Xenograft tumor model systems are powerful tools for the evaluation and development of anti-tumor therapeutics. These models are especially useful for ovarian cancer, where only limited mouse models have been developed . SKOV-3 ovarian cancer cells are one of the more commonly used cell lines to model ovarian cancer. We aimed to understand what may drive xenograft tumor growth, which likely differs from growth in cell culture conditions, and what factors may be important for anti-tumor treatment. As a model, we tested the effects of MT19c, a vitamin D derivative that shows promising pre-clinical properties as demonstrated here and elsewhere [6, 15, 16]. We used multiple approaches to identify significantly MT19c regulated genes, pathways and networks with experimental support suggesting the functional importance of the insulin and PPARγ networks for MT19c efficacy. In particular, we found that PPARγ and PPARγ-controlled networks are up-regulated in treated tumors and stimulation of PPARγ with Rosiglitazone inhibited the chemotherapeutic efficacy in SKOV-3 cells. Finally, we propose an approach integrating copy number and expression data to identify which genes within CNAs are most likely to be important for tumor progression and chemotherapy. We propose that genes with high or low copy number, along with significant gene expression changes in response to an anti-tumor agent indicate genes important tumorigenesis.
This approach of linking copy number and drug induced expression changes may be a viable approach to identify particularly important genes for tumor progression. Although the majority of high copy number and high expression genes were affected by MT19c, many were not. A few high copy number genes such as RPL23 and RPS29 were significantly stimulated by MT19c. These high copy number genes may be up-regulated to help SKOV-3 cells survive in response to a lethal compound such as MT19c. But, their up-regulation upon MT19c treatment suggests that their high expression and high copy number may be serving a different role than down-regulated genes. Combining gene expression and copy number can reduce the number of genes to consider for further study. These data also suggest that genes regulated by dosage play an important role in cancer cells' response to chemotherapy. Together, these data provide insights into general pathways important for tumor progression and survival as well as MT19c efficacy.
Our observations suggest that in some cases the PPARγ network is stimulated to help ovarian cancer cells survive as suggested by Rosiglitazone treatment increasing the IC50s of MT19c and cisplatin. The observations suggest that stimulation of PPARγ by Rosiglitazone increases SKOV-3 chemotherapy resistance (Figure 4B). Rosiglitazone has been reported to inhibit growth and enhance cisplatin efficacy in some ovarian cancer cell lines, though SKOV-3 was not tested . These findings suggest that additional study is warranted to understand the conditions in which Rosiglitazone may be an effective chemotherapeutic and when it may actually promote survival of ovarian cancer cells.
We aimed to understand which gene expression networks may be important for SKOV-3 xenograft tumors progression and MT19c response. We found that significantly different conclusions may be made depending on how long the tumor was treated before the specimen was collected. Because xenograft tumors, much like patient tumors, are continuously evolving during growth, drug responses may differ at each captured state of the tumor at the time the specimen is collected for analysis. Genes involved in the cell cycle, energy metabolism, and DNA replication machinery are significantly affected by MT19c at the earlier, day 8 time point, while regulation of protein synthesis and ribosomes were significantly up-regulated at the later day 16 time point. No significant enrichment of cell cycle and DNA replication machinery was observed after 16 days of MT19c treatment. These pathways are often observed to be affected in tumors as control of metabolism and the cell cycle are often critical for tumor growth and survival. From gene expression data, it is difficult to conclude whether the effects are direct or indirect. These data identify candidate genes to test for their importance in mediating chemosensitivity in ovarian cancer cells as well as possible specific factors related to MT19c that can be discriminated with further study.
A significant contribution to these differences appears to originate from changes in the tumor as it progresses. This is highlighted when comparing the naïve tumors at the day 8 and 16 time points. When comparing the day 8 and 16 naïve tumors, cell cycle and DNA replication machinery is expressed higher at later times. We then observed that MT19c down-regulates the cell cycle at early times and yet not at later times when many of these genes are expressed at higher levels. Many of these genes are controlled by E2F. These observations suggest that control of the cell cycle depends on when the tumor is collected. In patients, the exact place in tumor progression/growth is always different and thus this alone can explain expression differences, as opposed to the inherent character of genomic differences of the tumor. Among many other factors, these expression changes during tumor growth may be inherent to tumors and could be a source of heterogeneity in patient samples. In probing xenograft tumors, these observations highlight the importance of examining changes at multiple time points, and not just at an arbitrarily defined endpoint.
Integrating copy number and expression measurements has proven valuable to gain insight into tumor networks and regulation . However, determining which genes are drivers of tumorigenesis and which may be passengers remains a challenging problem, typically requiring extensive experiments to test the function of identified factors . Copy number can be a mechanism driving gene expression levels. Typically this is assessed by correlating the measured expression and copy number values. Here, we propose an extension of the copy number and expression comparison, in which we identify important genes in CNAs by examining changes driven by an anti-tumor therapy such as MT19c. Alternatively, these expression changes could also indicate chromatin deregulation of these genes, suggesting that the observed differential regulation is simply a result of chance. The many loci differentially regulated by MT19c suggest extensive changes in epigenetic control by MT19c. We believe the simplest explanation is that the MT19c induced down-regulation strongly suggests that at least some of these amplified genes are important for growth.
Together, these observations suggest that some of the amplified genes are important for tumorigenesis consistent with the hypothesis that amplified genes are selected for tumor growth and survival. Similarly, genes at lower gene dosage are stimulated by MT19c to induce cell death or at least limit growth. These observations suggest that a significant fraction of these CNA genes are important for tumor growth and survival. We speculate that these loci are being deregulated epigenetically by chromatin changes to overcome the gene dosage determined by the DNA copy number at the time points tested. Thus, the combination of DNA copy number and the mRNA expression behavior in response to MT19c suggest that these genes may be among the most important for SKOV-3 xenograft progression.
MT19c has strong anti-tumor effects in multiple ovarian cancer cell lines including SKOV-3; however, the mechanism of MT19c action remains unclear. We observed many factors and pathways affected by MT19c, including some related to tumor growth and survival, such as DNA damage response, apopotic genes, insulin, and PPARγ. Similar to PPARγ, it is likely that many of these factors are general survival and pathway factors for SKOV-3 cells. Identifying specific mechanisms from expression data based on small molecule perturbations remains challenging, and requires significant additional functional studies. These data highlight how MT19c affects specific pathways, such as PPARγ and insulin signaling, that can be tested in additional cell lines and in vivo models to determine their importance. These studies could lead to the development of biomarkers to help determine which features effect MT19c efficacy in pre-clinical and clinical models.
Combining copy number and changes in gene expression approach in multiple cell lines may prove useful to identify particularly important genes in mediating survival and drug responses in ovarian cancer. The major copy number changes in the SKOV3 xenografts are observed in the SKOV-3 cell line as reported  including the amplifications on chromosomes 3 and 17 and LOH on chromosome 1.