Estrogen-induced gene expression and patient survival in high-grade serous carcinoma of the ovary

Background. Assessment of estrogen receptor (ER) expression has inconsistent utility as a prognostic marker in epithelial ovarian carcinoma. In breast and endometrial cancers, the use of estrogen-induced gene panels, rather than ER expression alone, has shown improved prognostic capability. Specifically, over-expression of estrogen-induced genes in these tumors is associated with a better prognosis and signifies estrogen sensitivity that can be exploited with hormone antagonizing agents. It was therefore hypothesized that estrogen-induced gene expression in ovarian carcinoma would successfully predict outcomes and differentiate between tumors of varying estrogen sensitivities. Methods. Two hundred nineteen (219) patients with ovarian cancer who underwent surgery at M. D. Anderson between 2004 and 2007 were identified. Of these, eighty-three (83) patients were selected for inclusion because they had advanced stage, high-grade serous carcinoma of the ovary or peritoneum, had not received neoadjuvant chemotherapy, and had readily available frozen tissue for study. All patients had also received adjuvant treatment with platinum and taxane agents. The expression of seven genes known to be induced by estrogen in the female reproductive tract (EIG121, sFRP1, sFRP4, RALDH2, PR, IGF-1, and ER) was measured using qRT-PCR. Unsupervised cluster analyses of multiple gene permutations were used to categorize patients as high or low estrogen-induced gene expressors. QPCR gene expression results were then compared to ER and PR immunohistochemical (IHC) expression. Cox proportional hazards models were used to evaluate the effects of both individual genes and selected gene clusters on patient survival. Results. Median follow-up time was 38.7 months (range 1-68 months). In a multivariate model, overall survival was predicted by sFRP1 expression (HR 1.10 [1.02-1.19], p=0.01) and EIG121 expression (HR 1.28 [1.10-1.49], p<0.01). A cluster defined by EIG121 and ER was further examined because that combination appeared to reasonably segregate tumors into distinct groups of high and low estrogen-induced gene expressors. Shorter overall survival was associated with high estrogen-induced gene expressors (HR 2.84 [1.11-7.30], p=0.03), even after adjustment for race, age, body mass index, and residual disease at debulking. No difference in IHC ER or PR expression was noted between gene clusters. Conclusion. In sharp contrast to breast and endometrial cancers, high estrogen-induced gene expression predicts shorter overall survival in patients with high-grade serous ovarian carcinoma. An estrogen-induced gene biomarker panel may have utility as prognostic indicator and may be useful to guide management with estrogen antagonists in this population.^

Single nucleotide polymorphisms (SNPs) associated with TGF-beta pathway and their significance in systemic sclerosis - A multilevel analysis

Systemic sclerosis (SSc) or Scleroderma is a complex disease and its etiopathogenesis remains unelucidated. Fibrosis in multiple organs is a key feature of SSc and studies have shown that transforming growth factor-β (TGF-β) pathway has a crucial role in fibrotic responses. For a complex disease such as SSc, expression quantitative trait loci (eQTL) analysis is a powerful tool for identifying genetic variations that affect expression of genes involved in this disease. In this study, a multilevel model is described to perform a multivariate eQTL for identifying genetic variation (SNPs) specifically associated with the expression of three members of TGF-β pathway, CTGF, SPARC and COL3A1. The uniqueness of this model is that all three genes were included in one model, rather than one gene being examined at a time. A protein might contribute to multiple pathways and this approach allows the identification of important genetic variations linked to multiple genes belonging to the same pathway. In this study, 29 SNPs were identified and 16 of them located in known genes. Exploring the roles of these genes in TGF-β regulation will help elucidate the etiology of SSc, which will in turn help to better manage this complex disease. ^