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Drew et al., Sci. Adv. 10, eadk3121 (2024) 29 May 2024
SCIENCE ADVANCES | RESEARCH ARTICLE
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CANCER
Two genome- wide interaction loci modify the
association of nonsteroidal anti- inflammatory drugs
with colorectal cancer
David A. Drew1,2†*, Andre E. Kim3†, Yi Lin4, Conghui Qu4, John Morrison3, Juan Pablo Lewinger3,
Eric Kawaguchi3, Jun Wang5, Yubo Fu3, Natalia Zemlianskaia3, Virginia Díez- Obrero6,7,8,
Stephanie A. Bien4, Niki Dimou9, Demetrius Albanes10, James W. Baurley11,12, Anna H. Wu5,
Daniel D. Buchanan13,14,15, John D. Potter4,16, Ross L. Prentice4, Sophia Harlid17, Volker Arndt18,
Elizabeth L. Barry19, Sonja I. Berndt10, Emmanouil Bouras20, Hermann Brenner18,21,22,
Arif Budiarto11, Andrea Burnett- Hartman23, Peter T. Campbell24, Robert Carreras- Torres6,25,
Graham Casey26, Jenny Chang- Claude27,28, David V. Conti3, Matthew A.M. Devall29,
Jane C. Figueiredo3,30, Stephen B. Gruber31,32, Andrea Gsur33, Marc J. Gunter9,34,
Tabitha A. Harrison4, Akihisa Hidaka4, Michael Homeister18, Jeroen R. Huyghe4,
Mark A. Jenkins35, Kristina M. Jordahl4,36, Anshul Kundaje37,38, Loic Le Marchand39, Li Li29,40,
Brigid M. Lynch35,41, Neil Murphy9, Rami Nassir42, Polly A. Newcomb4,43, Christina C. Newton44,
Mireia Obón- Santacana6,45,46, Shuji Ogino47,48,49,50, Jennifer Ose51,52, Rish K. Pai53,
Julie R. Palmer54, Nikos Papadimitriou9, Bens Pardamean11, Andrew J. Pellatt55,
Anita R. Peoples51,52, Elizabeth A. Platz56, Gad Rennert57,58,59, Edward Ruiz- Narvaez60,
Lori C. Sakoda4,61, Peter C. Scacheri62, Stephanie L. Schmit63,64, Robert E. Schoen65,
Mariana C. Stern5, Yu- Ru Su66, Duncan C. Thomas3, Yu Tian27,67, Konstantinos K. Tsilidis34,68,
Cornelia M. Ulrich51,52, Caroline Y. Um44, Fränzel J.B. van Duijnhoven69, Bethany Van Guelpen17,70,
Emily White4,36, Li Hsu4,71‡*, Victor Moreno6,45,46,72‡, Ulrike Peters4,36‡*,
Andrew T. Chan1,2‡, W. James Gauderman3*‡
Regular, long- term aspirin use may act synergistically with genetic variants, particularly those in mechanistically
relevant pathways, to confer a protective eect on colorectal cancer (CRC) risk. We leveraged pooled data from
52 clinical trial, cohort, and case- control studies that included 30,806 CRC cases and 41,861 controls of European
ancestry to conduct a genome- wide interaction scan between regular aspirin/nonsteroidal anti- inammatory
drug (NSAID) use and imputed genetic variants. After adjusting for multiple comparisons, we identied statisti-
cally signicant interactions between regular aspirin/NSAID use and variants in 6q24.1 (top hit rs72833769),
which has evidence of inuencing expression of TBC1D7 (a subunit of the TSC1- TSC2 complex, a key regulator of
MTOR activity), and variants in 5p13.1 (top hit rs350047), which is associated with expression of PTGER4 (codes a
cell surface receptor directly involved in the mode of action of aspirin). Genetic variants with functional impact
may modulate the chemopreventive eect of regular aspirin use, and our study identies putative previously
unidentied targets for additional mechanistic interrogation.
INTRODUCTION
Aspirin, a nonsteroidal anti- inammatory drug (NSAID), is inversely
associated with colorectal cancer (CRC) risk. In meta- analyses and
systematic reviews of large observational studies, regular long- term
use of aspirin is associated with CRC risk reduction of 20 to 30%
(1–3). Reduction of CRC risk was also observed in well- designed
clinical trials of colorectal neoplasia outcomes among individuals
with Lynch syndrome or prior colorectal adenoma or CRC (4–10).
However, the precise mechanism of action has not yet been fully elu-
cidated, although several modes of action have been suggested
for aspirin’s anticancer eects (3, 11). Despite a potential overlap in
mechanism (i.e., inhibition of prostaglandin synthesis), the relationship
of non- aspirin NSAIDs (henceforth simply termed “NSAIDs”) and
CRC risk is less consistent, potentially owing to more heterogeneous
use in published studies, contamination of non- aspirin NSAID cat-
egories with aspirin use, or confounding by indication for use (i.e.,
individuals with higher inammatory states). Because not all studies
specically dierentiate between aspirin and other NSAID use, addi-
tional study of the impact of this broader drug class on CRC risk is
warranted. Genetic variation is a key individual factor that likely inter-
acts with aspirin and NSAIDs to ultimately determine CRC risk. In
general, gene- drug interaction studies aim to clarify these relationships
and implicate regions involved in the mode action (12), which may
identify subpopulations of individuals that might most benet from an
aspirin preventive strategy, particularly in light of the potential harms.
In this analysis, we conducted a genome- wide interaction scan
(GWIS) of regular aspirin/NSAID use and imputed genetic markers.
A previous GWIS, conducted on a smaller subset of individuals,
identied interaction loci in regions 12p12.3 and 15q25.2 (13). We
expanded upon that analysis by greatly increasing the sample size
and using additional statistical methods that improve power to
detect interaction loci and infer functional impact.
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Drew et al., Sci. Adv. 10, eadk3121 (2024) 29 May 2024
SCIENCE ADVANCES | RESEARCH ARTICLE
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RESULTS
Regular aspirin/NSAID use and CRC risk
Our combined study sample included 72,667 individuals (30,806
cases and 41,861 controls) with known aspirin/NSAID use status
and CRC outcome status (tableS1). We considered regular aspirin
and/or NSAID use as a combined variable (aspirin/NSAID) because
not all studies collected data on aspirin use separate from other
NSAID use, and aspirin and NSAIDs likely have common anticancer
mechanisms. Complete study inclusion and exclusion criteria and
how regular aspirin/NSAID use is dened are described in Materials
and Methods. Secondary analyses restricted to studies with infor-
mation on aspirin use only are also presented (N=72,137; 30,574
cases and 41,563 controls). Regular aspirin/NSAID use was less
prevalent among CRC cases compared to controls (34% versus 40%,
respectively) as was aspirin use alone (27% of cases versus 31% of
controls). As expected, CRC cases tended to be older, had a higher
body mass index (BMI) and energy intake, had a greater proportion
of family history of CRC, were less educated, and were more likely to
be exposed to other known risk factors including heavy alcohol in-
take and tobacco smoking compared to controls.
In meta- analyses of study- specic associations, regular aspirin/
NSAID use [odds ratio (OR)=0.76; 95% condence interval (CI)
0.72 to 0.81] and aspirin use alone (OR=0.80; 95% CI = 0.76 to 0.84)
were associated with reduced CRC risk (Fig.1). ere was statisti-
cally signicant cross- study heterogeneity in the aspirin/NSAIDS and
aspirin- only associations, which appeared to be largely due to study
design (fig. S1). The estimated reductions in CRC risk were
more pronounced in case- control studies for aspirin/NSAID use
(ORaspirin/NSAID=0.67, 95% CI=0.62 to 0.72) than in cohort studies
(ORaspirin/NSAID=0.85, 95% CI=0.80 to 0.91) (Phet<0.001). Similar
trends in estimates were observed for regular use of aspirin- only
(case- control studies: ORaspirin=0.72, 95% CI=0.67 to 0.77; co-
hort studies: ORaspirin=0.88, 95% CI=0.83 to 0.94) (Phet<0.001).
Further adjustment by established CRC risk factors—including BMI,
alcohol intake, smoking, and red meat consumption—did not sub-
stantially change OR estimates of aspirin/NSAID use (table S2).
