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Rare Event Detection Using Error-corrected DNA and RNA Sequencing
Published on: August 3, 2018
SYSMut: decoding the functional significance of rare somatic mutations in cancer
Sirvan Khalighi1,2, Peronne Joseph1, Deepak Babu1
1Division of General Medical Sciences-Oncology, Case Comprehensive Cancer Center.
Abstract:
Current tailored-therapy efforts in cancer are largely focused on a small number of highly recurrently mutated driver genes but therapeutic targeting of these oncogenes remains challenging. However, the vast number of genes mutated infrequently across cancers has received less attention, in part, due to a lack of understanding of their biological significance. We present SYSMut, an extendable systems biology platform that can robustly infer the biologic consequences of somatic mutations by integrating routine multiomics profiles in primary tumors. We establish SYSMut's improved performance vis-à-vis state-of-the-art driver gene identification methodologies by recapitulating the functional impact of known driver genes, while additionally identifying novel functionally impactful mutated genes across 29 cancers. Subsequent application of SYSMut on low-frequency gene mutations in head and neck squamous cell (HNSC) cancers, followed by molecular and pharmacogenetic validation, revealed the lipidogenic network as a novel therapeutic vulnerability in aggressive HNSC cancers. SYSMut is thus a robust scalable framework that enables the discovery of new targetable avenues in cancer.
Insights
SYSMut, a systems biology platform, identifies the biological impact of rare cancer mutations. It revealed a lipid network vulnerability in head and neck cancers, offering new therapeutic targets.
Area of Science:
- Computational Biology
- Genomics
- Cancer Research
Background:
- Current cancer therapies focus on common driver genes, facing targeting challenges.
- Infrequently mutated genes are understudied due to unclear biological significance.
- There is a need for methods to uncover the functional impact of rare mutations.
Purpose of the Study:
- To introduce SYSMut, a systems biology platform for inferring biological consequences of somatic mutations.
- To validate SYSMut's performance against existing driver gene identification methods.
- To discover novel, functionally impactful mutated genes, especially low-frequency ones.
Main Methods:
- SYSMut integrates multiomics profiles from primary tumors.
- The platform infers biological consequences of somatic mutations.
- Performance was validated by recapitulating known driver genes and identifying novel ones across 29 cancers.
Main Results:
- SYSMut outperforms state-of-the-art driver gene identification methods.
- It successfully identified known driver genes and novel functionally impactful mutated genes.
- In head and neck squamous cell carcinoma (HNSC), SYSMut identified the lipidogenic network as a therapeutic vulnerability.
Conclusions:
- SYSMut is a robust and scalable framework for discovering biologically significant mutations.
- The platform enables the identification of novel targetable avenues in cancer.
- The lipidogenic network represents a new therapeutic target for aggressive HNSC.
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