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The link between germline and somatic variation and lifestyle risk
factors in colorectal cancer
Ulrike (Riki) PetersAssociate Director, Public Health Science
Fred Hutchinson Cancer Research Center &University of Washington
Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO)
GECCO75+
publications
Statistical
Methods
Tumor Genome
Functional Genomics
Risk Prediction
Survival
Lifestyle & Environment
Human Genome
R01-CA059045, U01-CA137088, U19-CA148107, U01-CA164930, R01-CA176272, U01-CA185094, R01-CA201407, R01-CA206279 , R21-CA230486 X01-HG006196, X01-HG006662, X01-HG007585, X01HG009781, JUNO Therapeutics
A growing resource • 60+ studies
• CCFR and CORECT• 150,000+ participants with
genetic, clinical, epidemiologic & lifestyle data
• 3,000 participants with whole genome sequencing data
• 30,000+ patients with extended clinical and survival data
• 15,000+ patients with tumor characteristics data
• 7,000 patients with tumor sequencing data
Survival
Lifestyle & Environment
Human Genome
Mutated Genes
Mutational Burden
Mutated Pathways
Mutational Signatures
F. nucleatum Quantity
Existing Markers
Existing markers • BRAF• KRAS• CP Island methylation
phenotype (CIMP)• Microsatellite instability (MSI)• In 12,000 cases
Targeted tumor sequencing• 200-300 genes• Copy number changes• Fusobacterium and other
pathogens• In 7,000 cases
TumorHost
Smoking-CRC associations differ by molecular subtypes
3
Marker Status OR 95% CI P-trend P-difference
BRAF mut 1.20 (1.15, 1.25) 1.21E-15
wt 1.08 (1.06, 1.10) 3.76E-13 3.3E-06
KRAS mut 1.07 (1.04, 1.10) 1.11E-05
wt 1.10 (1.08, 1.13) 2.62E-16 5.5E-02
CIMP + 1.19 (1.15, 1.24) 1.31E-18
- 1.08 (1.06, 1.11) 5.48E-13 6.6E-07
MSI instable 1.16 (1.11, 1.21) 2.05E-12
stable 1.09 (1.07, 1.12) 7.83E-15 4.0E-03
0.95 1 1.05 1.1 1.15 1.2 1.25 1.3
Odds ratios
• Polytomous logistic regression • P-for difference case-only analysis
Smoking-CRC associations differ between groups (case-case analysis)
4
KRAS-mut
KRAS-wild
KRAS-mut
KRAS-wild
KRAS-mut
KRAS-wild
KRAS-mut
KRAS-wild
KRAS-mut
KRAS-wild
KRAS-mut
KRAS-wild
KRAS-mut
KRAS-wild
KRAS-mut
KRAS-wild
BRAF-mut
BRAF-wild
BRAF-mut
BRAF-wild
BRAF-mut
BRAF-wild
BRAF-mut
BRAF-wild
CIMP+
CIMP-
CIMP+
CIMP-
MSI-high
MSS/MSI-low
All Cases
Category P-difference
Group 1 1.4E-03
Group 10 3.5E-05
Group 8 1.7E-01
Group 5 9.1E-01
Group 2 4.3E-01
Group 6 9.6E-01
Group 9 3.1E-01
Group 7 3.0E-01
Group 3 9.1E-02
Group 4 reference
0.8 1 1.2 1.4 1.6Odds ratio
Molecular classification of CRC
Jass. Histopathology, 2007.
Colorectal cancer incidence trends by age and sex, US 1975-2014(SEER, National Program of Cancer Registries, North American Association of Central Cancer Registries)
Siegel et al, CA Cancer J Clin, 2017Year of Diagnosis
Tumor marker analyses by age of onset among CRC (cases-case analysis)
≥65 50-64 <50 ≥65 50-64 <50 ≥65 50-64 <50 ≥65 50-64 <500.0
0.5
1.0
1.5
Age Group
Odd
s Ra
tio
BRAF mutantKras mutantCIMP positive
P for trend = 0.22 P for trend < 0.001P for trend < 0.001
362/3377
116/2310
1105/2175
538/1165
365/2690
82/1035
MS unstable
885/4979 1877/3652 1153/4245 878/4401
412/3081
216/972
P for trend < 0.001
Ref. Ref. Ref.▪ Ref.
