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					 Use of gene expression to identify
heterogeneity of metastatic behavior
among high-grade pleomorphic soft
          tissue sarcomas
     Keith Skubitz1, Princy Francis2,
     Amy Skubitz1, Xianghua Luo1,
            and Mef Nilbert2,3

           1Universityof Minnesota,
               2Lund University,

              3Hvidovre Hospital
Sarcomas are heterogeneous

• Heterogeneity of biological behavior
  exists even within histologic
  subtypes of sarcomas, complicating
  clinical care, clinical trials, and drug
  development.
             Example
• Assume treatment A has no adverse
  effect
• Assume benefit of treatment A is all
  or none in a certain percentage of
  patients
• Some biological behaviors that do
  not correlate well with morphology
  may be determined by gene
  expression patterns
• A common approach to identify prognostic
  factors is to search for differences in gene
  expression between 2 groups defined by an
  outcome (eg survival)
  – Requires defining 2 groups
  – Irrelevant genes may obscure important
    patterns
  – Different genes could be important in different
    subsets
• Alternatively, identification of subsets
  independent of clinical information could be
  useful
• We used PCA with a variety of gene sets in
  an attempt to identify heterogeneity
  – Clear cell renal carcinoma (RCC)
  – Serrous ovarian carcinoma (OVCA)
  – Aggressive fibromatosis (AF)
PCA with 604 probes up or down >/=5-
  fold in ccRCC vs normal kidney



                                 B
PCA with probes from ubiquitylation in
    control of cell cycle pathway



     A
• Gene expression patterns that distinguished
  2 subsets of RCC (RCC gene set), OVCA
  (OVCA gene set), and AF (AF gene set) were
  identified
                Question

• Do the RCC-, OVCA-, and AF-gene sets
  identify subsets of high-grade pleomorphic
  STS?
              Samples

• 73 Samples obtained from Lund University

• 40 MFH
• 20 LMS
• 9 other high-grade pleomorphic STS
               Data

• cDNA microarray slides with ~16,000
  unique UniGene clusters

• About 50% of the genes in the RCC-,
  OVCA-, and AF- gene sets were
  present in this data set
              Methods
• Data were pooled to form a set of 234
  genes present in at least one of the
  RCC-, OVCA-, or AF-gene sets

• Hierarchical clustering using this
  gene set was performed
Hierarchical Clustering
Hierarchichal Clustering



1     2    3          4
       Important Caveats
• Clustering pattern depends on
  composition of sample set

• Many types of clustering and ways to
  modify data
            Conclusions
• Analysis of a set of STS using a gene set
  derived from other tumor systems without
  regard to clinical data, identified
  differences in time to metastasis

• Thus, an approach to subcategorizing
  samples before searching for variables
  that correlate with clinical behavior may
  be useful
             Conclusions
• Although no confirmation of clinical
  relevance is available, stratifying patients
  entering trials by a similar approach could
  be useful, and would not result in loss of
  information
             Conclusions
• Although no confirmation of clinical
  relevance is available, stratifying patients
  entering trials by a similar approach could
  be useful, and would not result in loss of
  information
• Banked samples should be obtained for all
  STS patients entering clinical trials for
  later analysis

				
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posted:6/13/2011
language:English
pages:23