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

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

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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.

项目摘要/摘要精确医学的承诺是医生可以调整治疗方案以适合每个患者。在癌症的情况下，这意味着一种个性化的治疗策略基于个体癌症的分子特征。但是，精确的成功近年来，医学引起了极大的关注，精密医学尚未引起对绝大多数癌症患者的影响。我们的总体目标是使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反应，其长期目标是扩大精密医学的前景。我们的研究将主要集中在表达k-ras突变形式的癌症上，这是最常见的突变癌症中的癌蛋白，也是癌症未能反应的最佳生物标志物之一治疗。使用各种实验和计算方法，该项目将解决与K-Ras有关的三个关键问题和精密医学的希望。首先，我们将利用一种相对罕见的情况，在这种情况下，结直肠癌表达特定突变形式的 K-RAS对抑制MEK激酶非常敏感。我们将使用质谱法和计算建模，以确定为什么表达K-rasg12d和k-rasa146t的癌症对MEK的抑制差异敏感。接下来，我们将解决单变量的限制通过确定遗传因素和表观遗传因素的遗传预测治疗功效互动以建立网络信号状态。我们将使用质量细胞术和计算建模以探索突变k-ras下游的信号传导如何受细胞谱系的影响以及通过肿瘤基因和肿瘤抑制基因的继发突变。最后，我们将移动除了基因型以外，通过开发一种算法来预测基因型作为治疗功效的预测指标基于磷酸蛋白酶测量值对激酶抑制的敏感性。我们将验证通过临床前疗法研究在患者衍生的异种移植物中进行的计算方法。这些研究总共利用了最先进的实验和计算方法对于患有K-RAS突变癌症患者的个性化医学是现实的目标。