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PhenoDriver: interpretable framework for studying personalized phenotype-associated driver genes in breast cancer
Yan Li1, Shao-Wu Zhang1,2, Ming-Yu Xie1
1School of Automation from Northwestern Polytechnical University, China.
Briefings in Bioinformatics
|September 22, 2023
Summary
PhenoDriver identifies personalized cancer driver genes and their mechanisms, improving breast cancer understanding and treatment. This framework reveals gene roles in cancer development and links them to clinical changes.
Area of Science:
- Oncology
- Genomics
- Bioinformatics
Background:
- Identifying personalized cancer driver genes is crucial for understanding cell transformation and clinical diagnosis.
- Existing methods often focus on gene identification at cohort or individual levels but lack mechanism elucidation.
- A gap exists in connecting driver genes to their specific oncogenic roles and clinical phenotypic alterations.
Purpose of the Study:
- To introduce PhenoDriver, an interpretable framework for personalized cancer driver gene identification.
- To elucidate the oncogenic mechanisms of identified driver genes and their association with clinical phenotypes.
- To discover novel breast cancer subtypes and their underlying molecular mechanisms.
Main Methods:
- Developed and applied the PhenoDriver framework to analyze genomic data from 988 breast cancer patients.
- Evaluated PhenoDriver's performance against state-of-the-art methods for cohort-level driver gene identification.
- Utilized PhenoDriver to identify both recurrent and rare driver mutations in individual patients and construct gene subnetworks.
Main Results:
- PhenoDriver demonstrated superior performance in identifying breast cancer driver genes at the cohort level.
- The framework effectively identified personalized driver genes, including those with rare mutations.
- Investigated oncogenic mechanisms of known and novel driver genes (e.g., TP53, MAP3K1, HTT) and their associated subnetworks.
- Discovered two known and one novel breast cancer subtype with elucidated molecular mechanisms.
Conclusions:
- PhenoDriver provides a robust method for personalized cancer driver gene identification and mechanism discovery.
- The findings enhance the understanding of breast cancer mechanisms and offer insights for therapeutic decisions.
- The framework aids in developing targeted anticancer therapies by revealing personalized driver profiles and subtypes.
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