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Polymorphisms in the mTOR-PI3K-Akt pathway, energy balance-related exposures and colorectal cancer risk in the Netherlands Cohort Study

Polymorphisms in the mTOR-PI3K-Akt pathway, energy balance-related exposures and colorectal... maastrichtuniversity.nl Department of Epidemiology, Background: The mTOR-PI3K-Akt pathway influences cell metabolism and GROW – School for Oncology and (malignant) cell growth. We generated sex-specific polygenic risk scores capturing Developmental Biology, Maastricht University, Maastricht, the natural variation in 7 out of 10 top-ranked genes in this pathway. We studied the Netherlands scores directly and in interaction with energy balance-related factors (body mass Full list of author information is index (BMI), trouser/skirt size, height, physical activity, and early life energy restriction) available at the end of the article in relation to colorectal cancer (CRC) risk in the Netherlands Cohort Study (NLCS) (n= 120,852). The NLCS has a case-cohort design and 20.3 years of follow-up. Participants completed a baseline questionnaire on diet and cancer in 1986 when 55–69 years old. ~75% of the cohort returned toenail clippings used for DNA isolation and genotyping (n subcohort=3,793, n cases=3,464). To generate the scores, the dataset was split in two and risk alleles were defined and weighted based on sex-specific associations with CRC risk in the other dataset half, because there were no SNPs in the top-ranked genes associated with CRC risk in previous genome-wide association −5 studies at a significance level p<1*10 . Results: Cox regression analyses showed positive associations between the sex- specific polygenic risk scores and colon but not rectal cancer risk in men and women, with hazard ratios for continuously modeled scores close to 1.10. There was no modifying effect observed of the scores on associations between the energy balance-related factors and CRC risk. However, BMI (in men), non-occupational physical activity (in women), and height (in men and women) were associated with the risk of CRC, in particular (proximal and distal) colon cancer, in the direction as expected in the lower tertiles of the sex-specific polygenic risk scores. Conclusions: Current data suggest that the mTOR-PI3K-Akt pathway may be involved in colon cancer development. This study thereby sheds more light on colon cancer etiology through use of genetic variation in the mTOR-PI3K-Akt pathway. Keywords: Body size, Cohort studies, Colorectal neoplasms, Mechanistic target of rapamycin, Polymorphisms © The Author(s). 2022 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. Simons et al. BioData Mining (2022) 15:2 Page 2 of 20 Introduction Altered cell metabolism is considered a cancer hallmark associated with malignant cell growth. [1] Cell metabolism is influenced by the mammalian target of rapamycin (mTOR)-phosphatidylinositide 3-kinases (PI3K)-Akt pathway, which could therefore in- fluence cancer development. In particular signaling by mTOR complex 1 (mTORC1) influences cell growth and survival via control of protein synthesis, autophagy, lipid synthesis, and mitochondrial metabolism. [2] Cellular energy status itself regulates mTOR-PI3K-Akt signaling, as do growth factors, stress, and nutrients. [2]. Genetic variation in the mTOR-PI3K-Akt pathway, which captures natural variation in the mTOR-PI3K-Akt pathway in the population, has been associated with cancer risk across organ sites. [3–12] Differences in associations between cancers may exist as, for example, MTOR rs2295080, a promotor variant associated with transcription [10] and mRNA expression [5] was oppositely associated with leukemia risk than with risk of other cancers. [7, 12, 13] To our knowledge, only one study investigated a potential interaction between MTOR rs2295080 and other variants in the mTOR-PI3K-Akt path- way and a diet risk score, showing evidence of interaction. [14]. Our aim was to extend on the existing evidence by studying mTOR-PI3K-Akt pathway genetic variation in relation to CRC risk and by investigating potential effect modification of mTOR-PI3K-Akt pathway genetic variation on associations between energy balance- related factors (body mass index, trouser/skirt size, height, physical activity, and early life energy restriction) and CRC risk in the large, prospective Netherlands Cohort Study. A higher body mass index, tallness, and a lack of physical activity are established CRC risk factors [15] which are thought to be associated with a positive energy balance and in- creased mTOR-PI3K-Akt signaling, stimulating malignant growth. Energy restriction dur- ing childhood and adolescence may favorably influence mTOR-PI3K-Akt signaling and could lower the potential for malignant growth. [2] Therefore, if we can show that the CRC risk conferred by these energy balance-related factors depends on genetic variation in the mTOR-PI3K-Akt pathway, which reflects core variation in the population, this pro- vides evidence for that the mTOR-PI3K-Akt pathway is a mechanism that underlies asso- ciations between energy balance-related factors and CRC risk. To achieve our aim, we generated sex-specific polygenic risk scores, capturing mul- tiple polymorphisms in one variable. We generated the scores by splitting the dataset in two halves and only including polymorphisms which showed the same direction of association in relation to CRC risk in both datasets, as effect alleles could not be de- fined based on literature or existing genome-wide association studies (GWAS). We weighted the polymorphisms in the scores with the standard error weighted regression coefficients from the other set. The scores were then standardized and the scores and data were merged back together again, after which Cox hazard ratios for CRC were es- timated for the scores (modeled in tertiles and continuously) and for the energy balance-related factors (modeled categorically) within tertiles of the scores. Results Baseline characteristics A flow diagram leading up to the number of subcohort members and cases available in the NLCS for the current analyses is shown in Fig. 1. The polygenic risk score in men Simons et al. BioData Mining (2022) 15:2 Page 3 of 20 was made up of the following 12 SNPs out of a set of 24 genotyped SNPs in 10 top- ranked genes in the mTOR-PI3K-Akt pathway (Supplemental Tables 1 and 2): MTOR rs1057079, TSC2 rs1800720, TSC2 rs2516739, PDK1 rs6723872, EIF4EBP1 rs6605631, RPS6KB2 rs12787021, AKT3 rs14403, AKT3 rs3006939, AKT3 rs946824, AKT2 rs16974157, AKT2 rs874269, and INSR rs891088. The polygenic risk score in women was made up of the following 11 SNPs out of the 24 genotyped SNPs (Supplemental Tables 1 and 2): MTOR rs2295080, TSC2 rs12918803, PDK1 rs6723872, RPS6KB2 rs12787021, AKT3 rs1352162, AKT3 rs14403, AKT3 rs7523198, AKT3 rs7523742, AKT2 rs16974157, AKT2 rs874269, and INSR rs891088. Supplemental Table 3 shows the regression coefficients, SEs, and resulting weights in the two dataset halves that were used to generate the sex-specific polygenic risk scores in each set. Supplemental Fig. 1 shows that the subcohort distributions of the standardized polygenic risk scores were similar in both sets. Since the scores were standardized, the mean equaled 0 and the SD equaled 1. The subcohort distributions of the sex-specific polygenic risk scores in the total population are shown in Fig. 2. The standardized score specific for men ranged from -2.25 to 3.70 in male subcohort members and from -2.25 to 3.55 in male CRC cases. The standardized score specific for women ranged from -1.79 to 4.70 in fe- male subcohort members and from -1.65 to 4.54 in female CRC cases. Table 1 shows that the mean scores within tertiles were comparable between subcohort members and CRC cases in both men and women. Table 1 furthermore shows baseline characteristics of the NLCS cohort, with no major differences in the distributions of most baseline characteristics between subcohort members and CRC cases in men and women. The most notable difference between subcohort members and CRC cases was in the Fig. 1 Flow diagram of subcohort members and colorectal cancer cases Simons et al. BioData Mining (2022) 15:2 Page 4 of 20 Fig. 2 Histogram of the sex-specific polygenic risk scores in male and female subcohort members percentage of first-degree family history of CRC (men: 8.8% versus 5.3%, respectively; women: 9.4% versus 5.5%, respectively). Sex-specific polygenic risk scores of mTOR-PI3K-Akt polymorphisms and CRC risk Positive associations were observed between the sex-specific polygenic risk scores and CRC risk when modeling these in tertiles and continuously (Table 2). Men had a 7% in- crease in CRC risk per unit increase on the polygenic risk score specific for men (HR = 1.07, 95% CI: 1.00-1.15; HR = 1.07, 95% CI: 0.91-1.26; HR continuous tertile 2 vs. 1 tertile = 1.14, 95% CI: 0.97-1.35). Women had a 9% increase in CRC risk per unit in- 3 vs. 1 crease on the polygenic risk score specific for women (HR = 1.09, 95% CI: continuous 1.01-1.17; HR = 0.97, 95% CI: 0.81-1.16; HR = 1.15, 95% CI: 0.97- tertile 2 vs. 1 tertile 3 vs. 1 1.38). Similar positive (borderline) statistically significant associations were observed for colon cancer risk and proximal and distal colon cancer risk in men and women. The Simons et al. BioData Mining (2022) 15:2 Page 5 of 20 Table 1 Baseline characteristics of subcohort members and CRC cases within the Netherlands Cohort Study (20.3 years of follow-up) Men Women Subcohort CRC cases Subcohort CRC cases Characteristic N (%) Mean N (%) Mean N (%) Mean N (%) Mean (SD) (SD) (SD) (SD) Polygenic risk score Tertile 1 -1.06 -1.02 -1.01 -0.99 (0.4) (0.4) (0.3) (0.3) Tertile 2 -0.06 -0.05 -0.13 -0.12 (0.3) (0.3) (0.2) (0.2) Tertile 3 1.12 1.15 1.14 1.17 (0.6) (0.6) (0.7) (0.7) Age in years 61.4 61.7 61.6 62.0 (4.2) (4.1) (4.3) (4.1) BMI at baseline, kg/m (sex- specific) Tertile 1 22.3 22.5 21.6 21.6 (1.4) (1.2) (1.4) (1.4) Tertile 2 24.8 24.9 24.7 24.8 (0.5) (0.6) (0.8) (0.8) Tertile 3 27.9 27.9 29.1 29.0 (1.7) (2.0) (2.7) (2.6) Non-occupational physical activity, min/d <=30 322 308 432 387 (17.8) (16.6) (25.6) (27.9) >30-60 576 556 541 433 (31.8) (29.9) (32.1) (31.2) >60-90 915 993 712 569 (50.5) (53.5) (42.3) (41.0) Height, cm (sex-specific) Tertile 1 169.4 169.2 158.9 159.1 (3.3) (3.4) (3.7) (3.2) Tertile 2 176.4 176.5 166.2 166.3 (1.6) (1.5) (1.5) (1.5) Tertile 3 184.0 184.1 172.4 172.9 (3.8) (4.1) (3.1) (3.5) Residence during the Hunger Winter (1944-45) Western city 903 962 911 725 (60.4) (61.9) (56.8) (54.8) Western rural area 228 232 250 223 (15.3) (14.9) (15.6) (16.9) Non-western area 364 360 442 375 (24.3) (23.2) (27.6) (28.3) Family history of CRC, yes 98 (5.3) 166 94 (5.5) 133 (8.8) (9.4) Smoking status Never 235 228 1010 844 (12.1) (12.1) (59.0) (60.0) Ex-smoker 957 1100 349 303 (52.2) (58.5) (20.4) (21.5) Current smoker 642 551 352 261 (35.0) (29.3) (20.6) (18.5) Simons et al. BioData Mining (2022) 15:2 Page 6 of 20 Table 1 Baseline characteristics of subcohort members and CRC cases within the Netherlands Cohort Study (20.3 years of follow-up) (Continued) Men Women Alcohol intake, g/d 0 250 227 512 416 (13.9) (12.3) (32.3) (31.3) 0.1-29 1278 1315 1018 856 (71.1) (71.2) (64.2) (64.4) ≥30 270 304 55 (3.5) 58 (15.0) (16.5) (4.4) Meat intake, g/d 104.7 105.4 92.5 89.6 (43.4) (43.0) (41.8) (40.7) Processed meat intake, g/d 16.6 17.4 10.7 10.8 (17.5) (17.2) (12.4) (11.6) Total energy intake, kcal/d 2140 2147 1658 1650 (505) (496) (411) (388) Abbreviations: BMI, body mass index; CRC, colorectal cancer; N, number of; SD, standard deviation The polygenic risk score was composed of MTOR rs1057079, TSC2 rs1800720, TSC2 rs2516739, PDK1 rs6723872, EIF4EBP1 rs6605631, RPS6KB2 rs12787021, AKT3 rs14403, AKT3 rs3006939, AKT3 rs946824, AKT2 rs16974157, AKT2 rs874269, and INSR rs891088 in men, and of MTOR rs2295080, TSC2 rs12918803, PDK1 rs6723872, RPS6KB2 rs12787021, AKT3 rs1352162, AKT3 rs14403, AKT3 rs7523198, AKT3 rs7523742, AKT2 rs16974157, AKT2 rs874269, and INSR rs891088 in women. Scores were standardized with a mean of 0 and an SD of 1. Numbers and percentages or means and SDs were given after additionally excluding individuals with incomplete/inconsistent questionnaires. associations between the polygenic risk scores and rectal cancer risk in men and women were positive in direction, but not statistically significant. Individual SNP-CRC risk associations are shown in Supplemental Table 4. Several statistically significant associations were observed between individual SNPs, predomin- antly AKT3 SNPs, and CRC risk in men and women after gene-based FDR adjustment. Energy balance-related exposures and CRC risk: effect modification by sex-specific polygenic risk scores of mTOR-PI3K-Akt polymorphisms? Table 3 shows the associations between BMI, trouser/skirt size, BMI at age 20, non- occupational physical activity, height, and energy restriction