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

Evolution under Spatially Heterogeneous Selection in Solid Tumors

Spatial genetic and phenotypic diversity within solid tumors has been well documented. Nevertheless, how this heterogeneity affects temporal dynamics of tumorigenesis has not been rigorously examined because solid tumors do not evolve as the standard population genetic model due to the spatial constraint. We therefore, propose a neutral spatial (NS) model whereby the mutation accumulation increases toward the periphery; the genealogical relationship is spatially determined and the selection efficacy is blunted (due to kin competition). In this model, neutral mutations are accrued and spatially distributed in manners different from those of advantageous mutations. Importantly, the distinctions could be blurred in the conventional model. To test the NS model, we performed a three-dimensional multiple microsampling of two hepatocellular carcinomas. Whole-genome sequencing (WGS) revealed a 2-fold increase in mutations going from the center to the periphery. The operation of natural selection can then be tested by examining the spatially determined clonal relationships and the clonal sizes. Due to limited migration, only the expansion of highly advantageous clones can sweep through a large part of the tumor to reveal the selective advantages. Hence, even multiregional sampling can only reveal a fraction of fitness differences in solid tumors. Our results suggest that the NS patterns are crucial for testing the influence of natural selection during tumorigenesis, especially for small solid tumors.

Variation in the life history strategy underlies functional diversity of tumors

Classical r- vs. K-selection theory describes the trade-offs between high reproductive output and competitiveness and guides research in evolutionary ecology. While its impact has waned in the recent past, cancer evolution may rekindle it. Herein, we impose r- or K-selection on cancer cell lines to obtain strongly proliferative r cells and highly competitive K cells to test ideas on life-history strategy evolution. RNA-seq indicates that the trade-offs are associated with distinct expression of genes involved in the cell cycle, adhesion, apoptosis, and contact inhibition. Both empirical observations and simulations based on an ecological competition model show that the trade-off between cell proliferation and competitiveness can evolve adaptively. When the r and K cells are mixed, they exhibit strikingly different spatial and temporal distributions. Due to this niche separation, the fitness of the entire tumor increases. The contrasting selective pressure may operate in a realistic ecological setting of actual tumors.

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

Hepatitis B virus (HBV) is a major human pathogen that causes liver diseases. The main HBV RNAs are unspliced transcripts that encode the key viral proteins. Recent studies have shown that some of the HBV spliced transcript isoforms are predictive of liver cancer, yet the roles of these spliced transcripts remain elusive. Furthermore, there are nine major HBV genotypes common in different regions of the world, these genotypes may express different spliced transcript isoforms. To systematically study the HBV splice variants, we transfected human hepatoma cells, Huh7, with four HBV genotypes (A2, B2, C2 and D3), followed by deep RNA-sequencing. We found that 13-28 % of HBV RNAs were splice variants, which were reproducibly detected across independent biological replicates. These comprised 6 novel and 10 previously identified splice variants. In particular, a novel, singly spliced transcript was detected in genotypes A2 and D3 at high levels. The biological relevance of these splice variants was supported by their identification in HBV-positive liver biopsy and serum samples, and in HBV-infected primary human hepatocytes. Interestingly the levels of HBV splice variants varied across the genotypes, but the spliced pregenomic RNA SP1 and SP9 were the two most abundant splice variants. Counterintuitively, these singly spliced SP1 and SP9 variants had a suboptimal 5' splice site, supporting the idea that splicing of HBV RNAs is tightly controlled by the viral post-transcriptional regulatory RNA element.

