MesKit: a tool kit for dissecting cancer evolution of multi-region tumor biopsies through somatic alterations

Author:

Liu Mengni12,Chen Jianyu1,Wang Xin1,Wang Chengwei1,Zhang Xiaolong2,Xie Yubin1,Zuo Zhixiang2ORCID,Ren Jian12ORCID,Zhao Qi2ORCID

Affiliation:

1. School of Life Sciences, Sun Yat-sen University, Guangzhou, Guangdong 510275, China

2. State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, 651 E Dongfeng Road, Guangzhou, Guangdong 510060, China

Abstract

Abstract Background Multi-region sequencing (MRS) has been widely used to analyze intra-tumor heterogeneity (ITH) and cancer evolution. However, comprehensive analysis of mutational data from MRS is still challenging, necessitating complicated integration of a plethora of computational and statistical approaches. Findings Here, we present MesKit, an R/Bioconductor package that can assist in characterizing genetic ITH and tracing the evolutionary history of tumors based on somatic alterations detected by MRS. MesKit provides a wide range of analysis and visualization modules, including ITH evaluation, metastatic route inference, and mutational signature identification. In addition, MesKit implements an auto-layout algorithm to generate phylogenetic trees based on somatic mutations. The application of MesKit for 2 reported MRS datasets of hepatocellular carcinoma and colorectal cancer identified known heterogeneous features and evolutionary patterns, together with potential driver events during cancer evolution. Conclusions In summary, MesKit is useful for interpreting ITH and tracing evolutionary trajectory based on MRS data. MesKit is implemented in R and available at https://bioconductor.org/packages/MesKit under the GPL v3 license.

Funder

National Natural Science Foundation of China

National Key Research and Development Program of China

Program for Guangdong Introducing Innovative and Entrepreneurial Teams

Natural Science Foundation of Guangdong Province

Fundamental Research Funds for the Central Universities

China Postdoctoral Science Foundation

Publisher

Oxford University Press (OUP)

Subject

Computer Science Applications,Health Informatics

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