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Prediction of response and survival following chemotherapy in patients with advanced bladder cancer

ABSTRACT: BACKGROUND Cisplatin-containing chemotherapy is the standard of care for patients with locally advanced and metastatic transitional cell carcinoma of the urothelium. The response rate is around 50% and tumor derived molecular prognostic markers are needed for estimation of response and survival. METHODS Affymetrix GeneChip expression profiling was carried out using tumor material from 30 patients. A set of genes having an expression with high correlation to survival time after chemotherapy was identified. Two of these genes were selected for validation by immunohistochemistry (IHC) in tumor tissue from 149 cisplatin treated patients having complete follow-up data. RESULTS Fifty-five differentially expressed genes correlated significantly to survival time. Two of these (Emmprin and Survivin) were validated using IHC, and multivariate analysis (n=145) identified Emmprin expression (hazard ratio 2.38; p<0.0001) and Survivin expression (hazard ratio 2.34; p<0.0001) as independent prognostic markers for poor outcome, together with the presence of visceral metastases (hazard ratio 2.72; p<0.0001). In the good prognosis group of patients without visceral metastases, both markers showed significant discriminating power as supplemental risk factors (p<0.0001). Within this group of patients, the subgroups of patients with no positive, one positive or two positive IHC stainings (Emmprin and Survivin) had estimated 5-year survival rates (+- SE) of 35.6+-?%, 6.3+-?%, and 0+?%, respectively. Response to chemotherapy could also be predicted with an OddsRatio of 4.60 (2.13-9.93) and 2.59 (1.25-5.38) for Emmprin and Survivin respectively. CONCLUSION Emmprin and Survivin proteins were identified as strong independent prognostic predictors for response and survival after cisplatin-containing chemotherapy in patients with bladder cancer. Keywords: Analysis of gene expression differences between responders and non-responders to chemotherapy

ORGANISM(S): Homo sapiens

PROVIDER: GSE5287 | GEO | 2007/08/01

SECONDARY ACCESSION(S): PRJNA96407

REPOSITORIES: GEO

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Project description:BACKGROUND; Cisplatin-containing chemotherapy is the standard of care for patients with locally advanced and metastatic transitional cell carcinoma of the urothelium. The response rate is around 50% and tumor derived molecular prognostic markers are needed for estimation of response and survival. METHODS; Affymetrix GeneChip expression profiling was carried out using tumor material from 30 patients. A set of genes having an expression with high correlation to survival time after chemotherapy was identified. Two of these genes were selected for validation by immunohistochemistry (IHC) in tumor tissue from 149 cisplatin treated patients having complete follow-up data. RESULTS; Fifty-five differentially expressed genes correlated significantly to survival time. Two of these (Emmprin and Survivin) were validated using IHC, and multivariate analysis (n=145) identified Emmprin expression (hazard ratio 2.38; p<0.0001) and Survivin expression (hazard ratio 2.34; p<0.0001) as independent prognostic markers for poor outcome, together with the presence of visceral metastases (hazard ratio 2.72; p<0.0001). In the good prognosis group of patients without visceral metastases, both markers showed significant discriminating power as supplemental risk factors (p<0.0001). Within this group of patients, the subgroups of patients with no positive, one positive or two positive IHC stainings (Emmprin and Survivin) had estimated 5-year survival rates (+- SE) of 35.6+-?%, 6.3+-?%, and 0+?%, respectively. Response to chemotherapy could also be predicted with an OddsRatio of 4.60 (2.13-9.93) and 2.59 (1.25-5.38) for Emmprin and Survivin respectively. CONCLUSION; Emmprin and Survivin proteins were identified as strong independent prognostic predictors for response and survival after cisplatin-containing chemotherapy in patients with bladder cancer. Experiment Overall Design: Tumors from 30 patients with advanced bladder cancer used in the study. A SAM analysis was used for identifying genes co-varying with treatment response.

