Breast cancer: The translation of big genomic data to cancer precision medicine

Abstract

Cancer is a complex genetic disease that consequence from the accumulation of genomic alterations, in which germline variations predispose individuals to cancer and somatic alterations initiate and trigger the progression of cancer. For the past two decades, genomic research has advanced remarkably, evolving from single-gene to whole-genome screening by using genome-wide association study (GWAS) and next generation sequencing (NGS) that contributing to big genomic data. International collaborative efforts have contributed in curating these data to identify clinically significant alterations that could be used in the clinical settings. Focusing on breast cancer, this review summarized the identification of genomic alterations with high throughput screening as well as the utilization of genomic information in clinical trials that match cancer patients to therapies, which further lead to cancer precision medicine. Furthermore, cancer screening and monitoring were enhanced greatly by the employment of liquid biopsies. With the growing data complexity and size, there are much anticipations in exploiting deep machine learning and artificial intelligence to curate integrative "–omics" data to refine the current medical practice to be applied in near future.

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