Download Sample pairwise correlation

Use of Samples in Research Rhabdomyosarcomas
Janet Shipley
Sarcoma Molecular Pathology Team
The Institute of Cancer Research
Sutton, UK
Childhood Cancers
~ 1,500 new cases in UK p.a.
1
1% Liver
2 3% Germ cell
3 3% Eye
4 5% Bone
5 6% Wilms
6 6% Soft tissue
7 7% Neuroblastoma
8 14% Lymphoma
9 18% CNS
10 30% Leukaemia
11 7% Other
6-8% soft tissue sarcomas (1% in adults)
> 50% rhabdomyosarcoma (RMS)
~ 70 new cases in UK p.a.
Clinical issues:
• Overall survival rhabdomyosarcomas (RMS) ~70%
• Certain categories and metastatic disease - dismal
• Major cause from cancer death in children
• Toxicity leads to survivorship issues
Rhabdomyosarcoma (RMS) histology
• Small round blue cell tumours
• Resemble developing skeletal muscle
• Two main histological subtypes:
– Embryonal (ERMS)
– Alveolar (ARMS)
Embryonal RMS (ERMS) (60-80% of RMS cases)
• Cells resemble
embryonic
skeletal muscle
• Predominant in younger
children
• Generally good
prognosis
ERMS genetics
Ploidy changes and aneuploidy (2, 8, 12, 13, 17 and 20)
- chromosomal instability CIN
Abnormalities of 11p:
Increased IGF2 expression through:
- Loss of heterozygosity (LOH)
80% affects IGF2, H19 and CDKN1C (p57KIP2) loci
(duplication of paternal non-silenced locus)
- Loss of imprinting (LOI) – 20%
(loss of maternal IGF2 imprinting)
- RAS mutations including HRAS at 11p
Alveolar RMS (ARMS) (20-40% of RMS cases)
• Older children
• Poor prognosis
• Characteristic translocations
ARMS genetics
• Ploidy changes, aneuploidy and amplification events
• TP53 mutations
• LOI loss of maternal silencing of IGF2 - biallelic expression
H19 affects IGF2 imprinting
• Characteristic chromosome translocations present in
most, but not all cases
Characteristic translocations present in ~70% of ARMS
– ~80% of which t(2;13)(q35;q14) PAX3/FOXO1
– ~20% t(1;13)(p36;q14) PAX7/FOXO1 and rare variants
FOXO1
PAX
Paired DNA
binding domain
Homeodomain
Transactivation
domain
Survival from rhabdomyosarcoma in GB, 1991-2000
Fig 5.13 Rhabdomyosarcoma, by subtype
Survival of patients diagnosed 1991-2000
% still alive
100
80
60
40
20
Embryonal N=341
Unspecified N=72
Alveolar N=123
0
0
1
2
3
4
5
6
7
8
9 10
Years since diagnosis
11
12
13
14
15
Charles Stiller, CCLG data
Use of PAX-FOXO1 Fusion vs Histology
in clinical stratification
• PAX fusion gene status has been used for years as
“unofficial” diagnostic aid
• Current and past treatment stratifications incorporate
histology into risk management strategies
• The definition of Alveolar histology changed in the 90s
(from majority to any)
• Differentiating Alveolar from Embryonal involves finding
histological evidence of alveolar spaces (with the
exception of solid alveolar)
As 30% of RMS with alveolar histology
do not appear to have fusion gene
Q: Clinical and biological
impact of fusion gene status
and histology
Williamson et al 2010 JCO
