European Journal of Cancer

This study compared machine-learning models for predicting recurrence-free survival (RFS), disease-specific survival (DSS), and overall survival (OS) using clinicopathologic data from 1,621 stage I/II primary cutaneous melanoma patients. Our time-to-event models achieved concordance indices of 0.829 for RFS, 0.812 for DSS, and 0.778 for OS. Tumor thickness and mitotic rate were the most important predictors for RFS. Charlson comorbidity score and insurance type were critical for DSS and OS.

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

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.

ObjectivesTo identify existing DNA methylation-based prognostic biomarkers and prediction models for colorectal cancer (CRC) prognosis and to validate them in a large external cohort. DesignSystematic review and external validation study. Data sourceSystematic search in PubMed and Web of Science until October 2022 to identify epigenome-wide studies reporting methylation at CpG sites (CpGs) associated with survival among CRC patients. Validation data were drawn from the 2310 CRC patients of the DACHS study recruited from 22 hospitals in the Rhine-Neckar region in the southwest of Germany. Main outcome measuresOverall survival (OS) in CRC patients. ResultsWe identified 200 unique CpGs and 10 CpG-based prognostic models derived from 15 studies. In the external validation analysis, 1252 of 2310 patients died during follow-up (median 10.4 years). Thirty-nine CpGs (20%) and five prognostic models (50%) were independently associated with overall survival after adjustment for clinical variables. The five models had unsatisfactory discrimination ability, with area under the receiver operating characteristic curves at five years ranging from 0.54 to 0.60. The calibration accuracy of the five models using recalibrated baseline survival was also poor, and no relevant added prognostic value to traditional clinical variables was observed. Based on the Prediction Model Risk of Bias Assessment Tool, all models were rated as high risk of bias. ConclusionsOnly 20% of published CpGs associated with survival in CRC patients could be externally validated. So far derived published CpG-based prognostic models for CRC do not seem to be useful for clinical practice. Summary boxO_ST_ABSWhat is already known on this topicC_ST_ABSO_LISeveral studies have suggested that DNA methylation biomarkers could have the potential to improve prognostic accuracy for patients with colorectal cancer (CRC), but these studies mostly did not include large-scale external validation C_LIO_LIMany CpG sites associated with CRC prognosis and prognostic models based on these CpGs have been proposed C_LIO_LIAn independent study to validate these biomarkers and prediction models is essential for assessing their utility in clinical practice, but has not yet performed C_LI What this study addsO_LIThis external validation study verified the prognostic relevance of a fraction of existing DNA methylation-based prognostic biomarkers for CRC C_LIO_LIPublished CpG-based prognostic models all performed poorly in our external validation and were rated as at high risk of bias, so they do not seem to be useful for clinical practice C_LI

