JCO Precision Oncology

BackgroundGastrointestinal stromal tumor (GIST) is the most common gastrointestinal mesenchymal tumor, driven by tyrosine-protein kinase KIT and platelet-derived growth factor receptor A (PDGFRA) mutations. Specific variants, such as KIT exon 11 deletions, carry prognostic and therapeutic implications, whereas wild-type (WT) variants derive limited benefit from tyrosine kinase inhibitors (TKIs). Given the limited reproducibility of established clinicopathological risk models, deep learning (DL) applied to whole-slide images (WSIs) emerged as a promising tool for molecular classification and prognostic assessment. Patients and methodsWe analyzed 8398 GIST cases from 21 centers in 7 countries, including 7238 with molecular data and 2638 with clinical follow-up. DL models were trained on WSIs to predict mutations, treatment sensitivity, and recurrence-free survival (RFS). ResultsDL predicted mutational status in GIST from WSIs, with area under the curve (AUC) of 0.87 for KIT, 0.96 for PDGFRA. High performance was observed for subtypes, including KIT exon 11 delinss 557-558 (0.67) and PDGFRA exon 18 D842V (0.93). For therapeutic categories, performance reached 0.84 for avapritinib sensitivity, 0.81 for imatinib sensitivity. DL models predicted RFS, with hazard-ratios (HR) of 8.44 (95%CI 6.14-11.61) in the overall cohort and 4.74 (95%CI 3.34-6.74) in patients receiving adjuvant therapy. Prognostic performance was comparable to pathology-based scores, with highest discrimination in the overall cohort and in patients without adjuvant therapy (9.44, 95%CI (5.87-15.20)). ConclusionDL applied to WSIs enables prediction of molecular alterations, treatment sensitivity, and RFS in GIST, performing comparably to established risk scores across international cohorts, providing a baseline for future multimodal predictors. HighlightsO_LIDeep learning on histology predicts KIT and PDGFRA mutations in a large international cohort of GISTs from multiple centers C_LIO_LIWhole-slide image models stratify recurrence-free survival comparable to pathology-based risk scores C_LIO_LIPrognostic value of deep learning is preserved in adjuvant therapy subgroups, supporting treatment duration decisions C_LI O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=117 SRC="FIGDIR/small/26345350v1_ufig1.gif" ALT="Figure 1"> View larger version (36K): org.highwire.dtl.DTLVardef@652548org.highwire.dtl.DTLVardef@729a2borg.highwire.dtl.DTLVardef@1e7b6b9org.highwire.dtl.DTLVardef@18d6721_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical abstract.C_FLOATNO Overview of study design and dataset characteristics. (A) Multinational collection of WSIs from seven countries (Spain, France, Italy, Germany, the Netherlands, Poland, and Japan), followed by standard image preprocessing with the STAMP pipeline and clinical data preprocessing/standardization via the Grammar Data Curation framework. The workflow was divided into two main branches: (i) molecular mutation and treatment sensitivity prediction, and (ii) RFS prediction. Model performance was evaluated using AUROC and F1 score for classification tasks, and Kaplan-Meier survival curves with hazard ratios for RFS. Model explainability was assessed through heatmaps of WSIs and identification of top predictive tiles. (B) Summary of clinical dataset composition: proportion of cases receiving adjuvant therapy, tumor location distribution, mutation distribution at the exon level, and mutation distribution at the codon level. C_FIG

PurposePrognosis in metastatic non-seminomatous germ cell tumors (mNSGCT) is currently guided by the IGCCCG classification, which incorporates tumor markers, organs involved with metastatic disease, and primary site but not histologic subtype. We aimed to evaluate whether specific histological components provide additional prognostic information in a large international mNSGCT cohort. Patient and MethodsWe analyzed clinical, pathologic, and outcome data from 662 patients with mNSGCT across multiple international centers. Cox regression and multivariable stepwise models were used to evaluate the impact of age, tumor histology, serum markers, primary site of disease, chemotherapy, IGCCCG, and post-chemotherapy surgery on overall survival. Analyses were performed using both complete-case and imputed datasets to account for missing values. ResultsThe presence of any percentage of embryonal carcinoma (EC) was independently associated with improved overall survival HR 0.603 (95% CI: 0.37-0.98, p=0.040), whereas yolk sac tumor (YST) predicted worse prognosis in complete-case analysis HR 2.27 (95% CI: 1.43 - 3.61 p = 0.001). Choriocarcinoma was also associated with a HR 1.58 (95% CI: 1.08 - 2.32 p= 0.019) adverse outcomes. IGCCCG risk classification remained a strong predictor of mortality HR up to 8.9 for Poor vs Good risk, (95% CI: 4.63 - 17.09 p < 0.001), but histologic components added significant independent prognostic value. Post-chemotherapy retroperitoneal lymph node dissection (RPLND) conferred a substantial survival benefit HR 0.44 (95% CI: 0.258 - 0.754 p=0.003). Interestingly, teratoma was not associated with mortality but was linked to younger age, testicular primaries, and higher likelihood of residual disease requiring surgery. ConclusionsHistological composition, particularly the presence of EC or YST, has a significant and independent impact on survival in mNSGCT, beyond established risk classifications. Integration of histological subtypes may enhance prognostic accuracy and guide individualized treatment strategies in advanced germ cell tumors.

