JCO Precision Oncology

Background. The European Society for Medical Oncology (ESMO) Scale for Clinical Actionability of molecular Targets (ESCAT) ranks genomic alterations by the evidence supporting the predictive value of the molecular target for response to targeted therapies. No openly available, systematically curated set of standard care biomarkers mapped to the ESCAT framework exists to support clinical decision-making or harmonize biomarker interpretation. Methods. We mapped all OncoKBTM Level 1 biomarkers to ESCAT tiers using evidence cited by OncoKBTM, excluding abstract-only data. Eight board-certified oncologists and hematologists independently assigned ESCAT tiers, with discrepancies resolved through structured consensus meetings. Recurring evidence scenarios that did not correspond to any existing ESCAT tier informed a set of a priori defined modifications, which were subsequently applied to biomarkers that could not be classified using native ESCAT criteria. Results. Of 188 OncoKBTM Level 1 biomarkers, 16 were excluded due to abstract-only evidence. Using native ESCAT criteria, 51% of the remaining biomarkers were classified as Tier 1, 3% Tier 2, 18% Tier 3, 6% Tier X and 22% could not be assigned to any tier. Applying the modified ESCAT criteria resolved all previously unclassifiable biomarkers and increased Tier 1 assignments to 73%. Inter-rater reliability (Krippendorffs alpha) was moderate (0.586) and 62% of classifications required consensus discussions. Comparison with ESCAT tiers reported in ESMO Clinical Practice Guidelines showed improved concordance when using the modified criteria. Conclusions. The native ESCAT criteria are highly stringent, resulting in many FDA-recognized, clinically validated biomarkers that are currently assigned level 1 by OncoKBTM not mapping to any existing tier. Our predefined modifications improved alignment with OncoKBTM Level 1 designations and with published ESMO clinical practice guidelines. The mapped set of standard care biomarkers are provided on the OncoKBTM website, offering a practical resource that harmonizes ESCAT tiers of evidence with a widely adopted levels of evidence schema.

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

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

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

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.

BackgroundFOLFIRINOX is a cornerstone regimen for eligible patients with pancreatic ductal adenocarcinoma (PDAC), but its clinical benefit is limited by substantial toxicity and frequent dose modification. In real-world practice, dose modifications are often individualized, and the clinical factors associated with these decisions remain incompletely characterized. ObjectiveTo develop and evaluate an electronic medical record (EMR)-based machine-learning framework for modeling cycle-specific FOLFIRINOX dose modification decisions in patients with PDAC. MethodsWe included patients with PDAC who received FOLFIRINOX at UCSF oncology clinics between November 2011 and December 2023. Predictors included demographic, clinical, laboratory, and treatment variables derived from the EMR. Logistic regression, random forest, and XGBoost models were trained using group-based 5-fold cross-validation to predict cycle-specific dose modifications for 5-fluorouracil, irinotecan, and oxaliplatin. Model performance was evaluated using area under the receiver operating characteristic curve. ResultsThe cohort included 514 patients receiving FOLFIRINOX across 5,041 treatment cycles. The mean age was 59 years, 60% of patients were White, 41% had a history of smoking, and patients received a median of 6 chemotherapy cycles. More than 60% of patients required at least one dose modification during treatment. XGBoost demonstrated the highest performance across component drugs, with AUCs ranging from 0.53 to 0.70. Clinically plausible predictors of irinotecan and oxaliplatin dose modification included hepatic and renal function markers, cumulative drug exposure, treatment-related symptoms, and demographic or behavioral characteristics. ConclusionWe developed an EMR-based machine-learning framework to model real-world FOLFIRINOX dose modification and identified clinically plausible, routinely available predictors, particularly for irinotecan and oxaliplatin. Variable model performance suggests that dosing decisions are only partially captured by structured EMR data, highlighting both the limitations of current data-driven approaches and clinical domains where ML-based models may support individualized dosing and toxicity surveillance. Future informatics efforts should incorporate dose-modification rationale, patient-reported and functional outcomes, and validation across diverse practice settings.

