Algorithms to link signaling pathways with transcriptional programs for precision medicine

项目概要/摘要 癌症是通过遗传和表观遗传改变的积累而产生的，这些改变通常针对 信号转导途径，导致下游转录效应子的失调， 广泛的基因表达变化。由于许多靶向疗法是小分子的抗肿瘤药物， 信号转导蛋白或抗生长因子受体的单克隆抗体， 一个主要的目标是在给定的肿瘤中解除对信号通路的调节， 癌症基因组学该项目的目的是开发将信号与 精准医疗的转录反应。在K99阶段，我将开发 整合公开可用的转录组学、蛋白质组学和基因组学的统计建模方法 在适当的细胞系中使用表观基因组数据的肿瘤类型数据， 转录程序和信号通路。有了这些方法，在R 00期间 第一阶段将研究常见和癌症特异性转录因子和信号传导的影响， 临床结果和药物反应的监管机构。我们希望我们的研究结果将导致新的见解， 癌症生物学，并进一步协助设计临床试验， 个性化治疗的签名。我提出了一个培训计划， 由临床医生、科学家和计算生物学家组成的跨学科团队， 在所提出的研究项目的各个方面的经验。这种专注的研究导师制， 通过频繁的成果展示和非正式的互动，将帮助我发展 沟通和领导技能对我向独立过渡至关重要。从长远来看，这种训练 我将准备领导一个实验室，重点是发展统计和计算方法 精准医疗的关键在于弥合基础科学和临床之间的差距。

Project Summary/Abstract Cancers arise through the accumulation of genetic and epigenetic alterations that often target signal transduction pathways, leading to dysregulation of downstream transcriptional effectors and widespread gene expression changes. Since many targeted therapies are small molecule inhibitors of signal transduction proteins or monoclonal antibodies against growth factor receptors, deciphering the signaling pathways that are deregulated in a given tumor in order to personalize therapy is a major goal of cancer genomics. The aim of this project is to develop algorithmic approaches linking signaling to transcriptional response for precision medicine. During the K99 phase of the award, I will develop statistical modeling approaches to integrate publicly available transcriptomic, proteomic and genomic data across tumor types with epigenomic data in appropriate cell lines in order to study altered transcriptional programs and signaling pathways in cancer. With these methods in hand, during the R00 phase I will study the impact of common and cancer-specific transcription factor and signaling regulators on clinical outcome and drug response. We expect that our results will lead to new insights in cancer biology and furthermore assist in the design of clinical trials that match actionable oncogenic signatures with personalized therapies. I propose a training plan under the mentorship of a broad, interdisciplinary team of clinicians, scientists, and computational biologists with extensive combined experience in all aspects of the proposed research project. This focused research mentorship, together with frequent presentation of results and informal interactions, will help me develop the communication and leadership skills vital for my transition to independence. In the long term, this training will prepare me to lead a laboratory that centers on developing statistical and computational approaches for precision medicine to bridge the gap between basic science and the clinic.