BackgroundThe province of Quebec has progressively implemented paired tumour-germline sequencing in paediatric oncology through two coordinated precision research programs, preceding a province-wide mainstream clinical genomics initiative. We report the prevalence, spectrum, and clinical relevance of germline findings (GFs) in children with primary extracranial cancers, integrating molecular, phenotypic, and pathological data. MethodsPatients enrolled between 2014 and 2022 underwent germline whole-exome sequencing (WES) using a virtual 352-cancer gene panel. Sequencing, bioinformatics and variant interpretation followed best practices standards based on GATK, ACMG/AMP and ClinGen recommendations. Somatic WES and transcriptomic data were integrated when available. GFs were categorised as diagnostic findings (DFs; established or suspicious association with the cancer phenotype) or as other findings further subcategorised according to actionability and age of disease onset. FindingsAmong 484 children, 130 (26.9%) carried 149 GFs, including 49 (10.1%) with a DF (42 with well-established associations with cancer phenotypes). DFs involved 21 genes related to childhood cancer predisposition, trisomy 21 and one clinical Beckwith-Wiedemann syndrome. Six DFs were initially missed by standard exome pipelines, and mosaic constitutional cancer predisposition syndrome (CPS) was confirmed in 4/49 children, underscoring the value of integrative analyses. A CPS was known at the time of primary cancer in 10/49 children. Among those diagnosed with a CPS after cancer onset, suggestive phenotypic features were present in 36/39. Other non-diagnostic findings were identified in 92 children; 21 (4.3% of the cohort) with actionable implications in childhood (n=7) or adulthood (n=14). Somatic sequencing was informative for refining causality, as somatic second hit alterations were identified in 29/33 (87.9%) DFs involving monoallelic tumour suppressor genes, whereas no such alterations were observed in non-DFs counterparts (0/57; p<0.0001). Interpretation: This provincial research experience highlights the analytical and practical challenges of germline evaluation in paediatric oncology and supports a shift toward integrative interpretation frameworks combining complementary germline, somatic, pathology, and phenotypic data. Flexibility in investigative strategies and nuanced categorisation of findings are warranted, guided by a child-centred interpretative framework. This approach underpins Quebecs paediatric oncology genomics mainstreaming initiative.

BackgroundIn immune checkpoint inhibitor (ICI) trials, overall survival (OS) benefits are well established, yet improvements in quality of life (QoL) are often inconsistent or absent in conventional analyses. This apparent discordance raises important questions: are QoL outcomes truly unrelated to survival, and how can QoL results be better utilized and interpreted? MethodsA model-based meta-analysis (MBMA) of longitudinal EORTC QLQ-C30 global health status/quality of life data from randomized ICI trials was conducted. Longitudinal QoL trajectories were analyzed using a nonlinear mixed-effects model to estimate treatment-related toxicity and long-term QoL improvement. Associations between QoL trajectory parameters and OS were assessed using spearman rank correlation tests and Cox proportional hazards models. ResultsTwenty-seven studies (8,149 ICI and 5,593 control patients) contributed longitudinal QoL data, and 18 studies provided matched OS data. Raw QoL trajectories showed overlap between treatment arms, while OS consistently favored ICIs. MBMA revealed that ICIs had similar toxicity but significantly faster QoL improvement than control therapies (p < 0.0001). Baseline QoL, toxicity, and QoL improvement rate were all significantly associated with OS (p < 0.001). MBMA-based QoL comparisons were more sensitive in detecting associations with survival than raw QoL data, with the strongest association observed at Week 24 (R = -0.37, p = 0.067). ConclusionsConventional analyses comparing QoL at a single time point may obscure meaningful patient-reported benefits. By capturing longitudinal QoL trajectories across trials, MBMA reveals how patient experience evolves alongside survival outcomes and supports improved interpretation and utilization of QoL data in treatment evaluation.