Analyses stratied by sex show nominally stronger inverse associa-
tions with regular aspirin/NSAID use and CRC risk among women
compared to men (multivariate model Pinteraction= 0.014), but
there were no statistically signicant sex dierences for aspirin use
1Clinical & Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA. 2Division of Gastroenterology, Massachusetts
General Hospital and Harvard Medical School, Boston, MA, USA. 3Division of Biostatistics, Department of Population and Public Health Sciences, Keck School of Medicine,
University of Southern California, Los Angeles, CA, USA. 4Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington, USA. 5Department of Popu-
lation and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, California, USA. 6Colorectal Cancer Group, ONCOBELL Program,
Bellvitge Biomedical Research Institute (IDIBELL), L’Hospitalet de Llobregat, Barcelona, Spain. 7Consortium for Biomedical Research in Epidemiology and Public Health
(CIBERESP), Madrid, Spain. 8Department of Clinical Sciences, Faculty of Medicine, University of Barcelona, Barcelona, Spain. 9Nutrition and Metabolism Branch, Interna-
tional Agency for Research on Cancer, World Health Organization, Lyon, France. 10Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Insti-
tutes of Health, Bethesda, MD, USA. 11Bioinformatics and Data Science Research Center, Bina Nusantara University, Jakarta, Indonesia. 12BioRealm LLC, Walnut, CA, USA.
13Colorectal Oncogenomics Group, Department of Clinical Pathology, The University of Melbourne, Parkville, Victoria 3010 Australia. 14University of Melbourne Centre for
Cancer Research, Victorian Comprehensive Cancer Centre, Parkville, Victoria 3010 Australia. 15Genomic Medicine and Family Cancer Clinic, The Royal Melbourne Hospital,
Parkville, Victoria, Australia. 16Research Centre for Hauora and Health, Massey University, Wellington, New Zealand. 17Department of Radiation Sciences, Oncology Unit,
Umeå University, Umeå, Sweden. 18Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany. 19Department of
Epidemiology, Geisel School of Medicine at Dartmouth, Hanover, NH, USA. 20Laboratory of Hygiene, Social & Preventive Medicine and Medical Statistics, Department of
Medicine, School of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece. 21Division of Preventive Oncology, German Cancer Research Center (DKFZ)
and National Center for Tumor Diseases (NCT), Heidelberg, Germany. 22German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany.
23Institute for Health Research, Kaiser Permanente Colorado, Denver, CO, USA. 24Department of Epidemiology and Population Health, Albert Einstein College of Medicine,
Bronx, NY, USA. 25Digestive Diseases and Microbiota Group, Girona Biomedical Research Institute (IDIBGI), Salt, 17190 Girona, Spain. 26Center for Public Health Genomics,
University of Virginia, Charlottesville, VA, USA. 27Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany. 28University Medical
Centre Hamburg- Eppendorf, University Cancer Centre Hamburg (UCCH), Hamburg, Germany. 29Department of Family Medicine, University of Virginia, Charlottesville, VA,
USA. 30Department of Medicine, Samuel Oschin Comprehensive Cancer Institute, Cedars- Sinai Medical Center, Los Angeles, CA, USA. 31Department of Medical Oncology
& Therapeutics Research, City of Hope National Medical Center, Duarte, CA, USA. 32Center for Precision Medicine, City of Hope National Medical Center, Duarte, CA, USA.
33Center for Cancer Research, Medical University Vienna, Vienna, Austria. 34Department of Epidemiology and Biostatistics, Imperial College London, School of Public
Health, London, UK. 35Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria,
Australia. 36Department of Epidemiology, School of Public Health, University of Washington, Seattle, WA, USA. 37Department of Genetics, Stanford University, Stanford, CA,
USA. 38Department of Computer Science, Stanford University, Stanford, CA, USA. 39University of Hawaii Cancer Center, Honolulu, HI, USA. 40UVA Comprehensive Cancer
Center, Charlottesville, VA, USA. 41Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia. 42Department of Pathology, School of Medicine,
Umm Al- Qura’a University, Mecca, Saudi Arabia. 43School of Public Health, University of Washington, Seattle, WA, USA. 44Department of Population Science, American
Cancer Society, Atlanta, GA, USA. 45Unit of Biomarkers and Susceptibility (UBS), Oncology Data Analytics Program (ODAP), Catalan Institute of Oncology (ICO), L’Hospitalet
del Llobregat, 08908 Barcelona, Spain. 46Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), 28029 Madrid, Spain. 47Department of Epi-
demiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA. 48Program in MPE Molecular Pathological Epidemiology, Department of Pa-
thology, Brigham and Women’s Hospital, Boston, MA, USA. 49Harvard Medical School, Boston, MA, USA. 50Broad Institute of MIT and Harvard, Cambridge, MA, USA.
51Huntsman Cancer Institute, Salt Lake City, UT, USA. 52Department of Population Health Sciences, University of Utah, Salt Lake City, UT, USA. 53Department of Laboratory
Medicine and Pathology, Mayo Clinic Arizona, Scottsdale, AZ, USA. 54Slone Epidemiology Center at Boston University, Boston, MA, USA. 55Department of Cancer Medicine,
University of Texas MD Anderson Cancer Center, Houston, TX, USA. 56Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
57Department of Community Medicine and Epidemiology, Lady Davis Carmel Medical Center, Haifa, Israel. 58Ruth and Bruce Rappaport Faculty of Medicine, Technion-
Israel Institute of Technology, Haifa, Israel. 59Clalit National Cancer Control Center, Haifa, Israel. 60Department of Nutritional Sciences, University of Michigan School of
Public Health, Ann Arbor, MI, USA. 61Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA. 62Department of Genetics and Genome Sciences, Case
Western Reserve University, Cleveland, OH, USA. 63Genomic Medicine Institute, Cleveland Clinic, Cleveland, OH, USA. 64Population and Cancer Prevention Program, Case
Comprehensive Cancer Center, Cleveland, OH, USA. 65Department of Medicine and Epidemiology, University of Pittsburgh Medical Center, Pittsburgh, PA, USA. 66Biosta-
tistics Division, Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA. 67School of Public Health, Capital Medical University, Beijing, China. 68Depart-
ment of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece. 69Division of Human Nutrition and Health, Wageningen University &
Research, Wageningen, Netherlands. 70Wallenberg Centre for Molecular Medicine, Umeå University, Umeå, Sweden. 71Department of Biostatistics, University of Washington,
Seattle, WA, USA. 72Department of Clinical Sciences, Faculty of Medicine and Health Sciences and Universitat de Barcelona Institute of Complex Systems (UBICS), University
of Barcelona (UB), L’Hospitalet de Llobregat, 08908 Barcelona, Spain.
*Corresponding author. Email: dadrew@ mgh. harvard. edu (D.A.D.); lih@ fredhutch. org (L.H.); upeters@ fredhutch. org (U.P.); jimg@ usc. edu ( W.J.G.)
†These authors contributed equally to this work.
‡These authors contributed equally to this work.
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Drew et al., Sci. Adv. 10, eadk3121 (2024) 29 May 2024
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only (tableS2). ere were no notable dierences in associations
when stratifying models by tumor site (tableS2).
GWIS results
We found no evidence of genomic ination or residual population
stratification in the genome- wide scan one degree of freedom
(df ) Gene x Environment (GxE) test P values for aspirin/NSAID
or aspirin- only exposure variables (g.S2). We identied a statistically
signicant interaction between regular aspirin/NSAID use and
rs72833769 (chr6:12577203 T/C, P=1.27 × 10−8; Table1), a marker
in locus 6q26 upstream of gene PHACTR1 (Fig.2A). e overall
minor allele frequency for this single- nucleotide polymorphism
(SNP) was 0.067 (Table1). is interaction for rs72833769 was similarly
signicantly associated when restricted to aspirin use only (Paspirin=
4.02 × 10−9; Fig.2B). While rs72833769 did not have a direct mar-
ginal association with CRC risk (OR=1.00; 95% CI = 0.96 to 1.05),
Odds ratio
Odds ratio
A
Aspirin/NSAIDs
B Aspirin only
Fig. 1. Association of regular aspirin/NSAID use with CRC according to sex and tumor location. Results from meta- analysis of association between regular use of (A) as-
pirin/NSAID or (B) aspirin- only and colorectal cancer, overall and stratied by sex and tumor site. Models adjusted for age and sex. Heterogeneity measures include Cochran’s
Q statistic p- value (Phet) and Higgin’s statistic (I2), which describes the proportion of observed variance due to heterogeneity and not attributed to sampling error.
