Calcium intake has been associated with a lower risk of colorectal cancer. Calcium signaling may enhance T-cell proliferation and differentiation, and contribute to T-cell–mediated antitumor immunity. In this prospective cohort study, we investigated the association between calcium intake and colorectal cancer risk according to tumor immunity status to provide additional insights into the role of calcium in colorectal carcinogenesis. The densities of tumor-infiltrating T-cell subsets [CD3+, CD8+, CD45RO (PTPRC)+, or FOXP3+ cell] were assessed using IHC and computer-assisted image analysis in 736 cancer cases that developed among 136,249 individuals in two cohorts. HRs and 95% confidence intervals (CI) were calculated using Cox proportional hazards regression. Total calcium intake was associated with a multivariable HR of 0.55 (comparing ≥1,200 vs. <600 mg/day; 95% CI, 0.36–0.84; Ptrend = 0.002) for CD8+ T-cell–low but not for CD8+ T-cell–high tumors (HR = 1.02; 95% CI, 0.67–1.55; Ptrend = 0.47). Similarly, the corresponding HRs (95% CIs) for calcium for low versus high T-cell–infiltrated tumors were 0.63 (0.42–0.94; Ptrend = 0.01) and 0.89 (0.58–1.35; Ptrend = 0.20) for CD3+; 0.58 (0.39–0.87; Ptrend = 0.006) and 1.04 (0.69–1.58; Ptrend = 0.54) for CD45RO+; and 0.56 (0.36–0.85; Ptrend = 0.006) and 1.10 (0.72–1.67; Ptrend = 0.47) for FOXP3+, although the differences by subtypes defined by T-cell density were not statistically significant. These potential differential associations generally appeared consistent regardless of sex, source of calcium intake, tumor location, and tumor microsatellite instability status. Our findings suggest a possible role of calcium in cancer immunoprevention via modulation of T-cell function.

Research on calcium intake and colorectal neoplasia has important public health implications. Calcium is a simple, modifiable, inexpensive agent, and approximately 43% of U.S. adults use supplemental calcium (1). Furthermore, most epidemiologic studies (2–4) have reported an inverse association between higher calcium intake and risk of developing colorectal adenoma and cancer. However, evidence from the randomized controlled trials of calcium supplementation has been inconsistent (5, 6). Partly because of these discrepant findings, the Institute of Medicine called for more targeted research on calcium and colorectal cancer (7). Most previous studies have investigated total colorectal cancer, but this tumor comprises a group of heterogeneous subtypes (8), and the association with calcium intake may therefore differ by specific molecular subtypes (9). Hence, integrating host factors (such as diet) and tumor molecular features (such as immunity status) may enhance our understanding of the mechanisms through which calcium may act on colorectal carcinogenesis.

Accumulating evidence suggests that effector or cytotoxic (CD3+ cells and CD8+ cells), memory [CD45RO (PTPRC)+ cells], and regulatory (FOXP3+ cells) T cells play an important role in colorectal cancer development and prognosis (10–12). Calcium acts as second messenger in lymphocytes that enhances T-cell proliferation and regulates its differentiation, and gene expression (13, 14). Hence, it is plausible that calcium may influence colorectal carcinogenesis through immunity. In fact, human trials showed that supplementation with calcium could reduce several tumor-promoting inflammation biomarkers (15–17), and reverse the upregulation of expression of genes involved in inflammation and immune response induced by Western-style diet which is low in calcium (18). In light of the biological evidence, we hypothesized that the association between calcium intake and colorectal cancer risk might differ by tumor immunity status defined by densities of infiltrated T cells in the tumor microenvironment.

To test this hypothesis, we conducted an immunologic molecular pathologic epidemiology study (8) by integrating data on calcium intake, colorectal cancer outcomes, and tumor pathologic immunity status from two large U.S. nationwide prospective cohorts, the Nurses' Health Study (NHS), and the Health Professionals Follow-up Study (HPFS). We examined the association between calcium intake and risk of colorectal cancer according to the T-cell densities in tumor tissue.