ImportanceEasy and accessible biomarkers to predict response to immune checkpoint inhibition (ICI)-treated melanoma are limited. ObjectiveTo evaluate artificial intelligence (AI) detected tumor-infiltrating lymphocytes (TILs) on pretreatment melanoma metastases as a biomarker for response and survival in ICI-treated patients. DesignMulticenter cohort study including patients with advanced melanoma treated with first-line anti-PD1 {+/-} anti-CTLA4 between 2016 and 2023. Median follow-up was 36.3 months. Setting11 melanoma treatment centers in the Netherlands. Participants1,202 patients with advanced cutaneous melanoma. ExposureAll patients received first-line anti-PD1 {+/-} anti-CTLA4. Main Outcome(s) and Measure(s)The percentage of TILs inside manually annotated tumor area in H&E stained pretreatment metastases was determined using the Hover-NeXt model trained and evaluated on an independent melanoma dataset containing 166,718 pathologist-verified manually annotated cells. The primary outcome was objective response rate (ORR); secondary outcomes were progression-free survival (PFS) and overall survival (OS). Correlation with manual TILs, scored according to the guidelines stated by the immune-oncology working group, was evaluated with Spearman correlation coefficients. Logistic regression and Cox proportional regression were conducted, adjusted for age, sex, disease stage, ICI type, BRAF status, brain metastases, LDH level, and performance status. ResultsMetastatic melanoma specimens were available for 1,202 patients, of whom 423 received combination therapy. Median TIL percentage was 9.9% (range 0.3% - 69.4%). A 10% increase in TILs was associated with increased ORR (adjusted OR 1.40 [95% 1.23-1.59]), PFS (adjusted HR 0.85 [95% CI 0.79 - 0.92]) and OS (adjusted HR 0.83 [95% CI 0.76 - 0.91]. Results were consistent for both patients treated with anti-PD1 monotherapy and combination treatment with anti-PD1 plus anti-CTLA4. When comparing manual TIL scoring with AI-detected TILs, associations with response and survival were consistently stronger for AI-detected TILs. Conclusions and RelevanceIn patients with advanced melanoma, higher levels of AI-detected TILs on pre-treatment H&E slides were independently associated with improved ICI response and survival. Given the accessibility of TIL scoring on routine histology, TILs may serve as a predictive biomarker for ICI outcomes. To facilitate broader validation, the Hover-NeXt architecture and model weights are publicly available. Key pointsQuestion: What is the predictive value of artificial intelligence-detected tumor-infiltrating lymphocytes (TILs) for clinical outcomes in patients with advanced melanoma receiving first-line immune checkpoint inhibition? Findings: In this multicenter cohort of 1202 patients, TILs in pretreatment metastases were quantified using a melanoma-specific publicly available AI model trained on an independent dataset. A 10% increase was associated with response (aOR 1.40 [95% CI 1.23-1.59]), progression-free survival (aHR 0.85 [95% CI 0.79-0.92]), and overall survival (aHR 0.83 [95% CI 0.76-0.91]). Associations were independent of clinical predictors. Meaning: AI-detected TILs in pretreatment melanoma metastases independently correlate with response and survival.

PurposeDespite their central role in the current staging system, Breslow thickness and ulceration do not fully identify early-stage melanoma patients who will develop distant metastases. We assessed whether gene expression profiling (GEP) improves prediction of distant metastases beyond standard staging factors in early-stage melanoma. MethodsData were derived from the population-based Dutch Early-Stage Melanoma (D-ESMEL) study, including a matched discovery set of 442 stage I/II melanomas (221 case-control pairs) and a validation cohort of 308 melanomas nested within 5,815 patients. The discovery set was used to identify genes associated with distant metastases, independent of age, sex, Breslow thickness, and ulceration. The validation cohort was partitioned into model development and independent validation subsets. Candidate genes from the discovery set were used to develop and validate a GEP model, evaluated by weighted area under the curve (AUC) and concordance index (C-index). ResultsRNA sequencing succeeded for 356 melanomas in the discovery set, 200 in the model development subset, and 94 melanomas in the independent validation subset. Differential gene expression analyses and modeling identified 558 candidate genes. In the independent validation subset, the GEP model achieved a weighted AUC of 0.77 (95% CI, 0.66-0.86) and weighted C-index of 0.79 (95% CI, 0.69-0.88), comparable to the clinical model based on Breslow thickness and ulceration (weighted AUC 0.82 (95% CI, 0.73-0.90), weighted C-index 0.84 (95% CI, 0.76-0.91)). Integration of GEP with the clinical model did not improve accuracy. Gene set enrichment analyses showed enrichment of proliferative and stress-related pathways. ConclusionWhile GEP captured biologically relevant signals, its predictive accuracy for distant metastases was comparable to that of Breslow thickness and ulceration in a population-based early-stage melanoma cohort.