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Table 1. Signicant results from genome- wide interaction scans of regular aspirin/NSAID use and CRC risk. SNP, single- nucleotide polymorphism; Chr,
chromosome; BP Position, base pair position based on NCBI Build37. “Overall” is the P value for the 1- df GxE test (rs72833769) or two- step procedure (rs350047).
Imputed SNPs were coded as expected gene dosage. Multiplicative interaction terms were modeled as the product of Aspirin/NSAIDs and each SNP of interest.
All statistical tests were two- sided.
Method SNP Chr BP Posi-
tion
Locus Gene Ref Alt Alt
allele
freq
(1000G)
Expo-
sure
P value
(overall)
EDGE
P value
(step 1)
EDGE
P value
(step 2)
P value
(3 df)
1–degree- of- freedom (df ) GxE
rs72833769 6 12577203 6p24.1 Upstream
of PHAC-
TR1
T C 0.02 Aspirin/
NSAID
1.27 ×
10−8
– – –
Aspirin
only
4.02 ×
10−9
– – –
Two- step (EDGE) and 3- df
rs350047 5 40252294 5p13.1 LINC00603
(upstream
of PTGER4)
C T 0.41 Aspirin/
NSAID
8.20 ×
10−8
5.22 ×
10−6
4.41 ×
10−5
6.50 ×
10−9
Aspirin
only
2.00 ×
10−8
9.40 ×
10−6
1.08 ×
10−5
3.12 ×
10−9
12 435678910 11 12 13 14 15 16 17 1819 202122
0
2
4
6
8
go
L
-
01
(P)
Chromosome
12 435678910 11 12 13 14 15 16 17 18 19202122
0
2
4
6
8
go
L
-
10
(P)
Chromosome
12 435678910 11 12 13 14 15 16 17 1819202122
0
2
4
6
8
go
L
-
01
(P)
Chromosome
10
12
14
12 435678910 11 12 13 14 15 16 17 1819 202122
0
2
4
6
8
go
L
-
10
(P)
Chromosome
A
DC
B
Fig. 2. Manhattan plots of genome- wide interaction scans. (A) traditional logistic regression interaction test (1- df ) for aspirin/NSAID (B) traditional logistic regression
interaction test (1- df) for aspirin- only, (C) 3- df joint test for aspirin/NSAID, and (D) 3- df joint test for aspirin- only. Red line represents a genome- wide signicance threshold
of 5 × 10−8/2.5 after adjustment for multiple testing.
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stratied analyses showed regular aspirin/NSAID use or aspirin-
only use were signicantly associated with lower CRC risk only
among homozygous carriers of the common allele T (aspirin/
NSAID: ORTT=0.73, 95% CI = 0.71 to 0.76; aspirin only: ORTT=0.76,
95% CI = 0.73 to 0.79) (Tables 2 and 3 and g.S3, A and B). In con-
trast, regular aspirin/NSAID use was not signicantly associated
with risk of CRC among individuals carrying the CT or CC genotype
(aspirin/NSAID: ORCT=0.94, 95% CI = 0.85 to 1.03; ORCC=1.44,
95% CI = 0.82 to 2.54; aspirin- only: ORCT=1.00, 95% CI = 0.90
to 1.11; ORCC=1.40, 95% CI = 0.75 to 2.59). Adjustment for addi-
tional CRC risk factors did not materially affect the estimate of
interaction (Tables 2 and 3, model 2). In addition, interaction ef-
fects for this SNP did not dier substantially by sex or tumor subsite,
although interaction estimates were modestly stronger for colon
locations than rectal tumors (tableS3A).
We identified a second locus rs350047 (chr5:40252294 C/T,
MAF1000 Genomes=0.48) of interest using both our two- step EDGE
method (g.S3) and our 3- df joint test (Table1). On the basis of the
two- step testing, this SNP shows signicant evidence of interaction
with aspirin- only use; interactions were marginally signicant for
aspirin/NSAID use. For aspirin/NSAID use, rs350047 achieved a
step 1 “EDGE” P value of 5.22 × 10−6 and step 2 “GxE” P value of
4.41 × 10−5 (overall two- step P value=8.2 × 10−8). e corresponding
P values for aspirin use only were 9.40 × 10−6 for step 1 and 1.08 × 10−5
for step 2, which in combination achieve genome- wide signicance
(overall two- step P value= 2.0 × 10−8) (Table 1). is locus was
statistically signicant for both exposure variables based on 3- df
joint test (aspirin/NSAID P=6.5 × 10−9; aspirin only P=3.12 × 10−9).
Combined, the consistency across methods supports the identica-
tion of this hit. is genetic variant lies in locus 5p13.1 upstream of
PTGER4 and LINC00603, within a genomic region identied in a
previously published GWAS of CRC risk and tagged by deletion-
insertion marker rs58791712 (14). Stratied analyses showed that
regular aspirin/NSAID use and aspirin only use was inversely asso-
ciated with CRC risk across all genotype groups, but the association
was of greater magnitude among homozygous carriers of the T allele
(aspirin/NSAID: ORTT=0.67, 95% CI = 0.63 to 0.72; aspirin- only:
ORTT=0.69, 95% CI = 0.64 to 0.74) compared to heterozygous or
homozygous carriers of the C allele (aspirin/NSAID: ORCT=0.78,
95% CI = 0.74 to 0.81; ORCC=0.81, 95% CI =0.76 to 0.87; aspirin-
only: ORCT=0.81, 95% CI = 0.77 to 0.85; ORCC=0.85, 95% CI = 0.79
to 0.91) (Tables 2 and 3 and g.S3, C and D). Adjustment for addi-
tional CRC risk factors similarly did not aect the estimates of this
interaction (Tables 2 and 3, model 2). e interaction is somewhat
stronger among men than women (tableS3B), although the three-
way interaction P value was not statistically signicant (PGxExSex=0.07).
Similarly, interaction eects at this locus were similar in magnitude
across tumor site (tableS3B). In our genome- wide scan for rare
variants, we did not nd any signicant interactions with aspirin/
NSAID or aspirin- only use.
Interactions stratied by CRC molecular subtypes
Case counts with available tumor marker information on BRAF and
KRAS mutation status and the presence of CpG island methylator
phenotype (CIMP) or microsatellite instability (MSI) are summarized
in tableS4. Generally, when tting traditional case- control logistic
regression models, interactions between aspirin/NSAID use and
rs72833769 or rs350047 are replicated within the subset of cases
with available tumor- marker data, at a signicance level P<0.05
(Fig.3; overall cases versus controls). In stratied analyses according
to the presence or absence of molecular subtype markers, signi-
cant association for the interaction between rs72833769 and aspirin/
NSAIDs were limited to tumors absent of each of the available tumor
markers (Fig.3A; BRAF wild- type, CIMP- low/negative, non–MSI-
high, and KRAS wild- type tumors versus controls: all P<0.05); how-
ever, estimates for the interaction between cases absent for these
molecular markers and those positive for the individual markers
were not signicantly heterogeneous (all Phet>0.05). In contrast,
signicant association for the interaction between rs350047 and
aspirin/NSAID is observed only among cases positive for BRAF
mutation, CIMP- high, or MSI- high compared to controls, but not
among cases with KRAS mutation nor absent any individual marker
(Fig.3B). Similarly, no signicant heterogeneity between cases with
the molecular marker present versus absent was observed (all Phet>
0.05), although the estimates for BRAF mutant versus wild- type and
CIMP- high versus CIMP- low/negative approached signicance.
Further restricting the analysis to aspirin use alone did not materially
alter estimates, but the reduced sample size resulted in slight attenua-
tion of observed statistical signicance.