during childhood and ado- lescence and CRC risk in men and women, stratified by tertiles of the sex-specific poly- genic risk scores. BMI was positively associated with CRC risk in men in the lowest tertile of the polygenic risk score specific for men; non-occupational physical activity was inversely associated with CRC risk in women in the lowest tertile of the polygenic risk score specific for women; and height was positively associated with CRC risk in men and women in the middle tertile of the polygenic risk score specific for each sex and in the lowest tertile of the polygenic risk score specific for women. No significant multiplicative interactions were observed. Analyses for subsite-specific CRC risks are shown in Supplemental Tables 5-8. In these stratified analyses for subsite-specific CRC risks, BMI was positively associated with proximal colon cancer risk in men, height was positively associated with colon, proximal colon, and distal colon cancer risk in both men and women and with rectal cancer risk in women, and non-occupational physical activity was inversely associated with colon, proximal colon, and distal colon cancer risk, with most associations observed in the lower tertiles of the polygenic risk score Simons et al. BioData Mining (2022) 15:2 Page 7 of 20 Table 2 Polygenic risk scores of mTOR-PI3K-Akt pathway polymorphisms in relation to CRC risk by sex and subsite in the Netherlands Cohort Study after 20.3 years of follow-up Men Women b b Endpoint Polygenic risk PT at N cases HR (95% CI) PT at risk N cases HR (95% CI) score risk CRC T2 vs. T1 9796 vs. 633 vs. 1.07 (0.91,1.26) 10,066 vs. 434 vs. 0.97 (0.81,1.16) 9540 587 10,207 460 T3 vs. T1 9506 vs. 662 vs. 1.14 (0.97,1.35) 10,125 vs. 517 vs. 1.15 (0.97,1.38) 9540 587 10,207 460 Continuous 28,841 1882 1.07 (1.00,1.15) 30,397 1411 1.09 (1.01,1.17) Colon T2 vs. T1 9796 vs. 397 vs. 1.05 (0.87,1.27) 10,066 vs. 312 vs. 0.95 (0.78,1.16) 9540 376 10,207 337 T3 vs. T1 9506 vs. 444 vs. 1.20 (1.00,1.44) 10,125 vs. 395 vs. 1.21 (1.00,1.46) 9540 376 10,207 337 Continuous 28,841 1217 1.10 (1.03,1.19) 30,397 1044 1.11 (1.03,1.20) Proximal T2 vs. T1 9796 vs. 177 vs. 1.02 (0.80,1.30) 10,066 vs. 185 vs. 0.96 (0.76,1.21) colon 9540 175 10,207 200 T3 vs. T1 9506 vs. 213 vs. 1.24 (0.98,1.57) 10,125 vs. 232 vs. 1.20 (0.96,1.51) 9540 175 10,207 200 Continuous 28,841 565 1.11 (1.01,1.22) 30,397 617 1.09 (0.99,1.20) Distal colon T2 vs. T1 9796 vs. 206 vs. 1.07 (0.85,1.35) 10,066 vs. 120 vs. 0.95 (0.72,1.25) 9540 190 10,207 129 T3 vs. T1 9506 vs. 222 vs. 1.18 (0.94,1.49) 10,125 vs. 153 vs. 1.21 (0.93,1.58) 9540 190 10,207 129 Continuous 28,841 618 1.11 (1.02,1.22) 30,397 402 1.14 (1.02,1.27) Rectum T2 vs. T1 9796 vs. 162 vs. 1.00 (0.78,1.29) 10,066 vs. 88 vs. 1.06 (0.77,1.47) 9540 159 10,207 85 T3 vs. T1 9506 vs. 163 vs. 1.04 (0.81,1.33) 10,125 vs. 87 vs. 1.05 (0.76,1.44) 9540 159 10,207 85 Continuous 28,841 484 1.00 (0.91,1.11) 30,397 260 1.07 (0.94,1.22) Abbreviations: CRC, colorectal cancer; CI, confidence interval; HR, hazard ratio; PT, person-time; T1-3, sex-specific tertile 1- 3; vs. versus. The polygenic risk score was composed of MTOR rs1057079, TSC2 rs1800720, TSC2 rs2516739, PDK1 rs6723872, EIF4EBP1 rs6605631, RPS6KB2 rs12787021, AKT3 rs14403, AKT3 rs3006939, AKT3 rs946824, AKT2 rs16974157, AKT2 rs874269, and INSR rs891088 in men, and of MTOR rs2295080, TSC2 rs12918803, PDK1 rs6723872, RPS6KB2 rs12787021, AKT3 rs1352162, AKT3 rs14403, AKT3 rs7523198, AKT3 rs7523742, AKT2 rs16974157, AKT2 rs874269, and INSR rs891088 in women. Adjusted for age (years). specific for each sex. Furthermore, one statistically significant interaction was observed between energy restriction during childhood and adolescence and the polygenic risk score specific for men in relation to distal colon cancer risk. Exposure to energy restric- tion during childhood and adolescence was inversely associated with distal colon cancer risk in men in the middle tertile of the polygenic risk score specific for men, while the association in the lowest tertile was positive in direction, though not statistically signifi- cant, nor was the association in the highest tertile statistically significant. Discussion The associations observed between the sex-specific polygenic risk scores and the risk of CRC overall, specifically colon cancer risk, suggest that the mTOR-PI3K-Akt pathway is involved in colon cancer development in both men and women. Involvement of the mTOR-PI3K-Akt pathway in rectal cancer development cannot be concluded based on the current data. There were no (multiplicative) interactions between the energy Simons et al. BioData Mining (2022) 15:2 Page 8 of 20 Table 3 Associations between exposures related to energy balance and CRC risk in men and women, stratified for tertiles of the sex-specific polygenic risk score of mTOR-PI3K- Akt pathway polymorphisms in the Netherlands Cohort Study (20.3 years of follow-up) Men Women BMI BMI T1 (sex-specific) T2 (sex-specific) T3 (sex-specific) T1 (sex-specific) T2 (sex-specific) T3 (sex-specific) a,b a,b a,b a,b a,b a,b N HR (95% N HR (95% CI) N HR (95% CI) P for N HR (95% N HR (95% CI) N HR (95% CI) P for cases/ CI) cases/ cases/ interaction cases/ CI) cases/ cases/ interaction PT at PT at PT at PT at PT at PT at risk risk risk risk risk risk Polygenic T1 165/ 1.00 (ref.) 166/ 1.07 (0.78,1.46) 203/ 1.48 (1.08,2.04) 133/ 1.00 (ref.) 141/ 1.16 (0.83,1.62) 118/ 1.03 (0.72,1.47) risk score 3343 2911 2588 3207 2999 2788 T2 165/ 1.00 (ref.) 207/ 1.12 (0.82,1.52) 206/ 1.26 (0.91,1.76) 139/ 1.00 (ref.) 132/ 0.98 (0.69,1.38) 109/ 0.81 (0.55,1.17) 2907 3196 2810 2829 2822 2775 T3 186/ 1.00 (ref.) 201/ 0.93 (0.68,1.27) 219/ 1.07 (0.77,1.47) 0.34 157/ 1.00 (ref.) 137/ 0.86 (0.62,1.20) 151/ 1.04 (0.74,1.44) 0.54 2688 3046 2726 3000 2900 2746 Trouser/skirt size Trouser/skirt size ≤median (sex- >median (sex-specific) ≤median (sex- >median (sex-specific) specific) specific) a,c a,c a,c a,c N HR (95% N HR (95% CI) P for N HR (95% N HR (95% CI) P for cases/ CI) cases/ interaction cases/ CI) cases/ interaction PT at PT at PT at PT at risk risk risk risk Polygenic T1 163/ 1.00 (ref.) 321/ 1.12 (0.81,1.54) 168/ 1.00 (ref.) 220/ 1.00 (0.69,1.45) risk score 3250 5042 3967 4910 T2 176/ 1.00 (ref.) 359/ 1.17 (0.86,1.60) 176/ 1.00 (ref.) 198/ 1.03 (0.70,1.50) 2932 5019 3849 4437 T3 177/ 1.00 (ref.) 376/ 1.25 (0.92,1.71) 0.84 178/ 1.00 (ref.) 263/ 1.11 (0.79,1.56) 0.76 3062 4775 3628 4937 Non-occupational physical activity Non-occupational physical activity ≤30 min/day >30-60 min/day >60 min/day ≤30 min/day >30-60 min/day >60 min/day a,d a,d a,d a,d a,d a,d NHR (95% N HR (95% CI) N HR (95% CI) P for N HR (95% N HR (95% CI) N HR (95% CI) P for Simons et al. BioData Mining (2022) 15:2 Page 9 of 20 Table 3 Associations between exposures related to energy balance and CRC risk in men and women, stratified for tertiles of the sex-specific polygenic risk score of mTOR-PI3K- Akt pathway polymorphisms in the Netherlands Cohort Study (20.3 years of follow-up) (Continued) Men Women cases/ CI) cases/ cases/ interaction cases/ CI) cases/ cases/ interaction PT at PT at PT at PT at PT at PT at risk risk risk risk risk risk Polygenic T1 89/ 1.00 (ref.) 156/ 0.79 (0.54,1.17) 289/ 1.01 (0.70,1.45) 113/ 1.00 (ref.) 104/ 0.57 (0.39,0.84) 175/ 0.70 (0.49,0.99) risk score 1380 3011 4452 1887 2964 4142 T2 83/ 1.00 (ref.) 177/ 1.09 (0.74,1.62) 318/ 1.14 (0.80,1.65) 98/ 1.00 (ref.) 128/ 0.84 (0.57,1.24) 154/ 0.72 (0.50,1.04) 1378 2811 4724 1775 2790 3860 T3 102/ 1.00 (ref.) 182/ 0.78 (0.53,1.16) 322/ 0.86 (0.60,1.23) 0.66 102/ 1.00 (ref.) 157/ 1.10 (0.76,1.59) 186/ 0.91 (0.64,1.29) 0.67 1341 2766 4353 1933 2815 3898 Height, Height, Height, Height, Height, Height, T1 (sex-specific) T2 (sex-specific) T3 (sex-specific) T1 (sex-specific) T2 (sex-specific) T3 (sex-specific) a,c a,c a,c a,c a,c a,c N HR (95% N HR (95% CI) N HR (95% CI) P for N HR (95% N HR (95% CI) N HR (95% CI) P for cases/ CI) cases/ cases/ interaction cases/ CI) cases/ cases/ interaction PT at PT at PT at PT at PT at PT at risk risk risk risk risk risk Polygenic T1 169/ 1.00 (ref.) 182/ 1.24 (0.90,1.71) 183/ 1.24 (0.91,1.71) 126/ 1.00 (ref.) 144/ 1.34 (0.96,1.88) 122/ 1.70 (1.18,2.44) risk score 3074 2862 2906 3605 3156 2233 T2 185/ 1.00 (ref.) 189/ 1.44 (1.04,1.98) 204/ 1.30 (0.95,1.77) 133/ 1.00 (ref.) 131/ 1.00 (0.71,1.42) 116/ 1.05 (0.72,1.52) 3205 2616 3093 3130 2905 2390 T3 181/ 1.00 (ref.) 205/ 1.14 (0.83,1.56) 220/ 1.28 (0.93,1.76) 0.94 138/ 1.00 (ref.) 162/ 1.60 (1.13,2.26) 145/ 1.57 (1.09,2.27) 0.30 2826 2987 2647 3285 2761 2600 Exposure to energy restriction during childhood and adolescence as based on Exposure to energy restriction during childhood and adolescence as based on place of residence during the Hunger Winter place of residence during the Hunger Winter Non-Western area Western rural area Western city Non-Western area Western rural area Western city a,c a,c a,c a,c a,c a,c N HR (95% N HR (95% CI) N HR (95% CI) P for N HR (95% N HR (95% CI) N HR (95% CI) P for cases/ CI) cases/ cases/ interaction cases/ CI) cases/ cases/ interaction PT at PT at PT at PT at PT at PT at risk risk risk risk risk risk Polygenic T1 269/ 1.00 (ref.) 73/ 1.24 (0.82,1.86) 107/ 0.98 (0.70,1.38) 201/ 1.00 (ref.) 60/ 1.42 (0.92,2.19) 108/ 1.24 (0.88,1.75) Simons et al. BioData Mining (2022) 15:2 Page 10 of 20 Table 3 Associations between exposures related to energy balance and CRC risk in men and women, stratified for tertiles of the sex-specific polygenic risk score of mTOR-PI3K- Akt pathway polymorphisms in the Netherlands Cohort Study (20.3 years of follow-up) (Continued) Men Women risk score 4418 1127 1918 4989 1182 2291 T2 305/ 1.00 (ref.) 69/ 0.85 (0.57,1.27) 112/ 0.84 (0.60,1.19) 194/ 1.00 (ref.) 63/ 1.20 (0.79,1.82) 101/ 1.02 (0.72,1.43) 4307 1230 1696 4413 1222 2333 T3 324/ 1.00 (ref.) 74/ 1.15 (0.76,1.74) 108/ 0.72 (0.50,1.02) 0.56 225/ 1.00 (ref.) 70/ 1.12 (0.75,1.67) 127/ 1.04 (0.75,1.43) 0.61 4194 946 1805 4412 1283 2462 Abbreviations: BMI, body mass index; CI, confidence interval; HR, hazard ratio; N, number of; PT, person-time; ref., reference; T1-3, tertile 1-3. Adjusted for age (years), first-degree family history of colorectal cancer (yes/no); smoking status (never, ex, current); alcohol intake (0, 0.1-29, ≥30 g/d); meat intake (g/d), processed meat intake (g/d), and total energy intake (kcal/d). Additionally adjusted for non-occupational physical activity (≤30, >30-60, >60 min/day), respectively. c 2 Additionally adjusted for BMI (kg/m ) and non-occupational physical activity (≤30, >30-60, >60 min/day), respectively. d 2 Additionally adjusted for BMI (kg/m ). Simons et al. BioData Mining (2022) 15:2 Page 11 of 20 balance-related exposures studied and the polygenic risk scores specific for each sex in relation to CRC risk overall or by subsite, except for one, i.e. there was an interaction with energy restriction during childhood and adolescence in relation to distal colon cancer risk in men. However, associations within tertiles of the polygenic risk score did not provide a clear indication for a modifying effect. Overall, in the stratified analyses, we predominantly observed associations between energy balance-related exposures and CRC risk in the lower tertiles of the sex-specific polygenic risk scores, with the direc- tion of the associations generally in line with what would be expected for these factors in relation to CRC risk based on literature [15] and with what was previously observed in the NLCS after 16.3 years of follow-up. [16, 17] However, these stratum-specific as- sociations on their own, without (statistical) interaction present, do not form sufficient evidence for concluding that there was a modifying effect by mTOR-PI3K-Akt genetic variation on associations between energy balance-related factors and CRC risk. That said, if we allow for speculation and view these data in a broader sense, these data raise the question whether environmental factors predominate when genetic risk is low. As regards to our findings for the polygenic risk scores and subsite-specific CRC risks, stronger involvement of the mTOR-PI3K-Akt pathway in the development of more proximally located colorectal tumors is plausible considering that higher (over)ex- pression of Akt1, Akt2, and p-p70S6K(Thr389) genes has been reported in proximal colon tumors than distal colon tumors. [18] PTEN gene expression was also found to show a positive expression gradient towards the proximal colon, starting at the rectum. [19] Furthermore, PTEN and PIK3CA mutations are more prevalent in tumors of the proximal colon. [20] These literature findings provide some confidence in that the asso- ciations observed in this study, which suggest mTOR-PI3K-Akt involvement in colon cancer development, were not chance findings. There are few data characterizing associations between energy balance-related expo- sures and CRC risk within genetic risk strata based on mTOR-PI3K-Akt pathway poly- morphisms