BackgroundImmune checkpoint inhibitors (ICI) have improved outcomes in Merkel cell carcinoma (MCC). Population analyses suggest improved survival following the 2017 approval of ICI, but registry data lack treatment-level information including type of systemic therapy and initiation timepoint to directly estimate the benefit attributable to immunotherapy. This study compared Merkel Cell Carcinoma-specific survival between patients treated with first-line ICI versus cytotoxic chemotherapy. MethodsPatients were identified from the Seattle Merkel Cell Carcinoma Registry. Among 1,517 patients with MCC, 463 received first-line systemic therapy with either ICI or chemotherapy. Propensity scores were estimated using logistic regression including AJCC 8th stage, age, sex, MCPyV status, and immunosuppression. One-to-one nearest-neighbor matching produced balanced cohorts of 133 ICI-treated and 133 chemotherapy-treated patients. Merkel Cell Carcinoma-specific survival from therapy initiation was analyzed using Kaplan-Meier and Cox proportional hazards models with follow-up administratively censored at five years. ResultsBaseline clinical characteristics were comparable between matched cohorts. ICI therapy was associated with significantly improved Merkel Cell Carcinoma-specific survival compared with chemotherapy (log-rank p<0.0001). Five-year Merkel Cell Carcinoma-specific survival was 56.8% (95% CI 46.8-65.6) for ICI versus 23.9% (95% CI 16.9-31.6) for chemotherapy. In multivariable stage-stratified Cox analysis, ICI remained independently associated with improved Merkel Cell Carcinoma-specific survival (HR 0.32, 95% CI 0.21-0.50; p<0.0001), while immunosuppression was associated with worse Merkel Cell Carcinoma-specific survival (HR 2.03, 95% CI 1.10-3.74; p=0.0228). ConclusionsICI therapy was associated with substantially improved MCC-specific survival compared with chemotherapy.

Oncology research and clinical practice in North America increasingly rely on complex endpoints, heterogeneous study designs, and high-dimensional molecular data. In this landscape, data visualization serves as a critical analytic instrument for study design communication, model diagnostics, safety reporting, and real-time clinical decision support. Despite its importance, the oncology visualization ecosystem remains fragmented across commercial platforms and bespoke scripts, lacking a unified, code-first reference that emphasizes reproducibility and auditability in the R programming environment. This paper addresses this gap by presenting a North American collaborative atlas of 62 oncology visualization templates: 24 for clinical trials, 12 for real-world evidence (RWE), and 26 common to both settings. A core innovation of this atlas is its simulation-driven approach; each plot is illustrated using transparent, reproducible data-generating mechanisms. This allows users to deterministically recreate figures and easily adapt templates to alternative endpoints, censoring patterns, and subgroup structures. The paper provides foundational notation for oncology endpoints, an operational taxonomy based on data geometry, and a consolidated review of relevant R software. We further synthesize the practical utility of these methods through four representative case studies and provide a comparative analysis of the strengths, limitations, and future challenges of oncology data visualization. A detailed tutorial on fishplot is included to demonstrate a publication-ready workflow for clonal evolution.

Introduction Agreement between radiologists regarding treatment response in Pleural Mesothelioma (PM) is acknowledged to be poor, but downstream effects in clinical trials have not been quantified. Methods We performed a mixed methods study, composed of a multicentre, retrospective cohort study and in silico modelling. CT images and data were retrieved from 4 UK centres regarding chemotherapy-treated patients. Expert radiologists classified response using modified Response Evaluation Criteria In Solid Tumours criteria (mRECIST) v1.1, generating discordance rate (%) and agreement. In silico modelling simulated two-arm trials of an active therapy with intended 80% power and confidence intervals for four endpoints (objective response rate (ORR), disease control rate (DCR), progression-free survival (PFS), overall survival (OS)) covering 95% of the true effect. Actual power and endpoint coverage were modelled against mRECIST misclassification rate (a single reporter equivalent of discordance rate). Consecutive simulations varied misclassification rate from 0-100% in 1% increments, each repeated 10,000 times. Results 172 cases were included. Discordance rate was 35% (60/172), kappa=0.456. In silico modelling demonstrated reduced power and endpoint precision with increasing misclassification. At 17% misclassification, corresponding to the observed 35% discordance, power dropped from 80% to 55% for ORR, 53% for DCR, 65% for PFS and 66% for OS, with endpoint coverage reduced to 88%, 89%, 92% and 92%, respectively. 50/60 (83%) discordances reflected interpretation or measurement differences intrinsic to mRECIST. Discordance was not associated with tumour volume. Conclusions Inconsistent response classification is common in PM and substantially reduces statistical power and endpoint precision in clinical trials.

PurposeGermline deletions affecting the Y-chromosomal gr/gr region were reported in 2005 as associated with susceptibility to testicular germ cell tumor (TGCT), a highly heritable tumor type that is the most common cancer type affecting adult men under the age of 45. Attempts to replicate this association have been equivocal, primarily due to limited power. MethodsHere, we compare and validate two computational approaches to gr/gr deletion calling in high-, low- and ultra-low-coverage whole genome sequencing data, applying these to two datasets from UK Biobank and the TECAC consortium. We generate dataset-specific effect size estimates for the gr/gr deletion-TGCT association using Firths bias-reduced logistic regression across a total of 198,306 men of European-like ancestry (2231 with and 196,075 without TGCT). ResultsUpon random-effects meta-analysis of estimated effect sizes in the two datasets, we found no significant association between gr/gr deletion status and TGCT risk (combined odds ratio=1.24, 95% CI=0.74-2.07, p=0.42), nor upon stratification of seminoma and non-seminoma/mixed histological subtypes. ConclusionOur analysis suggests gr/gr deletion status alone is likely not predictive of TGCT risk in population-scale analyses of European-like individuals; however, the importance of other proposed determinants of gr/gr deletion impact, including Y-haplogroups and semen phenotype, remains unexplored at scale.