Functional follow- up
e regional plot for rs72833769 (6p24.1) shows several genes in the
vicinity of this locus, including PHACTR1 and END1 (g.S5A).
Several data sources provide evidence that the region tagged by
rs72833769 plays a regulatory role in the transcription of neighboring
genes. In CRC tumor and normal tissue, and CRC cell lines derived
from work by Cohen etal. (15), the lead SNP rs72833769 showed little
evidence of functional activity. However, several SNPs in linkage
disequilibrium (LD) with rs72833769 coincided with accessible chro-
matin regions based on H3K27ac markers primarily in CRC tumors
(tableS5A and fig.S6A). This region also contains overlaps with
regulatory regions based on H3K4me1, H3K4me3, H3K9ac, and H3K27
histone modication signals in connective, gastrointestinal, and im-
mune cell types (16). ENSEMBL queries also show several regulatory
features in this locus, in addition to a transcript region for lncRNA
RP11- 125 M16.1 (tableS5A).
Evidence for an expression quantitative trait locus (eQTL) was less
pronounced. None of the lead or LD SNPs were signicant eQTLs for
any gene/tissue in the GTEx v.8 database. In the Barcelona and Uni-
versity of Virginia genotyping and RNA sequencing (BarcUVa- Seq)
dataset, a single SNP rs12194512 [LD SNP, coecient of determination
(R2)=0.36] was a signicant eQTL with GFOD1, a gene approximately
800 kb upstream of the main nding. While rs72833769 was not
specically identied as an eQTL for PHACTR1, it was signicantly
associated with predicted PHACTR1 expression (P=8.4 × 10−6). Last,
in the eQTLGEN database, we found signicant eQTLs with LD
SNPs rs499627 (R2=0.32) and rs538788 (R2=0.24) for the expres-
sion of TBC1D7 (tableS5A).
e regional plot for rs350047 (5p13.1) shows that the SNP lies
within a long noncoding RNA region, LINC00604, and is in LD
with a known GWAS region identied in 2018 by Schmit etal.
(14) (g.S5B). is locus appears to reside in a region with little
histone modications or deoxyribonuclease (DNAse) accessible
sites based on evidence from CRC normal and tumor tissues and
CRC cell lines (tableS5B and g.S6B). However, SNPs in LD with
rs350047 overlap with functionally active sites in connective and
immune cells (17). Several LD SNPs are signicant eQTLs for
PTGER4 in GTEx v.8 suprapubic epithelial cells. This finding is
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Table 2. Associations between aspirin/NSAID intake and CRC risk stratied by rs72833769. Odds ratios (ORs) and 95% condence intervals (CIs) calculated
from traditional interaction model with an interaction term. Model 1: Covariates include age (continuous), sex, study, and the rst three principal components.
Model 2: Includes all covariates in Model 1+smoking (never/ever), alcohol consumption (nondrinkers; moderate, 1 to 28 g/day; heavy, >28 g/day), BMI
(continuous), and red meat intake (study and sex specic quartiles of red meat intake based on controls only).
TT CT CC
Cases Controls OR (95% CI) Cases Controls OR (95% CI) Cases Controls OR (95% CI)
Aspirin/NSAID
Model 1
Nonregular use 17,861 21,554 1.0 (Ref ) 2,410 3,349 1.0 (Ref) 89 120 1.0 (Ref)
Regular use 9,011 14,697 0.73
(0.71–0.76)
1,374 2,073 0.94
(0.85–1.03)
61 68 1.44
(0.82–2.54)
Model 2
Nonregular use 15,957 19,917 1.0 (Ref ) 2,138 3,113 1.0 (Ref) 81 114 1.0 (Ref)
Regular use 8,159 13,423 0.72
(0.70–0.75)
1,243 1,930 0.90 (0.81,
0.99)
54 65 1.33 (0.72,
2.46)
Aspirin only
Model 1
Nonregular use 19,535 24,775 1.0 (Ref ) 2,689 3,874 1.0 (Ref) 108 142 1.0 (Ref )
Regular use 7,134 11,229 0.76
(0.73–0.79)
1,066 1,497 1.00 (0.90,
1.11)
42 46 1.40 (0.75,
2.59)
Model 2
Nonregular use 17,517 22,969 1.0 (Ref ) 2,399 3,615 1.0 (Ref) 98 136 1.0 (Ref)
Regular use 6,427 10,180 0.76
(0.73–0.79)
958 1,384 0.96 (0.86,
1.07)
37 43 1.28 (0.66,
2.49)
Table 3. Associations between aspirin/NSAID intake and CRC risk stratied by rs350047. Odds ratios (ORs) and 95% condence intervals (CIs) calculated
from traditional interaction model with an interaction term. Model 1: Covariates include age (continuous), sex, study, and the rst three principal components.
Model 2: Includes all covariates in Model 1+smoking (never/ever), alcohol consumption (nondrinkers; moderate, 1 to 28 g/day; heavy, >28 g/day), BMI
(continuous), and red meat intake (study and sex specic quartiles of red meat intake based on controls only).
CC CT TT
Cases Controls OR (95% CI) Cases Controls OR (95% CI) Cases Controls OR (95% CI)
Aspirin/NSAID
Model 1
Nonregular
use
5,179 6,921 1.0 (Ref ) 10,141 12,453 1.0 (Ref ) 5040 5649 1.0 (Ref )
Regular use 2,765 4,496 0.81
(0.76–0.87)
5,301 8,425 0.78
(0.74–0.81)
2380 3917 0.67
(0.63–0.72)
Model 2
Nonregular
use
4,630 6,410 1.0 (Ref ) 9,057 11,480 1.0 (Ref ) 4489 5254 1.0 (Ref )
Regular use 2,504 4,104 0.80
(0.75–0.86)
4,811 7,725 0.76
(0.72–0.80)
2141 3589 0.66
(0.61–0.71)
Aspirin only
Model 1
Nonregular
use
5,690 7,958 1.0 (Ref ) 11,157 14,340 1.0 (Ref ) 5485 6493 1.0 (Ref )
Regular use 2,197 3,374 0.85
(0.79–0.91)
4,173 6,389 0.81
(0.77–0.85)
1872 3009 0.69
(0.64–0.74)
Model 2
Nonregular
use
5,107 7,394 1.0 (Ref ) 9,999 13,272 1.0 (Ref ) 4908 6054 1.0 (Ref )
Regular use 1,978 3,055 0.85
(0.79–0.91)
3,774 5,815 0.81
(0.77–0.85)
1670 2737 0.69
(0.64–0.74)
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corroborated when querying eQTLGEN, where essentially the
entire region contains significant eQTLs for PTGER4 in blood
cells (tableS5B). Similarly, rs350047 was not an identied eQTL
for PTGER4 expression but was signicantly correlated with pre-
dicted expression in our data (P=2.2 × 10−16). In addition to
PTGER4, several LD SNPs were also signicant eQTLs for DAB2
in eQTLGEN, approximately 800kb downstream from the lead
SNP (tableS5B). Last, we developed genetic models to t interac-
tions between regular aspirin use and predicted PHACTR1 (upstre am
of rs72833769) and DAB2/PTGER4 (near rs350047) expression
based on the BarcUVa- Seq dataset but did not identify any sig-
nicant interactions.
