npj Precision Oncology

BackgroundFluoropyrimidines, specifically 5-fluorouracil (5-FU), remain the cornerstone of colorectal cancer (CRC) therapy. However, intrinsic and acquired resistance, alongside dose-limiting systemic toxicities, often result in treatment failure and disease relapse. There is a pressing clinical need for next-generation fluoropyrimidines that can retain the antitumor activity in 5-FU-refractory CRC models while maintaining a favorable safety profile. MethodsWe evaluated the antitumor efficacy of CF10, a novel polymeric fluoropyrimidine designed for the sustained delivery of FdUMP, against equimolar 5-FU. We utilized a diverse panel of six patient-derived CRC organoid (PDO) models to assess 3D growth inhibition under both normoxic ([~]20% O2) and physioxic (5% O2) conditions. Mechanisms of action were investigated via {gamma}H2AX signaling (DNA damage), Annexin V/PI flow cytometry (death kinetics), and ALDEFLUOR assays (stem-like populations). Functional suppression of metastasis-associated phenotypes was evaluated using 3D Matrigel invasion assays. Finally, the therapeutic index and overall survival were validated in vivo using two independent patient-cell-derived xenograft (PCDX) models (TX-CC-199 and TX-CC-201). ResultsCF10 demonstrated significantly greater suppression of organoid growth compared to equimolar 5-FU across all patient-derived lines, regardless of morphological heterogeneity or oxygen tension. In 3D invasion assays, CF10 achieved superior anti-invasive activity even at a 10-fold lower molar dose than 5-FU. This functional advantage was mirrored by a marked depletion of the ALDH-high stem-like subpopulation, which was largely recalcitrant to 5-FU. Mechanistically, CF10 induced intensified replication stress, DNA damage and repair signaling ({gamma}H2AX, Top1cc/pRPA32, FANCD2), and pushed the CRC to irreversible/terminal, PI-positive death states. In vivo, CF10 treatment resulted in profound tumor growth inhibition and a robust survival advantage in two patient cell-derived xenograft (PCDX) models (Log-rank P<0.01) without inducing systemic weight loss or noticeable toxicity. ConclusionsBy integrating 3D patient-derived modeling with in vivo validation, we demonstrate that CF10 effectively overcomes the biological and pharmacological limitations of 5-FU. CF10 targets the aggressive, invasive, and stem-like subpopulations of CRC that drive clinical relapses. These findings provide a compelling translational rationale for the clinical development of CF10 as a superior alternative to standard fluoropyrimidines in both treatment-naive and refractory CRC. Significance StatementDespite the foundational role of 5-fluorouracil (5-FU) in colorectal cancer (CRC) therapy, resistance and systemic toxicity remain major barriers to curative outcomes. This study identifies CF10, a novel polymeric fluoropyrimidine, as a superior alternative that overcomes 5-FU resistance in biologically diverse patient-derived organoids and xenograft models. Crucially, CF10 demonstrates a unique capacity to suppress the invasive, aldehyde dehydrogenase (ALDH)-high stem-like subpopulations that likely survive standard chemotherapy (5-FU) by maintaining efficacy under physiological oxygen levels and providing a significant survival advantage in vivo with improved tolerability. CF10 represents a promising translational candidate for the treatment of both treatment-naive and refractory CRC.