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

Uncovering the functionally essential variations related to tumorigenesis and tumor progression from cancer genomics data is still challenging due to the genetic diversity among patients, and extensive inter- and intra-tumoral heterogeneity at different levels of gene expression regulation, including but not limited to the genomic, epigenomic, and transcriptional levels. To minimize the impact of germline genetic heterogeneities, in this study, we establish multiple primary cultures from the primary and recurrent tumors of a single patient with hepatocellular carcinoma (HCC). Multi-omics sequencing was performed for these cultures that encompass the diversity of tumor cells from the same patient. Variations in the genome sequence, epigenetic modification, and gene expression are used to infer the phylogenetic relationships of these cell cultures. We find the discrepancy among the relationships revealed by single nucleotide variations (SNVs) and transcriptional/epigenomic profiles from the cell cultures. We fail to find overlap between sample-specific mutated genes and differentially expressed genes (DEGs), suggesting that most of the heterogeneous SNVs among tumor stages or lineages of the patient are functionally insignificant. Moreover, copy number alterations (CNAs) and DNA methylation variation within gene bodies, rather than promoters, are significantly correlated with gene expression variability among these cell cultures. Pathway analysis of CNA/DNA methylation-related genes indicates that a single cell clone from the recurrent tumor exhibits distinct cellular characteristics and tumorigenicity, and such an observation is further confirmed by cellular experiments both in vitro and in vivo. Our systematic analysis reveals that CNAs and epigenomic changes, rather than SNVs, are more likely to contribute to the phenotypic diversity among subpopulations in the tumor. These findings suggest that new therapeutic strategies targeting gene dosage and epigenetic modification should be considered in personalized cancer medicine. This culture model may be applied to the further identification of plausible determinants of cancer metastasis and relapse.

Genetic Load and Potential Mutational Meltdown in Cancer Cell Populations

Large genomes with elevated mutation rates are prone to accumulating deleterious mutations more rapidly than natural selection can purge (Muller's ratchet). As a consequence, it may lead to the extinction of small populations. Relative to most unicellular organisms, cancer cells, with large and nonrecombining genome and high mutation rate, could be particularly susceptible to such "mutational meltdown." However, the most common type of mutation in organismal evolution, namely, deleterious mutation, has received relatively little attention in the cancer biology literature. Here, by monitoring single-cell clones from HeLa cell lines, we characterize deleterious mutations that retard the rate of cell proliferation. The main mutation events are copy number variations (CNVs), which, estimated from fitness data, happen at a rate of 0.29 event per cell division on average. The mean fitness reduction, estimated reaching 18% per mutation, is very high. HeLa cell populations therefore have very substantial genetic load and, at this level, natural population would likely face mutational meltdown. We suspect that HeLa cell populations may avoid extinction only after the population size becomes large enough. Because CNVs are common in most cell lines and tumor tissues, the observations hint at cancer cells' vulnerability, which could be exploited by therapeutic strategies.

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

The processes by which tumors evolve are essential to the efficacy of treatment, but quantitative understanding of intratumoral dynamics has been limited. Although intratumoral heterogeneity is common, quantification of evolution is difficult from clinical samples because treatment replicates cannot be performed and because matched serial samples are infrequently available. To circumvent these problems we derived and assayed large sets of human triple-negative breast cancer xenografts and cell cultures from two patients, including 86 xenografts from cyclophosphamide, doxorubicin, cisplatin, docetaxel, or vehicle treatment cohorts as well as 45 related cell cultures. We assayed these samples via exome-seq and/or high-resolution droplet digital PCR, allowing us to distinguish complex therapy-induced selection and drift processes among endogenous cancer subclones with cellularity uncertainty <3%. For one patient, we discovered two predominant subclones that were granularly intermixed in all 48 co-derived xenograft samples. These two subclones exhibited differential chemotherapy sensitivity–when xenografts were treated with cisplatin for 3 weeks, the post-treatment volume change was proportional to the post-treatment ratio of subclones on a xenograft-to-xenograft basis. A subsequent cohort in which xenografts were treated with cisplatin, allowed a drug holiday, then treated a second time continued to exhibit this proportionality. In contrast, xenografts from other treatment cohorts, spatially dissected xenograft fragments, and cell cultures evolved in diverse ways but with substantial population bottlenecks. These results show that ecosystems susceptible to successive retreatment can arise spontaneously in breast cancer in spite of a background of irregular subclonal bottlenecks, and our work provides to our knowledge the first quantification of the population genetics of such a system. Intriguingly, in such an ecosystem the ratio of common subclones is predictive of the state of treatment susceptibility, showing how measurements of subclonal heterogeneity could guide treatment for some patients.