European Journal of Cancer

Background: Adjuvant therapy decision in early-stage node-negative breast cancer remains one of the most challenging problems in oncology. Although most patients in this population achieve favorable outcomes with endocrine therapy alone, a subset remains at risk of relapse and may benefit from chemotherapy. Accurate identification of high-risk and low-risk patients is therefore essential to prevent both undertreatment and overtreatment. Methods: In this study, machine learning models were assessed using MSK-IMPACT (n=645) and METABRIC (n=503) cohorts comprising node-negative stage I-II breast cancer patients. Three experimental settings were evaluated: training on MSK-IMPACT and testing on METABRIC, the reverse direction, and five-fold cross-validation on the combined cohort. Sensitivity analysis and data harmonization were applied to identify and mitigate cohort-specific bias. Survival analysis, SHapley Additive exPlanations (SHAP) interpretability, and ablation analyses were performed to validate clinical relevance and characterize feature contributions. Results: The combined cohort model achieved an area under the receiver operating characteristic curve (AUC) of 0.788{+/-}0.028 across five-fold cross-validation, while bidirectional cross-cohort validation yielded AUC of 0.741 when testing on MSK-IMPACT and AUC of 0.714 when testing on METABRIC. Survival validation on METABRIC out-of-fold predictions from the combined cohort model showed hazard ratios of 3.01 (95\% CI: 2.28-3.98) for disease-free survival and 2.35 (95\% CI: 1.79-3.09) for overall survival, consistent across Luminal A, Luminal B, and Triple-Negative subtypes. Conclusion: Validated across two geographically independent cohorts, the proposed model enables cost-effective, gene expression-free identification of patients at high-risk of distant relapse and may facilitate adjuvant chemotherapy decision-making in early-stage node-negative breast cancer.

BackgroundNHS-Galleri did not reach its prespecified primary endpoint of a reduction in combined stage III and IV cases, but reported fewer stage IV cancers after annual multi-cancer early detection screening. We estimated the potential five-year life-years associated with the cancer-specific stage IV differences observed during the two incident screening rounds. MethodsWe used intervention and control stage IV counts for ten prespecified cancers reported in the NHS-Galleri ASCO 2026 presentation. For each cancer, the randomized-arm count difference was multiplied by the difference in five-year restricted mean survival time between stage III and IV, estimated from England stage-specific five-year net survival using a constant-hazard fit. Adverse cancer-specific differences remained in the model. Results were annualized across the two incident rounds and scaled per million people screened. Sampling intervals used an independent Poisson approximation for arm-specific counts. FindingsAcross the ten evaluable cancers, the intervention and control arms had 180 and 239 stage IV diagnoses during the incident rounds. The conditional projection was 66.3 potential life-years over five years in the trial intervention cohort, equivalent to 466 per million people screened annually (95% sampling interval 190-743). For a population of 10 million, the corresponding projection was 4,664 annually (1,896-7,432). Colorectal, lung, and esophageal cancers accounted for 79% of the net projection. Pancreatic, ovarian, and lymphoid cancers contributed negatively because their observed stage IV counts favored control. InterpretationIf the observed incident-round reduction in stage IV diagnoses represents true downstaging to stage III, it could produce meaningful population health gains. This conditional estimate is not evidence of mortality reduction. Longer follow-up remains necessary to distinguish benefit from chance, lead-time and length biases, overdiagnosis, and differences in tumor biology.