0.5 1.0 1.5 2.0
Overall case vs. controls
KRAS wild-type vs. controls
KRAS mutant vs. controls
Non
-
MSI-high vs. controls
MSI-high vs. controls
CIMP-low/negative vs. controls
CIMP-high vs. controls
BRAF wild-type vs. controls
BRAF mutant vs. controls
Odds ratios
1.38 (1.11, 1.71)
0.81
0.79
0.22
0.72
A
rs72833769 x Aspirin/NSAIDs
1.27 (0.77, 2.11)
1.27 (1.01, 1.61)
1.26 (0.78, 2.03)
1.41 (1.10, 1.80)
0.96 (0.58, 1.59)
1.41 (1.13, 1.78)
1.24 (0.88, 1.76)
1.34 (1.04, 1.73)
OR (95% CI) Phet
0.40.5 0.60.7 0.80.9 1.01.1
Overall case vs. controls
KRAS wild-type vs. controls
KRAS mutant vs. controls
Non
-
MSI-high vs. controls
MSI-high vs. controls
CIMP-low/negative vs. controls
CIMP-high vs. controls
BRAF wild-type vs. controls
BRAF mutant vs. controls
Odds ratios
B rs350047 x Aspirin/NSAIDs
0.89 (0.80, 0.99)
0.098
0.075
0.15
0.73
0.76 (0.59, 0.98)
0.92 (0.78, 1.08)
0.73 (0.58, 0.93)
0.93 (0.83, 1.05)
0.77 (0.62, 0.97)
0.89 (0.80, 1.00)
0.92 (0.78, 1.08)
0.89 (0.80, 1.01)
OR (95% CI) Phet
Fig. 3. Forest plots of GxE interactions for identied SNPs and aspirin/NSAID and risk of CRC stratied by tumor molecular subtypes. ORs (dots) and 95% CIs (error bars)
are plotted for each case stratied by the presence (BRAF mutant; CIMP- high; MSI- high; KRAS mutant) or absence (BRAF wild type; CIMP- low/negative; Non- MSI- high; KRAS wild
type) of the marker in cases versus controls (referent) for each identied SNP: (A) rs72833769 and (B) rs350047. The P value for heterogeneity (Phet) between estimates for cases
with the molecular marker present versus absent and the overall association of the interaction for cases with molecular marker data versus controls is also provided.
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DISCUSSION
We report results of the largest genome- wide GxE interaction scan
to date for CRC focused on aspirin and NSAIDs. Consistent with
previous epidemiological studies, regular use of aspirin/NSAIDs
was inversely associated with CRC risk in this analysis. e direct
aspirin/NSAIDs associations did not dier substantially when stratied
by sex or tumor site. Gene- environment interaction scans identied
two previously undescribed interaction SNPs, rs350047 (5p13.1)
and rs72833769 (6p24.1), and subgroup- specic analyses show dif-
ferent magnitudes of eects of aspirin/NSAIDs on CRC risk at each
locus dened by genotype.
Our nding of a signicant interaction for rs350047 (5p13.1) and
aspirin’s preventive capacity is biologically plausible and potentially
functionally relevant. PTGER4 encodes PTGER4 (EP4), a major recep-
tor for prostaglandin E2 (PGE2). PGE2 is one of the major proinam-
matory factors that is produced constitutively in the intestinal
epithelium and is elevated during periods of inammation (18, 19).
Aberrant PGE2 signaling via PTGER4 within the mucosal microenvi-
ronment promotes tumorigenesis and aects cellular dierentiation
processes central to mucosal injury repair (20–23). In the setting of
cancer, PGE2 has been found to induce proliferation of CRC stem
cells and CRC liver metastasis in mouse models via EP4- dependent
signaling pathways (24, 25). Increased synthesis of PGE2 [due to
increased prostaglandin endoperoxide synthase 2 (PTGS2) expression]
is observed in patients with CRC (24, 25). Genetic deletion of Ptgs2 or
downstream PGE2 receptors results in protection from tumorigenesis
in CRC mouse models (26–30). In addition, 15- hydroxyprostaglandin
dehydrogenase (HPGD; 15- PGDH), the primary enzyme that ca-
tabolizes PGE2, has been characterized as a tumor suppressor for
several human cancers, including colorectal, and is ubiquitously
down- regulated in CRC (31–36).
However, in the context of prevention, carefully orchestrated PGE2-
PTGER4 signaling mediated by PTGS2 in the mucosal microenviron-
ment has been demonstrated to have a key role in healthy stem cell
function and regenerative programming (20, 21, 37). e leading
putative biological mechanism for the chemopreventive eects of
aspirin and other nonselective NSAIDs centers on inhibition of
PTGS1 and PTGS2, the enzymes that mediate conversion of arachi-
donic acid to PGE2 (3). Direct inhibition of PGE2 synthesis by aspirin
has been conrmed in separate clinical trials, where aspirin interven-
tion in patients at risk for CRC signicantly reduces the major urinary
metabolite of PGE2 (38–40), 11α- hydroxy- 9,15- dioxo- 2,3,4,5- tetranor-
prostane- 1,20- dioic acid (PGE- M), an inammatory biomarker that
may predict individual risk for colorectal neoplasia and may have
utility as an ecacy marker for aspirin prevention (11, 39, 41).
us, it is biologically plausible that the interaction SNP rs350047
as an eQTL for PTGER4 expression may substantially modify the PGE2-
PTGER4 signaling axis, aspirin’s eect on this pathway and resultant
tumorigenesis, and in turn an individual’s risk for CRC. Moreover,
the locus 5p13.1 has been previously implicated in investigations of
cancer and inammatory bowel disease. Genome- wide association
analyses of CRC risk using data from a broader subset of the same Ge-
netics and Epidemiology of Colorectal Cancer Consortium (GECCO)/
Colon Cancer Family Registry (CCFR)/Colorectal Transdisciplinary
Study (CORECT) consortia identied two independent risk loci in
this region; Schmit etal. (14) reported a signicant association between
indel rs58791712 (G/GT) and CRC risk, while Huyghe etal. (42)
reported a separate hit upstream of this variant, (G/A). e associa-
tion for rs7708610 was genome- wide signicant upon conditioning
models for the previously identied hit at rs58791712 (using a surrogate
SNP), indicating the presence of an independent susceptibility locus
(42). e correlation of the SNP rs350047 showing interaction with
rs58791712 and rs7708610 in 1000G European population is R2=
0.38 and 0.042, respectively. Furthermore, several analyses of inam-
matory diseases, specically for, but not limited to, Crohn’s disease,
also implicated the 5p13.1 region as a risk locus (43–46), although
none of the reported markers are in LD with our main nding.
In contrast to rs350047, the functional evidence for rs72833769
(6p24.1) is less clear. However, a signicant eQTL with TBC1D7
might be of interest. is gene is a subunit of the TSC1- TSC2 complex,
a key regulator of mammalian target of rapamycin (mTOR) activity,
which is a proto- oncogene and downstream eector of the phospha-
tidylinositol 3- kinase/AKT pathway. As previously described in the
context of pancreatic cancer (47), there is potential cross- talk between
PGE2 expression, PTGER4 activation, and the AKT/mTOR pathways.
Moreover, in a large, recent study using human normal colon organoids,
network analysis of RNA- seq data revealed that both TBC1D7 and
GFOD1 were present within modules that were both signicantly
modulated by aspirin treatment in vitro and enriched for PGE2-
related pathways (48). us, it appears biologically plausible that
aspirin/NSAID- mediated changes in PGE2 expression can modulate
the eectiveness of signal transduction to proliferative signals by
these signaling axes.
ese results also add to the existing evidence base for SNPs
predicting aspirin’s chemopreventive benet for CRC. Nan et al.
(13) previously reported signicant interactions between aspirin
use and variants in 12p12.3 (rs2965667 and rs10505806) and 15q25.2
(rs16973225) for CRC risk in a subset of studies included in this inves-
tigation. Although these SNPs were not identied at genome- wide
signicance in our main analysis, we did conrm nominally signicant
interactions for these SNPs. While other prior studies have identied
putative SNP- based biomarkers for an interaction of aspirin/NSAID
use and CRC risk [summarized in prior reviews (3, 49)], the functional
relevance of the SNPs to their preventive mechanism for CRC has
been limited, beyond putative indirect linkages with CRC- relevant
or prostaglandin- signaling adjacent pathways or enzymes linked to
aspirin pharmacokinetics. Similarly, prior studies that have performed
targeted genotyping of specic pathways related to aspirin/NSAID
chemoprevention (e.g., prostaglandin synthesis) include important
a priori signals (50–54), such that the absence of previously studied
functional genetic variants from the results here does not preclude
their potential utility in precision prevention. Nonetheless, our study
is not only the largest study to date to perform a GWIS and, thus,
has the greatest power to eliminate potential false negatives, but also
by extending our ndings to our functional dataset, we have demon-
strated that rs350047 is within an eQTL for PTGER4, implicating a
mechanistic link for this SNP and the eects of NSAIDs on PGE2-
PTGER4 signaling.