BackgroundColorectal cancer (CRC) is a molecularly heterogeneous disease shaped by both genetic and epigenetic alterations. Approximately 15% of CRCs display widespread CpG island hypermethylation, known as the CpG Island Methylator Phenotype (CIMP). CIMP-high (CIMP-H) tumours frequently exhibit MLH1 promoter hypermethylation, leading to mismatch repair deficiency (MMRd) and microsatellite instability (MSI). However, DNA methylation patterns associated with MSI, independent of CIMP and MLH1 silencing, and the influence of clinical variables such as anatomical location and patient age on the CRC methylome remain poorly characterised. MethodsWe performed epigenome-wide DNA methylation profiling of 259 primary CRC tissue samples using the Illumina EPICv2 array, comparing differential methylation between MSI and microsatellite stable (MSS) CRC, adjusting for tumour purity, MLH1 promoter methylation, CIMP status, and anatomical location, to account for known confounders. We further evaluated the independent effects of anatomical location and patient age on global methylation patterns. ResultsEpigenome-wide differential methylation between MSS and MSI CRC was dominated by MLH1 promoter hypermethylation. After adjusting for MLH1 hypermethylation and CIMP status, we identified a distinct set of 656 CpG sites associated with MMRd independent of MLH1 silencing. These included hypermethylation at LRP6, GSK3{beta}, and CDK12, implicating altered WNT signalling and transcriptional regulation pathways. Comparison of MSI subgroups revealed the co-occurrence of MLH1 hypermethylation with promoter hypermethylation at TXNRD1. Anatomical location showed a strong independent effect on methylation patterns, while we observed only modest effects of patient age on the CRC methylome after adjustment for confounders. ConclusionsWe identified a distinct methylation profile distinguishing MSS and MSI CRC, including MLH1-independent markers of MMRd, as well as novel differentially methylated loci within MSI subgroups. We further showed that anatomical location has a strong independent impact on the CRC methylome. Together, these findings refine the molecular characterisation of CRC and highlight potential epigenetic markers that could inform patient stratification and precision oncology.

BackgroundImmune checkpoint inhibitors (ICIs) have improved outcomes across multiple cancer types, yet reliable predictors of survival remain limited. While genomic features such as tumor mutational burden (TMB) are widely used, their contribution to predictive modeling in heterogeneous real-world cohorts remains unclear. We evaluated the relative contributions of clinical and whole-genome sequencing (WGS) features in pan-cancer survival modeling. MethodsWe analyzed 658 patients treated with ICIs with matched WGS data from the Genomics England. Using a leakage-controlled machine learning framework with strict train-test separation, we compared four models: TMB-only, clinical-only, clinical+TMB, and an integrated 11-feature clinico-genomic XGBoost survival model. Model performance was assessed using Harrells concordance index (C-index) with bootstrap confidence intervals. ResultsTMB alone demonstrated near-random discrimination (C-index 0.50; 95% CI 0.44-0.56). Clinical variables substantially improved predictive performance (0.59; 95% CI 0.53-0.64), with marginal gain from adding TMB (0.59). The integrated model achieved a C-index of 0.60 (95% CI 0.55-0.65). While improvement over TMB alone was significant, incremental gain beyond optimized clinical models was modest. Feature attribution analysis showed that model performance was dominated by clinical variables, with genomic features contributing limited additional signal. ConclusionsThese findings suggest that, in heterogeneous pan-cancer cohorts, predictive performance is constrained by the underlying data structure, in which dominant clinical signals overshadow genome-scale features. This study highlights fundamental limitations in integrating genomic data into survival models across diverse cancer types and provides a benchmark for future computational approaches.

Emerging multi-omic profiling has made it feasible to subtype disease using multiple molecular layers. However, inconsistent preprocessing, heterogeneous implementations, variable evaluation, and limited reproducibility often constrain method selection. Here, we systematically benchmark 22 publicly available unsupervised approaches for bulk data on the TCGA-BRCA cohort across five modalities (RNA-seq, miRNA, DNA methylation, copy numbers, single nucleotide polymorphisms) and validate findings in two independent datasets, enabling a multi-layered comparison of performance, heterogeneous data support and interpretability. Most approaches fuse multi-omic data to produce a two-cluster solution largely aligned with ER status, with higher-resolution approaches further refining these into four coherent subclasses (angiogenic luminal, oxidative-phosphorylation/HER2-low luminal, immune-inflamed basal-like, and hyper-proliferative basal-like). Our benchmarking results indicate that methods based on similarity networks can efficiently produce stable, reliable partitions. Matrix factorisation and Bayesian factorisation algorithms produce rich latent representations, allowing quantification of feature and modality contributions, albeit at higher computational cost. Consensus clustering can be used on a case-by-case basis and refine partitions into more robust and generalisable findings. We aggregate our insights into a decision workflow that aligns with study goals, data characteristics, and computational resources, enabling optimal analytic strategies. This comprehensive assessment provides a practical roadmap for investigators seeking to extract reproducible, biologically meaningful subtypes from complex multi-omic datasets. We higlight the different technical and practical benefits and trade-offs that shape the selection and development of multi-omic approaches applied in precision oncology.