Background: Self-supervised learning may improve segmentation when labelled data are limited, but its benefit may depend on model and initialisation strategy. This study evaluated in-domain masked-reconstruction pretraining across limited annotation budgets. Methods: The International Skin Imaging Collaboration (ISIC) 2016 development set was partitioned into 765 images for self-supervised pretraining, 94 for fine-tuning and 41 for validation. Five nested annotation budgets (12-94 images) and five seeds were evaluated on the 379-image test set. A custom U-Net compared random and in-domain self-supervised initialisation; a ResNet-34 U-Net compared random, ImageNet, in-domain self-supervised and sequential ImageNet-to-self-supervised initialisation. Models trained with 94 labelled images were evaluated without adaptation on ISIC 2017. A separate experiment examined restoration-based pretraining. Results: In the custom U-Net, self-supervised pretraining achieved higher mean Dice than random initialisation at all five budgets, including 0.664 versus 0.615 with 12 labels and 0.811 versus 0.768 with 94 labels. Seed-level 95% confidence intervals for paired differences excluded zero at 47 and 94 labels but not at the other budgets. With 47 labels, the self-supervised model achieved mean Dice of 0.789, compared with 0.768 for random initialisation with 94 labels. In ResNet-34, ImageNet initialisation achieved higher mean Dice than in-domain self-supervised pretraining at all five budgets, and adding self-supervised pretraining after ImageNet reduced mean Dice at four of five budgets. All six 94-label configurations achieved lower mean Dice on ISIC 2017 than ISIC 2016. In the restoration experiment, mean Dice was 0.686 with random initialisation and 0.638-0.642 following pretraining. Conclusions: In-domain masked-reconstruction pretraining improved mean Dice relative to random initialisation in the custom U-Net, but not consistently in the ResNet-34 U-Net, where ImageNet initialisation performed better at every budget. Adding self-supervised pretraining after ImageNet provided no consistent gain. These findings are specific to the dermoscopic lesion-segmentation experiments and do not establish melanoma-specific diagnostic or clinical performance.

Background and Objective: Adding belzutifan to adjuvant pembrolizumab improved disease-free survival (DFS) in LITESPARK-022, but its durability beyond the 1-year treatment is unknown. Methods: Following a registered plan, we reconstructed individual patient data from published Kaplan-Meier curves of ten phase 3 adjuvant renal cell carcinoma trials. Within each trial, we estimated restricted mean survival time differences ({Delta}RMST), hazard ratios (HRs) before and after planned treatment ended, and DFS differences. Key Findings and Limitations: In LITESPARK-022 (central review), the 24-month {Delta}RMST was 1.34 months (95% confidence interval 0.73-1.95), and the belzutifan arm remained ahead after month 12 ({Delta}RMST increment 0.74 months/yr, 0.32-1.19). The HR was 0.55 (0.43-0.71) before month 12 and 1.16 (0.87-1.54) afterwards (post/pre ratio 2.10); investigator assessment showed similar attenuation. Similar ratios occurred in KEYNOTE-564 at its first interim analysis (2.02), whose benefit persisted, and in SORCE and PROTECT (2.09, 2.59), whose benefit faded. From months 12 to 30 (post hoc), the DFS difference changed by -3.7 percentage points (-8.1 to 0.8) with central review and 0.6 (-4.0 to 5.0) with investigator assessment. Data were reconstructed, and follow-up was short. Conclusions and Clinical Implications: Relative hazard reductions with belzutifan were greater before month 12; durability of the absolute DFS advantage is uncertain and assessment-dependent, so single-HR projections should be avoided.

Clinical records preserve patient findings and final recommendations but rarely capture how specialists select evidence, develop alternative interpretations, disagree, and converge on a management strategy. We describe a dataset derived from ten retrospectively selected, completed cases of histologically confirmed colorectal cancer. For each case, an administrator uploaded de-identified textual clinical records to an authenticated web-based annotation platform. A medical oncologist, surgical oncologist, and radiation oncologist, each with more than ten years of clinical experience, independently linked relevant source fragments to patient-specific interpretations and documented clinical problems, differential hypotheses, uncertainty, and diagnostic and treatment strategies. After the three records were locked, a large language model generated candidate questions for review by the tumour-board chair. The four physicians then discussed each case online; recordings were automatically transcribed and verified by two medical residents. The dataset comprises 30 source-linked specialist reasoning records, 10 verified case-level discussion transcripts, 10 final tumour-board decisions, and associated provenance and quality-control metadata. It supports research on evidence-grounded reasoning, cross-specialty disagreement, multidisciplinary decision-making, and multi-agent clinical decision support.

