Unravelling the Molecular Taxonomies of Gastroesophageal Cancers
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1 Unravelling the Molecular Taxonomies of Gastroesophageal Cancers Patrick Tan, MD PhD Professor, Duke-NUS Medical School Deputy Executive Director, Biomedical Research Council, Agency for Science, Technology and Research (A*STAR) ESMO World Congress on Gastrointestinal Cancer Barcelona, June 2018
2 Molecular and Clinical Heterogeneity in Gastric Cancer Tay et al., (2003) Cancer Research Tay et al., Cancer Research (2003)
3 TCGA Study (~3-4 Major GC Genomic Subtypes) A) Chromosomal Instability (CIN) B) Microsatellite Instability (MSI) C) Genome Stable (GS) D) Epstein-Barr Virus (EBV) USA TCGA (2014) Nature
4 Today s Topics 1) What are the most Frequent Genetic Events in Gastric Cancers? - Mutations in the *Non-Coding* Genome 2) What are the Key Determinants for Development of Gastric Cancer? - Pre-Malignant Condition (Intestinal Metaplasia)
5 Genetic Mutations in GC Have Largely Focused on Protein Coding Genes Amaro Taylor-Weiner (USA TCGA)
6 Genetic Mutations in GC Have Largely Focused on Protein Coding Genes Amaro Taylor-Weiner (USA TCGA)
7 Non-Coding Driver Mutations are Still Unknown for Many Cancer Types (Including GC) Driver Mutations are Mutated More Frequently than the Background Mutation Rate
8 Whole-Genome Analysis of Gastric Cancer Uniform Mutation Calling on 212 GC WGS Anders Skanderup, GIS
9 Many Factors (Covariates) Affect Whole-Genome Mutation Rates Mutation Count Single-Nucleotide Variants (SNVs) Indels Mutation Count
10 Statistically Significant Non-Coding Mutation Hotspots are Enriched in CTCF-Binding Sites (CBSs) ** Significance Remains Even After Correcting for Elevated CBS Mutation Rates
11 CTCF: Regulator of 3D Genome Organization Wikipedia
12 CBS Hotspot Mutations are enriched in GCs with copy-number instability (CIN) Overall CBS Hotspot Mutation Frequency = 25% TP53 Mutations = 50% ARID1A Mutations = 14%
13 CBS hotspot mutations are associated with local chromosomal breaks
14 CBS hotspots Frequently and Specifically Mutated in Gastrointestinal Cancers Top-4 Gastric Colorectal Liver Pancreas
15 Clinical Use-Case : Use of Non-coding Hotspots in Liquid Biopsies Gene Sensitivity (%) Size (bp) Sensitivity / bp Cumulative Sensitivity KRAS % Hotspots % TP % RHOA % Cancer cfdna fraction very low in blood Non-coding hotspots represent densely mutated regions (=TP53) Disease monitoring of gastrointestinal cancers
16 Summary Slide Whole-genome analysis of GC reveals frequent mutations at CTCF Binding Sites (CBSs) CBS Hotspots are significantly mutated even after adjusting for covariates, consistent with positive selection CBS Hotspot Mutations Occur at Frequencies Exceeded Only by TP53 Mutations, in GCs with Chromosomal Instability CBS Hotspot Mutations are Associated with Local Chromosomal Breaks and Regional Changes in Gene Expression CBS Hotspot Mutations are Specifically Observed in Gastrointestinal Malignancies Guo et al., (2018) Nature Communications
17 Today s Topics 1) What are the most Frequent Genetic Events in Gastric Cancers? - Mutations in the *Non-Coding* Genome 2) What are the Key Determinants for Development of Gastric Cancer? - Pre-Malignant Condition (Intestinal Metaplasia)
18 Gastric Cancer and Intra-Patient Heterogeneity Sundar and Tan (2018) Cancer Discovery
19 Most Gastric Cancers* Follow a Multi-step Carcinogenesis Sequence Yeoh and Tan (2015) Gastroenterology *Diffuse-type GC does not involve metaplasia