Immune checkpoint inhibitors such as anti-PD1 antibodies are essential in cancer therapy. Emerging data suggest that lower doses may be effective and more economical, though further evidence is needed. We conducted a retrospective study at Centre Leon Berard to assess the efficacy and safety of low-dose nivolumab (20 mg every three weeks) in patients with advanced cancer, mainly squamous cell carcinomas (SCC). Between 2023 and 2024, 53 patients were treated, with a median age of 74 years; 39.6% were over 80. Most were male (64%) and had ECOG >1 (69.9%). Primary tumor sites included cutaneous SCC (34%), head and neck SCC (32%), and soft tissue sarcoma (15%). After a median follow-up of 8.3 months, median overall survival was 7.5 months. The objective response rate (ORR) was 20.8% overall, rising to 35.3% in cutaneous SCC and 23.5% in head and neck SCC-comparable to standard-dose nivolumab. Toxicity was manageable: 18.7% experienced immune-related adverse events, with only 3.7% grade 3. Low-dose nivolumab demonstrates encouraging efficacy and tolerability in a frail population, supporting its potential role in resource-limited settings. Prospective trials are warranted to confirm these findings in broader populations.

Background: Predicting whether a treatment will demonstrate meaningful clinical benefit before committing to a large-scale trial remains a major unmet need in oncology. Patient-derived organoids (PDOs) recapitulate individual tumor drug sensitivity, but have not been used to forecast population-level trial outcomes. We developed SCOPE (Screening-to-Clinical Outcome Prediction Engine), a platform that integrates PDO drug screening with clinical prognostic modeling to predict arm-level median progression-free survival (mPFS) and objective response rate (ORR) without access to any trial outcome data. Patients and methods: SCOPE was trained on 54 treatment lines from patients with metastatic colorectal cancer (mCRC, n=15) and metastatic pancreatic ductal adenocarcinoma (mPDAC, n=39) with matched clinical data and PDO drug screening across 9 compounds. A Clinical Score module captures baseline prognosis; a Drug Screen Score module quantifies treatment-specific organoid sensitivity. To predict trial outcomes, synthetic patient profiles are generated from published eligibility criteria and matched to a biobank of 81 PDO lines. Predictions were externally validated against 32 arms from 23 published trials, treatment ranking was assessed across 8 head-to-head comparisons, and prospective applicability was tested for daraxonrasib (RMC-6236), a novel pan-RAS inhibitor in mPDAC. Results: Predicted mPFS strongly agreed with published outcomes (R2=0.85, MAE=0.82 months; Pearson r=0.92, P<0.001), approaching the empirical concordance between two independently measured clinical endpoints (ORR vs. mPFS, R2=0.87). ORR prediction was similarly robust (R2=0.71, MAE=7.3 percentage points). Integrating organoid and clinical data significantly outperformed either alone (P=0.001). SCOPE correctly identified the superior arm in 7 of 8 head-to-head comparisons (88%, P<0.05). Applied to daraxonrasib prior to phase 3 data availability, the platform predicted superiority over standard chemotherapy in KRAS-mutant mPDAC, consistent with emerging clinical data. Conclusion: By combining functional organoid drug screening with clinical modeling, SCOPE generates calibrated efficacy predictions for both established regimens and novel agents without prior clinical data. This approach could support clinical trial design, treatment arm selection, and go/no-go decisions, offering a new tool to improve the efficiency of gastrointestinal cancer drug development.

A recent randomized clinical trial in non-small cell lung cancer1 confirms what numerous observational studies have reported - time-of-day (ToD) may dramatically influence treatment outcomes in cancer patients2-9. In this recent trial median overall survival (OS) decreased from 28 months in the early ToD arm to 16.8 months in the late ToD arm. We raise the concern that clinical trial outcomes may be influenced by seemingly minor biases in treatment time across arms. We also suggest that by measuring or randomizing treatment-time in clinical trials, we may identify beneficial ToD-dependent treatments that would otherwise be overlooked.