Background: Patients with ER-positive/HER2-negative early breast cancer of low or intermediate grade are generally considered to have a favourable prognosis, yet a number will recur. This risk is poorly captured by standard clinicopathological factors. AI-based medical devices for whole slide image (WSI) analyses used for risk-stratification often predict on slide-level thereby overlooking spatial localisation of the biomarkers. We hypothesise that the spatial distribution of a previously reported AI-based marker provides independent prognostic value beyond its global score. Patients and Methods: This Swedish multicentre cohort study includes 6106 patients with primary invasive breast cancer: 3078 from the Stockholm region (training) and 3028 from Skane (independent test). Tile-level high-grade morphology scores were generated with ViT_DG, a vision-transformer adaptation of DeepGrade, and the presence of clusters of high-risk tiles localised at the invasive tumour front was defined as a binary biomarker. Primary analyses were pre-specified in the ER-positive/HER2-negative/lymph-node-negative and Grade 1-2 subgroup. Association with breast-cancer recurrence-free interval (BCRFI) and distant metastasis were assessed using multivariable Cox proportional hazards models (HR). Results: In the primary subgroup (n=1536 training; n=1403 test), tumour front clusters were present in 37% and 39% of patients. Cluster-positive status was associated with a 2.30-fold increased risk in the external test. Cluster-negative patients achieved a 10-year BCRFI of 95.3%, equivalent to Grade 1 patients and better than Grade 2 (92.4%), whereas cluster-positive patients (90.2%) did worse than the Grade 3 reference. The association was retained in patients classified as low-risk by ViT_DG (test set multivariable HR 2.86), identifying high-risk patients not captured by the global score. Conclusions: The absence of high-risk morphology clusters at the tumour front is an independent prognostic indicator, identifying a low-risk group independently of the global AI biomarker classification. More broadly, preserving the spatial distribution of AI-derived predictions enables prognostic stratification beyond slide-level aggregation.

Objective: To develop and evaluate a dynamic model that repeatedly estimates the 30-day risk of a first grade [≥]2 immune-related adverse event (irAE) during immune checkpoint inhibitor (ICI) therapy. Materials and Methods: Adults initiating ICI therapy during 2015-2022 comprised the development cohort; 2023 initiators formed a nonoverlapping internal temporal evaluation cohort. Every 21 days during the first treatment year, models used available EHR information to predict a first structured phenotype-defined above Grade 2 irAE during the next 30 days. Evaluation included pooled and within-landmark discrimination, precision-recall performance, calibration, and patient-clustered bootstrap confidence intervals. Feature-state ablations and alternative update schedules examined the contribution of dynamic information. Results: Development included 1,522 patients and 18,744 evaluable landmarks; temporal evaluation included 526 patients and 6,263 landmarks, of which 184 were positive (2.94%). The multiscale XGBoost model achieved pooled AUROC 0.716 (95% CI, 0.676-0.755), AUPRC 0.067, within-landmark AUROC 0.672 (95% CI, 0.631-0.716), Brier score 0.0281, and log loss 0.1245. The time-aware baseline achieved AUROC 0.631 and within-landmark AUROC 0.536. Updated treatment exposure and laboratory trajectories accounted for most of the gain. A complete-state extension achieved AUROC 0.719 and AUPRC 0.073. The 21-day schedule had the highest discrimination among tested cadences. Conclusion: Longitudinal EHR updating improved near-term irAE prediction beyond pretreatment characteristics and treatment time. Phenotype adjudication and independent evaluation of calibration, transportability, and clinical utility are needed before implementation

Background Immune checkpoint blockade (ICB) has become the standard of treatment for metastatic melanoma (MM). However, patients with melanoma brain metastases (MBM) are often excluded from clinical trials. Here we analyse patients within the real-world OxCITE cohort receiving ICB treatment for metastatic melanoma, with a focus on MBM, aiming to discern outcomes, prognostic factors and peripheral biomarkers. Methods Patients eligible for ICB treatment were recruited prospectively. Demographic, clinical and radiological data were collected from electronic patient records. Plasma samples were collected prior to the first cycle of treatment for plasma proteomic analysis. Results 307 patients were included, 81 of whom had MBM on commencing ICB. No difference in overall survival between patients with and without brain metastases was noted on multivariable analysis (HR=1.1, 95% CI 0.73-1.6, p=0.68), whereas hepatic metastases were associated with increased risk of death (HR 1.7, 95% CI 1.2-2.4, p=0.0019). Within the MBM group, steroid treatment (HR=2.5, 95% CI 1.3-5.0, p=0.006) was associated with poor OS, whilst treatment with single agent ICB (HR=1.9, 95% CI 0.86-3.9, p=0.11), symptomatic first presentation of MBM (HR=1.5, 95% CI 0.76-2.8, p=0.25) and anti-seizure drug treatment (HR=1.74, 95% CI 0.89-3.4, p=0.10) were not. Amongst all patients carriage of a BRAF mutation was associated with increased risk of MBM (OR 2.49, 95% CI 1.4-4.4, p=0.0007), and increased propensity to new brain metastases on ICB treatment (OR 2.5, 95% CI 1.1-5.9, p=0.036). Analysis of blood and plasma samples demonstrated that patients with MBM have altered haematological profiles, with raised NLR and increased plasma glial fibrillary acidic protein levels, reinforcing the distinct systemic features of MBM. Conclusions In this real-world cohort of patients receiving ICB for metastatic melanoma, the presence of MBM did not associate with worse OS. Conversely, baseline corticosteroid use was adversely prognostic. Intriguingly, plasma GFAP was significantly elevated in patients with MBM and correlated with the burden of intracranial disease, highlighting a potential role for GFAP as a candidate biomarker for MBM in high-risk surveillance. Our observations indicate the adversely prognostic weight historically attached to M1d disease is no longer applicable in the ICB era.