Beyond host genetics, other molecular epidemiology studies
have demonstrated that aspirin/NSAID chemopreventive eects
may be limited to tumors that would have otherwise developed via
pathways sensitive to aspirin prevention. Specically, these prior
studies demonstrate that aspirin may have greater protective eects
against BRAF wild- type (55) and PIK3CA mutant CRCs (56) or those
tumors with higher PTGS2 expression (57) or against CRCs arising
in individuals with intact HPGD expression (58). Our ndings that
interactions between aspirin/NSAID use and rs350047 were statisti-
cally signicant only when comparing BRAF mutant, CIMP- high,
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or MSI- high cases to controls, whereas interactions between aspirin/
NSAID use and rs72833769 were limited to cases absent of these
molecular markers compared to controls, suggests that host genetics
may further inuence aspirin’s ability to dierentially prevent tumors
arising via separate tumorigenesis pathways (e.g., traditional versus
serrated pathways). Combined, our ndings support calls for a more
nuanced, precision prevention approach to specically identify subsets
of individuals likely to benet from aspirin/NSAIDs and improve
broader, one- size- ts- all recommendations [i.e., only on the basis of
age or conditioning on added risk for cardiovascular disease as has
been the case for past U.S. Preventive Services Task Force recommen-
dations (59, 60)]. While some markers, such as rs72833769, may
provide simpler, more qualitative guidance (i.e., go/no- go) for indi-
vidual stratication, quantitative interaction markers, such as rs350047,
will be critical to calibrating precision prevention recommendations
to maximize net benet among those more likely to benet, par-
ticularly when they are linked to potential mechanisms of action.
ese ndings can help identify interrelated modes of action that
may help clarify dierential anticancer eects associated with aspirin/
NSAIDs, new and more eective therapeutic or prevention targets,
or specic pathways individuals are on toward the development of
cancer that may or may not be responsive to these agents. Moreover,
quantitative measures may further help explain observed interindi-
vidual responses to preventive interventions, even among patients
identied as likely to respond by qualitative measures (11). In all, a
truly precise precision prevention approach likely requires the incor-
poration of both qualitative and quantitative genomic interaction
markers of risk and response in context of the potential tumorigenesis
pathways to which an individual is particularly susceptible to and
additional individual risk factors.
Our study has several strengths, including being the largest GWIS
of CRC and aspirin/NSAIDS to date, pairing this data with functional
datasets that allow for the identication of eQTLs and using ecient
and powerful statistical methods to improve power over standard GxE
tests. A limitation of our study is that the resolution of data collected
on aspirin use alone varied across studies and required us in some
cases to group aspirin use, for which there is a clearer chemopreven-
tion benet established, with other non- aspirin NSAID use. While
not ideal, we have performed careful data harmonization across the
studies to ensure that we accurately have differentiated between
aspirin/NSAID use, which may include aspirin only users along
with users of both aspirin and/or other NSAIDs, and those who
explicitly recorded aspirin use separately from other NSAID use. In
addition, our study focuses on harmonized self- reported regular
aspirin/NSAID use presumed to be representative of standard dose
recommendations (≤325 mg/day) as this was most consistently col-
lected across all studies. Although multiple lines of evidence support
the notion that the chemopreventive association with aspirin is
strongest 5 to 10 years aer commencing continued regular use, we
did not have sucient information to consider the duration of ex-
posure. Despite this measurement error, which is likely to attenuate
risk estimates and lead to reduced power, we were able to show
strong protective associations for aspirin/NSAIDs and identify two
signicant interactions. Nonetheless, future studies will need to ex-
amine whether the interactions observed for aspirin/NSAID use are
specic to use of aspirin or extend to other non- aspirin NSAIDs and
in context of other key factors including dose and duration of use.
Our analysis is limited to individuals of European ancestry, thereby
limiting the direct extension of these ndings to dierent racial and
ethnic populations. Last, we cannot rule out the eects of residual
confounding, including from additional CRC risk factors (e.g., in-
ammatory bowel disease history) not otherwise accounted for in
our analysis, or recall bias in our analysis.
In summary, we identied previously undescribed genetic loci
that modify the protective eect of regular aspirin/NSAID use on
CRC risk. Functional evidence presented in our investigation im-
plicates genes directly involved in CRC- associated signaling path-
ways, such as PGE2 synthesis/signaling in the case of PTGER4, and
downstream pathways involved in tumorigenesis and proposed to
be central to aspirin’s protective mode of action, suggesting biological
plausibility. Validation and additional functional work are necessary
to conrm these ndings. Furthermore, the likelihood of identifying
additional interaction loci in the future can be improved via imple-
mentation of tissue- /cell- specic functional annotations, along with
multi- ethnic GWIS.
MATERIALS AND METHODS
Study design
We pooled individual level genomic and epidemiological data from
three consortia comprising individuals of European ancestry—the
GECCO, the CORECT, and the CCFR consortia comprising a total of
52 studies. Study details have been previously published (14, 42, 61)
and can be found in tableS6. For cohort studies, nested case- control
sets were assembled via risk- set sampling, while population- based
controls were used for case- control studies. Clinical trials were
treated as cohort studies and participants were matched according to
trial arm. Cases were dened as CRC or advanced adenomas and
were conrmed by medical records, pathological reports, or death
certicate information. Controls were matched on age, sex, race, and
enrollment date/trial group, when applicable. For the small subset
of advanced adenoma cases, matched controls were found to be
polyp- free on sigmoidoscopy or colonoscopy at the time of adenoma
selection. All participants gave written informed consent, and studies
were approved by their respective Institutional Review Boards.
Exposure and aspirin/NSAID use ascertainment
Analyses include individuals with complete exposure and covariate
information. We excluded individuals based on discrepancies between
reported and genotypic sex, cryptic relatedness, and duplicates. For
any individual included in multiple studies, we selected a single
record for them, prioritizing the study that genotyped on the more
comprehensive platform. Collection of risk factor data and harmo-
nization across contributing studies has been previously described
(62). Combined use of any aspirin or non- aspirin NSAIDs at reference
time is dened as “aspirin/NSAID use” (yes or no), comprising a
nal study sample size of 72,667 individuals (30,806 cases and 41,861
controls). When aspirin use was specically queried separately from
NSAID use, we dened regular use of aspirin at reference time as
“aspirin only use” (yes or no) to conduct analyses limited to aspirin
users (N=72,137; 30,574 cases and 41,563 controls). Multivariate
models include age (continuous), sex (male/female), study, smok-
ing status (never/ever), alcohol consumption (nondrinkers; moder-
ate, 1 to 28 g/day; heavy, >28 g/day), BMI (continuous), and red
meat intake (study and sex- specic quartiles of red meat intake
based on controls only) as specically denoted. Individuals with
missing covariate data were excluded from any model using the
covariate.
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Genotyping
e genotyping platforms used in each study are summarized in
tableS6. Briey, genotyped SNPs were excluded on the basis of call
rate (<95 to 98%), lack of Hardy- Weinberg equilibrium (P<1 × 10−4),
and discordant calls between duplicates. All autosomal SNPs of all
studies were imputed to the Haplotype Reference Consortium r1.1
(2016) reference panel via the Michigan Imputation Server (63) and
converted into a binary format for data management and analyses
using R package BinaryDosage. Imputed common SNPs were l-
tered on the basis of a pooled minor allele frequency (MAF)≥0.01
and imputation accuracy R2>0.8. Aer imputation and quality
control analyses, a total of more than 7.2 million SNPs were avail-
able for analysis. Principal components analysis for population
stratication assessment was performed using PLINK 1.9 on 30,000
randomly sampled imputed SNPs with MAF>0.05 and imputation
accuracy R2> 0.99. Additional details on genotyping and quality
control have been previously published (42).
Statistical analysis
We evaluated associations of CRC risk with regular aspirin/NSAID
use and with aspirin only use and CRC risk using random- eects
meta- analysis of study specic results to obtain summary ORs and
95% CIs across studies. Association tests were stratied by study de-
sign, sex, and tumor subsite. e latter was categorized into the fol-
lowing groups based on the following ICD- 9 codes (otherwise
excluded): proximal colon (153.0, 153.1,153.4, 153.6), distal colon
(153.2,153.3, 153.7), and rectum (154.0, 154.1).
