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Molecular determinants for lymph node metastasis in early-stage endometrial cancer
Abstracts / Gynecologic Oncology 130 (2013) e1–e169
Objective: To identify and validate transcription factors that regulate cisplatin resistance in high-grade serous ovarian cancer. Methods: We assembled a genomewide, regulatory network from The Cancer Genome Atlas (TCGA) ovarian cancer dataset. We further interrogated this dataset by comparing patients who were resistant to cisplatin treatment with those who were sensitive to treatment. Using Master Regulator Interference algorithm (MARINa), we identified master regulators, transcription factors that are differentially active between the 2 groups of patients. MARINa is an algorithm for the unbiased inference of transcription factors that implement a specific cellular phenotype to produce experimentally validated, cell contextspecific maps of molecular interactions. Candidate master regulators were assessed for expression and siRNA-mediated silencing, followed by assessment of cisplatin sensitivity in intrapatient, paired tumor cell lines. The cell lines were derived from high-grade serous ovarian cancer patients with initial platinum sensitivity and subsequent platinum resistance, PEO1/PEO4. Western blot analysis was used to assess putative master regulator expression, and cell viability was measured after cisplatin treatment. Results: The Ovarian Cancer Interactome from TCGA identified 30 potential master regulators of cisplatin resistance and sensitivity. Among other genes, MARINa inferred that PAX2 expression was downregulated in cisplatin resistance. Western blots showed that PAX2 was undetected in PEO4, the cisplatin-resistant cell line, and expressed in PEO1, the cisplatin-sensitive cell line. PAX2 siRNA silencing was confirmed by western blot, and reduction of PAX2 decreased the sensitivity of PEO1, as shown using a cisplatin cell viability assay (Figure). Conclusions: Computational analysis has identified 30 potential regulators or biomarkers of cisplatin sensitivity. Among these, PAX2 is differentially expressed in cisplatin-resistant and -sensitive cell lines, and PAX2 silencing results in acquisition of the resistant phenotype in the PEO1 cell line. We conclude that PAX2 is a potential target for in vivo study of cisplatin resistance in mouse xenografts, and if validated, for study in targeted pharmacologic therapy in humans.
e5
Objective: While the majority of patients with endometrioid type endometrial cancer (EC) present with early-stage disease, a significant subset have occult nodal metastasis and are prone to develop recurrence with worse outcomes. Identified clinicopathologic risk factors for nodal metastasis have low predictive value and are not uniformly applied. Our objective is to add molecular parameters (gene and protein expression) to the existing clinicopathologic criteria to improve lymph node (LN) metastasis prediction. Methods: Using publicly available data on patients with EC collected by The Cancer Genome Atlas, we performed a univariate analysis of differentially expressed genes (n=262), proteins, and clinicopathologic parameters (n=200), including myometrial invasion and tumor grade), comparing EC patients with and without LN metastasis. Only those molecular and pathologic parameters found to be significant in the univariate analysis were introduced in the multivariate model. All independently significant molecular factors were evaluated in a pathway enrichment analysis with MetaCore 6.0 (www.genego.com) to identify biologic processes that may participate in LN invasion in EC. Results: LN metastasis was associated with the expression of 268 unique genes (P=0.001), 19 unique proteins (Pb0.05), tumor grade, and myometrial invasion in univariate analysis. The multivariate analysis demonstrated 10 genes independently associated with LN metastasis in EC (RSI, RNF183, DNER, DUSP9, TEX19, RPS6KA6, FBN3, MUC6, GABRQ, FLJ16779), and 4 independently associated proteins (EF2K, EGFR, PDK1, YB). Myometrial invasion was the only independent clinicopathologic parameter associated with LN status in EC. The enrichment pathway analysis demonstrated that the expression of EGFR, Bcl2 antagonist of cell death, and PTEN pathways (P ≤10-4) to be significantly involved in LN metastasis. A gene expression signature to predict LN status in EC was created for future reference and validation. Conclusions: Few studies have focused on the association between molecular characteristics of EC and the presence of nodal metastasis. Defining molecular risk factors for EC LN metastasis may help to individualize surgical and overall EC treatment and improve outcomes of patients. doi:10.1016/j.ygyno.2013.04.068
10 Separating the good, the bad, and the ugly: New directions in genomic prediction of outcome in ovarian cancer B. Zand, C. Ivan, C. Pecot, R. Rupaimoole, H. Dalton, J. Bottsford-Miller, W. Hu, A. Nick, A. Sood. The University of Texas, MD Anderson Cancer Center, Houston, TX.