BRAF (mut.) KRAS (mut.) CIMP (pos.) MSI (unstable)
Yin Cao
Targeted Tumor Sequence of 2,100 tumors
Most Frequently Mutated Genes POLE, POLD1, MSI Status Among Hypermutated Samples (n=395, 19%)
CRC Pathway and Gene Mutations by NHM and HM Status
Zaidi, Harrison, Phipps et al. submitted
Tabitha Harrison Hassan Zaidi
Survival by Mutated Genes
CRC Survival by Predicted TP53 Transcriptional Activity
P-value = 3x10-5
Residual activity >5%Residual activity = 0Residual activity 0-5%
Days since diagnosis
Zaidi, Harrison, Phipps et al. submitted
Amanda Phipps
30 Mutational Signatures Identified
Signatures have been linked to– Aging– Smoking– UV radiation– Aristolochic acid– Aflatoxin– Mutation in APOBEC family
BRCA1/2, MMR, POLE– Treatments with alkylating agent
temozolomide– Most signatures have not been
linked to risk factors
Nik-Zainal et al. Cell 2012; Alexandrov et al. Cell Reports 2013; Alexandrov et al. Nature2013 Helleday et al. Nat Rev Genet 2014; Alexandrov et al. Curr Opin Genet Dev 2014
Ludmil Alexandrov
GERMLINE GENETIC RISK AND INTERACTIONS WITH ENVIRONMENTAL RISK FACTORS
Interactions
Colorectal Cancer Risk
Why are we interested in discovering genetic risk loci and gene-environment interactions?
• Identification of the underlying risk factors to advance understanding of disease biology
• Inform drug targets to improve treatment of disease and chemoprevention
• Modify the genetic risk that is fixed by changing the environmental exposures
• Precision prevention - Enable genetic risk prediction of diseases to guide screening and targeted interventions
Chromosome
Genome-wide scan of 125,000 samples identified 40 new independent CRC risk variants
Jeroen Huyghe Stephanie Bien Tabitha Harrison
Total # of genetic variants tested 16,900,000
-log
10P
Summary:• Replicate 55 previously reported signals• Discover 30 new loci with P < 5×10-8
• Discover 10 additional new conditionally independent signals with P < 5×10-8 in known and new loci
• Rare (0.6% MAF) protective variant (gene CHD1) for sporadic CRC
• Low-frequency risk variants (genes BOC, TERT, POLD3 and KLF5)
• Pathways not previously implicated by GWAS (Hedgehog signaling, Krüppel-like factors, Hippo-YAP signaling)
• Role for lncRNAs, somatic drivers and immune function
Huyghe, Bien, Harrison et al. Nat Genet 2019
Genetic analysis by tumor sites
Substantial effect heterogeneity between sites at established CRC risk loci- Only ½ of risk loci have shared effects
across sites and Phet>0.05- ¼ of risk loci are associated with left-
sided CRC risk, but show little evidence for association with right-sided CRC risk
Distal colon and rectal tumors share most polygenic risk
Jeroen Huyghe
Analyses based on 64,506 shared controls.
• 5 GWAS meta-analyses:
Discovery of 14 additional risk loci in GWAS stratified by primary tumor site
Left-sided CRC and right-sided CRC have to a large extent different genetic etiologies
Huyghe et al. in preparation
Tumor site N cases
Right-sided/ Proximal colon
15,706
Distal colon 14,376Rectal 16,212Colon 32,002Left-sided 30,588
Functionally informed GWAS
Integration of colorectal-specific super enhancers
11 novel risk loci discovered
Bien et al. in preparation
Stephanie BienPeter Scacheri
0
50,000,000
100,000,000
150,000,000
200,000,000
250,000,000Single variants
Indels
Based on TOPMed Freeze 5 data; Taliun et al. BioRxiv 2019
Most genetic variation is rare
Whole genome sequencing of 65,000 samples, total 470M variants detected
Integration of functional genomics to improve discoveries for GxE interactions
RNA sequencing gene expression
DNA accessibility• ATAC-Seq
Machine learning
R01-CA201407, MPI: Casey, Gauderman, Le Marchand and Peters
GxE100,000
cases and controls
with GWAS & E
Genotyping500biopsies
50organoids
Harmonized epidemiological data across studies
Odds ratios adjusted for age, sex and study
Harmonization of individual level data ongoing for 60+ studies across CCFR, CORECT and GECCO at the coordinating center using a consistent pipeline
Genome-wide GxE findings for colorectal cancerFindings with p-value < 5x10-08 in up to 27k participants from CCFR and GECCO
Epi Variable Chr / gene P-value for GxE Publication/StatusProcess. Meat 10p14/GATA3 8.7E-09
Figueiredo et al. PLoS Genet 2014Red meat NoneFruits NoneVegetables NoneFiber NoneNSAIDS 12p12.3/HGST1 4.6E-09 Nan et al. JAMA 2015
15q25.2/IL16 8.2E-09HRT 20q13.2/CY24A1 4.8E-09 Garcia d.A., Rudolph Br J Can 2016Calcium None Du et al. CEBP 2015Alcohol 9q22.32/HIATL1 1.8E-08
