Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, 1400 Pressler St, Houston, TX, USA.
Present Address: Precision Medicine Institute, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
Genome Biol. 2021 Feb 23;22(1):70. doi: 10.1186/s13059-021-02291-5.
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 .
我们提出了一种最小事件距离非整倍体谱系树(MEDALT)算法,该算法基于单细胞拷贝数(SCCN)谱推断细胞群体的进化历史,并提供了一种名为谱系特化分析(LSA)的统计程序,该程序有助于从 SCCN 谱系树中发现与适应性相关的改变和基因。在重建拷贝数谱系方面,MEDALT 比系统发育方法更准确。从 20 名三阴性乳腺癌患者的数据中,我们的方法有效地确定了对乳腺癌细胞适应性至关重要的基因,并预测了患者的生存情况,其中包括那些涉及趋同进化的基因。我们研究的源代码可在 https://github.com/KChen-lab/MEDALT 获得。