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dc.contributor.authorKonstantinopoulos, Panagiotis
dc.contributor.authorFountzilas, Elena
dc.contributor.authorGoldsmith, Jeffrey David
dc.contributor.authorBhasin, Manoj
dc.contributor.authorPillay, Kamana
dc.contributor.authorFrancoeur, Nancy
dc.contributor.authorLibermann, Towia Aron
dc.contributor.authorGebhardt, Mark Clyde
dc.contributor.authorSpentzos, Dimitrios
dc.date.accessioned2011-03-27T20:09:42Z
dc.date.issued2010
dc.identifier.citationKonstantinopoulos, Panagiotis A., Elena Fountzilas, Jeffrey D. Goldsmith, Manoj Bhasin, Kamana Pillay, Nancy Francoeur, Towia A. Libermann, Mark C. Gebhardt, and Dimitrios Spentzos. 2010. Analysis of multiple sarcoma expression datasets: Implications for classification, oncogenic pathway activation and chemotherapy resistance. PLoS ONE 5(4): e9747.en_US
dc.identifier.issn1932-6203en_US
dc.identifier.urihttp://nrs.harvard.edu/urn-3:HUL.InstRepos:4774189
dc.description.abstractBackground: 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.en_US
dc.language.isoen_USen_US
dc.publisherPublic Library of Scienceen_US
dc.relation.isversionofdoi:10.1371/journal.pone.0009747en_US
dc.relation.hasversionhttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC2848563/pdf/en_US
dash.licenseLAA
dc.subjectoncologyen_US
dc.subjectsarcomasen_US
dc.subjectgenetics and genomicsen_US
dc.subjectgenomicsen_US
dc.subjectgene expressionen_US
dc.subjectbioinformaticsen_US
dc.titleAnalysis of Multiple Sarcoma Expression Datasets: Implications for Classification, Oncogenic Pathway Activation and Chemotherapy Resistanceen_US
dc.typeJournal Articleen_US
dc.description.versionVersion of Recorden_US
dc.relation.journalPLoS ONEen_US
dash.depositing.authorKonstantinopoulos, Panagiotis
dc.date.available2011-03-27T20:09:42Z
dash.affiliation.otherHMS^Medicine- Beth Israel-Deaconessen_US
dash.affiliation.otherHMS^Medicine- Beth Israel-Deaconessen_US
dash.affiliation.otherHMS^Medicine- Beth Israel-Deaconessen_US
dc.identifier.doi10.1371/journal.pone.0009747*
dash.contributor.affiliatedKonstantinopoulos, Panagiotis
dash.contributor.affiliatedGebhardt, Mark
dash.contributor.affiliatedSpentzos, Dimitrios
dash.contributor.affiliatedGoldsmith, Jeffrey
dash.contributor.affiliatedLibermann, Towia


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