Risk Stratification for Early-Onset Colorectal Cancer Using a Combination of Genetic and Environmental Risk Scores

An International Multi-Center Study

Alexi N. Archambault, PhD, MPH; Jihyoun Jeon, PhD, MS; Yi Lin, MS; Minta Thomas, PhD, MPhil; Tabitha A. Harrison, MPH; D. Timothy Bishop, PhD, MSC; Hermann Brenner, MD, MPH; Graham Casey, PhD; Andrew T. Chan, MD, MPH; Jenny Chang-Claude, PhD; Jane C. Figueiredo, PhD; Steven Gallinger, MD, MSC; Stephen B. Gruber, MD, PhD; Marc J. Gunter, PhD; Feng Guo, PhD, MSC; Michael Hoffmeister, PhD; Mark A. Jenkins, PhD; Temitope O. Keku, PhD, MSPH, MSC; Loïc Le Marchand, MD, PhD; Li Li, MD, PhD; Victor Moreno, PhD; Polly A. Newcomb, PhD, MPH; Rish Pai, MD, PhD; Patrick S. Parfrey, MD; Gad Rennert, MD, PhD; Lori C. Sakoda, PhD MPH; Jeffrey K. Lee, MD, MAS; Martha L. Slattery, PhD; Mingyang Song, ScD, MS; Aung Ko Win, PhD, MPH; Michael O. Woods, PhD; Neil Murphy, PhD; Peter T. Campbell, PhD, MSc; Yu-Ru Su, PhD, MS; Iris Lansdorp-Vogelaar, PhD; Elisabeth F. P. Peterse, PhD; Yin Cao, ScD, MPH; Anne Zeleniuch-Jacquotte, MD, MS; Peter S. Liang, MD, MPH; Mengmeng Du, ScD; Douglas A. Corley, MD, PhD, MPH; Li Hsu, PhD; Ulrike Peters, PhD, MPH; Richard B. Hayes, PhD, MPH, DDS

Disclosures

J Natl Cancer Inst. 2022;114(4):528-539.

Abstract and Introduction

Background: The incidence of colorectal cancer (CRC) among individuals aged younger than 50 years has been increasing. As screening guidelines lower the recommended age of screening initiation, concerns including the burden on screening capacity and costs have been recognized, suggesting that an individualized approach may be warranted. We developed risk prediction models for early-onset CRC that incorporate an environmental risk score (ERS), including 16 lifestyle and environmental factors, and a polygenic risk score (PRS) of 141 variants.

Methods: Relying on risk score weights for ERS and PRS derived from studies of CRC at all ages, we evaluated risks for early-onset CRC in 3486 cases and 3890 controls aged younger than 50 years. Relative and absolute risks for early-onset CRC were assessed according to values of the ERS and PRS. The discriminatory performance of these scores was estimated using the covariate-adjusted area under the receiver operating characteristic curve.

Results:Increasing values of ERS and PRS were associated with increasing relative risks for early-onset CRC (odds ratio per SD of ERS = 1.14, 95% confidence interval [CI] = 1.08 to 1.20; odds ratio per SD of PRS = 1.59, 95% CI = 1.51 to 1.68), both contributing to case-control discrimination (area under the curve = 0.631, 95% CI = 0.615 to 0.647). Based on absolute risks, we can expect 26 excess cases per 10 000 men and 21 per 10 000 women among those scoring at the 90th percentile for both risk scores.

