Download Gene signatures: What`s new to predict outcome and drug

Gene signatures: What’s new to
predict outcome and drug sensitivity?
Christos Sotiriou, MD, PhD
Jules Bordet Institute
Université Libre de Bruxelles (ULB)
Brussels, Belgium
What’s new
(1)Molecular taxonomy and prognosis
(2)Drug sensitivity (chemo, targeted
agents)
(3)Future directions (RNA seq)
Gene prognostic signatures:
Genomic Grade Index
H/I + MGI
Mammaprint
Oncotype DX
Ma et al. Cancer Cell. 2004
Sotiriou et al. J Natl Cancer Inst. 2006
Paik et al. NEJM, 2004
Van’t Veer et al. Nature,2002
Population: Untreated
Tissue: Fresh/Frozen
Population: Tamoxifen-treated
Tissue: FFPE
May add additional information to current clinico-pathological
parameters for treatment decision making for some patients
Recurrence score (TransATAC)
TAM
Postmenopausal
ER+, No chemo
R
Anastrazole
Anastrazole+ TAM
TAM (N=609)
Anastrazole (N=622)
Dowsett M et al, JCO 2010
(1) Proliferation = driving force
Proliferation
Genes
Non-Proliferation
Genes
Sotiriou and Pusztai, N Engl J Med 2009
Desmedt et al. Clin Cancer Res, 2008
Wirapati et al. Breast Cancer Res, 2008
Sotiriou and Piccart Nat Rev Cancer, 2007
Most published signatures
are not significantly better
outcome predictors than
random signatures of
identical size…
Venet et al. PLoS Comput Biol. 2011
Proliferation score
(2) Informative for ER+/HER2- BC
High proliferative
high risk tumors
(luminal B)
low proliferative
low risk tumors
(luminal A)
ER/HER2-
HER2+
ER+/HER2Sotiriou and Pusztai, N Engl J Med 2009
Desmedt et al. Clin Cancer Res, 2008
Wirapati et al. Breast Cancer Res, 2008
Sotiriou and Piccart Nat Rev Cancer, 2007
(3) Currently
defined
molecular
subtypes are
mainly driven by
ER, HER2 and
proliferation
Basal-like
(ER-/HER2)
HER2 +
Luminal (ER+)
Normal-like
Perou et al., Nature 2000
Intrinsic
ER/HER2/
classification proliferation
Comparison in 5715 breast cancer
samples (36 datasets)
Haibe-Kains et al., JNCI 2012
Clinical relevance –
prognosis
PAM 50
Intrinsic classification
Single genes (mRNA)
ER, HER2, Aurka
ER/HER2/proliferation
Modules
Haibe-Kains et al., JNCI 2012
Clinical utility?
Molecular classification (PAM50) vs
1st generation prognostic signatures vs
IHC?
Attend the next guidelines
session…
Basallike
HER2 +
Luminal B
Luminal A
Next
Steps…
Identify molecular
drivers
Patients
selection
for clinical
trials
Methods
Discovery set
-Primary BC
(N=997)
-Matched normal
(N=258)
Validation set
-Primary BC
(N=995)
METABRIC
consortium
Illumina HT-12
(gene expression)
Affymetrix SNP 6.0
(CAN, CNV, SNPS)
Integration of genomic and
transcriptomic analysis of
breast cancer
(1) Germline and somatic variants
influence breast tumor expression
architecture
(39% 11,198/28609 probes )
• Cis = a variant at a locus has an impact on its own expression
• Trans = a variant at a locus is associated with genes at other sites in the genome
(2) Patterns of cis outlying expression
refine putative breast cancer drivers
(3) Integrative clustering reveals
10 novel IntClust molecular subgroups beyond
the intrinsic subtypes
Clinical
outcome
*
*
*
*
*
*
* = good outcome
* = poor outcome
What is the clinical utility of the
Intclust classification today?
• Limited (prognosis)
• Potential to identify targetable
drivers in the future
• NGS will provide additional
information
What’s new
(1)Molecular taxonomy and prognosis
(2)Drug sensitivity (chemo, targeted
agents)
(3)Future directions (RNA seq)
Pooled analysis of gene expression studies to predict
neoadjuvant (taxanes and/or anthracyclines)
chemotherapy response (pCR)
Several molecular
Processes
and molecular
pathways
? Response to chemotherapy
M Ignatiadis JCO 2012
All N= 845 pts
M Ignatiadis JCO 2012
ER-/HER2-
Different
processes/pathways
are associated with
pCR in different BC
subtypes
HER2+
ER+/HER2-
Main message
Chemotherapy sensitivity
=
tumor microenvironment
matters!
What about targeted agents?
• High prevalence of PIK3CA
mutation in ER+ BC
(irrespective of molecular
subtypes LumA vs LumB)
• PIK3CA mutation = better
clinical outcome (low
mTORC1 signaling)
Does PIK3CA GS predicts for response
to PI3K pathway inhibitors ?