Nurse-led skin cancer screening extends specialist reach but depends on the nurse selecting which lesions to forward for diagnosis, and performance varies with experience. We evaluated whether real-time decision support using artificial intelligence (AI) improves malignancy detection in routine nurse-led teledermoscopy screening across MoleMap clinics in New Zealand and Australia (January 2024-July 2025). In this real-world study, clinics using AI decision support were compared with standard clinics. Nurses examined patients, captured dermoscopic images, and forwarded selected lesions to teledermatologists, who provided the reference diagnosis. The analytic cohort comprised 1,102,382 lesions from 98,422 patients across 577 sites. AI-assisted screening was associated with a higher malignancy detection rate than standard screening (25.5 vs 15.7 malignancies per 1,000 lesions; odds ratio adjusted for nurse experience 1.73, 95% CI 1.68-1.77), a finding consistent across all nurse-experience tiers and the three major malignant subtypes. AI assistance was also associated with a shift in recommended management: 21 additional intervention recommendations and 13 additional safety-netting recommendations (self-monitoring, short-term follow-up, or specialist referral) per 1,000 lesions, approximately balanced by 34 fewer no-action recommendations. The reference standard was teledermatologist diagnosis and allocation was not randomised; findings are therefore associational and require prospective, outcome-based confirmation.

Purpose: Cyclin-dependent kinase 4/6 inhibitors in combination with endocrine therapy have become widely adopted as a first-line treatment approach for hormone receptor-positive (HR+)/human epidermal growth factor receptor 2-negative (HER2-) advanced breast cancer (ABC). Through a collaboration between Novartis and the US Food and Drug Administration, we integrated multimodal data from phase III MONALEESA trials to identify novel prognostic factors for HR+/HER2- ABC. Experimental Design: Baseline clinical and genomics data were collected from patients in the MONALEESA-2, -3, and -7 trials. Circulating tumor DNA (ctDNA) from plasma samples and gene expression in archival tumor biopsies were analyzed. Machine learning models were trained (n = 1,355) on progression-free survival (PFS) using multimodal features via fusion models. In a test set (n = 382), prognostic performances were evaluated using the Kaplan-Meier, Cox, and Weibull models. Results: The late fusion model achieved a prognostic performance (C-index: 0.727, 95% CI: 0.641-0.799) slightly outperforming early fusion (C-index: 0.718, 95% CI: 0.631-0.791) and single-modality models. Model-based risk stratification identified distinct low- and high-risk groups (PFS HR, 0.28; 95% CI: 0.20-0.38). Among the top 40 features for progression risk (18 clinical, 20 gene expression, and 2 ctDNA), these were associated with higher risk: liver involvement, higher histological grades, early relapse; lower STC2 and RBBP8 expressions and higher FBX05 expression; and higher tumor mutational burden and ctDNA purity. Conclusions: Integrating clinical and genomic modalities improved progression risk prediction compared with single-modality models, while highlighting salient features that inform the underlying biology and support hypothesis generation.