Abstract and Introduction
Methods
Results
Discussion
References

Table 1. Baseline participant characteristics of participants
Characteristic	Cases (n = 3486)	Controls (n = 3890)
Mean age (SD), y	44.43 (7.39)	44.52 (5.38)
Sex, No (%)
Female	1818 (52.2)	2043 (52.5)
Male	1668 (47.8)	1847 (47.5)
Disease site, No. (%)
Proximal colon	891 (27.5)	—
Distal colon	1056 (32.5)	—
Rectum	1298 (40.0)	—
Family history, No. (%)
No	2407 (76.5)	2327 (87.3)
Yes	741 (23.5)	340 (12.7)
Combined risk scores
ERS
Quartile 1	828 (23.8)	1019 (26.2)
Quartile 2	801 (23.0)	1081 (27.8)
Quartile 3	915 (26.2)	960 (24.7)
Quartile 4	942 (27.0)	830 (21.3)
PRS
Quartile 1	640 (18.4)	1209 (31.1)
Quartile 2	820 (23.5)	1089 (28.0)
Quartile 3	920 (26.4)	933 (24.0)
Quartile 4	1106 (31.7)	659 (16.9)
Education, highest level completed, No. (%)
<High school graduate	483 (13.9)	622 (16.0)
High school graduate or completed GED	762 (21.9)	538 (13.8)
Some college or technical school	1058 (30.3)	1190 (30.6)
≥College graduate	1183 (33.9)	1540 (39.6)
Mean height (SD), cm	171.2 (9.8)	170.8 (9.5)
Mean BMI (SD), kg/m²	27.2 (5.6)	26.9 (5.2)
Red meat, No. (%), servings/d
Quartile 1^a	828 (24.7)	1004 (26.4)
Quartile 2^a	843 (25.2)	1234 (32.4)
Quartile 3^a	888 (26.5)	998 (26.2)
Quartile 4^a	791 (23.6)	573 (15.0)
Processed meat, No. (%), servings/d
Quartile 1^a	263 (13.7)	385 (13.0)
Quartile 2^a	580 (30.1)	1006 (34.0)
Quartile 3^a	698 (36.2)	1296 (43.8)
Quartile 4^a	385 (20.0)	272 (9.2)
Fruit, No. (%), servings/d
Quartile 1^a	1045 (31.3)	1241 (33.0)
Quartile 2^a	1054 (31.5)	1097 (29.1)
Quartile 3^a	711 (21.3)	750 (19.9)
Quartile 4^a	531 (15.9)	678 (18.0)
Vegetable, No. (%), servings/d
Quartile 1^a	801 (23.7)	1173 (30.9)
Quartile 2^a	1271 (37.6)	1101 (29.0)
Quartile 3^a	882 (26.1)	878 (23.2)
Quartile 4^a	424 (12.6)	639 (16.9)
Total fiber, No. (%), g/d
Quartile 1^a	354 (26.4)	238 (27.1)
Quartile 2^a	331 (24.7)	217 (24.7)
Quartile 3^a	309 (23.0)	202 (23.0)
Quartile 4^a	348 (25.9)	221 (25.2)
Total calcium intake, No. (%), mg/d
Quartile 1^a	298 (8.5)	215 (5.5)
Quartile 2^a	1926 (55.2)	2426 (62.4)
Quartile 3^a	1011 (29.0)	1027 (26.4)
Quartile 4^a	251 (7.2)	222 (5.7)
Total folate intake, No. (%), mcg/d
Quartile 1^a	787 (23.7)	467 (12.4)
Quartile 2^a	1331 (40.1)	2138 (56.7)
Quartile 3^a	646 (19.4)	774 (20.5)
Quartile 4^a	559 (16.8)	393 (10.4)
Sedentary lifestyle, No. (%)
No	654 (78.9)	1697 (82.2)
Yes	175 (21.1)	367 (17.8)
Pack-years of smoking, No. (%)
Never smoker	1772 (55.9)	2196 (63.2)
Quartile 1 ^a	395 (12.5)	413 (11.9)
Quartile 2^a	401 (12.6)	368 (10.6)
Quartile 3^a	376 (11.9)	336 (9.7)
Quartile 4^a	226 (7.1)	162 (4.7)
Alcohol use, No. (%), g/d
0	1450 (43.1)	1104 (28.7)
1–28	1490 (44.3)	2222 (57.9)
>28	424 (12.6)	514 (13.4)
Aspirin use, No. (%)
No	3090 (91.7)	3520 (91.9)
Yes	281 (8.3)	312 (8.1)
NSAID use, No. (%)
No	2967 (89.4)	3115 (82.5)
Yes	353 (10.6)	661 (17.5)
Diabetes diagnosis, No. (%)
No	3234 (95.5)	3693 (97.4)
Yes	154 (4.5)	100 (2.6)