Study population

The study population included 121,700 female participants from NHS and 51,529 male participants from HPFS (19, 20). Briefly, for NHS, the recruitment of 121,700 U.S. female registered nurses ages 30–55 years was completed in 1976. For HPFS, the recruitment of 51,529 U.S. male professionals ages 40–75 years was completed in 1986. In both cohorts, questionnaires were administrated biennially to collect and update information on demographic characteristics, lifestyle factors, and medical history, with follow-up rates over 90% in each cohort. This study was approved by the institutional review boards of the Brigham and Women's Hospital and Harvard T.H. Chan School of Public Health (Boston, MA). In this study, we excluded participants with a history of cancer (except for non-melanoma skin cancer), polyposis syndrome, ulcerative colitis/Crohn disease, implausible energy intakes at baseline (<600 or >3,500 kcal/day for women, or <800 or >4,200 kcal/day for men), or with no reports of calcium intake. After exclusion, a total of 136,249 participants (88,509 women and 47,740 men) were included in this analysis. A flow chart showing how the study population for analysis was developed is presented in Supplementary Fig. S1.

Assessments of calcium intake and other dietary factors

Details on assessments of calcium intake, as well as other dietary factors were described previously (2, 9, 21). In brief, we used validated (22, 23) semiquantitative food frequency questionnaires (FFQ) to collect dietary information at baseline and every 4 year thereafter. The energy-adjusted correlation coefficients of total calcium intake comparing the FFQs and the average of multiple 1-week diet records were 0.61 for men (22) and 0.63 for women (23). The correlation coefficients for dietary calcium intake were 0.60 for men (22) and 0.70 for women (23). We also collected information on dietary factors including intakes of alcohol, vitamin D, folate, red meat, and processed meat (22, 24).

Assessments of covariates

We collected information on potential colorectal cancer risk factors including height, adult body weight, physical activity [metabolic equivalent task score (METS)-hours/week], cigarette smoking, sigmoidoscopy/colonoscopy screening, family history of colorectal cancer, aspirin use, and menopausal status and use of menopausal hormones on the baseline and updated in biennial follow-up questionnaires.

Ascertainment of colorectal cancer cases

The incident colorectal cancer cases were defined as a primary tumor with International Classification of Diseases-9 codes of 153 and 154. Participants from the two cohorts were asked for written permission to obtain medical records and pathologic reports if they reported colorectal cancer on biennial questionnaires. We searched state vital statistics records, the National Death Index, to identify additional unreported cancer deaths. For all deaths attributable to colorectal cancer, we requested permission from next-of-kin to review medical records. All possible cancer cases were further confirmed through review of medical and pathologic records. A study physician who was blinded to exposure data abstracted information on tumor anatomic location, stage, and histology type. We included colon and rectal carcinoma cases based on the colorectal continuum model (25, 26).

Tumor immunity and molecular analyses

We constructed tissue microarray (TMA; ref. 27), and assessed CD3+ cell, CD8+ cell, CD45RO (PTPRC)+ cell, and FOXP3+ cell densities in tumor tissue using IHC. We used image analysis through an automated scanning microscope and the Ariol Image Analysis System (Genetix) to calculate the average density (cells/mm2) of each T-cell subset in TMA cores, as reported previously (10). We classified each of the T-cell densities (cells/mm2) into quartiles (Q1–Q4) and divided cases into two groups: low (Q1–Q2) or high (Q3–Q4) in the analyses for statistical efficiency. We also analyzed tumor microsatellite instability (MSI) status and calcium sensing receptor (CASR) expression as reported previously (9, 28, 29). DNA from paraffin-embedded tissue was extracted. The status of MSI was determined by analyzing variability in the length of the microsatellite markers from tumor DNA compared with normal DNA, including D2S123, D5S346, D17S250, BAT25, BAT26, BAT40, D18S55, D18S56, D18S67, and D18S487 (29). As described previously (9), we constructed TMAs from colorectal cancer blocks, and conducted IHC for CASR. CASR expression levels in all cases were reviewed by Y. Masugi. For agreement study, selected tumors (n = 118) were independently examined by a second observer (Z.R. Qian), and the concordance between the two observers (Y. Masugi and Z.R. Qian) was reasonable with a weighted κ of 0.71 [95% confidence interval (CI), 0.61–0.82; ref. 9].

