Sarcomas with Aberrant Transcription Factors: Biology and Expression Profiling Marc Ladanyi Memorial Sloan-Kettering Cancer Center New York, NY, USA.
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Sarcomas with Aberrant Transcription Factors: Biology and Expression Profiling
Marc Ladanyi
Memorial Sloan-Kettering Cancer CenterNew York, NY, USA
Translocation-associated sarcomas
1. General biological features and comparison to sarcomas with non-specific cytogenetic alterations
2. Insights from microarray-based expression profiling of translocation-associated sarcomas
Prom oter Substitution
Chim ericTranscription Factor
Chim ericTyrosine Kinase
Gene Fusion
Balanced
Loss o f Tum or Suppressor Gain of Oncogene
Unbalanced
Translocations
Pathologic genetic rearrangements in human cancers: a family tree
Deregulated gene expression
Deregulated growth signaling
Molecular pathology of sarcomas: two major classes
• approx. 1/3 of all sarcomas• 15 different sarcoma types
with over 25 different translocations
• fusion genes: aberrant chimeric transcription factors (most) or aberrant kinases (some)
• biology: transcriptional deregulation or aberrant signaling
1. Sarcomas with specific reciprocal translocations and relatively simple karyotypes
• approx. 2/3 of all sarcomas• biology: genetic gains &
losses, chromosomal instability, telomere dysfunction
2. Sarcomas with complex unbalanced karyotypes
and no specific translocations
Sarcomas with specific
translocations
Sarcomas with non-specific genetic
alterations
Karyotypes Usually simple Usually complex
Translocations Reciprocal & specific, producing fusion genes
Non-reciprocal & non-specific, causing gene copy number changes
Telomere maintenance mechanisms
Telomerase common, ALT mechanism rare
ALT mechanism more common than telomerase
P53 pathway alterations
Relatively rare, but strong prognostic impact*
More frequent, but limited or no prognostic impact
Incidence in P53-mutant or knockout mice
Not observed Common
Incidence in bilateral retinoblastoma and Li-Fraumeni syndrome
Rare Common
Global gene expression profiles
Robust clustering Looser clustering
Molecular pathology of sarcomas: two major classes
* MSKCC study: Genes Chromos Cancer 2004
Evidence of “alternative lengthening of telomeres”
(ALT) telomere maintenance mechanism:
Ewing sarcoma (0/30) vs osteosarcoma (38/60)
(P<0.0001)*
Sarcomas with specific
translocations
Sarcomas with non-specific genetic
alterations
Karyotypes Usually simple Usually complex
Translocations Reciprocal & specific, producing fusion genes
Non-reciprocal & non-specific, causing gene copy number changes
Telomere maintenance mechanisms
Telomerase common, ALT mechanism rare
ALT mechanism more common than telomerase
P53 pathway alterations
Relatively rare, but strong prognostic impact*
More frequent, but limited or no prognostic impact
Incidence in P53-mutant or knockout mice
Not observed Common
Incidence in bilateral retinoblastoma and Li-Fraumeni syndrome
Rare Common
Global gene expression profiles
Robust clustering Looser clustering
Molecular pathology of sarcomas: two major classes
* Hopkins study: AJP 2004
Evidence of “alternative lengthening of telomeres”
(ALT) telomere maintenance mechanism:
Translocation sarcomas (0/9) vs other sarcomas (7/9)
(P=0.002)*
Sarcomas with specific
translocations
Sarcomas with non-specific genetic
alterations
Karyotypes Usually simple Usually complex