New prognostic biomarkers for oesophageal adenocarcinoma (OAC) may help treatment planning as current staging does not accurately predict survival for many patients. We used a 451 cancer-associated gene panel to assess genomic alterations associated with survival for a cohort of 65 OAC patients who underwent surgical resection without perioperative treatment, reflecting institutional practice at the time. Median age at operation was 72 years, 88% were male, median 32 lymph nodes were removed per patient, and minimum follow-up exceeded 11 years. TNM stages (pTNM, 8th ed.) were 14% stage 1, 14% stage 2, 45% stage 3, and 6% stage 4 (non-locoregional nodal metastases). Copy number changes were common, and somatic mutations were similar to those reported previously. Genetic associations with worse survival included amplification of CCND1 (HR=3.08, p=0.02), EGFR (HR=2.98, p=0.01), loss of ARID2 (HR=2.12, p=0.05), KMT2D (HR=1.99, p=0.07) and missense mutation within RYR1 (HR=2.68, p=0.01). This study identifies candidate genomic prognostic biomarkers in OAC using a cohort treated by surgery alone, enabling assessment of tumour biology independent of the confounding effects of peri-operative systemic therapy and radiotherapy. Several of the identified alterations are potentially targetable, supporting further investigation of genomic profiling to refine prognostication and guide treatment selection.

Background Immune checkpoint inhibitor (ICI) therapies have revolutionized oncology, but identifying patients who benefit remains a challenge, particularly in populations where ICIs are not the standard of care, including microsatellite stable colorectal cancer (MSS CRC) and rare solid cancers. We evaluated real-world performance of the Immune Profile Score (IPS), a validated DNA- and RNA-based molecular signature, in these cohorts. Methods In an exploratory analysis, we analyzed two real-world cohorts: 1) MSS CRC; and 2) MSS, tumor mutational burden-low rare solid cancers treated with off-label ICI. IPS-High and IPS-Low were calculated using a previously validated threshold. Cox proportional hazards models were fit to demonstrate prognostic utility for real-world overall survival (rwOS). In MSS CRC, time-to-next-treatment (TTNT) on prior non-ICI therapy was compared to rwOS on subsequent ICI therapy. Results In MSS CRC (n=46), IPS-High patients (13%) demonstrated clinically meaningful improvement in rwOS versus IPS-Low (Median OS: not reached vs 7.3 months; HR = 0.22, 90% CI 0.04-1.16). While IPS was not associated with TTNT on the preceding non-ICI line (HR = 1.07, 90% CI 0.60-1.91), it was associated with OS in patients receiving later line ICI (HR 0.21, 90% CI 0.04-1.22). In the rare cancers cohort (n=90; 26 unique malignancies), IPS-High (17.8%) was associated with longer OS to ICI therapy (HR = 0.26, 90% CI 0.11-0.61), even when restricted to histologies represented in both IPS groups (HR = 0.18, 90%, CI 0.05-0.62). Conclusions Preliminary evidence suggests that IPS may serve as a generalizable, pan-cancer biomarker capable of stratifying outcomes for ICI treatment in populations traditionally excluded from immunotherapy benefit.

Background Chemotherapy (CT) de-escalation strategies based on dual HER2 blockade with trastuzumab and pertuzumab (HP) have shown promising efficacy in HER2-positive (HER2+) early breast cancer (EBC), but biomarkers to guide patient selection are lacking. The Ataraxis Breast platform (ATX) uses artificial intelligence (AI) models integrating hematoxylin-eosin (H&E) whole-slide images and clinical variables to predict pathological complete response (pCR) and long-term outcomes. We evaluated ATX in the phase II PHERGain (NCT03161353) and PHERGain-2 (NCT04733118) trials. Methods The ATX NEO model was assessed for pCR prediction in PHERGain (n=306 evaluable patients) and PHERGain-2 (n=383 evaluable patients). The ATX RISK model was evaluated for prognosis in PHERGain (n=309 evaluable patients). Prespecified cutoffs classified patients into low-, medium-, and high-probability groups for pCR and low- or high-risk groups for recurrence. Associations with pCR were assessed using logistic regression. Prognostic performance was evaluated using Cox proportional hazards models, Kaplan-Meier estimates, and Harrell's C-index. Results Overall pCR rates were 40.2% in PHERGain and 60.3% in PHERGain-2. Continuous NEO scores were significantly associated with pCR in both PHERGain (odds ratio [OR], 1.28; 95% CI, 1.11-1.48; P<0.001) and PHERGain-2 (OR, 1.30; 95% CI, 1.13-1.52; P<0.001). Using prespecified cutoffs, pCR rates increased across low-, medium-, and high-probability groups (19.2%, 41.3%, and 48.3% in PHERGain; 29.5%, 61.6%, and 70.1% in PHERGain-2). In PHERGain, the RISK score was significantly associated with event-free survival (hazard ratio per 1-SD increase, 1.56; 95% CI, 1.17-2.08; P=0.002; C-index, 0.66). Five-year event-free survival was 93.9% in the low-risk group and 83.1% in the high-risk group. Conclusions NEO score was consistently associated with pCR across two independent HER2+ EBC trials, while RISK score provided prognostic information for long-term outcomes in PHERGain. Prespecified score categories identified clinically distinct response and risk groups. These findings support further evaluation of AI-based biomarkers to refine patient selection for CT de-escalation strategies in HER2+ EBC.

