The candidate pre dictors proposed here could inform such clinical deci sions for nearly all patients. Therefore, by considering diverse molecular data, we might suggest treatment options for not only the approximately 20% of patients who are ERBB2 /ER but also secondary treatment options for those who will suboptimally respond to ER or ERBB2 directed treatments. While our efforts to develop predictive drug response signatures are quite promising, they come with several conceptual caveats. Although the cell line panel is a reasonable model system, it does not capture several features known to be of critical importance in primary tumors. In particular, we have not modeled influences of the microenvironment, including additional cell types known to contribute to tumorigenesis, as well as variation in oxygen content, which has been shown to influence therapeutic response.

Expanding these experiments to three dimensional model systems or mouse xenografts would aid in translation to the clinic. Additionally, validating these predictors in independent data sets will be important for determining how robust they are. Despite these limitations, our observation that we could find evidence of these predictive signatures in the TCGA data suggests that our cell line system is likely captur ing many of the key elements involved in mediating therapeutic response. Of course, the cell line derived predictive signatures described in this study require substantial clinical val idation.

One possibility is in neoadjuvant trials like the I SPY 2 TRIAL, in which in vitro derived signatures for individual compounds are tested for power in predicting pathologic complete response or change in tumor volume measured with magnetic resonance imaging. An alternative approach for validation of signatures for approved drugs is to compare outcomes in patients assigned compounds according to in vitro predictors with outcomes in patients assigned drugs according to physicians first treatment choice. This study constitutes the basis for such a trial, with the development of a portfolio of in vitro predictors and a computational tool that physicians might use to select compounds from that portfolio for individual patients. Regardless of the specific design of the clinical trial, gene expression, methylation and copy number levels should be collected for all patients.

High throughput sequencing techniques can provide all three with the additional AV-951 benefits of alternative splicing information. As outlined in Figure 1, measurements of expression, methylation and copy number would serve as input to the predictor toolbox. The output of the toolbox consists of a report for each individualized patient, with the 22 thera peutic compounds ranked according to a patients likeli hood of response and in vitro GI50 dynamic range. The full panel of 22 drug compounds could be tested simultan eously in a multi arm trial to speed up the validation of the in vitro approach.