We investigate transcriptomic data across multiple breast tumour datasets using differential variability analysis to identify genes that are associated with BRCA1 and BRCA2 pathogenic variant status and with different tumour subtypes
2021
Key concepts
Scholarcy highlights
- Gene expression profiles have been used extensively in the study of cancer development, treatment response and prognosis
- We investigate transcriptomic data across multiple breast tumour datasets using differential variability analysis to identify genes that are associated with BRCA1 and BRCA2 pathogenic variant status and with different tumour subtypes
- Using gene-specific standard deviations, BRCA1-associated breast tumours had a 22.8% increase in gene expression variability
- Increased variability in BRCA1associated breast tumours was observed in linear models of gene-specific coefficient of variance and median absolute deviation
- We explored publicly available familial breast cancer microarray datasets for phenotypes associated with BRCA1- and BRCA2-related breast tumours
- BRCA1-associated and basal-like breast tumours displayed a phenotype of greater gene expression variability, with no change in global RNA abundance
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