We recently described a methodology that reliably predicted chemotherapeutic response in multiple independent clinical trials. The method worked by building statistical models from gene expression and drug sensitivity data in a very large panel of cancer cell lines, then applying these models to gene expression data from primary tumor biopsies. Here, to facilitate the development and adoption of this methodology we have created an R package called pRRophetic. This also extends the previously described pipeline, allowing prediction of clinical drug response for many cancer drugs in a user-friendly R environment. We have developed several other important use cases; as an example, we have shown that prediction of bortezomib sensitivity in multiple myeloma may be improved by training models on a large set of neoplastic hematological cell lines. We have also shown that the package facilitates model development and prediction using several different classes of data.

Cell line drug screening datasets can be utilized for a range of different drug discovery applications from drug biomarker discovery to building translational models of drug response. Previously, we described three separate methodologies to (1) correct for general levels of drug sensitivity to enable drug-specific biomarker discovery, (2) predict clinical drug response in patients and (3) associate these predictions with clinical features to perform in vivo drug biomarker discovery. Here, we unite and update these methodologies into one R package (oncoPredict) to facilitate the development and adoption of these tools. This new OncoPredict R package can be applied to various in vitro and in vivo contexts for drug and biomarker discovery.

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.

Population differences observed for complex traits may be attributed to the combined effect of socioeconomic, environmental, genetic and epigenetic factors. To better understand population differen...

Large interindividual variance has been observed in sensitivity to drugs. To comprehensively decipher the genetic contribution to these variations in drug susceptibility, we present a genome-wide model using human lymphoblastoid cell lines from the International HapMap consortium, of which extensive genotypic information is available, to identify genetic variants that contribute to chemotherapeutic agent-induced cytotoxicity. Our model integrated genotype, gene expression, and sensitivity of HapMap cell lines to drugs. Cell lines derived from 30 trios of European descent (Center d'Etude du Polymorphisme Humain population) and 30 trios of African descent (Yoruban population) were used. Cell growth inhibition at increasing concentrations of etoposide for 72 h was determined by using alamarBlue assay. Gene expression on 176 HapMap cell lines (87 Center d'Etude du Polymorphisme Humain population and 89 Yoruban population) was determined by using the Affymetrix GeneChip Human Exon 1.0ST Array. We evaluated associations between genotype and cytotoxicity, genotype and gene expression and correlated gene expression of the identified candidates with cytotoxicity. The analysis identified 63 genetic variants that contribute to etoposide-induced toxicity through their effect on gene expression. These include genes that may play a role in cancer ( AGPAT2 , IL1B , and WNT5B ) and genes not yet known to be associated with sensitivity to etoposide. This unbiased method can be used to elucidate genetic variants contributing to a wide range of cellular phenotypes induced by chemotherapeutic agents.