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

Abstract

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.

Keywords: breast cancer; clinical sequencing; genome-wide association study; liquid biopsy; next-generation sequencing.

Figure 1

Summary workflow of genome‐wide association studies (GWAS). GWAS starts from the determination of phenotypes. Genomic DNA extracted from samples was genotyped with chips that contained up to hundreds of thousands of single nucleotide polymorphisms (SNP). Quality control (QC) was carried out on samples and SNP before association studies. Sample quality control includes: (1) sample quality to exclude poorly genotyped samples; (2) identity‐by‐state analysis to exclude close relatedness samples; and (3) principal component analysis to evaluate population stratification of the sample sets to obtain a homogeneous sample set before carrying out the association study. SNP QC were set to exclude SNP if: (1) they were of low genotype quality; (2) if SNP deviated from normal distribution by evaluating the Hardy‐Weinberg equilibrium in control samples; and (3) if they contained non‐polymorphic SNP (minor allele frequency = 0). To evaluate the association distribution, quantile‐quantile plots (Q‐Q plot) of observed P‐value vs expected P‐value and genomic inflation factor (λ value) were evaluated to eliminate the possibility of population substructure. Manhattan plots of P‐value (−log₁₀) vs chromosome loci were used to depict an overview of the GWAS, with each dot representing a SNP and each color representing a chromosome. Post‐GWAS included: (A) a meta‐analysis that combined multiple studies to identify significantly associated SNP; and (B) functional analysis. Two of the most common functional analyses of the identified variants are: (i) electrophoretic mobility shift assay (EMSA) to check the existence of proteins, mainly transcription factors, binding to SNP‐contained DNA fragments; and (ii) luciferase reporter assay (comparison of relative luciferase activity) to assess the associated SNP that could affect differential gene expression (as shown in the figure). (C) Other analyses, including gene‐based analysis, pathway analysis, polygenic risk estimation, SNP‐SNP interaction, SNP‐environment interaction etc. could be carried out after GWAS