upregulated (log2FC=+2.92, P=5.0e-37), accompanied by TFRC upregulation (log2FC=+1.38, HR=2.30, P=3.6e-42) and NEO1 downregulation (log2FC=-0.57, HR=0.59, P=4.6e-6). De novo HM450K methylation analysis revealed HAMP as the dominant epigenetic target in the iron network, exhibiting the strongest hypomethylation signal (DeltaBeta=-0.265, P=1.4e-48), while NEO1 and TFRC showed constitutively low baseline methylation (Beta<0.05). Gene set enrichment analysis identified ferroptosis driver genes (NES=+1.861, P=0.030) and the iron deficiency response pathway (NES=+1.698, P=0.010) as the most significantly enriched pathways in GBM. Molecular subtype analysis revealed that the mesenchymal GBM subtype exhibits the highest iron metabolism gene expression. Mendelian randomization established a causal relationship between PTGS2 expression and glioma risk (IVW OR=1.31, P=1.1e-4). Single-cell RNA-seq analysis validated that iron metabolism gene expression is heterogeneously distributed across malignant cell states, with the mesenchymal state exhibiting the highest HAMP expression and elevated ferroptosis vulnerability. GPX4 was universally highly expressed across all cell states, indicating pan-GBM dependence on GPX4-mediated ferroptosis suppression. Conclusions: This multi-omics investigation reveals that the NEO1/hepcidin iron regulatory axis is epigenetically reprogrammed in glioma, driving iron-dependent vulnerability that bridges COX-2 signaling with ferroptosis susceptibility. The convergent evidence from transcriptomics, proteomics, epigenomics, and causal inference provides a comprehensive mechanistic framework for aspirin's protective effects against glioma and identifies the NEO1/HAMP/TFRC axis as a promising therapeutic target.

Background: Severe cytokine release syndrome (CRS) and immune effector cell-associated neurotoxicity syndrome (ICANS) are major dose-limiting toxicities of chimeric antigen receptor (CAR) T-cell therapy. Existing pre-infusion biomarkers offer modest discrimination, motivating non-invasive alternatives. Methods: We prospectively enrolled 26 patients with relapsed/refractory large B-cell lymphoma receiving axicabtagene ciloleucel. Pre-infusion (day -1) exhaled breath samples were analyzed by gas chromatography-mass spectrometry for 40 volatile organic compounds (VOCs). Candidates with univariate AUC > 0.65 for severe (grade >=2) CRS or ICANS were carried forward to sensitivity-maximization-at-given-specificity with LASSO regularization (SMAGS-LASSO), which selected separate panels for each outcome. Model performance was assessed by leave-one-out cross-validation with permutation p-values and Harrell bootstrap optimism correction. Results: The 4-VOC CRS panel (heptanal, benzaldehyde, 2-butanone, ethylbenzene) achieved LOOCV AUC 82.5% (80% sensitivity at 88% specificity) and the 3-VOC ICANS panel (nonanal, allyl methyl sulfide, levomenthol) achieved AUC 86.3% (67% sensitivity at 86% specificity). By tertile, severe CRS occurred in 8/9 (89%) high-risk versus 2/9 (22%) low-risk patients (Cox HR 6.82, 95% CI 1.41-32.9, p=0.017) and severe ICANS occurred in 8/9 (89%) versus 2/9 (22%) (HR 8.28, 95% CI 1.73-39.6, p=0.008). Each 1-SD score increase corresponded to a 3.80-fold higher hazard of severe CRS (p<0.001) and 4.36-fold higher hazard of severe ICANS (p<0.001). In head-to-head comparison, the 3-VOC ICANS panel outperformed the modified Endothelial Activation and Stress Index (mEASIX) (delta-AUC +0.36, DeLong 1-sided p=0.008). The 4-VOC CRS panel had numerically higher AUC than mEASIX (delta-AUC +0.19, p=0.150). Conclusions: Pre-infusion exhaled breath VOC panels stratify CAR T-cell recipients by severity and timing of severe CRS and ICANS, providing a non-invasive complement to existing serum biomarkers. Multi-institutional validation is warranted.

