Jul 2026· International Journal of Cancer· 0 citations· 35 references
Medicine
TL;DR
A regression-based transmission score (TS_R2) is developed to quantify driver alteration signal propagation across DNA, mRNA, and protein layers and provides a framework for integrated functional driver prioritization.
Abstract
Precision oncology relies primarily on DNA-level alterations for therapeutic decisions, but the extent to which driver mutations propagate to protein abundance has not been systematically evaluated. Here, I developed a regression-based transmission score (TS_R2) to quantify driver alteration signal propagation across DNA, mRNA, and protein layers. Applying this framework to matched genomic, transcriptomic, proteomic, and phosphoproteomic data from 754 Clinical Proteomic Tumor Analysis Consortium (CPTAC) tumors across seven cancer types, I analyzed 86 driver gene-cancer type pairs, of which 83 were evaluable for the full two-layer transmission score. I employed covariate-adjusted regression for each molecular transition, assessing significance via permutation testing (n = 1000). Mixed-effects modeling then partitioned gene-intrinsic from cancer-type-dependent effects. Only 5 of 83 evaluable pairs (6%) demonstrated high transmission (TS_R2 > 0.05), with receptor tyrosine kinases (EGFR, FGFR2) exemplifying this class. The primary bottleneck occurred at the mutation-mRNA transition, not mRNA-protein translation. Gene identity accounted for 49% of transmission efficiency variance, nearly double the contribution of cancer type (29%). Copy number alterations transmitted signals 13.8-fold more efficiently than point mutations, and truncating mutations showed higher transmission than missense variants (Wilcoxon p = 0.005). Microsatellite instability attenuated mRNA-protein transmission in UCEC and COAD. These findings demonstrate that many driver alterations show limited propagation to protein abundance. This challenges DNA-only interpretations in precision oncology and provides a framework for integrated functional driver prioritization.
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.
Frederik Hauke, Jeremias Krause, P. Wienholt et al.· arXiv.org· 0 citations
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.
N. Calonaci, E. Krasniqi, D. Čolić et al.· Nature Genetics· 0 citations
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.
Tho Ngoc-Quynh Le, Nhi Doan Yen Nguyen, Thanh Quang Nguyen et al.· Discover Oncology· 0 citations
Abstract Background/Aim: Lung adenocarcinoma (LUAD) exhibits substantial molecular heterogeneity and variable clinical outcomes, highlighting the need for biomarkers that reflect core tumor biological processes. Centrosome-associated proteins regulate mitotic fidelity and genome stability, yet their roles in LUAD remain incompletely defined. In this study, we systematically characterized mitotic spindle organizing protein 1 (MOZART1; MZT1) and related family members in LUAD. Materials and Methods: We performed integrated analyses combining bulk transcriptomic datasets, survival modeling, gene set enrichment, immune deconvolution, machine-learning based prognostic modeling, and single-cell RNA sequencing. Expression patterns and clinical associations of MZT family genes were evaluated across pan-cancer and LUAD cohorts. Results: MZT family genes were consistently upregulated in tumor tissues, with MZT1 showing the most robust expression pattern. Elevated MZT1 expression was significantly associated with reduced overall survival. Functional analyses revealed coordinated activation of proliferative and genome maintenance pathways, including G2/M checkpoint regulation, E2F and MYC signaling, and DNA repair. A multivariable analysis indicated that the prognostic association of MZT1 was reduced after adjusting for canonical proliferation markers, suggesting partial overlap with established proliferation signals. The LASSO-based Cox model demonstrated stable time-dependent predictive performance at 1-, 3-, and 5-year survival. Immune analyses indicated associations between MZT1 expression and tumor microenvironmental features. Single-cell analysis showed that MZT1 expression was predominantly enriched in malignant epithelial cells and associated with proliferative cellular states. Protein-level validation supported concordance with transcriptomic findings. Conclusion: MZT1 is a proliferation-associated marker that integrates clinical risk, transcriptional programs, cellular heterogeneity, and predictive modeling in LUAD, providing a potential framework for biomarker development and risk stratification.
Dahlak Daniel Solomon, Hui-Ru Lin, Yung-Kuo Lee et al.· Cancer Genomics & Proteomics· 0 citations
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].
Tania J. González-Robles, Maha Khan, Paul Sastourné et al.· Cell Death and Differentiati...· 0 citations
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.
