title: Copy number losses define subgroups of dedifferentiated liposarcoma with poor prognosis and genomic instability.
abstract: OBJECTIVE
  Molecular events underlying progression of well-differentiated liposarcoma (WDLS) to dedifferentiated liposarcoma (DDLS) are poorly defined. This study sought to identify copy number alterations (CNA) associated with dedifferentiation of WDLS, with DDLS morphology, and with patient outcomes.
  
  
  METHODS
  Fifty-five WDLS and 52 DDLS were analyzed using Agilent 244K comparative genomic hybridization and Affymetrix U133A expression arrays. CNAs were identified by RAE analysis. Thirty-nine of the DDLS specimens were categorized morphologically by a single pathologist.
  
  
  RESULTS
  Nine regions of CNA were identified as recurrent in DDLS but not WDLS; 79% of DDLS had at least one of these CNAs. Loss of the chromosome segment 11q23-24, the most common event, was observed only in DDLS that morphologically resembled the genomically complex sarcomas, undifferentiated pleomorphic sarcoma and myxofibrosarcoma. 11q23-24 loss was itself associated with increased genomic complexity in DDLS. Loss of 19q13, but not 11q23-24, was associated with poor prognosis. Median disease-specific survival was shorter for patients with19q13 loss (27 months) than for patients with diploid 19q13 (>90 months; P < 0.0025), and 19q13 loss was associated with local recurrence (HR, 2.86; P = 0.013). Common copy number losses were associated with transcriptional downregulation of potential tumor suppressors and adipogenesis-related genes (e.g., EI24 and CEBPA).
  
  
  CONCLUSIONS
  Dedifferentiation of WDLS is associated with recurrent CNAs in 79% of tumors. In DDLS, loss of 11q23-24 is associated with genomic complexity and distinct morphology whereas loss of 19q13 predicts poor prognosis. CNAs in liposarcoma improve risk stratification for patients and will help identify potential tumor suppressors driving liposarcoma progression.
authors.affiliation: Sarcoma Disease Management Program, Department of Surgery, Bioinformatics Core, Computational Biology Center, Memorial Sloan-Kettering Cancer Center, New York, New York 10065, USA.
authors.forename: Aimee M
authors.initials: AM
authors.lastname: Crago
authors.forename: Nicholas D
authors.initials: ND
authors.lastname: Socci
authors.forename: Penelope
authors.initials: P
authors.lastname: DeCarolis
authors.forename: Rachael
authors.initials: R
authors.lastname: O'Connor
authors.forename: Barry S
authors.initials: BS
authors.lastname: Taylor
authors.forename: Li-Xuan
authors.initials: LX
authors.lastname: Qin
authors.forename: Cristina R
authors.initials: CR
authors.lastname: Antonescu
authors.forename: Samuel
authors.initials: S
authors.lastname: Singer
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D002880:Chromosomes, Human, Pair 11; D002888:Chromosomes, Human, Pair 19; D056915:DNA Copy Number Variations; D018450:Disease Progression; D005260:Female; D016147:Genes, Tumor Suppressor; D042822:Genomic Instability; D006801:Humans; D008080:Liposarcoma / Q000235:genetics* / Q000401:mortality / Q000473:pathology; D008297:Male; D008875:Middle Aged; D011379:Prognosis; D012307:Risk Factors; D016019:Survival Analysis; D055815:Young Adult