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

Sixue Liu,Zuyu Yang,Guanghao Li,Chunyan Li,Yanting Luo,Qiang Gong,Xin Wu,Tao Li,Zhiqian Zhang,Baocai Xing,Xiaolan Xu,Xuemei Lu

doi:10.1016/j.gpb.2018.07.008

Abstract

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.

Highlights

Most large-scale cancer omics studies aim to discover functionally significant alterations that contribute to cancer phenotypes, or to characterize cancer evolution during tumorigenesis and progression before or after treatment, for potential personalized medicine [1,2,3]
The TILs were used as a matched normal control of this patient for somatic variation calling from both wholegenome sequencing (WGS) and whole-exome sequencing (WES) data
Our results indicate that gene body methylation was more closely associated with gene expression than promoter methylation, which is consistent with the idea that the ‘‘gene body methylation is a stronger indicator of expression class than promoter methylation”, as described for human samples and cell lines [41]

Summary

Introduction

Most large-scale cancer omics studies aim to discover functionally significant alterations that contribute to cancer phenotypes, or to characterize cancer evolution during tumorigenesis and progression before or after treatment, for potential personalized medicine [1,2,3]. The integration of multi-omics data and large cohorts become necessary due to the emerging cancer hallmarks based on in-depth multiple genetic and epigenetic studies on somatic tumor cells [4,5,6,7]. It appears that when larger sample population is interrogated and massive data are produced, the number of false positive genes, which often lead to enormous complexity in interpreting molecular mechanisms, increases remarkably [1,8]. Since diversity in tumors has not been sophisticatedly considered in most drug development programs employing artificial tumor models, empirical systems that can distinguish impacts of causative intratumoral alterations from genetic background and reflect the diversity within a tumor are of essence for better prognostics and treatment

Methods

Results

Conclusion