or vice versa. We specifically chose the former modulation in light of future translation of the results towards prevention, because polymorphisms are static vari- ables and energy balance-related exposures such as BMI are modifiable; that is, a healthy BMI and physical activity level could be especially important for specific genetic risk groups. Previous studies, however, investigated cancer risks associated with carry- ing more risk alleles within strata of energy balance-related factors, [21–24] under the hypothesis that a positive energy imbalance activates the mTOR-PI3K-Akt pathway. These studies did not uniformly suggest that activation of the mTOR-PI3K-Akt path- way by a positive energy imbalance influences cancer risk, as some observed associa- tions between mTOR-PI3K-Akt pathway variants and cancer risk in normal weight instead of overweight/obese individuals. [23] Meanwhile, energy balance has been shown to modulate signaling through Akt and mTOR in multiple epithelial tissues in mice, with diet-induced obesity enhancing and calorie restriction inhibiting activation. [25] The mixed observational results in the literature might be explained by differences in effect on CRC risk of the specific variants included, perhaps suggesting the import- ance of capturing a sufficient and representative amount of genetic variation present in the mTOR-PI3k-Akt pathway in the population. For example, one of the studies refer- enced above utilized both a polygenic risk score of mTOR-PI3K-Akt pathway polymor- phisms and an energy balance index and found a joint effect of the two on bladder Simons et al. BioData Mining (2022) 15:2 Page 12 of 20 cancer risk. [21] This study, however, may have had limited power, leading to unstable (and extreme) risk estimates, as based on the case numbers and the wide confidence in- tervals reported. In addition, this study selected SNPs for inclusion in the risk score based on p-values for main effects and tested the risk score in the same population as in which the single SNPs were tested, which might have led to overfitting of the risk score model to the underlying data and inflation of the results. Alternatively, the mixed results in the literature in relation to CRC risk and the absence of interaction in the present study could mean that an interaction between energy balance-related exposures and genetic variation in the mTOR-PI3K-Akt pathway in relation to CRC risk is absent or not strong enough to be detected given the average statistical power achieved in a large observational cohort. Despite the absence of (statistical) interaction between energy balance-related expo- sures and the polygenic risk score of mTOR-PI3K-Akt pathway polymorphisms in rela- tion to CRC risk, one particular finding in this study is noteworthy. This is the observation that height was a colon cancer risk factor in both men and women in the lowest and middle tertiles of the polygenic risk score. Previously, after 16.3 years of follow-up, height was observed to be a colon cancer risk factor in women but not men, [16] whereas accounting for genetic variation in the mTOR-PI3K-Akt pathway ap- peared to remove the sex difference observed overall in our cohort. We have observed the same phenomenon when accounting for genetic variation in the insulin-like growth factor pathway. [26] The absence of a sex difference is in accordance with the literature that shows energy balance-related exposures such as BMI and height to be CRC risk factors regardless of sex. [15] Interestingly, BMI and height were colon but not rectal cancer risk factors in this study and in previous studies from the NLCS regardless of which other variables were taken into account, [16, 26] which may be a cohort-specific effect (e.g. residual confounding in this specific population), as the literature shows these factors to also be rectal cancer risk factors. [15]. The methodology used to select genes and polymorphisms in the mTOR-PI3K-Akt pathway and the methodology used to generate the sex-specific weighted polygenic risk scores of mTOR-PI3K-Akt polymorphisms deserves some further discussion. Firstly, the assumptions made to select key genes in the mTOR-PI3K-Akt pathway using the relative betweenness centrality measure may not accurately represent the biology of the mTOR-PI3K-Akt pathway. For example, it was assumed that the information flow (sig- nals) between nodes (genes) in a pathway is undividable and always takes the shortest path. In addition, we have assumed an undirected graph (pathway), meaning the infor- mation flow between connected nodes can go both ways. These assumptions were nevertheless necessary and resulted in a list of top-ranked genes that fit with prior knowledge of key players in the mTOR-PI3K-Akt pathway, reassuring us that no major bias occurred because of a potentially inaccurate representation of the biology of the pathway. Secondly, our method of SNP selection, i.e. we selected tagging variants in order to cover as much of the genetic variation in the top-ranked genes as possible, did not immediately allow us to consider correlations of SNPs with other biological levels, such as gene or protein expression. Many selected SNPs, however, turned out to be ex- pression quantitative trait loci (eQTLs) for the gene that they were tagging and/or other genes according to the Genotype-Tissue expression (GTex) project (https://gtexportal. org/home/; National Institutes of Health, United States). Thirdly, we were limited in Simons et al. BioData Mining (2022) 15:2 Page 13 of 20 the number of SNPs that we could genotype, and thus the number of genes in the mTOR-PI3K-Akt pathway that we could cover, because of budgetary constraints that allowed us to genotype only one multiplex assay. Given the genes that we covered, this may have led to insufficient coverage of genes encoding for proteins of which signaling is under the influence of a negative energy imbalance. For example, we could not in- clude SNPs encoding for adenosine monophosphate-activated protein kinase (AMPK), which phosphorylates TSC2 in the TSC1-2 complex [2] and stabilizes the mTOR- RAPTOR bond in mTORC1 under conditions of a negative energy balance, inhibiting mTORC1 signaling. [2, 27]. Strengths of this study include that it is a large, population-based prospective cohort with long follow-up, resulting in a large number of CRC cases and making selection and information bias unlikely. Limitations include the single baseline measurement of exposures. The NLCS population has been found stable in its dietary habits, [28] but diminishing physical activity levels and changes in body composition may be inevitable with increasing age, possibly having led to attenuation of associations over time. Conclusions The findings of this study suggest that the mTOR-PI3K-Akt pathway may be involved in the development of colon cancer, but not rectal cancer. Energy balance-related fac- tors were associated with CRC risk as hypothesized, mostly within the lower tertiles of the polygenic risk score specific for each sex, but there was no clear modifying effect of the scores. The relevance of this study lies in its contribution to the evidence base on mechanisms involved in colon cancer development through use of a polygenic risk score, capturing natural variation in the mTOR-PI3K-Akt pathway in the population. Methods Population and design The NLCS [29] includes 120,856 men and women who completed a questionnaire on diet and cancer at baseline in 1986 when 55-69 years old. The baseline questionnaire included a 150-item semi-quantitative food frequency questionnaire, which was found to rank individuals’ dietary intake adequately as compared to a 9-day dietary record [30] and was shown a good indicator of intake for at least 5 years. [28] Approximately 75% of the cohort returned toenail clippings, which are a valid and long-term DNA source. [31, 32] The NLCS is characterized by a case-cohort approach for reasons of ef- ficiency related to questionnaire processing, follow-up, and genotyping. A random sub- cohort (n=5000), selected immediately after baseline and independent of any exposure, is followed up for vital status through record linkage to the Central Bureau of Geneal- ogy and municipal population registries (>99.9% completeness) to estimate the accu- mulated person-time at risk. Participants were excluded if they reported a history of cancer other than skin cancer at baseline, leaving 4774 subcohort members for follow- up (Fig. 1). The whole cohort is followed up for incident cancer cases through record linkage to the population-based cancer registry and PALGA (the Netherlands pathology database) (>96%completeness). [33, 34] The case-cohort design allows for the estima- tion of hazard ratios as would be done in a full cohort under the assumption that the fraction of the accumulated person-time at risk observed for exposed and unexposed Simons et al. BioData Mining (2022) 15:2 Page 14 of 20 individuals is equal. This is reasonable considering that the subcohort was selected in- dependent of any exposure. The extra variance introduced by sampling the subcohort from the total cohort can be adjusted for using the robust variance estimator. [35]A detailed description of the NLCS is available in [29]. After 20.3 years of follow-up from September 1986 until the end of 2006, there were 3144 incident colon cancer cases (ICD-O-3 code C19) (among which 1623 incident proximal colon cancer cases (ICD- O-3 codes C18-C18.4) and 1430 incident distal colon cancer cases (ICD-O-3 codes C18.5-C18.7)), 427 incident rectosigmoid cancer cases (ICD-O-3 code C20), and 1026 incident rectal cancer cases (ICD-O-3 code C21), totaling to 4597 incident CRC cases (Fig. 1). Baseline information Baseline information included height (cm) and weight (kg) used to derive body mass index (kg/m ) (BMI) (reflecting body fatness), trouser/skirt size (Dutch clothing sizes) which is used as a marker for waist circumference (reflecting abdominal fatness when adjusting for BMI), weight at age 20 used together with height to derive BMI at age 20 (kg/m ), and energy restriction during childhood and adolescence as based on place of residence during the Dutch Hunger Winter in 1944-45. Self-reports on weight and height have been shown valid measures in other cohort studies with >10 years of follow-up. [36, 37] Trouser/skirt size correlated with hip and waist circumferences in a subset of weight-stable NLCS men (r=0.63 and 0.64, respectively) and women (r=0.78 and 0.71, respectively) and was associated with endometrial and renal cancer risk in a fashion as would be expected for waist circumference. [38] BMI and height measures were divided into sex-specific tertiles based on the distribution in the subcohort. Trou- ser/skirt size was split into two sex-specific categories based on the median in the sub- cohort. Information on non-occupational physical activity in categories of ≤30, 30-60, >60 min of physical activity per day was a sum measure of daily walking/cycling (min/ day), weekly recreational walking/cycling, weekly gardening/doing odd jobs, and weekly sports/gymnastics (categories: never, 1, 1-2, >2 h/week). More details on energy restric- tion during childhood and adolescence as measured in the NLCS are available in [39]. Baseline information on relevant covariates in diet and lifestyle was also available from the baseline questionnaire. DNA isolation and genotyping Toenail clippings were stored without further treatment or climate control of the stor- age room. The DNA isolation protocol has been described in [31] and [32]. DNA iso- lated from toenail material was stored at -30 °C at the BioBank Maastricht University Medical Center+ (Maastricht, the Netherlands). Toenail DNA is suitable for genotyping on the Agena BioScience MassARRAY® platform (Hamburg, Germany), allowing the genotyping of 36-40 SNPs at once, although, in practice, not all SNPs can be combined due to sequence incompatibilities between the sequences flanking the SNPs. Gene and SNP selection We identified 10 top ranked genes in the mTOR-PI3K-Akt pathway according to their relative betweenness centrality, which provides an indication of the strength of node Simons et al. BioData Mining (2022) 15:2 Page 15 of 20 involvement in the information flow through a network: MTOR (alias: FRAP1), TSC2, PDPK1 (alias: PDK1), EIF4EBP1 (alias: 4EBP1), IRS1, RPS6KB1 (alias: S6K1), RPS6KB2 (alias: S6K2), AKT3, AKT1, and AKT2 (Supplemental Table 1). The Kyoto Ecyclopedia of Genes and Genomes (KEGG) mTOR signaling (map04150) was used as input (http://www.genome.jp/kegg/) (R software, version 3.2.2, KeggGraph package). Since there were no SNPs in these genes associated with colorectal cancer risk at a signifi- −5 cance level of p<1*10 in GWAS (https://www.ebi.ac.uk/gwas/), we selected tagging single nucleotide polymorphisms (tagSNPs) at a minor allele frequency of 5% or higher for the top 10 ranked genes using