Copy Number Alterations in Low Mutation Cancer

摘要 两种常见的基因改变会推动癌症的进展；突变和拷贝数改变 (CNAS)。对ABL融合和BRAF等突变的研究已经产生了强大的靶向疗法。 然而，并不是所有的癌症都在靶向基因上发生突变；48%的浆液性卵巢癌(OV)没有 P53以外的致癌突变。对于这些低突变的肿瘤和癌症类型，最有可能的罪魁祸首 肿瘤发生和耐药的关键在于CNAs。卵子基因组非常不稳定；平均而言 肿瘤，2/3的基因呈现拷贝数变化：大约1/3的基因缺失，1/3的基因增加 剂量。一个已知的卵巢癌CNA驱动因素是BRCA1/2基因纯合缺失，在约10%的 患者；然而，SOC中99%的缺失是杂合性的，而不是纯合子缺失。剩下的99% 的缺失必须包含导致癌症进展的肿瘤抑制基因，且仅为杂合子 损失，这些损失沿着个别路径累积。我们开发了新的“HAPTRIG”途径分析 全基因组数据集中的丢失事件，能够在高度变化的背景下工作，如OV和Can 同时执行多条路径的计算。我们发现，细胞的循环途径 自噬是普遍的(98%的肿瘤)，是冗余的(平均每个肿瘤至少有4个基因缺失)，以及 在浆液性卵巢癌中唯一被缺失抑制的(比任何其他肿瘤类型都多)。最多的 缺失的自噬基因有BECN1和LC3B。我们发现BECN1和LC3B的损失是对OV的贡献 非整倍体和单等位基因BECN1缺失可加速小鼠卵巢肿瘤的发生。我们建议 通过分析&gt；3,000中的每个肿瘤的通路中断来发展我们对肿瘤CNA的理解[1] 使用自动HAPTRIG生物信息学工具的已知分子路径，[2]对最有效的评分 每个途径/肿瘤对中的基因，以识别癌症的新CNA驱动因素，并[3]在用户中释放该工具- 为任何肿瘤学家提供友好的门户，以便对任何肿瘤队列进行CNA途径分析。从我们的顶层开始 来自CNA网络的预测被验证为影响基因组拷贝数变异性、肿瘤发生、 和针对OV的治疗，我们建议通过[1]提供对CNA损失的进一步的机制理解。 分析BECN1和LC3B耗竭引起的CNA的类型和异质性，[2]代谢， CNA，以及BECN1+/-小鼠OV肿瘤中存在的干细胞变化，以及[3]检测自噬通量和 氯喹治疗的代谢改变，选择性地杀死BECN1和LC3B耗尽的OV细胞。

Abstract Two general types of genetic alterations drive cancer progression; mutations and copy number alterations (CNAs). Research into mutations such ABL fusions and BRAF have yielded powerful targeted therapeutics. However, not all cancers are mutated in targetable genes; 48% of serous ovarian cancers (OV) have no oncogenic mutation other than in p53. For these low-mutation tumors and cancer types, the most likely culprit for tumorigenesis and drug resistance lies in CNAs. The OV genome is remarkably unstable; in the average tumor, 2/3 of genes display a copy number change: roughly 1/3 are deleted and 1/3 are increased in gene dosage. One known CNA driver in ovarian cancer is a homozygous loss in BRCA1/2 genes in ~10% of patients; however 99% of deletions in SOC are heterozygous, not homozygous deletions. This remaining 99% of deletions must contain tumor suppressors which contribute to cancer progression with only heterozygous losses, which accumulate along individual pathways. We developed novel "HAPTRIG" pathway analysis of loss events in whole genome datasets with the ability to work in highly altered backgrounds like OV and can perform calculations of multiple pathways at once. We discovered that the cellular recycling pathway of autophagy is universally (98% of tumors), redundantly (at least 4 genes are deleted in the average tumor), and uniquely (more than any other tumor type) suppressed by deletions in serous ovarian cancer. The most impactful lost autophagy genes are BECN1 and LC3B. We found BECN1 and LC3B loss is to contribute to OV aneuploidy and monoallelic BECN1 loss to accelerate OV tumorigenesis in a mouse model. We propose to develop our understanding of tumor CNAs by [1] analyzing every tumor for pathway disruptions in >3,000 known molecular pathways using an automated HAPTRIG bioinformatics tool, [2] scoring the most impactful genes in each pathway/tumor pair to identify novel CNA drivers of cancer, and [3] release the tool in a user- friendly portal for any oncologist to perform CNA pathway analysis on any cohort of tumors. Since our top predictions from the CNA networks were validated to impact genomic copy number variability, oncogenesis, and therapy targeting in OV, we propose to provide further mechanistic understanding of CNA losses by [1] analyzing the types and heterogeneity of CNAs caused by BECN1 and LC3B depletion, [2] the metabolic, CNA, and stem cell changes present in BECN1+/- murine OV tumors, and [3] assaying autophagic flux and metabolic alterations for chloroquine therapy, which selectively kills BECN1 and LC3B depleted OV cells.