BackgroundNeoadjuvant chemoimmunotherapy (nCIT) has become a standard treatment for locally advanced resectable non-small cell lung cancer (NSCLC), yet the spatial biology underlying treatment resistance remains poorly understood. We used spatial transcriptomics to define the microenvironmental architecture of residual cancers in patients who did not achieve major pathologic response (non-MPR) compared with those who did (MPR). MethodsSpatial transcriptomics was performed on 10 formalin-fixed paraffin-embedded (FFPE) tumor blocks (5 MPR, 5 non-MPR) obtained from 8 patients treated with nCIT. A deep learning algorithm was applied to detect viable residual cancer spots from treatment-induced fibrosis and necrosis. Spatial deconvolution, distance modeling, ligand-receptor analysis, and functional pathway scoring were integrated to characterize niche-specific programs. ResultsMPR cancer core displayed an immune-permissive remodeling environment with deep infiltration of cytotoxic CD8+ T cells, mature dendritic cells (LAMP3+, CCR7+), and active efferocytosis signaling (APOE-TREM2), alongside robust MHC class II expression. Non-MPR cancer core, by contrast, exhibited spatial immune exclusion: a dense fibroblast barrier reinforced by TIMP1-CD63 signaling and Treg-enriched boundaries physically restricted effector T cell access to the cancer core. Residual cancer cells in non-MPR samples maintained active cell cycling and independently upregulated cytochrome P450-mediated drug detoxification and DNA damage response pathways without inducing MHC class II expression -- effectively decoupling intrinsic survival from immune recognition. The non-MPR core also showed a hyper-metabolic profile, including elevated glutathione metabolism consistent with antioxidant buffering against chemotherapy-induced oxidative stress. TROP2 was broadly expressed across the non-MPR cancer core and co-localized with DNA damage response and nuclear factor erythroid 2-related factor 2 resistance signatures. ConclusionsResidual cancer cores in non-MPR tumors appear to represent evolved resistant niches sustained by structural immune exclusion, metabolic rewiring, and DNA repair proficiency. These findings highlight the spatial co-localization of epithelial anchors, such as TROP2, with intrinsic resistance pathways, providing a structural rationale for developing novel precision therapeutic strategies to bypass stromal barriers and overcome the cancer cores intrinsic repair capacity.

Mechanistic modeling has long been used as a tool to describe the dynamics of biological systems, especially cancer in response to treatment. Their key advantage lies in interpretability of relationships between input parameters and outcomes of interest. In contrast, machine learning techniques offer strong prediction performance, especially for high dimensional datasets that are common in oncology. Here, we employ a Mechanstic Learning framework that combines the advantages of both approaches by training machine learning models on mechanistic parameters inferred from clinical patient data. The mechanistic model (a Markov chain model) contains sixteen parameters that describe the rate of cell fate transitions that occur in patients with B-cell precursor acute lymphoblastic leukemia. The machine learning (a ridge logistic regression model) is trained on these parameters to predict two clinically-relevant features: BCR::ABL1 fusion gene status (positive or negative) and minimal residual disease status (positive or negative) post-induction chemotherapy. Model training is done in an iterative fashion to assess which (and how many) parameters are critical to maintain high predictive performance. Using machine learning models trained on the clinical flow-cytometry data, we find that the stem-like cell state alone is the most predictive feature for both BCR::ABL1-positive and MRD-positive disease, with combination scores (defined as the average of accuracy, balanced accuracy, and area under the curve) of 0.80 and 0.67, respectively. By comparison, mechanistic learning achieves comparable or improved combination scores for BCR::ABL1-positive and MRD-positive disease, with scores of 0.81 and 0.71, respectively, using only de-differentiation for BCR::ABL1 and primitive-state persistence together with differentiation-directed exit for MRD. Thus, the mechanistic-learning approach not only preserves predictive performance, but also provides a biological hypothesis for why stemness is predictive of these clinically relevant outcomes.