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:summary Ovarian cancer is the leading cause of death among gynecologic malignancies. This is partly due to a non-durable response to chemotherapy. Prediction of resistance to chemotherapy could be a key role in more personalized treatment. In the current study we aimed to examine if microRNA based predictors could predict resistance to chemotherapy in ovarian cancer, and to investigate if the predictors could be prognostic factors for progression free and overall survival. Methods Predictors of chemotherapy-resistance were developed based on correlation between miRNA expression and differences in measured growth inhibition in a variety of human cancer cell lines in the presence of Carboplatin, Paclitaxel and Docetaxel (deposited under accession number E-MTAB-327 in the ArrayExpress database of the European Bioinformatics Institute, 2012). These predictors were then, retrospectively, blindly validated in a cohort of 170 epithelial ovarian cancer patients treated with Carboplatin and Paclitaxel or Docetaxel as first line treatment. Results In a multivariate cox proportional analysis the predictors of chemotherapy-resistance were not able to predict time to progression after end of chemotherapy (hazard ratio: 0.64, 95% CI: 0.36 – 1.12, P=0.117). However, in a multivariate logistic analysis, where time to progression was considered as either more or less than 6 months, the predictors match clinical observed chemotherapy-resistance (odds ratio: 0.19, 95% CI: 0.05-0.73, P=0.015). Neither univariate nor multivariate, time-dependent, cox analysis for progression free survival (PFS) or overall survival (OS) in all 170 patients showed to match predicted resistance to chemotherapy (PFS: hazard ratio: 0.69, 95% CI: 0.40-1.19, P=0.183, OS: hazard ratio: 0.76, 95% CI: 0.42-1.40, P=0.386). Conclusion In the current study, microRNA based predictors of chemotherapy-resistance did not demonstrate any convincing correlation to clinical observed chemotherapy-resistance, progression free survival, or overall survival, in patients with epithelial ovarian cancer. However the predictors did reflect relapse more or less than 6 months.

Project description:TP53 mutations are a poor prognostic factor in breast cancers. This study sets out to identify the gene set that determine expression signature of the TP53 status (TP53 signature) and to correlate it with clinical outcome. Using comprehensive expression analysis and DNA sequencing of the TP53 gene in 38 Japanese breast cancer patients, we have isolated a gene set of 33 genes from differentially expressed genes in the learning set (n=26), depending on the TP53 status. Predictive accuracy of TP53 status by gene expression profile was 83.3% in the test set (n=12). As independent external datasets, two published datasets were introduced for validation of TP53 status prediction (251 Swedish samples) and survival analysis (both the Swedish and 295 Dutch samples). TP53 signature has the ability to predict recurrence-free survival (RFS) of 29 stage I and II Japanese breast cancers (log rank, P = 0.032), and RFS, overall survival of two independently published datasets (log rank, both P < 0.0001). Multivariate analysis has shown an independent and significant prognostic factor with a hazard ratio (HR) for recurrence and survival in two external datasets (P < 0.0001). The TP53 signature is also a strong prognostic factor in the subgroups: estrogen-receptor positive, lymph node (LN) positive and negative, intermediate/high risk in St. Gallen criteria, and high risk in National Cancer Institute (NCI) criteria (log rank, P < 0.0001). TP53 signature is a reliable and independent predictor of the outcome of disease in operated breast cancer. Experiment Overall Design: Microarray hybridizations (Agilent: Whole Human Genome Oligo Microarray; 41k unique probe) were carried out with 1Î¼g Cy3 labeled cRNA and 1 Î¼g Cy5 labeled cRNA, both prepared from each sample and reference pool, respectively. Experiment Overall Design: Fluorescent intensities of scanned images were quantified by ArrayVision Ver.8 rev.4 (Imaging research). Experiment Overall Design: The gene expression data was quantified and analyzed by GeneSpring 6.2 (Silicon Genetics). To identify the TP53 status predictor gene set, a Wilcoxon rank sum test along with Benjamini and Hochberg false discovery rate (FDR) was used.

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Prediction of response and survival following chemotherapy in patients with advanced bladder cancer

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