Pan-Cancer Quantification of Driver Alteration Transmission Across Molecular Layers Reveals Limited Propagation to Protein Abundance.

Foundation-model-guided radiogenomic discovery linking cancer genomes to cancer scans

The function of many genes is still unknown, and conventional driver-discovery methods, which rely on how frequently a gene is mutated, cannot assess genes that are only rarely affected. Here we pair Evo~2-based genome analysis with routine clinical imaging to identify gene--phenotype associations at genome-wide scale. For every somatic mutation across three TCGA cohorts (cRCC=clear cell renal cell carcinoma, HCC=hepatocellular carcinoma, and BC=breast cancer; $n = 340$ total), Evo~2 predicts a severity score, with no task-specific training. Per-gene severity summaries are then correlated with radiomic features extracted from paired tumor segmentations, controlling for total mutation burden. In TCGA-cRCC ($n = 162$), this sweep recovers established renal-cancer drivers and identifies 46 additional genes reaching false discovery rate (FDR) significance absent from curated cancer-gene panels, several of which are Mendelian ciliopathy and cytoskeletal-disease genes. These results demonstrate that pairing a genomic language model with widely available clinical imaging can serve as a hypothesis-free discovery tool for gene--imaging associations invisible to conventional approaches.

Gene mutant dosage is associated with prognosis and metastatic tropism in 60,000 clinical cancer samples

The interplay between somatic mutations and copy number alterations influences tumor evolution and prognosis. These alterations are often treated independently, overlooking gene mutant dosage (GMD)—a key property of their interaction. Here we develop a computational framework that infers mutation copy number and multiplicity from targeted sequencing panels without requiring matched normal samples. We derive GMD for over 500,000 mutations across 60,000 pan-cancer samples. By stratifying more than 20,000 patients according to GMD across multiple genes, we identify 46 tumor-type-specific biomarkers predictive of survival, 13 of which were undetectable using binary mutant/wild-type models, 26 were associated with metastatic spread and 20 predicted metastatic tropism. Our method reveals GMD patterns as independent predictors of disease prognosis, metastatic potential and site-specific dissemination across diverse tumor types. This augmented insight into genomic drivers enhances our understanding of cancer progression and metastasis and holds the potential to substantially enhance biomarker discovery. The authors present INCOMMON, an open-source Bayesian inference tool that determines the multiplicity and copy number of driver mutations from tumor sequencing datasets.

Integrative analysis of bulk, single-cell, and spatial transcriptomes reveals a clinically relevant transcriptional program from hepatocellular carcinoma to pan-cancer

Hepatocellular carcinoma (HCC) remains a leading cause of cancer-related mortality worldwide, underscoring the urgent need for robust molecular signatures that support multiple clinical tasks and generalize across diverse transcriptomic platforms. We performed an integrative analysis of 1,300 HCC transcriptomic profiles spanning microarray (n = 869), bulk RNA sequencing (n = 370), spatial transcriptomics (n = 10), and single-cell RNA sequencing (n = 51). Using a multi-task machine learning framework, we distilled a foundational 284-gene program into a compact 25-gene transcriptional signature, including the under-characterized gene IPO9. This signature captured the core proliferative state of HCC while maintaining high diagnostic accuracy (AUC = 0.987–0.991) and served as an independent prognostic factor for both overall and progression-free survival, with activity levels that progressively scale with advancing tumor grade. Spatial and single-cell analyses localized the signature almost exclusively to malignant hepatocytes within the tumor core, confirming its cell-intrinsic nature. Extending beyond HCC, the signature demonstrated conserved oncogenic pathway alignment across 31 additional cancer types and prognostic relevance in nine of them, identifying it as a pan-cancer malignant proliferative-proteostatic marker. This study defines a mechanistically interpretable 25-gene malignant axis that generalizes from HCC to multiple cancer types . Our findings suggest that this signature could serve as a reliable framework for clinical risk assessment and personalized management in HCC.

