一种新的计算组学和非组学方法用于识别亚洲前列腺癌的真正致病风险变异。

Anusha Chimmiri, Haitao Wang, E. Yeo, K. Low, A. Tan, Wai Yee Woo, E. Ong, T. Tan, W. S. Looi, W. Nei, J. Tuan, Michael L C Wang, J. S. Tan, L. Lee, K. Tay, R. Kanesvaran, L. Khor, J. Yeong, Chien Sheng Tan, M. Chua
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引用次数: 0

摘要

47背景:大规模全基因组关联研究已经建立了种系多基因风险基因座,这些基因座支持了对前列腺癌症(PCa)的易感性。然而,大多数试验都是在欧洲血统的男性中进行的,亚洲男性前列腺癌的数据缺失。在这里,我们报道了一个内部多维生物信息学管道,该管道整合了OMICS和非OMICS方法,用于识别亚洲男性前列腺癌的真正种系风险变体。方法:我们对新诊断为前列腺癌的亚洲男性进行了前瞻性队列研究。对血液(100X)进行全外显子组测序(Illumina Hiseq,CA)。基于OMICS的方法需要逐步筛选癌症特异性途径的特征。然后开发了一个基因组蛋白质组网络来过滤已知的致病性变体;随后与已报道与PCa易感性相关的聚集种系变异(N=95000)的大型人工数据库进行比较。最后,通过非OMICS管道过滤突变,该管道需要与人群水平的统计数据和临床结果(复发和存活率)进行数据同步。结果:对277例前列腺癌进行了初步分析;其中M1型50例。使用非组合无偏方法进行筛选产生36157个种系变异。这与我们基于OMICS的方法形成了对比,后者将变体调用减少到6144个显著相关的突变。接下来,通过关注与激素调节和已知癌症热点突变相关的路径特异性基因,我们可以进一步加强对3562种激素相关变体(HLA-DRB1上的rs9269958)和已知癌症基因中2125种变体的变体调用,特别是(BRCA1/2上的rs8176320、LILRA2上的rs2555691、TP53BP1上的rs8036934)。结论:在这里,我们表明,OMICS方法的应用结合了路径驱动的分析和人工数据集,以及人群水平的统计和临床相关性,对与前列腺癌相关的种系变异进行了更有力的注释。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A novel computational OMICS and non-OMICS approach for identifying true pathogenic risk variants for Asian prostate cancer.
47 Background: Large-scale genome-wide association studies have established germline polygenic risk loci that underpin the susceptibility to prostate cancer (PCa). However, most trials conducted are in men of European ancestry with data missing for Asian male PCa. Here, we report on an in-house multidimensional bioinformatics pipeline that integrates OMICS and non-OMICS approaches in identifying true germline risk-variants for PCa in Asian men. Methods: We utilized a prospective cohort study of Asian men who were newly diagnosed with PCa. Whole exome sequencing (Illumina Hiseq, CA) of blood (100X) was performed. The OMICS-based approach entailed a stepwise screen for hallmarks of cancer-specific pathways. A genome-proteome network was then developed to filter for known pathogenic variants; this was followed by comparison against a large artificial database of aggregated germline variants (N = 95,000) with reported linkage to PCa susceptibility. Finally, mutations were filtered through a non-OMICS pipeline that entailed data synchronization with population-level statistics and clinical outcomes (recurrence and survival). Results: Preliminary analyses were based on 277 PCa cases; of which 50 were M1 cases. Screening using a non-combined unbiased approach yielded 36,157 germline variants. This contrast against our OMICS-based approach, which reduced the variant calls to 6,144 significantly associated mutations. Next, by focusing on pathway-specific genes related to hormonal regulation and known cancer hotspot mutations, we could further tighten our variant calls to 3,562 hormone-related variants (rs9269958 on HLA-DRB1) and 2,125 variants in known cancer genes, notably (rs8176320 on BRCA1/2, rs2555691 on LILRA2, rs8036934 on TP53BP1). Conclusions: Here, we show that application of an OMICS approach that combines pathway-driven analyses and an artificial dataset, along with population-level statistics and clinical relevance resulted in more robust annotation of germline variants that were associated with PCa.
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来源期刊
自引率
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0
审稿时长
20 weeks
期刊介绍: The Journal of Global Oncology (JGO) is an online only, open access journal focused on cancer care, research and care delivery issues unique to countries and settings with limited healthcare resources. JGO aims to provide a home for high-quality literature that fulfills a growing need for content describing the array of challenges health care professionals in resource-constrained settings face. Article types include original reports, review articles, commentaries, correspondence/replies, special articles and editorials.
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