Structured AbstractO_ST_ABSImportanceC_ST_ABSThe genetic architecture of disease risk may not be independent of social determinants. This can be leveraged to investigate for causality. ObjectiveTo investigate for a possible causal relationship between socioeconomic status (SES) and melanoma in situ (MIS) based on their genetic architectures. DesignGenetic correlation study SettingMulticenter and population-based data sources. ParticipantsThe Ingold et al., 2024 GWAS summary statistic dataset is derived from 3,564 MIS cases, 10,552 invasive melanoma (MM) cases, and 1,022,070 melanoma-free controls. Individuals with both MIS and MM were only included as MM cases. The Kweon et al., GWAS summary statistic dataset on income, a proxy for SES, is derived from an effective sample size of 668,288 individuals. Main Outcome(s) and Measure(s)Genetic correlation between MIS, MM, and SES. ResultsWe obtained European genomic ancestries-based GWAS summary statistics for MIS (3,564 cases and 1,022,070 melanoma-free controls), MM (10,552 cases and 1,022,070 melanoma-free controls), and SES (668,288 individuals). We identify a positive and significant genetic correlation between MIS and SES (0.14, 95% CI 0.06 to 0.21; p = 5.55 x 10-04) but not MM and SES (0.05, 95% CI -0.01 to 0.11; p = 0.11). The genetic architecture of MIS subtracting that of MM (MIS-MM) remained positively and significantly correlated with the genetic architecture of SES (0.23, 95% CI 0.07 to 0.39; p = 4.18 x 10-03). In contrast, the genetic architecture of MM subtracting that of MIS (MM-MIS) is negatively correlated with the genetic architecture of SES (-0.24, 95% CI -0.42 to -0.06; p = 8.01 x 10-03). Finally, taking a Mendelian Randomization approach, we identify consistent evidence for a causal pathway between MIS and SES but not MM and SES. Conclusions and RelevanceThe genetic architecture of SES correlates with that of MIS but not MM. There is also evidence for a causal link between SES and MIS. Both these findings support an overdiagnosis of MIS. Importantly, our results demonstrate genetic risk scores for disease are not inherently independent of social determinants of diagnosis. Clinical application of genetics-based risk stratification without consideration of social determinants may have limited utility. Key PointsO_ST_ABSQuestionC_ST_ABSIs socioeconomic status genetically correlated with melanoma in situ and is there any evidence for a causal relationship? FindingsIn this genetic correlation study based on data from 3,564 melanoma in situ cases, 10,552 invasive melanoma cases, 1,022,070 melanoma-free controls, and 668,288 individuals with income data; we identify a positive and significant genetic correlation between socioeconomic status and melanoma in situ but not invasive melanoma. We also identify Mendelian Randomization-based evidence for a causal relationship between socioeconomic status and melanoma in situ but not invasive melanoma MeaningThe genetic architecture of disease risk is not independent of social determinants of diagnosis. Clinical application of genetics-based risk stratification without consideration of social determinants may have limited utility.

BackgroundNumerous studies have shown that elderly women with endometrial cancer (EC) have a higher risk of recurrence and cancer-related death. It is, however, unclear whether aging is a causal prognostic factor, or whether other risk factors become increasingly common with age. We address to this with a unique multi-method study design using state of the art statistical and causal inference techniques on datasets of three large randomised trials. MethodsData of 1801 women participating in the randomised PORTEC-1, -2 and -3 trials were used for statistical analyses and causal inference. The cohort included 714 patients with intermediate-risk EC, 427 high-intermediate risk EC patients and 660 high-risk EC patients. Associations of age with clinicopathological and molecular features were analysed using non-parametric tests. Multivariable competing risk analyses were performed to determine the independent prognostic value of age. To analyse age as a causal prognostic variable a deep learning Causal Inference model called AutoCI was used. FindingsMedian follow-up was 12{middle dot}3 years for PORTEC-1, 10{middle dot}5 years for PORTEC-2 and 6{middle dot}1 years for PORTEC-3. Both overall recurrence and EC-specific deaths significantly increased with age. Moreover, elderly women had a higher incidence of deep myometrial invasion, serous tumour histology and p53abn tumours. Age was an independent risk factor for both overall recurrence (HR 1{middle dot}02 per year, 95%CI 1{middle dot}01-1{middle dot}04; p=0{middle dot}0012) and EC-specific death (HR 1{middle dot}03 per year, 95%CI 1{middle dot}01-1{middle dot}05; p=0{middle dot}0012), and was identified as a significant causal variable. InterpretationThis study shows that advanced age is associated with more aggressive tumour features, and independently and causally related to worse oncological outcomes. Therefore, treatment for endometrial cancer in elderly women should not be de-escalated based on their age alone. FundingThe PORTEC-1, -2 and -3 trials and the associated translational studies are supported by the Dutch Cancer Society.

