ArticleComputational and structural biotechnology journal2022
A unified platform enabling biomarker ranking and validation for 1562 drugs using transcriptomic data of 1250 cancer cell lines.
Article in Computational and structural biotechnology journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- A whole genomic CRISPR-Cas9 screen identifies the amino acid transporter SLC43A1 (LAT3) as a major determinant of oxaliplatin sensitivity in colorectal cancer cells.Molecular cancer therapeutics · 2026Article
- Expression Profiles and Biomarker Potential of Long Non-Coding RNAs H19, NEAT1, MALAT1 and HOTAIR in Locally Advanced Rectal Cancer Patients.International journal of molecular sciences · 2026Article
- GX15-070 enhances niraparib efficacy in ovarian cancer by promoting a shift in Mcl1-mediated DNA repair pathway from HR to NHEJ.Journal of translational medicine · 2025Article
- ST-deconv: an accurate deconvolution approach for spatial transcriptome data utilizing self-encoding and contrastive learning.NAR genomics and bioinformatics · 2025Article
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- SensitiveCancerGPT: Leveraging Generative Large Language Model on Structured Omics Data to Optimize Drug Sensitivity Prediction.bioRxiv : the preprint server for biology · 2025Article
- Disrupting DDB2-DNA Interaction by Lapatinib Enhances Chemotherapy Sensitivity.International journal of biological sciences · 2025Article
- Evaluation of Spliceosome Protein SmD2 as a Potential Target for Cancer Therapy.International journal of molecular sciences · 2024Article
- Integrative analysis of a novel immunogenic PANoptosis‑related gene signature in diffuse large B-cell lymphoma for prognostication and therapeutic decision-making.Scientific reports · 2024Article
- Polo-like kinase 2 targeting as novel strategy to sensitize mutant p53-expressing tumor cells to anticancer treatments.Journal of molecular medicine (Berlin, Germany) · 2024Article
- Oxidative Phosphorylation as a Predictive Biomarker of Oxaliplatin Response in Colorectal Cancer.Biomolecules · 2024Article
- Artificial intelligence methods available for cancer research.Frontiers of medicine · 2024Review
- Cone photoreceptor phosphodiesterase PDE6H inhibition regulates cancer cell growth and metabolism, replicating the dark retina response.Cancer & metabolism · 2024Article
- Unlocking the Molecular Secrets of Antifolate Drug Resistance: A Multi-Omics Investigation of the NCI-60 Cell Line Panel.Biomedicines · 2023Article
- Enhancement of TKI sensitivity in lung adenocarcinoma through m6A-dependent translational repression of Wnt signaling by circ-FBXW7.Molecular cancer · 2023Article
- New Transcriptomic Biomarkers of 5-Fluorouracil Resistance.International journal of molecular sciences · 2023Article
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- CRABP2 regulates infiltration of cancer-associated fibroblasts and immune response in melanoma.Oncology research · 2023Article
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Authors and funding
2 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Intro: In vitro cell line models provide a valuable resource to investigate compounds useful in the systemic chemotherapy of cancer. However, the due to the dispersal of the data into several different databases, the utilization of these resources is limited. Here, our aim was to establish a platform enabling the validation of chemoresistance-associated genes and the ranking of available cell line models. Methods: We processed four independent databases, DepMap, GDSC1, GDSC2, and CTRP. The gene expression data was quantile normalized and HUGO gene names were assigned to have unambiguous identification of the genes. Resistance values were exported for all agents. The correlation between gene expression and therapy resistance is computed using ROC test. Results: We combined four datasets with chemosensitivity data of 1562 agents and transcriptome-level gene expression of 1250 cancer cell lines. We have set up an online tool utilizing this database to correlate available cell line sensitivity data and treatment response in a uniform analysis pipeline (www.rocplot.com/cells). We employed the established pipeline to by rank genes related to resistance against afatinib and lapatinib, two inhibitors of the tyrosine-kinase domain of ERBB2. Discussion: The computational tool is useful 1) to correlate gene expression with resistance, 2) to identify and rank resistant and sensitive cell lines, and 3) to rank resistance associated genes, cancer hallmarks, and gene ontology pathways. The platform will be an invaluable support to speed up cancer research by validating gene-resistance correlations and by selecting the best cell line models for new experiments.
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