Using valproic acid as an example, the authors demonstrate that drug response signatures derived from genome-wide expression data can identify individuals likely to respond to a drug, and propose that this method could select optimal populations for clinical trials of new therapies.
Drug response signatures that accurately reflect the cellular response to a drug can be generated from Connectivity Map and publically available gene expression data.Predictions from the drug response signature for valproic acid correlate with sensitivity to valproic acid in breast cancer cell lines and patient tumors grown in three-dimensional culture and mouse xenografts.The MATCH algorithm provides an efficient approach for using genome-wide gene expression data to identify a target population for a drug prior to clinical trials.MATCH can predict drug sensitivity in tumors without knowledge of mechanism of action.
Unlike traditional chemotherapy, targeted cancer therapies are expected to work in only a subset of people with a particular cancer. However, biomarkers of response are not always known before clinical trial initiation. We present MATCH (Merging genomic and pharmacologic Analyses for Therapy CHoice), an algorithm for using genome-wide gene expression data to identify and validate a genomic biomarker of sensitivity (see Figure 1). Our proof-of-principle example is valproic acid (VPA), but we also show that an estrogen blocking drug currently used for breast cancer and a B-RAF inhibitor in trials for melanoma give predictions that correspond to their clinical uses.
We use genome-wide gene expression data from treated and untreated samples from the Connectivity Map to generate a VPA response signature. We validate that the VPA signature can identify treated and untreated cells in an independent data set of normal cells and in independent samples from the Connectivity Map. The AUC for the ROC curve is 0.86. We then apply the VPA signature to publically available data sets from a panel of cancer cell lines and from primary tumor and normal tissue samples. These data suggest that there is a subset of women with breast cancer who will be sensitive to VPA. Finally, we validate that our predictions correlate with sensitivity to VPA in breast cancer cell lines grown in two-dimensional culture, primary breast tumor samples grown in three-dimensional culture, and in vivo mouse breast cancer xenografts. Together, these studies show that MATCH can identify cancer patients most likely to respond to a specific drug treatment.
Identifying the best drug for each cancer patient requires an efficient individualized strategy. We present MATCH (Merging genomic and pharmacologic Analyses for Therapy CHoice), an approach using public genomic resources and drug testing of fresh tumor samples to link drugs to patients. Valproic acid (VPA) is highlighted as a proof-of-principle. In order to predict specific tumor types with high probability of drug sensitivity, we create drug response signatures using publically available gene expression data and assess sensitivity in a data set of >40 cancer types. Next, we evaluate drug sensitivity in matched tumor and normal tissue and exclude cancer types that are no more sensitive than normal tissue. From these analyses, breast tumors are predicted to be sensitive to VPA. A meta-analysis across breast cancer data sets shows that aggressive subtypes are most likely to be sensitive to VPA, but all subtypes have sensitive tumors. MATCH predictions correlate significantly with growth inhibition in cancer cell lines and three-dimensional cultures of fresh tumor samples. MATCH accurately predicts reduction in tumor growth rate following VPA treatment in patient tumor xenografts. MATCH uses genomic analysis with in vitro testing of patient tumors to select optimal drug regimens before clinical trial initiation.