aggressive tagging [40] (HaploView version 4.2, Broad Institute). Not all 10 genes could be included in the assay because not all combi- nations of SNPs can be included because of sequence incompatibilities between the se- quences flanking the SNPs on the basis of which the primer design took place. We firstly fixed the replicated cancer risk-associated MTOR SNP rs2295080 in the assay de- sign. The assay design next allowed for the inclusion of the following tagSNPs covering 7 of the 10 top-ranked genes: MTOR rs1057079; TSC2 rs2516739, rs1800720, rs2074969, rs9928737, and rs12918803; PDK1 rs6723872; EIF4EBP1 rs6605631; RPS6KB2 rs12787021; AKT3 rs3006939 rs14403, rs7523198, rs7523742, rs1352162, and rs946824; and AKT2 rs874269, rs16974157, and rs7250897. The assay was furthermore filled up as much as possible with single genome-wide association study (GWAS) hits for anthropometric traits, physical activity, or CRC annotated to mTOR-PI3K-Akt pathway genes (https://www.ebi.ac.uk/gwas/). Included were RPTOR rs7503807 for its association with obesity, [41] RICTOR rs2043112 and S6K1 rs1051424 for their associ- ation with (childhood) obesity-associated traits, [42] and IGF1R rs2871865 [43, 44] and INSR rs891088 for their association with height (abbreviations are explained in Supple- mental Table 1). [43, 45]. Genotyping Genotyping was performed for 3793 (79.5%) subcohort members and 3464 (75.3%) CRC cases with available toenail DNA (Fig. 1). Potentially contaminated samples as noted by the laboratory technicians were excluded (2.6%) to ensure the quality of the data. Mean sample call rates were 97.4% (median: 100%). SNP call rates were between 97 and 99%, except for one SNP, which had a SNP call rate of 92% (rs1051424). A sam- ple call rate of 95% or higher was present in samples from 3550 subcohort members (93.6%) and in samples from 3293 CRC cases (95.1%). (Two SNPs genotyped for use in another project were also enumerated when calculating the sample call rate.) Allele fre- quencies in the subcohort, which is representative of the whole cohort, are given in Supplemental Table 2. Hardy-Weinberg Equilibrium was violated on five occasions, but we did not exclude these SNPs from further analysis, because we had no reason to sus- pect genotyping errors since all SNPs were genotyped using a single assay and because multiple tests increased the risk of a significant finding by chance. Statistical analysis The main exposure variable used in the analyses was a sex-specific polygenic risk score. Since no GWAS summary statistics were available to generate a polygenic risk score, we generated this score using the data at hand. First, the dataset was divided into two Simons et al. BioData Mining (2022) 15:2 Page 16 of 20 random sets of approximately equal size (datasets A and B). In each set, each individual SNP was modelled continuously in relation to the risk of CRC in men and women sep- arately, adjusting the model for age. We deemed it important to do this in a sex- specific manner, because energy balance-related risk factors for CRC have been shown to differ between men and women in the NLCS. [16, 17, 26, 39, 46–48] Specifically, a larger BMI and trouser size, used as a proxy for waist circumference, were previously shown to be risk factors in men but not women, whereas height was a risk factor in women but not men in the NLCS. Genotypes in individuals were coded ‘0’ when homo- zygote for the major allele, ‘1’ when heterozygote, and ‘2’ when homozygote for the minor allele. We used the standard error (SE) weighted regression coefficients (beta / SE) from set A to generate the polygenic risk scores in set B and vice versa (i.e. two polygenic risk scores were generated in each set, one for men and one for women). The polygenic risk scores were calculated by weighting the number of risk alleles carried by an individual in one set using the standard error weighted regression coefficient from the other set (SNP x: n risk alleles * (beta / SE), with n being 0, 1, or 2) and then sum- ming the weighted number of risk alleles for all SNPs into a single score. In case of a negative standard error weighted regression coefficient, the coding of the SNP was re- versed, as in these instances the major allele instead of the minor allele was considered the risk-conferring allele, and the absolute value of the weighted regression coefficient was used. We only included SNPs in the polygenic risk scores that showed the same direction of effect in both sets so that the risk scores in each dataset would include the same SNPs, though different weights were used to generate the scores. The scores were allowed to include different SNPs between men and women. We also only included SNPs in the polygenic risk scores that were in low linkage disequilibrium (LD) which was defined as r ≤0.6, because SNPs in low LD are most likely to add new information to the score in terms of the variance explained in the outcome. LD was evaluated for the data under study using default settings in Haploview version 4.2 and defining CRC cases as affected individuals and subcohort members without CRC as unaffected indi- viduals. There were two pairs of SNPs that were in LD with r >0.6 (AKT3 rs946824 and AKT3 rs7523742 and MTOR rs2295080 and MTOR rs1057079), of which only one of a pair (chosen at random) was included in the polygenic risk score in case of consist- ent betas in sets A and B for both SNPs. To adjust for missing SNP data (one SNP was missing at most because of exclusion of samples with <95% call rate), we divided the polygenic risk scores in each set by the proportion of successfully genotyped risk alleles [(n successfully genotyped SNPs*2) / (n genotyped SNPs*2)]. We then standardized the set-specific polygenic risk scores by deducting the mean and dividing the scores by their standard deviation (SD) [(x-mean) / SD], with sex-specific means and SDs based on the subcohort. This allowed us to merge the scores and the datasets back together again, resulting in one dataset which included two polygenic risk scores, i.e. one for men and one for women. Cox regression was then used to study the (subsite-specific) CRC risks associated with the sex-specific polygenic risk scores in men and women separately using R (R software, version 3.2.2). Models were age-adjusted and the polygenic risk scores were modelled in tertiles (based on the distribution in the male and female subcohort, re- spectively) and continuously. We also analyzed individual SNPs in relation to CRC risks in men and women in the total dataset, assuming a codominant and additive Simons et al. BioData Mining (2022) 15:2 Page 17 of 20 inheritance mode, in order to facilitate potential future meta-analyses for any of these individual SNPs. To investigate whether associations of BMI, trouser/skirt size, BMI at age 20, non- occupational physical activity, height, and energy restriction during childhood and ado- lescence with overall and subsite-specific CRC risks in men and women were modified by the polygenic risk scores, we stratified associations by tertiles of the sex- specific polygenic risk scores and tested multiplicative interactions using the Wald test in men and women (wald.test, aod package in R). Participants with incomplete or in- consistent baseline questionnaires were excluded from these analyses, leaving 2191 male and 2248 female subcohort members and 2409 male and 1870 CRC cases, al- though the total number per analysis differed because of missing values on specific ex- posure variables and covariates (Fig. 1). Since BMI, trouser/skirt size, BMI at age 20, non-occupational physical activity, height, and energy restriction during childhood and adolescence have been studied as CRC risk factors after 16.3 years of follow-up in our cohort, we used the same confounder sets as before, which included established CRC risk factors and confounders derived using a backward procedure. [16, 17, 26, 39, 46– 48]. Our approach of using polygenic risk scores was aimed at reducing the risk of overfitting, which can lead to inflated estimates or false positive findings. Since most of theSNPsusedinthisstudy were tagging polymorphismsthatwerese- lected to cover as much genetic variation as possible in the set of identified top- ranked genes in the mTOR-PI3K-Akt pathway, we did not have data from an inde- pendent population available on the risk-conferring allele for the majority of the SNPs. By generating a risk score in each half of the data and then merging the data back together again, we benefited from optimal power to carry out subsite- specific analyses and investigate potential effect modification by the polygenic risk scores of associations between energy balance-related factors and (subsite-specific) CRC risks in men and women separately. All Cox models (coxph, survival package in R) were adjusted for the additional vari- ance introduced by sampling the subcohort from the total cohort by entering the par- ticipant identification number as cluster term in the model (robust variance option). [35] We checked potential violations of the proportional hazards assumption by plot- ting the scaled Schoenfeld residuals against time and violations appeared negligible (cox.zph, survival package in R). Statistical significance was indicated by a P-value <0.05 for two-sided testing. Gene-based false discovery rate-adjusted P-values across men and women were calculated according to the method of Benjamini and Hochberg for P- values for individual SNP-CRC associations. The FDR adjustment entailed ranking P- values in ascending order and multiplying a predefined FDR threshold (0.20 [49]) with the inverse of the rank order over the total number of P-values considered to be part of the multiple testing. [50] If the original P-value was below 0.05 and the FDR-adjusted P-value, we considered the result statistically significant. Supplementary information The online version contains supplementary material available at https://doi.org/10.1186/s13040-021-00286-3. Additional file 1: Supplemental Fig. 1. Histogram of the sex-specific polygenic risk scores in male and female subcohort members in datasets A and B used to generate the scores. Simons et al. BioData Mining (2022) 15:2 Page 18 of 20 Additional file 2. Acknowledgements We are indebted to the participants of this study and wish to thank the Netherlands Cancer Registry and the Netherlands nationwide registry of pathology (PALGA). We also thank Drs. A. Volovics, and A. Kester for statistical advice; S. van de Crommert, H. Brants, J. Nelissen, C. de Zwart, M. Moll, W. van Dijk, and A. Pisters for data management; Dr. H. Hoofs, H. van Montfort, T. van Moergastel, L. van den Bosch, R. Schmeitz, and J. Berben for programming assistance; L. Maas, L. Jonkers, J. Goessens, K. Lemmens, and S. Lumeij for the laboratory work involved; Dr. J. Hogervorst for conceiving the idea of using toenail clippings for DNA isolation and genotyping; and the Biobank Maastricht UMC+ for sample storage. Authors’ contributions The authors’ contributions were as follows: C.C.J.M. Simons conducted the analyses and drafted the manuscript; L.J. Schouten, R.W.L. Godschalk, F.J. van Schooten, M. Stoll, and K. Van Steen critically reviewed the manuscript; Piet A. van den Brandt critically reviewed the manuscript and conceived the Netherlands Cohort Study; Matty P. Weijenberg critically reviewed the manuscript and conceived the idea for this particular study. The author(s) read and approved the final manuscript. Funding This work was supported by grants of the World Cancer Research Fund (grant numbers 2012/618 and 2013/673 to MPW), the Biobanking and Biomolecular Research Infrastructure Netherlands (to MPW), and the Health Foundation Limburg (to MPW). Availability of data and materials The authors declare that the data supporting the findings of this study are available within the article and its supplementary information files. Declarations Ethics approval and consent to participate The review boards of the TNO Nutrition and Food Research Institute (Zeist, the Netherlands) and Maastricht University (Maastricht, the Netherlands) approved the NLCS, the use of toenail DNA for genotyping, and this study. Participants consented to participation in the NLCS by returning the baseline questionnaire on diet and cancer. Consent for publication Not applicable. Competing interests None of the authors had a conflict of interest. Author details Department of Epidemiology, GROW – School for Oncology and Developmental Biology, Maastricht University, Maastricht, the Netherlands. Department of Pharmacology and Toxicology, NUTRIM – School for Nutrition and Translational Research in Metabolism, Maastricht University, Maastricht, the Netherlands. Institute of Human Genetics, Genetic Epidemiology, University of Münster, Münster, Germany. 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Polymorphisms in the mTOR-PI3K-Akt pathway, energy balance-related exposures and colorectal cancer risk in the Netherlands Cohort Study

