Kinase-Dependent Chemotherapy Resistance Mechanisms in Small Cell Lung Cancer Project Summary/Abstract ~22,800-34,000 new Small Cell Lung Cancer (SCLC) diagnoses are made yearly and ~22,000-32,000 patients die of SCLC in the U.S.A. (1). SCLC therapy has not changed in almost 30 years, and long term survival has not improved during this time. Yet, SCLC can be cured. SCLC is sensitive to chemotherapy with a 70-85% partial response rate, and a 20-30% complete response rate. However, almost every patient acquires resistance to chemotherapy and relapses with chemoresistant SCLC, resulting in only a 5% long term survival rate. Understanding the mechanisms behind acquired chemoresistance in SCLC is critical to improved therapy and clinical outcomes. We hypothesize that chemotherapy alters the essential kinase profile in chemosensitive vs. chemoresistant SCLC. Alterations in the essential kinase profile represent acquired kinase-dependent survival mechanisms that protect chemoresistant SCLC cells from chemotherapy induced death. We will test our hypothesis through: Aim I. In chemosensitive and chemoresistant SCLC identify kinases essential for cell survival and alterations in the essential kinase profile induced by chemotherapy. We will comprehensively elucidate kinases essential to SCLC survival using a kinome-wide shRNA approach. By comparing the essential kinases in chemosensitive vs. chemoresistant SCLC derived from the same primary tumor, we will define chemotherapy induced changes in essential kinases to support our first hypothesis. Aim II. Validate chemotherapy induced alterations in the essential kinase profile as mechanisms of chemoresistance. Focusing on essential kinases found in chemoresistant SCLC, we will determine whether essential kinases induced by chemotherapy in chemoresistant SCLC represent a mechanism of chemoresistance in vitro and in vivo in Patient Derived Xenograft models (PDX). We will take novel approaches to our hypothesis by; 1) focusing on functional analyses of kinase-dependent cell survival mechanisms, 2) using an unbiased screen of all kinases, 3) utilizing a novel bioinformatics pipeline developed by our research team, and 4) modeling our studies in PDXs instead of long established cell lines. The assembled research team is particularly qualified to perform these studies with combined expertise in lung cancer, kinases, functional genomics, bioinformatics methodologies and animal modeling

R21CA209121

2016-08-19

2018-07-31