Translocations Reciprocal & specific, producing fusion genes
Non-reciprocal & non-specific, causing gene copy number changes
Telomere maintenance mechanisms
Telomerase common, ALT mechanism rare
ALT mechanism more common than telomerase
P53 pathway alterations
Relatively rare, but strong prognostic impact
More frequent, but limited or no prognostic impact
Incidence in P53-mutant or knockout mice
Not observed Common
Incidence in bilateral retinoblastoma and Li-Fraumeni syndrome
Rare Common
Global gene expression profiles
Robust clustering Looser clustering
Molecular pathology of sarcomas: two major classes
* MSKCC study: JCO in press0 20 40 60 80 100 120
0.0
0.2
0.4
0.6
0.8
1.0
P53 mutated (n=8)
No P53 mutation (n=52)
P53 in Ewing sarcoma*
p<0.0001
Sarcomas with specific
translocations
Sarcomas with non-specific genetic
alterations
Karyotypes Usually simple Usually complex
Translocations Reciprocal & specific, producing fusion genes
Non-reciprocal & non-specific, causing gene copy number changes
Telomere maintenance mechanisms
Telomerase common, ALT mechanism rare
ALT mechanism more common than telomerase
P53 pathway alterations
Relatively rare, but strong prognostic impact
More frequent, but limited or no prognostic impact
Incidence in P53-mutant or knockout mice
Not observed Common
Incidence in bilateral retinoblastoma and Li-Fraumeni syndrome
Rare Common
Global gene expression profiles
Robust clustering Looser clustering
Molecular pathology of sarcomas: two major classes
Major fusion genes in sarcomas: biological overview
Sarcoma type Translocation Fusion gene Transcriptional Deregulation
Aberrant Signaling
Ewing sarcoma t(11;22) EWS-FLI1 X
t(21;22) EWS-ERG X
Clear cell sarc. t(12;22) EWS-ATF1 X
Myxoid LPS t(12;16) TLS-CHOP X
Alveolar rhabdomyo-sarcoma
t(2;13) PAX3-FKHR X
t(1;13) PAX7-FKHR X
DSRCT t(11;22) EWS-WT1 X
Extr. myx. CS t(9;22) EWS-CHN X
Synovial sarc. t(X;18) SYT-SSX1,2 X
DFSP t(17;22) COL1A1-PDGFB X
Cong. FS t(12;15) ETV6-NTRK3 X
IMT t(2p23) ALK fusions X
End. str. sarc. t(7;17) JAZF1-JJAZ1 X
ASPS t(X;17) ASPL-TFE3 X
Low grade MFS t(7;16) FUS-BBF2H7 X
Pericytoma t(7;12) ACTB-GLI X
• Slow progress in identifying genuine biologically critical target genes of chimeric transcription factors – e.g. well-established targets:
• EWS-FLI1 <15• EWS-WT1, PAX3-FKHR <5 each
– Low throughput gene-by-gene studies– False leads generated by assays using exogenous target
promoters and/or heterologous cells– Most representative cellular background for inducible
systems still subject of active investigation– Need for more alternative higher throughput approaches
Target Genes of Chimeric Transcription Factors in Sarcomas
Target Genes of Chimeric Transcription Factors in Selected Sarcomas
Tumor Fusion protein Type of DNA BD
Direct or indirect targets
Ewing sarcoma / PNET
EWS-FLI1EWS-ERG
ETS TGFBR2 (), p57KIP2 (), MYC, PDGF-C, ID2, CCND1, UBE2C, IGFBP3()
DSRCT EWS-WT1 Zn finger PDGF-A, IL-2/15R, TALLA1, BAIAP3
Alveolar RMS PAX3-FKHRPAX7-FKHR
Paired box MET, CXCR4
Synovial Sarcoma
SYT-SSX1SYT-SSX2
(none) XRCC4, TLE1
Alveolar Soft Part Sarcoma
ASPL-TFE3 bHLH-LZ CYP17A1, UPP1
Sarcomas with Aberrant Transcription Factors
1. General biological features and comparison to sarcomas with non-specific cytogenetic alterations
2. Insights from microarray-based expression profiling of translocation-associated sarcomas
Expression Profiling of Sarcomas with Chimeric Transcription Factors
Why is it interesting?