BackgroundAppendiceal adenocarcinoma (AA) is a rare cancer with limited treatment options. KRAS is the most commonly mutated gene in AA and a promising therapeutic target, but its preclinical and translational relevance in AA remains unclear. MethodsWe evaluated KRASG12D-specific (MRTX1133) and pan-KRAS inhibitor (RMC-6236) in KRASmut organoid and orthotopic PDX models of AA. Tumor-intrinsic and microenvironmental responses were characterized using multi-omics profiling. Clinical outcomes were also assessed in six heavily pre-treated AA patients treated with KRAS inhibitors. ResultsMRTX1133 was highly effective for KRASG12D organoids (IC50=4.1 nM); both KRASG12D and KRASG12V organoids were sensitive to RMC-6236 (IC50=4.4 nM vs 0.5 nM, respectively). In orthotopic PDX models of peritoneal carcinomatosis from AA, MRTX1133 significantly reduced tumor growth in the KRASG12D model TM00351, and RMC-6236 reduced tumor growth in KRASG12V model AAPDX-16. Pathologic evaluation showed dramatically reduced tumor cellularity, proliferation, and pERK expression as well as induction of apoptosis. Gene Sets Enrichment Analysis (GSEA) revealed significant downregulations of E2F targets (NES=-1.9, p-adj=0.06) and the newly developed RAS/ERK (NES=-2.3, p-adj=0.06) gene set, consistent with the observed decrease in cell proliferation. There was marked upregulation of EMT (NES=2.7, FDR<0.001) and TGF-{beta} signaling (NES=2.3, FDR=0.004) in remaining tumor cells, suggesting these pathways could confer resistance. scRNA-seq analysis of TME showed dramatic shifts in cancer-associated fibroblasts (CAFs), with KRAS inhibition driving a shift from normal fibroblasts to inflammatory CAFs, and upregulation of interferon alpha and gamma pathways, suggesting that KRAS inhibition can activate innate immune response in the setting of peritoneal metastases. In a cohort of 6 heavily pre-treated patients with AA treated with KRAS inhibitors (1 G12D, 3 G12C, 2 pan-KRAS), all had biochemical response based on CEA/Ca19-9 or ctDNA and clinical benefit by RECIST criteria (1 CR, 1 PR, 4 SD). ConclusionsWhile effective suppression of RAS/ERK signaling by KRAS inhibitors reduces tumor growth, adaptive activation of EMT and TGF-{beta} pathways may mediate resistance in KRASmut AA. Additionally, KRAS inhibition remodels TME and may enhance innate immune signaling. These findings support continued clinical development of KRAS inhibitors in AA and provide a rationale for combination strategies targeting resistance pathways and stromal remodeling.

Confirming target engagement in tumor experimental models remains a major challenge in oncology drug development. Pharmacodynamic biomarkers can help address this, but few systematic resources link drug targets to candidate biomarkers. We developed TargetTrace, a comprehensive resource to identify and prioritize pharmacodynamic biomarkers across nine key target classes, including transcription factors/cofactors, kinases, phosphatases, ubiquitin ligases, deubiquitinases, acetyltransferases, deacetylases, methyltransferases, and demethylases. Biomarker candidates were gathered from curated molecular interaction resources and refined using external annotations to improve accuracy. For enzyme targets with measurable substrate changes, we applied a two-agent large language model workflow, followed by manual review, to harmonize antibody information from the antibody resources and ensure that the selected biomarkers are measurable with existing laboratory tests. From more than 92,000 input interactions and over 2,300 targets, we compiled 71,323 target-biomarker relationships involving 2,270 potential drug targets, encompassing both transcription factor/cofactor-target gene and enzyme-substrate interactions. Commercial antibodies were available for over 1,400 biomarkers, supporting laboratory validation. This resource provides a structured and reusable resource for systematic identification and prioritization of pharmacodynamic biomarkers in oncology.

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.

