Identifying rational combination therapies for colorectal cancer using functional genetics

The deployment of cancer therapeutic agents that target specific molecular pathways has been the hallmark of drug development in oncology over the past decade. This has helped us rea lize the dream of personalized cancer medicine since we can ta ilor the treatment to individual patients based on the genetic makeup of the tumor. In a limited number of specific instances, a s ingle molecule or antibody directed against an oncogenic target that sustains tumor growth and survival has proved remarkably effective in providing long-term conh·ol of a specific d isease process. Some of these Molecular Targeted Agents (MTA) include imatinib (BCR-ABL), Vemurafenib (BRAFV600E), Herceptin (HER2), Gefitinib (EGFR TKI) and Cetuximab (EGFRmab) respectively. However, considering the complexity of our genome and the existence of redundancy and crosstalk in signaling pathways, resistance to these MTA is inevitable. We have come to the realization that more combinations of MTAs would be required to maintain a steady response in cancer patients. This is not surprising as multiple combinations of antiviral /antibiotics drugs are administered to patients with HIV or tuberculosis to manage the disease. Since we have an arsenal of MT As in the pipeline, its difficult to assess which combination treatment will deliver the best therapeutic effect. It is often seen that most of the combination treatments that are experimented in clinical trials often seem to deliver modest clinical responses. This is due to the lack of proper mechanistic insight underlying the complexity of targeting signal transduction pathways. In this thes is we use RNA interference technology to understand signaling crosstalk, res istance mechanism and rational combination therapies for colon cancer. Chapter I is a review that summarizes the challenges and opportunities provided by crosstalk between signaling pathways during therapeutic intervention. This chapter discusses the problem of drug res istance seen with targeted monotherapies and provides a comprehensive over view about identifying rational combinations to fight resistance using functional genetic screening technology. In Chapter 2 we have solved a trivial clinical problem i.e. why do BRAF mutant colon cancers respond poorly to BRAF inhibition as against BRAF mutant melanoma? Using a kinome-cen tered loss of function genetic screening we identified EGFR as synthetic lethal with BRAF inhibition in BRAF mutant CRC. It is counterintuitive to inhibit EGFR, which is upstream of BRAF in the MAPK pathway, but the re lief of negative feedback upon BRAF inhibition that is mediated via CDC25C phospnatase, aids in the activation of EGFR upstream of BRAF. Using a combination of clinically approved EGFR inhibitors and the BRAF inhibitor, we were able to show strong synergistic effect on cell proliferation in-vitro and ivvivo. Here we have a mechanistic rational to combine EGFR inhibitors with BRAF inhibitor in the context of BRAF mutant CRC. Moreover, this is an elegant example to il llustrate that the genotype alone is a bad predictor of therapy response and emphasizes the point of context dependency and tissue specificity. • The above study has now led to the design of three clinical trials, in which BRAF mutant CRC patients are being treated with the combination of an EGFR and a BRAF inhibitor. We are beginning to see impressive responses in patients that have been treated so far. ClinicalTrials.gov Identifier; NCTO l 7 19380, NCTO J 750918 and NCTOl791309. In Chapter 3 we carried out a phosphatase centered loss of function genetic screen in BRAF mutant CRC in order to identify phosphatases that disable the feedback engagement of EGFR upon BRAF inhibition. We identified PTPNJ I suppression as highly synergistic with BRAF inhibitors in BRAF mutant CRC. PTPNJ I is a positive regulator of receptor tyros ine kinase signaling. We find that inhibition of PTPNI 1 disrupts the feedback engagement of EGFR signaling in BRAF mutant CRC and also abrogated growth factor driven resistance in melanoma. We show that PTPNJ I pTYR 542 can be used as a biomarker to identify the involvement ofRTKs during drug resistance. In Chapter 4 we focus on identify ing resistance mechanism to cetuximab in colorectal cancer. Using a chromatin modifier RNAi library we identified SIRT2 loss as a modulator of cetuximab sensitivity. SIRT2 is a deacetylase of MEK I. Increased Acetylation of MEKI upon loss of SIRT2 leads to MEK I hyper phosphorylation and increased MAP kinase pathway signaling and confers resi stance to cetuximab. We a lso show that loss of SIRT2 confers res istance to BRAF and MEK inhibitors in BRAF mutant and KRAS mutant cancers. In Chapter S we discuss about the general advantages and limitations of cell lines to identify combination treatments and highlight other possible invitro (organoids) or invivo (PDX) models that can be used for preclinical studies.