Systems approaches to understanding the relationships between genotype, signaling, and therapeutic efficacy

项目摘要/摘要 精确医学的前景是，医生可以根据需要量身定做一种治疗方案 每个单独的病人。就癌症而言，这意味着个性化的治疗策略。 基于个体癌症的分子特征。但是，尽管在精确度方面取得了成功 近年来，医学引起了极大的关注，而精准医学尚未取得 这对绝大多数癌症患者来说是一种影响。我们的首要目标是利用蛋白质组学和 系统生物学理解癌症基因分型与治疗之间的关系 应对，以拓展精准医疗前景为长远目标。我们的研究 主要关注表达突变形式的K-RAS的癌症，K-RAS是最常见的突变 癌蛋白与癌症无反应的最佳生物标志物之一 心理治疗。利用各种实验和计算方法，这个项目将解决 与K-RAS和精准医疗承诺相关的三个关键问题。首先，我们将利用 在相对罕见的情况下，结直肠癌表达一种特定的突变形式的 K-RAS对MEK激酶的抑制是唯一敏感的。我们将使用质谱仪 和计算模型来确定为什么表达K-RasG12D和K-RasA146T的癌症 对MEK的抑制具有不同的敏感性。接下来，我们将解决单变量的限制 通过确定遗传和表观遗传因素对疗效的遗传预测 交互以建立网络信令状态。我们将使用质量细胞术和计算 探索突变K-RAS下游信号如何受细胞谱系影响的建模 癌基因和肿瘤抑制基因的二次突变。最后，我们将搬家 通过开发一种预测疗效的算法，超越基因作为疗效预测因子 基于磷酸化蛋白质组学测量的对激酶抑制的敏感性。我们将验证 通过患者来源的异种移植物的临床前治疗研究的计算方法。 总之，这些研究将利用最先进的实验和计算方法 使个性化医疗成为K-RAS突变癌症患者的现实目标。

Project Summary/Abstract The promise of precision medicine is that a physician can tailor a therapeutic regimen to suit each individual patient. In the case of cancer, this means a personalized therapeutic strategy based on the molecular features of an individual's cancer. But while successes in precision medicine have garnered significant attention in recent years, precision medicine has not made an impact for the vast majority of cancer patients. Our overarching goal is to use proteomics and systems biology to understand the relationships between cancer genotype and therapeutic response, with the long-term goal of expanding the prospects of precision medicine. Our study focuses primary on cancers expressing mutant forms of K-Ras, the most commonly mutated oncoprotein in cancer and one of the best biomarkers for the failure of a cancer to respond to therapy. Using a variety of experimental and computational approaches, this project will address three key questions related to K-Ras and the promise of precision medicine. First, we will exploit a relatively rare circumstance in which colorectal cancers expressing a specific mutant form of K-Ras are uniquely sensitive to inhibition of the MEK kinase. We will use mass spectrometry and computational modeling to determine why cancers expressing K-RasG12D and K-RasA146T are differentially sensitive to inhibition of MEK. Next, we will address the limitation of univariate genetic prediction of therapeutic efficacy by determining how genetic and epigenetic factors interact to establish network signaling state. We will use mass cytometry and computational modeling to explore how signaling downstream of mutant K-Ras is affected by cellular lineage and by secondary mutations in oncogenes and tumor suppressor genes. Finally, we will move beyond genotype as a predictor of therapeutic efficacy by developing an algorithm to predict sensitivity to kinase inhibition based on phospho-proteomic measurements. We will validate the computational approach via preclinical therapeutics studies in patient-derived xenografts. Altogether these studies will utilize state-of-the-art experimental and computational approaches to make personalized medicine a realistic goal for patients suffering from K-Ras mutant cancer.