doi:10.1016/j.ygyno.2013.04.067
9 Molecular determinants for lymph node metastasis in early-stage endometrial cancer N. Bou Zgheib1, D. Marchion2, I. Ramirez2, P. Teefey1, P. Judson Lancaster2, R. Wenham2, S. Apte2, J. Lancaster2, J. Gonzalez Bosquet2. 1 University of South Florida College of Medicine, Tampa, FL, 2H. Lee Moffitt, Cancer Center, Tampa, FL.
Objective: To identify genomic predictors of overall survival in women with high-grade serous ovarian cancer. Methods: Massive data analysis of The Cancer Genome Atlas dataset of high-grade serous epithelial ovarian cancer was carried out with “training” (n=375) and “validation” (n=188) datasets. A software program code was written specifically to identify genes with high expression that gave the greatest predictive potential for patient survival. The top 10 lowest sums of the validation set were chosen to be included with Kaplan-Meier survival curves for analysis. Results: The 10 genes with high expression that had the smallest P values in their respective order were: SLC6A1, LIPK, EHBP1, SUSD5, PEX3, SLC22A3, RABGEF1, PPM2C, KIAA1219, and GALNT10. The range of P value sum ranged from 2.42E-4 to 9.94E-4. The lowest P value was for SLC6A1, which encodes for GABA transporter-1 (GAT1) that removes GABA from extracellular to intracellular space. Interestingly, 4 of the 10 genes predictive for poor outcome are directly involved in cell metabolism: LIPK, PPM2C, PEX3, and GALNT10. The median overall survival (OS) of the high gene expression group ranged from 28.3 to 41.5 months. The median OS of the low gene expression group ranged
Objective: To identify and validate transcription factors that regulate cisplatin resistance in high-grade serous ovarian cancer. Methods: We assembled a genomewide, regulatory network from The Cancer Genome Atlas (TCGA) ovarian cancer dataset. We further interrogated this dataset by comparing patients who were resistant to cisplatin treatment with those who were sensitive to treatment. Using Master Regulator Interference algorithm (MARINa), we identified master regulators, transcription factors that are differentially active between the 2 groups of patients. MARINa is an algorithm for the unbiased inference of transcription factors that implement a specific cellular phenotype to produce experimentally validated, cell contextspecific maps of molecular interactions. Candidate master regulators were assessed for expression and siRNA-mediated silencing, followed by assessment of cisplatin sensitivity in intrapatient, paired tumor cell lines. The cell lines were derived from high-grade serous ovarian cancer patients with initial platinum sensitivity and subsequent platinum resistance, PEO1/PEO4. Western blot analysis was used to assess putative master regulator expression, and cell viability was measured after cisplatin treatment. Results: The Ovarian Cancer Interactome from TCGA identified 30 potential master regulators of cisplatin resistance and sensitivity. Among other genes, MARINa inferred that PAX2 expression was downregulated in cisplatin resistance. Western blots showed that PAX2 was undetected in PEO4, the cisplatin-resistant cell line, and expressed in PEO1, the cisplatin-sensitive cell line. PAX2 siRNA silencing was confirmed by western blot, and reduction of PAX2 decreased the sensitivity of PEO1, as shown using a cisplatin cell viability assay (Figure). Conclusions: Computational analysis has identified 30 potential regulators or biomarkers of cisplatin sensitivity. Among these, PAX2 is differentially expressed in cisplatin-resistant and -sensitive cell lines, and PAX2 silencing results in acquisition of the resistant phenotype in the PEO1 cell line. We conclude that PAX2 is a potential target for in vivo study of cisplatin resistance in mouse xenografts, and if validated, for study in targeted pharmacologic therapy in humans.