^aStudy and sex-specific quartiles. Note that the majority of lifestyle and environmental variables were modeled as ordinal sex- and study-specific quartiles throughout the analysis. BMI = body mass index; ERS = environmental risk score; GED = general educational development; NSAID = nonsteroidal antiinflammatory drug; PRS = polygenic risk score.

Table 2. Odds ratio of ERS and PRS associated with early-onset CRC risk using repeated 10-fold cross-validation
Model	OR (95% CI)	P ^a	K-Fold cross-validation accuracy (SD)
Models with ERS as predictor
Model 1: ERS per 1 SD ^b	1.14 (1.08 to 1.20)	<.001	0.721 (0.011)
Model 2: ERS by quartile ^c			0.721 (0.013)
1	1 (Referent)	—	—
2	1.00 (0.86 to 1.16)	.97	—
3	1.22 (1.05 to 1.42)	.009	—
4	1.36 (1.16 to 1.58)	<.001	—
Models with PRS as predictor
Model 3: PRS per 1 SD^d	1.59 (1.51 to 1.68)	<.001	0.720 (0.014)
Model 4: PRS by quartile^e			0.717 (0.016)
1	1 (Referent)	—	—
2	1.54 (1.32 to 1.80)	<.001	—
3	2.15 (1.84 to 2.51)	<.001	—
4	3.50 (3.00 to 4.09)	<.001	—
Models with ERS and PRS as predictors
Model 5^f:			0.737 (0.014)
ERS per 1 SD	1.12 (1.06 to 1.19)	<.001	—
PRS per 1 SD	1.59 (1.50 to 1.68)	<.001	—
Model 6^g:			0.734 (0.011)
ERS by quartile
1	1 (Referent)	—	—
2	0.99 (0.85 to 1.16)	.91	—
3	1.24 (1.06 to 1.44)	.008	—
4	1.32 (1.12 to 1.54)	<.001	—
PRS by quartile
1	1 (Referent)	—	—
2	1.50 (1.28 to 1.75)	<.001	—
3	2.06 (1.77 to 2.41)	<.001	—
4	3.52 (3.00 to 4.14)	<.001	—

^a2-sided P values per the Wald test. CI = confidence interval; CRC = colorectal cancer; ERS = environmental risk score; OR = odds ratio; PRS = polygenic risk score.
^bThe model includes age, sex, total energy consumption, study, family history, and a continuous z-transformed ERS.
^cThe model includes age, sex, total energy consumption, study, family history, and ERS in quartiles.
^dThe model includes age, sex, genotype platform, family history, principal components, and a continuous z-transformed PRS.
^eThe model includes age, sex, genotype platform, family history, principal components, and PRS in quartiles.
^fThe model includes age, sex, total energy consumption, study, family history, principal components, genotype platform, and continuous z-transformed ERS and PRS.
^gThe model includes age, sex, total energy consumption, study, family history, principal components, genotype platform, and ERS and PRS in quartiles.

