ArticlePLoS computational biology2020
Individualized genetic network analysis reveals new therapeutic vulnerabilities in 6,700 cancer genomes.
Article in PLoS computational biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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Who cites it
29 citing papers in PubMed, 52 citations in OpenAlex.
- Network based analysis identifies TP53m-BRCA1/2wt-homologous recombination proficient (HRP) population with enhanced susceptibility to Vigil immunotherapy.Cancer gene therapy · 2022Trial
- OncoBERT: Context-Aware Modeling of Somatic Mutations for Precision Oncology.bioRxiv : the preprint server for biology · 2026Article
- Genetic landscape of cancer: mechanisms, key genes, and therapeutic implications.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Review
- From Data to Cure: A Comprehensive Exploration of Multi-omics Data Analysis for Targeted Therapies.Molecular biotechnology · 2025Review
- Revolutionising Cardio-Oncology Care with Precision Genomics.International journal of molecular sciences · 2025Review
- Analysis of immune cell infiltration in the tumor microenvironment of cervical cancer and its impact on immunotherapy.Frontiers in oncology · 2025Article
- Prognostic prediction models for postoperative patients with stage I to III colorectal cancer based on machine learning.World journal of gastrointestinal oncology · 2024Article
- Familial Pancreatic Cancer Research: Bridging Gaps in Basic Research and Clinical Application.Biomolecules · 2024Review
- Individual-network based predictions of microbial interaction signatures for response to biological therapies in IBD patients.Frontiers in molecular biosciences · 2024Article
- Targeting the DNA Damage Response for Cancer Therapy.International journal of molecular sciences · 2023Review
- A new vulnerability to BET inhibition due to enhanced autophagy in BRCA2 deficient pancreatic cancer.Cell death & disease · 2023Article
- Genomic Landscape and Potential Regulation of RNA Editing in Drug Resistance.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2023Article
- Interrogating the Human Diplome: Computational Methods, Emerging Applications, and Challenges.Methods in molecular biology (Clifton, N.J.) · 2023Article
- CoMutDB: the landscape of somatic mutation co-occurrence in cancers.Bioinformatics (Oxford, England) · 2023Article
- A New View of Activating Mutations in Cancer.Cancer research · 2022Article
- NESM: a network embedding method for tumor stratification by integrating multi-omics data.G3 (Bethesda, Md.) · 2022Article
- Prioritization of cancer driver gene with prize-collecting steiner tree by introducing an edge weighted strategy in the personalized gene interaction network.BMC bioinformatics · 2022Article
- Artificial intelligence in cancer target identification and drug discovery.Signal transduction and targeted therapy · 2022Review
- Network approaches for omics studies of neurodegenerative diseases.Frontiers in genetics · 2022Review
- Nucleotide-based genetic networks: Methods and applications.Journal of biosciences · 2022Article
Corrections and comments
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Authors and funding
9 authors at 6 institutions in 3 countries.
Funding
Abstract
Tumor-specific genomic alterations allow systematic identification of genetic interactions that promote tumorigenesis and tumor vulnerabilities, offering novel strategies for development of targeted therapies for individual patients. We develop an Individualized Network-based Co-Mutation (INCM) methodology by inspecting over 2.5 million nonsynonymous somatic mutations derived from 6,789 tumor exomes across 14 cancer types from The Cancer Genome Atlas. Our INCM analysis reveals a higher genetic interaction burden on the significantly mutated genes, experimentally validated cancer genes, chromosome regulatory factors, and DNA damage repair genes, as compared to human pan-cancer essential genes identified by CRISPR-Cas9 screenings on 324 cancer cell lines. We find that genes involved in the cancer type-specific genetic subnetworks identified by INCM are significantly enriched in established cancer pathways, and the INCM-inferred putative genetic interactions are correlated with patient survival. By analyzing drug pharmacogenomics profiles from the Genomics of Drug Sensitivity in Cancer database, we show that the network-predicted putative genetic interactions (e.g., BRCA2-TP53) are significantly correlated with sensitivity/resistance of multiple therapeutic agents. We experimentally validated that afatinib has the strongest cytotoxic activity on BT474 (IC50 = 55.5 nM, BRCA2 and TP53 co-mutant) compared to MCF7 (IC50 = 7.7 μM, both BRCA2 and TP53 wild type) and MDA-MB-231 (IC50 = 7.9 μM, BRCA2 wild type but TP53 mutant). Finally, drug-target network analysis reveals several potential druggable genetic interactions by targeting tumor vulnerabilities. This study offers a powerful network-based methodology for identification of candidate therapeutic pathways that target tumor vulnerabilities and prioritization of potential pharmacogenomics biomarkers for development of personalized cancer medicine.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.