Common variant analysis
Genome- wide scans were conducted using GxEScanR, an R package
that implements several methods for detecting GxE interactions
(https://CRAN.R- project.org/package=GxEScanR). Imputed al-
lelic dosages were modeled as continuous variables. Our analysis
employs three primary methods: (i) traditional 1- df GxE logistic
regression models, (ii) a two- step method using “D versus G”
and “E versus G” joint information [the EDGE method (64)] as
the step 1 filtering statistic and the 1- df GxE statistic for the step
2 test accounting for LD- based correlation among SNPs in step 2
(65), and (iii) a 3- df joint test that incorporates information
from main effects, gene- exposure associations, and GxE inter-
action statistics in a single model. (66) For the 3- df test, we re-
port only results with GxE P value < 1 × 10−4, since this test
captures markers with significant results from any of the three
sources of statistics. We adjusted the overall genome- wide sig-
nificance threshold for each testing procedure to 5 × 10−8/2.5 to
account for multiple testing with methods that are statistically
correlated. We adopt the following notation: E, regular aspirin/
NSAID use; G, SNP; D, CRC outcome; and C, set of adjustment
covariates. Traditional logistic regression models assessed inter-
actions on a multiplicative scale by including an interaction
term in the model logit[Pr(D = 1∣G, E, C)] = β0 + βGG + βEE +
βGxEGxE + βCC, testing H0 : βGxE = 0. The typical focus of a
GWIS is on the 1- df test of GxE interaction based on the null
hypothesis H0 : βGxE = 0. This test is known to generally have
low power, particularly in the context of discovering new inter-
actions in a GWIS. To enhance our power to discover new GxE
loci, we used the two- step EDGE method and 3- df joint tests
summarized above. Additional details of these approaches are in
the Supplementary Materials.
Functional follow- up
We leveraged the BarcUVa- Seq (https://barcuvaseq.org/) resource
(67) to develop prediction models to test interactions between pre-
dicted gene expression and regular aspirin/NSAID use, in a method
previously explored in GECCO consortium using GTEx data (68).
Weights were generated using elastic net regularized regression
models t on BarcUVa- Seq gene expression (20,693 measured gene
expressions) and HRC imputed genotypes. Imputed SNPs were l-
tered on the basis of MAF>0.1 and imputation quality (R2>0.7);
models were adjusted for age, sex, RNA- seq batch, tissue location,
principal components, and probabilistic estimation of expression
residuals (PEER) factors (69). Weights were then used to predict
gene expression in our study sample of 72,667 subjects. In total,
13,393 gene expressions were successfully predicted. Interaction
tests were conducted using logistic regression with an interaction
term between predicted genetic expression and aspirin/NSAID use
and aspirin use alone.
We used LocusZoom v1.3 (70) to generate regional plots for
significant findings to inspect and extend the association sig-
nal and LD, and position of findings relative to genes in the
region. Measures of LD were estimated using European popu-
lations from the 1000 Genomes Project. The putative function-
al role of these SNPs and those in LD (R2>0.2) at 500- kb flanking
regions was investigated with relation to their potential con-
tribution to gene expression regulation in two ways: first, by
their physical location in regions of chromatin accessibility or
histone modifications (variant enhancer loci) and, second,
through their direct association with expression of nearby genes
(eQTLs).
To assess the physical location of the SNPs in regions of chroma-
tin accessibility or histone modications, we queried overlaps be-
tween our ndings and regions containing active enhancer elements
in tissue from healthy and tumor colon samples in addition to CRC
cell lines, obtained from previously analyzed assays for transposase-
accessible chromatin with sequencing (ATAC- seq) data, DNase hy-
persensitivity sequencing, and H3K27ac histone chromatin
immunoprecipitation sequencing datasets. (15) We extended this
analysis to include additional tissue types by incorporating regula-
tory annotations of histone modications from 10 groups of tissues,
obtained from several resources (71, 72) and compiled by Finucane
etal. (16) Furthermore, we queried lead and LD SNPs against func-
tional annotation databases from ENSEMBL using the Variant Eect
Predictor tool. (73)
We checked for eQTLs using several resources: (i) GTEx v8, (ii)
the colon transcriptome explorer (CoTrEx 2.0; https://barcuvaseq.
org/cotrex/, accessed May 2021), a resource for transcriptomic data
jointly developed by the University of BarcUVa- Seq, which includes
eQTL from 445 epithelium- enriched healthy colon biopsies from
ascending, transverse, and descending colon, and 3) eQTL results
from eQTLGEN, a consortium of 37 cohorts pooling RNA sequenc-
ing data from whole blood samples.
Supplementary Materials
This PDF le includes:
Supplementary Materials and Methods
Sources of consortium funding
Figs.S1 to S6
TablesS1 to S6
References
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Acknowledgments: ASTERISK: We are very grateful to B. Buecher without whom this project
would not have existed. We also thank all those who agreed to participate in this study,
including the patients and the healthy control persons, as well as all the physicians, technicians,
and students. CCFR: The Colon CFR graciously thanks the generous contributions of their study
participants, dedication of study sta, and the nancial support from the U.S. National Cancer
Institute, without which this important registry would not exist. CLUE II: We thank the
participants of Clue II and appreciate the continued eorts of the sta at the Johns Hopkins
George W. Comstock Center for Public Health Research and Prevention in the conduct of the
Clue II Cohort Study. Cancer data were provided by the Maryland Cancer Registry, Center for
Cancer Prevention and Control, Maryland Department of Health, with funding from the State of
Maryland and the Maryland Cigarette Restitution Fund. The collection and availability of cancer
registry data are also supported by the Cooperative Agreement NU58DP006333, funded by the
Centers for Disease Control and Prevention. Its contents are solely the responsibility of the
authors and do not necessarily represent the ocial views of the Centers for Disease Control
and Prevention or the Department of Health and Human Services. CPS- II: We express sincere
appreciation to all Cancer Prevention Study- II participants and to each member of the study and
biospecimen management group. We would like to acknowledge the contribution to this study
from central cancer registries supported through the Centers for Disease Control and
Prevention’s National Program of Cancer Registries and cancer registries supported by the
National Cancer Institute’s Surveillance Epidemiology and End Results Program. We assume full
responsibility for all analyses and interpretation of results. The views expressed here are those of
the authors and do not necessarily represent the American Cancer Society or the American
Cancer Society–Cancer Action Network. DACHS: We thank all participants and cooperating
clinicians, and everyone who provided excellent technical assistance. EDRN: We acknowledge all
contributors to the development of the resource at University of Pittsburgh School of Medicine,
Department of Gastroenterology, Department of Pathology, Hepatology and Nutrition and
Biomedical Informatics. Harvard cohorts: The study protocol was approved by the institutional
review boards of the Brigham and Women’s Hospital and Harvard T.H. Chan School of Public
Health, and those of participating registries as required. We acknowledge Channing Division of
Network Medicine, Department of Medicine, Brigham and Women’s Hospital as home of the
NHS. We would like to acknowledge the contribution to this study from central cancer registries
supported through the Centers for Disease Control and Prevention’s National Program of Cancer
Registries (NPCR) and/or the National Cancer Institute’s Surveillance, Epidemiology, and End
Results (SEER) Program. Central registries may also be supported by state agencies, universities,
and cancer centers. Participating central cancer registries include the following: Alabama,
Alaska, Arizona, Arkansas, California, Colorado, Connecticut, Delaware, Florida, Georgia, Hawaii,
Idaho, Indiana, Iowa, Kentucky, Louisiana, Massachusetts, Maine, Maryland, Michigan,
Mississippi, Montana, Nebraska, Nevada, New Hampshire, New Jersey, New Mexico, New York,
North Carolina, North Dakota, Ohio, Oklahoma, Oregon, Pennsylvania, Puerto Rico, Rhode
Island, Seattle SEER Registry, South Carolina, Tennessee, Texas, Utah, Virginia, West Virginia, and
Wyoming. We assume full responsibility for analyses and interpretation of these data. Kentucky:
We would like to acknowledge the sta at the Kentucky Cancer Registry. LCCS: We acknowledge
the contributions of Jennifer Barrett, Robin Waxman, Gillian Smith and Emma Northwood in
conducting this study. NCCCS I & II: We would like to thank the study participants, and the NC
Colorectal Cancer Study sta. PLCO: We thank the PLCO Cancer Screening Trial screening center
investigators and the stas from Information Management Services Inc. and Westat Inc. We also
thank the study participants for their contributions that made this study possible. Cancer
incidence data have been provided by the District of Columbia Cancer Registry, Georgia Cancer
Registry, Hawaii Cancer Registry, Minnesota Cancer Surveillance System, Missouri Cancer
Registry, Nevada Central Cancer Registry, Pennsylvania Cancer Registry, Texas Cancer Registry,
Virginia Cancer Registry, and Wisconsin Cancer Reporting System. All are supported in part by
funds from the Center for Disease Control and Prevention; National Program for Central
Registries; local states or by the National Cancer Institute; Surveillance, Epidemiology, and End
Results program. The results reported here and the conclusions derived are the sole
responsibility of the authors. PPS3 and PPS4 Polyp Prevention Study trials: We would like to
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thank the study participants, investigators, and sta. SEARCH: We thank the SEARCH team.