Table 3. Covariate-adjusted AUC comparisons between risk prediction models
Model	All participants AUC (95% CI)	Men AUC (95% CI)	Women AUC (95% CI)
Model 1: Family history^a	0.563 (0.555 to 0,571)	0.568 (0.558 to 0.580)	0.558 (0.547 to 0.569)
Model 2: ERS per 1 SD^b	0.536 (0.519 to 0.552)	0.546 (0.519 to 0.571)	0.525 (0.494 to 0.543)
Model 3: PRS per 1 SD^c	0.628 (0.613 to 0.644)	0.621 (0.592 to 0.651)	0.633 (0.612 to 0.655)
Model 4: ERS and PRS per 1 SD^d	0.631 (0.615 to 0.647)	0.629 (0.604 to 0.654)	0.630 (0.607 to 0.652)

^aThe model includes family history as the predictor, adjusting for sex (for the model including all participants) and age. AUC = area under the receiver operating characteristic curve; CI = confidence interval; ERS = environmental risk score; PRS = polygenic risk score.
^bThe model includes a z-transformed ERS as the predictor, adjusting for age, sex (for the model including all participants), total energy consumption, study, and family history.
^cThe model includes a z-transformed PRS as the predictor, adjusting for age, sex (for the model including all participants), family history, genotype platform, and principal components.
^dThe model includes z-transformed ERS and PRS as predictors, adjusting for age, sex (for the model including all participants), study, family history, total energy consumption, principal components, genotype platform, and a z-transformed ERS.

Table 4. Ten-year absolute risk estimates for early-onset CRC with variable risk factor profiles and starting ages
ERS risk percentile	PRS risk percentile	Starting age of 30 y		Starting Age of 40 years
ERS risk percentile	PRS risk percentile	Men %, (95% CI)	Women %, (95% CI)	Men %, (95% CI)	Women %, (95% CI)
Average risk^a		0.06 (—)	0.05 (—)	0.21 (—)	0.18 (—)
ERS and PRS combined^b
1	1	0.02 (0.02 to 0.02)	0.02 (0.02 to 0.02)	0.06 (0.06 to 0.07)	0.06 (0.06 to 0.07)
10	10	0.02 (0.02 to 0.02)	0.02 (0.02 to 0.02)	0.08 (0.08 to 0.08)	0.08 (0.07 to 0.08)
50	50	0.05 (0.05 to 0.05)	0.05 (0.05 to 0.05)	0.19 (0.19 to 0.19)	0.17 (0.17 to 0.17)
90	90	0.13 (0.13 to 0.14)	0.11 (0.11 to 0.12)	0.47 (0.46 to 0.49)	0.39 (0.38 to 0.41)
99	99	0.16 (0.16 to 0.17)	0.14 (0.13 to 0.14)	0.58 (0.56 to 0.60)	0.47 (0.46 to 0.49)
PRS^c
—	1	0.03 (0.02 to 0.03)	0.02 (0.02 to 0.02)	0.09 (0.09 to 0.09)	0.07 (0.07 to 0.08)
—	10	0.03 (0.03 to 0.03)	0.02 (0.02 to 0.03)	0.10 (0.10 to 0.11)	0.09 (0.08 to 0.09)
—	50	0.05 (0.05 to 0.05)	0.05 (0.05 to 0.05)	0.20 (0.19 to 0.20)	0.17 (0.17 to 0.17)
—	90	0.10 (0.10 to 0.11)	0.10 (0.10 to 0.10)	0.37 (0.36 to 0.38)	0.34 (0.34 to 0.35)
—	99	0.12 (0.12 to 0.12)	0.11 (0.11 to 0.12)	0.43 (0.42 to 0.44)	0.40 (0.39 to 0.41)
ERS^d
1	—	0.04 (0.04 to 0.04)	0.04 (0.04 to 0.04)	0.15 (0.15 to 0.16)	0.15 (0.15 to 0.16)
10	—	0.05 (0.04 to 0.05)	0.04 (0.04 to 0.05)	0.16 (0.16 to 0.17)	0.16 (0.15 to 0.16)
50	—	0.06 (0.06 to 0.06)	0.05 (0.05 to 0.05)	0.21 (0.21 to 0.21)	0.18 (0.18 to 0.19)
90	—	0.07 (0.07 to 0.08)	0.06 (0.06 to 0.06)	0.27 (0.26 to 0.27)	0.22 (0.21 to 0.22)
99	—	0.08 (0.08 to 0.08)	0.06 (0.06 to 0.07)	0.28 (0.28 to 0.29)	0.23 (0.22 to 0.23)

