RESUMEN
BACKGROUND: PTEN loss contributes to the development of liver diseases including hepatic steatosis and both hepatocellular carcinoma (HCC) and cholangiocarcinoma (CC). The factors that influence the penetrance of these conditions are unclear. We explored the influence of sustained hypoxia signaling through co-deletion of Pten and Vhl in a murine model. METHODS: We used a CreER-linked Keratin 18 mouse model to conditionally delete Pten, Vhl or both in somatic cells of adult mice, evaluating the resultant tumors by histology and gene expression microarray. Existing sets of gene expression data for human HCC and CC were examined for pathways related to those observed in the murine tumors, and a cohort of human CC samples was evaluated for relationships between HIF-1α expression and clinical outcomes. RESULTS: Both Pten deletion genotypes developed liver tumors, but with differing phenotypes. Pten deletion alone led to large hepatic tumors with widespread hepatosteatosis. Co-deletion of Pten and Vhl with the Keratin 18 promoter resulted in reduced steatosis and a reduced tumor burden that was characterized by a trabecular architecture similar to CC. Genes associated with hepatic steatosis were coordinately expressed in the human HCC dataset, while genes involved in hypoxia response were upregulated in tumors from the human CC dataset. HIF-1α expression and overall survival were examined in an independent cohort of human CC tumors with no statistical differences uncovered. CONCLUSION: Pten deletion in Keratin 18 expressing cells leads to aggressive tumor formation and widespread steatosis in mouse livers. Co-deletion of Vhl and Pten results in lower tumor burden with gene expression profiling suggesting a switch from a profile of lipid deposition to an expression profile more consistent with upregulation of the hypoxia response pathway. A relationship between tumor hypoxia signaling and altered hepatic steatotic response suggests that competing influences may alter tumor phenotypes.
RESUMEN
BACKGROUND: Gene expression signatures have proven to be useful tools in many cancers to identify distinct subtypes of disease based on molecular features that drive pathogenesis, and to aid in predicting clinical outcomes. However, there are no current signatures for kidney cancer that are applicable in a clinical setting. OBJECTIVE: To generate a signature biomarker for the clear cell renal cell carcinoma (ccRCC) good risk (ccA) and poor risk (ccB) subtype classification that could be readily applied to clinical samples to develop an integrated model for biologically defined risk stratification. DESIGN, SETTING, AND PARTICIPANTS: A set of 72 ccRCC sample standards was used to develop a 34-gene classifier (ClearCode34) for assigning ccRCC tumors to subtypes. The classifier was applied to RNA-sequencing data from 380 nonmetastatic ccRCC samples from the Cancer Genome Atlas (TCGA), and to 157 formalin-fixed clinical samples collected at the University of North Carolina. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Kaplan-Meier analyses were performed on the individual cohorts to calculate recurrence-free survival (RFS), cancer-specific survival (CSS), and overall survival (OS). Training and test sets were randomly selected from the combined cohorts to assemble a risk prediction model for disease recurrence. RESULTS AND LIMITATIONS: The subtypes were significantly associated with RFS (p<0.01), CSS (p<0.01), and OS (p<0.01). Hazard ratios for subtype classification were similar to those of stage and grade in association with recurrence risk, and remained significant in multivariate analyses. An integrated molecular/clinical model for RFS to assign patients to risk groups was able to accurately predict CSS above established, clinical risk-prediction algorithms. CONCLUSIONS: The ClearCode34-based model provides prognostic stratification that improves upon established algorithms to assess risk for recurrence and death for nonmetastatic ccRCC patients. PATIENT SUMMARY: We developed a 34-gene subtype predictor to classify clear cell renal cell carcinoma tumors according to ccA or ccB subtypes and built a subtype-inclusive model to analyze patient survival outcomes.