Objective: To determine how cohort versus nested case-control (NCC) design affects EHR-based prediction of a rare outcome. Materials and Methods: We compared two cohort and five NCC designs for predicting early-onset gastrointestinal cancer using L1-regularized logistic regression (LASSO), XGBoost, and multilayer perceptron (MLP). Models underwent nested five-fold cross-validation with model-specific hyperparameter tuning and temporal evaluation in a held-out prospective cohort representing intended deployment. Model discrimination was assessed using area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve (AUPRC). Model calibration was assessed using Brier scores and calibration plots before and after recalibration with isotonic regression. Feature-ranking similarity was quantified using rank-biased overlap. Results: The cohort development datasets included 256,854-415,529 patients, with 2-yr cumulative incidence of 0.12%-0.17%, compared with approximately 9% in the 1:10 matched NCC datasets. NCC designs generally produced higher internal discrimination, but temporal performance varied by algorithm and index-date definition. Cohort designs achieved the highest temporal AUPRC for LASSO (0.068), whereas diagnosis-anchored NCC achieved the highest temporal AUPRC for XGBoost (0.086) and MLP (0.070). Temporal AUPRC generally decreased as the required lead time before diagnosis increased. Raw predicted probabilities substantially overestimated observed risks in the temporal cohort. Isotonic recalibration improved probability scaling while largely preserving discrimination, although residual miscalibration remained. Cohort-based feature rankings were more consistent across algorithms, whereas NCC-based rankings depended more strongly on algorithm and index-date anchoring. Conclusion: Study design should be selected and evaluated according to the intended clinical prediction task and deployment setting.

The FORESEE trial (NCT04450706) was a prospective study to evaluate the feasibility of integrating organoid-based drug testing with comprehensive genomic characterization to inform treatment selection in patients with HER2-negative metastatic breast cancer. The primary objective of the study was to determine the feasibility of providing personalized therapy recommendations using a combination of genomic vulnerability information with drug testing in patient-derived organoids (PDOs) in a clinically relevant time frame. Of the 15 patients enrolled, 10 cases had biopsies with adequate tumor content, and PDOs were successfully established in 4 of 10 cases. Drug profiling and genomic testing was completed in all established organoids, yielding an overall success rate of 40% in cases of biopsies with adequate tumor content. Drug sensitivity and genomic results were returned within 12 weeks and informed subsequent treatment selection in all evaluable cases. The primary endpoint - demonstrating that PDO-based drug screening combined with genomic characterization would identify clinically actionable outcomes in at least 46% of patients based on prior data - was not met. In conclusion, successful PDO establishment represents a limiting step in the implementation of FPO for metastatic breast cancer; however when PDO were successful, drug responses were always obtained and physicians chose to use the information in 100% of cases. These findings highlight the need for improved strategies optimizing use of patient biopsy material for drug testing in order to fully realize the clinical potential of FPO.