^aAverage risks in general population were calculated based on SEER incidence rates for men and women separately. CI = confidence interval; CRC = colorectal cancer; ERS = environmental risk score; PRS = polygenic risk score.
^bAdjusted for age, study, total energy consumption, family history, genotype platform, and principal components.
^cAdjusted for age, family history, genotype platform, and principal components.
^dAdjusted for age, study, total energy consumption, and family history.

Authors and Disclosures

Alexi N. Archambault, PhD, MPH¹, Jihyoun Jeon, PhD, MS ², Yi Lin, MS³, Minta Thomas, PhD, MPhil³, Tabitha A. Harrison, MPH³, D. Timothy Bishop, PhD, MSC⁴, Hermann Brenner, MD, MPH^5–7, Graham Casey, PhD⁸, Andrew T. Chan, MD, MPH^9–14, Jenny Chang-Claude, PhD^15,16, Jane C. Figueiredo, PhD^17,18, Steven Gallinger, MD, MSC¹⁹, Stephen B. Gruber, MD, PhD²⁰, Marc J. Gunter, PhD²¹, Feng Guo, PhD, MSC⁵, Michael Hoffmeister, PhD⁵, Mark A. Jenkins, PhD²², Temitope O. Keku, PhD, MSPH, MSC²³, Loïc Le Marchand, MD, PhD²⁴, Li Li, MD, PhD²⁵, Victor Moreno, PhD^26–29, Polly A. Newcomb, PhD, MPH^3,30, Rish Pai, MD, PhD³¹, Patrick S. Parfrey, MD³², Gad Rennert, MD, PhD^33–35, Lori C. Sakoda, PhD MPH^3,36, Jeffrey K. Lee, MD, MAS³⁶, Martha L. Slattery, PhD³⁷, Mingyang Song, ScD, MS^9,11,38, Aung Ko Win, PhD, MPH²², Michael O. Woods, PhD³⁹, Neil Murphy, PhD⁴⁰, Peter T. Campbell, PhD, MSc⁴¹, Yu-Ru Su, PhD, MS⁴², Iris Lansdorp-Vogelaar, PhD⁴³, Elisabeth F. P. Peterse, PhD⁴³, Yin Cao, ScD, MPH^44–46, Anne Zeleniuch-Jacquotte, MD, MS¹, Peter S. Liang, MD, MPH⁴⁷, Mengmeng Du, ScD⁴⁸, Douglas A. Corley, MD, PhD, MPH³⁶, Li Hsu, PhD^3,49,1, Ulrike Peters, PhD, MPH^3,50,1 and Richard B. Hayes, PhD, MPH, DDS^1,*^,†

