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The MEF transformation model was established by immortalization of primary MEF cells via introduction of SV40LT (S) oncogene followed by over expression of HRAS V12 (R) and c-MYC (M) oncogenes either individually or in combination using retroviral transductions. Each construct used contained a different selectable drug resistance gene. Once the drug selection was completed, we obtained 4 cell cultures created from over expression of single, double or triple oncogenes, and we named them after the oncogenes introduced, for example S for the culture of SV40LT transduced cells, and SR for SV40LT and HRASV12 transduced cells etc. Each cell culture consisted of a heterogenous cell population, as, in addition to expected differences between cells in a culture, cells differed from each other by the copy number of oncogenes inserted and their insertion sites. Thus, these cultures are referred to as "mix populations". We performed the soft agar assay in order to test these mix populations for their ability to grow attachment free which is a hallmark for malignant cell transformation. Generally, only a small proportion of cells with the right amount of oncogene insertion at the right sites along with acquired stochastic changes during the assay complete the transformation process and give colonies. In our assay, despite the difference in transformation ability, each mix population was able to give rise to colonies on soft agar as shown below albeit at different time points (Figure 1). We then picked colonies derived from a single transformed cell and expanded them further. Populations obtained from such colonies were clonal and are referred to as "transformed clones (t-clones)". Initially, we expected only triple oncogene transduced cell colonies to give a high number of transformed colonies in a very short time period. Unexpectedly, other combinations of transductions also gave rise to colonies quickly. Therefore, even though we focus on SRM during this project, we also included others in the microarray experiment. For microarrays, we prepared RNA from 5 of these t-clones as well as from 3 clones of iPS (for 2 out of 3 clones, 2 different passages (P) were used, so totally 5 samples), and 3 different Ps of mix populations and primary MEF cells. We performed the microarray experiment as 2 separate sets. In the first set, "t-clone array", we hybridized primary MEF cells' RNA together with iPS and transformed clones' RNAs. In the second set, "mix array", we hybridized RNAs of primary MEF cells and of mix populations that we obtained from different transductions. For both arrays we used the Illumina whole genome expression chips (Illumina WG6 v2.0). Data obtained from both sets were analyzed separately but following a similar differential expression analysis scheme using Gene Spring GX. Each dataset was first normalized. Then, probes which were not expressed in any sample were excluded, and oneway ANOVA was performed. Next, volcano test was performed to find significantly and differentially expressed genes with at least a Benjamini corrected p-value of 0.05 and a fold difference of 1.5 between different pairs of comparisons.
Figure 1. Soft agar assay. (a) Pictures of colonies were taken on the 7th day of the assay for SR, SM and SRM, and on the 19th day for S cells because S cells formed colonies a week later than the others. SRM cells gave rise to biggest colonies and S cells to smallest ones. (b) Soft agar colonies (SAC) in 10 random fields from each of triplicate 6-wells for a particular tMEF type were counted on the 7th day of the assay, and the average SAC number was calculated. The number of SRM colonies was much higher than SR and SM colonies. Considering the timeline of colony formation along with the size and number of colonies obtained from each mix population, it can be suggested that over expression of one oncogene is enough for few cells to gain the transformation ability which can be increased by subsequent addition of more oncogenes. (c) Over expression of the constructs was confirmed by western blot.
For simplicity, from this point onwards, iPS and SRM cells will be called as stem and cancer cells. In the differential expression analysis of "t-clone array" dataset, we compared primary, stem and cancer cell transcriptomes to identify gene cassette that were common between stem and cancer cells with either the same or distinct expression pattern, as well as stem cell specific and cancer cell specific expression patterns (Figure 2). To validate our classifications, we checked each group for presence of genes that we expected to be present in that group. The stem cell specific group should include genes that are crucial for stem cell biology. As iPS cells have a very similar expression profile to ESCs, we used the list of self-renewal genes that are specifically expressed in ESCs and tested for induction of pluripotency in somatic cells by Shinya Yamanaka . 17 genes out of 24 in that list were found to be differentially and over expressed uniquely in our stem cells supporting that gene expression pattern in this group was specific to stem cells. When we had a look at the cancer specific group, we found many genes like Tm4sf1, Ccne2, Pparg, Vcam1 and Igfbp3 which are involved in cancer, or in mechanisms related to cancer such as apoptosis (Bcl2l11) and epithelial-mesenchymal transition (Gsc, Mitf, Wnt5a, Col3a1 etc.), as expected. In addition, expression trend for these genes when compared to human cancers were the same.
(a)stem vs cancer.png
Figure 2. Differential expression analysis: Stem vs cancer cells. (a) 3 comparisons were done to find differentially expressed genes between stem and primary cells, between cancer and primary cells, and stem and cancer cells. (b) As a result of these comparisons, 4 important expression patterns were identified: stem cell specific (2132 genes), cancer cell specific (813 genes), stem-cancer shared component with distinct expression pattern (495 genes) and stem-cancer shared component with same expression pattern (stem-cancer identical component, 1766 genes).
