Computer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA.
Center for Population Genomics, MAVERIC, VA Boston Healthcare System, Jamaica Plain, MA, USA.
BMC Med Genomics. 2022 Jul 6;15(1):151. doi: 10.1186/s12920-022-01298-6.
Genome-wide Association Studies (GWAS) aims to uncover the link between genomic variation and phenotype. They have been actively applied in cancer biology to investigate associations between variations and cancer phenotypes, such as susceptibility to certain types of cancer and predisposed responsiveness to specific treatments. Since GWAS primarily focuses on finding associations between individual genomic variations and cancer phenotypes, there are limitations in understanding the mechanisms by which cancer phenotypes are cooperatively affected by more than one genomic variation.
This paper proposes a network representation learning approach to learn associations among genomic variations using a prostate cancer cohort. The learned associations are encoded into representations that can be used to identify functional modules of genomic variations within genes associated with early- and late-onset prostate cancer. The proposed method was applied to a prostate cancer cohort provided by the Veterans Administration's Million Veteran Program to identify candidates for functional modules associated with early-onset prostate cancer. The cohort included 33,159 prostate cancer patients, 3181 early-onset patients, and 29,978 late-onset patients. The reproducibility of the proposed approach clearly showed that the proposed approach can improve the model performance in terms of robustness.
To our knowledge, this is the first attempt to use a network representation learning approach to learn associations among genomic variations within genes. Associations learned in this way can lead to an understanding of the underlying mechanisms of how genomic variations cooperatively affect each cancer phenotype. This method can reveal unknown knowledge in the field of cancer biology and can be utilized to design more advanced cancer-targeted therapies.
全基因组关联研究（GWAS）旨在揭示基因组变异与表型之间的联系。它们已被广泛应用于癌症生物学领域，以研究变异与癌症表型之间的关联，如对某些类型癌症的易感性和对特定治疗方法的预先敏感性。由于 GWAS 主要侧重于发现个体基因组变异与癌症表型之间的关联，因此在理解癌症表型如何受多个基因组变异协同影响的机制方面存在局限性。
本文提出了一种网络表示学习方法，使用前列腺癌队列来学习基因组变异之间的关联。所学习到的关联被编码为表示，可以用于识别与早发性和晚发性前列腺癌相关的基因中基因组变异的功能模块。该方法应用于退伍军人管理局百万退伍军人计划提供的前列腺癌队列，以鉴定与早发性前列腺癌相关的功能模块的候选基因。该队列包括 33159 名前列腺癌患者、3181 名早发性患者和 29978 名晚发性患者。该方法的可重复性清楚地表明，该方法可以提高模型在稳健性方面的性能。
据我们所知，这是首次尝试使用网络表示学习方法来学习基因内基因组变异之间的关联。以这种方式学习到的关联可以帮助我们理解基因组变异如何协同影响每个癌症表型的潜在机制。该方法可以揭示癌症生物学领域的未知知识，并可用于设计更先进的癌症靶向治疗方法。
