Multi-context modeling of driver pathways reveals common and specific mechanisms across 23 cancer types
Discovery of cancer driver pathways is essential for targeted therapies, since these pathways govern tumor progression and treatment resistance. However, their context-specific patterns across populations remain poorly understood. Leveraging pan-cancer genomic data, we apply our two models, EntCDP a...
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| Published in | PLoS computational biology Vol. 21; no. 8; p. e1013349 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Public Library of Science
06.08.2025
Public Library of Science (PLoS) |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1553-7358 1553-734X 1553-7358 |
| DOI | 10.1371/journal.pcbi.1013349 |
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| Abstract | Discovery of cancer driver pathways is essential for targeted therapies, since these pathways govern tumor progression and treatment resistance. However, their context-specific patterns across populations remain poorly understood. Leveraging pan-cancer genomic data, we apply our two models, EntCDP and ModSDP, to perform stratified analyses from four perspectives: region, tumor type, age group, and risk factors. Our results reveal the regional biases in perturbed pathways, such as PI3K-Akt in Chinese patients and GPCR in American patients with bladder cancer. Subtype comparisons highlight the mTOR signaling in lung adenocarcinoma and the FoxO signaling in lung squamous cell carcinoma. Pediatric-adult comparisons emphasize the enrichment of Ras signaling in pediatric acute myeloid leukemia and PAK signaling in pediatric glioblastoma, respectively. Risk factor associations further link Notch-mediated pathways to alcohol consumption and CDKN-regulated pathways to obesity-related cancers. Our findings demonstrate the utility of stratified driver pathway analysis in uncovering common and specific mechanisms, which can help prioritize context-aware therapeutic targets. |
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| AbstractList | Discovery of cancer driver pathways is essential for targeted therapies, since these pathways govern tumor progression and treatment resistance. However, their context-specific patterns across populations remain poorly understood. Leveraging pan-cancer genomic data, we apply our two models, EntCDP and ModSDP, to perform stratified analyses from four perspectives: region, tumor type, age group, and risk factors. Our results reveal the regional biases in perturbed pathways, such as PI3K-Akt in Chinese patients and GPCR in American patients with bladder cancer. Subtype comparisons highlight the mTOR signaling in lung adenocarcinoma and the FoxO signaling in lung squamous cell carcinoma. Pediatric-adult comparisons emphasize the enrichment of Ras signaling in pediatric acute myeloid leukemia and PAK signaling in pediatric glioblastoma, respectively. Risk factor associations further link Notch-mediated pathways to alcohol consumption and CDKN-regulated pathways to obesity-related cancers. Our findings demonstrate the utility of stratified driver pathway analysis in uncovering common and specific mechanisms, which can help prioritize context-aware therapeutic targets.
