Precision Medicine for Cancer Patients: Lessons Learned and the Path Forward

Abstract

An explosion in our knowledge of cancer biology has occurred in the decade since the elucidation of the human genome. The potential for translation of this genomic information to the therapeutic management of patients has generated great excitement, although, despite some stellar successes, progress has been slower than expected. However, the concept of personalized or “precision” medicine that integrates genomic knowledge (such as molecular analysis of the patient’s tumor) and other laboratory research with input from health records, along with social and environmental data, for the selection of the optimal therapy for the individual patient remains attractive. Striking examples of success with this approach are the use of BCR-ABL mutations to predict clinical responses to imatinib in chronic myelogenous leukemia and EGFR mutations to predict clinical response to EGFR tyrosine kinase inhibitors in non–small cell lung cancer. These approaches use “enrollment biomarkers” to identify mutant targets to attack with specific therapies. An example of a newly recognized potential enrollment marker is expression of the SLFN11 gene for prediction of sensitivity to topoisomerase inhibitors (1). Another approach is to use tumor mRNA expression patterns, “molecular signatures,” for response prediction for the selection of conventional cytotoxic therapies. Enrollment biomarker approaches are mechanistically based in that they are directly related to the targeted pathway and thus directly connect the therapy to the tumor’s “oncogene addiction.” Tumor mRNA phenotypes, however, have not been shown to directly relate to the targeted tumor pathway(s) or addiction in most cases. Thus, such approaches are less specific and often rely on gene panels whose mechanistic roles and relevance are not apparent. All such approaches may be facilitated by preclinical models (e.g., tumor cell lines, xenografts, and genetically engineered mouse models of cancer) for which both molecular analyses and therapy response phenotypes can be determined independent of the patient and which can lead to the development of molecular signatures predictive of response to specific therapies. This latter approach permits the widespread, unbiased testing of new therapies and their correlation with molecular markers. Such preclinical models also allow for totally independent testing by multiple investigators of proposed therapies and their molecular correlations and for systematic genetic (e.g., small interfering RNA or short hairpinRNA) and chemical library-wide searches for “tumor acquired vulnerabilities” (synthetic lethalities) to identify previously unknown cancer therapies that have specificity for tumor over normal tissues and also specificities for subtypes within tumors of the same primary type. These approaches are being used by programs such as the National Cancer Institute’s (NCI’s) Cancer Target Discovery and Development (CTD) Network (2) as well as many pharmaceutical and biotechnology companies. In this issue of the Journal, Wang et al. (3) address an important related issue: how to evaluate the clinical relevance of a drug sensitivity signature developed from a preclinical model without knowledge of all relevant information. The US-based academic authors collaborated with a Danish pharmacodiagnostic company, which developed a drug response predictor for the relevant cancer therapeutic drugs using the NCI-60 panel of cell lines (4). The academic group scoured the literature to find all deposited tumor databases that had used a single commercial gene expression microarray and had at least 100 patients with the same type of cancer who had received the same treatment and for whom clinical outcome information was available. When this information was coupled with only those drugs used in the treatment for which information from the NCI-60 datasets met criteria for analyses, only three datasets were finally available: one each for breast cancer, Hodgkin’s lymphoma, and acute lymphoblastic leukemia. The