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

项目总结/摘要 精准医疗的承诺是，医生可以根据需要定制治疗方案， 每一个病人。在癌症的情况下，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，虽然在精确度方面取得了成功， 近年来，精准医学已经引起了人们的极大关注，但精准医学并没有使 对绝大多数癌症患者都有影响。我们的首要目标是利用蛋白质组学， 系统生物学，以了解癌症基因型和治疗之间的关系 响应，长期目标是扩大精准医疗的前景。我们的研究 主要关注表达K-Ras突变形式的癌症， 癌症中的癌蛋白和癌症失败的最佳生物标志物之一， 疗法使用各种实验和计算方法，该项目将解决 与K-Ras相关的三个关键问题以及精准医疗的前景。首先，我们将利用 一种相对罕见的情况，其中结肠直肠癌表达特定的突变形式， 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-RasG 12 D和K-RasA 146 T的癌症 对MEK抑制的敏感性不同。接下来，我们将讨论单变量的限制。 通过确定遗传和表观遗传因素 交互以建立网络信令状态。我们将使用大量的细胞计数和计算 建模以探索突变型K-Ras下游的信号传导如何受到细胞谱系的影响 以及癌基因和抑癌基因的继发性突变。最后，我们将 超越基因型作为疗效的预测因子， 基于磷酸化蛋白质组学测量的激酶抑制敏感性。我们将验证 通过临床前治疗学研究在患者来源的异种移植物中使用计算方法。 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.