Unknown,Transcriptomics,Genomics,Proteomics

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Immune-activation as a therapeutic direction for patients with high-risk ovarian cancer based on gene expression signature (2)

ABSTRACT: Advanced-stage ovarian cancer is one of the most lethal gynecologic malignancies. To improve prognosis of patients with ovarian cancers, a predictive biomarkers leading to personalized treatments are required. In this large-scale cross-platform study of six microarray datasets consisting of 1054 ovarian cancer patients, we developed a novel risk classification system based on a 126-gene expression signature for predicting overall survival by applying elastic net7 and 10-fold cross validation to a Japanese dataset A (n = 260). We further validated its predictive ability with the five other datasets using multivariate analysis. Also, through gene ontology and pathway analyses of 1109 high-risk ovarian cancer specific transcripts, we identified a significant reduction of expression of immune-response related genes, especially on the antigen presentation pathway. Furthermore, an immunohistochemical analysis demonstrated that the number of CD8 T lymphocytes infiltrating into tumor tissue was significantly decreased in high-risk ovarian cancers. These predictive biomarkers based on the 126-gene expression signature will identify high-risk ovarian cancer patients who need novel immune-activating therapeutic approaches, leading to improved outcomes for such patients. 40 patients who were diagnosed as advanced-stage high-grade serous ovarian cancer were recruited in this study.

ORGANISM(S): Homo sapiens

SUBMITTER: Kosuke Yoshihara

PROVIDER: E-GEOD-32063 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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High-risk ovarian cancer based on 126-gene expression signature is uniquely characterized by downregulation of antigen presentation pathway.

Yoshihara Kosuke K Tsunoda Tatsuhiko T Shigemizu Daichi D Fujiwara Hiroyuki H Hatae Masayuki M Fujiwara Hisaya H Masuzaki Hideaki H Katabuchi Hidetaka H Kawakami Yosuke Y Okamoto Aikou A Nogawa Takayoshi T Matsumura Noriomi N Udagawa Yasuhiro Y Saito Tsuyoshi T Itamochi Hiroaki H Takano Masashi M Miyagi Etsuko E Sudo Tamotsu T Ushijima Kimio K Iwase Haruko H Seki Hiroyuki H Terao Yasuhisa Y Enomoto Takayuki T Mikami Mikio M Akazawa Kohei K Tsuda Hitoshi H Moriya Takuya T Tajima Atsushi A Inoue Ituro I Tanaka Kenichi K

Clinical cancer research : an official journal of the American Association for Cancer Research 20120112 5

<h4>Purpose</h4>High-grade serous ovarian cancers are heterogeneous not only in terms of clinical outcome but also at the molecular level. Our aim was to establish a novel risk classification system based on a gene expression signature for predicting overall survival, leading to suggesting novel therapeutic strategies for high-risk patients.<h4>Experimental design</h4>In this large-scale cross-platform study of six microarray data sets consisting of 1,054 ovarian cancer patients, we developed a ...[more]

PMID: 22241791

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Project description:To identify and evaluate the prognostic ability of progression-free survival-related profile for advanced-stage ovarian cancer, advanced-stage serous ovarian cancer tissues from 110 patients who received primary surgery and a platinum/taxane-based chemotherapy were profiled using oligonucleotide microarrays of more than 40,000 transcripts. We first selected 88 genes by a univariate Cox proportional hazard analysis (p<0.01) and next optimized regression coefficients by ridge regression model using 10-fold cross-validation. The prognostic index was independently associated with PFS time compared to other clinical factors in multivariate analysis [hazard ratio (HR), 3.72; 95% confidence interval (CI), 2.66–5.43; p,0.0001]. In an external dataset, multivariate analysis revealed that this prognostic index was significantly correlated with PFS time (HR, 1.54; 95% CI, 1.20–1.98; p = 0.0008). Furthermore, the correlation between the prognostic index and overall survival time was confirmed in the two independent external datasets (log rank test, p = 0.0010 and 0.0008). In multivariable analysis, our prognostic index was independently associated with progression-free survival times compared to other clinical factors (p<0.001). Furthermore, the prognostic ability of our prognostic index was validated in external, publicly available dataset (n = 87), and was proved in multivariate analysis (p = 0.0032). These results suggest that disease progression or recurrence of advanced-stage serous ovarian cancer can be predicted by gene expression profile. The prognostic ability of our index based on the 88-gene expression profile in ridge regression Cox hazard model was shown to be independent of other clinical factors in predicting cancer prognosis across two distinct datasets. Further study will be necessary to improve predictive accuracy of the prognostic index toward clinical application for evaluation of the risk of recurrence in patients with advanced-stage serous ovarian cancer.

Project description:Advanced ovarian cancer is the most lethal gynecologic malignancy in the United States. Currently patients are treated by surgical cytoreductive surgery with the aim of reducing tumor burden to microscopic disease followed by adjuvant combined treatment with a platinum and taxane containing chemotherapy, which affords 80% of patients an initial complete response. However, Abdominal and pelvic recurrence rates are high and response to further chemotherapy is limited. Attempts at introducing biologic therapeutic agents to improve outcome in this disease are ongoing, while prognostic or predictive biomarkers that can stratify patients for treatment are still lacking. Using a 60-mer 22K oligonucleotide-based array comparative genome hybridization (CGH) platform combined with DNA isolated from microdissected tumor tissue samples, Birrer et. al. reported that the amplification of 5q31-35.3 in ovarian cancer cells is a negative prognostic indicator for patients with advanced stage high-grade serous ovarian cancer (HGSC). Further studies showed that fibroblast growth factor 1 (FGF1) located in the amplicon, may be one of the driving genes for ovarian cancer progression (Birrer et. al., 2007). Besides FGF1, located on the same amplicon is one of its receptors fibroblast growth factor receptor 4 (FGFR4), suggesting that it may also be amplified and may be another driving gene involved in ovarian cancer pathogenesis.In this study, we used microarrays to explore and compare gene expression profiles between FGFR4 knock down ovarian cancer cell lines and their corresponding parental cell lines. Two high grade serous ovarian cancer cell lines: SKOV3 and OVCA432 were selected for this study. For each of the cell lines, two different siRNA sequences which both target human FGFR4 were used. While a non-target scramble siRNA sequence was used as control. Ovarian cancer cells were transfected with siRNA using lipofectamine (Invitrogen). RNA extraction was performed 72 hours post-transfection, followed by hybridization on Affymetrix microarrays.

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Dataset Information

Immune-activation as a therapeutic direction for patients with high-risk ovarian cancer based on gene expression signature (2)

Publications

High-risk ovarian cancer based on 126-gene expression signature is uniquely characterized by downregulation of antigen presentation pathway.

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