1. Translocation-associated sarcomas already have an objective molecular classification (detection of specific translocation) that can be used to evaluate unsupervised clustering based on expression profiles
2. Transcriptional deregulation is likely to be central to the pathogenesis of translocation-associated sarcomas with chimeric transcription factors
Alveolar Rhabdomyosarcoma(ARMS)
23 16 PAX3-FKHR 7 PAX7-FKHR
Desmoplastic Small Round Cell Tumor (DSRCT)
32 32 EWS-WT1
Synovial Sarcoma (SS) 46 25 SYT-SSX121 SYT-SSX2
Ewing Sarcoma/PNET (ES)
38 22 EWS-FLI1 type 111 EWS-FLI1 type 2 5 EWS-ERG
Alveolar Soft Part Sarcoma(ASPS)
14 11 ASPL-TFE3 type 1 3 ASPL-TFE3 type 2
• 137 tumor samples from MSKCC and U.Penn. (F. Barr)• + 4 xenografts + 12 cell lines = 153 total samples• Classification of all samples confirmed by fusion transcript RT-PCR
Expression profiling of sarcomas with chimeric transcription factors
Gene expression data analysis
• Hybridization to Affymetrix U133A GeneChip (22215 probe sets, 18500 transcripts, 14500 genes)
• Unsupervised Hierarchical Clustering
• Selection of differentially expressed genes Two-tailed t-tests; p<0.01 after Bonferroni correction
Raw data processing Clustering Number of genes
MAS v5.0 Cluster/Treeview Subsets
RMA method Pearson correlationAll probe sets (22215)
Unsupervised hierarchical clustering n°1MAS v5.0, Cluster/Treeview, different subsets of most variable probe sets
ARMS SS ES DSRCT SS ES ASPS
7200 probe sets
2200 probe sets
3200 probe sets
ASPS DSRCT ARMS ES SS ES SS
DSRCT ASPS SS ARMS SS ES ARMS
Variability of clustering results according to number of probe sets selected
Unsupervised Hierarchical Clustering n°2
RMA method, Pearson correlation, all 22215 probe sets
ES ASPS DSRCT ARMS ES SS
n=153
celllines
Unsupervised hierarchical clustering n°2
RMA method, Pearson correlation, all 22215 probe sets
ASPS ES SS DSRCT ARMS n=137tumors
only
ES/PNET orphan
case
Ewing’s sarcoma
Alveolar rhabdomyosarcoma
Alveolar soft part sarcoma
Synovial sarcoma
Desmoplastic small round cell tumor
Unsupervised hierarchical clustering Multi-dimensional scaling analysis
n=137
3 different views of same data
Distribution of tumor types and numbers of differentially expressed genes by sarcoma type
Numbers of differentially expressed genes “inflated” by comparison to reference group of remaining four sarcomas, each with strong distinctive expression profile
Samples
Genes significantly over- or under-expressed*
Subset with 2 fold
overexpression*
SS 46 6816 638
ARMS 23 1518 282
DSRCT 28 3163 554
ASPS 12 1590 531
ES/PNET
28 2157 294
* all significant at Bonferroni p<0.01 relative to 4 other sarcoma types
The gene expression profiles of translocation sarcomas contain many previously reported differentially expressed genes
“Literature validation”
Sarcoma Gene P-value Fold change
ARMS MyoD1 10-18 12
FGFR4 10-11 7.7
Myogenin 10-9 4.5
ES MIC2 10-18 2.8
MYC 10-17 5.2
CCND1 10-12 4.5
SS PRAME 10-39 5.6
TLE1 10-33 8.5
FZD1 10-24 8.4
• Differentially expressed genes in sarcomas with chimeric transcription factors– Target genes regulated by fusion protein (direct or indirect) – Genes reflecting pre-existing phenotype of host cell– Genes reflecting secondary genetic or epigenetic alterations
Use of microarray data to predict classification of translocation sarcomas
Multiple-class Prediction using Supervised Methods Support Vector Machine Prediction Rule
Validation set
Creation of a Prediction Model
Predictor/Classifier
Selection of a subset of informative genes for each class
10 fold Cross
validation
Training set
90% 10%
Assess accuracy on the validation set
Performance of microarray-based predictor
136/137 sarcomas were accurately predicted
23 0 0 0 0
0 12 0 0 0
0 0 28 0 0
0 0 0 46 0
1 0 0 0 27
ARM
SAS
PSD
SRC
TSS ES
ARMS
ASPS
DSRCT
SS
ES
Model Prediction
Histological and
Molecular Diagnosis
Similar results with other predictions methods Single misclassified case = orphan case in unsupervised clustering
ES with EWS-FLI1 type 1 fusion + p16 deletion and no PAX-FKHR