Background Cardiovascular adverse events (CVAEs) after chemoradiotherapy (CRT) for lung cancer are major concerns in Appalachia due to high rates of smoking and pre-existing cardiovascular diseases (CVD). The objectives of this study were to characterize the incidence of CVAEs in this population and evaluate machine learning (ML) models for CVAEs risk stratification and mortality prediction. Methods A retrospective study was conducted among Appalachian patients with lung cancer treated with definitive CRT at a single institution between 2013 and 2025. Baseline clinical variables, including demographics, smoking status, pre-existing CVD, and post-CRT CVAEs were collected. Heart dosimetric parameters were also obtained. ML models [Random Forest (RF), Gradient Boosting (GBM), Support Vector Machine (SVM), Logistic Regression (LR)] were trained using 5 fold cross validation and evaluated using AUC, sensitivity, specificity, and F1 score. Feature importance was assessed using permutation analysis. Wilcoxon and Chi-squared tests were used for descriptive comparisons. Results Eighty-six patients (mean age 66 years, 47% male) were included. At diagnosis, 80% (n=69) had NSCLC and 20% (n=17) had LS-SCLC. CVAEs occurred in 51 patients (59%). The most frequent events were NSTEMI (n=15, 29.4%), pericardial disease (n=15, 29.4%), and arrhythmia (n=8, 15.7%). Mean heart dose was higher in the CVAE group (13.4 vs 9.4 Gy, p=0.27). For CVAE prediction, GBM achieved the highest AUC (0.55, 95% CI 0.44-0.69) and sensitivity (75%), while RF showed the highest sensitivity (80%, 95% CI 69-90%). Key predictors included age and cardiac dosimetrists (Heart V20, V40, V50, and mean heart dose). For mortality prediction, RF achieved the highest discrimination (AUC = 0.63, 95% CI 0.496-0.750). Age, cardiac dosimetry, disease stage, and cardiovascular comorbidity were the most influential predictors. Conclusion High incidence of CVAEs occurred among patients with lung cancer treated with CRT in this Appalachian cohort. While ML models demonstrated modest predictive performance, tree-based approaches demonstrated high sensitivity for identifying patients at risk for CVAEs and mortality. Age and cardiac radiation dose metrics consistently emerged as key predictors, highlighting the importance of cardiac dose optimization and ML-based risk stratification for cardio-oncology surveillance.

BackgroundOlder age and comorbidities complicate initial therapy in non-small-cell lung cancer (NSCLC), as platinum ineligibility has not been systematically characterized. MethodsAll 2592 patients presenting with metastatic NSCLC between 2018-2023 at Thoraxklinik Heidelberg were analyzed. ECOG status (PS), comorbidities, molecular testing, therapy, toxicities, and outcomes were verified from individual patient records. ResultsAmong 1306 patients with PD-L1 0-49%, systemic therapy was initiated in 74%. With availability of monoimmunotherapy, the treatment rate for patients with PD-L1[&ge;]50% (n=507) was higher by 5% (p=0.01), while best supportive care (BSC) by own choice was reduced (1.8% vs. 4.5%, p=0.005) more than medical BSC (mBSC 14.6% vs. 17.8%, p=0.11), and early death remained unchanged (ca. 4%). Initial suitability for systemic therapy was documented for 70% of cases eventually receiving mBSC after deterioration associated with comorbidities, metastatic burden, longer workup duration, or radiotherapy upfront (all p<0.001). The atezolizumab Summary of Medicinal Product Characteristics (SmPC) criteria, i.e. >80 years, or PS [&ge;]3, or comorbidities with PS [&ge;]2 or with age [&ge;]70, were fulfilled by 38% of patients (n=501) and associated with a >3-fold higher risk of BSC or early death (230/501), as well as significantly higher toxicity under platinum and shorter survival, which for a platinum dose ratio [&le;]60% across 4 cycles (9% of 1306) was similar to that with single-agent chemotherapy (median 5.1 months, p<0.001). SmPC criteria correlated better than comorbidity scores with foregoing platinum, but predictive performance for individual patients remained modest (AUC 0.71, p<0.001). ConclusionsThe high initial attrition of approximately 25% in NSCLC could improve with availability of monoimmunotherapy, but requires optimized, faster patient workflows for better mitigation. Adoption of the SmPC criteria could support a priori identification of patients at risk for mBSC or platinum overtreatment to enhance utilization of monoimmunotherapy and other novel platinum-free first-line options in the future. HighlightsO_LIA high initial attrition of approximately 25% is caused by deterioration after histologic diagnosis in advanced NSCLC. C_LIO_LIMonoimmunotherapy and optimized workflows may facilitate treatment for ca. 15% additional stage IV NSCLC patients. C_LIO_LISmPC criteria indicate cases at higher risk for BSC (>3x) or platinum overtreatment (i.e. platinum dose ratio [&le;]60%). C_LIO_LISmPC patients receiving platinum have higher toxicity and shorter survival than non-SmPC patients. C_LIO_LIImproved therapeutic allocation will be essential for utilization of any novel platinum-free option in the future. C_LI