Cancer develops as a result of disruptions in signaling pathways driven by gene mutations. However, the heterogeneity of these pathways across patient groups with distinct clinical characteristics remains inadequately understood. Here, we aim to bridge the existing knowledge gap by utilizing our newly developed models: EntCDP and ModSDP. These models are applied to analyze gene mutation data and clinical information from 55 cohorts, encompassing 23 different cancer types across various platforms. We explore how geographic factors drive regional biases, uncover differences among cancers with similar functions or locations (e.g., digestive system cancers, lung subtypes, sex-specific cancers, and metastatic tumors with preferred sites), and examine age-related shifts to reveal developmental and physiological impacts. Additionally, we investigate how lifestyle factors, such as smoking, alcohol consumption, and obesity, influence signaling pathways through environmental exposures. These findings offer a comprehensive framework for understanding the heterogeneity of cancer signaling across diverse populations and clinical contexts. Our study not only elucidates the mechanisms underlying cancer progression, but also identifies potential targets for group-based therapeutic strategies. Discovery of cancer driver pathways is essential for targeted therapies, since these pathways govern tumor progression and treatment resistance. However, their context-specific patterns across populations remain poorly understood. Leveraging pan-cancer genomic data, we apply our two models, EntCDP and ModSDP, to perform stratified analyses from four perspectives: region, tumor type, age group, and risk factors. Our results reveal the regional biases in perturbed pathways, such as PI3K-Akt in Chinese patients and GPCR in American patients with bladder cancer. Subtype comparisons highlight the mTOR signaling in lung adenocarcinoma and the FoxO signaling in lung squamous cell carcinoma. Pediatric-adult comparisons emphasize the enrichment of Ras signaling in pediatric acute myeloid leukemia and PAK signaling in pediatric glioblastoma, respectively. Risk factor associations further link Notch-mediated pathways to alcohol consumption and CDKN-regulated pathways to obesity-related cancers. Our findings demonstrate the utility of stratified driver pathway analysis in uncovering common and specific mechanisms, which can help prioritize context-aware therapeutic targets.Discovery of cancer driver pathways is essential for targeted therapies, since these pathways govern tumor progression and treatment resistance. However, their context-specific patterns across populations remain poorly understood. Leveraging pan-cancer genomic data, we apply our two models, EntCDP and ModSDP, to perform stratified analyses from four perspectives: region, tumor type, age group, and risk factors. Our results reveal the regional biases in perturbed pathways, such as PI3K-Akt in Chinese patients and GPCR in American patients with bladder cancer. Subtype comparisons highlight the mTOR signaling in lung adenocarcinoma and the FoxO signaling in lung squamous cell carcinoma. Pediatric-adult comparisons emphasize the enrichment of Ras signaling in pediatric acute myeloid leukemia and PAK signaling in pediatric glioblastoma, respectively. Risk factor associations further link Notch-mediated pathways to alcohol consumption and CDKN-regulated pathways to obesity-related cancers. Our findings demonstrate the utility of stratified driver pathway analysis in uncovering common and specific mechanisms, which can help prioritize context-aware therapeutic targets. Discovery of cancer driver pathways is essential for targeted therapies, since these pathways govern tumor progression and treatment resistance. However, their context-specific patterns across populations remain poorly understood. Leveraging pan-cancer genomic data, we apply our two models, EntCDP and ModSDP, to perform stratified analyses from four perspectives: region, tumor type, age group, and risk factors. Our results reveal the regional biases in perturbed pathways, such as PI3K-Akt in Chinese patients and GPCR in American patients with bladder cancer. Subtype comparisons highlight the mTOR signaling in lung adenocarcinoma and the FoxO signaling in lung squamous cell carcinoma. Pediatric-adult comparisons emphasize the enrichment of Ras signaling in pediatric acute myeloid leukemia and PAK signaling in pediatric glioblastoma, respectively. Risk factor associations further link Notch-mediated pathways to alcohol consumption and CDKN-regulated pathways to obesity-related cancers. Our findings demonstrate the utility of stratified driver pathway analysis in uncovering common and specific mechanisms, which can help prioritize context-aware therapeutic targets. |
| Author | Zhang, Junhua Zhou, Wenjia |
| AuthorAffiliation | Georgia Institute of Technology and Emory University, UNITED STATES OF AMERICA 2 School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing, China 1 CEMS, NCMIS, RCSDS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China |
| AuthorAffiliation_xml | – name: 2 School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing, China – name: 1 CEMS, NCMIS, RCSDS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China – name: Georgia Institute of Technology and Emory University, UNITED STATES OF AMERICA |
| Author_xml | – sequence: 1 givenname: Wenjia surname: Zhou fullname: Zhou, Wenjia – sequence: 2 givenname: Junhua orcidid: 0009-0001-4809-5446 surname: Zhang fullname: Zhang, Junhua |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/40768543$$D View this record in MEDLINE/PubMed |
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| Title | Multi-context modeling of driver pathways reveals common and specific mechanisms across 23 cancer types |
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