Background: Clinicopathologic (SARCULATOR) and transcriptomic (CINSARC) models are established prognostication tools in soft tissue sarcoma, but neither directly represent the sequence context of tumor genomic alterations. Evo2 is a genomic foundation model trained to learn the grammar of DNA/RNA sequences. We tested whether Evo2-derived features from tumor short variants, fusions, and rearrangements improve survival prognostication beyond clinicopathologic and transcriptomic risk models. Methods: We analysed formalin-fixed, paraffin-embedded tumor material and clinical data from 102 patients with dedifferentiated liposarcoma (n = 25), high-grade leiomyosarcoma (n = 25), and undifferentiated pleomorphic sarcoma (n = 52). A separate TCGA-SARC cohort of comparable histologies (n = 129) served as external validation. Reconstructable alterations (short variants, fusions and rearrangements) were converted into sequence inputs, embedded with Evo2 and aggregated into patient-level genomic representations. Cox proportional hazards models incorporated genomic features alone (Evo2) or together with clinicopathologic (SARCULATOR) or transcriptomic (CINSARC) predictors across overall survival (OS), progression-free survival (PFS), disease-free survival (DFS), relapse-free survival (RFS), and metastasis-free survival (MFS). Discrimination was assessed using Harrell's concordance index (C-index). Results: Evo2 features were generated from 174 study-cohort sequences and 7298 TCGA-SARC sequences. In the study cohort, adding Evo2 to SARCULATOR increased the reported C-index for OS from 0.620 to 0.770, PFS from 0.625 to 0.755, DFS from 0.612 to 0.714, and RFS from 0.633 to 0.771. In TCGA-SARC, SARCULATOR plus Evo2 increased the OS C-index from 0.595 to 0.716 and the MFS C-index from 0.548 to 0.667. Also in TCGA-SARC, adding Evo2 to CINSARC increased the OS C-index from 0.476 to 0.653 and the MFS C-index from 0.553 to 0.650. These are descriptive point-estimate comparisons. Moreover, in TCGA-SARC Evo2 alone outperformed SARCULATOR alone and CINSARC alone, supporting independent prognostic information within altered sequence-context representations. Conclusions: Evo2-derived genomic representations improved clinicopathologic and transcriptomic prognostication across sarcoma cohorts and survival endpoints. These findings support genomic foundation models as an independent and complementary layer for sarcoma risk stratification through direct modeling of altered tumor sequence biology. Confirmation of model transportability, calibration and clinical utility is required before clinical application.

Background: The organization of pancreatic cancer care may be associated with survival. Using health administrative claims data, we reconstructed the pathway from diagnosis to first-line therapy and examined associations of multidisciplinary team review and treatment at specialized centers with overall survival. Methods: Longitudinal historical cohort study using anonymized health administrative claims data from six Czech health insurance funds, covering approximately 17% of patients diagnosed with pancreatic cancer nationally between 2018 and 2020. Adults with an incident ICD-10 C25 record confirmed by imaging and histopathology were followed from the index date (the date of the first qualifying imaging or histopathological examination) until death, censoring, or 31 December 2021. Overall survival was analyzed using Kaplan-Meier methods and Cox regression adjusted for age and first-line therapy type. Results: Of 1,255 patients, 920 (73.3%) received first-line therapy and 266 (21.2%) died untreated. Among treated patients, 436 (47.4%) underwent multidisciplinary team review, 335 (36.4%) underwent surgery, and 667 (72.5%) were treated at a complex oncological center. Surgery was associated with longer overall survival than chemotherapy (age-adjusted hazard ratio [HR] 0.32, P < 0.001). Treatment at a complex oncological center was associated with longer overall survival (adjusted HR 0.77, 95% confidence interval [CI] 0.64-0.92, P = 0.004; crude median 532 versus 394 days). Multidisciplinary team review was not significantly associated with overall survival (adjusted HR 0.86, 95% CI 0.73-1.01, P = 0.073). Conclusions: Treatment at a complex oncological center was associated with longer survival, while fewer than half of patients had a recorded multidisciplinary team review. How care is organized, not only which therapy is delivered, is associated with outcome. The observational design and absence of key clinical covariates limit causal interpretation.

Background: This study compared functional and cognitive trajectories in older women treated for early breast cancer with matched women without cancer. Methods: Data were derived from two Dutch cohorts: the CLIMB study (women aged 75 years and older with operable breast cancer) and the ISCOPE study (community-dwelling women aged 75 years and older without cancer). Breast cancer patients were matched 1:2 to controls on age and baseline functional or cognitive status. Functional status was assessed using the Groningen Activity Restriction Scale (GARS), and cognitive function using the Mini-Mental State Examination (MMSE). Longitudinal changes over 12 months were analyzed using both unadjusted and confounder-adjusted linear mixed models. Results: 168 breast cancer patients and 336 matched breast cancer-free controls were included with a mean age of 80 years. Most breast cancer patients had stage 0-I or II disease and received surgery and/or radiotherapy; 48% received adjuvant endocrine therapy, chemotherapy was rare (<1%). At baseline, both cohorts were comparable in age and functional or cognitive status, although there was a higher comorbidity burden in the control cohort. No significant differences in functional or cognitive decline were observed between the cohorts over 12 months. Conclusions: In older women, early breast cancer and its treatment did not lead to accelerated decline in functional status or cognitive function within the first year compared to matched older women without cancer. These findings suggest that, for relatively fit older patients, breast cancer treatment may not accelerate functional decline in the short term.

