ArticleCommunications biology2022
Machine learning multi-omics analysis reveals cancer driver dysregulation in pan-cancer cell lines compared to primary tumors.
Article in Communications biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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Who cites it
10 citing papers in PubMed.
- Systematic pathway-level analysis defines conserved transcriptional divergence between primary lung tumors and cell line models.Animal models and experimental medicine · 2026Article
- The role of hematological biomarkers as diagnostic tool in pre-cancerous patients.Discover oncology · 2026Review
- Common gene mutations in 103 authenticated colorectal cancer cell lines.Oncogenesis · 2026Article
- AI-based methods for the assessment of DNA damage and repair mechanisms.Frontiers in systems biology · 2026Review
- A machine learning approach to a nine-SNP immunogenetic score for prognostic stratification in cervical cancer.Frontiers in immunology · 2026Article
- Consistently processed RNA sequencing data from 50 sources enriched for pediatric data.Scientific data · 2025Article
- Pan-cancer analysis shapes the understanding of cancer biology and medicine.Cancer communications (London, England) · 2025Review
- Identification of AKTIP as a biomarker for fibrolamellar carcinoma using WGCNA and machine learning.3 Biotech · 2025Article
- Novel research and future prospects of artificial intelligence in cancer diagnosis and treatment.Journal of hematology & oncology · 2023Review
- Article
Corrections and comments
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Authors and funding
16 authors.
Funding
Abstract
Cancer cell lines have been widely used for decades to study biological processes driving cancer development, and to identify biomarkers of response to therapeutic agents. Advances in genomic sequencing have made possible large-scale genomic characterizations of collections of cancer cell lines and primary tumors, such as the Cancer Cell Line Encyclopedia (CCLE) and The Cancer Genome Atlas (TCGA). These studies allow for the first time a comprehensive evaluation of the comparability of cancer cell lines and primary tumors on the genomic and proteomic level. Here we employ bulk mRNA and micro-RNA sequencing data from thousands of samples in CCLE and TCGA, and proteomic data from partner studies in the MD Anderson Cell Line Project (MCLP) and The Cancer Proteome Atlas (TCPA), to characterize the extent to which cancer cell lines recapitulate tumors. We identify dysregulation of a long non-coding RNA and microRNA regulatory network in cancer cell lines, associated with differential expression between cell lines and primary tumors in four key cancer driver pathways: KRAS signaling, NFKB signaling, IL2/STAT5 signaling and TP53 signaling. Our results emphasize the necessity for careful interpretation of cancer cell line experiments, particularly with respect to therapeutic treatments targeting these important cancer pathways.
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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.