The phase 1b TICIMEL clinical trial evaluated the safety, tolerability, and anti-tumor activity of combining the immune checkpoint inhibitors (ICI), ipilimumab and nivolumab, with tumor necrosis factor (TNF) blockers, certolizumab or infliximab, to treat advanced melanoma patients. A higher proportion of responses was observed in patients receiving ICI and certolizumab, while patients treated with ICI and infliximab demonstrated superior tolerability. Moreover, CITE-Seq analyses of circulating CD8 T cells showed that ICI plus certolizumab promoted an IFN signature, whereas ICI plus infliximab reduced the induction of genes associated with T cell activation. In preclinical models, ICI and TNF blockade with certolizumab increased IFN-{gamma}+ CD8 T cells and reduced regulatory T cells in tumors. The IgG1 Fc fragment of infliximab was identified as counteracting the benefits of TNF blockade. These findings underscore the importance of selecting the optimal TNF blocker to combine with ICI to enhance therapy efficacy in melanoma patients. ClinicalTrials.gov identifiers: NCT03293784; NCT05867004.

Cancer survivors are at an increased risk of cardiovascular disease (CVD) compared to the general population. Here, we evaluated the impact of somatic mosaic chromosomal alterations (mCAs) on death of CVD causes, coronary artery disease (CAD) causes, from cancer, and of any cause in patients with a cancer diagnosis within the UK Biobank (n=48 919). mCAs were derived from DNA genotyping array intensity data and long-range chromosomal phase inference from participants. Overall, 10 070 individuals (20.6%) carried [≥]1 mCA clone. In adjusted analyses, mCA was associated with an increased risk of death of CAD causes (hazard ratio [HR]: 1.37, 95% confidence interval [CI]: 1.09-1.71, P=0.006), from cancer (HR: 1.06, 95% CI: 1.00-1.11, P=0.041), and death of any cause (HR: 1.07, 95% CI: 1.02-1.12, P=0.005). Among cancer survivors, carriers of any mCA are at an increased risk of death of CAD causes and of any cause as compared to non-carriers.

BackgroundA retrospective study in Nature recently reported improved overall survival (OS) when COVID-19 mRNA vaccination was administered around initiation of immune checkpoint inhibitor (ICI) therapy. Whether this association depends on vaccination timing relative to ICI treatment remains unclear. MethodsWe conducted a single-centre retrospective cohort study of patients receiving palliative-intent ICI therapy at a UK tertiary cancer centre (October 2014-December 2025). Two vaccination exposure definitions were evaluated: (1) vaccination within 100 days of the first ICI cycle (initial window); and (2) vaccination from 100 days before the first ICI cycle to 100 days after the final ICI cycle (extended window). OS was analysed using Kaplan-Meier methods and Cox models relative to unvaccinated patients. ResultsAmong 2109 patients, 515 (24.4%) received [&ge;]1 COVID-19 vaccine dose. Under the initial window, mRNA vaccination was associated with a longer OS in the all-tumours cohort only (HR 0.76; 95% CI 0.58-0.99; p=0.04). Under the extended window, mRNA vaccination was associated with longer OS in the all-tumours cohort (HR 0.58; 95% CI 0.46-0.75; p<0.0001), including melanoma (HR 0.35; 95% CI 0.18-0.69; p=0.002) and kidney cancer (HR 0.47; 95% CI 0.28-0.79; p=0.004), but not NSCLC. In an era-restricted analysis limited to patients receiving ICI therapy from 2020 onwards, the all-tumours association persisted (HR 0.76; 95% CI 0.59-0.98; p=0.04) with no significant tumour-specific associations. ConclusionsCOVID-19 mRNA vaccination was associated with improved OS, with magnitude and tumour specificity dependent on vaccination exposure definition. Prospective studies are required to assess causality and tumour-specific effects.

