Human hepatic cancer stem cells are characterized by common stemness traits and diverse oncogenic pathways

Abstract

Epigenetic mechanisms play critical roles in stem cell biology by maintaining pluripotency of stem cells and promoting differentiation of more mature derivatives. If similar mechanisms are relevant for the cancer stem cell (CSC) model, then epigenetic modulation might enrich the CSC population, thereby facilitating CSC isolation and rigorous evaluation. To test this hypothesis, primary human cancer cells and liver cancer cell lines were treated with zebularine (ZEB), a potent DNA methyltransferase-1 inhibitor, and putative CSCs were isolated using the side population (SP) approach. The CSC properties of ZEB-treated and untreated subpopulations were tested using standard in vitro and in vivo assays. Whole transcriptome profiling of isolated CSCs was performed to generate CSC signatures. Clinical relevance of the CSC signatures was evaluated in diverse primary human cancers. Epigenetic modulation increased frequency of cells with CSC properties in the SP fraction isolated from human cancer cells as judged by self-renewal, superior tumor-initiating capacity in serial transplantations, and direct cell tracking experiments. Integrative transcriptome analysis revealed common traits enriched for stemness-associated genes, although each individual CSC gene expression signature exhibited activation of different oncogenic pathways (e.g., EGFR, SRC, and MYC). The common CSC signature was associated with malignant progression, which is enriched in poorly differentiated tumors, and was highly predictive of prognosis in liver and other cancers.

Conclusion: Epigenetic modulation may provide a tool for prospective isolation and in-depth analysis of CSC. The liver CSC gene signatures are defined by a pernicious interaction of unique oncogene-specific and common stemness traits. These data should facilitate the identifications of therapeutic tools targeting both unique and common features of CSCs.

Fig. 1

Treatment with Zebularine Reduces Frequency while Increasing Clonogenicty of SP Cells. (A) Effect of Zebularine on SP frequency. Data presented as mean percentage ± SD of 3 independent experiments. (B) Live-cell FACS profiles for Huh7 cells untreated and treated with 100 µM zebularine for 3 days. SP cells were identified by Hoechst 33342 staining and the use of blue and red filters. Cells were incubated with Hoechst 33342 in presence (top) and absence (bottom) of Fumitremorgin C to identify the SP gate with cell percentages noted. (C and D) Bar graph showing the number of spheres formed in soft agar by untreated (C) or zebularine-treated cells (D). Data are presented as mean ± SD of triplicate experiments and expressed relative to corresponding non-SP cells. (* P<0.05; ** P<0.01, *** P<0.001). (E) Representative images of spheres formed in soft agar by SP and non-SP cells sorted by FACS form untreated and zebularine-treated PLC/PRF/5 cells. (X40).

Fig. 2

SP Cells Possess Self-Renewal Capacity in Serial Transplantations. (A) Changes in SP frequency after serial transplantations of SP-ZEB cells from Huh7 and KMCH. Data presented as mean percentage ± SD of three independent experiments. Cells were isolated from tumors derived from 100 SP-ZEB cells, re-treated with ZEB, and FACS sorted for SP and non-SP cells before serial re-transplantation. (B) Representative FACS profiles for Huh7 cells. SP cells were gated based on inhibition with Fumitremorgin C. (C) Graphs show kinetics of tumor development after serial transplantations of ZEB-treated SP and non-SP cells isolated from Huh7 (upper panels) and KMCH (lower panels) cell lines. 1000 cells of each fraction were transplanted into flanks of NOD/SCID mice (n=4) and tumor formation was monitored by weekly palpation. P <0.05 was considered statistically significant.

