pRRophetic: an R package for prediction of clinical chemotherapeutic response from tumor gene expression levels

Abstract

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.

Figure 1. The prediction accuracy achieved in external datasets.

(a) A boxplot showing the predicted clinical bortezomib sensitivity for multiple myeloma patients. The predictions where made using the pRRopheticPredict() function for only hematological cancer cell lines. (NR, Clinical Non-responders; R, Clinical Responders) (b) The predicted PD0325901 sensitivity in CCLE, plotted against the measured activity area (a measure of drug response) in CCLE. A linear regression line and 95% confidence intervals are included.