Background: Sezary syndrome (SS) is an aggressive leukemic variant of cutaneous T-cell lymphoma (CTCL) with distinct clinical and biological features compared to rarer entities such as primary cutaneous CD8+ aggressive epidermotropic cytotoxic T-cell lymphoma (PCAECTCL). Although recurrent genomic alterations in CTCL have been described, comparative analyses at the pathway level across biologically divergent subtypes remain limited. Here, we leveraged a conversational artificial intelligence (AI) platform for precision oncology to enable rapid, integrative, and hypothesis-driven interrogation of publicly available genomic datasets. Methods: We conducted a secondary analysis of somatic mutation and clinical data from the Columbia University CTCL cohort accessed via cBioPortal. Cases were stratified into SS (n=26) and PCAECTCL (n=13). High-confidence coding variants were curated and mapped to biologically relevant signaling pathways and functional gene categories implicated in CTCL pathogenesis. Pathway-level mutation frequencies were compared using Chi-square or Fisher's exact tests, with effect sizes quantified as odds ratios. Tumor mutational burden (TMB) was compared using the Wilcoxon rank-sum test. Subtype-specific co-mutation patterns were evaluated using pairwise association analyses and visualized through oncoplots and network heatmaps. Conversational AI agents, AI-HOPE, were used to iteratively refine cohort definitions, prioritize pathway-level signals, and contextualize findings. Results: TMB was comparable between SS and PCAECTCL (p = 0.96), indicating no significant difference in global mutational load. In contrast, pathway-centric analyses revealed marked qualitative differences. SS demonstrated enrichment of alterations in epigenetic regulators, tumor suppressor and cell-cycle control pathways, NFAT signaling, and DNA damage response mechanisms, consistent with transcriptional dysregulation and immune modulation. PCAECTCL exhibited relatively higher frequencies of alterations involving epigenetic regulators and MAPK pathway signaling, suggesting distinct oncogenic dependencies. Co-mutation analysis revealed a more constrained and focused interaction landscape in SS, whereas PCAECTCL displayed broader and more heterogeneous co-mutation networks, indicative of divergent evolutionary trajectories. Notably, ERBB2 mutations were significantly enriched between subtypes (p = 0.031), highlighting a potential subtype-specific therapeutic vulnerability. Conclusions: This study demonstrates that SS is distinguished from PCAECTCL not by increased mutational burden but by distinct pathway-level architectures, particularly involving epigenetic regulation, immune signaling, and transcriptional control. These findings generate biologically grounded, testable hypotheses for subtype-specific therapeutic targeting and underscore the value of conversational AI as a scalable framework for accelerating discovery in translational cancer genomics.

ObjectivePopulation-based information regarding adherence to first-line chemotherapy in epithelial ovarian cancer is scarce. This study aimed to evaluate chemotherapy adherence, reasons for chemotherapy modifications, and associations with overall survival. MethodsAdvanced-stage epithelial ovarian cancer patients diagnosed between January 2015 and December 2021 were identified from the Netherlands Cancer Registry. Patients who underwent cytoreductive surgery combined with platinum- and taxane-based chemotherapy were included. Patients were categorized into two groups: adherent (patients without modifications) and non-adherent (patients with modifications: dose reduction, chemotherapy interruption, and/or reduction in chemotherapy cycles). Reasons for modifications were assessed. Kaplan-Meier survival curves and Cox proportional hazards models were used to analyze overall survival. ResultsAmong the cohort (N = 3,687), 54% of patients underwent chemotherapy modifications. Dose reduction (38%) was the most common, followed by interruption (24%) and reduction in chemotherapy cycles (9%). Non-adherence was associated with poorer performance scores, higher comorbidity indices, and undergoing primary cytoreductive surgery. Neurotoxicity and hematologic toxicity were the primary reasons for modifications in platinum (33% and 37%) and taxane (47% and 35%) agents. No association with survival was found for dose reduction and interruption. However, reduction in chemotherapy cycles was associated with lower 5-year overall survival (32% (95% CI 26%-38%) vs. 36% (95% CI 34%-38%)), remaining significant after multivariable adjustment (hazard ratio 1.36; 95% CI 1.17-1.59). ConclusionA significant proportion of Dutch advanced-stage epithelial ovarian cancer patients undergo chemotherapy modifications. No impact on overall survival was found for dose reduction or chemotherapy interruption, warranting prospective studies. Reduction in chemotherapy cycles was negatively associated with overall survival, possibly reflecting underlying treatment ineffectiveness. Key messagesO_ST_ABSWhat is already known on this topicC_ST_ABSGuideline-recommended chemotherapy for advanced epithelial ovarian cancer is often difficult to deliver in routine practice, and real-world data on adherence and its impact on survival are limited. What this study addsIn this nationwide retrospective cohort, over half of patients experienced chemotherapy modifications; dose reductions and interruptions were not associated with poorer overall survival, whereas a reduction in the number of cycles showed an association with worse outcomes, although this may partly reflect underlying disease severity or treatment response. How this study might affect research, practice or policyOur findings suggest that standard dosing and treatment duration of six cycles may not always be necessary, emphasizing the need to tailor treatment plans to optimize both efficacy and tolerability in advanced-stage epithelial ovarian cancer patients