Criteria for Inclusion
• Patients with RMS all stages less than 21 years old, both sexes
• RMS diagnosed or treated in France or UK (through CCLG) between
1989 and 2005 - SIOP protocols
• Review of histological diagnosis of RMS alveolar and embryonal
according to morphology and immunohistochemistry by members of
the French/UK panel of pathologists
Analyses
• Molecular analysis of a representative sample by the
Institut Curie or ICR for presence of PAX3/FOXO1,
PAX7/FOXO1 or PAX3/NCOA1 by multiplex RT-PCR
• DNA array CGH profiling
• Gene expression profiling (Affymetrix Plus 2)
 Issue of RNA quality critical – rapid snap freezing
Used in Survival Analysis
ARMSp
ARMSn
ERMS
No. of Patients
Median Age (Years)
Sex
Male
Female
Site of Disease
Parameningeal
Limb
Genitourinary
Head and Neck
Bladder/Prostate
Orbit
Other
Unknown
Favorable/Unfavorable
Sites
No. of Patients
% Favorable
SIOP Stage
I
II
III
IV
NA
Size
<=5cm
>5cm
NA
Used in Microarray Analysis
ARMSp
ARMSn
ERMS
Used in CGH Analysis
ARMSp
ARMSn
ERMS
94
7.0
39
4.5
77
5.0
45
7.1
20
6.0
36
6.8
50
8.6
27
5.8
51
5.4
53
41
21
18
48
29
21
24
11
9
22
14
24
26
14
13
33
18
13
35
5
9
0
4
19
9
12
2
9
2
1
7
5
1
16
3
13
5
7
12
13
8
10
17
0
4
0
2
9
3
9
1
7
0
0
2
1
0
8
2
6
3
3
7
7
0
12
19
0
4
0
1
10
4
12
3
6
0
1
4
1
-
12
6
6
3
4
9
11
-
18/67
18/20
30/39
6/36
9/11
16/20
5/41
10/17
18/33
21
47
43
14
45
44
10
37
35
21
12
16
43
2
10
10
7
8
4
24
34
6
12
1
9
9
5
20
2
8
5
4
3
-
10
18
1
7
-
8
8
7
25
2
7
10
4
6
-
15
24
3
8
1
10
28
56
13
12
14
17
16
44
12
23
10
11
8
1
15
15
6
11
27
12
13
13
1
20
22
9
Overall and Event Free Survival
Cox Regression – Risk of
Recurrence
Relative Risks of Recurrence
Variable
Univariate model
RR (95%CI)
Multivariate model
p-value
RR (95%CI)
p-value
Fusion
NEG
1.00
1.00
4 10-9
POS
2.89 (1.99-4.17)
6 10-4
3.0 (1.6-5.6)
Histology
ERMS
1.00
ARMS
2.03 (1.36-3.02)
4 10-4
1.00
0.51
0.8 (0.4-1.5)
Stage
1-3
1.00
1.00
1
4
2.3 (1.58-3.35)
10-5
1.7 (1.2-2.6)
 Fusion gene positive cases greater risk of recurrence
0.005
Cox Regression – Risk of Death
Relative Risk of Death
Variable
Univariate model
RR (95%CI)
Multivariate model
p-value
RR (95%CI)
p-value
Fusion
NEG
1.00
1.00
3
POS
10-10
3.85 (2.46-6.04)
0.012
2.5 (1.2-5.1)
Histology
7 10-6
ERMS
1.00
1.00
ARMS
3.04 (1.83-5.06)
1.3 (0.6-2.9)
1.00
1.00
0.48
Stage
1-3
7 10-9
4
3.55 (2.31-5.45)
2.6 (1.7-4.1)
 Fusion gene positive cases greater risk of death
2 10-5
Frequency of Metastases
Expression profiling of 101 RMS
Negative Matrix Factorisation (NMF) - Metagenes
•
ARMSp
ERMS
– 101
This
Study
• 69 Alveolar
• 37 Embryonal
•
ARMSn
• 64 Alveolar
• 55 Embryonal
Davicioni
et al
•
HGU133a – Wachtel et al
– 30
ERMS
ERMS
HGU133a – Davicioni et al
– 118
ARMS
ARMSn
ARMSp
ERMS
ARMSn
ARMS
HGU133 plus 2 Our Study
• 15 Alveolar
• 15 Embryonal
Wachtel
et al
Laé et al
•
HGU133a – Laé
– 38
• 23 Alveolar
• 15 Embryonal
Negative Matrix Factorisation (NMF) - Metagenes
