Predicting Cancer Drug Response Using a Recommender System

Suphavilai, Chayaporn; Bertrand, Denis; Nagarajan, Niranjan

doi:10.1101/215327

Cited by 32 publications

(60 citation statements)

References 38 publications

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“…For example, cancer cell lines with mutations in the BRAF genes are significantly more sensitive to PLX4730, a BRAF-inhibitor, than those with wild-type BRAF (Yang et al, 2013). Additional computational methods for predicting sensitivity to drugs using gene expression data from cancer cell lines have been developed, for example (Azuaje et al, 2018;Nguyen et al, 2016;Suphavilai et al, 2018;Wei et al, 2019). Several publications have recent efforts in this area (Azuaje, 2017;Guan et al, 2019;Guvenc Paltun et al, 2019;Reinhold et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Predicting Tumor Response to Drugs based on Gene-Expression Biomarkers of Sensitivity Learned from Cancer Cell Lines

Umbach

Krahn

et al. 2020

Preprint

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SUMMARYHuman cancer cell line profiling and drug sensitivity studies provide valuable information about the therapeutic potential of drugs and their possible mechanisms of action. The goal of those studies is to translate the findings from in vitro studies of cancer cell lines into in vivo therapeutic relevance and, eventually, patients’ care. Tremendous progress has been made. In this work, we built predictive models for 453 drugs using data on gene expression and drug sensitivity (IC50) from cancer cell lines. We identified many known drug-gene interactions and uncovered several potentially novel drug-gene associations. Importantly, we further applied these predictive models to ∼17,000 bulk RNA-seq samples from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) database to predict drug sensitivity for both normal and tumor tissues. We created a web site for users to visualize and download our predicted data (https://edelgene.niehs.nih.gov/cancerRxTissue). Using trametinib as an example, we showed that our approach can faithfully recapitulate the known tumor specificity of the drug. We further demonstrated that our approach can predict drugs that 1) are tumor-type specific; 2) elicit higher sensitivity from tumor compared to corresponding normal tissue; 3) elicit differential sensitivity across breast cancer subtypes. If validated, our predictions could have clinical relevance for patients’ care.

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Section: Introductionmentioning

confidence: 99%

Predicting Tumor Response to Drugs based on Gene-Expression Biomarkers of Sensitivity Learned from Cancer Cell Lines

Umbach

Krahn

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…For example, cancer cell lines with mutations in the BRAF genes are significantly more sensitive to PLX4730, a BRAF-inhibitor, than those with wild-type BRAF [3]. Additional computational methods for predicting sensitivity to drugs using gene expression data from cancer cell lines have been developed, for example [11][12][13][14]. Several publications have recent efforts in this area [15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Predicting Tumor Response to Drugs Based on Gene-expression Biomarkers of Sensitivity Learned From Cancer Cell Lines

Umbach

Krahn³

et al. 2020

Preprint

View full text Add to dashboard Cite

Background: human cancer cell line profiling and drug sensitivity studies provide valuable information about the therapeutic potential of drugs and their possible mechanisms of action. The goal of those studies is to translate the findings from in vitro studies of cancer cell lines into in vivo therapeutic relevance and, eventually, patients’ care. Tremendous progress has been made. Results: in this work, we built predictive models for 453 drugs using data on gene expression and drug sensitivity (IC50) from cancer cell lines. We identified many known drug-gene interactions and uncovered several potentially novel drug-gene associations. Importantly, we further applied these predictive models to ~17,000 bulk RNA-seq samples from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) database to predict drug sensitivity for both normal and tumor tissues. We created a web site for users to visualize and download our predicted data (https://edelgene.niehs.nih.gov/cancerRxTissue). Using trametinib as an example, we showed that our approach can faithfully recapitulate the known tumor specificity of the drug. Conclusions: we demonstrated that our approach can predict drugs that 1) are tumor-type specific; 2) elicit higher sensitivity from tumor compared to corresponding normal tissue; 3) elicit differential sensitivity across breast cancer subtypes. If validated, our predictions could have clinical relevance for patients’ care.

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“…have proposed a matrix factorization based recommender system (CaDRReS) method, which considers essential genes for drug-response prediction. 19 …”

Section: Introductionmentioning

confidence: 99%

Anti-cancer Drug Response Prediction Using Neighbor-Based Collaborative Filtering with Global Effect Removal

Liu

Zhao

Zhang

et al. 2018

Molecular Therapy - Nucleic Acids

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Patients of the same cancer may differ in their responses to a specific medical therapy. Identification of predictive molecular features for drug sensitivity holds the key in the era of precision medicine. Human cell lines have harbored most of the same genetic changes found in patients’ tumors and thus are widely used in the research of drug response. In this work, we formulated drug-response prediction as a recommender system problem and then adopted a neighbor-based collaborative filtering with global effect removal (NCFGER) method to estimate anti-cancer drug responses of cell lines by integrating cell-line similarity networks and drug similarity networks based on the fact that similar cell lines and similar drugs exhibit similar responses. Specifically, we removed the global effect in the available responses and shrunk the similarity score for each cell line pair as well as each drug pair. We then used the K most similar neighbors (hybrid of cell-line-oriented and drug-oriented) in the available responses to predict the unknown ones. Through 10-fold cross-validation, this approach was shown to reach accurate and reproducible outcomes of drug sensitivity. We also discussed the biological outcomes based on the newly predicted response values.

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Predicting Cancer Drug Response Using a Recommender System

Cited by 32 publications

References 38 publications

Predicting Tumor Response to Drugs based on Gene-Expression Biomarkers of Sensitivity Learned from Cancer Cell Lines

Predicting Tumor Response to Drugs based on Gene-Expression Biomarkers of Sensitivity Learned from Cancer Cell Lines

Predicting Tumor Response to Drugs Based on Gene-expression Biomarkers of Sensitivity Learned From Cancer Cell Lines

Anti-cancer Drug Response Prediction Using Neighbor-Based Collaborative Filtering with Global Effect Removal

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