Background: Polygenic risk scores (PRS) for breast cancer are increasingly used for risk stratification to inform screening and prevention. However, for PRSs to be equitable and clinically useful, they need to perform well across diverse populations. While PRS performance is known to be ancestry-dependent, it is not well understood how environmental context, such as that of socioeconomic status (SES), affects PRS transferability. Here, we assess whether SES, measured via self-reported household income, modifies breast cancer PRS performance and, if so, whether socioeconomic context contributes predictive information beyond genetic risk alone. Methods: We used the US-based All of Us biobank to evaluate how SES impacts breast cancer PRS performance. First, we quantified changes in breast cancer PRS performance by modeling a commonly-cited polygenic score for breast cancer previously described by Mavaddat et al. with SES. We then reestimated the genetic effect sizes of the 3,820 variants from Mavaddat et al. in All of Us with and without income as a covariate. Because social determinants of health affect breast cancer detection and outcomes, we stratified analyses by socially defined populations on the basis of self-identified race and ethnicity. We further stratified individuals whose self-identified race is White (''White'') into three SES groups (high, middle, low) based on self-reported income and re-estimated genetic effect sizes to create SES-specific PRSs. We then applied these PRSs to White participants, the largest group in the study, and to Black or African American (''Black'') and Hispanic or Latino (''Hispanic'') participants, groups underrepresented in breast cancer research. Model discrimination between cases and controls was measured by area under the curve (AUC). Results: We analyzed 163,715 women from the All of Us biobank, which included 8,833 breast cancer cases (6,619 White, 1,178 Black, and 1,036 Hispanic), with relative income available for a subset of these cases (5,525 White, 848 Black, and 566 Hispanic). The ancestry-dependent performance of the breast cancer PRS described in Mavaddat et al. was replicated in All of Us. In Black individuals, this PRS (AUC and 95% CI: 0.576 [0.571, 0.582]) produced a similar increase in AUC as relative income (AUC: 0.573 [0.568, 0.577]) when added to an age-only model. Incorporating income with PRS, age, and genetic PCs 1-3 improved AUC by 0.007 in White Americans and 0.018 in Black Americans (both p < 10-11), while attenuating the contribution of PRS in the full model. PRS performance also varied among SES categories. Notably, PRSs with variant effect sizes that were recalibrated in low-SES White participants performed best in low-SES White participants (AUC: 0.605 [0.583, 0.628]) and Black Americans (AUC: 0.588 [0.586, 0.591]), both better than performance in high-SES White Americans (AUC: 0.579 [0.577, 0.580]) and middle-SES White Americans (AUC: 0.578 [0.569, 0.586]). Conclusion: Socioeconomic context, measured by income, significantly impacts the transferability of a PRS for breast cancer within and among groups defined by self-identified race and ethnicity. Accounting for SES improves PRS performance, most notably in Black Americans and low-SES White individuals.

Abstract Background Cancer is an increasing public health challenge in Kenya, particularly in rural and underserved regions where surveillance systems and diagnostic capacity remain limited. Kilifi County, located along the Kenyan coast, lacks a population-based cancer registry, and data on the local cancer burden is not available. This study aimed to characterize the demographic distribution of patients, cancer burden in the county, and management of cancer cases diagnosed at Kilifi County Referral Hospital (KCRH) over ten years. Methods This retrospective study analyzed the patterns of cancer in Kilifi County using patient records from KCRH during the study period (January 1, 2014, to January 1, 2024). Results A total of 101 patients with cancer were identified, 58% female, with a mean age of 54 years. Most patients were from Kilifi North (47%), with a high proportion reporting no formal occupation (41%) or farming (26%). Esophageal and cervical cancers were the most common (18% each), followed by breast and prostate cancers (5% each), with other malignancies occurring infrequently. Histopathology was the primary diagnostic modality (88%). Staging data were incomplete in 70% of cases; among documented cases, the majority presented with advanced disease (21% stage IV). Due to limited local treatment capacity, approximately half of the patients were referred to tertiary centers for chemotherapy, radiotherapy, or surgery. At data cut-off, 43% had died, 25% were on treatment, and 29% were lost to follow-up, with only 2% completing treatment or under follow-up. Conclusions This study demonstrates a substantial cancer burden in Kilifi County and highlights critical gaps in diagnostic capacity, staging, and continuity of care. Strengthening cancer surveillance systems, expanding diagnostic and treatment infrastructure, and establishing a population-based cancer registry are essential to improving cancer outcomes and advancing equitable care in rural Kenya