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.

BackgroundPredicting 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 fore-cast 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 methodsSCOPE was trained on 54 treatment lines from 52 independent patients with metastatic colorectal cancer (mCRC, n=15) and metastatic pancreatic ductal adeno-carcinoma (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. ResultsPredicted 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 out-performed 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 trial data. ConclusionBy combining functional organoid drug screening with clinical modeling, SCOPE generates calibrated efficacy predictions for both established regimens and novel agents without prior trial 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.

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 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.

Importance Prognostic tools beyond staging are needed to guide treatment and counseling in head and neck squamous cell carcinoma (HNSCC). Objective To develop and externally validate a machine learning model predicting survival in advanced HNSCC using routinely collected clinical and biomarker data. Design, Setting, and Participants Retrospective, multi-institutional cohort study including 2,385 patients with stage III-IV HNSCC diagnosed from 2012-2022 in the University of California Health Data Warehouse (UCHDW). Patients were randomly split into training (n = 1,908) and test (n = 477) sets. Partial external validation used 7,749 patients from the Surveillance, Epidemiology, and End Results (SEER) registry (2010-2020). Exposures Demographic, tumor, treatment, comorbidity, and biomarker variables recorded at or before diagnosis. Main Outcomes and Measures The primary outcome was all-cause mortality within 70 months. Cox proportional hazards models included all predictors. Discrimination was assessed with Harrell's concordance index (C-index), calibration with predicted vs observed survival, and stratification with Kaplan-Meier curves. A Random Survival Forest (RSF) was trained for benchmarking and interpretability using Shapley Additive exPlanations (SHAP). Results Among 2,385 patients in UCHDW (median age, 63 years; 29.0% mortality), the Cox model achieved a C-index of 0.735 in the internal test set. Risk quartiles showed clear separation on Kaplan-Meier curves (log-rank p < 0.0001). In the SEER cohort (n = 7,749), where only demographic, staging, subsite, and treatment variables were available, the reduced Cox model achieved a C-index of 0.688, with calibration showing modest underestimation of survival in high-risk groups. Age, T stage, Charlson Comorbidity Index, neutrophil-to-lymphocyte ratio, and platelet count were among the strongest predictors, while surgery was associated with improved survival. The RSF achieved a C-index of 0.758 internally, with SHAP highlighting nonlinear effects of albumin, BMI, and inflammatory markers. Conclusions and Relevance A machine learning model using routine clinical and biomarker data demonstrated good prognostic performance in advanced HNSCC, with partial external validation. Such approaches may support individualized survival estimates, risk stratification, and treatment discussions, but broader validation is required before clinical adoption.

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

Chimeric antigen receptor T-cell (CAR-T) therapy targeting CD19 has transformed outcomes for children with relapsed or refractory (R/R) B-cell acute lymphoblastic leukemia (B-ALL), yet the influence of molecular subtype on outcomes remains unclear. We evaluated the impact of cytogenetic and molecular signatures on complete response (CR), overall survival (OS), and leukemia-free survival (LFS) after CD19 CAR-T therapy in eighty-six pediatric patients with R/R B-ALL treated with tisagenlecleucel. CR was assessed 30 days after infusion. Cytogenetic data were available for 84 patients and molecular profiling for 62. Survival analyses included 72 patients who received CD19 CAR-T as the sole cellular therapy. Seventy-seven patients achieved CR (89.5%). Pre-infusion bone marrow blasts of [&ge;]20% were associated with lower CR rates (53.8% vs 95.9%, p<0.0001) and significantly reduced OS and LFS (both p<0.0001). Among molecular markers, RAS mutations correlated with inferior OS (p=0.0222) and LFS (0.0402). In multivariate analysis, bone marrow blasts >20% and RAS mutations independently predicted inferior OS. Post CAR-T, CD19 negative relapses showed almost twice higher prevalence of RAS mutations (66% vs 37.5%). These findings highlight RAS mutations as a key molecular predictor of outcome after CD19 CAR-T therapy and suggest emergence of unique risk stratification for patients receiving CD19-targeting therapy. Key PointsO_LIRAS mutations independently predict unfavorable survival after CAR-T CD19 in pediatric B-ALL. C_LIO_LIRAS mutations increase risk of CD19 negative relapse after CAR-T CD19 therapy in pediatric B-ALL. C_LI

