* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
Download C-Type
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
Gene therapy wikipedia , lookup
Ridge (biology) wikipedia , lookup
Point mutation 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
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