MEDALT: single-cell copy number lineage tracing enabling gene discovery

Fang Wang; Qihan Wang; Vakul Mohanty; Shaoheng Liang; Jinzhuang Dou; Jincheng Han; Darlan Conterno Minussi; Ruli Gao; Li Ding; Nicholas Navin; Ken Chen

doi:10.1186/s13059-021-02291-5

MEDALT: single-cell copy number lineage tracing enabling gene discovery

Genome Biol. 2021 Feb 23;22(1):70. doi: 10.1186/s13059-021-02291-5.

Authors

Fang Wang^#^{1

2}, Qihan Wang^#^{1

3}, Vakul Mohanty¹, Shaoheng Liang¹, Jinzhuang Dou¹, Jincheng Han⁴, Darlan Conterno Minussi⁵, Ruli Gao⁶, Li Ding⁷, Nicholas Navin⁵, Ken Chen⁸

Affiliations

¹ Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, 1400 Pressler St, Houston, TX, USA.
² Present Address: Precision Medicine Institute, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
³ Department of Computer Science, Rice University, Houston, USA.
⁴ Department of Cancer Biology, The University of Texas MD Anderson Cancer Center, Houston, USA.
⁵ Department of Genetics, The University of Texas MD Anderson Cancer Center, Houston, USA.
⁶ Department of Cardiovascular Sciences, Center for Bioinformatics and Computational Biology, Houston Methodist Research Institute, Houston, USA.
⁷ Department of Medicine, McDonnell Genome Institute Washington University School of Medicine, St. Louis, USA.
⁸ Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, 1400 Pressler St, Houston, TX, USA. kchen3@mdanderson.org.

^# Contributed equally.

Abstract

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 .

Keywords: Copy number alteration; Driver discovery; Lineage tracing; Single-cell; Tumor evolution; scDNA-seq; scRNA-seq.

Publication types

Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't

MeSH terms

Algorithms
Aneuploidy*
Computational Biology / methods*
Evolution, Molecular
Gene Dosage*
Genetic Association Studies
Genetic Fitness
Genetic Predisposition to Disease
High-Throughput Nucleotide Sequencing
Humans
RNA-Seq* / methods
Single-Cell Analysis* / methods
Software*

Abstract

Publication types

MeSH terms

Grants and funding