BackgroundSezary 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. MethodsWe 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 Fishers 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. ResultsTMB 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. ConclusionsThis 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.

IntroductionCraniopharyngioma (CP) comprises two distinct histological subtypes, adamantinomatous craniopharyngioma (ACP) and papillary craniopharyngioma (PCP), which are often challenging to distinguish preoperatively. Approximately 95% of PCP harbor the BRAF V600E mutation, whereas ACP lacks this alteration, making PCP uniquely sensitive to BRAF and MEK inhibition. However, in the absence of a reliable preoperative classification strategy, targeted therapy has been limited to recurrent disease or to cases with histological confirmation. This study aims to describe and prospectively evaluate a pragmatic preoperative classification strategy and short-course neoadjuvant BRAF and MEK inhibition followed by surgery in newly diagnosed, preoperatively classified PCP. Methods and analysisThis is a prospective, single-arm, open-label study. Patients with newly diagnosed craniopharyngioma will be screened using an integrated preoperative strategy combining imaging-based prediction and selective cerebrospinal fluid (CSF) cell-free DNA testing for BRAF V600E in indeterminate cases. Twelve participants preoperatively predicted as PCP and BRAF V600E positive will receive dabrafenib 150 mg twice daily plus trametinib 2 mg once daily for up to three 28-day cycles, followed by transnasal endoscopic surgery. Assessments are scheduled at days 7, 14, 28, 56, and 84 until surgery. The primary endpoint is objective response rate, assessed by contrast-enhanced MRI using RANO 2.0 criteria. Secondary outcomes include progression-free survival, local disease control, endocrine outcomes of the hypothalamic-pituitary-adrenal and hypothalamic-pituitary-thyroid axes, visual and cognitive outcomes, postoperative diabetes insipidus, surgical complexity, and concordance between the preoperative classification strategy and postoperative pathology and BRAF V600E status. Exploratory analyses will evaluate treatment-related changes in tumor vascularity, tissue characteristics, and post-treatment molecular alterations in tumor tissue. Ethics and disseminationThis protocol has been approved by the Ethics Committee of Huashan Hospital, Fudan University (KY2024-028). Written informed consent will be obtained from all participants. Results will be disseminated through peer-reviewed publications and scientific conferences. Trial registration numberChiCTR2400081636 STRENGTHS AND LIMITATIONS OF THIS STUDYO_ST_ABSStrengthC_ST_ABS[tpltrtarr] This study proposes an integrated, clinically applicable preoperative strategy that combines imaging-based prediction with selective cerebrospinal fluid cell-free DNA analysis to identify papillary craniopharyngioma (PCP) prior to surgery. [tpltrtarr]It prospectively evaluates short-course neoadjuvant BRAF and MEK inhibition in newly diagnosed PCP, addressing a clinically relevant gap in current management. [tpltrtarr]Standardized, multidimensional assessments are performed across the neoadjuvant, perioperative, and early postoperative periods, capturing radiographic, surgical, endocrine, visual, and cognitive outcomes. Limitation[tpltrtarr] The single-arm, open-label design without a surgical control group limits direct comparison with upfront surgery. [tpltrtarr]Despite the integrated prediction strategy, preoperative misclassification cannot be excluded entirely.

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.

