| Literature DB >> 28369576 |
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.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