Baselga et al.
Neoadjuvant study
Letrozole (N=27)
vs
Letrozole +
Everolimus (N=31)
Sabine et al.
Pre-surgical window study
Everolimus 15 days
(N=23)
Total 81 patients used for this analysis
Collaboration with Novartis and John Bartlett
The PIK3CA-GS is associated with anti-proliferative
response to Everolimus (Baselga et al. dataset)
Interaction p test= 0.02
Letrozole + Everolimus
Interaction p test= 0.02
Letrozole
Unpublished data
The PIK3CA-GS is associated with anti-proliferative
response to Everolimus by PIK3CA genotype
PIK3CA wild type
PIK3CA mutation
Interaction p test= 0.05
Interaction p test= 0.07
Letrozole + Everolimus
Letrozole
Unpublished data
What’s new
(1)Molecular taxonomy and prognosis
(2)Drug sensitivity (chemo)
(3)Future directions (RNA seq)
Rational for RNA-Seq
1. Study of the entire transcriptome
2. It can identify small non-synonymous
mutations that alter protein-coding
sequencing (1-2% of whole genome)
3. Splice variants, gene fusions
4. It can identify RNA editing events
Belgian National Initiative
N = 60 BC + normal breast
Exome seq
RNA seq
(+Affymetrix)
Methylation
Infinium 450K
(1) Excellent correlation between
Affymetrix and RNA-seq
2.0
Correlation for all genes
AFFY vs ILLUMINA RNA−seq
1.0
0.5
0.0
Density
1.5
75% genes correlate at 0.82
−1.0
−0.5
0.0
0.5
Spearman correlation
Quantiles5%: −0.025; 25%: 0.41; 50%: 0.69; 75%: 0.82; 95%: 0.92
1.0
(2) Identification of the 4 main
molecular subtypes
(3) Lack of correlation between Allred
score and ER mRNA transcripts…
RNA seq = Higher dynamic range
HER2
Lum B
Basal
Lum A
(4) Good correlation between HER2
copy number and mRNA levels
Basal
Lum A
HER2
Lum B
(5) Some
expression
basedsignatures
could be
reproduced
GGI using RNA-seq data
GGI
AFFYMETRIX vs ILLUMINA RNA−seq
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Low−risk
High−risk
Discordance
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GGI scores (ILLUMINA RNA−seq)
2
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Spearman correlation 0.98
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−1
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GGI scores (AFFY)
Spearman correlation: 0.98
2
Mammaprint using RNA-seq data
MAMMAPRINT
AFFYMETRIX vs ILLUMINA RNA−seq
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−0.6
MAMMAPRINT scores (ILLUMINA RNA−seq)
0.2
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Low−risk
High−risk
Discordance
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Spearman correlation 0.97
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−0.4
−0.2
0.0
MAMMAPRINT scores (AFFY)
Spearman correlation: 0.97
0.2
0.4
Many other things…
1. Study of the entire transcriptome
2. It can identify small non-synonymous
mutations that alter protein-coding
sequencing (1-2% of whole genome)
3. Splice variants, gene fusions
4. It can identify RNA editing events
Messages
1. Novel molecular classification
- Clinical utility?
- Potential to identify targetable drivers in the future…
2. Tumor microenvironment matters for
chemotherapy response!
3. Predict response to targeted agents may be easier
(i.e. PIK3CA signature).
3. RNA-seq: lot of promises…
Sotiriou’s Translational lab
Christine Desmedt
Michail Ignatiadis
Sherene Loi
Françoise Rothé
Marion Maetens
Debora Fumagalli
Hatem Azim
Stefan Michiels
David Brown
Sandeep Singhal
Vinu Jose
Laurence Buisseret
Samira Majjaj
Naïma Kheddoumi
Ghizlane Rouas PierreYves Adnet
Delphine Vincent
Laurence Simon
Dominique Roels
Martine Piccart (IJB)
Major Collaborators
IRIBHM:
Vincent Detours
David Gacquer
Marc Abramowicz
ULB-Epigenetics:
François Fuks
Sarah Dedeurwaerder
Montreal University:
Benjamin Haibe-Kains
Immunology lab-IJB:
Karen Willard-Gallo
MDACC:
Lajos Pusztai
Fraser Symmans
Les Amis de
l'Institut Bordet
MEDIC
Foundation
Sanger:
Peter Campbell
Mike Stratton
Sancha Martin
UCL UK:
Charles Swanton
Institut Gustave Roussy:
Fabrice Andre
ER+ = 8 pts; HER2+ = 8 pts
TNBC = 8 pts
131 fusion transcripts
Cancer Res. 2012 Apr 15;72(8):1921-8.
86 fusion
transcripts
PRIVATE
45 fusion
transcripts
REDUNDANT