The COXEN principle: translating signatures of in vitro chemosensitivity into tools for clinical outcome prediction and drug discovery in cancer

Abstract

Substantial effort has been devoted to in vitro testing of candidate chemotherapeutic agents. In particular, the United States National Cancer Institute Developmental Therapeutics Program (NCI-DTP) Human Tumor Cell Line Screen has screened hundreds of thousands of compounds and extracts, for which data on more than 40,000 compounds tested on a panel of 60 cancer cell lines (NCI-60) are publically available. In tandem, gene expression profiling has brought about a sea change in our understanding of cancer biology, allowing discovery of biomarkers or signatures able to characterize, classify, and prognosticate clinical behavior of human tumors. Recent studies have used tumor profiling matched to clinical trial outcome data to derive gene expression models predicting therapeutic outcomes, though such efforts are costly, time-consuming, tumor type-specific, and not amenable to rare diseases. Furthermore, addition of new or established drugs to multidrug combinations in which such models are already available requires the entire model to be re-derived. Can the aforementioned in vitro testing platform, coupled to the universal language of genomics, be used to develop, a priori, gene expression models predictive of clinical outcomes? Recent advances, including the CO-eXpression ExtrapolatioN (COXEN) algorithm, suggest that development of these models may be possible and raise important implications for future trial design and drug discovery.

Figure 1. The COXEN algorithm and its implementations

Reported uses of the COXEN algorithm have included both intercohort prediction of sensitivity between different cell line panels for drug discovery and cell line to tumor prediction for response stratification in clinical trials. Either way, the workflow proceeds per the sequence illustrated at left. The algorithm starts by deriving candidate biomarkers by comparing gene expression data between sensitive and resistant cell lines from the NCI-60, a step which can be regarded as biomarker discovery. Then candidate biomarkers are triaged by COeXpression ExtrapolatioN (COXEN) to ensure concordant expression between datasets. Then concordant biomakers are used to derive gene expression models (GEMs), predictive of sensitivity to individual drugs or combinations. Last, such models used to classify cell lines or tumors based on their own gene expression data, and prediction scores examined with respects to empirical (in vitro) or observed (clinical trial) response outcomes. In the case of drug discovery, GEMs can be derived for each of thousands of drugs tested against the NCI-60 panel, then drugs ranked by percentage predicted responders to prioritize them for preclinical evaluation.