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Abstract

maastrichtuniversity.nl Department of Epidemiology, Background: The mTOR-PI3K-Akt pathway influences cell metabolism and GROW – School for Oncology and (malignant) cell growth. We generated sex-specific polygenic risk scores capturing Developmental Biology, Maastricht University, Maastricht, the natural variation in 7 out of 10 top-ranked genes in this pathway. We studied the Netherlands scores directly and in interaction with energy balance-related factors (body mass Full list of author information is index (BMI), trouser/skirt size, height, physical activity, and early life energy restriction) available at the end of the article in relation to colorectal cancer (CRC) risk in the Netherlands Cohort Study (NLCS) (n= 120,852). The NLCS has a case-cohort design and 20.3 years of follow-up. Participants completed a baseline questionnaire on diet and cancer in 1986 when 55–69 years old. ~75% of the cohort returned toenail clippings used for DNA isolation and genotyping (n subcohort=3,793, n cases=3,464). To generate the scores, the dataset was split in two and risk alleles were defined and weighted based on sex-specific associations with CRC risk in the other dataset half, because there were no SNPs in the top-ranked genes associated with CRC risk in previous genome-wide association −5 studies at a significance level p<1*10 . Results: Cox regression analyses showed positive associations between the sex- specific polygenic risk scores and colon but not rectal cancer risk in men and women, with hazard ratios for continuously modeled scores close to 1.10. There was no modifying effect observed of the scores on associations between the energy balance-related factors and CRC risk. However, BMI (in men), non-occupational physical activity (in women), and height (in men and women) were associated with the risk of CRC, in particular (proximal and distal) colon cancer, in the direction as expected in the lower tertiles of the sex-specific polygenic risk scores. Conclusions: Current data suggest that the mTOR-PI3K-Akt pathway may be involved in colon cancer development. This study thereby sheds more light on colon cancer etiology through use of genetic variation in the mTOR-PI3K-Akt pathway. Keywords: Body size, Cohort studies, Colorectal neoplasms, Mechanistic target of rapamycin, Polymorphisms © The Author(s). 2022 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. Simons et al. BioData Mining (2022) 15:2 Page 2 of 20 Introduction Altered cell metabolism is considered a cancer hallmark associated with malignant cell growth. [1] Cell metabolism is influenced by the mammalian target of rapamycin (mTOR)-phosphatidylinositide 3-kinases (PI3K)-Akt pathway, which could therefore in- fluence cancer development. In particular signaling by mTOR complex 1 (mTORC1) influences cell growth and survival via control of protein synthesis, autophagy, lipid synthesis, and mitochondrial metabolism. [2] Cellular energy status itself regulates mTOR-PI3K-Akt signaling, as do growth factors, stress, and nutrients. [2]. Genetic variation in the mTOR-PI3K-Akt pathway, which captures natural variation in the mTOR-PI3K-Akt pathway in the population, has been associated with cancer risk across organ sites. [3–12] Differences in associations between cancers may exist as, for example, MTOR rs2295080, a promotor variant associated with transcription [10] and mRNA expression [5] was oppositely associated with leukemia risk than with risk of other cancers. [7, 12, 13] To our knowledge, only one study investigated a potential interaction between MTOR rs2295080 and other variants in the mTOR-PI3K-Akt path- way and a diet risk score, showing evidence of interaction. [14]. Our aim was to extend on the existing evidence by studying mTOR-PI3K-Akt pathway genetic variation in relation to CRC risk and by investigating potential effect modification of mTOR-PI3K-Akt pathway genetic variation on associations between energy balance- related factors (body mass index, trouser/skirt size, height, physical activity, and early life energy restriction) and CRC risk in the large, prospective Netherlands Cohort Study. A higher body mass index, tallness, and a lack of physical activity are established CRC risk factors [15] which are thought to be associated with a positive energy balance and in- creased mTOR-PI3K-Akt signaling, stimulating malignant growth. Energy restriction dur- ing childhood and adolescence may favorably influence mTOR-PI3K-Akt signaling and could lower the potential for malignant growth. [2] Therefore, if we can show that the CRC risk conferred by these energy balance-related factors depends on genetic variation in the mTOR-PI3K-Akt pathway, which reflects core variation in the population, this pro- vides evidence for that the mTOR-PI3K-Akt pathway is a mechanism that underlies asso- ciations between energy balance-related factors and CRC risk. To achieve our aim, we generated sex-specific polygenic risk scores, capturing mul- tiple polymorphisms in one variable. We generated the scores by splitting the dataset in two halves and only including polymorphisms which showed the same direction of association in relation to CRC risk in both datasets, as effect alleles could not be de- fined based on literature or existing genome-wide association studies (GWAS). We weighted the polymorphisms in the scores with the standard error weighted regression coefficients from the other set. The scores were then standardized and the scores and data were merged back together again, after which Cox hazard ratios for CRC were es- timated for the scores (modeled in tertiles and continuously) and for the energy balance-related factors (modeled categorically) within tertiles of the scores. Results Baseline characteristics A flow diagram leading up to the number of subcohort members and cases available in the NLCS for the current analyses is shown in Fig. 1. The polygenic risk score in men Simons et al. BioData Mining (2022) 15:2 Page 3 of 20 was made up of the following 12 SNPs out of a set of 24 genotyped SNPs in 10 top- ranked genes in the mTOR-PI3K-Akt pathway (Supplemental Tables 1 and 2): MTOR rs1057079, TSC2 rs1800720, TSC2 rs2516739, PDK1 rs6723872, EIF4EBP1 rs6605631, RPS6KB2 rs12787021, AKT3 rs14403, AKT3 rs3006939, AKT3 rs946824, AKT2 rs16974157, AKT2 rs874269, and INSR rs891088. The polygenic risk score in women was made up of the following 11 SNPs out of the 24 genotyped SNPs (Supplemental Tables 1 and 2): MTOR rs2295080, TSC2 rs12918803, PDK1 rs6723872, RPS6KB2 rs12787021, AKT3 rs1352162, AKT3 rs14403, AKT3 rs7523198, AKT3 rs7523742, AKT2 rs16974157, AKT2 rs874269, and INSR rs891088. Supplemental Table 3 shows the regression coefficients, SEs, and resulting weights in the two dataset halves that were used to generate the sex-specific polygenic risk scores in each set. Supplemental Fig. 1 shows that the subcohort distributions of the standardized polygenic risk scores were similar in both sets. Since the scores were standardized, the mean equaled 0 and the SD equaled 1. The subcohort distributions of the sex-specific polygenic risk scores in the total population are shown in Fig. 2. The standardized score specific for men ranged from -2.25 to 3.70 in male subcohort members and from -2.25 to 3.55 in male CRC cases. The standardized score specific for women ranged from -1.79 to 4.70 in fe- male subcohort members and from -1.65 to 4.54 in female CRC cases. Table 1 shows that the mean scores within tertiles were comparable between subcohort members and CRC cases in both men and women. Table 1 furthermore shows baseline characteristics of the NLCS cohort, with no major differences in the distributions of most baseline characteristics between subcohort members and CRC cases in men and women. The most notable difference between subcohort members and CRC cases was in the Fig. 1 Flow diagram of subcohort members and colorectal cancer cases Simons et al. BioData Mining (2022) 15:2 Page 4 of 20 Fig. 2 Histogram of the sex-specific polygenic risk scores in male and female subcohort members percentage of first-degree family history of CRC (men: 8.8% versus 5.3%, respectively; women: 9.4% versus 5.5%, respectively). Sex-specific polygenic risk scores of mTOR-PI3K-Akt polymorphisms and CRC risk Positive associations were observed between the sex-specific polygenic risk scores and CRC risk when modeling these in tertiles and continuously (Table 2). Men had a 7% in- crease in CRC risk per unit increase on the polygenic risk score specific for men (HR = 1.07, 95% CI: 1.00-1.15; HR = 1.07, 95% CI: 0.91-1.26; HR continuous tertile 2 vs. 1 tertile = 1.14, 95% CI: 0.97-1.35). Women had a 9% increase in CRC risk per unit in- 3 vs. 1 crease on the polygenic risk score specific for women (HR = 1.09, 95% CI: continuous 1.01-1.17; HR = 0.97, 95% CI: 0.81-1.16; HR = 1.15, 95% CI: 0.97- tertile 2 vs. 1 tertile 3 vs. 1 1.38). Similar positive (borderline) statistically significant associations were observed for colon cancer risk and proximal and distal colon cancer risk in men and women. The Simons et al. BioData Mining (2022) 15:2 Page 5 of 20 Table 1 Baseline characteristics of subcohort members and CRC cases within the Netherlands Cohort Study (20.3 years of follow-up) Men Women Subcohort CRC cases Subcohort CRC cases Characteristic N (%) Mean N (%) Mean N (%) Mean N (%) Mean (SD) (SD) (SD) (SD) Polygenic risk score Tertile 1 -1.06 -1.02 -1.01 -0.99 (0.4) (0.4) (0.3) (0.3) Tertile 2 -0.06 -0.05 -0.13 -0.12 (0.3) (0.3) (0.2) (0.2) Tertile 3 1.12 1.15 1.14 1.17 (0.6) (0.6) (0.7) (0.7) Age in years 61.4 61.7 61.6 62.0 (4.2) (4.1) (4.3) (4.1) BMI at baseline, kg/m (sex- specific) Tertile 1 22.3 22.5 21.6 21.6 (1.4) (1.2) (1.4) (1.4) Tertile 2 24.8 24.9 24.7 24.8 (0.5) (0.6) (0.8) (0.8) Tertile 3 27.9 27.9 29.1 29.0 (1.7) (2.0) (2.7) (2.6) Non-occupational physical activity, min/d <=30 322 308 432 387 (17.8) (16.6) (25.6) (27.9) >30-60 576 556 541 433 (31.8) (29.9) (32.1) (31.2) >60-90 915 993 712 569 (50.5) (53.5) (42.3) (41.0) Height, cm (sex-specific) Tertile 1 169.4 169.2 158.9 159.1 (3.3) (3.4) (3.7) (3.2) Tertile 2 176.4 176.5 166.2 166.3 (1.6) (1.5) (1.5) (1.5) Tertile 3 184.0 184.1 172.4 172.9 (3.8) (4.1) (3.1) (3.5) Residence during the Hunger Winter (1944-45) Western city 903 962 911 725 (60.4) (61.9) (56.8) (54.8) Western rural area 228 232 250 223 (15.3) (14.9) (15.6) (16.9) Non-western area 364 360 442 375 (24.3) (23.2) (27.6) (28.3) Family history of CRC, yes 98 (5.3) 166 94 (5.5) 133 (8.8) (9.4) Smoking status Never 235 228 1010 844 (12.1) (12.1) (59.0) (60.0) Ex-smoker 957 1100 349 303 (52.2) (58.5) (20.4) (21.5) Current smoker 642 551 352 261 (35.0) (29.3) (20.6) (18.5) Simons et al. BioData Mining (2022) 15:2 Page 6 of 20 Table 1 Baseline characteristics of subcohort members and CRC cases within the Netherlands Cohort Study (20.3 years of follow-up) (Continued) Men Women Alcohol intake, g/d 0 250 227 512 416 (13.9) (12.3) (32.3) (31.3) 0.1-29 1278 1315 1018 856 (71.1) (71.2) (64.2) (64.4) ≥30 270 304 55 (3.5) 58 (15.0) (16.5) (4.4) Meat intake, g/d 104.7 105.4 92.5 89.6 (43.4) (43.0) (41.8) (40.7) Processed meat intake, g/d 16.6 17.4 10.7 10.8 (17.5) (17.2) (12.4) (11.6) Total energy intake, kcal/d 2140 2147 1658 1650 (505) (496) (411) (388) Abbreviations: BMI, body mass index; CRC, colorectal cancer; N, number of; SD, standard deviation The polygenic risk score was composed of MTOR rs1057079, TSC2 rs1800720, TSC2 rs2516739, PDK1 rs6723872, EIF4EBP1 rs6605631, RPS6KB2 rs12787021, AKT3 rs14403, AKT3 rs3006939, AKT3 rs946824, AKT2 rs16974157, AKT2 rs874269, and INSR rs891088 in men, and of MTOR rs2295080, TSC2 rs12918803, PDK1 rs6723872, RPS6KB2 rs12787021, AKT3 rs1352162, AKT3 rs14403, AKT3 rs7523198, AKT3 rs7523742, AKT2 rs16974157, AKT2 rs874269, and INSR rs891088 in women. Scores were standardized with a mean of 0 and an SD of 1. Numbers and percentages or means and SDs were given after additionally excluding individuals with incomplete/inconsistent questionnaires. associations between the polygenic risk scores and rectal cancer risk in men and women were positive in direction, but not statistically significant. Individual SNP-CRC risk associations are shown in Supplemental Table 4. Several statistically significant associations were observed between individual SNPs, predomin- antly AKT3 SNPs, and CRC risk in men and women after gene-based FDR adjustment. Energy balance-related exposures and CRC risk: effect modification by sex-specific polygenic risk scores of mTOR-PI3K-Akt polymorphisms? Table 3 shows the associations between BMI, trouser/skirt size, BMI at age 20, non- occupational physical activity, height, and energy restriction during childhood and ado- lescence and CRC risk in men and women, stratified by tertiles of the sex-specific poly- genic risk scores. BMI was positively