Statistical analysis

Age-adjusted and multivariable-adjusted cohort-specific HRs and 95% CIs for each colorectal cancer subtype according to the densities of tumor-infiltrated T-cell subsets (i.e., CD3+ cells, CD8+ cells, CD45RO+ cells, and FOXP3+ cells) were calculated using the duplication method Cox proportional hazards regression model (30). This method permits the estimation of separate regression coefficients for the exposure stratified by CRC subtype defined by the densities of tumor-infiltrated T-cell subsets (30). The model was stratified simultaneously by age (in months) and year of questionnaire return (every 2 year since baseline questionnaire), accounting for the finest possible control of confounding for age and secular trends. Person-years of follow-up were calculated from the date of baseline questionnaire return to the date of diagnosis of colorectal cancer, date of death, loss to follow-up, or the end of follow-up (June 1, 2012 for the NHS and January 31, 2012 for the HPFS), whichever came first. Cancer cases without tumor immunity data were censored at diagnosis. We used the energy-adjusted (31) cumulative average intake of total calcium as reported on all available questionnaires up to the start of each 4-year follow-up interval as the main exposure (2), to minimize within-person variation and to better reflect long-term intake. Likewise, we used cumulative average for covariates and modeled them as time-varying variables when appropriate to allow for potential changes over follow-up periods. The adjusted covariates, as well as their categorizations in the multivariable models are shown in Tables 1 and 2 footnotes. We found no violation of proportional hazard assumption.

Table 1.

Baseline characteristics of participants by frequency of total calcium intake in the NHS (1980) and HPFS (1986)

