Targeted Sequencing of Taiwanese Breast Cancer with Risk Stratification by the Concurrent Genes Signature: A Feasibility Study.
Ching-Shui HuangChih-Yi LiuTzu-Pin LuChi-Jung HuangJen-Hwey ChiuLing-Ming TsengChi-Cheng HuangPublished in: Journal of personalized medicine (2021)
Breast cancer is the most common female malignancy in Taiwan, while conventional clinical and pathological factors fail to provide full explanation for prognostic heterogeneity. The aim of the study was to evaluate the feasibility of targeted sequencing combined with concurrent genes signature to identify somatic mutations with clinical significance. The extended concurrent genes signature was based on the coherent patterns between genomic and transcriptional alterations. Targeted sequencing of 61 Taiwanese breast cancers revealed 1036 variants, including 76 pathogenic and 545 likely pathogenic variants based on the ACMG classification. The most frequently mutated genes were NOTCH, BRCA1, AR, ERBB2, FANCA, ATM, and BRCA2 and the most common pathogenic deletions were FGFR1, ATM, and WT1, while BRCA1 (rs1799965), FGFR2 (missense), and BRCA1 (rs1799949) were recurrent pathogenic SNPs. In addition, 38 breast cancers were predicted into 12 high-risk and 26 low-risk cases based on the extended concurrent genes signature, while the pathogenic PIK3CA variant (rs121913279) was significantly mutated between groups. Two deleterious SH3GLB2 mutations were further revealed by multivariate Cox's regression (hazard ratios: 29.4 and 16.1). In addition, we identified several significantly mutated or pathogenic variants associated with differentially expressed signature genes. The feasibility of targeted sequencing in combination with concurrent genes risk stratification was ascertained. Future study to validate clinical applicability and evaluate potential actionability for Taiwanese breast cancers should be initiated.
Keyphrases
- genome wide
- copy number
- single cell
- bioinformatics analysis
- genome wide identification
- locally advanced
- cancer therapy
- dna damage
- squamous cell carcinoma
- gene expression
- breast cancer risk
- transcription factor
- autism spectrum disorder
- machine learning
- cell proliferation
- dna damage response
- childhood cancer
- heat shock
- tyrosine kinase
- data analysis