associated with CRC risk in men in the lowest tertile of the polygenic risk score specific for men; non-occupational physical activity was inversely associated with CRC risk in women in the lowest tertile of the polygenic risk score specific for women; and height was positively associated with CRC risk in men and women in the middle tertile of the polygenic risk score specific for each sex and in the lowest tertile of the polygenic risk score specific for women. No significant multiplicative interactions were observed. Analyses for subsite-specific CRC risks are shown in Supplemental Tables 5-8. In these stratified analyses for subsite-specific CRC risks, BMI was positively associated with proximal colon cancer risk in men, height was positively associated with colon, proximal colon, and distal colon cancer risk in both men and women and with rectal cancer risk in women, and non-occupational physical activity was inversely associated with colon, proximal colon, and distal colon cancer risk, with most associations observed in the lower tertiles of the polygenic risk score Simons et al. BioData Mining (2022) 15:2 Page 7 of 20 Table 2 Polygenic risk scores of mTOR-PI3K-Akt pathway polymorphisms in relation to CRC risk by sex and subsite in the Netherlands Cohort Study after 20.3 years of follow-up Men Women b b Endpoint Polygenic risk PT at N cases HR (95% CI) PT at risk N cases HR (95% CI) score risk CRC T2 vs. T1 9796 vs. 633 vs. 1.07 (0.91,1.26) 10,066 vs. 434 vs. 0.97 (0.81,1.16) 9540 587 10,207 460 T3 vs. T1 9506 vs. 662 vs. 1.14 (0.97,1.35) 10,125 vs. 517 vs. 1.15 (0.97,1.38) 9540 587 10,207 460 Continuous 28,841 1882 1.07 (1.00,1.15) 30,397 1411 1.09 (1.01,1.17) Colon T2 vs. T1 9796 vs. 397 vs. 1.05 (0.87,1.27) 10,066 vs. 312 vs. 0.95 (0.78,1.16) 9540 376 10,207 337 T3 vs. T1 9506 vs. 444 vs. 1.20 (1.00,1.44) 10,125 vs. 395 vs. 1.21 (1.00,1.46) 9540 376 10,207 337 Continuous 28,841 1217 1.10 (1.03,1.19) 30,397 1044 1.11 (1.03,1.20) Proximal T2 vs. T1 9796 vs. 177 vs. 1.02 (0.80,1.30) 10,066 vs. 185 vs. 0.96 (0.76,1.21) colon 9540 175 10,207 200 T3 vs. T1 9506 vs. 213 vs. 1.24 (0.98,1.57) 10,125 vs. 232 vs. 1.20 (0.96,1.51) 9540 175 10,207 200 Continuous 28,841 565 1.11 (1.01,1.22) 30,397 617 1.09 (0.99,1.20) Distal colon T2 vs. T1 9796 vs. 206 vs. 1.07 (0.85,1.35) 10,066 vs. 120 vs. 0.95 (0.72,1.25) 9540 190 10,207 129 T3 vs. T1 9506 vs. 222 vs. 1.18 (0.94,1.49) 10,125 vs. 153 vs. 1.21 (0.93,1.58) 9540 190 10,207 129 Continuous 28,841 618 1.11 (1.02,1.22) 30,397 402 1.14 (1.02,1.27) Rectum T2 vs. T1 9796 vs. 162 vs. 1.00 (0.78,1.29) 10,066 vs. 88 vs. 1.06 (0.77,1.47) 9540 159 10,207 85 T3 vs. T1 9506 vs. 163 vs. 1.04 (0.81,1.33) 10,125 vs. 87 vs. 1.05 (0.76,1.44) 9540 159 10,207 85 Continuous 28,841 484 1.00 (0.91,1.11) 30,397 260 1.07 (0.94,1.22) Abbreviations: CRC, colorectal cancer; CI, confidence interval; HR, hazard ratio; PT, person-time; T1-3, sex-specific tertile 1- 3; vs. versus. The polygenic risk score was composed of MTOR rs1057079, TSC2 rs1800720, TSC2 rs2516739, PDK1 rs6723872, EIF4EBP1 rs6605631, RPS6KB2 rs12787021, AKT3 rs14403, AKT3 rs3006939, AKT3 rs946824, AKT2 rs16974157, AKT2 rs874269, and INSR rs891088 in men, and of MTOR rs2295080, TSC2 rs12918803, PDK1 rs6723872, RPS6KB2 rs12787021, AKT3 rs1352162, AKT3 rs14403, AKT3 rs7523198, AKT3 rs7523742, AKT2 rs16974157, AKT2 rs874269, and INSR rs891088 in women. Adjusted for age (years). specific for each sex. Furthermore, one statistically significant interaction was observed between energy restriction during childhood and adolescence and the polygenic risk score specific for men in relation to distal colon cancer risk. Exposure to energy restric- tion during childhood and adolescence was inversely associated with distal colon cancer risk in men in the middle tertile of the polygenic risk score specific for men, while the association in the lowest tertile was positive in direction, though not statistically signifi- cant, nor was the association in the highest tertile statistically significant. Discussion The associations observed between the sex-specific polygenic risk scores and the risk of CRC overall, specifically colon cancer risk, suggest that the mTOR-PI3K-Akt pathway is involved in colon cancer development in both men and women. Involvement of the mTOR-PI3K-Akt pathway in rectal cancer development cannot be concluded based on the current data. There were no (multiplicative) interactions between the energy Simons et al. BioData Mining (2022) 15:2 Page 8 of 20 Table 3 Associations between exposures related to energy balance and CRC risk in men and women, stratified for tertiles of the sex-specific polygenic risk score of mTOR-PI3K- Akt pathway polymorphisms in the Netherlands Cohort Study (20.3 years of follow-up) Men Women BMI BMI T1 (sex-specific) T2 (sex-specific) T3 (sex-specific) T1 (sex-specific) T2 (sex-specific) T3 (sex-specific) a,b a,b a,b a,b a,b a,b N HR (95% N HR (95% CI) N HR (95% CI) P for N HR (95% N HR (95% CI) N HR (95% CI) P for cases/ CI) cases/ cases/ interaction cases/ CI) cases/ cases/ interaction PT at PT at PT at PT at PT at PT at risk risk risk risk risk risk Polygenic T1 165/ 1.00 (ref.) 166/ 1.07 (0.78,1.46) 203/ 1.48 (1.08,2.04) 133/ 1.00 (ref.) 141/ 1.16 (0.83,1.62) 118/ 1.03 (0.72,1.47) risk score 3343 2911 2588 3207 2999 2788 T2 165/ 1.00 (ref.) 207/ 1.12 (0.82,1.52) 206/ 1.26 (0.91,1.76) 139/ 1.00 (ref.) 132/ 0.98 (0.69,1.38) 109/ 0.81 (0.55,1.17) 2907 3196 2810 2829 2822 2775 T3 186/ 1.00 (ref.) 201/ 0.93 (0.68,1.27) 219/ 1.07 (0.77,1.47) 0.34 157/ 1.00 (ref.) 137/ 0.86 (0.62,1.20) 151/ 1.04 (0.74,1.44) 0.54 2688 3046 2726 3000 2900 2746 Trouser/skirt size Trouser/skirt size ≤median (sex- >median (sex-specific) ≤median (sex- >median (sex-specific) specific) specific) a,c a,c a,c a,c N HR (95% N HR (95% CI) P for N HR (95% N HR (95% CI) P for cases/ CI) cases/ interaction cases/ CI) cases/ interaction PT at PT at PT at PT at risk risk risk risk Polygenic T1 163/ 1.00 (ref.) 321/ 1.12 (0.81,1.54) 168/ 1.00 (ref.) 220/ 1.00 (0.69,1.45) risk score 3250 5042 3967 4910 T2 176/ 1.00 (ref.) 359/ 1.17 (0.86,1.60) 176/ 1.00 (ref.) 198/ 1.03 (0.70,1.50) 2932 5019 3849 4437 T3 177/ 1.00 (ref.) 376/ 1.25 (0.92,1.71) 0.84 178/ 1.00 (ref.) 263/ 1.11 (0.79,1.56) 0.76 3062 4775 3628 4937 Non-occupational physical activity Non-occupational physical activity ≤30 min/day >30-60 min/day >60 min/day ≤30 min/day >30-60 min/day >60 min/day a,d a,d a,d a,d a,d a,d NHR (95% N HR (95% CI) N HR (95% CI) P for N HR (95% N HR (95% CI) N HR (95% CI) P for Simons et al. BioData Mining (2022) 15:2 Page 9 of 20 Table 3 Associations between exposures related to energy balance and CRC risk in men and women, stratified for tertiles of the sex-specific polygenic risk score of mTOR-PI3K- Akt pathway polymorphisms in the Netherlands Cohort Study (20.3 years of follow-up) (Continued) Men Women cases/ CI) cases/ cases/ interaction cases/ CI) cases/ cases/ interaction PT at PT at PT at PT at PT at PT at risk risk risk risk risk risk Polygenic T1 89/ 1.00 (ref.) 156/ 0.79 (0.54,1.17) 289/ 1.01 (0.70,1.45) 113/ 1.00 (ref.) 104/ 0.57 (0.39,0.84) 175/ 0.70 (0.49,0.99) risk score 1380 3011 4452 1887 2964 4142 T2 83/ 1.00 (ref.) 177/ 1.09 (0.74,1.62) 318/ 1.14 (0.80,1.65) 98/ 1.00 (ref.) 128/ 0.84 (0.57,1.24) 154/ 0.72 (0.50,1.04) 1378 2811 4724 1775 2790 3860 T3 102/ 1.00 (ref.) 182/ 0.78 (0.53,1.16) 322/ 0.86 (0.60,1.23) 0.66 102/ 1.00 (ref.) 157/ 1.10 (0.76,1.59) 186/ 0.91 (0.64,1.29) 0.67 1341 2766 4353 1933 2815 3898 Height, Height, Height, Height, Height, Height, T1 (sex-specific) T2 (sex-specific) T3 (sex-specific) T1 (sex-specific) T2 (sex-specific) T3 (sex-specific) a,c a,c a,c a,c a,c a,c N HR (95% N HR (95% CI) N HR (95% CI) P for N HR (95% N HR (95% CI) N HR (95% CI) P for cases/ CI) cases/ cases/ interaction cases/ CI) cases/ cases/ interaction PT at PT at PT at PT at PT at PT at risk risk risk risk risk risk Polygenic T1 169/ 1.00 (ref.) 182/ 1.24 (0.90,1.71) 183/ 1.24 (0.91,1.71) 126/ 1.00 (ref.) 144/ 1.34 (0.96,1.88) 122/ 1.70 (1.18,2.44) risk score 3074 2862 2906 3605 3156 2233 T2 185/ 1.00 (ref.) 189/ 1.44 (1.04,1.98) 204/ 1.30 (0.95,1.77) 133/ 1.00 (ref.) 131/ 1.00 (0.71,1.42) 116/ 1.05 (0.72,1.52) 3205 2616 3093 3130 2905 2390 T3 181/ 1.00 (ref.) 205/ 1.14 (0.83,1.56) 220/ 1.28 (0.93,1.76) 0.94 138/ 1.00 (ref.) 162/ 1.60 (1.13,2.26) 145/ 1.57 (1.09,2.27) 0.30 2826 2987 2647 3285 2761 2600 Exposure to energy restriction during childhood and adolescence as based on Exposure to energy restriction during childhood and adolescence as based on place of residence during the Hunger Winter place of residence during the Hunger Winter Non-Western area Western rural area Western city Non-Western area Western rural area Western city a,c a,c a,c a,c a,c a,c N HR (95% N HR (95% CI) N HR (95% CI) P for N HR (95% N HR (95% CI) N HR (95% CI) P for cases/ CI) cases/ cases/ interaction cases/ CI) cases/ cases/ interaction PT at PT at PT at PT at PT at PT at risk risk risk risk risk risk Polygenic T1 269/ 1.00 (ref.) 73/ 1.24 (0.82,1.86) 107/ 0.98 (0.70,1.38) 201/ 1.00 (ref.) 60/ 1.42 (0.92,2.19) 108/ 1.24 (0.88,1.75) Simons et al. BioData Mining (2022) 15:2 Page 10 of 20 Table 3 Associations between exposures related to energy balance and CRC risk in men and women, stratified for tertiles of the sex-specific polygenic risk score of mTOR-PI3K- Akt pathway polymorphisms in the Netherlands Cohort Study (20.3 years of follow-up) (Continued) Men Women risk score 4418 1127 1918 4989 1182 2291 T2 305/ 1.00 (ref.) 69/ 0.85 (0.57,1.27) 112/ 0.84 (0.60,1.19) 194/ 1.00 (ref.) 63/ 1.20 (0.79,1.82) 101/ 1.02 (0.72,1.43) 4307 1230 1696 4413 1222 2333 T3 324/ 1.00 (ref.) 74/ 1.15 (0.76,1.74) 108/ 0.72 (0.50,1.02) 0.56 225/ 1.00 (ref.) 70/ 1.12 (0.75,1.67) 127/ 1.04 (0.75,1.43) 0.61 4194 946 1805 4412 1283 2462 Abbreviations: BMI, body mass index; CI, confidence interval; HR, hazard ratio; N, number of; PT, person-time; ref., reference; T1-3, tertile 1-3. Adjusted for age (years), first-degree family history of colorectal cancer (yes/no); smoking status (never, ex, current); alcohol intake (0, 0.1-29, ≥30 g/d); meat intake (g/d), processed meat intake (g/d), and total energy intake (kcal/d). Additionally adjusted for non-occupational physical activity (≤30, >30-60, >60 min/day), respectively. c 2 Additionally adjusted for BMI (kg/m ) and non-occupational physical activity (≤30, >30-60, >60 min/day), respectively. d 2 Additionally adjusted for BMI (kg/m ). Simons et al. BioData Mining (2022) 15:2 Page 11 of 20 balance-related exposures studied and the polygenic risk scores specific for each sex in relation to CRC risk overall or by subsite, except for one, i.e. there was an interaction with energy restriction during childhood and adolescence in relation to distal colon cancer risk in men. However, associations within tertiles of the polygenic risk score did not provide a clear indication for a modifying effect. Overall, in the stratified analyses, we predominantly observed associations between energy balance-related exposures and CRC risk in the lower tertiles of the sex-specific polygenic risk scores, with the direc- tion of the associations generally in line with what would be expected for these factors in relation to CRC risk based on literature [15] and with what was previously observed in the NLCS after 16.3 years of follow-up. [16, 17] However, these stratum-specific as- sociations on their own, without (statistical) interaction present, do not form sufficient evidence for concluding that there was a modifying effect by mTOR-PI3K-Akt genetic variation on associations between energy balance-related factors and CRC risk. That said, if we allow for speculation and view these data in a broader sense, these data raise the question whether environmental factors predominate when genetic risk is low. As regards to our findings for the polygenic risk scores and subsite-specific CRC risks, stronger involvement of the mTOR-PI3K-Akt pathway in the development of more proximally located colorectal tumors is plausible considering that higher (over)ex- pression of Akt1, Akt2, and p-p70S6K(Thr389) genes has been reported in proximal colon tumors than distal colon tumors. [18] PTEN gene expression was also found to show a positive expression gradient towards the proximal colon, starting at the rectum. [19] Furthermore, PTEN and PIK3CA mutations are more prevalent in tumors of the proximal colon. [20] These literature findings provide some confidence in that the asso- ciations observed in this study, which suggest mTOR-PI3K-Akt involvement in colon cancer development, were not chance findings. There are few data characterizing associations between energy balance-related expo- sures and CRC risk within genetic risk strata based on mTOR-PI3K-Akt pathway poly- morphisms or vice versa. We specifically chose the former modulation in light of future translation of the results towards prevention, because polymorphisms are