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Abstract IA12: Developing precision medicine-based new lung cancer therapeutics IA12:发展基于精准医学的肺癌新疗法
Pub Date : 2018-09-01 DOI: 10.1158/1557-3265.AACRIASLC18-IA12
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
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
我们使用“化学优先”的方法来发现非小细胞肺癌(NSCLC)发病机制中获得的可药物获得性脆弱性。我们筛选了化学文库(约200,000种化合物),以寻找杀死非小细胞肺癌亚群而不是正常人肺上皮细胞(HBECs)的化学毒素。我们首先筛选了12个NSCLC细胞系,这些细胞系代表了各种已知的癌基因型,并确定了具有大Z分数和适当特性的化学物质,包括再供应、化学和可重复的药物反应表型,并从中缩小了202种化学物质和18种已知靶向药物的列表(我们称之为“精确肿瘤探针”集,POPS)。这些药物和30种临床可用药物、靶向疗法和药物组合组成的小组,已经在使用或在试验中用于非小细胞肺癌治疗,然后在96个非小细胞肺癌系的小组上进行12点剂量反应曲线的药物反应表型测试。使用扫描排序KS (Kolmogorov-Smirnov)和弹性网络生物统计学方法对这些信息进行分析,以确定分子生物标志物(突变、mRNA表达、拷贝数变异、蛋白质表达和代谢组学),这些标志物可以预测对特定化学毒素或治疗方案的敏感性或耐药性。由此我们发现,我们的方法可以识别已知的分子生物标志物药物敏感性(例如,EGFR突变和EGFR TK抑制剂);许多临床可用的化疗药物具有预测临床前模型药物反应的分子生物标志物;POP化学毒素组在大型NSCLC组中提供了新的药物反应表型模式,不同于临床可用的药物,包括治疗窗口;许多POP毒素只击中一小部分(~5%)的非小细胞肺癌组,但POP作为一个整体提供了整个非小细胞肺癌组的“覆盖”;有简单的单组分或双组分分子生物标志物(突变、mRNA表达)可预测NSCLC患者对不同化学毒素的反应;这些分子生物标记物提供了一些关于目标和途径的信息,这些目标和途径涉及对化学毒素的反应。因此,我们已经确定了一组对非小细胞肺癌亚群和相关肿瘤分子生物标志物具有选择性的化学毒素,以促进它们在精准医学方面的发展,并且在某些情况下,还可以获得这些化合物阻断的靶点和途径的信息。此外,我们已经发现了非小细胞肺癌的预测性生物标志物的临床可用的药物。德克萨斯大学肺癌孢子(P50CA70907), NCI CTD2N (U01 CA176284)和CPRIT资助。引文格式:John D. Minna, Elizabeth McMillan, Luc Girard, Michael Peyton, Kenneth Huffman, Dhruba Deb, Paul Yenerall, Amit Das, Longshan Li, Maithili Dalvi, Boning Gao,谢杨,俞永豪,Suzie ight, Rachel Vaden, Caroline Diep, Michael Roth, Bruce Posner, John MacMillan, Ralph Deberardinis, David Wheeler, John V. Heymach, Ignacio I. Wistuba, Adi Gazdar, Michael White。发展基于精准医学的肺癌新疗法[摘要]。第五届AACR-IASLC国际联合会议论文集:肺癌转化科学从实验室到临床;2018年1月8日至11日;费城(PA): AACR;临床肿瘤杂志,2018;24(增刊):1 - 12。
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