Multi-level Transcriptomic and Machine-learning Analyses Identify MZT1 as a Proliferation-associated Prognostic Marker in Lung Adenocarcinoma

Pan-cancer proteogenomic interrogation of the ubiquitin-proteasome system.

Somatic mutations rewire the ubiquitin-proteasome system (UPS) to support tumor growth, but the proteome-wide consequences of cancer-driver alterations on UPS composition remain incompletely understood. Using harmonized proteogenomic data from up to 11 CPTAC cohorts, we performed an integrated pan-cancer analysis of UPS protein dysregulation, prognostic associations, and mutation-driven remodeling. We show that mRNA poorly predicts UPS protein abundance, that a defined set of E3 ligases is recurrently dysregulated across cancers, and that somatic mutations (most strikingly TP53 loss) produce coherent UPS protein-quantitative trait locus (pQTL) signatures. Two case studies (UBR5 and TRIM28) illustrate orthogonal modes of UPS rewiring: a mutation-driven axis in which TP53-mutant tumors elevate UBR5 to support replication stress tolerance, and a lineage-driven axis in which TRIM28 engages tissue-restricted regulatory networks with opposing prognostic effects in glioblastoma versus head and neck cancer. Each axis exposes context-specific therapeutic vulnerabilities, including sensitivity to DNA damage response inhibitors (UBR5-high) and lineage-specific drug responses (TRIM28-high). Together, these analyses define a mechanistic framework for how cancer-driver mutations reshape proteostasis through the UPS and nominate mutation- and lineage-defined dependencies for precision degrader therapy. The harmonized pan-tissue atlas and the UbiDash interactive resource that underpin parts of this analysis are reported in our companion paper [1].

Multi-omics and single-cell analysis link the pan-cancer TOP2A-immune exclusion paradox to a non-cell-autonomous dilution effect

TOP2A is universally upregulated in human cancers, yet its negative correlation with immune infiltration in bulk transcriptomes remains mechanistically unresolved at single-cell resolution. We integrated multi-omics data across 34 cancer types with external prognostic validation in 59 independent datasets. Single-cell deconvolution resolved cell-type-specific expression, and confounder-adjusted analyses distinguished proliferation-dependent from TOP2A -specific phenotypes. Pharmacogenomic profiling employed bidirectional Connectivity Map screening with cross-validation across four drug sensitivity databases. TOP2A was broadly upregulated at mRNA and protein levels across cancers, with high expression associated with shorter survival in most malignancies yet a protective effect in THYM and READ. Copy-number amplification, rather than somatic mutation, emerged as the predominant genomic correlate of TOP2A overexpression. TOP2A expression correlated positively with tumor mutation burden, homologous recombination deficiency, aneuploidy, and loss of heterozygosity, indicating widespread genomic instability. Single-cell analysis revealed that TOP2A expression is stringently restricted to malignant epithelial cells and proliferating immune subsets. Tumor purity and proliferation-adjusted analyses demonstrated that the bulk-level immune exclusion signature reflects stoichiometric dilution driven by malignant cell expansion rather than direct immunosuppression, whereas associations with genomic instability were largely proliferation-independent. Pharmacogenomic cross-validation revealed enhanced sensitivity of TOP2A -high tumors to topoisomerase, Aurora kinase, and microtubule inhibitors, but intrinsic resistance to MEK and EGFR inhibitors. CMap screening prioritized the HDAC inhibitor MS-275 as a candidate with pan-cancer reversal potential across 22 cancer types. This study clarifies that TOP2A functions as a pan‑cancer barometer of proliferative burden and genomic instability rather than a direct immune suppressor. Resolving the bulk-level immune paradox as a non-cell-autonomous dilution effect through single-cell deconvolution and confounder-adjusted analyses, and identifying putative therapeutic vulnerabilities, we provide a framework for deploying TOP2A as a prognostic biomarker and a hypothesis-generating therapeutic target.