Nelson Stefanie, Carrick Danielle, Daee Danielle, Fingerman Ian, Gillanders Elizabeth
Division of Cancer Control and Population Sciences, National Cancer Institute, Rockville, MD, USA.
Division of Cancer Biology, National Cancer Institute, Rockville, MD, USA.
J Natl Cancer Inst. 2024 Dec 1;116(12):1882-1889. doi: 10.1093/jnci/djae173.
Research conducted over the past 15+ years has identified hundreds of common germline genetic variants associated with cancer risk, but understanding the biological impact of these primarily non-protein coding variants has been challenging. The National Cancer Institute sought to better understand and address those challenges by requesting input from the scientific community via a survey and a 2-day virtual meeting, which focused on discussions among participants. Here, we discuss challenges identified through the survey as important to advancing functional analysis of common cancer risk variants: 1) When is a variant truly characterized; 2) Developing and standardizing databases and computational tools; 3) Optimization and implementation of high-throughput assays; 4) Use of model organisms for understanding variant function; 5) Diversity in data and assays; and 6) Creating and improving large multidisciplinary collaborations. We define these 6 challenges, describe how success in addressing them may look, propose potential solutions, and note issues that span all the challenges. Implementation of these ideas could help develop a framework for methodically analyzing common cancer risk variants to understand their function and make effective and efficient use of the wealth of existing genomic association data.
过去15年多开展的研究已识别出数百种与癌症风险相关的常见种系基因变异，但要理解这些主要为非蛋白质编码变异的生物学影响一直具有挑战性。美国国家癌症研究所试图通过一项调查和为期两天的虚拟会议向科学界征求意见，以更好地理解并应对这些挑战，该会议聚焦于参与者之间的讨论。在此，我们讨论通过调查确定的对推进常见癌症风险变异功能分析至关重要的挑战：1）变异何时真正得到特征描述；2）开发和规范数据库及计算工具；3）高通量检测的优化与实施；4）利用模式生物理解变异功能；5）数据和检测的多样性；6）建立和改进大型多学科合作。我们对这6项挑战进行定义，描述应对这些挑战取得成功可能是什么样子，提出潜在解决方案，并指出贯穿所有挑战的问题。实施这些想法有助于构建一个框架，用于系统地分析常见癌症风险变异，以了解其功能，并有效且高效地利用现有的大量基因组关联数据。
