Carboplatin-Induced Gene Expression Changes In Vitro are Prognostic of Survival in Epithelial Ovarian Cancer

Background: We performed a time-course microarray experiment to define the transcriptional response to carboplatin in vitro, and to correlate this with clinical outcome in epithelial ovarian cancer (EOC). RNA was isolated from carboplatin and control-treated 36M2 ovarian cancer cells at several time points, followed by oligonucleotide microarray hybridization. Carboplatin induced changes in gene expression were assessed at the single gene as well as at the pathway level. Clinical validation was performed in publicly available microarray datasets using disease free and overall survival endpoints. Results: Time-course and pathway analyses identified 317 genes and 40 pathways (designated time-course and pathway signatures) deregulated following carboplatin exposure. Both types of signatures were validated in two separate platinum-treated ovarian and NSCLC cell lines using published microarray data. Expression of time-course and pathway signature genes distinguished between patients with unfavorable and favorable survival in two independent ovarian cancer datasets. Among the pathways most highly induced by carboplatin in vitro, the NRF2, NF-kB, and cytokine and inflammatory response pathways were also found to be upregulated prior to chemotherapy exposure in poor prognosis tumors. Conclusion: Dynamic assessment of gene expression following carboplatin exposure in vitro can identify both genes and pathways that are correlated with clinical outcome. The functional relevance of this observation for better understanding the mechanisms of drug resistance in EOC will require further evaluation

Analysis of Multiple Sarcoma Expression Datasets: Implications for Classification, Oncogenic Pathway Activation and Chemotherapy Resistance

Background: Diagnosis of soft tissue sarcomas (STS) is challenging. Many remain unclassified (not-otherwise-specified, NOS) or grouped in controversial categories such as malignant fibrous histiocytoma (MFH), with unclear therapeutic value. We analyzed several independent microarray datasets, to identify a predictor, use it to classify unclassifiable sarcomas, and assess oncogenic pathway activation and chemotherapy response. Methodology/Principal Findings: We analyzed 5 independent datasets (325 tumor arrays). We developed and validated a predictor, which was used to reclassify MFH and NOS sarcomas. The molecular “match” between MFH and their predicted subtypes was assessed using genome-wide hierarchical clustering and Subclass-Mapping. Findings were validated in 15 paraffin samples profiled on the DASL platform. Bayesian models of oncogenic pathway activation and chemotherapy response were applied to individual STS samples. A 170-gene predictor was developed and independently validated (80-85% accuracy in all datasets). Most MFH and NOS tumors were reclassified as leiomyosarcomas, liposarcomas and fibrosarcomas. “Molecular match” between MFH and their predicted STS subtypes was confirmed both within and across datasets. This classification revealed previously unrecognized tissue differentiation lines (adipocyte, fibroblastic, smooth-muscle) and was reproduced in paraffin specimens. Different sarcoma subtypes demonstrated distinct oncogenic pathway activation patterns, and reclassified MFH tumors shared oncogenic pathway activation patterns with their predicted subtypes. These patterns were associated with predicted resistance to chemotherapeutic agents commonly used in sarcomas. Conclusions/Significance: STS profiling can aid in diagnosis through a predictor tracking distinct tissue differentiation in unclassified tumors, and in therapeutic management via oncogenic pathway activation and chemotherapy response assessment

Integrated Analysis of Multiple Microarray Datasets Identifies a Reproducible Survival Predictor in Ovarian Cancer