¹Division of Epidemiology, Department of Population Health, New York University School of Medicine, New York, NY, USA; ²Department of Epidemiology, University of Michigan, Ann Arbor, MI, USA; ³Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA; ⁴Leeds Institute of Medical Research, St. James's University of Leeds, Leeds, UK; ⁵Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany; ⁶Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg, Germany; ⁷German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany; ⁸Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA; ⁹Division of Gastroenterology, Massachusetts General Hospital, Boston, MA, USA; ¹⁰Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, USA; ¹¹Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA, USA; ¹²Broad Institute of Harvard and MIT, Cambridge, MA, USA; ¹³Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA; ¹⁴Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA; ¹⁵Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany; ¹⁶University Medical Centre Hamburg-Eppendorf, University Cancer Centre Hamburg (UCCH), Hamburg, Germany; ¹⁷Department of Medicine, Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA; ¹⁸Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; ¹⁹Lunenfeld Tanenbaum Research Institute, Mount Sinai Hospital, University of Toronto, Toronto, ON, Canada; ²⁰Center for Precision Medicine, City of Hope National Medical Center, Duarte, CA, USA; ²¹Nutrition and Metabolism Section, International Agency for Research on Cancer, World Health Organization, Lyon, France; ²²Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia; ²³Center for Gastrointestinal Biology and Disease, University of North Carolina, Chapel Hill, NC, USA; ²⁴Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, USA; ²⁵Department of Family Medicine, University of Virginia, Charlottesville, VA, USA; ²⁶Oncology Data Analytics Program, Catalan Institute of Oncology-IDIBELL, L'Hospitalet de Llobregat, Barcelona, Spain; ²⁷CIBER Epidemiolog_ıa y Salud P_ublica (CIBERESP), Madrid, Spain; ²⁸Department of Clinical Sciences, Faculty of Medicine, University of Barcelona, Barcelona, Spain; ²⁹ONCOBEL Program, Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, Barcelona, Spain; ³⁰School of Public Health, University of Washington, Seattle, WA, USA; ³¹Department of Laboratory Medicine and Pathology, Mayo Clinic Arizona, Scottsdale, AZ, USA; ³²Faculty of Medicine, Memorial University, St John's, NL, Canada; ³³Department of Community Medicine and Epidemiology, Lady Davis Carmel Medical Center, Haifa, Israel; ³⁴Ruth and Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology, Haifa, Israel; ³⁵Clalit National Cancer Control Center, Haifa, Israel; ³⁶Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA; ³⁷Department of Internal Medicine, University of Utah, Salt Lake City, UT, USA; ³⁸Department of Nutrition, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA; ³⁹Discipline of Genetics, Memorial University of Newfoundland, St John's, NL, Canada; ⁴⁰Section of Nutrition and Metabolism, International Agency for Research on Cancer, Lyon, France; ⁴¹Department of Population Science, American Cancer Society, Atlanta, GA, USA; ⁴²Biostatistics Unit, Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA; ⁴³Department of Public Health, Erasmus University Medical Center, Rotterdam, the Netherlands; ⁴⁴Division of Public Health Sciences, Department of Surgery, Washington University School of Medicine, St Louis, MO, USA; ⁴⁵Washington University School of Medicine, Alvin J. Siteman Cancer Center, St Louis, MO, USA; ⁴⁶Division of Gastroenterology, Department of Medicine, Washington University School of Medicine, St Louis, MO, USA; ⁴⁷Department of Medicine, New York University School of Medicine, New York, NY, USA; ⁴⁸Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA; ⁴⁹Department of Biostatistics, University of Washington, Seattle, WA, USA; and ⁵⁰Department of Epidemiology, University of Washington School of Public Health, Seattle, WA, USA

*Correspondence to
Richard B. Hayes, PhD, MPH, DDS, NYU Langone Health, 180 Madison Ave, Rm 415, New York, NY 10016, USA (e-mail: Richard.B.Hayes@nyulangone.org).

‡These authors jointly supervised this work.

Disclosures
The authors have no conflicts of interest to report and assume full responsibility for all aspects of this study.

Author contributions
Conceptualization: ANA, RBH, UP, LH; Formal analysis: ANA, JJ, YL; Investigation: ANA, JJ; Methodology: ANA, RBH, UP, LH, JJ, DAC, PSL, MD; Supervision: RBH, UP, LH, DAC; Writing—original draft: ANA, RBH, UP, LH, DAC; Writing—review & editing: ANA, JJ, YL, MT, TAH, DTB, HB, GC, ATC, JC-C, JCF, SG, SBG, MJG, FG, MH, MAJ, TOK, LLM, LL, VM, PAN, RP, PSP, GR, LCS, JKL, MLS, MS, AKW, MOW, NM, PTC, Y-RS, IL-V, EFPP, YC, AZ-J, PSL, MD, DAC, LH, UP, RBH; Funding acquisition: ANA, RBH, UP.