– Global analyses / Microarray-based classifier• Translocation sarcomas are associated with very
distinctive gene expression profiles – can be used to classify these sarcomas as accurately
as translocation detection
• Parameters for raw data processing and clustering can have strong effects on unsupervised analyses
– Contribution of chimeric transcription factor target genes to specific expression profiles
Expression Profiling of Sarcomas with Chimeric Transcription Factors
Contribution of chimeric transcription factor target genes to specific expression profiles of translocation sarcomas
Approaches
1. Differential expression of known target genes2. Cross-referencing of profiles from microarray
experiments using inducible cell lines3. Identification of candidates for target gene
analyses based on expression profiling data from primary tumors
The expression profiles of translocation sarcomas contain known target genes of their chimeric transcription factors
ES: EWS-FLI1 targets
ProposedTarget Gene
Reported Effect of EWS-FLI1
Differential expression in ES/PNET (n=38 vs 115)
P-value Fold change
MYC 10-17 5.2ID2 10-9 3.2
CCND1 10-12 4.5UBE2C 10-8 2.7PDGFC 10-9 3.5
The expression profiles of translocation sarcomas contain known target genes of their chimeric transcription factors
DSRCT: EWS-WT1 targets
Proposed Target Gene
Reported Effect of
EWS-WT1
Differential expression in DSRCT (n=32 vs 121)
P-value Fold change
TALLA-1 10-15 5.5
PDGFA 10-14 2.7
IL2RB 10-13 4.6
BAIAP3 10-9 4
EWS-WT1 target genes defined in a heterologous cell line are over-represented among genes
differentially expressed in DSRCTs
• Induction of EWS-WT1 protein expression in U2OS human osteosarcoma cells with tetracycline-inducible EWS-WT1 construct
• Hybridized to U133A chips• W. Gerald Lab, MSKCC
• 102 genes demonstrated at least a 3 fold alteration in expression level at 24h following induction of EWS-WT1 – 22 down-regulated, 80 upregulated
EWS-WT1 target genes defined in a heterologous cell line are over-represented among genes
differentially expressed in DSRCTs
• 17-fold enrichment for EWS-WT1 target genes among genes in the DSRCT expression profile (Chi-square p<0.0001)
• include several previously validated EWS-WT1 targets: – BAIAP3, PDGFA, TALLA1, IL2RB
80 genes upregulated at least 3 fold
553 genes that were 2 fold overexpressed in DSRCT relative to other translocation
sarcomas
U2OS cell experiment Expression profiles of DSRCT tumors
35 genes in common
EWS-WT1 target genes defined in a heterologous cell line are over-represented among genes
differentially expressed in DSRCTs
• 44% of genes upregulated by induction of EWS-WT1 in the U2OS human osteosarcoma cell line were also significantly overexpressed in DSRCTs
• 6% of the DSRCT expression profile corresponds to genes induced by EWS-WT1 in the model system
EWS-WT1 target genes defined in a heterologous cell line are over-represented among genes
differentially expressed in DSRCTs
• 44% of genes upregulated by induction of EWS-WT1 in the U2OS human osteosarcoma cell line were also significantly overexpressed in DSRCTs
• 6% of the DSRCT expression profile corresponds to genes induced by EWS-WT1 in the model system
• Comparison with similar data in Ewing’s sarcoma• Lessnick SL, Dacwag CS, Golub TR. Cancer Cell 1:393-401, 2002
• 46% of the EWS-FLI1-upregulated genes appeared in the ES/PNET expression profile obtained from primary tumors
• 8% of the ES/PNET expression profile corresponded to genes induced by EWS-FLI1 in the model system
Alveolar Soft Part SarcomaTop 20 significantly overexpressed genes
Fold change P-value Gene
121 10-11 CYP17A1
86 10-24 INHBE
75 10-9 MIBP
62 10-37 GPNMB
52 10-28 GOS2
51 10-34 Hs.57548
49 10-9 DEFB1
47 10-11 SULT1C1
47 10-10 UPP1
43 10-11 SULT1C1
43 10-15 SV2B
42 10-9 GPR56
41 10-7 NTSR2
40 10-19 Hs.37189
36 10-7 AGXT2L1
30 10-14 CLI
29 10-16 CLI
27 10-2 3 PTDGS
25 10-29 PTDGS
24 10-39 PTDGS
Ranked by fold-change
Can we use this list to identify new target genes of sarcoma
fusion proteins?