Training
Test
ARMSp
ARMSn
ERMS
Supervised Analysis - Support Vector Machine Classification
Training Set
Class
Count
No Call
No Call (%)
Real Error
Real Error
(%)
Correct
Call
Correct Call
(%)
ARMSp
45
0
0
1
2
44
98
ARMSn
20
2
10
17
94
1
6
ERMS
36
0
0
1
3
35
97
Total
101
2
2
19
19
80
81
Count
No Call
No Call (%)
Real Error
Real Error
(%)
Correct
Call
Correct Call
(%)
ARMSp
83
1
1.2
1
1.2
81
99
ARMSn
18
0
0
18
100
0
0
ERMS
85
2
2.4
4
4.7
79
95
Total
186
4
1.7
37
17.7
186
82.3
Test Set
Class
Supervised Analysis – SAM (Significance Analysis Microarray)
DNA analysis - ArrayCGH – 128 RMS
Gain of Chromosome 8 is Characteristic of Fusion
Negative RMS
Expression
Copy number
Chromosome 8 genes are enriched in
Metagene F2 linked to fusion neg cases
Highly correlated with
F2 metagene
Highly anti-correlated with
F2 metagene
Metagenes associated with outcome
•Davicioni et al MG34
• New metagene we derived, less efficient in their dataset
- overfitting
• Heavy association with fusion gene status, PAX3-FOXO1 versus PAX7-FOXO1 cases
PAX3-FOXO1 versus PAX7-FOXO1 cases
• Similar gene expression profiles
• Predictive metagenes linked to PAX3 v PAX7-FOXO1
• Direct comparison?
- COG study, PAX7-FOXO1 better outcome
- German study, no difference
- Limited numbers
Pilot data
N=450 from MMT89, 95 , 98
Plus current EpSSG cases
PAX3-FOXO1 fusion
dual-color assay
5’ PAX3
3’ FOXO1
Telomeric Probes (BACs)
Centromeric Probes (BACs)
RP11-81I18
RP11-452K11
RP11-16P6
RP11-805F18
PAX3-FOXO1
RP11-612G6
RP11-350A18
Chimeric der(13)
t(2;13) (q35,q14)
Normal
PAX3-FOXO1
PAX7-FOXO1 fusion
dual color assay
5’ PAX7
3’ FOXO1
Telomeric Probes (BACs)
Centromeric Probes (BACs)
RP11-468N9
RP11-452K11
CTD-2009F7
RP11-805F18
PAX7-FOXO1
RP11-121A23
RP11-350A18
Chimeric der(13)
t(1;13) (p36;q14)
Normal
PAX7-FOXO1
Plus RT-PCR analyses where possible
Conclusions 1
• PAX fusion negative ARMS clinically and
molecularly indistinguishable from ERMS
• Fusion negative RMS characterised by a distinct
and common expression signature including
chromosome 8 gain
• Implications for the ongoing risk stratification
strategies in current RMS treatment protocols under versus over treatment
PLANS:
• Prospective study to assess classifier
• Additional/refinement of potential
prognostic markers
• Identify and validate presence of potential
therapeutic targets
Thanks to…
INSERM U830 Institut Curie
Olivier Delattre
Daniel Williamson
Gaelle Pierron
Benedicte Thuille
Stephanie Reynaud
Départment de Pédiatrie,
Institut Curie
Daniel Orbach
Gilles Palenzuela
Pathology Dept. Institut Curie
Paul Fréneaux
Marick Laé
Ligue Nationale Contre le
Cancer
Aurélien de Reyniès
Manchester Children’s Hospital
Anna Kelsey
Swiss Bioinformatics Institute
Edoardo Missiaglia
GOS
Kathy Pritchard-Jones
Department of Pediatric and Adolescent
Oncology,
Institut Gustave Roussy
Odile Oberlin
Children's Cancer and
Leukaemia Group