DeVoe Elias, Reddi Honey V, Taylor Bradley W, Stachowiak Samantha, Geurts Jennifer L, George Ben, Shaker Reza, Urrutia Raul, Zimmermann Michael T
Computational Structural Genomics Unit, Linda T. and John A. Mellowes Center for Genomic Sciences and Precision Medicine, Medical College of Wisconsin, Milwaukee, Wisconsin, USA.
Clinical and Translational Sciences Institute, Medical College of Wisconsin, Milwaukee, Wisconsin, USA.
J Comput Biol. 2025 Jan;32(1):89-103. doi: 10.1089/cmb.2023.0461. Epub 2024 Dec 11.
Expanded analysis of tumor genomics data enables current and future patients to gain more benefits, such as improving diagnosis, prognosis, and therapeutics. Here, we report tumor genomic data from 1146 cases accompanied by simultaneous expert analysis from patients visiting our oncological clinic. We developed an analytical approach that leverages combined germline and cancer genetics knowledge to evaluate opportunities, challenges, and yield of potentially medically relevant data. We identified 499 cases (44%) with variants of interest, defined as either potentially actionable or pathogenic in a germline setting, and that were reported in the original analysis as variants of uncertain significance (VUS). Of the 7405 total unique tumor variants reported, 462 (6.2%) were reported as VUS at the time of diagnosis, yet information from germline analyses identified them as (likely) pathogenic. Notably, we find that a sizable number of these variants (36%-79%) had been reported in heritable disorders and deposited in public databases before the year of tumor testing. This finding indicates the need to develop data systems to bridge current gaps in variant annotation and interpretation and to develop more complete digital representations of actionable pathways. We outline our process for achieving such methodologic integration. Sharing genomics data across medical specialties can enable more robust, equitable, and thorough use of patient's genomics data. This comprehensive analytical approach and the new knowledge derived from its results highlight its multi-specialty value in precision oncology settings.
对肿瘤基因组学数据进行扩展分析，能让当下及未来的患者获得更多益处，比如改善诊断、预后和治疗效果。在此，我们报告了1146例患者的肿瘤基因组数据，并同时对前来我们肿瘤诊所就诊的患者进行了专家分析。我们开发了一种分析方法，利用种系和癌症遗传学的综合知识来评估潜在医学相关数据的机会、挑战和产出。我们在最初分析中被判定为意义不明确的变异（VUS）中，识别出了499例（44%）具有感兴趣变异的病例，这些变异在种系背景下要么具有潜在的可操作性，要么具有致病性。在总共报告的7405个独特肿瘤变异中，有462个（6.2%）在诊断时被报告为VUS，但种系分析信息将它们识别为（可能）致病性变异。值得注意的是，我们发现这些变异中有相当一部分（36%-79%）在肿瘤检测年份之前就已在遗传性疾病中被报道并存入公共数据库。这一发现表明，需要开发数据系统来弥合当前变异注释和解释方面的差距，并开发更完整的可操作途径数字表示。我们概述了实现这种方法整合的过程。跨医学专科共享基因组学数据能够更有力、公平和全面地利用患者的基因组学数据。这种全面的分析方法及其结果所产生的新知识凸显了其在精准肿瘤学环境中的多专科价值。
