Area C: Genome-wide identification and targeting of resistance to cancer therapy

总结：领域C：全基因组鉴定和靶向癌症治疗耐药性 对抗癌疗法的耐药性的频繁出现仍然是癌症治疗中的主要挑战 这一点至关重要。最近针对这一问题的临床和实验研究需要 对于每一种新的特定治疗方法和研究的癌症类型，都需要费力地收集治疗前和治疗后的数据。 因此，一种可以加速鉴定抗性分子决定因素的计算方法， 通过对现有大规模癌症队列的分析。 我们的建议旨在确定新的方法来对抗新生和获得性耐药， 组合。我们专注于基因相互作用，而不是单个基因，并利用大规模基因组 数据集和患者反应数据。我们的方法是基于Ruppin和其他实验室最近的工作 这表明遗传相互作用可以通过分析组学肿瘤数据来计算识别。 为了破译对癌症疗法的抗性途径，我们在这里集中研究一种新型的遗传学。 相互作用，称为合成救援（SR）。SR表示两个基因之间的功能性相互作用， 两个基因中的一个（称为脆弱基因）的活性的适应性降低变化被补偿， 改变另一个基因（称为拯救基因）的活性，恢复细胞的健康和拯救它。 最近开发的用于SR数据驱动的大型癌症肿瘤队列识别的工具，成功地 从而能够预测患者的药物反应和耐药性的出现。根据这些 100.本提案的具体目标是： 具体目标1。执行泛癌症和癌症类型特异性基于SR的分析，重点关注 黑色素瘤，乳腺癌，头颈癌和结肠癌，确定每种癌症的主要拯救基因， 癌症类型，以及减轻耐药性的组合疗法的具体建议。 具体目标2。开发一个新版本的SR分析工具，该工具将在市场上提供 供其他人以标准的、用户友好的方式使用，包括基因甲基化分析， 基因组范围的突变数据，在我们以前的SR推理工具中已经使用的其他组学数据之上。 具体目标3。实验测试预测救援目标和联合治疗， 患者来源的耐药癌细胞。 总之，拟议的研究，将提出一个变革性的SR为基础的方法， 针对整个癌症基因组的耐药途径。

Summary: Area C: Genome-wide identification and targeting of resistance to cancer therapy The frequent emergence of resistance to anti-cancer therapies remains a major challenge in cancer treatment that is of utmost importance. Recent clinical and experimental studies addressing this problem require the arduous collection of pre- and post- treatment data for every new specific treatment and cancer type studied. Thus, a computational approach that can expedite the identification of molecular determinants of resistance via the analysis of existing large-scale cancer cohorts is called for. Our proposal seeks to identify novel ways to counter de novo and acquired resistance through drug combinations. We focus on gene interactions rather than individual genes and leverage large scale genomic datasets and patient response data. Our approach is based on recent work in the Ruppin and other labs showing that genetic interactions can be computationally identified by analyzing omics tumor data. To decipher pathways of resistance to cancer therapies, we focus here on studying a new type of genetic interactions, termed synthetic rescues (SRs). SRs denote a functional interaction between two genes whereby a fitness reducing change in the activity of one of the two genes (termed the vulnerable gene) is compensated by altered activity of another gene (termed the rescuer gene), which restores cell fitness and rescues it. We have recently developed tools for SR data-driven identification from large cancer tumors cohorts, successfully enabling the prediction of drug response and emergence of resistance in patients. Building on these transformational results the specific aims of this proposal are: Specific Aim 1. Perform a pan-cancer and cancer type-specific SR-based analyses, focusing on melanoma, breast, head and neck, and colon cancer, identifying the major rescuer genes in each cancer type, together with specific recommendations of combinatorial therapies mitigating resistance. Specific Aim 2. Develop a new version of SR analysis tools that will be made publically available for use by others in a standard, user friendly manner and includes analysis of gene methylation and genome wide mutation data, on top of other omics data already utilized in our previous SR inference tools. Specific Aim 3. Experimentally test predicted rescuer targets and combination therapies in patient derived resistant cancer cells. Taken together the proposed study, will present a transformative SR based approach for identifying and targeting resistance pathways across the whole cancer genome.