PurposePDPK1 functions downstream of PI3K and is essential for activating AKT and other AGC kinases. Although PDPK1 has a central role in the PI3K/AKT/mTOR signaling pathway, there has been limited evaluation of it as a target for cancer therapy. Anaplastic thyroid cancer (ATC) has one of the highest mortality rates of all human malignancies. Although combined BRAF and MEK inhibition in BRAF V600E-mutant ATC (45% of cases) results in response, resistance is common, and there is no curative treatment. The majority (up to 95.8%) of ATC cases have activation in the PI3K/AKT/mTOR and RAS/RAF/MEK/MAPK pathways due to genetic alterations (including driver mutations and genomic gains/losses), involved in these pathways. In this study, we investigated PDPK1 as a therapeutic target for ATC. Experimental designWe used in vitro, ex vivo, and in vivo ATC models to evaluate the effect of targeting PDPK1 (BX795) alone and in combination with mutated BRAF V600E inhibition (dabrafenib), and the mechanism of action that resulted in ATC cell death. ResultsBX795 monotherapy significantly reduced ATC cell proliferation, invasion, colony formation, and spheroid size. The combination of BX795 with dabrafenib produced strong synergistic antitumor activity in BRAF V600E-mutant ATC models. Dual inhibition led to simultaneous and sustained suppression of PDPK1/AKT and MAPK signaling, preventing the compensatory pathway reactivation observed with single-agent treatment. This integrated blockade induced pronounced oxidative stress, DNA damage, and G2-phase cell-cycle arrest, accompanied by mitochondrial dysfunction and robust activation of apoptotic cascades. These effects translated into marked tumor regression in in vitro, ex vivo, and in vivo experimental systems. ConclusionsOur findings identify PDPK1 as a critical and therapeutically tractable vulnerability in anaplastic thyroid cancer. Co-targeting PDPK1 and BRAF V600E produces potent synergistic antitumor activity by shutting down convergent oncogenic signaling nodes and amplifying apoptotic stress responses. These data support PDPK1 inhibition--alone and in combination with BRAF blockade acts as a promising strategy to improve outcomes for patients with BRAF V600E-mutant ATC.

IntroductionBRCA1/2 alterations are increasingly recognized as biologically and clinically relevant features in prostate cancer, yet the prognostic and therapeutic significance of zygosity status remains uncertain. Understanding differences between monoallelic and biallelic inactivation may refine risk stratification and guide therapeutic decision-making. Materials and MethodsA retrospective, desk-based observational analysis was performed using publicly accessible datasets from TCGA-PRAD (primary disease) and SU2C/PCF (metastatic disease). BRCA1/2 status was categorized as wild-type, monoallelic, or biallelic based on mutation, copy-number, and loss-of-heterozygosity profiles. Overall survival was evaluated using Kaplan-Meier estimates and Cox models. Systemic therapy outcomes were assessed by treatment class, incorporating exploratory interaction tests. ResultsIn TCGA-PRAD (n=300), OS did not significantly differ by zygosity (global log-rank p=0.45), with median OS of 80.0 months (wild-type), 78.0 months (monoallelic), and 55.0 months (biallelic). In SU2C/PCF (n=200), zygosity stratified outcomes significantly (global log-rank p=0.04): median OS was 22.0 months (wild-type), 14.0 months (monoallelic), and 16.0 months (biallelic). Treatment analyses showed ARSI exposure improved OS in wild-type disease (HR 0.60; 95% CI 0.38-0.95), while interaction testing suggested potential heterogeneity without statistical confirmation (interaction p=0.092). PARP inhibitor exposure showed directionally favorable HRs in wild-type and monoallelic groups but no significant interaction (interaction p=0.757). No therapy class demonstrated consistent effect modification by zygosity. ConclusionBRCA1/2 zygosity shows prognostic relevance in metastatic prostate cancer but not clearly in primary disease. While zygosity did not consistently modify systemic therapy associations in this dataset, findings support zygosity-aware reporting as a practical tool for molecular stratification and future research design.

Background: The tumor suppressor gene TP53 and the oncogene KRAS are among the most frequently altered core drivers in human malignancies. Although they cooperatively regulate critical biological processes, the prognostic impact of their co alterations remains poorly defined and exhibits striking inconsistency across different cancer types. Methods: We comprehensively analyzed genomic and clinical data from multi-cancer cohorts sourced from the cBioPortal database and The Cancer Genome Atlas (TCGA). Genetic alterations, including sequence variations and copy number alterations (CNAs), were classified for TP53 and KRAS. Patients were stratified into four subgroups based on individual or combined alteration status. Survival analyses were performed using Kaplan-Meier methods. Integrated multi-omics analyses were conducted to assess the relationship between genetic alterations and mRNA/protein expression, and to characterize co-occurring genetic events and their prognostic implications. Results: Patients harboring concurrent TP53 and KRAS alterations exhibited significantly shorter overall survival in pancreatic cancer, colorectal cancer, and ampullary carcinoma, but surprisingly demonstrated the longest survival in gastric cancer. Distinct KRAS mutation subtype distributions were observed across cancer types: G12D/G12V predominated in pancreatic and colorectal cancers, G12C in non small cell lung cancer, and G13D in gastric cancer, with copy number alterations representing a substantial proportion of KRAS alterations in gastric and lung cancers. Multi-omics analysis revealed a lack of concordance between genetic alterations and mRNA/protein expression, indicating that mutation status alone does not reliably reflect downstream molecular changes. Concurrent genetic events displayed striking cancer-type specificity: CDKN2A alterations frequently co-occurred with TP53/KRAS double alterations in pancreatic cancer and were associated with worse prognosis, whereas APC mutations co-occurred in colorectal cancer and correlated with improved survival. Integrated analysis further demonstrated that KRASaltered/TP53altered patients were highly enriched in pancreatic, colorectal, and lung cancers, each exhibiting unique background genomic landscapes. Conclusions: The prognostic significance of TP53 and KRAS alterations is profoundly cancer-type specific, driven by differences in mutation subtype distribution, copy number alteration patterns, co-occurring genetic events, and the discordance between genotype and functional expression. These findings challenge the simplistic view of dual-gene alterations as universal markers of poor prognosis and underscore the necessity of incorporating cancer-specific molecular contexts into prognostic models and precision oncology strategies.

