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

了解基因型、信号传导和治疗功效之间关系的系统方法

基本信息

批准号：
9904544
负责人：
Wilhelm Haas
金额：
$ 77.01万
依托单位：
MASSACHUSETTS INSTITUTE OF TECHNOLOGY
依托单位国家：
美国
项目类别：
财政年份：
2017
资助国家：
美国
起止时间：
2017-04-05 至 2022-03-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9904544
关键词：
Address Affect Algorithms Attention Biological Markers Biology Cancer Patient Cells Colon Colorectal Colorectal Cancer Communities Complex Computer Analysis Computer Models Computing Methodologies Cues Cytometry Cytostatics Data Epidermal Growth Factor Receptor Epigenetic Process Event Exposure to FDA approved Failure Fuzzy Logic Genes Genetic Genetically Engineered Mouse Genotype Goals Heterogeneity Human Human Cell Line Hyperactive behavior Individual KRAS2 gene Knowledge Least-Squares Analysis Lung MAP3K1 gene MEK inhibition MEKs Malignant Neoplasms Mass Spectrum Analysis Measurement Measures Molecular Movement Mutate Mutation Oncogenic Oncoproteins Output Pancreas Pathway interactions Patients Phosphotransferases Physicians Prize Proteome Proteomics Regimen Research Resistance Sampling Signal Pathway Signal Transduction System Systems Biology TP53 gene Testing Therapeutic Therapeutic Studies Therapy trial Time Tissues Treatment Efficacy Tumor Suppressor Genes Work Xenograft procedure antibody inhibitor base cancer cell cancer therapy combinatorial conventional therapy cytotoxic design effective therapy experimental study forest individual patient inhibitor/antagonist insight kinase inhibitor multidimensional data mutant personalized medicine personalized therapeutic phosphoproteomics pre-clinical pre-clinical therapy precision medicine precision oncology prospective response small molecule stem success targeted treatment therapeutic candidate therapy resistant treatment response tumor

项目摘要

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.

项目概要/摘要精准医学的承诺是医生可以根据需要定制治疗方案每个患者。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。但尽管在精度方面取得了成功近年来医学受到广泛关注，但精准医学尚未取得进展对绝大多数癌症患者产生影响。我们的首要目标是利用蛋白质组学和系统生物学了解癌症基因型与治疗之间的关系响应，长期目标是扩大精准医学的前景。我们的研究主要关注表达 K-Ras 突变形式的癌症，K-Ras 是最常见的突变形式癌症中的癌蛋白，也是癌症未能做出反应的最佳生物标志物之一治疗。该项目将使用各种实验和计算方法来解决与 K-Ras 和精准医学的前景相关的三个关键问题。首先，我们将利用一种相对罕见的情况，结直肠癌表达特定的突变形式 K-Ras 对 MEK 激酶的抑制特别敏感。我们将使用质谱分析和计算模型以确定为什么癌症表达 K-RasG12D 和 K-RasA146T 对 MEK 的抑制有不同的敏感性。接下来我们要解决单变量的局限性通过确定遗传和表观遗传因素对治疗效果的遗传预测交互建立网络信令状态。我们将使用质谱流式细胞术和计算建模以探索突变型 K-Ras 下游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二次突变。最后，我们将搬家通过开发预测算法，超越基因型作为治疗效果的预测因子基于磷酸化蛋白质组测量的激酶抑制敏感性。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。总的来说，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。