20 Gastric Cancer Epidemiology Program(GCEP) Four Singapore Hospitals : NUH, SGH, TTSH, CGH Funded by National Medical Research Council
21 GCEP Translational Study ( TransGCEP ) Selection of High-Risk IM Patients (n=148) Normal Mild IM IM ( 30% Mucosa (<30%) cellularity) n=43 n=22 n=83 p value (IM vs Normal) Age (year), mean ±SD 62±7 60±7 62± Race Chinese 43 (100) 22 (100) 83 (100) -- Gender (%) Male 22 (51) 12 (55) 42 (51) 1 Female 21 (49) 10 (45) 41 (49) Smoking (%) Current/ Ex- Smoker 9 (21) 4 (18) 32 (39) Non-smoker 34 (79) 18 (82) 51 (61) Alcohol consumption (%) 5 (12) 5 (23) 20 (24) 0.1 Family history of GC in first-degree relative (%) 7 (16) 3 (14) 13 (16) 1 Hp serology positivity (%) 43 (100) 22 (100) 83 (100) -- Chronic gastritis (%) 38 (88) 22 (100) 83 (100) Atrophic gastritis (%) 0 (0) 0 (0) 67 (81) -- Low Grade Dysplasia (%) 0 (0) 0 (0) 2 (2) -- EGN (%) 0 (0) 0 (0) 4 (5) -- Endoscopy surveillance (months), mean ±SD 56±12 58±8 49± % moderate/marked IM All Chinese All positive for Hp serology (Previous Infection)
22 DNA Mutations Point Mutations and Indels (MAF>4%) IMs Exhibit Low Mutation Burdens Compared to GC TP53 and ARID1A Clonal Mutations are Rare (TP53 2%; ARID1A 3%) GC IM NL Laser Capture Microdissection Confirms Mutations in IM Cells
23 Copy Number Alterations and Telomere Erosion MYC NL IM 10% of IMs have scnas Most scnas target Chr 8q IMs have significantly shorter Telomeres (Genome Instability)
24 Sequencing Detects More Hp-Infected IMs Compared to Histology All 15 have Hp DNA (100%) 33 cases have Hp DNA (27%)
25 Genomic Sequencing Can Detect Active Low-Level Hp Infection Histology-confirmed HP cases Show no Hp reads after eradication (ie Hp DNA is transient) Giemsa staining confirms Hp infection in Sequencing positive cases
26 IMs Exhibit Global DNA Methylation Alterations and a Subgroup is Hypermethylated Normal Intestinal Metaplasia IMs Exhibit Increased DNA Methylation
27 Integration with Clinical Outcome (Regression, Persistence, Progression) TransGCEP Samples LGD/HGD /EGC Regression criteria based on Rugge et al (2003) Progression includes both LGD and HGD, as both have higher GC risk than IM
28 Factors influencing IM regression and progression Mutation Burden Telomere Length Regression DNA Methylation Copy Number Alterations
29 Summary Slide: What our Data Supports Genomic profiling reveals that IMs exhibit low mutational burdens compared with GCs In general, TP53 and ARID1A mutations are rare in IM Some IMs have FBXW7 mutations, chromosome 8q amplifications, or shortened telomeres Sequencing detects more IM patients with active H. pylori infection than histology (Epi)genomic alterations in IM predict subsequent GC progression or regression Huang et al., 2018 Cancer Cell
30 Acknowledgements Amanda Yu Guo Mei Mei Chang Weitai Huang Wen Fong Ooi Manjie Xing Anders Skanderup Christopher Khor Choon-Jin Ooi Kwong Ming Fock Jimmy So Wee Chian Lim Khoon Lin Ling Tiing Leong Ang Andrew Wong Kie Kyon Huang Kalpana Ramnarayanan Feng Zhu Supriya Srivastava Ming Teh Andrea Rajnakova Lee Guan Lim Wai Ming Yap Soh Ee Lee Jia Wei Lee Khay Guan Yeoh Chang Xu Angie Lay Keng Tan Minghui Lee Suting Tay Kakoli Das Manjie Xing Aliya Fatehullah Syed Muhammad Fahmy Alkaff Tony Kiat Hon Lim Jonathan Lee Khek Yu Ho Steven George Rozen Bin Tean Teh Nick Barker Chung King Chia
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