title: Analysis of multiple sarcoma expression datasets: implications for classification, oncogenic pathway activation and chemotherapy resistance.
abstract: 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.
  
  
  RESULTS
  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
  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.
authors.affiliation: Division of Hematology/Oncology, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA.
authors.forename: Panagiotis A
authors.initials: PA
authors.lastname: Konstantinopoulos
authors.forename: Elena
authors.initials: E
authors.lastname: Fountzilas
authors.forename: Jeffrey D
authors.initials: JD
authors.lastname: Goldsmith
authors.forename: Manoj
authors.initials: M
authors.lastname: Bhasin
authors.forename: Kamana
authors.initials: K
authors.lastname: Pillay
authors.forename: Nancy
authors.initials: N
authors.lastname: Francoeur
authors.forename: Towia A
authors.initials: TA
authors.lastname: Libermann
authors.forename: Mark C
authors.initials: MC
authors.lastname: Gebhardt
authors.forename: Dimitrios
authors.initials: D
authors.lastname: Spentzos
mesh_terms: D001499:Bayes Theorem; D016000:Cluster Analysis; D030561:Databases, Nucleic Acid; D019008:Drug Resistance, Neoplasm / Q000235:genetics; D005103:Expert Systems; D015972:Gene Expression Regulation, Neoplastic; D015894:Genome, Human; D006801:Humans; D016571:Neural Networks, Computer; D020411:Oligonucleotide Array Sequence Analysis; D012509:Sarcoma / Q000145:classification* / Q000188:drug therapy / Q000235:genetics*; D012983:Soft Tissue Neoplasms / Q000145:classification