Comprehensive identification of RAS mutations and allelic co-segregation patterns in colorectal cancer

项目摘要 结直肠癌(CRC)是第四大常见癌症，也是全球第二大致死癌症。 美国。激活KRAS或NRAS的致癌基因突变在45%的结直肠癌中被发现，从而导致肿瘤 细胞毒和靶向治疗的进展和影响疗效；这些突变经常同时发生 与其他癌基因和抑癌基因的突变有关，包括APC、TP53、BRAF、PI3KCA等。 重要的是，G12、G13、Q61和其他密码子中相对常见的RAS突变的结构-功能分析 表明它们具有不同的转化潜力和作用模式。这些差异可能具有 临床影响；例如，G12突变会对EGFR靶向药物产生耐药性，但G13D突变会 不。我们的目标是更好地理解KRAS突变在CRC中的意义，并利用这项工作来 更好地对结直肠癌患者进行分层，以便在治疗方案之间进行选择。在初步研究中，使用以下数据集 13,000例结直肠癌标本中，我们研究了结直肠癌RAS家族蛋白的基因组变异模式。 从最近发表的这项工作中，我们发现了新的热点突变，并揭示了显著的差异 在不同患者亚组中发生的RAS突变模式中，基于微卫星不稳定(MSI)与 微卫星稳定(MSS)状态，以结肠和直肠为原发肿瘤亚区，患者年龄，在一例中， 耐心的性行为。在第二项研究中，我们还确定了额外驱动基因与年龄相关的突变模式， 包括APC、BRAF和FAM123B，具有不同的患者亚群特征。然而，我们受到以下因素的限制 这项研究的样本量来自于解决一些重要的问题；特别是，特定的等位基因是否 KRAS和NRAS以预测信号的方式与额外驱动基因的突变分离 特定患者亚组中的路径依赖性。我们的目标是详细解决这些和其他问题， 使用Foundation Medicine提供的大约34,000个结直肠癌的信息，以及大约8,400个结直肠癌的信息，我们 从公共数据集汇编而成。为此，我们提出了两个目标。在目标1中，我们将确定完整的 RAS热点突变曲目，包括3D热点。我们将使热点与RAS结构保持一致，并 建立它们与临床病理变量(包括肿瘤部位、年龄、性别、肿瘤突变)的相关性 负担(TMB，包括对高突变肿瘤的具体考虑)和MSI/MSS状态)和潜在的 靶向与特定突变过程相关的三核苷酸背景。我们将分析突变 给出最近的数据，两种主要的KRAS亚型KRAS4B和KRAS4A之间的不同模式 这表明KRAS4A具有重要的特定作用。在目标2中，我们将阐明基因、密码子和变异水平 分析RAS突变与结直肠癌中常见的其他驱动基因突变的共存情况，我们将 确定协方差模式是否基于临床病理特征而不同。语境分析 特定等位基因的共生模式将有助于定义不同等位基因的等位基因功能和临床可操作性 病人队列。

Project Summary Colorectal cancer (CRC) is the fourth most common cancer, and the second leading cause of cancer death in the United States. Activating oncogenic mutations in KRAS or NRAS are found in >45% of CRC, driving tumor progression and influencing efficacy of both cytotoxic and targeted therapies; these mutations often co-occur with mutations of other oncogenes and tumor suppressors, including APC, TP53, BRAF, PI3KCA, and others. Importantly, structure-function analysis of relatively common RAS mutations in G12, G13, Q61, and other codons indicate these have non-equivalent transforming potential and modes of action. These differences can have clinical impact; for example, G12 mutations confer resistance to EGFR-targeting drugs, but G13D mutations do not. Our goal is to better understand the significance of KRAS mutations in CRC, and to leverage this work to better stratify CRC patients for selection between treatment options. In preliminary studies, using a dataset of 13,000 CRC specimens, we have investigated genomic patterns of variance in RAS-family proteins in CRC. From this recently published work, we have identified novel hotspot mutations, and revealed striking differences in RAS mutational pattern occurring in distinct patient subgroups, based on microsatellite instable (MSI) versus microsatellite stable (MSS) status, colon versus rectum as primary tumor subsite, patient age, and in one case, patient sex. In a second study, we have also identified age-related patterns of mutation of additional driver genes, including APC, BRAF, and FAM123B, characterizing distinct patient subgroups. However, we were limited by the study sample size from addressing a number of important questions; in particular, whether specific alleles of KRAS and NRAS segregated with mutations in additional driver genes in a manner that predicted signaling pathway dependency in specific patient subgroups. Our goal is to address these and other questions in detail, using information for ~34,000 CRC tumors provided by Foundation Medicine, and for ~8,400 CRC tumors we compiled from public datasets. To this end, we propose two Aims. In Aim 1, we will identify the complete repertoire of RAS hotspot mutations, including 3D hotspots. We will align hotspots to RAS structures, and establish their covariance with clinicopathological variables (including tumor subsite, age, sex, tumor mutation burden (TMB, including specific consideration of hypermutated tumors), and MSI/MSS status) and underlying targeting of trinucleotide contexts associated with specific mutational processes. We will analyze mutational patterns that differ between the two primary KRAS isoforms, KRAS4B and KRAS4A, given recent data suggesting an important specific role for KRAS4A. In Aim 2, we will elucidate gene, codon, and variant level analysis of co-occurrence of RAS mutations with other driver genes commonly mutated in CRC, and we will determine whether co-variance patterns differ based on clinicopathological features. Analysis of the context (comutational patterns) for specific alleles will help define allelic function and clinical actionability for distinct patient cohorts.