Abstract IA13: Finding biomarkers of response to targeted cancer drugs through functional genetics

Abstract

Abstract Unresponsiveness to therapy remains a significant problem in the treatment of cancer, also with the new classes of cancer drugs. In my laboratory, we use functional genetic approaches to identify biomarkers that can predict responsiveness to targeted cancer therapeutics; drugs that specifically inhibit molecules or pathways that are often activated in cancer. Nevertheless, it remains poorly explained why a significant number of tumors do not respond to these therapies. We aim to elucidate the molecular pathways that contribute to unresponsiveness to targeted cancer therapeutics using a functional genetic approach. This will yield biomarkers that may be useful to predict how individual patients will respond to these drugs. Furthermore, this work may allow the development of drugs that act in synergy with the established drug to prevent or overcome drug resistance. To identify biomarkers that control tumor cell responsiveness to cancer therapeutics, we use multiple complementary approaches. First, we use genome wide loss-of-function genetic screens (with shRNA interference libraries) in cancer cells that are sensitive to the drug-of-interest to search for genes whose down-regulation confers resistance to the drug-of-interest (resistance screens). In addition, we use shRNA screens with a low dose of the drug to screen for genes whose inhibition enhances the toxicity of the cancer drug (sensitizer screens). As a third approach, we use gain of function genetic screens in which we search for genes whose over-expression modulates drug responsiveness. Once we have identified candidate drug response biomarkers in relevant cell line models, we ask if the expression of these genes is correlated with clinical response to the drug-of-interest. For this, we use tumor samples of cancer patients treated with the drug in question and whose response to therapy is documented. In a fourth and distinct approach we perform high throughput sequencing of the “kinome” (some 600 genes) of tumor samples to identify connections between cancer genotype and drug responses. Examples of these approaches to identify biomarkers of response to different cancer drugs will be presented.