Abstract 1599: Determining patient ancestry based on targeted tumor comprehensive genomic profiling
Abstract 1599: Determining patient ancestry based on targeted tumor comprehensive genomic profiling
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摘要 1599：基于靶向肿瘤综合基因组分析确定患者血统
DOI：
10.1158/1538-7445.sabcs18-1599
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发表时间：
2019
期刊：
影响因子：
5.4
通讯作者：
G. Frampton
中科院分区：
文献类型：
--
作者：
J. Newberg;Caitlin F. Connelly;G. Frampton
背景癌症基因突变表现出在不同祖先群体中不同的流行突变模式。例如，EGFR变异在具有亚洲血统的人中的非小细胞肺癌中更常见，并且在具有非洲裔美国人血统的患者中的结直肠癌中更频繁地观察到KRAS变异。此外，许多癌症的组织学亚型在不同血统的人之间表现出患病率的差异。然而，许多癌症研究缺乏识别这些细微差别的统计能力。在Foundation Medicine接受全面癌症基因组分析的大型患者队列可能为表征这些突变模式提供了一个有用的起点。方法.为了在去识别样本上建立祖先，我们将我们的每一种综合基因组分析测试（FoundationOne，FoundationOne CDx，FoundationOne Heme）靶向的SNP与Phase 3 1000 Genomes数据叠加。使用已建立的方法，我们将SNP向下投影到前五个主成分，并使用随机森林集成学习来训练每个诱饵集的分类器。10-折叠交叉验证表明该方法对于不同的基因组谱分析测试具有98-99%的精确度和召回率。结果对超过17万个同意进行研究的去识别样本进行了调查。初步分析表明，对美国样本的分类不如其他群体那么可靠。为了解决这个问题，我们在每个染色体的基础上训练了分类器，并将染色体间一致性低于80%的样本重新分配到混合物组。数据集中患者祖先的总体患病率为75.9%欧洲人、8.3%非洲人、4.7%东亚人、0.8%南亚人和0.8%美国人，以及9.5%混合。从所得数据中，我们总结了在人群中具有良好代表性的癌症类型，确定了至少28种肿瘤类型，我们可能有能力确定祖先依赖性体细胞突变。讨论所描述的数据集包含一组先前不可用的癌症类型，用于挖掘祖先依赖性癌症驱动改变。将提交这些结果。这项工作中描述的祖先分类方法可以应用于一系列基因组分析测试，对这种方法的改进可以整合到临床试验和最终的临床护理中，以更好地阐明晚期癌症的各种生物学行为。引文格式：贾斯汀·纽伯格，凯特琳·康奈利，加勒特·弗兰普顿。基于靶向肿瘤综合基因组分析确定患者血统[摘要]。在：2019年美国癌症研究协会年会论文集; 2019年3月29日至4月3日;亚特兰大，佐治亚州。Philadelphia（PA）：AACR; Cancer Res 2019;79（13 Suppl）：Abstract nr 1599.
Background . Cancer gene mutations exhibit mutation patterns of prevalence that vary across different ancestry groups. For example, EGFR variants are more frequent in non-small cell lung cancer among people with Asian ancestry, and KRAS variants are observed more frequently in colorectal cancer among patients with African American ancestry. Additionally, many histological subtypes of cancers demonstrate differences in prevalence between people of different ancestry. However, many cancer studies lack the statistical power to identify such nuances. The large cohort of patients who have undergone comprehensive cancer genomic profiling at Foundation Medicine may provide a useful starting point for characterizing these mutation patterns across ancestry groups. Methods . To establish the ancestry on de-identified samples, we superimposed SNPs targeted by each of our comprehensive genomic profiling tests (FoundationOne, FoundationOne CDx, FoundationOne Heme) with Phase 3 1000 Genomes data. Using an established approach, we projected the SNPs down to the top five principal components and used random forest ensemble learning to train a classifier on each bait set. 10-fold cross-validation indicates this approach performs with 98-99% precision and recall for the different genomic profiling tests. Results . Ancestry calls were made on over 170,000 de-identified samples consented for research. Initial analyses indicated that classification of American samples was not as robust as other groups. To address this, we trained classifiers on a per-chromosome basis, and re-assigned samples which exhibited less than 80% consensus across chromosomes to an admixture group. Overall prevalence of patient ancestry in the dataset is 75.9% European, 8.3% African, 4.7% East Asian, 0.8% South Asian, and 0.8% American, and 9.5% admixed. From the resulting data, we summarize cancer types that are well-represented across populations, identifying at least 28 tumor types for which we likely have power to identify ancestry-dependent somatic mutations. Discussion . The dataset described contains a previously unavailable set of cancer types to be mined for ancestry-dependent cancer-driving alterations. Those results will be presented. The ancestry classification approach described in this work can be applied to a range of genomic profiling tests, and refinements on this approach can be integrated into clinical trials and ultimately clinical care to better elucidate varied biologic behavior across advanced cancer. Citation Format: Justin Newberg, Caitlin Connelly, Garrett Frampton. Determining patient ancestry based on targeted tumor comprehensive genomic profiling [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1599.