Makoto Nagai, MD PhD
Alveolar Soft Part SarcomaTop 20 significantly overexpressed genes
Fold change P-value Gene
121 10-11 CYP17A1
86 10-24 INHBE
75 10-9 MIBP
62 10-37 GPNMB
52 10-28 GOS2
51 10-34 Hs.57548
49 10-9 DEFB1
47 10-11 SULT1C1
47 10-10 UPP1
43 10-11 SULT1C1
43 10-15 SV2B
42 10-9 GPR56
41 10-7 NTSR2
40 10-19 Hs.37189
36 10-7 AGXT2L1
30 10-14 CLI
29 10-16 CLI
27 10-2 3 PTDGS
25 10-29 PTDGS
24 10-39 PTDGS
Ranked by fold-change
0
5000
10000
15000
20000
25000
30000
35000
40000
45000
1 11 21 31 41 51 61 71 81 91 101 111 121 131 141 151
Cytochrome P450 subfamily 17A1 (CYP17A1)
• Promoter region of CYP17A1 contains 3 potential TFE3 sites
• CYP17A1 promoter strongly activated by ASPL-TFE3, but not native TFE3
• Strong physical binding of ASPL-TFE3 to sites #1 and #3 by EMSA
• In vivo presence of ASPL-TFE3 at CYP17A1 promoter in model cell line (293) by chromatin IP
• Induction of ASPL-TFE3 in model cell line (293) results in upregulation of endogenous CYP17A1 by real-time Q-RT-PCR
Identification of CYP17A1 as a direct target of ASPL-TFE3 based on its strong differential overexpression
in the expression profile of ASPS
Alveolar Soft Part SarcomaTop 20 significantly overexpressed genes
Fold change P-value Gene
121 10-11 CYP17A1
86 10-24 INHBE
75 10-9 MIBP
62 10-37 GPNMB
52 10-28 GOS2
51 10-34 Hs.57548
49 10-9 DEFB1
47 10-11 SULT1C1
47 10-10 UPP1
43 10-11 SULT1C1
43 10-15 SV2B
42 10-9 GPR56
41 10-7 NTSR2
40 10-19 Hs.37189
36 10-7 AGXT2L1
30 10-14 CLI
29 10-16 CLI
27 10-2 3 PTDGS
25 10-29 PTDGS
24 10-39 PTDGS
Ranked by fold-change
Can we do this with another gene from the list?
