Molecularly heterogeneous cancers such as triple-negative breast cancer are challenging to treat, because they often lack the “driver” mutations that are targeted by the newest cancer therapies. These cancers exhibit genomic instability, resulting in chromosomal rearrangements and gene fusions, and identifying these alterations is technically difficult.
Timothy Shaver and Brian Lehmann, Ph.D., working with Jennifer Pietenpol, Ph.D., developed a new algorithm, Segmental Transcript Analysis (STA), to predict gene rearrangements.
Using STA, they identified multiple known and novel gene rearrangements in triple-negative breast cancer and then expanded their analysis to other malignancies using a cohort from The Cancer Genome Atlas.
Two of the gene rearrangements that the team characterized in triple-negative breast cancer involve molecular targets for therapies already in clinical investigation or development.
The findings, reported Aug. 15 in Cancer Research, provide evidence that STA is an effective prediction tool for gene rearrangements and highlight the need to advance gene fusion detection for molecularly heterogeneous cancers.
This research was supported by grants from the National Institutes of Health (CA183531, GM008554, CA098131, CA105436, CA068485) and from the Susan G. Komen Foundation.
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