Clinical chart abstraction extracts structured patient variables from longitudinal clinical notes but is labor-intensive and difficult to scale. We evaluated LLM agents for question-guided chart review using lung cancer molecular testing guideline concordance as a use case. Two configurations were compared: (1) sequential note review using metadata and chronology, and (2) the same framework augmented with keyword-based note search. Gold-standard labels were established by human annotators. The search-enabled agent achieved higher accuracy (92.4% vs. 83.5%) and reduced errors by more than half (41 vs. 89) by retrieving evidence from long, heterogeneous note histories. In guideline concordance evaluation, most determinate patient-rule assessments were concordant (80.7%), while most apparent non-concordance reflected missing molecular testing documentation rather than documented care deviations. These results suggest tool-augmented LLM agents can approximate key aspects of human chart review and support scalable information extraction from longitudinal clinical documentation.

Some melanoma-prone families linked to the 9p21 locus, harboring the established susceptibility gene CDKN2A, lack pathogenic protein-coding variants. Using whole-exome and targeted sequencing, we identified three rare single-nucleotide variants in two melanoma-prone families and one sporadic melanoma case. Variants map to a conserved CTCF-bound region within the first intron of CDKN2B that physically interacts with CDKN2A. Analysis of UK Biobank showed significant enrichment of variants in this region in melanoma cases. Variants result in diminished CTCF binding in vitro. CTCF ChIP-seq in fibroblasts from the carriers of the largest family demonstrated loss of CTCF binding, accompanied by weakened promoter interactions and allele-specific reduction of CDKN2A p16 transcript expression from the variant haplotype. CRISPR-based perturbation of this region and editing of the large family variant into melanocytes resulted in reduced expression of p14 and p16 CDKN2A transcripts. These findings suggest that non-coding regulatory variants function as high-penetrance susceptibility alleles in melanoma families by altering CDKN2A function.

Polygenic risk scores (PRSs) are emerging as powerful tools for quantifying inherited risk for common diseases and, in some cases, are approaching clinical implementation. A major concern for PRS implementation is their limited accuracy in non-European populations, particularly in those of African ancestry. However, past evaluations have focused on metrics such as relative risk or AUC, which do not capture background risk arising from contextual factors. We introduce a novel measure of variable importance, the conditional average derivative estimator (CADE), to evaluate PRS utility across diverse contexts and populations within absolute risk models that integrate PRSs with other relevant risk factors. We illustrate this framework by integrating PRSs for breast and prostate cancer within age-specific absolute risk models for incidence and mortality fit using individual-level data from the All of Us Research Program with inputs from the National Cancer Institute SEER cancer registry. Our projections show that although the PRSs are known to have the lowest discriminatory accuracy in African Americans (AA), there are contexts in which they provide greater utility, such as for the stratification of prostate cancer risk and mortality, where the CADE values for AA were 2- and 7-fold higher than for European Americans. These findings suggest that conclusions about the limited clinical utility of PRS in non-European populations may be premature and underscore the need to quantify PRS risk-stratification utility at the absolute-risk level, while accounting for disease onset, survival, and broader health and economic factors.