static vari- ables and energy balance-related exposures such as BMI are modifiable; that is, a healthy BMI and physical activity level could be especially important for specific genetic risk groups. Previous studies, however, investigated cancer risks associated with carry- ing more risk alleles within strata of energy balance-related factors, [21–24] under the hypothesis that a positive energy imbalance activates the mTOR-PI3K-Akt pathway. These studies did not uniformly suggest that activation of the mTOR-PI3K-Akt path- way by a positive energy imbalance influences cancer risk, as some observed associa- tions between mTOR-PI3K-Akt pathway variants and cancer risk in normal weight instead of overweight/obese individuals. [23] Meanwhile, energy balance has been shown to modulate signaling through Akt and mTOR in multiple epithelial tissues in mice, with diet-induced obesity enhancing and calorie restriction inhibiting activation. [25] The mixed observational results in the literature might be explained by differences in effect on CRC risk of the specific variants included, perhaps suggesting the import- ance of capturing a sufficient and representative amount of genetic variation present in the mTOR-PI3k-Akt pathway in the population. For example, one of the studies refer- enced above utilized both a polygenic risk score of mTOR-PI3K-Akt pathway polymor- phisms and an energy balance index and found a joint effect of the two on bladder Simons et al. BioData Mining (2022) 15:2 Page 12 of 20 cancer risk. [21] This study, however, may have had limited power, leading to unstable (and extreme) risk estimates, as based on the case numbers and the wide confidence in- tervals reported. In addition, this study selected SNPs for inclusion in the risk score based on p-values for main effects and tested the risk score in the same population as in which the single SNPs were tested, which might have led to overfitting of the risk score model to the underlying data and inflation of the results. Alternatively, the mixed results in the literature in relation to CRC risk and the absence of interaction in the present study could mean that an interaction between energy balance-related exposures and genetic variation in the mTOR-PI3K-Akt pathway in relation to CRC risk is absent or not strong enough to be detected given the average statistical power achieved in a large observational cohort. Despite the absence of (statistical) interaction between energy balance-related expo- sures and the polygenic risk score of mTOR-PI3K-Akt pathway polymorphisms in rela- tion to CRC risk, one particular finding in this study is noteworthy. This is the observation that height was a colon cancer risk factor in both men and women in the lowest and middle tertiles of the polygenic risk score. Previously, after 16.3 years of follow-up, height was observed to be a colon cancer risk factor in women but not men, [16] whereas accounting for genetic variation in the mTOR-PI3K-Akt pathway ap- peared to remove the sex difference observed overall in our cohort. We have observed the same phenomenon when accounting for genetic variation in the insulin-like growth factor pathway. [26] The absence of a sex difference is in accordance with the literature that shows energy balance-related exposures such as BMI and height to be CRC risk factors regardless of sex. [15] Interestingly, BMI and height were colon but not rectal cancer risk factors in this study and in previous studies from the NLCS regardless of which other variables were taken into account, [16, 26] which may be a cohort-specific effect (e.g. residual confounding in this specific population), as the literature shows these factors to also be rectal cancer risk factors. [15]. The methodology used to select genes and polymorphisms in the mTOR-PI3K-Akt pathway and the methodology used to generate the sex-specific weighted polygenic risk scores of mTOR-PI3K-Akt polymorphisms deserves some further discussion. Firstly, the assumptions made to select key genes in the mTOR-PI3K-Akt pathway using the relative betweenness centrality measure may not accurately represent the biology of the mTOR-PI3K-Akt pathway. For example, it was assumed that the information flow (sig- nals) between nodes (genes) in a pathway is undividable and always takes the shortest path. In addition, we have assumed an undirected graph (pathway), meaning the infor- mation flow between connected nodes can go both ways. These assumptions were nevertheless necessary and resulted in a list of top-ranked genes that fit with prior knowledge of key players in the mTOR-PI3K-Akt pathway, reassuring us that no major bias occurred because of a potentially inaccurate representation of the biology of the pathway. Secondly, our method of SNP selection, i.e. we selected tagging variants in order to cover as much of the genetic variation in the top-ranked genes as possible, did not immediately allow us to consider correlations of SNPs with other biological levels, such as gene or protein expression. Many selected SNPs, however, turned out to be ex- pression quantitative trait loci (eQTLs) for the gene that they were tagging and/or other genes according to the Genotype-Tissue expression (GTex) project (https://gtexportal. org/home/; National Institutes of Health, United States). Thirdly, we were limited in Simons et al. BioData Mining (2022) 15:2 Page 13 of 20 the number of SNPs that we could genotype, and thus the number of genes in the mTOR-PI3K-Akt pathway that we could cover, because of budgetary constraints that allowed us to genotype only one multiplex assay. Given the genes that we covered, this may have led to insufficient coverage of genes encoding for proteins of which signaling is under the influence of a negative energy imbalance. For example, we could not in- clude SNPs encoding for adenosine monophosphate-activated protein kinase (AMPK), which phosphorylates TSC2 in the TSC1-2 complex [2] and stabilizes the mTOR- RAPTOR bond in mTORC1 under conditions of a negative energy balance, inhibiting mTORC1 signaling. [2, 27]. Strengths of this study include that it is a large, population-based prospective cohort with long follow-up, resulting in a large number of CRC cases and making selection and information bias unlikely. Limitations include the single baseline measurement of exposures. The NLCS population has been found stable in its dietary habits, [28] but diminishing physical activity levels and changes in body composition may be inevitable with increasing age, possibly having led to attenuation of associations over time. Conclusions The findings of this study suggest that the mTOR-PI3K-Akt pathway may be involved in the development of colon cancer, but not rectal cancer. Energy balance-related fac- tors were associated with CRC risk as hypothesized, mostly within the lower tertiles of the polygenic risk score specific for each sex, but there was no clear modifying effect of the scores. The relevance of this study lies in its contribution to the evidence base on mechanisms involved in colon cancer development through use of a polygenic risk score, capturing natural variation in the mTOR-PI3K-Akt pathway in the population. Methods Population and design The NLCS [29] includes 120,856 men and women who completed a questionnaire on diet and cancer at baseline in 1986 when 55-69 years old. The baseline questionnaire included a 150-item semi-quantitative food frequency questionnaire, which was found to rank individuals’ dietary intake adequately as compared to a 9-day dietary record [30] and was shown a good indicator of intake for at least 5 years. [28] Approximately 75% of the cohort returned toenail clippings, which are a valid and long-term DNA source. [31, 32] The NLCS is characterized by a case-cohort approach for reasons of ef- ficiency related to questionnaire processing, follow-up, and genotyping. A random sub- cohort (n=5000), selected immediately after baseline and independent of any exposure, is followed up for vital status through record linkage to the Central Bureau of Geneal- ogy and municipal population registries (>99.9% completeness) to estimate the accu- mulated person-time at risk. Participants were excluded if they reported a history of cancer other than skin cancer at baseline, leaving 4774 subcohort members for follow- up (Fig. 1). The whole cohort is followed up for incident cancer cases through record linkage to the population-based cancer registry and PALGA (the Netherlands pathology database) (>96%completeness). [33, 34] The case-cohort design allows for the estima- tion of hazard ratios as would be done in a full cohort under the assumption that the fraction of the accumulated person-time at risk observed for exposed and unexposed Simons et al. BioData Mining (2022) 15:2 Page 14 of 20 individuals is equal. This is reasonable considering that the subcohort was selected in- dependent of any exposure. The extra variance introduced by sampling the subcohort from the total cohort can be adjusted for using the robust variance estimator. [35]A detailed description of the NLCS is available in [29]. After 20.3 years of follow-up from September 1986 until the end of 2006, there were 3144 incident colon cancer cases (ICD-O-3 code C19) (among which 1623 incident proximal colon cancer cases (ICD- O-3 codes C18-C18.4) and 1430 incident distal colon cancer cases (ICD-O-3 codes C18.5-C18.7)), 427 incident rectosigmoid cancer cases (ICD-O-3 code C20), and 1026 incident rectal cancer cases (ICD-O-3 code C21), totaling to 4597 incident CRC cases (Fig. 1). Baseline information Baseline information included height (cm) and weight (kg) used to derive body mass index (kg/m ) (BMI) (reflecting body fatness), trouser/skirt size (Dutch clothing sizes) which is used as a marker for waist circumference (reflecting abdominal fatness when adjusting for BMI), weight at age 20 used together with height to derive BMI at age 20 (kg/m ), and energy restriction during childhood and adolescence as based on place of residence during the Dutch Hunger Winter in 1944-45. Self-reports on weight and height have been shown valid measures in other cohort studies with >10 years of follow-up. [36, 37] Trouser/skirt size correlated with hip and waist circumferences in a subset of weight-stable NLCS men (r=0.63 and 0.64, respectively) and women (r=0.78 and 0.71, respectively) and was associated with endometrial and renal cancer risk in a fashion as would be expected for waist circumference. [38] BMI and height measures were divided into sex-specific tertiles based on the distribution in the subcohort. Trou- ser/skirt size was split into two sex-specific categories based on the median in the sub- cohort. Information on non-occupational physical activity in categories of ≤30, 30-60, >60 min of physical activity per day was a sum measure of daily walking/cycling (min/ day), weekly recreational walking/cycling, weekly gardening/doing odd jobs, and weekly sports/gymnastics (categories: never, 1, 1-2, >2 h/week). More details on energy restric- tion during childhood and adolescence as measured in the NLCS are available in [39]. Baseline information on relevant covariates in diet and lifestyle was also available from the baseline questionnaire. DNA isolation and genotyping Toenail clippings were stored without further treatment or climate control of the stor- age room. The DNA isolation protocol has been described in [31] and [32]. DNA iso- lated from toenail material was stored at -30 °C at the BioBank Maastricht University Medical Center+ (Maastricht, the Netherlands). Toenail DNA is suitable for genotyping on the Agena BioScience MassARRAY® platform (Hamburg, Germany), allowing the genotyping of 36-40 SNPs at once, although, in practice, not all SNPs can be combined due to sequence incompatibilities between the sequences flanking the SNPs. Gene and SNP selection We identified 10 top ranked genes in the mTOR-PI3K-Akt pathway according to their relative betweenness centrality, which provides an indication of the strength of node Simons et al. BioData Mining (2022) 15:2 Page 15 of 20 involvement in the information flow through a network: MTOR (alias: FRAP1), TSC2, PDPK1 (alias: PDK1), EIF4EBP1 (alias: 4EBP1), IRS1, RPS6KB1 (alias: S6K1), RPS6KB2 (alias: S6K2), AKT3, AKT1, and AKT2 (Supplemental Table 1). The Kyoto Ecyclopedia of Genes and Genomes (KEGG) mTOR signaling (map04150) was used as input (http://www.genome.jp/kegg/) (R software, version 3.2.2, KeggGraph package). Since there were no SNPs in these genes associated with colorectal cancer risk at a signifi- −5 cance level of p<1*10 in GWAS (https://www.ebi.ac.uk/gwas/), we selected tagging single nucleotide polymorphisms (tagSNPs) at a minor allele frequency of 5% or higher for the top 10 ranked genes using aggressive tagging [40] (HaploView version 4.2, Broad Institute). Not all 10 genes could be included in the assay because not all combi- nations of SNPs can