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MOLECULAR CLASSIFICATION OF COLON CANCER: IS IT READY FOR CLINICAL PRACTICE?. Dr Omer Dizdar Hacettepe University Cancer Institute 6 th International Gastrointestinal Cancer Conference, 7-9 Dec 2018, Istanbul.
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MOLECULAR CLASSIFICATION OF COLON CANCER: IS IT READY FOR CLINICAL PRACTICE? Dr Omer Dizdar Hacettepe University Cancer Institute 6th International Gastrointestinal Cancer Conference, 7-9 Dec 2018, Istanbul
CRC carcinogenesis is the result of a stepwise accumulation of genetic events in oncogenes and tumor suppressor genes
ONCOGENIC DRIVERS:Ras MUTATION • More than 50% of CRC tumors • Resistance to anti-EGFR mabs.
BRAF mutation • 8% of patients with mCRC • Resistance to anti-EGFR Regimens and significantly worse survival • Targeted therapies evolving • BEACON TRIAL: Encorafinib + binimetinim + cetuximab ORR 48% • BRAF non-V600E mutations do not negatively affect patient prognosis
HER2 amplifications • 2% in unselected patients • Primary resistance to anti-EGFR agents in the refractory setting • Response to HER2-targeted agents in mCRC
MET Amplifications • Fewer than 2% of primary CRC tumors • ctDNA NGS in patients refractory to anti-EGFR treatment: as high as 20% • Resistance to anti-EGFR agents and BRAF inhibitor combinations
Kinase Fusions • RAS and BRAF wild-type CRC cells resistant to EGFR blockade • Functionally “addicted” to other kinase genes, including ALK , ROS1 , NTRK1 , NTRK2 , NTRK3 , and RET • Case reports of exceptional responses to the ALK and TRK inhibitor entrectinibin fusion positive mCRC
Microsatellite instability • The prevalence of MSI in the mCRC is 5% • Most patients have sporadic MLH1 loss via promoter methylation or biallelic somatic genomic alterations
Hypermutated Many neoantigens • High infiltration of MSI tumors with CD8(+) cytotoxic T lymphocytes and activated Th1 cells • Susceptible to immune checkpoint inhibitors
Overall response rate with nivolumab alone was 31% and with the combination regimen was 55%, with a 1-year PFS rate of 71%. • Responses rates not influenced by PD-L1 expression, BRAF mut. or genetic basis for MMR deficiency
POLE mutation • MSS but has the highest mutation rates in CRC high neoantigenloads and tumor-infiltratinglymphocytes • Fewer than 1% of patients with early stage CRC • Favorable prognosis • Case report of response to pembrolizumab
“MSI-like” gene expression signature • MSS (+) • Up to 10% of mCRC cases display an “MSI-like” phenotype • High mutation load • Response to checkpoint inhibitors?
Mesenchymal or “TGFβ-Active” Signature • 25%–30% in mCRC • Reduced sensitivity to standard chemotherapies and anti-EGFR drugs • TGFβ-signaling inhibitors (galunisertib)? • Chaperone (HSP90) inhibitors? • Trials ongoing
Between 2012 and 2014, 6 different classification systems developed in CRC
Differences in • Discovery cohort patients • Gene exp. profiling platforms • Bioinformatics analyses and data interpretation Discrepent subtypes
Colorectal cancer subtyping consortium (CRCSC) identifies consensus molecular subtypes Presented By Rodrigo Dienstmann at 2014 ASCO Annual Meeting
How many CRC subtypes? Presented By Rodrigo Dienstmann at 2014 ASCO Annual Meeting
CRC Subtyping Consortium Presented By Rodrigo Dienstmann at 2014 ASCO Annual Meeting
Population Presented By Rodrigo Dienstmann at 2014 ASCO Annual Meeting
Population Presented By Rodrigo Dienstmann at 2014 ASCO Annual Meeting
Slide 9 Presented By Rodrigo Dienstmann at 2014 ASCO Annual Meeting
How to define a Consensus Molecular Subtype (CMS)? Presented By Rodrigo Dienstmann at 2014 ASCO Annual Meeting
Results - network of subtypes Presented By Rodrigo Dienstmann at 2014 ASCO Annual Meeting
Results - network of samples Presented By Rodrigo Dienstmann at 2014 ASCO Annual Meeting
Results – distribution of subtypes Presented By Rodrigo Dienstmann at 2014 ASCO Annual Meeting
Results – clinical correlates Presented By Rodrigo Dienstmann at 2014 ASCO Annual Meeting
Results – clinical and molecular correlates Presented By Rodrigo Dienstmann at 2014 ASCO Annual Meeting
Results – molecular correlates Presented By Rodrigo Dienstmann at 2014 ASCO Annual Meeting
Results – mutation profile (n=2,386) Presented By Rodrigo Dienstmann at 2014 ASCO Annual Meeting
Results – pathway analysis (n=3,891) Presented By Rodrigo Dienstmann at 2014 ASCO Annual Meeting
Summary – clinical and molecular correlates Presented By Rodrigo Dienstmann at 2014 ASCO Annual Meeting
Slide 22 Presented By Rodrigo Dienstmann at 2014 ASCO Annual Meeting
Slide 23 Presented By Rodrigo Dienstmann at 2014 ASCO Annual Meeting
Slide 24 Presented By Rodrigo Dienstmann at 2014 ASCO Annual Meeting
Making Sense of Consensus Molecular Subtypes (CMS) Presented By Wells Messersmith at 2017 ASCO Annual Meeting
Abstracts Discussed Presented By Wells Messersmith at 2017 ASCO Annual Meeting
Colorectal Cancer Trial Designs Presented By Wells Messersmith at 2017 ASCO Annual Meeting
CMS subtype frequency Presented By Wells Messersmith at 2017 ASCO Annual Meeting
CMS and Sidedness Presented By Wells Messersmith at 2017 ASCO Annual Meeting
CMS and clinical features Presented By Wells Messersmith at 2017 ASCO Annual Meeting
Agreement with CMS (2015 publication) Presented By Wells Messersmith at 2017 ASCO Annual Meeting
Agreement with CMS (2015 publication) Presented By Wells Messersmith at 2017 ASCO Annual Meeting
Cetuximab vs Bevacizumab Presented By Wells Messersmith at 2017 ASCO Annual Meeting
FOLFIRI cetuximab vs. FOLFIRI bevacizumab FIRE3 Presented By Sebastian Stintzing at 2017 ASCO Annual Meeting
CMS subtypes in 3 large clinical trials recapitulate 2015 classification by Guinney et al. • Prognostic • NOT predictive for FOLFOX vs FOLFIRI • NOT predictive for cetuximabvsbevacizumab except for CMS1 • Overlap between CMS and current clinical subgroups not perfect (RAS, BRAF, MSI)
CMS- CHALLENGES • Transcriptomic subtyping alone cannot capture heterogenity of disease • Multi-omic data (DNA methylation, proteomics) more accurate classification • Consensus with emphasis on the role of individual genesLack of incorporation of biological knowledge • Effective bioinformatic integration of multi-omic data for classification • Gene expression profiling affected by technical platform variations