Genomic Diversity in Barrett s esophagus predicts long term progression.., Soesterberg, Prof. dr. Sheila Krishnadath

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1 Genomic Diversity in Barrett s esophagus predicts long term progression.., Soesterberg, Prof. dr. Sheila Krishnadath

2 Esophageal squamous cell carcinoma Risk factors - Alcohol - Smoking - Male gender - Age Esophageal cancers Esophageal adenocarcinoma Risk factors - Reflux (GERD) - Barrett s esophagus - Male gender - Age - Caucasian - Smoking

3 The incidence of esophageal adenocarcinoma (EAC) is increasing Incidence in NL: 10 per /year > EAC in males Total: 2500 per year in NL

4 Survival depends on EAC disease stage and disease response to treatment Overall 5 yr survival of EAC is 15% Diagnosis is through endoscopy and Biopsy Siddiqui et al, J Gastrointest Oncol 2014;5:86-91.

5 Incidence of esophageal adenocarcinoma in comparison to breast and colon cancer CRC EAC

6 Screening and/or surveillance for prevention of cancer Population screening (Colon and breast) Clear survival benefit in case of early detection/treatment Relative high incidence of the disease to be cost effective Population screening for EAC by endoscopy is not be cost effective. Surveillance of subpopulations Clearly defined high risk subgroups Easy detectable premalignant lesions Cost effective eradication/surveillance with good outcomes

7 Barrett s esophagus: metaplastic premalignant lesion for EAC Normal Barrett s esophagus Adenocarcinoma

8 Facts about Barrett s and EAC >90% of Barrett patients are not detected Total Barrett population no surveillance surveillance Estimation: > 1% of the Caucasian population >45 has Barrett s >90% of EAC cases are incident cases that present in late stage!!!

9 Problem with Barrett s surveillance: Non dysplastic Barrett s esophagus (NDBE) has a relative low risk to develop cancer NDBE 0.3% risk Surveillance 95% (3-5 year) LGD HGD Therapy Wang et al,am J Gastroenterol 2008; Curvers Am J Gastroenterol 2010 ; Jenzen et al N Engl J Med.2011; Timmer et al, GUT 2015)

10 Future: Novel algortihm for cost effective treatment/surveillance of NDBE NDBE 0.3% risk 95% Biomarkers Surveillance??? Eradication therapy? No Surveillance Endoscopic therapy

11 How to increase cost efficacy for surveillance of Barrett s esophagus Biomarkers Eradication therapy

12 Progression of Barrett s and genetic abnormalities Barrett Dysplasia Adenocarcinoom Gut 2008, Maley et al.

13 Biomarker studies of Barrett s esophagus 95% Number of publications on Biomarkers in Barrett s Dis. Es Timmer et al.

14 Genetic biomarker studies in BE References Anueploidy, P16, p53 aberrations are most frequent Less frequent are C-Myc and Her-2 amplifications Bird Lieberman EL, Dunn JM, Coleman HG, et al. Population-Based Study Reveals New Risk-Stratification Biomarker Panel for Barrett s Esophagus. Gastroenterology 2012; 143: e3. Reid BJ, Levine DS, Longton G, Blount PL, Rabinovitch PS. Predictors of progression to cancer in Barrett s esophagus: baseline histology and flow cytometry identify lowand high-risk patient subsets. Am J Gastroenterol 2000; 95: Galipeau PC, Li X, Blount PL, et al. NSAIDs modulate CDKN2A, TP53, and DNA content risk for progression to esophageal adenocarcinoma. PLoS Med 2007; 4: e67. Barrett MT, Sanchez CA, Prevo LJ, et al. Evolution of neoplastic cell lineages in Barrett oesophagus. Nat Genet 1999; 22: Rygiel AM, Milano F, Ten Kate FJ, et al. Gains and amplifications of c-myc, EGFR, and 20.q13 loci in the no dysplasia-dysplasia-adenocarcinoma sequence of Barrett s esophagus. Cancer Epidemiol Biomark Prev Publ Am Assoc Cancer Res Cosponsored Am Soc Prev Oncol 2008; 17: Shortcomings of studies Mostly based on case control studies. Cohort studies included mixed populations of Barrett patients, including LGD and NDBE Prospective cohort studies of patients with non-dysplastic Barrett s esophagus (NDBE) are required

15 Study design Prospective, multi-center study ( ) Inclusion Exclusion Non dysplastic Barrett s esophagus (NDBE) (>1cm M length) History of any dysplasia/previous treatment Progression < 6 months At index endoscopy Baseline brush cytology & biopsies FISH analysis for biomarker panel End-point: high-grade dysplasia/adenocarcinoma