Fig. 3

Cell Tracking Experiments Demonstrate Superior Self-Renewal Ability of SP Cells versus non-SP Cells. (A) Experimental design. Huh7 cells stably transduced with either green fluorescent protein (GFP) or red fluorescent protein (mCherry) were FACS-sorted for SP (green) and non-SP (red) cells to allow cell tracking in vitro and in vivo. (B) Graph bars showing the number of colonies formed by 1000 SP (green) and nonSP (red) cells 14 days after plating. Direct fluorescence image of colonies formed by SP and non-SP cells (X40) shown on the right. Data represent means of 3 replicates ± SD. P <0.05 calculated by paired t-test were considered statistically significant. (C) Bar graphs showing the number of spheres formed in Matrigel by 1000 SP and non-SP cells 21 days after plating. Direct fluorescence (left) and bright (right) field images showing a sphere formed by a green SP cell. Scale bars, 100µm. Data represent means of 6 replicates ± SD. P-values were calculated by paired t-test and P <0.05 were considered statistically significant. (D) Representative ex vivo confocal image of the whole tumor at 8 wk after s.c. transplantation of 1000 SP and non-SP (1:1) cells into NOD/SCID mice (n=4). The vast majority of tumor composed from cells with green fluorescence phenotype indicating that SP cells were responsible for tumor growth. Arrows point to minor areas formed by non-SP-red cells. Scale bar, 200 µm. Inset, macroscopic image of tumor at 6 wk after transplantation.

Fig. 4

ZEB effect on primary human cancer cells. (A) Effect of Zebularine on SP frequency of primary human cancers cells from gastrointestinal and hepatobilary cancers. Data presented as mean percentage ± SD. All experiments performed in triplicates. (B) Representative SP profiles of untreated and zebularine- treated cells from liver, pancreatic and colon cancer (top) gated based on inhibition with Fumitremorgin C (bottom). (C) Bar graph showing the number of colonies and spheres formed by untreated or zebularine-treated sorted liver and pancreatic cancer cells cultured on monolayer or Matrigel. The data presented as mean ± SD of triplicate experiment. (* P<0.05; ** P<0.01, *** P<0.001)

Fig. 5

Common Gene expression Signatures of Putative Liver CSCs. (A) Unsupervised hierachial cluster analysis of SP and non-SP fractions from Huh7, WRL68 and KMCH cell lines after zebularine treatment. A common gene expression signature of 617 differentially expressed genes from three SP-ZEB fractions identified by Bootstrap t-test (P-values ≤ 0.05 and fold-change ≥1.5). Note clear separation of SP (red) and non-SP (green) cells into 2 clusters. (B) GSEA based on two previously published gene sets: Adult tissue stem cell genes(23) and upregulated genes of the HB-signature.(24) For the adult tissue stem module available homologue genes were used. Results demonstrate enrichment of both gene sets in SP-ZEB cells. Enrichment score (ES) reflects degree of over-representation for each group at the peak of the entire set. Statistical significance calculated by nominal P-value of the ES by using an empirical phenotype-based permutation test. False positives are calculated by the false discovery rate (FDR).(C) Top 5 functional networks for the SP-ZEB-signature.

Fig. 6

SP-ZEB Signature Predicts HCC Patients Prognosis. (A) Hierarchical cluster analysis of 53 human HCCs based on the SP-ZEB signature (579 of 617 genes remained when filtered for 80% presence of genes). Bars under cluster tree represent integrated cell fractions and overlap with previously generated HCC subclasses (subtype A, B, HB and HC). (B) Identification of gene classifiers predictive for patient survival. A class random variance model was used to identify classifier genes (α≤0.001) and validation of correct classification was performed by seven different algorithm using 1000 random permutations. (C) Validation of the SP-ZEB gene signature in poor prognosis patients (subtype A) by GSEA.(D) Kaplan-Meier plots of overall survival and (E) recurrence of the HCC (Mantel-Cox test). Recurrence data were available only for 20 patients. (F) Association of the classifier signature with clinical outcome of cancer patients from different types of cancer. Integrative meta-analysis of genomic data from 40 primary tumors using the Oncomine Microarray database. Data presented as mean odds ratio ± SD with P-values < 0.001 and odds ratios >2 set as a threshold.