BackgroundSezary syndrome (SS) represents an aggressive leukemic variant of cutaneous T-cell lymphoma (CTCL) with distinct clinical behavior compared with other CTCL subtypes. While prior studies have identified recurrent genomic alterations in CTCL, a systematic pathway-centric comparison between SS and non-SS CTCL remains limited. We applied our conversational artificial intelligence (AI) platform for precision oncology, to accelerate hypothesis generation and integrative interpretation of public genomic data. MethodsWe performed a secondary analysis of somatic mutation and clinical data from the Columbia University CTCL cohort available via cBioPortal. Samples were stratified into SS (n=26) and non-SS CTCL (n=17). High-impact coding variants were retained and annotated to curated functional gene groups and signaling pathways relevant to CTCL biology. Pathway-level mutation frequencies were compared using Fishers exact test, with effect sizes summarized by odds ratios. Tumor mutation burden (TMB) was compared using Wilcoxon rank-sum testing. Subtype-specific gene-gene co-mutation patterns were assessed using pairwise association testing and visualized with heatmaps and oncoplots, with our conversational AI agents facilitating interactive exploration and prioritization of results. ResultsOverall TMB did not differ between SS and non-SS CTCL (p=0.83), indicating comparable global mutational burden. Pathway-level analyses revealed enrichment of alterations affecting epigenetic regulators, tumor suppressor and cell-cycle control genes, NFAT signaling, and apoptosis/immune regulation in SS, whereas MAPK and JAK-STAT pathway alterations were relatively more frequent in non-SS CTCL. Co-mutation analysis demonstrated fewer but more focused gene-gene interactions in SS compared with broader co-mutation networks in non-SS CTCL, suggesting divergent evolutionary constraints. Several genes (including ERBB2, WWC1, POSTN) showed borderline subtype-specific enrichment, warranting further validation. ConclusionsConversational AI-enhanced analysis reveals that SS is distinguished from other CTCL subtypes not by higher mutational load, but by qualitative differences in pathway involvement, particularly epigenetic dysregulation, immune escape, and transcriptional control. These findings generate testable hypotheses for downstream validation in patient-level datasets and demonstrate the utility of conversational AI agents as accelerators of translational cancer genomics.

BackgroundCirculating tumor DNA (ctDNA) detection after curative-intent surgery is being used to identify minimal residual disease (MRD) in colorectal cancer (CRC). However, MRD classification is dependent on analytical sensitivity, and the impact of detection threshold on observed post-operative positivity remains incompletely characterized. We evaluated MRD positivity in stage I-III CRC using a CRISPR-based plasma sequencing assay, MUTE-Seq. MethodsPatients were prospectively enrolled and analyzed using customized tumor-informed panels applied to baseline and post-operative plasma samples collected at 4-week and 3-month. We report preliminary results from 39 plasma samples obtained from the first 14 patients. MRD positivity was assessed across multiple hypothetical detection thresholds (1-100 ppm). ResultsAll 14 patients (100%) had detectable mutations at baseline. Mutation-positive call number significantly decreased after surgery (baseline vs 4-week, p = 0.006; baseline vs 3-month, p = 0.004), and ctDNA concentration likewise declined (baseline vs 4-week, p = 0.002; baseline vs 3-month, p = 0.003). Among stage II-III patients, MRD positivity at 4-week was 20% at a 100-ppm threshold but increased to 70% at 10 ppm and 100% at 1 ppm. At 3-month, MRD positivity was 11% at a 100-ppm threshold and 78% at 1 ppm. At both time points, approximately 80% of MRD-positive stage II-III patients harbored ctDNA levels below 100 ppm, and half of these cases were below 15 ppm. Two patients (one stage I and one stage II) developed recurrence; both were MRD-positive at 4-week and demonstrated increasing mutation-positive calls at 3-month, with a median radiologic lead time of 4 months. ConclusionsPost-operative MRD classification in CRC is strongly influenced by analytical sensitivity. A substantial proportion of residual disease signals reside below the conventional ctDNA detection threshold of 100 ppm, supporting the clinical relevance of ultrasensitive ctDNA detection.