16 Brush cytology and multi colour DNA FISH Cytospin Cell suspension in cytolite Cells are Fixed and Hybridised with a Probe Mixture for 48 Hours at 37 C p p53 Y DNA FISH probe sets: CEP 7, CEP17, 9p (p16), 17p (p53)/ Her-2/Neu, C-Myc, 20q

17 DNA Fluorescence in situ hybridization Cells from the whole Barrett s segment Multi-colour FISH ~ 100 cells per sample

18 Inclusion 601 patients with Barrett s esophagus 52 patients without IM 63 patients with dysplasia 486 patients without dysplasia 428 patients included in the final analysis 23 patients awaiting FU 35 patients lost to FU

19 Baseline characteristics No. of subjects 428 Male sex - no. (%) 345 (81) Age yr 59 ± 12 Body-mass index - kg/m ± 6.5 Circumferential Barrett s length - cm 1.6 (0-4) Maximum Barrett length - cm 3 (2-6) Tobacco use 294 (70) Family history of BE 52 (13) Family history of EAC 39 (9)

20 Results 428 patients Median follow-up 45 months 22 patients developed HGD / EAC High-grade dysplasia, N=9 Adenocarcinoma, N=13 Progression to HGD/EAC: 0.9% per patient-year Progression to EAC: 0.56% per patient-year

21 Distribution of all FISH abnormalities 21

22 Predictors of progression Univariate analysis MYC, P16 and/or aneusomy* *) Marker score variable

23 Testing of different models 1. BASIS: Clinical model comprising the significant clinical variables in univariate analysis : Age and Barrett C length 2. Clinical+ molecular models: models with each individual biomarker combined with the clinical variables 3. Biomarkers only model comprising all molecular markers 4. Clinical + Significant biomarkers model with all variables that were significant in the univariate analysis (i.e. age, Barrett s segment length, p16, MYC, and aneusomy) 5. All model comprising clinical and all molecular markers. 6. Clinical + Marker score model combining the significant clinical variables and the incremental number of abnormal markers (p16 loss, MYC, and/or aneusomy) Models were tested by performing bootstrapping on the cohort, which was split into training and validation set to determine the AUC Models were compared by calculating Akaike s Information Criterion (AIC) 23

24 Multivariate models Evaluation of different models: Using bootstrap analysis - High AUC corresponds to good classifier performance - Low AIC* indicates low information loss *) Akaike s information criterion Clinical + Marker score model Area under the Curve

25 Multi variate analysis Multivariate model Variable Variable type Mean Hazard ratio Coefficient 95% p-value Confidence Interval Age Continuous Circumferential Barrett s Continuous length Marker score Continuous Multivariate model predicting progression among patients with non-dysplastic Barrett s esophagus. The marker score was defined as the number of abnormal markers (p16 loss, MYC, and/or aneusomy). *Hazard ratios were calculated with the use of multivariate Cox proportional-hazards analysis. 25

26 Stratification into high and low risk score Risk of Sens:0. 6 Spec: 0.8

27 Conclusion The markers P16, MYC and aneuploidy can be used in combination with the clinical variables of age and Barrett length risk stratify nondysplastic BE patients into low and high risk groups for the developing of HGD/EAC. Future: Implement/validate in surveillance programs and evaluate cost efficacy of the FISH/cytology method as a risk stratification tool for NDBE in adjunct to histology

28 Clonal diversity as a prognostic factor in Barrett s N=268 (including different stages of BE) Retrospectively analyzed Different techniques for detecting subclones Diversity measures (e.g., Richness of clones and Shannon index) predicted cancer outcome. Nat Genetics 2006,Maley et al.

29 Evolution is associated with diversity Ecology Evolution of humans Cancer evolution

30 Cancer development is an evolutionary process Is clonal diversity a biomarker for Barrett progression?

31 Per cell analysis of DNA FISH data to investigate Per Cell analysis with clonal DNA diversity FISH and is excellent clonal expansions to distinguish over time different clones DNA FISH on brush cytology specimens of BE patients effective to detect subclones in brush specimens Diversity of the subclones can be related to progression risk Analysis of DNA FISH obtained at different time points for studying clonal expansions

32 Study design Prospective, multi-center study ( ) Inclusion Exclusion Non dysplastic Barrett s esophagus (NDBE) (>1cm M length) History of any dysplasia/previous treatment Progression < 6 months At index endoscopy Baseline brush cytology & biopsies FISH analysis for biomarker panel End-point: high-grade dysplasia/adenocarcinoma

