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A Direct Test of Selection in Cell Populations Using the Diversity in Gene Expression within Tumors
Author(s) -
Chunyan Li,
Yali Hou,
Jin Xu,
Aiqun Zhang,
Zhenzhen Liu,
Furong Qi,
Zuyu Yang,
Ke Chen,
Sixue Liu,
Huanwei Huang,
Qianfei Wang,
Jiahong Dong,
ChungI Wu,
Xuemei Lu
Publication year - 2017
Publication title -
molecular biology and evolution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.637
H-Index - 218
eISSN - 1537-1719
pISSN - 0737-4038
DOI - 10.1093/molbev/msx115
Subject(s) - biology , divergence (linguistics) , selection (genetic algorithm) , neutral theory of molecular evolution , genetic divergence , genetic diversity , gene , natural selection , genetic variation , population , gene expression , evolutionary biology , genetics , philosophy , linguistics , demography , artificial intelligence , sociology , computer science
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.

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