e5
Objective: While the majority of patients with endometrioid type endometrial cancer (EC) present with early-stage disease, a significant subset have occult nodal metastasis and are prone to develop recurrence with worse outcomes. Identified clinicopathologic risk factors for nodal metastasis have low predictive value and are not uniformly applied. Our objective is to add molecular parameters (gene and protein expression) to the existing clinicopathologic criteria to improve lymph node (LN) metastasis prediction. Methods: Using publicly available data on patients with EC collected by The Cancer Genome Atlas, we performed a univariate analysis of differentially expressed genes (n=262), proteins, and clinicopathologic parameters (n=200), including myometrial invasion and tumor grade), comparing EC patients with and without LN metastasis. Only those molecular and pathologic parameters found to be significant in the univariate analysis were introduced in the multivariate model. All independently significant molecular factors were evaluated in a pathway enrichment analysis with MetaCore 6.0 (www.genego.com) to identify biologic processes that may participate in LN invasion in EC. Results: LN metastasis was associated with the expression of 268 unique genes (P=0.001), 19 unique proteins (Pb0.05), tumor grade, and myometrial invasion in univariate analysis. The multivariate analysis demonstrated 10 genes independently associated with LN metastasis in EC (RSI, RNF183, DNER, DUSP9, TEX19, RPS6KA6, FBN3, MUC6, GABRQ, FLJ16779), and 4 independently associated proteins (EF2K, EGFR, PDK1, YB). Myometrial invasion was the only independent clinicopathologic parameter associated with LN status in EC. The enrichment pathway analysis demonstrated that the expression of EGFR, Bcl2 antagonist of cell death, and PTEN pathways (P ≤10-4) to be significantly involved in LN metastasis. A gene expression signature to predict LN status in EC was created for future reference and validation. Conclusions: Few studies have focused on the association between molecular characteristics of EC and the presence of nodal metastasis. Defining molecular risk factors for EC LN metastasis may help to individualize surgical and overall EC treatment and improve outcomes of patients. doi:10.1016/j.ygyno.2013.04.068
10 Separating the good, the bad, and the ugly: New directions in genomic prediction of outcome in ovarian cancer B. Zand, C. Ivan, C. Pecot, R. Rupaimoole, H. Dalton, J. Bottsford-Miller, W. Hu, A. Nick, A. Sood. The University of Texas, MD Anderson Cancer Center, Houston, TX.
doi:10.1016/j.ygyno.2013.04.067
9 Molecular determinants for lymph node metastasis in early-stage endometrial cancer N. Bou Zgheib1, D. Marchion2, I. Ramirez2, P. Teefey1, P. Judson Lancaster2, R. Wenham2, S. Apte2, J. Lancaster2, J. Gonzalez Bosquet2. 1 University of South Florida College of Medicine, Tampa, FL, 2H. Lee Moffitt, Cancer Center, Tampa, FL.
Objective: To identify genomic predictors of overall survival in women with high-grade serous ovarian cancer. Methods: Massive data analysis of The Cancer Genome Atlas dataset of high-grade serous epithelial ovarian cancer was carried out with “training” (n=375) and “validation” (n=188) datasets. A software program code was written specifically to identify genes with high expression that gave the greatest predictive potential for patient survival. The top 10 lowest sums of the validation set were chosen to be included with Kaplan-Meier survival curves for analysis. Results: The 10 genes with high expression that had the smallest P values in their respective order were: SLC6A1, LIPK, EHBP1, SUSD5, PEX3, SLC22A3, RABGEF1, PPM2C, KIAA1219, and GALNT10. The range of P value sum ranged from 2.42E-4 to 9.94E-4. The lowest P value was for SLC6A1, which encodes for GABA transporter-1 (GAT1) that removes GABA from extracellular to intracellular space. Interestingly, 4 of the 10 genes predictive for poor outcome are directly involved in cell metabolism: LIPK, PPM2C, PEX3, and GALNT10. The median overall survival (OS) of the high gene expression group ranged from 28.3 to 41.5 months. The median OS of the low gene expression group ranged