Gong et al. PLoS Genet 2016Smoking NoneFolate 1p22.1/ABCA4 3.0E-08 Du et al. (in preparation)
9p21.3/FOCAD 7.9E-09BMI 118q21.1/SMAD7 Cocktail Campbell et al. (in preparation)Height NonePhysical activity None Joshi et al. (in preparation)Diabetes None Murphy (expanding #s)
NIH Director’s Blog highlighted our NSAIDsxG discovery (Nan et al. JAMA 2015) as an example for precision medicine
GxE enables precision medicine
Utilizing or discoveries for risk prediction
Survival depends on early detection
STAGEI II III IV
5-year survival 75%
5-year survival 30-70%
5-year survival 6%
STAGE STAGE STAGE
Screening is effective in early detection
• Available screening tools endoscopy, blood in stool test
• About 1/3 of eligible people do not receive colorectal cancer screeningSerious side effects, inconvenience, costs, others
• Factors influencing screening decision in general population Age, family history
Average risk vs. personal risk
Average risk to develop colorectal cancer in the next 10 years is
close to 1% for an adult age 50
Risk of colorectal cancer
highlow
Freq
uenc
y in
the
popu
latio
n
Build comprehensive risk prediction model
• Improve screening efficiency through risk stratified screening
• Identify high risk group for targeted preventive intervention– Lifestyle (diet, smoking, etc)– NSAIDs
Recommended age to start colorectal cancer screening based on genetic and environmental risk factors profile
Current recommended starting age for screening
Risk Score (%) Risk Score (%)
Jeon, Hsu et al. Gastroenterol 2016 and 2018
Risk threshold set to 0.89% (average 10-year risk for an individual aged 50 years without prior endoscopy)
Li Hsu Jihyoun Jeon
Genome-wide risk prediction
• Genome-wide risk prediction model:• Pruning SNPs and then build (non-linear)
prediction model using machine learning algorithms (Prune + ML)
• Using the entire genome to build prediction model (LDPred)
Model # Variants AUC
Known Loci 120 0.63
Prune + ML 13,000 0.64
LDPred 3.6million 0.68
Chad He Jessica Minnier Minta Thomas
90
10%
5%
Dise
ases
Pro
babi
lity
40 50 60 70 80Age
90%
25-75%
<10%
Net discounted costs compare uniform and risk-stratified screening with equal effectiveness
Risk-stratified screening based on risk prediction algorithms that has an AUC=0.70 would reduce resources needed for screening by >$250,000 per 1,000 individuals screened at similar effectiveness as current screening recommendations (Costs for risk assessment, i.e. genetic testing and questionnaire data were not taken into account)
Blue = current screening guidelines; red = risk-stratified screening
Preliminary data based on the MISCAN-Colon model
Iris Lansdorp-Vogelaar
Elleke Peterse
Ann Zauber
Josh Roth
Evaluation of risk-stratified screening in Kaiser Permanente Northern CA cohort and Estonia Biobank
Study population Total
All successfully genotyped 102,979
Colorectal cancer 744
Colorectal adenoma 11,830
Advanced adenoma 4,614
Hyperplastic polyp 3,589
Had colonoscopy 32,733
Had hemoccult test 69,965
R01-CA206279; MPI: Corley, Hayes, Peters
Doug Corley
Peeter Padrik
Tonis Tasa
Estonian Biobank • 150,000 participants in
Biobank – ~12% of entire population– 700 Colorectal cancer cases– GWAS– Nationwide EHR system
Kaiser Permanente cohort
Genic risk score (based on known GWAS loci) is particularly predictive for early onset cases without a family history
Archambault et al. under review
Early Onset CRCWe have >5000 CRC cases diagnosed <50 years of age
Alexi Archambault Richard Hayes
Moving towards risk stratified screening in a prospective trial
60,000 participants age 45-60
Risk stratified screening
Current screening recommendations
Follow up for advanced adenoma & cancer
Commercialization of polygenic risk scores
• Myriad has included polygenic risk score in genetic testing since 2017
• Other companies are following, such as Ambry Genetics, Color Genetics,…
• Polygenic risk scores as one of the top 10 Breakthrough Technologies in 2018 by MIT Technology Review
Summary
• Some environmental and clinical factors show strong associations with mutational profiles
• About 120 common genetic risk variants have been identified for colorectal cancer
• 8 gene-environment interactions have been detected• A large fraction of the common and rare genetic variation remains to be
discovered• Using genetic + environmental data to inform screening holds promise and
needs to be evaluated in a prospective setting
GECCO meeting, Seattle 2019THANK YOU!