Download C-Type

Survey
yes no Was this document useful for you?
   Thank you for your participation!

* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project

Document related concepts

Epigenetics of diabetes Type 2 wikipedia , lookup

Epigenetics of neurodegenerative diseases wikipedia , lookup

Pathogenomics wikipedia , lookup

Frameshift mutation wikipedia , lookup

Biology and consumer behaviour wikipedia , lookup

Genomic imprinting wikipedia , lookup

Mutation wikipedia , lookup

Gene therapy wikipedia , lookup

Ridge (biology) wikipedia , lookup

Point mutation wikipedia , lookup

Gene wikipedia , lookup

Therapeutic gene modulation wikipedia , lookup

Public health genomics wikipedia , lookup

Epigenetics of human development wikipedia , lookup

BRCA mutation wikipedia , lookup

Minimal genome wikipedia , lookup

Polycomb Group Proteins and Cancer wikipedia , lookup

Site-specific recombinase technology wikipedia , lookup

Gene expression programming wikipedia , lookup

Cancer epigenetics wikipedia , lookup

Artificial gene synthesis wikipedia , lookup

Microevolution wikipedia , lookup

Genome evolution wikipedia , lookup

Mir-92 microRNA precursor family wikipedia , lookup

Designer baby wikipedia , lookup

Nutriepigenomics wikipedia , lookup

Gene expression profiling wikipedia , lookup

NEDD9 wikipedia , lookup

Genome (book) wikipedia , lookup

RNA-Seq wikipedia , lookup

Oncogenomics wikipedia , lookup

Transcript
Colorectal cancer intrinsic subtypes are
associated with prognosis,
chemotherapy response, deficient
mismatch repair and epithelial to
mesenchymal transition (EMT)
Josep Tabernero, Vall d’Hebron Hospital
and Iris Simon, Paul Roepman, Andreas Schlicker, Ian Majewski, Victor Moreno,
Christine Chresta, Robert Rosenberg, Ulrich Nitsche, Teresa Macarulla, Gabriel
Capella, Ramon Salazar, George Orphanides, Lodewyk Wessels, Rene Bernards
Disclosure Information
Relationships relevant to this session
Tabernero, Josep
VHIO has received research funding from Agendia
Please note, all disclosures are reported as submitted to ASCO, and are always
available at gicasym.org
Colorectal cancer  different subtypes
• Colorectal cancer is the second leading
cause of cancer death
• Although several treatments exist we do
not have an optimal way to select
treatments for individual patients
• Only KRAS status has been established
as a predictor of anti-EGFR treatment
activity
• New technology platforms allow genetic
definition of different types of cancer
based on gene expression and
characterization
• Unbiased genome-wide analyses of gene
expression patterns have been successful
for molecular classification of BC & GBM
Development Set (stage I-IV) (n=188)
Netherlands Cancer Institute, Leiden Medical Center, Slotervaart
Molecular classification based on nearest centroid single sample predictor
(SSP)  3 gene expression profiles (A, B & C)
Validation
Validation Set (stage II-III) (n=543)
Technical University Munich,
Institut Catala d’Oncologia & Vall d’Hebron Hospital Barcelona,
Medical University Vienna, University of Ferrara
Development
Whole Genome Array
MSI analysis by IHC – All samples
MSI/dMMR gene expression pattern (64 gene signature1) – All samples
All sets
Analysis of mutations in BRAF(V600), KRAS (codons 12, 13 & 61) and PIK3CA
(exons 9 & 20) – All samples
Analysis of 615 (incl. kinome) by NGS – 73 samples
Analysis of Epithelial and Mesenchymal genes – All samples
OS and Distant Metastasis-free survival (DMS) – All samples
Effect of adjuvant treatment – Stage III samples, validation cohort (n=123)
1Tian
S et al. J Pathol. 2012
Development and Validation of the Molecular
Subtype Signature
N
Hospital
Development Cohort
Validation Cohort
188
543
NKI, LUMC, Slotervaart
Vall d’Hebron, ICO
Barcelona, Munich
Stage
I
II
III
IV
24 (13%)
100 (53%)
56 (30%)
8 (4%)
320 (59%)
223 (41%)
-
Gender
F
M
104 (55%)
84 (45%)
226 (42%)
317 (58%)
Subtype
A
B
C
65 (35%)
98 (52%)
25 (13%)
117 (22%)
336 (62%)
90 (16%)
Unsupervised hierarchical clustering of whole
genome reveals 3 distinct patient groups
Gene profiles were developed to identify these subgroups
A-Type
B-Type
C-Type
Unsupervised hierarchical clustering of whole
genome reveals 3 distinct patient groups
Gene profiles were developed to identify these subgroups
A-Type
B-Type
C-Type
(32 genes)
(53 genes)
(102 genes)
Clinical Characterization
PROGNOSIS, MSI AND BENEFIT
FOM ADJUVANT CHEMOTHERAPY
C-Type
B-Type
A-Type
Death
DM Risk
Subtypes are significantly associated with
prognosis
Risk of Distant Metastasis
Risk of Death
Subtypes are significantly correlated with
benefit from adjuvant 5-FU-based treatment
Difference in proliferation between subtypes
might explain difference in treatment benefit
• significantly reduced expression of Ki67 and AURKA in C-type
compared to A- and B-type
– Ki67 p=6.06e-5, AURKA p=4.53e-6
C-Type
B-Type
A-Type
Subtypes differ significantly in mutation and
MSI frequency (Mismatch Repair deficiency)
•
Cancer kinome sequencing (~600 kinases and other cancer related genes)
– high mutation frequency in A and C-type (dMMR)
– B type represent proficient mismatch repair (pMMR)
Subtypes differ significantly in mutation and
MSI frequency (Mismatch Repair deficiency)
•
Cancer kinome sequencing (~600 kinases and other cancer related genes)
– high mutation frequency in A and C-type (dMMR)
– B type represent proficient mismatch repair (pMMR)
Observed mutations in the cancer kinome
Types
Mutated genes
MSI/dMMR
A
36%
68%
B
17%
1%
C
34%
36%
Biological characteristics
EPITHELIAL VS.
MESENCHYMAL
Epithelial-Mesenchymal Transition
EMT markers are differently expressed in subtypes
•
•
•
Mesenchymal markers (higher in C-type)
– VIM, CDH2, FN1, FGFR1, FLT1, TWIST1, AXL, TGFB1
Epithelial markers
– CDH1, CDH3, CLDN9, EGFR, MET
Mesenchymal Character of C-type was confirmed by EMT signature
developed at MDACC (Loboda et al. 2011)
Molecular Subtypes
CONCLUSION
Colon molecular subtype model
Subtype
Clinical features
Biological features
Clinical utility
No adjuvant
or 5FU
Chemotherapy
New targeted
therapy?
(companion Dx)
Acknowledgements
All collaborators and patients
•
•
•
•
•
•
•
•
•
•
Vall d'Hebron Hospital
Agendia
Institut Català d'Oncologia
Technische Universität Munich
Netherland Cancer Institute
Slotervaart Hospital
Leiden Medical Center
Medical University of Vienna
University of Ferrara
COLTHERES, EU-FP7