PurposeLimited CNS bioavailability and pharmacodynamics are obstacles to effective systemic therapies for glioblastoma. One strategy to overcome these challenges is drug combinations enhancing CNS penetration and/or tumor chemosensitivity. LP-184, a synthetic acylfulvene class alkylator, induces DNA damage and inhibits glioblastoma cell viability in pre-clinical models. LP-184 is a prodrug converted to active metabolites by intracellular prostaglandin reductase 1 (PTGR1) that is over-expressed in >70% of glioblastoma. DNA damage induced by LP-184 is MGMT agnostic and reversed by transcription-dependent NER. PatientsLP-184 was evaluated in a Phase 1a study (NCT05933265) in 63 adult patients with advanced malignancies including 16 patients with recurrent glioblastoma. All patients with glioblastoma received prior standard-of-care therapy and most had received 1 or more additional therapies before enrollment. ResultsPatients with glioblastoma experienced more frequent transaminitis, Grade 1-2 nausea and a trend towards more frequent and severe thrombocytopenia compared to the non-glioblastoma cohort. Otherwise, overall toxicity profiles were similar. Clinical pharmacokinetic analysis combined with published pre-clinical intra-tumoral bioavailability data ([~]20% penetration) predicted that LP-184 at the recommended dose for expansion (RDE) would achieve cytotoxic levels if combined with spironolactone, a BBB permeable ERCC3 degrader and TC-NER inhibitor that sensitizes glioblastoma cells to LP-184 3-6-fold. We show that three daily doses of spironolactone deplete orthotopic glioblastoma PDX ERCC3 protein by [~] 80% and increases tumor LP-184 cytotoxicity 2-fold. ConclusionsLP-184 is well tolerated at the RDE, and we establish a clinically translatable scheme for dosing spironolactone in combination with LP-184 for a future Phase 1b clinical trial. Statement of translational relevanceTreatment failure in glioblastoma reflects inadequate drug brain exposure and DNA repair- mediated resistance. LP-184, a novel acylfulvene alkylator, generates MGMT-independent DNA lesions predominantly repaired by transcription-coupled NER. In a Phase 1a dose finding trial, LP-184 was well-tolerated at the recommended dose for expansion (RDE) in participants with advanced cancers, including recurrent glioblastoma. Plasma drug levels achieved predicted effective systemic exposures but not brain concentrations based on projected 20% brain penetrance. Pharmacokinetic modeling indicates that NER inhibition could increase tumor chemosensitivity with the addition of spironolactone. The optimal dosing regimen for spironolactone combined with LP-184 was identified in orthotopic PDX models, facilitating advancement to Phase 1b/2a testing of LP-184 plus spironolactone.

PurposeEarly detection of cancer can improve survival following diagnosis. However, routine screening is limited to a few cancer types. Multi-cancer early detection (MCED) tests could substantially expand cancer screening by simultaneously detecting multiple cancer types. This modeling study evaluates the potential impact of an MCED test on cancer outcomes in the US general population. MethodsWe developed a microsimulation model of 14 solid tumor cancer types which account for nearly 80% of cancer incidence and mortality. The model was calibrated to reproduce annual incidence rates reported in the Surveillance, Epidemiology, and End Results database. Cancer diagnosis could arise from standard-of-care (SoC) procedures or annual MCED testing. MCED sensitivities were derived from a case-control clinical validation study. We simulated the 10-year life course of 5 million US adults aged 50-84 years. The primary outcome was cancer mortality reduction due to MCED testing. ResultsIn the best case with perfect uptake and adherence, MCED testing added to the SoC led to a 23% decrease in 10-year cancer mortality relative to the SoC alone, translating to 668,600 cancer deaths averted over 10 years. The largest mortality reductions, in absolute terms, were observed for lung (160; 802 versus 962 per 100,000), colorectal (118; 168 versus 284), and pancreatic (50; 238 versus 288) cancer. The largest relative reductions were in cervical (52%), colorectal (41%), and breast (34%) cancer. The population-level life-year gain was 7,158 years per 100,000. ConclusionMCED testing has the potential to substantially reduce cancer-related deaths, improve outcomes across multiple cancer types.

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.

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