BackgroundThe gut microbiome has emerged as a promising non-invasive biomarker for early cancer detection. However, evidence remains fragmented across individual studies with limited cross-cancer comparisons. ObjectivesTo systematically evaluate the diagnostic accuracy of gut microbiome-based signatures across five major cancer types: colorectal cancer (CRC), gastric cancer (GC), pancreatic ductal adenocarcinoma (PDAC), hepatocellular carcinoma (HCC), and lung cancer (LC). MethodsWe conducted a systematic literature search in PubMed, Embase, and Web of Science (January 2000 - April 2026), following PRISMA 2020 guidelines. Studies reporting area under the receiver operating characteristic curve (AUC) for microbiome-based cancer classification were included. Pooled AUC estimates were derived using a DerSimonian-Laird random-effects model. Study quality was assessed using the Newcastle-Ottawa Scale (NOS). ResultsEighteen studies (2,587 participants) met inclusion criteria. Pooled AUC values were: CRC 0.785 (95%CI 0.750-0.819; I2=30.6%), GC 0.834 (0.781-0.887; I2=56.6%), PDAC 0.853 (0.785-0.921; I2=60.8%), HCC 0.809 (0.747-0.871; I2=70.3%), and LC 0.780 (0.738-0.822; I2=25.0%). Fusobacterium nucleatum was consistently enriched across CRC, GC, and PDAC, while Faecalibacterium prausnitzii and Akkermansia muciniphila were depleted in all five cancer types. Porphyromonas gingivalis showed the highest fold-change in PDAC (log{blacksquare}FC=+2.8). Risk of bias was moderate-to-high in all studies. ConclusionsGut microbiome profiling demonstrates good-to-excellent diagnostic accuracy (AUC 0.78-0.85) across five major cancer types. Shared cross-cancer biomarkers suggest common dysbiotic mechanisms amenable to pan-cancer screening. These findings support integration of microbiome signatures into multi-modal cancer detection platforms.

Mucinous Ovarian Carcinoma (MOC) is a rare ovarian cancer histological subtype with distinct pathology, genomics and clinical outcomes compared to other epithelial ovarian cancers. Accordingly, there is little evidence to guide clinical care, particularly in the use of systemic therapies, and the field has lacked informative and diverse pre-clinical models. We developed MOC-specific methods for generating tumour organoids with a success rate of 70% for long-term cultured lines (n=19). Organoid lines were developed from localised, advanced and recurrent tumours, including from biopsy tissue, and represent diverse genomic features not previously captured by existing cell lines. The organoid lines were highly similar to the tumours of origin for genomic and immunohistochemical markers. Screening using a panel of 11 chemotherapy agents highlighted resistance to standard-of-care agents such as carboplatin. Gastrointestinal cancer chemotherapy agents and their combination regimens lacked activity. Paclitaxel was often highly potent at low doses but failed to kill all cells. However, less frequently used drugs such as gemcitabine, topotecan and doxorubicin inhibited many of the lines more effectively than paclitaxel. Available, but non-standard of care, chemotherapy agents should be considered for clinical management of MOC. This is the largest (by [~]10 fold) cohort of fully characterised patient-derived MOC organoid lines described and the first with extensive drug screening data affording an opportunity for drug discovery and screening for personalised treatment.

Accurate histopathological differentiation between High-Grade Serous Carcinoma (HGSC) and Low-Grade Serous Carcinoma (LGSC) remains a critical yet challenging aspect of ovarian cancer diagnosis due to their similar morphology and different clinical outcomes. This study presents a deep learning framework that uses custom attention mechanisms, including the Convolutional Block Attention Module (CBAM), Squeeze-and-Excitation (SE) blocks, and a Differential Attention module within five CNN architectures for automated binary classification of ovarian cancer subtypes from H&E WSI patches. Although individual models achieved higher accuracy, the ensemble stacking framework with a shallow MLP meta-learner delivered the best overall performance, with a ROC-AUC of 0.9211, an accuracy of 0.85, and F1-scores of 0.84 and 0.85 across both subtypes. These findings demonstrate that attention-guided feature recalibration combined with ensemble stacking provides robust and clinically interpretable discrimination of ovarian carcinoma subtypes.

