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

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

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

8 Scopus citations

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.

Original language	English (US)
Article number	70
Journal	Genome biology
Volume	22
Issue number	1
DOIs	https://doi.org/10.1186/s13059-021-02291-5
State	Published - Dec 2021
Externally published	Yes

Keywords

Copy number alteration
Driver discovery
Lineage tracing
Single-cell
Tumor evolution
scDNA-seq
scRNA-seq

ASJC Scopus subject areas

Ecology, Evolution, Behavior and Systematics
Genetics
Cell Biology

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1186/s13059-021-02291-5

Cite this

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title = "MEDALT: single-cell copy number lineage tracing enabling gene discovery",

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",

author = "Fang Wang and Qihan Wang and Vakul Mohanty and Shaoheng Liang and Jinzhuang Dou and Jincheng Han and Minussi, {Darlan Conterno} and Ruli Gao and Li Ding and Nicholas Navin and Ken Chen",

note = "Funding Information: This work was supported in part by the NIH [R01CA172652, U01CA211006, U01CA247760], the CPRIT [RP180248, RP180684], the MD Anderson Cancer Center Sheikh Khalifa Ben Zayed Al Nahyan Institute of Personalized Cancer Therapy grant [U54CA112970], and the NCI Cancer Center Support Grant [P30 CA016672]. This work was also supported by the Human Breast Cell Atlas Seed Network Grant (CZF2019-002432) from the Chan Zuckerberg Initiative DAF, an advised fund of Silicon Valley Community Foundation. Publisher Copyright: {\textcopyright} 2021, The Author(s).",

year = "2021",

month = dec,

doi = "10.1186/s13059-021-02291-5",

language = "English (US)",

volume = "22",

journal = "Genome Biology",

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T2 - single-cell copy number lineage tracing enabling gene discovery

AU - Wang, Fang

AU - Wang, Qihan

AU - Mohanty, Vakul

AU - Liang, Shaoheng

AU - Dou, Jinzhuang

AU - Han, Jincheng

AU - Minussi, Darlan Conterno

AU - Gao, Ruli

AU - Ding, Li

AU - Navin, Nicholas

AU - Chen, Ken

N1 - Funding Information: This work was supported in part by the NIH [R01CA172652, U01CA211006, U01CA247760], the CPRIT [RP180248, RP180684], the MD Anderson Cancer Center Sheikh Khalifa Ben Zayed Al Nahyan Institute of Personalized Cancer Therapy grant [U54CA112970], and the NCI Cancer Center Support Grant [P30 CA016672]. This work was also supported by the Human Breast Cell Atlas Seed Network Grant (CZF2019-002432) from the Chan Zuckerberg Initiative DAF, an advised fund of Silicon Valley Community Foundation. Publisher Copyright: © 2021, The Author(s).

PY - 2021/12

Y1 - 2021/12

N2 - 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.

AB - 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.

KW - Copy number alteration

KW - Driver discovery

KW - Lineage tracing

KW - Single-cell

KW - Tumor evolution

KW - scDNA-seq

KW - scRNA-seq

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

Abstract

Keywords

ASJC Scopus subject areas

UN SDGs

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