Background: Cancer patients face a significantly elevated risk of severe COVID-19 outcomes. Although vaccination is a cornerstone of prevention, data on vaccination coverage and association with survival among Russian cancer patients remain scarce. We assessed COVID-19 vaccination coverage and its association with overall survival in a population-based cohort of cancer patients in Northwest Russia. Methods: This retrospective registry-based cohort study integrated data from the Population-Based Cancer Registry (PBCR) of the Arkhangelsk Region and the Nenets Autonomous Okrug, regional vaccination records, and an artificial intelligence (AI)-based analysis of chest computed tomography (CT) scans. The cohort consisted of 1,144 cancer patients who underwent CT between August 2020 and April 2021. Vaccination status was ascertained via data linkage. Overall survival (OS) was compared between vaccinated and unvaccinated patients using Kaplan-Meier analysis and multivariable Cox proportional hazards regression, adjusted for demographic, clinical, and AI-derived CT biomarkers. To mitigate immortal time bias, the primary analysis used a landmark approach restricted to patients alive on March1, 2021 (N=1,136). A sensitivity analysis used a November1, 2021 landmark (N=894). Vaccination status was defined as receipt of at least one dose before the landmark date. Results: Only 6.6% (n=76) of patients received at least one dose of a COVID-19 vaccine (Sputnik V or Sputnik Light). Vaccinated patients were more likely to have earlier-stage cancer and were less likely to be receiving active treatment. In the primary landmark analysis, vaccination was associated with a significantly higher 3-year OS (80.3% vs. 45.7%, p<0.001). In multivariable analysis, vaccination was independently associated with a 60% lower hazard of all-cause mortality (adjusted HR 0.40, 95% CI 0.25-0.64). AI-detected COVID-19 pneumonia on baseline CT was also an independent predictor of poorer survival (adjusted HR 1.32, 95% CI 1.09-1.59). In the sensitivity analysis (November1, 2021 landmark), the adjusted HR for vaccination was 0.56 (95% CI 0.34-0.92), showing a consistent but attenuated association. Conclusion: This study reveals critically low COVID-19 vaccination coverage among cancer patients in Northwest Russia. Nevertheless, vaccination was strongly associated with a substantial survival benefit, underscoring its importance as a potentially lifesaving intervention for this vulnerable population.

Introduction: Perioperative systemic therapy improves outcomes in gastroesophageal adenocarcinoma, but many patients do not receive postoperative treatment. We examined whether host characteristics and perioperative inflammatory and albumin changes provided information about adjuvant initiation beyond tumour pathology, and whether initiation was associated with survival and disease control. Method: This retrospective cohort included patients undergoing curative-intent resection for gastroesophageal adenocarcinoma at the McGill University Health Centre between 2006 and 2025. Sequential logistic models evaluated pathological characteristics, pre-treatment host variables and perioperative changes in CBC-derived neutrophil-to-lymphocyte ratio (cNLR) and albumin. Postoperative measurements preceded adjuvant initiation. Nested comparisons used matched complete cases. Day-90 landmark and time-varying Cox models assessed associations between adjuvant initiation and survival. Results: Among 243 patients, 78 (32.1%) did not initiate adjuvant therapy. Adding host variables improved apparent discrimination for initiation beyond pathology (AUC 0.672 to 0.763; P=0.002). Greater perioperative cNLR increase was associated with lower odds of initiation (adjusted OR 0.67 per unit increase in {Delta}log2; cNLR, 95% CI 0.47-0.95), whereas more favourable albumin change was associated with higher odds (OR 1.07 per g/L, 95% CI 1.01-1.13). Neither marker retained a clear independent association when modelled together. Adjuvant initiation by day 90 was associated with longer overall survival (adjusted HR 0.53, 95% CI 0.36-0.78) and disease-free survival (HR 0.57, 95% CI 0.39-0.84), with similar time-varying findings. Conclusion: Host inflammatory and albumin changes added information about adjuvant initiation beyond tumour pathology, and initiation was associated with longer survival. Prospective evaluation should determine whether host-directed perioperative strategies can improve postoperative treatment delivery.