33 Single-cell evaluation by DNA FISH Cells from the whole Barrett s segment 4 markers identified (centromeres, single genes) Multi-colour FISH Record copy number for each marker in individual cells Two Probe sets: 1: Her-2/Neu, CEP17, 9p (p16), 17p (p53)/ 2: CEP 17, CEP 7, C-Myc, 20q 13.2 ~ 100 cells per sample

34 Single cell analysis for detection of subclones 2 x Yellow CEP17 2 x Red Her2 2 x Green TP53 2 x Blue P16 3 x Yellow CEP17 2 x Red Her2 1 x Green TP53 2 x Blue P16 2 x Yellow CEP17 1 x Red Her2 1 x Green TP53 2 x Blue P16 2 x Yellow CEP17 2 x Red Her2 2 x Green TP53 1 x Blue P16 Clone 1 Clone 2 Clone 3 Clone 4 Clone Observations Richness no. of different clones Simpson index relative abundance of each clone Shannon index both the no. and abundance of clones

35 Results 320 NDBE, per cell FISH data Prospective Follow Up: median 43 months 20 patients progressed to HGD/EAC Single Cell analysis of a total of ~ cells with 7 FISH markers (2 probe sets) 1: Her-2/Neu, CEP17, 9p (p16), 17p (p53)/ 2: CEP 17, CEP 7, C-Myc, 20q 13.2

36 Baseline genetic abnormalities Most cases seem normal Most frequent; Loss of p16 locus (cut off 6%) Loss of p53 locus (cut off 1%)

37 Baseline diversity multiple clones Genetic diversity higher for set 1 no. of different clones no. and abundance of clones

38 Progressors have a higher baseline diversity

39 Genetic diversity is predictive of progression Variable Unit P value HR Richness per cell (Set2) per % Richness per cell (Set1) per % Shannon diversity (Set2) per Aneusomy per % Age per yr Simpson diversity (Set2) per Shannon diversity (Set1) per C length per cm MYC gain per % Simpson diversity (Set1) per Avg. pairwise distance (Set1) per

40 Genetic diversity predicts progression of Barrett s

41 Conclusion II None dysplastic Barrett s has clonal diversity Baseline clonal is prognostic for progression Diversity measures can be used as long term prognostic markers for Barrett s 1) Martinez, Timmer et al, Nat Comm, 2016

42 Genetic Abnormalities Is the concept of clonal expansion Barrett true? Intestinal Metaplasia Dysplasia Adenocarcinoma Gut 2008, Maley et al.

43 Clonal dynamics in Barrett s 320 Barrett, patients 195 patients, Brush on 2e time point 14 progressed to adenocarcinoma median 37 months Baseline Brush (n=320) Brush 2 (n=195)

44 Dynamic equilibrium between clones: clones appear and disappear But no significant change in clonal diversity!

45 Clonal diversity is stabile over time

46 Progression risk is stabile over time

47 A new concept for clonal dynamics in Barrett s Progressor Old concept Non-Progressor Martinez et al, Nat Comm 2016

48 Conclusion III Progression Risk of Barrett (NDBE) is predetermined Progression risk can be determined using diversity measures Born to be Bad principe

49 Current surveillance Biomarker/diversity strategy & low diversity or high diversity Cost efficacy of DNA FISH? Timmer et al, GUT 2015 Martinez et al, Nat Comm 2016 Or treatment? Endoscopic treatment?

50 Future : Whole Genome analysis of single cells DEP-ARRAY Cell suspension in cytolite p p5 3 Y Single Cell WGS

51 Acknowledgements AMC M. Timmer W.M. Rosmolen S.Calpe H. Verhulst W.M. Westra B. Elzer S. Hoefnagel P. Fockens M. Sancho-Serra J.J. Bergman L. Mari P. De Koning A. Correia C. T Hoen L. Lau S.L. Meijer Collaborators R.C.Mallant-Hent A.H.A.M.van Oijen A.H.Naber P.Scholten L.C.Baak C.J.M. Bohmer Flevo Ziekenhuis Almere MCA Tergooiziekenhuizen SLAZ OLVG Spaarneziekenhuis T.A. Graham Barts Cancer Institute, London P. Martinez Barts Cancer Institute, London C.C. Maley Center for Evolution and Cancer, UCSF Matthew Read Peter MacCallum Oncology Center, Melbourne Wayne Phillips Peter MacCallum Oncology Center, Melbourne Funding: KWF, NWO, Fonds NutsOhra, Gut Club, Abbott Molecular Barrett Pateints DDW 2014 DDW 2014