Special Lecture最新文献

{"title":"Abstract IA12: Developing precision medicine-based new lung cancer therapeutics","authors":"J. Minna, E. McMillan, L. Girard, M. Peyton, K. Huffman, Dhruba Deb, P. Yenerall, A. Das, L. Li, Maithili P. Dalvi, B. Gao, Yang Xie, Yonghao Yu, Suzie K. Hight, Rachel M Vaden, Caroline H. Diep, M. Roth, B. Posner, J. MacMillan, R. Deberardinis, D. Wheeler, J. Heymach, I. Wistuba, A. Gazdar, M. White","doi":"10.1158/1557-3265.AACRIASLC18-IA12","DOIUrl":"https://doi.org/10.1158/1557-3265.AACRIASLC18-IA12","url":null,"abstract":"We have used a “chemistry first” approach to discover druggable acquired vulnerabilities that have been acquired in the pathogenesis of non-small cell lung cancer (NSCLC). We screened chemical libraries (~200,000 compounds) for chemical toxins that killed subsets of NSCLC but not normal human lung epithelial cells (HBECs). We first screened a panel of 12 NSCLC lines that represented a variety of known oncogenotypes and identified chemicals with large Z scores and appropriate properties, including re-supply, chemistry, and reproducible drug response phenotypes, and from this narrowed down a list of 202 chemicals and 18 drugs with known targeting (we called our “Precision Oncology Probe” set, POPS). These and a panel of 30 clinically available drugs, targeted therapies, and drug combinations, already in use or in trials for NSCLC treatment, were then tested on a panel of 96 NSCLC lines for their drug response phenotypes in 12-point dose response curves. This information was analyzed using scanning ranked KS (Kolmogorov–Smirnov) and elastic net biostatistics approaches to identify molecular biomarkers (mutations, mRNA expression, copy number variation, protein expression, and metabolomics) that could predict for sensitivity or resistance to a particular chemical toxin or treatment regimen. From this we have discovered that our approach identifies already known molecular biomarker drug sensitivities (e.g., EGFR mutations and EGFR TK inhibitors); many clinically available chemotherapy agents have molecular biomarkers predicting preclinical model drug responses; the POP set of chemical toxins provides novel drug-response phenotype patterns in the large NSCLC panel different from those found with clinically available agents including a therapeutic window; many of the POP toxins only hit a small % (~5%) of the NSCLC panel but the POP set as a whole provides “coverage” of the entire NSCLC panel; there are simple, one- or two-component molecular biomarkers (mutations, mRNA expression) that predict responses to the different chemical toxins in the NSCLC panel; and that the molecular biomarkers provide some information on the targets and pathways involved in response to the chemical toxins. Thus, we have identified a group of chemical toxins with selectivity for subsets of NSCLC and associated tumor molecular biomarkers to facilitate their development for precision medicine and also, in some cases, information on the targets and pathways interdicted by these chemical compounds. In addition, we have discovered NSCLC predictive biomarkers for clinically available agents. University of Texas SPORE in Lung Cancer (P50CA70907), NCI CTD2N (U01 CA176284), and CPRIT Grants. Citation Format: John D. Minna, Elizabeth McMillan, Luc Girard, Michael Peyton, Kenneth Huffman, Dhruba Deb, Paul Yenerall, Amit Das, Longshan Li, Maithili Dalvi, Boning Gao, Yang Xie, Yonghao Yu, Suzie Hight, Rachel Vaden, Caroline Diep, Michael Roth, Bruce Posner, John MacMillan, Ralph Deberard","PeriodicalId":227047,"journal":{"name":"Special Lecture","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123896453","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}