BackgroundDespite improved cancer outcomes with kinase inhibitors (KIs), their cardiotoxicity remains a significant clinical challenge. Current approaches to predict and prevent KI-induced cardiac adverse events (CAEs) are limited by an incomplete understanding of underlying mechanisms, including the contribution of off-target kinase engagement. ObjectivesTo establish links between kinase inhibition profiles and cardiotoxic phenotypes using empirical proteomic data, and to leverage these profiles in machine learning (ML) models capable of predicting KI cardiotoxicity. MethodsWe curated a database connecting kinome-wide target binding profiles of FDA-approved KIs (n=44) with documented incidence rates of six distinct CAEs. Binding profiles were derived from unbiased chemoproteomics and used to assess associations between KI selectivity, specific kinase targets, and CAEs. Profiles were further used to develop ML models to predict CAE risk, with SHAP-based model interpretation applied to identify cardiotoxicity-associated kinases. ResultsKI promiscuity was not a significant predictor of cardiotoxicity across all six CAEs. Frequency analysis revealed that kinases including RET, PDGFRB, and DDR1 are recurrently inhibited across CAE-linked compounds, with nearly all identified as off-targets not annotated by the FDA. Network and pathway enrichment analyses supported a systems-level model in which cardiotoxicity arises from coordinated disruption of cardiac-relevant signaling networks. ML models achieved 66-84% cross-validated accuracy (ROC-AUC 0.75-0.8) across CAE endpoints, with SHAP analysis identifying PDGFRB, EGFR, and MEK1/2 among the most predictive kinases. ConclusionsProteomic kinome profiling combined with machine learning provides a mechanistically grounded framework for predicting KI cardiotoxicity and supports off-target-aware drug design to minimize cardiovascular risk.

BackgroundGallium (Ga) is a promising anti-tumor agent; however, its precise molecular targets in osteosarcoma remain debated. While current paradigms largely attribute its toxicity to reactive oxygen species (ROS) and ferroptosis, understanding its true mechanism is essential for overcoming therapeutic resistance. This highlights the need for interdisciplinary approaches, such as metabolomics, to unveil novel vulnerabilities in cancer metabolism. MethodsWe employed an interdisciplinary strategy utilizing high-resolution liquid chromatography-mass spectrometry (LC-MS) metabolomics and 13C2-glutamine stable isotope tracing in osteosarcoma cells to elucidate the cytotoxic mechanisms of gallium nitrate. Scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM-EDS) was utilized for elemental mapping, and in silico modeling was applied to evaluated metal binding dynamics. Furthermore, synergistic effects were tested by combining gallium with the DNA-damaging agent cisplatin. ResultsOur metabolic profiling revealed a profound bifurcation characterized by the systemic depletion of glycolysis and pentose phosphate pathway intermediates, coupled with a novel ribonucleotide accumulation bottleneck. The observed distinct signature strongly implicated ribonucleotide reductase (RNR) as the primary enzymatic target. In silico modeling and SEM-EDS visually and thermodynamically confirmedthat gallium acts as a structural decoy for iron within the RNR active site. The co-localization induces functional iron starvation rather than canonical ferroptosis. Furthermore, isotope tracing confirmed that elevated ROS is a consequence of overall metabolic failure, not the primary driver of cell death. Crucially, gallium functioned as a metabolic DNA repair inhibitor, synergizing potently with cisplatin to prevent the repair of platinum-induced DNA lesions. ConclusionsGallium selectively sensitizes highly proliferative sarcoma cells by disrupting RNR-mediated DNA precursor synthesis, while sparing normal osteoblasts. Leveraging metabolomics to uncover this state of functional iron starvation provides a rational, interdisciplinary framework for developing gallium-based combination therapies designed to break platinum resistance in clinical oncology.

Transcriptomic data is highly sensitive to cancer state and progression, making transcriptome-based foundation models a great promise for diverse clinical ontological inference. However, analyses of transcriptome are conventionally hindered by technical batch effects and limited generalization across platforms. Here, we introduce RNABag, a foundation model designed to generalize well to external datasets. In particular, the model only focuses on highly variable genes to reduce noise; and extensive data augmentation was utilized to pretrain RNABag to learn robust representations, invariant to batch variations. We demonstrate that RNABag achieves superior performance in pan-cancer tissue-of-origin classification and cancer detection in internal validation sets, as well as in zero-shot generalization to external cohorts and in-house clinical samples. Furthermore, RNABag, after specialized finetuning, exhibits strong capabilities in a wide range of clinical applications. The model effectively stratifies patient survival and predicts relapse risks, highlighting key molecular pathways driving tumor progression. Crucially, we extend RNABags utility to liquid biopsies, achieving high diagnostic accuracy in plasma cfRNA and tumor-educated platelets (TEPs), thereby supporting its application in non-invasive cancer monitoring. Interpretability analysis revealed pivotal role of tumor immune escape in the cancer induced plasma cfRNA signals. In summary, our study indicates that cancer states and progression may be monitored in details and precision via comprehensive modeling of transcriptome across biopsy modalities.

