Transcript Gene Expression in Lipoma and Liposarcoma

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
1University
of 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 >/=5fold 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