Genomic testing has transformed treatment decisions for hormone receptor-positive, HER2-negative (HR+/HER2-) early breast cancer; however, it remains inaccessible to many patients worldwide due to high costs and logistical barriers. Here, we developed an artificial intelligence (AI) model using a multimodal deep learning approach that estimates Oncotype DX 21-gene recurrence scores (RS) from routine histopathology images and clinicopathologic variables, including age at diagnosis, tumor size, and receptor status. Using a foundation model pre-trained on 171,189 histopathological slides, we fine-tuned and validated our AI model on the TAILORx randomized trial (n=8,284). Among 2,407 patients in the TAILORx validation, the model classifies 45.6% of patients as low-risk, 42.4% as intermediate risk, and 12.0% as high-risk. For predicting high genomic risk disease (RS[&ge;]26), occurring in 15.9% in the TAILORx validation set, the model achieves AUC=0.898. Patient stratification by our model shows strong prognostic value across multiple clinical endpoints, including recurrence-free interval, distant recurrence-free interval, and disease-free survival. Importantly, chemotherapy benefit is demonstrated for premenopausal patients classified by our model as high AI risk and chemotherapy benefit is ruled out for postmenopausal patients classified as low AI risk. External validation across six independent cohorts (n=5,497 patients) demonstrates robust generalization of the AI model for prognostication and prediction of RS. Notably, in postmenopausal patients, the AI model reclassifies approximately 30% of clinically high-risk cases, defined by the MINDACT criteria, as low-risk. These findings demonstrate that artificial intelligence applied to standard histopathology can be a valuable tool for chemotherapy decision-making in HR+/HER2- early breast cancer. This approach can help reduce unnecessary chemotherapy and extend precision medicine, particularly in resource-limited settings, where genomic testing is not widely accessible.

BackgroundMelanoma is a lethal form of skin cancer with a high propensity for metastasizing, making early detection crucial. This study aims to develop a machine learning model using electronic health record data to identify patients at high risk of developing melanoma to prioritize them for dermatology screening. MethodsThis retrospective study included patients diagnosed with melanoma (cases), as well as matched patients without melanoma (controls), from Massachusetts General Hospital (MGH), Brigham and Womens Hospital (BWH), Dana-Farber Cancer Institute (DFCI), and other hospital centers within the Research Patient Data Registry at Mass General Brigham healthcare system between 1992 and 2022. Patient demographics, family history, diagnoses, medications, procedures, laboratory tests, reasons for visits, and allergy data six months prior to the date of first melanoma diagnosis or date of censoring were extracted. A machine learning framework for health outcomes (MLHO) was utilized to build the model. Performance was evaluated using five-fold cross-validation of the MGH cohort (internal validation) and by using the MGH cohort for model training and the non-MGH cohort for independent testing (external validation). The Area Under the Receiver Operating Characteristic Curve (AUC-ROC) and the Area Under the Precision-Recall Curve (AUC-PR), along with 95% Confidence Intervals (CIs), were computed. ResultsThis study identified 10,778 patients with melanoma and 10,778 matched patients without melanoma, including 8,944 from MGH and 1,834 from non-MGH hospitals in each cohort, both with an average follow-up duration of 9 years. In the internal and external validations, the model achieved AUC-ROC values of 0.826 (95% CI: 0.819-0.832) and 0.823 (95% CI: 0.809-0.837) and AUC-PR scores of 0.841 (95% CI: 0.834-0.848) and 0.822 (95% CI: 0.806-0.839), respectively. Important risk features included a family history of melanoma, a family history of skin cancer, and a prior diagnosis of benign neoplasm of skin. Conversely, medical examination without abnormal findings was identified as a protective feature. ConclusionsMachine learning techniques and electronic health records can be effectively used to predict melanoma risk, potentially aiding in identifying high-risk patients and enabling individualized screening strategies for melanoma.

