ArticleScientific reports2018
Personalised drug repositioning for Clear Cell Renal Cell Carcinoma using gene expression.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 16 citations in OpenAlex.
- Prognostic importance of splicing-triggered aberrations of protein complex interfaces in cancer.NAR genomics and bioinformatics · 2024Article
- Article
- Systems Biology Approaches to Decipher the Underlying Molecular Mechanisms of Glioblastoma Multiforme.International journal of molecular sciences · 2021Review
- Drug Repurposing to Identify a Synergistic High-Order Drug Combination to Treat Sunitinib-Resistant Renal Cell Carcinoma.Cancers · 2021Article
- Characterization of Molecular Heterogeneity Associated With Tumor Microenvironment in Clear Cell Renal Cell Carcinoma to Aid Immunotherapy.Frontiers in cell and developmental biology · 2021Article
- A Multi-Objective Approach for Anti-Osteosarcoma Cancer Agents Discovery through Drug Repurposing.Pharmaceuticals (Basel, Switzerland) · 2020Article
- Integrating Differential Gene Expression Analysis with Perturbagen-Response Signatures May Identify Novel Therapies for Thyroid-Associated Orbitopathy.Translational vision science & technology · 2020Article
- Influence of batch effect correction methods on drug induced differential gene expression profiles.BMC bioinformatics · 2019Article
- The impact of estimated tumour purity on gene expression-based drug repositioning of Clear Cell Renal Cell Carcinoma samples.Scientific reports · 2019Article
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Reversal of cancer gene expression is predictive of therapeutic potential and can be used to find new indications for existing drugs (drug repositioning). Gene expression reversal potential is currently calculated, in almost all studies, by pre-aggregating all tumour samples into a single group signature or a limited number of molecular subtype signatures. Here, we investigate whether drug repositioning based on individual tumour sample gene expression signatures outperforms the use of tumour group and subtype signatures. The tumour signatures were created using 534 tumour samples and 72 matched normal samples from 530 clear cell renal cell carcinoma (ccRCC) patients. More than 20,000 drug signatures were extracted from the CMAP and LINCS databases. We show that negative enrichment of individual tumour samples correlated (Spearman's rho = 0.15) much better with the amount of differentially expressed genes in drug signatures than with the tumour group signature (Rho = 0.08) and the 4 tumour subtype signatures (Rho 0.036-0.11). Targeted drugs used against ccRCC, such as sirolimus and temsirolimus, which could not be identified with the pre-aggregated tumour signatures could be recovered using individual sample analysis. Thus, drug repositioning can be personalized by taking into account the gene expression profile of the individual's tumour sample.
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