期刊
GENOME BIOLOGY
卷 22, 期 1, 页码 -出版社
BMC
DOI: 10.1186/s13059-021-02291-5
关键词
Single-cell; scDNA-seq; scRNA-seq; Copy number alteration; Tumor evolution; Lineage tracing; Driver discovery
资金
- NIH [R01CA172652, U01CA211006, U01CA247760]
- CPRIT [RP180248, RP180684]
- MD Anderson Cancer Center Sheikh Khalifa Ben Zayed Al Nahyan Institute of Personalized Cancer Therapy grant [U54CA112970]
- NCI Cancer Center Support Grant [P30 CA016672]
- Human Breast Cell Atlas Seed Network Grant from Chan Zuckerberg Initiative DAF, an advised fund of Silicon Valley Community Foundation [CZF2019-002432]
The MEDALT algorithm and LSA statistical routine accurately infer the evolutionary history of cell populations based on SCCN lineage trees to discover fitness-associated genes. By analyzing data from triple-negative breast cancer patients, essential genes for breast cancer fitness and patient survival can be effectively predicted.
We present a Minimal Event Distance Aneuploidy Lineage Tree (MEDALT) algorithm that infers the evolution history of a cell population based on single-cell copy number (SCCN) profiles, and a statistical routine named lineage speciation analysis (LSA), whichty facilitates discovery of fitness-associated alterations and genes from SCCN lineage trees. MEDALT appears more accurate than phylogenetics approaches in reconstructing copy number lineage. From data from 20 triple-negative breast cancer patients, our approaches effectively prioritize genes that are essential for breast cancer cell fitness and predict patient survival, including those implicating convergent evolution. The source code of our study is available at https://github.com/KChen-lab/MEDALT.
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