Patients with advanced melanoma are treated with immune checkpoint inhibitors (ICIs), yet less than 50% of patients achieve a durable response while all patients are exposed to the risk of severe side effects. Tumor-infiltrating lymphocytes (TILs) in pathology images are associated with ICI outcomes, but manual assessment is subjective. In addition, the predictive value of other immune cell subsets, including plasma cells, neutrophils, histiocytes, and melanophages, remains unclear. We organized the Panoptic segmentation of nUclei and tissue in advanced MelanomA (PUMA) challenge to evaluate whether the spatial localization of TILs and other immune cell subsets on melanoma H&E slides collected before start of treatment was associated with treatment outcomes. Algorithm performance was evaluated on a hidden test set, after which top-ranked algorithms were applied to pre-treatment metastatic whole-slide images from a large, multicenter cohort of patients with advanced melanoma treated with first-line ICIs (n=1102). Automatically quantified tissue features and immune cell subsets were then associated with clinical outcomes. Top-performing algorithms improved detection of immune cell subsets, although accuracy for rare classes remained limited. Across challenge participants, TIL density showed the most consistent association with treatment response and survival. Associations for stromal TILs were weaker, while plasma cells, histiocytes, melanophages, neutrophils, necrosis and blood vessels did not show independent associations with outcomes. Overall, the results from the PUMA challenge improved the state of the art of immune cell detection in melanoma histopathology and show that intra-tumoral lymphocytes are the immune cell subset most consistently associated with treatment response and survival. HighlightsO_LIWe organized the first melanoma-specific tissue and nuclei segmentation competition C_LIO_LIWinning algorithms were applied to 1102 whole-slide images for biomarker analysis C_LIO_LIIntra-tumoral TILs were associated with response to immune checkpoint inhibitors C_LIO_LIOther immune cell subsets showed no independent association with treatment outcomes C_LIO_LITissue segmentation on WSIs was limited by low heterogeneity in training data. C_LI Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=140 SRC="FIGDIR/small/26347935v1_ufig1.gif" ALT="Figure 1"> View larger version (39K): org.highwire.dtl.DTLVardef@13838e4org.highwire.dtl.DTLVardef@1f34a6org.highwire.dtl.DTLVardef@b9a65borg.highwire.dtl.DTLVardef@58d300_HPS_FORMAT_FIGEXP M_FIG C_FIG

PurposeReal-world data (RWD) from electronic health records (EHRs) and next-generation sequencing are increasingly used to study treatment effectiveness in molecularly refined patient populations. Incomplete mortality data in EHR can overestimate survival rates in RWD studies. While the National Death Index (NDI) is the gold standard for mortality data in the United States, its limited accessibility and reporting delays hinder timely research. Instead, EHR datasets are often supplemented with external mortality data sources to improve mortality data capture. This study evaluated a composite mortality variable against NDI records using a large cohort of advanced cancer patients from a real-world oncology database. MethodsDe-identified clinical and molecular data from patients with advanced solid tumors were linked with third-party mortality and claims datasets using deterministic tokenization. Vital status and death dates were harmonized across sources. Patient identifiers were submitted to NDI, and true matches were de-identified and joined for analysis. Performance metrics (sensitivity, specificity, positive predictive value [PPV], negative predictive value [NPV]) were calculated using NDI as ground truth. Date agreement was assessed at 0, {+/-}15, and {+/-}30-day tolerances. Subgroup analyses and a cumulative cases/dynamic controls (CC/DC) approach were also performed. ResultsAmong 17,597 patients, the composite mortality variable demonstrated 82% sensitivity and 95% specificity against NDI. PPV was 96%, and NPV was 77%. Exact date agreement was 86%, increasing to 94% within a {+/-}15-day tolerance and 96% within a {+/-}30-day tolerance. Incorporating third-party mortality and claims data substantially improved sensitivity from 17% (EHR alone) to 82%. Sensitivity remained stable across subgroups but showed variation by age, cancer type, geographic region, and race. With the CC/DC approach, sensitivity was 96% at 6 months, 97% at 12 months, and 98% at 24 months, with specificity above 98% across these timeframes. ConclusionsThe composite mortality variable is a robust, reliable endpoint for real-world evidence analyses. Its high accuracy for identified deaths and appropriate censoring of lost-to-follow-up patients support its use in overall survival analyses. This validation is a foundational step towards high-quality research to improve patient outcomes and advance cancer drug development using this multimodal dataset. Clinical trial number: not applicable