Alveolar Soft Part SarcomaTop 20 significantly overexpressed genes
Fold change P-value Gene
121 10-11 CYP17A1
86 10-24 INHBE
75 10-9 MIBP
62 10-37 GPNMB
52 10-28 GOS2
51 10-34 Hs.57548
49 10-9 DEFB1
47 10-11 SULT1C1
47 10-10 UPP1
43 10-11 SULT1C1
43 10-15 SV2B
42 10-9 GPR56
41 10-7 NTSR2
40 10-19 Hs.37189
36 10-7 AGXT2L1
30 10-14 CLI
29 10-16 CLI
27 10-2 3 PTDGS
25 10-29 PTDGS
24 10-39 PTDGS
Ranked by fold-change
203234_at gb:NM_003364.1 Uridine phosphorylase (UP)
02000400060008000
1000012000140001600018000
1 11 21 31 41 51 61 71 81 91 101 111 121 131 141 151
Uridine Phosphorylase (UPP1)
• UPP1 promoter more strongly activated by ASPL-TFE3 than by native TFE3
• Induction of ASPL-TFE3 in model cell line (293) results in upregulation of endogenous UPP1, as measured by real-time quantitative RT-PCR
Identification of UPP1 as a direct target of ASPL-TFE3 based on its strong differential overexpression
in the expression profile of ASPS
Identification of UPP1 as a direct target of ASPL-TFE3 based on its strong differential overexpression
in the expression profile of ASPS
• UPP1 promoter more strongly activated by ASPL-TFE3 than by native TFE3
• Induction of ASPL-TFE3 in model cell line (293) results in upregulation of endogenous UPP1, as measured by real-time quantitative RT-PCR
Identification of UPP1 as a direct target of ASPL-TFE3 based on its strong differential
overexpression in the expression profile of ASPS
• UPP1 promoter more strongly activated by ASPL-TFE3 than by native TFE3
• Induction of ASPL-TFE3 in model cell line (293) results in upregulation of endogenous UPP1, as measured by real-time quantitative RT-PCR
• Potential therapeutic interest of uridine phosphorylase – converts the pyrimidine
analogue, 5'-deoxy-5'fluorouridine, to 5-FU
– allows administration of 5'-deoxy-5'fluorouridine as a prodrug with low toxicity to non-neoplastic cells expressing only basal levels of uridine phosphorylase
Identification of potential target genes of SYT-SSX based on strong differential overexpression in the
expression profile of synovial sarcoma
Top 5 ranked by p-value
TLE1 (transducin-like enhancer of split 1) a transcriptional repressor of
Wnt/-catenin signaling
Tsuyoshi Saito, M.D. Ph.D.
See Pathology Poster # 5(not in printed program)
– Global analyses / Microarray-based classifier– Contribution of chimeric transcription factor
target genes to specific expression profiles• Significant subsets of genes in specific expression
profiles may be chimeric transcription factor target genes
• Specific expression profiles can be used to identify new candidates for target gene analyses
Expression Profiling of Sarcomas with Chimeric Transcription Factors
Kinases as therapeutic targets in sarcomas with chimeric transcription factors
• kinases or signaling pathways directly activated by the specific aberrant transcription factor
Tumor Fusion protein Signaling proteins confirmed or proposed to be upregulated
Ewing sarcoma /PNET
EWS-FLI1EWS-ERG
PIM3
Alveolar RMS PAX3-FKHRPAX7-FKHR
MET
Desmoplastic small round cell tumor
EWS-WT1 PDGF-A, FGFR4
Kinases as therapeutic targets in sarcomas with chimeric transcription factors
• kinases or signaling pathways directly activated by the specific aberrant transcription factor
• kinases or signaling pathways overexpressed in specific sarcomas apparently unrelated to direct action of aberrant transcription factor
• ERBB2/Her2/neu in synovial sarcoma
• EGFR in synovial sarcoma
• KIT in Ewing’s sarcoma• kinases activated by mutations as secondary or
cooperating events in sarcomas with aberrant transcription factors (like FLT3 mutations in leukemias)
• none identified so far• chimeric transcription factors regulated by
phosphorylation • EWS-WT1
MSKCC • Ladanyi Lab
– Tsuyoshi Saito – Makoto Nagai – Marick Laé– Violetta Barbashina– Man Yee Lui– Zhiquan Zhao
• Biostatistics– Adam Olshen– Shannon Chuai
• W. Gerald Lab– William Gerald– Lishi Chen
• Other MSKCC collaborators– Cristina Antonescu– John Healey, Murray Brennan, Sam Singer– Paul Meyers, Len Wexler, Robert Maki
• Johns Hopkins– Pete Argani
Expression profiling of sarcomas with aberrant transcription factors and related studies
• U. Pennsylvania – Fred Barr
• U. Michigan – Larry Baker– Dafydd Thomas
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