Background: Current deep learning models in computational pathology, radiology, and digital pathology produce opaque predictions that lack the explainable artificial intelligence (xAI) capabilities required for clinical adoption. Despite achieving radiologist-level performance in tasks from whole-slide image (WSI) classification to mammographic screening, these models function as black boxes: clinicians cannot trace predictions to specific biological features, verify outputs against established morphological criteria, or integrate AI reasoning into precision oncology workflows and tumor board decision-making. Methods: We present Virtual Spectral Decomposition (VSD), a modality-agnostic, interpretable-by-design framework that decomposes medical images into six biologically interpretable tissue composition channels using sigmoid threshold functions - the same mathematical structure as CT windowing. Unlike post-hoc xAI methods (Grad-CAM, SHAP, LIME) applied to black-box deep learning models, VSD channels have pre-defined biological meanings derived from tissue physics, providing inherent explainability without sacrificing quantitative rigor. For whole-slide image (WSI) analysis in digital pathology, we introduce the dendritic tile selection algorithm, a biologically-inspired hierarchical architecture achieving 70-80% computational reduction while preferentially sampling the tumor immune microenvironment. VSD is validated across three cancer types and imaging modalities: pancreatic ductal adenocarcinoma (PDAC) on CT imaging, lung adenocarcinoma (LUAD) on H&E-stained pathology slides using TCGA data, and breast cancer on screening mammography. Composition entropy of the six-channel vector is computed as a visual Biological Entropy Index (vBEI) - an imaging biomarker quantifying the diversity of active biological defense systems. Results: In pancreatic cancer, the fat-to-stroma ratio (a novel CT-derived radiomics biomarker) declines from >5.0 (normal) to <0.5 (advanced PDAC), enabling early detection of desmoplastic invasion before mass formation on standard imaging. In lung cancer, composition entropy from H&E whole-slide images correlates with tumor immune microenvironment markers from RNA-seq (CD3: rho=+0.57, p=0.009; CD8: rho=+0.54, p=0.015; PD-1: rho=+0.54, p=0.013) and predicts overall survival (low entropy immune-desert phenotype: 71% mortality vs 29%, p=0.032; n=20 TCGA-LUAD), providing immune phenotyping for checkpoint immunotherapy patient selection from a $5 H&E slide without molecular assays. In breast cancer, each lesion type produces a characteristic six-channel fingerprint functioning as an interpretable computer-aided diagnosis (CAD) system for quantitative BI-RADS assessment and subtype classification (IDC vs ILC vs DCIS vs IBC). A five-level xAI audit trail provides complete traceability from clinical decision support output to specific biological structures visible on the original images. Conclusion: VSD establishes a unified, interpretable-by-design mathematical framework for explainable tissue composition analysis across imaging modalities and cancer types. Unlike black-box deep learning and post-hoc xAI approaches, VSD provides inherently interpretable, clinically verifiable cancer detection and immune phenotyping from standard clinical imaging at existing costs - without requiring foundation model infrastructure, specialized hardware, or molecular assays. The open-source pipeline (Google Colab, Supplementary Material) enables immediate reproducibility and extension to additional cancer types across the pan-cancer TCGA atlas.

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.

Diffuse Large B-cell lymphoma (DLBCL) is the most common aggressive lymphoma in the Western world. First-line immunochemotherapy fails in approximately 30-40% of patients, with refractory and relapse patients presenting a dismal prognosis. Currently, these high-risk patients cannot be accurately identified at diagnosis. Using statistical modeling and machine learning approaches applied to large public DLBCL datasets, we identified a novel predictive signature based on the reactivation of eight normally silent tissue-dependent genes associated with survival. We then developed a multiplex RT-MLPseq based assay, compatible with formalin-fixed paraffin-embedded (FFPE) samples and transferable into routine clinical practice, enabling analysis of expression of these eight genes and validated their prognosis impact in an independent real-life cohort. This signature could be integrated with current prognostic indices and molecular classifications to improve patient stratification and guide treatment selection toward a personalized theragnostic approach, thereby enhancing management of non-responder patients.