SARS-CoV-2 mRNA vaccination (COVID-19 vaccination) within 100 days of immune checkpoint inhibitor (ICI) treatment was reported to improve survival and prevent disease progression in patients with non-small cell lung cancer (NSCLC) and metastatic melanoma (Grippin et al., Nature, 2025). However, the clinical evidence, derived from real-world observational data, might suffer from methodological limitations, including immortal-time bias. These key limitations can be overcome by carefully designing a target trial emulation analysis. Using the data made publicly available by the authors, we emulated a target trial that would identify the causal effect of COVID-19 vaccination within 100 days of first ICI on overall survival and progression-free survival in patients with NSCLC and metastatic melanoma. In contrast to the original analysis, we found no evidence that COVID-19 vaccination improves survival outcomes in these populations. The original results likely reflect biases inherent to non-causal observational analyses. To clarify the true effect of COVID-19 vaccination in this setting, larger and suitably designed studies are needed.

Guideline-based recommendations for metastatic lines of therapy (mLoTs), especially second lines and beyond, are comparatively sparse due to challenges in later-line treatment efficacy quantification. Scalable real-world evidence that captures the interaction between treatment and disease progression is therefore especially valuable, as regimens become increasingly individualized, confounding intensifies, and progression is rarely recorded as a structured EHR endpoint. We present a framework to (i) reconstruct clinically coherent mLoTs from longitudinal EHR using radiology-anchored progression evidence and (ii) generate individualized progression-free survival (PFS) estimates from a line-start multimodal snapshot in a highly heterogeneous cohort. In 2,881 patients contributing 8,791 metastatic mLoTs, the selected model shows strong discrimination over a 2-year horizon (Antolinis C = 0.680 {+/-} 0.006; cumulative/dynamic AUC at 1 year = 0.824 {+/-} 0.006). Predicted risk strata closely track Kaplan-Meier trends across line number and tumor subtypes, enabling calibrated risk stratification even in smaller sub-cohorts. Model prediction primarily relies on clinically plausible signals of recent metastatic burden and tumor markers, with limited dependence on surveillance cadence or subtype labels, and is robust to missingness. Together, this framework supports scalable evidence generation and interpretable, calibrated prognostication to inform risk assessment and care planning in heterogeneous metastatic practice.

Backgroundfollowing a first cutaneous squamous cell carcinoma (CSCC), one-third of patients develop new primaries, escalating their risk of metastasis and poor outcomes. However, current follow-up strategies are not risk-stratified, representing a critical gap in patient management. Objectiveto develop and validate a prognostic model to quantify individualized absolute risk of a first metachronous CSCC after an index tumor, accurately accounting for the high competing risk of mortality in this typically elderly population. Methodswe conducted a nationwide, population-based cohort study of 11,737 patients with a first histologically confirmed CSCC (Netherlands Cancer Registry, 2007-2008) with up to 10 years of follow-up. Data on subsequent tumors was retrieved via linkage to the Automated National Pathological Anatomy Archive (Palga). A Fine-Gray competing-risk model was developed using routinely available clinical and pathological predictors (age, sex, hematologic malignancy, basal cell carcinoma (BCC) and actinic keratosis (AK) history, presence of synchronous CSCC, primary tumor location, and differentiation). Model performance was assessed 10-fold cross-validation, quantifying discrimination (time-dependent C-index) and calibration. Resultsduring follow-up, 3,288 (28%) developed a first metachronous CSCC. The model identified key predictors: markers of cumulative UV-exposure (included AK history, [&ge;]5 prior BCCs), and immunosuppression (chronic lymphocytic leukaemia/small lymphocytic leukaemia). Male sex, presence of synchronous CSCC at baseline were also associated with higher risk. While discrimination was modest (cross-validated 5-year C-index: 0.64), the model demonstrated excellent calibration. Conclusionsthis competing-risk model provides individualized, well-calibrated absolute risk estimates for a first metachronous CSCC. Based on routinely available clinical features, it offers insight into how established predictors shape risk in this high-susceptibility population. External validation and the identification of novel predictors are necessary to further refine the model and support personalized dermatologic care.