Background Public data integration may help overcome challenges in clinical implementation of microarray profiles. We integrated several ovarian cancer datasets to identify a reproducible predictor of survival. Methodology/Principal Findings Four microarray datasets from different institutions comprising 265 advanced stage tumors were uniformly reprocessed into a single training dataset, also adjusting for inter-laboratory variation (“batch-effect”). Supervised principal component survival analysis was employed to identify prognostic models. Models were independently validated in a 61-patient cohort using a custom array genechip and a publicly available 229-array dataset. Molecular correspondence of high- and low-risk outcome groups between training and validation datasets was demonstrated using Subclass Mapping. Previously established molecular phenotypes in the 2nd validation set were correlated with high and low-risk outcome groups. Functional representational and pathway analysis was used to explore gene networks associated with high and low risk phenotypes. A 19-gene model showed optimal performance in the training set (median OS 31 and 78 months, p<0.01), 1st validation set (median OS 32 months versus not-yet-reached, p = 0.026) and 2nd validation set (median OS 43 versus 61 months, p = 0.013) maintaining independent prognostic power in multivariate analysis. There was strong molecular correspondence of the respective high- and low-risk tumors between training and 1st validation set. Low and high-risk tumors were enriched for favorable and unfavorable molecular subtypes and pathways, previously defined in the public 2nd validation set. Conclusions/Significance Integration of previously generated cancer microarray datasets may lead to robust and widely applicable survival predictors. These predictors are not simply a compilation of prognostic genes but appear to track true molecular phenotypes of good- and poor-outcome

Carboplatin-induced gene expression changes in vitro are prognostic of survival in epithelial ovarian cancer

<p>Abstract</p> <p>Background</p> <p>We performed a time-course microarray experiment to define the transcriptional response to carboplatin <it>in vitro</it>, and to correlate this with clinical outcome in epithelial ovarian cancer (EOC). RNA was isolated from carboplatin and control-treated 36M2 ovarian cancer cells at several time points, followed by oligonucleotide microarray hybridization. Carboplatin induced changes in gene expression were assessed at the single gene as well as at the pathway level. Clinical validation was performed in publicly available microarray datasets using disease free and overall survival endpoints.</p> <p>Results</p> <p>Time-course and pathway analyses identified 317 genes and 40 pathways (designated time-course and pathway signatures) deregulated following carboplatin exposure. Both types of signatures were validated in two separate platinum-treated ovarian and NSCLC cell lines using published microarray data. Expression of time-course and pathway signature genes distinguished between patients with unfavorable and favorable survival in two independent ovarian cancer datasets. Among the pathways most highly induced by carboplatin <it>in vitro</it>, the NRF2, NF-kB, and cytokine and inflammatory response pathways were also found to be upregulated prior to chemotherapy exposure in poor prognosis tumors.</p> <p>Conclusion</p> <p>Dynamic assessment of gene expression following carboplatin exposure <it>in vitro </it>can identify both genes and pathways that are correlated with clinical outcome. The functional relevance of this observation for better understanding the mechanisms of drug resistance in EOC will require further evaluation.</p

Integrated Analysis of Multiple Microarray Datasets Identifies a Reproducible Survival Predictor in Ovarian Cancer

BACKGROUND: Public data integration may help overcome challenges in clinical implementation of microarray profiles. We integrated several ovarian cancer datasets to identify a reproducible predictor of survival. METHODOLOGY/PRINCIPAL FINDINGS: Four microarray datasets from different institutions comprising 265 advanced stage tumors were uniformly reprocessed into a single training dataset, also adjusting for inter-laboratory variation ("batch-effect"). Supervised principal component survival analysis was employed to identify prognostic models. Models were independently validated in a 61-patient cohort using a custom array genechip and a publicly available 229-array dataset. Molecular correspondence of high- and low-risk outcome groups between training and validation datasets was demonstrated using Subclass Mapping. Previously established molecular phenotypes in the 2(nd) validation set were correlated with high and low-risk outcome groups. Functional representational and pathway analysis was used to explore gene networks associated with high and low risk phenotypes. A 19-gene model showed optimal performance in the training set (median OS 31 and 78 months, p < 0.01), 1(st) validation set (median OS 32 months versus not-yet-reached, p = 0.026) and 2(nd) validation set (median OS 43 versus 61 months, p = 0.013) maintaining independent prognostic power in multivariate analysis. There was strong molecular correspondence of the respective high- and low-risk tumors between training and 1(st) validation set. Low and high-risk tumors were enriched for favorable and unfavorable molecular subtypes and pathways, previously defined in the public 2(nd) validation set. CONCLUSIONS/SIGNIFICANCE: Integration of previously generated cancer microarray datasets may lead to robust and widely applicable survival predictors. These predictors are not simply a compilation of prognostic genes but appear to track true molecular phenotypes of good- and poor-outcome