Finally, the stem-cancer shared component was divided into 2 subgroups, the stem-cancer component with distinct expression pattern and the stem-cancer identical component, based on the expression level difference between stem cells and cancer cells. Both subgroups contained genes associated with stemness and/or oncogenesis. As the next step, we checked the enrichment of known ESC genes in gene lists obtained from our classification to comprehend which groups might be important for stem cell phenotype. For the list of ESC genes, we used H. Chang's module map generated compiling microarray, RNAi and ChIP data obtained from mouse ESC and ASC . From the module map, Chang et al. had identified a transcriptional program shared by ESCs with a subset of ASCs, "mESC-like cassette", which contained many transcriptional regulators associated with pluripotency, and a "ASC cassette" which included many transcriptional regulators of differentiation. They also examined the expression of mESC-like and ASC cassettes in human cancer microarray data. They found that mESC-like cassette was activated in various cancer tissues relative to their corresponding normal tissue, in contrast to ASC cassette which was repressed. Interestingly, in our study the stem-cancer component subgroup but not the stem-specific group was significantly enriched for ESC genes, and most of the overlapping genes were included in the stem-cancer identical subgroup. With few exceptions, ESC genes in this group were up-regulated whereas ASC genes were down-regulated in agreement with Chang et al's observation about the activation of ESC-like cassette and repression of ASC cassette in cancer. We thus decided to prioritize stem-cancer identical component for further investigation as this group was the richest for ESC genes, plus they were shared by cancer cells. This group was the most likely to contain the list for genes that drive cancer and to maintain networks which are normally involved in stem cell biology or more specifically in self-renewal (Figure3).
Figure 3. Distribution of mESC-like cassette. (a) Stem-cancer component was divided into 2 subgroups. (b) 57% of mESC-like cassette overlapped with one of our gene lists, 28% of mESC genes were common in stem cells and cancer cells, and most of them had same expression level in both cell types. (c) Enrichment of mESC genes in both subgroups of the stem-cancer component was significant.
To further discover candidates in this group for playing a role in oncogenesis, we first detected transformation cassettes specific to each mix population (S, SM, SR and SRM) by differential expression analysis of the second microarray dataset: "mix array" and we intersected them to define the common transformation cassette in all (Figure 4). Next, we overlapped the common transformation cassette with the stem-cancer identical component to reduce the gene list to a smaller subset which was thought to contain the strongest candidates: we obtained 166 genes which were expressed with same levels in stem cells and cancer cells, and which were also common in transformation of all mix populations. Then, we checked for the distribution of different cassettes like c-Myc targets, in this subset. c-Myc oncogene was the common factor in reprogramming for transformation and for pluripotency. Therefore, we checked the percentage of c-Myc targets using MYC database and S. Orkin's paper and showed that these 166 genes were not solely c-Myc targets [3,4].
Figure 4. Differential expression analysis: Mix vs T-Clone. For each mix population, differentially expressed genes relative to primary MEFs were found: changes at those genes' expression levels were most probably induced by the over expression of S, R and/or M. Then, these genes were compared to differentially expressed genes in the corresponding t-clone relative to primary MEFs. The intersection of these 2 groups was the expression pattern maintained during the transformation process. This expression pattern might be important for the transformation but it wasn't sufficient to complete the process as only a small portion of cells in the mix population could transform. We suggest that, in these cells, changes were complemented with additional "transformation specific changes" which we term "the transformation cassette". We believe that aside from passenger ones, this group is rich in cancer-driving changes.mix vs clone.png
We next suppose that if the above mentioned 166 genes included genes which were important for a cancer phenotype, then they would be (frequently) altered in human cancers as well. To verify this, we utilized the Oncomine expression database and found that out of 166 genes, 50 genes were upregulated and 32 genes were downregulated in multiple human cancers when compared to normal match tissues. We also checked the overlap of ESC, ASC and MYC cassettes within these 2 lists. We demonstrated again that those lists included non-MYC targets in addition to show that there were no ESC genes which were down-regulated, nor any ASC gene which was up-regulated. As a result of this final overlap, we obtained 18 ESC genes which were expressed in stem and cancer cells with similar expression values, which were involved in transformation of all our mix MEF populations, and which were frequently altered in human cancers. Therefore, we picked them as potential targets to perturb oncogenesis.
At present, we are designing RNA interference based functional assays to test our candidates for their importance in oncogenesis and in pluripotency. In addition, we are doing a network analysis using Ingenuity Pathway Analysis to discover stem cell- and cancer cell-specific networks that regulate gene expression for stem-cancer identical component. We have also started to analyze RNA-seq and gPET data that we obtained from 6 samples we had chosen for expression array (primary MEF, 2 iPS and 3 SRM clones). Finally, we have completed the establishment of the HMEC linear transformation model by retroviral transductions of hTERT, SV40st, p53 shRNA, and HRAS. After confirmation of the over-expression of oncogenes we introduced, we will proceed with the whole genome expression array experiment.