BackgroundThe Immunoscore(R) is a validated tool for predicting colorectal cancer (CRC) prognosis, yet its adoption is impeded by complex commercial software and patient reimbursement challenges. Utilizing open-source methods, this study aimed to explore whether an immune cell score can be facilitated by focusing on single T-cell markers, to provide a simplified prognostic model in non-metastatic CRC. MethodsA multicentric prospective cohort study was conducted in non-metastatic CRC patients who underwent curative surgical resection. CD3+ and CD8+ tumor infiltrating lymphocytes (TILs) were quantified in both invasive margin (IM) and tumor core (TC) using QuPath. A composite score, termed immune cell score, mirroring the methods employed for the Immunoscore(R), was calculated based on the TIL densities (CD3-IM, CD8-IM, CD3-TC, CD8-TC]. We used a split sample approach (70:30) to estimate adjusted hazard ratios of cancer-specific survival (CSS) in a training and a validation set. Classification and regression tree analysis (CART) was performed to select the most prognostic TIL. The model incorporating the CART-selected TIL was compared to a two-tiered immune cell score model for overall performance (Brier score) and discrimination (concordance probability estimate, CPE). ResultsDuring a median follow-up time of 9.0 years, among 1260 patients, there were 203 CRC specific deaths. CART-selected CD8-IM was the most prognostic TIL at a cut-off of 231 cells/mm2. Patients with CD8-IMHi had better CSS than CD8-IMLow in both training (HR 0.58, 95% CI 0.40-0.84) and validation sets (HR 0.35, 95% CI 0.21-0.60). Brier scores of CD-8IM and immune cell score survival models were comparable in both training and validation cohort, whereas the survival discrimination of CD8-IM slightly outperformed the immune cell score in the validation set (CPE: CD8-IM 0.748, IS 0.730). ConclusionA single TIL marker, specifically CD8-IM, provided prognostic information comparable to the immune cell score. Simplified and cost-effective TIL assessments could enhance their bench to bedside translation and may guide adjuvant therapy in early-stage CRC.

BackgroundPatients with high-risk muscle-invasive urothelial carcinoma (MIUC) remain at substantial risk of recurrence following radical surgery. Adjuvant immune checkpoint inhibitors (ICIs) targeting the PD-1/PD-L1 axis have emerged as a potential strategy to reduce recurrence and improve survival outcomes. MethodsWe conducted a systematic review and meta-analysis of randomized controlled trials (RCTs) evaluating adjuvant ICIs versus observation or placebo in high-risk MIUC. Data were extracted for disease-free survival (DFS), overall survival (OS), distant metastasis-free survival (DMFS), recurrence-free survival beyond the urothelial tract (RFS-extraUT), and health-related quality of life (HRQoL). Standardized mean differences (SMDs) were used to express effect sizes with 95% confidence intervals (CIs), and pooled using inverse-variance weighting under common- and random-effects models. Heterogeneity and subgroup analyses were performed according to drug class, PD-L1 status, and clinical covariates. ResultsNine study-level comparisons from four phase III RCTs (CheckMate 274, IMvigor010, AMBASSADOR, and related updates; n > 2,200) were included. Adjuvant ICIs significantly prolonged DFS compared with control (SMD -0.32; 95% CI -0.44 to -0.21; p < 0.001), with consistent benefits across subgroups and low heterogeneity (I{superscript 2} = 25%). OS benefit was emerging, with nivolumab demonstrating significant advantage at extended follow-up (HR 0.76; 95% CI 0.61-0.96), while pembrolizumab and atezolizumab yielded neutral OS results. ConclusionsAdjuvant PD-1/PD-L1 inhibitors improve DFS and show emerging OS benefits without compromising HRQoL in high-risk MIUC. Nivolumab has demonstrated the most robust long-term survival evidence to date, supporting its role as standard of care. Further biomarker-driven trials are warranted to refine patient selection.