SELECT: We thank the research and clinical sta at the sites that participated on SELECT study,
without whom the trial would not have been successful. We are also grateful to the 35,533
dedicated men who participated in SELECT. WHI: The authors thank the WHI investigators and
sta for their dedication, and the study participants for making the program possible. A full
listing of WHI investigators can be found at www- whi- org.s3.us- west- 2.amazonaws.com/
wp- content/uploads/WHI- Investigator- Long- List.pdf. Disclaimer: Where authors are identied
as personnel of the International Agency for Research on Cancer/World Health Organization,
the authors alone are responsible for the views expressed in this article and they do not
necessarily represent the decisions, policy or views of the International Agency for Research on
Cancer/World Health Organization. Funding: Individual authors report the following grants
and/or funding support: D.A.D.: K01 DK120742; A.E.K.: R01CA201407; J.M.: R01CA201407,
P01CA196569; R01CA273198; J.P.L.: R01CA201407, P01CA196569; R01CA273198; E.K.:
R01CA201407, P01CA196569; R01CA273198; Y.F.: P01CA196569; R01CA273198; E.L.B.:
R01CA059005 and R01CA098286; S.I.B.: Intramural Research Program, Division of Cancer
Epidemiology and Genetics, NCI, NIH; J.C.F.: R01 CA155101; S.B.G.: U19 CA148107, R01
CA263318; M.A.J.: U01 CA167551, U01 CA122839, R01 CA143247, U19 CA148107, R01 CA81488,
U19 CA148107; B.M.L.: VCA MCRF18005; J.O.: R03CA270473, R01CA207371,
R01CA189184,R01CA254108, and U01CA206110; C.M.U.: R03CA270473, R01CA207371,
R01CA189184, R01CA254108, U01CA206110, and P30CA042014; U.P.: R01CA059045,
U01CA164930, R01CA244588, R01CA201407, and R01 CA273198; W.J.G.: R01CA201407,
P01CA196569; R01CA273198; and Genetics and Epidemiology of Colorectal Cancer Consortium
(GECCO): National Cancer Institute, National Institutes of Health, U.S. Department of Health and
Human Services (R01 CA059045, U01 CA164930, R01 CA244588, R01 CA201407). Genotyping/
Sequencing services were provided by the Center for Inherited Disease Research (CIDR) contract
number HHSN268201700006I and HHSN268201200008I. This research was funded in part
through the NIH/NCI Cancer Center Support Grant P30 CA015704. Scientic Computing
Infrastructure at Fred Hutch was funded by ORIP grant S10OD028685. Additional funding
supporting the GECCO Consortium and participating studies is listed in the Supplemental
Materials. Author contributions: Conceptualization: D.A.D., Y.L., J.P.L., S.A.B., N.D., H.B., P.T.C.,
J.C.- C., S.B.G., M.J.G., R.N., D.C.T., K.T.T., V.M., U.P., A.T.C., and W.J.G. Methodology: D.A.D., A.E.K., Y.L.,
J.P.L., E.K., N.D., J.B., H.B., A.B., P.T.C., D.V.C., R.C.- T., S.B.G., M.J.G., R.N., D.C.T., C.M.U., L.H., U.P., and
W.J.G. Investigation: D.A.D., A.E.K., Y.L., Y.F., J.B., J.D.P., H.B. A.B.- H., P.T.C., G.C., D.V.C., S.B.G., A.G.,
M.J.G., P.A.N., M.O.- S., S.O., R.K.P., B.P., P.C.S., C.M.U., U.P., and A.T.C. Visualization: D.A.D., A.E.K., Y.L.,
A.H., and W.J.G. Resources: D.A.D., D.A., A.W., R.L.P., V.A., E.L.B., S.I.B., H.B., A.B.- H., P.T.C., G.C., J.C.- C.,
J.C.F., S.B.G., A.G., M.H., J.R.H., M.A.J., A.K., L.L.M., L.L., B.L., P.A.N., C.C.N., S.O., A.J.P., E.A.P., R.K.P.,
P.C.S., R.E.S., M.C.S., C.M.U., C.Y.U., E.W., V.M., A.T.C., and W.J.G. Software: A.E.K., Y.L., J.M., J.P.L., E.K.,
Y.F., J.B., D.V.C., D.C.T., and W.J.G. Formal analysis: D.A.D., A.E.K., C.Q., J.M., J.P.L., E.K., Y.F., N.Z.,
V.D.- O,. J.B., D.V.C., S.B.G., M.J.G., J.R.H., S.L.S., and W.J.G. Data curation: D.A.D., A.E.K.,. Y.L., C.Q.,
V.A., S.B.G., T.A.H., M.H., J.R.H., E.W., V.M., and W.J.G. Validation: D.A.D., A.E.K., H.B., A.B., A.G., R.N.,
J.O., and W.J.G. Funding acquisition: D.D.B., H.B., P.T.C., D.V.C., S.B.G., M.J.G., M.H., M.A.J., A.K.,
L.L.M., R.N., P.A.N., E.A.P., D.C.T., C.M.U., E.W., V.M., U.P., A.T.C., and W.J.G. Project administration:
D.A.D., S.A.B., S.B.G., L.L., M.H., E.W., U.P., A.T.C., and W.J.G. Supervision: D.A.D., V.M., U.P., A.T.C., and
W.J.G. Writing—original draft: D.A.D., A.E.K., and W.J.G. Writing—review and editing: All authors.
Competing interests: E.K. owns stock in Abbvie (ABBV) and Pzer (PFE). J.W.B. is an owner and
employee of BioRealm LLC. S.A.B. owns stock and is an employee of Adaptive Biotechnologies.
S.B.G. has equity in Brogent International LLC not related to this work. K.A.J. has received
personal fees from Bristol- Myers Squibb outside the submitted work and reports grants from
the National Cancer Institute. A.K. is on the scientic advisory board of PatchBio, TensorBio,
SerImmune, and OpenTargets; was a consultant with Illumina; and owns shares in
DeepGenomics, ImmunAI, and Freenome. C.M.U. has as cancer center director oversight over
research funded by several pharmaceutical companies but has not received funding directly
herself. U.P. was a consultant with Abbvie and her husband is holding individual stocks for the
following companies: BioNTech SE–ADR, Amazon, CureVac BV, NanoString Technologies,
Google/Alphabet Inc. Class C, NVIDIA Corp, and Microsoft Corp. All other authors declare that
they have no competing interests. Data and materials availability: All data needed to evaluate
the conclusions in the paper are present in the paper and/or the Supplementary Materials.
Summary- level data for the GWAS datasets are available through the GWAS catalog (accession
no. GCST012876). For individual- level data, datasets are deposited in dbGaP (accession nos.
phs001415.v1.p1, phs001315.v1.p1, phs001078.v1.p1, phs001903.v1.p1, phs001856.v1.p1, and
phs001045.v1.p1).
Submitted 14 August 2023
Accepted 26 April 2024
Published 29 May 2024
10.1126/sciadv.adk3121
Downloaded from https://www.science.org on May 30, 2024