Literature DB >> 28369576

A Direct Test of Selection in Cell Populations Using the Diversity in Gene Expression within Tumors.

Chunyan Li1, Yali Hou1, Jin Xu1, Aiqun Zhang2, Zhenzhen Liu1, Furong Qi1, Zuyu Yang1, Ke Chen1, Sixue Liu1, Huanwei Huang1, Qianfei Wang1, Jiahong Dong2,3, Chung-I Wu1,4, Xuemei Lu1.   

Abstract

Although intratumor diversity driven by selection has been the prevailing view in cancer biology, recent population genetic analyses have been unable to reject the neutral interpretation. As the power to reject neutrality in tumors is often low, it will be desirable to have an alternative means to test selection directly. Here, we utilize gene expression data as a surrogate for functional significance in intra- and intertumor comparisons. The expression divergence between samples known to be driven by selection (e.g., between tumor and normal tissues) is always higher than the divergence between normal samples, which should be close to the neutral level of divergence. In contrast, the expression differentiation between regions of the same tumor, being lower than the neutral divergence, is incompatible with the hypothesis of selectively driven divergence. To further test the hypothesis of neutral evolution, we select a hepatocellular carcinoma tumor that has large intratumor SNV and CNV (single nucleotide variation and copy number variation, respectively) diversity. This tumor enables us to calibrate the level of expression divergence against that of genetic divergence. We observe that intratumor divergence in gene expression profile lags far behind genetic divergence, indicating insufficient phenotypic differences for selection to operate. All these expression analyses corroborate that natural selection does not operate effectively within tumors, supporting recent interpretations of within-tumor diversity. As the expected level of genetic diversity, hence the potential for drug resistance, would be much higher under neutrality than under selection, the issue is of both theoretical and clinical significance.
© The Author 2017. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Entities:  

Keywords:  genetic diversity; intra- and intertumor heterogeneity; selection; variation in gene expression

Mesh:

Year:  2017        PMID: 28369576     DOI: 10.1093/molbev/msx115

Source DB:  PubMed          Journal:  Mol Biol Evol        ISSN: 0737-4038            Impact factor:   16.240


  4 in total

1.  Quantitative analysis of the splice variants expressed by the major hepatitis B virus genotypes.

Authors:  Chun Shen Lim; Vitina Sozzi; Margaret Littlejohn; Lilly K W Yuen; Nadia Warner; Brigid Betz-Stablein; Fabio Luciani; Peter A Revill; Chris M Brown
Journal:  Microb Genom       Date:  2021-01

2.  High-resolution deconstruction of evolution induced by chemotherapy treatments in breast cancer xenografts.

Authors:  Hyunsoo Kim; Pooja Kumar; Francesca Menghi; Javad Noorbakhsh; Eliza Cerveira; Mallory Ryan; Qihui Zhu; Guruprasad Ananda; Joshy George; Henry C Chen; Susan Mockus; Chengsheng Zhang; Yan Yang; James Keck; R Krishna Murthy Karuturi; Carol J Bult; Charles Lee; Edison T Liu; Jeffrey H Chuang
Journal:  Sci Rep       Date:  2018-12-18       Impact factor: 4.379

3.  Evolution under Spatially Heterogeneous Selection in Solid Tumors.

Authors:  Guanghao Li; Zuyu Yang; Dafei Wu; Sixue Liu; Xuening Li; Tao Li; Yawei Li; Liji Liang; Weilong Zou; Chung-I Wu; Hurng-Yi Wang; Xuemei Lu
Journal:  Mol Biol Evol       Date:  2022-01-07       Impact factor: 16.240

4.  Multi-omics Analysis of Primary Cell Culture Models Reveals Genetic and Epigenetic Basis of Intratumoral Phenotypic Diversity.

Authors:  Sixue Liu; Zuyu Yang; Guanghao Li; Chunyan Li; Yanting Luo; Qiang Gong; Xin Wu; Tao Li; Zhiqian Zhang; Baocai Xing; Xiaolan Xu; Xuemei Lu
Journal:  Genomics Proteomics Bioinformatics       Date:  2020-03-20       Impact factor: 7.691

  4 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.