be included because of sequence incompatibilities between the se- quences flanking the SNPs on the basis of which the primer design took place. We firstly fixed the replicated cancer risk-associated MTOR SNP rs2295080 in the assay de- sign. The assay design next allowed for the inclusion of the following tagSNPs covering 7 of the 10 top-ranked genes: MTOR rs1057079; TSC2 rs2516739, rs1800720, rs2074969, rs9928737, and rs12918803; PDK1 rs6723872; EIF4EBP1 rs6605631; RPS6KB2 rs12787021; AKT3 rs3006939 rs14403, rs7523198, rs7523742, rs1352162, and rs946824; and AKT2 rs874269, rs16974157, and rs7250897. The assay was furthermore filled up as much as possible with single genome-wide association study (GWAS) hits for anthropometric traits, physical activity, or CRC annotated to mTOR-PI3K-Akt pathway genes (https://www.ebi.ac.uk/gwas/). Included were RPTOR rs7503807 for its association with obesity, [41] RICTOR rs2043112 and S6K1 rs1051424 for their associ- ation with (childhood) obesity-associated traits, [42] and IGF1R rs2871865 [43, 44] and INSR rs891088 for their association with height (abbreviations are explained in Supple- mental Table 1). [43, 45]. Genotyping Genotyping was performed for 3793 (79.5%) subcohort members and 3464 (75.3%) CRC cases with available toenail DNA (Fig. 1). Potentially contaminated samples as noted by the laboratory technicians were excluded (2.6%) to ensure the quality of the data. Mean sample call rates were 97.4% (median: 100%). SNP call rates were between 97 and 99%, except for one SNP, which had a SNP call rate of 92% (rs1051424). A sam- ple call rate of 95% or higher was present in samples from 3550 subcohort members (93.6%) and in samples from 3293 CRC cases (95.1%). (Two SNPs genotyped for use in another project were also enumerated when calculating the sample call rate.) Allele fre- quencies in the subcohort, which is representative of the whole cohort, are given in Supplemental Table 2. Hardy-Weinberg Equilibrium was violated on five occasions, but we did not exclude these SNPs from further analysis, because we had no reason to sus- pect genotyping errors since all SNPs were genotyped using a single assay and because multiple tests increased the risk of a significant finding by chance. Statistical analysis The main exposure variable used in the analyses was a sex-specific polygenic risk score. Since no GWAS summary statistics were available to generate a polygenic risk score, we generated this score using the data at hand. First, the dataset was divided into two Simons et al. BioData Mining (2022) 15:2 Page 16 of 20 random sets of approximately equal size (datasets A and B). In each set, each individual SNP was modelled continuously in relation to the risk of CRC in men and women sep- arately, adjusting the model for age. We deemed it important to do this in a sex- specific manner, because energy balance-related risk factors for CRC have been shown to differ between men and women in the NLCS. [16, 17, 26, 39, 46–48] Specifically, a larger BMI and trouser size, used as a proxy for waist circumference, were previously shown to be risk factors in men but not women, whereas height was a risk factor in women but not men in the NLCS. Genotypes in individuals were coded ‘0’ when homo- zygote for the major allele, ‘1’ when heterozygote, and ‘2’ when homozygote for the minor allele. We used the standard error (SE) weighted regression coefficients (beta / SE) from set A to generate the polygenic risk scores in set B and vice versa (i.e. two polygenic risk scores were generated in each set, one for men and one for women). The polygenic risk scores were calculated by weighting the number of risk alleles carried by an individual in one set using the standard error weighted regression coefficient from the other set (SNP x: n risk alleles * (beta / SE), with n being 0, 1, or 2) and then sum- ming the weighted number of risk alleles for all SNPs into a single score. In case of a negative standard error weighted regression coefficient, the coding of the SNP was re- versed, as in these instances the major allele instead of the minor allele was considered the risk-conferring allele, and the absolute value of the weighted regression coefficient was used. We only included SNPs in the polygenic risk scores that showed the same direction of effect in both sets so that the risk scores in each dataset would include the same SNPs, though different weights were used to generate the scores. The scores were allowed to include different SNPs between men and women. We also only included SNPs in the polygenic risk scores that were in low linkage disequilibrium (LD) which was defined as r ≤0.6, because SNPs in low LD are most likely to add new information to the score in terms of the variance explained in the outcome. LD was evaluated for the data under study using default settings in Haploview version 4.2 and defining CRC cases as affected individuals and subcohort members without CRC as unaffected indi- viduals. There were two pairs of SNPs that were in LD with r >0.6 (AKT3 rs946824 and AKT3 rs7523742 and MTOR rs2295080 and MTOR rs1057079), of which only one of a pair (chosen at random) was included in the polygenic risk score in case of consist- ent betas in sets A and B for both SNPs. To adjust for missing SNP data (one SNP was missing at most because of exclusion of samples with <95% call rate), we divided the polygenic risk scores in each set by the proportion of successfully genotyped risk alleles [(n successfully genotyped SNPs*2) / (n genotyped SNPs*2)]. We then standardized the set-specific polygenic risk scores by deducting the mean and dividing the scores by their standard deviation (SD) [(x-mean) / SD], with sex-specific means and SDs based on the subcohort. This allowed us to merge the scores and the datasets back together again, resulting in one dataset which included two polygenic risk scores, i.e. one for men and one for women. Cox regression was then used to study the (subsite-specific) CRC risks associated with the sex-specific polygenic risk scores in men and women separately using R (R software, version 3.2.2). Models were age-adjusted and the polygenic risk scores were modelled in tertiles (based on the distribution in the male and female subcohort, re- spectively) and continuously. We also analyzed individual SNPs in relation to CRC risks in men and women in the total dataset, assuming a codominant and additive Simons et al. BioData Mining (2022) 15:2 Page 17 of 20 inheritance mode, in order to facilitate potential future meta-analyses for any of these individual SNPs. To investigate whether associations of BMI, trouser/skirt size, BMI at age 20, non- occupational physical activity, height, and energy restriction during childhood and ado- lescence with overall and subsite-specific CRC risks in men and women were modified by the polygenic risk scores, we stratified associations by tertiles of the sex- specific polygenic risk scores and tested multiplicative interactions using the Wald test in men and women (wald.test, aod package in R). Participants with incomplete or in- consistent baseline questionnaires were excluded from these analyses, leaving 2191 male and 2248 female subcohort members and 2409 male and 1870 CRC cases, al- though the total number per analysis differed because of missing values on specific ex- posure variables and covariates (Fig. 1). Since BMI, trouser/skirt size, BMI at age 20, non-occupational physical activity, height, and energy restriction during childhood and adolescence have been studied as CRC risk factors after 16.3 years of follow-up in our cohort, we used the same confounder sets as before, which included established CRC risk factors and confounders derived using a backward procedure. [16, 17, 26, 39, 46– 48]. Our approach of using polygenic risk scores was aimed at reducing the risk of overfitting, which can lead to inflated estimates or false positive findings. Since most of theSNPsusedinthisstudy were tagging polymorphismsthatwerese- lected to cover as much genetic variation as possible in the set of identified top- ranked genes in the mTOR-PI3K-Akt pathway, we did not have data from an inde- pendent population available on the risk-conferring allele for the majority of the SNPs. By generating a risk score in each half of the data and then merging the data back together again, we benefited from optimal power to carry out subsite- specific analyses and investigate potential effect modification by the polygenic risk scores of associations between energy balance-related factors and (subsite-specific) CRC risks in men and women separately. All Cox models (coxph, survival package in R) were adjusted for the additional vari- ance introduced by sampling the subcohort from the total cohort by entering the par- ticipant identification number as cluster term in the model (robust variance option). [35] We checked potential violations of the proportional hazards assumption by plot- ting the scaled Schoenfeld residuals against time and violations appeared negligible (cox.zph, survival package in R). Statistical significance was indicated by a P-value <0.05 for two-sided testing. Gene-based false discovery rate-adjusted P-values across men and women were calculated according to the method of Benjamini and Hochberg for P- values for individual SNP-CRC associations. The FDR adjustment entailed ranking P- values in ascending order and multiplying a predefined FDR threshold (0.20 [49]) with the inverse of the rank order over the total number of P-values considered to be part of the multiple testing. [50] If the original P-value was below 0.05 and the FDR-adjusted P-value, we considered the result statistically significant. Supplementary information The online version contains supplementary material available at https://doi.org/10.1186/s13040-021-00286-3. Additional file 1: Supplemental Fig. 1. Histogram of the sex-specific polygenic risk scores in male and female subcohort members in datasets A and B used to generate the scores. Simons et al. BioData Mining (2022) 15:2 Page 18 of 20 Additional file 2. Acknowledgements We are indebted to the participants of this study and wish to thank the Netherlands Cancer Registry and the Netherlands nationwide registry of pathology (PALGA). We also thank Drs. A. Volovics, and A. Kester for statistical advice; S. van de Crommert, H. Brants, J. Nelissen, C. de Zwart, M. Moll, W. van Dijk, and A. Pisters for data management; Dr. H. Hoofs, H. van Montfort, T. van Moergastel, L. van den Bosch, R. Schmeitz, and J. Berben for programming assistance; L. Maas, L. Jonkers, J. Goessens, K. Lemmens, and S. Lumeij for the laboratory work involved; Dr. J. Hogervorst for conceiving the idea of using toenail clippings for DNA isolation and genotyping; and the Biobank Maastricht UMC+ for sample storage. Authors’ contributions The authors’ contributions were as follows: C.C.J.M. Simons conducted the analyses and drafted the manuscript; L.J. Schouten, R.W.L. Godschalk, F.J. van Schooten, M. Stoll, and K. Van Steen critically reviewed the manuscript; Piet A. van den Brandt critically reviewed the manuscript and conceived the Netherlands Cohort Study; Matty P. Weijenberg critically reviewed the manuscript and conceived the idea for this particular study. The author(s) read and approved the final manuscript. Funding This work was supported by grants of the World Cancer Research Fund (grant numbers 2012/618 and 2013/673 to MPW), the Biobanking and Biomolecular Research Infrastructure Netherlands (to MPW), and the Health Foundation Limburg (to MPW). Availability of data and materials The authors declare that the data supporting the findings of this study are available within the article and its supplementary information files. Declarations Ethics approval and consent to participate The review boards of the TNO Nutrition and Food Research Institute (Zeist, the Netherlands) and Maastricht University (Maastricht, the Netherlands) approved the NLCS, the use of toenail DNA for genotyping, and this study. Participants consented to participation in the NLCS by returning the baseline questionnaire on diet and cancer. Consent for publication Not applicable. Competing interests None of the authors had a conflict of interest. Author details Department of Epidemiology, GROW – School for Oncology and Developmental Biology, Maastricht University, Maastricht, the Netherlands. Department of Pharmacology and Toxicology, NUTRIM – School for Nutrition and Translational Research in Metabolism, Maastricht University, Maastricht, the Netherlands. Institute of Human Genetics, Genetic Epidemiology, University of Münster, Münster, Germany. 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Journal

BioData Mining – Springer Journals

Published: Jan 10, 2022

Keywords: Body size; Cohort studies; Colorectal neoplasms; Mechanistic target of rapamycin; Polymorphisms

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