Clinical drug response can be predicted using baseline gene expression levels and in vitro drug sensitivity in cell lines

By Paul Geeleher, Nancy J. Cox, R. Huang. Genome Biology, 2014

773 citations recorded by the source corpus.

We demonstrate a method for the prediction of chemotherapeutic response in patients using only before-treatment baseline tumor gene expression data. First, we fitted models for whole-genome gene expression against drug sensitivity in a large panel of cell lines, using a method that allows every gene to influence the prediction. Following data homogenization and filtering, these models were applied to baseline expression levels from primary tumor biopsies, yielding an in vivo drug sensitivity prediction. We validated this approach in three independent clinical trial datasets, and obtained predictions equally good, or better than, gene signatures derived directly from clinical data.

Subfields: Cancer Research, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/5088cc9024e67375df4582fd593a3bcf3e96549f

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Citations in the corpus

pRRophetic: An R Package for Prediction of Clinical Chemotherapeutic Response from Tumor Gene Expression Levels (2014, methodology): “We previously reported that this pipeline (using models developed on all available CGP cell lines) could enrich for responders to the proteosome inhibitor bortezomib in multiple myeloma [1].”
STITCH 5: augmenting protein–chemical interaction networks with tissue and affinity data (2015, background): “This is related to the concentration of the drug, the strength with which it modulates the activity of the target, and the distribution of target proteins among different tissues (16).”
MOLI: multi-omics late integration with deep neural networks for drug response prediction (2019, methodology): “3 Predicting drug response for TCGA patients To study MOLI’s performance, similar to Geeleher et al. (2017), we employ the model trained on the pan-drug input for the EGFR inhibitors to predict the drug response for patients in several TCGA datasets for which there was no drug response recorded.”
Large Scale Foundation Model on Single-cell Transcriptomics (2023, neutral): “This may be due to the fact that specific gene mutations often have important impacts on targeted therapy 34 but such information is hardly revealed in gene expression data, while chemotherapy drugs were widely reported to be related to gene expression 38,39 so their IC 50 is easier to predict.”
Machine learning and feature selection for drug response prediction in precision oncology applications (2018, methodology): “2016), although there are also a few recent examples aiming at clinical treatment predictions in patient samples (Sadanandam et al. 2013; Geeleher et al. 2014; Majumder et al. 2015; Ding et al. 2016; Noren et al. 2016; Yao et al. 2018; Turki et al. 2018).”