Copy Number Alterations in Low Mutation Cancer

摘要 两种一般类型的遗传改变驱动癌症进展;突变和拷贝数改变 （CNA）。对ABL融合和BRAF等突变的研究已经产生了强大的靶向治疗。 然而，并非所有的癌症都有靶基因突变; 48%的浆液性卵巢癌（OV）没有靶基因突变。 除了p53以外的致癌突变。对于这些低突变肿瘤和癌症类型，最有可能的罪魁祸首 肿瘤发生和耐药性的关键在于CNAs。OV基因组是非常不稳定的;平均而言， 在肿瘤中，2/3的基因显示拷贝数变化：大约1/3的基因缺失，1/3的基因增加。 剂量.卵巢癌中一个已知的CNA驱动因素是BRCA 1/2基因的纯合缺失，约10%的卵巢癌患者存在BRCA 1/2基因的纯合缺失。 患者;然而，SOC中99%的缺失是杂合缺失，而不是纯合缺失。剩下的99% 的缺失必须含有肿瘤抑制因子，其导致癌症进展，仅杂合 沿着沿着单个路径累积的损失。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OV等高度改变的背景下工作， 一次执行多个路径的计算。我们发现， 自噬是普遍的（98%的肿瘤），冗余的（平均肿瘤中至少有4个基因缺失）， 在浆液性卵巢癌中，这种基因缺失是唯一（比任何其他肿瘤类型都多）受到抑制的。最 有影响力的自噬基因是BECN 1和LC 3B。我们发现BECN 1和LC 3B的丢失是导致OV的原因 非整倍体和单等位基因BECN 1丢失加速小鼠模型中的OV肿瘤发生。我们建议 通过[1]分析每种肿瘤中> 3000种的通路中断，发展我们对肿瘤CNA的理解。 使用自动化HAPTRIG生物信息学工具，[2]对最具影响力的分子途径进行评分 基因在每个途径/肿瘤对，以确定新的CNA驱动程序的癌症，和[3]释放的工具，在用户- 任何肿瘤学家都可以对任何肿瘤队列进行CNA通路分析。自从我们的顶部 来自CNA网络的预测被验证影响基因组拷贝数变异性，肿瘤发生， 和治疗靶向OV，我们建议通过[1]提供对CNA损失的进一步机制理解 分析BECN 1和LC 3B缺失引起的CNA的类型和异质性，[2]代谢， CNA和BECN 1 +/-鼠OV肿瘤中存在的干细胞变化，以及[3]测定自噬通量和 代谢改变的氯喹治疗，选择性地杀死BECN 1和LC 3B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.