Researchers from Georgia Tech have recently published a study on ovarian cancer using whole genome sequence (control + tumor), RNA-Seq and microarray gene expression chip data from the same patients. Data was acquired from TCGA. The paper titles:
Here is the brief summary:
- First they designed an integrated bioinformatics workflow (Figure S1) to process and analyze the large volumes of genomic and transcriptomic data, and to detect structural variants (SVs) breakpoint accurately at the single nucleotide resolution.
- Using whole genome sequencing (WGS) data they first detected various classes of SVs and further classify them as ‘germline derived’ and ‘somatically derived’.
- Then based on their structure and underlyong genomic regions, determined their potential to create functional gene-fusions at the RNA level.
- Using RNA-Seq and micraoarray data, they measured the proportion potential gene-fusion forming SVs that actually get transcribed.
- The observations suggests existence of regulatory mechanism(s) that suppress the expression of more established germline SVs (could be segregating in natural population) but facilitates the selected somatically derived SVs at the RNA level in ovarian tumors.
- These findings resonate with the observations of Bueno et al. (Pubmed) that the expression of BCR-ABL gene fusion, a very well known tumor driver in non-solid tumors, can be regulated by the genetic and epigenetic silencing of miR-203
- These findings are also relevant to the fact that recent studies have found several gene-fusions that are considered as cancer biomarkers in healthy individuals. Simply put, not the ocurrance of SVs at the genomic level but the regulation of the expression of SVs at RNA level contributes to their biological and clinical significance in the onset and progression of cancer.
Integrative data analysis workflow for structural variants. The upper workflow summarizes detection and validation of SVs using whole genome sequence data. The bottom left workflow summarizes the detection of fusion-transcripts using RNA-Seq data. The estimation of differential gene-expression using microarray data is summarized in the bottom right of the figure.
Contact – Vinay Mittal – email@example.com
Source – Bioinformatics Blog