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

• Published in: Cancer Science Vol. 109; no. 3; pp. 497 - 506
• Main Authors: Low, Siew‐Kee, Zembutsu, Hitoshi, Nakamura, Yusuke
• Format: Journal Article
• Language: English
• Published: England Wiley 01.03.2018; John Wiley & Sons, Inc; John Wiley and Sons Inc
• Subjects: Artificial intelligence; Biomarkers; Breast cancer; Breast Neoplasms; Breast Neoplasms - diagnosis; Breast Neoplasms - drug therapy; Breast Neoplasms - genetics; Cancer screening; Cancer therapies; clinical sequencing; Clinical trials; Databases, Genetic; Deoxyribonucleic acid; Disease; DNA; Female; Genes; Genetic Predisposition to Disease; Genome-wide association studies; Genome-Wide Association Study; Genomes; Genomics; Germ-Line Mutation; High-Throughput Nucleotide Sequencing; Humans; Learning algorithms; Liquid Biopsy; Machine Learning; Medical prognosis; Metastasis; Molecular Targeted Therapy; Mutation; next‐generation sequencing; Pharmacogenomic Variants; Precision Medicine; Precision Medicine - methods; Principal components analysis; Quality control; Review; Thematic Section: Cancer Genomics; clinical sequencing; breast cancer; next-generation sequencing; liquid biopsy; genome-wide association study
• ISSN: 1347-9032; 1349-7006; 1349-7006
• DOI: 10.1111/cas.13463

Cancer is a complex genetic disease that develops 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 2 decades, genomic research has advanced remarkably, evolving from single‐gene to whole‐genome screening by using genome‐wide association study and next‐generation sequencing that contributes to big genomic data. International collaborative efforts have contributed to curating these data to identify clinically significant alterations that could be used in clinical settings. Focusing on breast cancer, the present review summarizes the identification of genomic alterations with high‐throughput screening as well as the use of genomic information in clinical trials that match cancer patients to therapies, which further leads to cancer precision medicine. Furthermore, cancer screening and monitoring were enhanced greatly by the use of liquid biopsies. With the growing data complexity and size, there is much anticipation in exploiting deep machine learning and artificial intelligence to curate integrative “−omics” data to refine the current medical practice to be applied in the near future. Focusing on breast cancer, this review summarizes the discovery of germline variations and somatic alterations with genome‐wide association studies and next‐generation sequencing that contributes to big genomic data. These genetic biomarkers could be integrated in clinical settings to identify individuals who are at risk for cancer, drug‐induced toxicity, as well as match cancer patients to therapies, which further leads to cancer precision medicine. This review will also discuss the potential use of liquid biopsies in cancer screening and monitoring.