High-grade serous ovarian carcinoma (HGSC) is an aggressive malignancy for which bulk transcriptomic subtypes are used to stratify tumors, interpret biology, and guide biomarker development. The four TCGA-derived subtypes, mesenchymal (C1.MES), immunoreactive (C2.IMM), proliferative (C5.PRO), and differentiated (C4.DIF), are consistently observed across cohorts. However, despite their prominence, these subtypes have not translated into therapeutic utility, and their biological basis remains unresolved. Here, we show that HGSC transcriptomic subtypes are largely determined by tumor cellular composition rather than intrinsic malignant transcriptional programs. By integrating controlled single-cell-derived pseudobulk simulations with deconvolution-based analysis of 1,834 primary HGSC tumors across RNA-seq and microarray cohorts, we demonstrate that subtype probabilities align along a composition-driven axis of stromal and immune variation. Cellular composition alone predicted subtype labels with high accuracy (ROC-AUC = 0.81-0.95) and explained a substantial fraction of subtype-associated transcriptomic variation, with the mesenchymal (C1.MES) subtype representing the most robust and reproducible example of composition-driven signal. Although a secondary, composition-independent expression signal is detectable, it does not define the dominant structure of subtype classification. These findings redefine HGSC transcriptomic subtypes as features of the tumor ecosystem rather than discrete malignant states. This reinterpretation has immediate implications for studies that use subtype labels to infer tumor-intrinsic biology and provides a generalizable framework for separating composition-driven and intrinsic signals in bulk tumor data. Significance StatementHGSC transcriptomic subtypes lack consistent clinical utility and remain biologically ambiguous. We show subtype assignments are largely driven by tumor cellular composition, and less so by distinct intrinsic tumor states.

Although the retinoblastoma protein (pRB) is functionally inactivated by hyperphosphorylation in the majority of colorectal cancers (CRC) - with RB1 rarely mutated and even amplified at the genomic level - three critical gaps remain unaddressed: no study has systematically compared which first-line chemotherapeutic agent best synergizes with CDK4/6 inhibition using head-to-head quantitative analysis; functional differences between palbociclib and abemaciclib in chemotherapy combinations have not been characterized in CRC; and direct genetic evidence of RB dependency in this combinatorial context is lacking. Here, we addressed these gaps by evaluating palbociclib and abemaciclib combined with oxaliplatin, 5-fluorouracil, and SN-38 in HCT116 CRC cells, with validation in SW480 cells, RB1-silenced HCT116 cells (shRNA-RB), and non-tumoral intestinal epithelial cells (IEC-6), using quantitative drug interaction analysis (Chou-Talalay), cell cycle profiling, apoptosis assessment, and pRB phosphorylation measurement. Oxaliplatin was the most consistently synergistic partner for both CDK4/6 inhibitors (CI < 1 across all tested concentrations), while combinations with SN-38 yielded variable results and 5-FU combinations approached additivity. The oxaliplatin combination reinforced G1 arrest and enhanced cell death, with abemaciclib producing more pronounced apoptotic induction than palbociclib - an effect not explained by differential pRB target engagement (both inhibitors reduced pRB Ser807/811 phosphorylation by [~]50%), likely reflecting abemaciclibs broader kinase inhibitory profile. shRNA-mediated RB1 silencing partially attenuated the combinatorial effect, providing direct genetic evidence that the synergy is RB-dependent. Importantly, the combination did not significantly potentiate oxaliplatin cytotoxicity in non-tumoral IEC-6 intestinal epithelial cells, in contrast to the pronounced enhancement observed in tumor cells, and synergistic benefit was preserved at sub-cytotoxic inhibitor concentrations. These findings identify oxaliplatin as the optimal chemotherapeutic partner for CDK4/6 inhibition in CRC, with a mechanism involving RB-dependent potentiation of apoptosis that is preferentially active against tumor cells and maintained at clinically relevant inhibitor doses.