Total calcium intake (mg/d)
<600600–799800–9991,000–1,199≥1,200
Women (NHS) 
 Number 34,137 24,290 14,732 8,325 7,022 
 Age, yearsa 46.5 (7.0) 46.8 (7.2) 46.8 (7.3) 46.7 (7.4) 47.1 (7.4) 
 White, % 96.4 98.0 98.3 98.4 98.2 
 Body mass index, kg/m² 24.0 (4.2) 24.0 (4.2) 24.1 (4.1) 24.2 (4.1) 24.4 (4.4) 
 Activity, METS-hours/week 12.5 (18.0) 14.2 (19.7) 15.2 (21.5) 15.3 (21.0) 16.2 (26.4) 
 Family history of colorectal cancer, % 7.9 7.8 7.9 7.9 7.7 
 Regular aspirin use (2 or more tablets/week), % 33.1 33.4 32.7 31.4 30.6 
 Past smoking, % 25.3 28.8 29.2 28.9 28.5 
 Current smoking, % 32.0 27.5 26.4 26.0 26.1 
 Multivitamin use, % 28.5 33.8 37.9 40.6 45.0 
 History of sigmoidoscopy/endoscopy, % 9.9 10.0 10.0 10.6 10.4 
 Postmenopausal status, % 45.0 44.0 44.2 43.9 44.8 
 Postmenopausal hormone use, % 18.3 18.5 18.8 19.3 19.2 
 Total energy intake, kcal/day 1,573 (513) 1,546 (484) 1,565 (518) 1,602 (481) 1,569 (497) 
 Dietary calcium intake, mg/day 457 (97) 691 (61) 883 (71) 1,078 (88) 1,376 (263) 
 Dairy calcium intake, mg/day 211 (94) 413 (97) 595 (113) 791 (132) 1,082 (287) 
 Supplemental calcium intakeb, mg/day 358 (434) 372 (426) 382 (424) 392 (433) 402 (456) 
 Alcohol, g/day 7.8 (12.5) 6.2 (9.6) 5.5 (8.8) 4.8 (8.1) 3.9 (7.2) 
 Total folate intake, μg/day 311 (228) 365 (236) 399 (253) 417.2 (262) 503 (504) 
 Total vitamin D, IU/day 238 (227) 309 (238) 378 (252) 451 (268) 606 (489) 
 Red meat, servings/week 3.2 (2.3) 2.4 (1.8) 2.1 (1.7) 1.9 (1.5) 1.5 (1.4) 
 Processed meat, servings/week 1.3 (1.9) 1.2 (1.8) 1.0 (1.6) 0.9 (1.6) 0.7 (1.2) 
 Total fat, g/day 73.6 (14.3) 69.9 (12.6) 66.9 (12.8) 64.9 (12.8) 61.9 (13.6) 
 Total fiber, g/day 15.4 (5.6) 17.4 (6.2) 18.1 (6.9) 17.9 (6.9) 17.6 (7.4) 
 ω-3 polyunsaturated fatty acids, g/day 0.2 (0.1) 0.2 (0.2) 0.2 (0.2) 0.2 (0.2) 0.2 (0.2) 
 ω-6 polyunsaturated fatty acids, g/day 6.4 (2.5) 6.3 (2.4) 6.1 (2.4) 6.0 (2.4) 5.9 (2.6) 
Men (HPFS) 
 Number 10,817 13,820 9,049 5,328 8,726 
 Age, yearsa 54.0 (9.6) 53.9 (9.7) 54.5 (9.9) 54.6 (9.9) 55.8 (9.8) 
 White, % 93.3 95.8 96.7 97.0 97.3 
 Body mass index, kg/m² 25.6 (3.4) 25.6 (3.3) 25.5 (3.2) 25.4 (3.3) 25.4 (3.3) 
 Activity, METS-hours/week 18.3 (27.3) 20.8 (28.5) 22.3 (31.7) 21.8 (31.5) 22.6 (30.5) 
 Family history of colorectal cancer, % 8.7 8.3 8.3 8.6 8.5 
 Regular aspirin use (2 or more tablets/week), % 26.9 28.9 30.1 31.0 31.3 
 Past smoking, % 43.5 42.9 40.9 40.5 39.6 
 Current smoking, % 12.4 9.7 8.2 8.9 8.1 
 Multivitamin use, % 50.6 57.5 62.0 67.3 74.2 
 History of sigmoidoscopy/endoscopy, % 24.0 26.2 26.9 26.7 27.1 
 Total energy intake, kcal/day 1,957 (638) 1,994 (605) 1,956 (632) 2,111 (631) 1,959 (583) 
 Dietary calcium intake, mg/day 500 (76) 683 (78) 845 (115) 982 (109) 1,180 (395) 
 Dairy calcium intake, mg/day 201 (76) 357 (98) 506 (136) 643 (206) 838 (409) 
 Supplemental calcium intake, mg/day 7 (22) 21 (55) 52 (103) 118 (180) 423 (550) 
 Alcohol, g/day 15.5 (19.3) 11.8 (14.9) 9.6 (13.3) 9.9 (14.2) 8.2 (12.1) 
 Total folate intake, μg/day 381 (210) 447 (227) 497 (251) 529 (287) 612 (363) 
 Total vitamin D, IU/day 272 (241) 338 (253) 407 (279) 488 (291) 637 (371) 
 Red meat, servings/week 2.2 (1.9) 1.9 (1.6) 1.6 (1.5) 1.7 (1.5) 1.4 (1.4) 
 Processed meat, servings/week 1.4 (2.0) 1.3 (1.8) 1.1 (1.8) 1.2 (1.9) 1.0 (1.7) 
 Total fat, g/day 73.5 (14.6) 72.2 (13.3) 70.2 (13.7) 70.6 (13.8) 68.6 (14.5) 
 Total fiber, g/day 19.1 (6.4) 21.1 (6.5) 22.3 (7.1) 21.6 (7.6) 21.8 (7.9) 
 ω-3 polyunsaturated fatty acids, g/day 0.3 (0.3) 0.3 (0.3) 0.3 (0.3) 0.3 (0.3) 0.3 (0.2) 
 ω-6 polyunsaturated fatty acids, g/day 12.2 (3.8) 12.0 (3.5) 11.6 (3.4) 11.5 (3.4) 10.9 (3.4) 
Total calcium intake (mg/d)
<600600–799800–9991,000–1,199≥1,200
Women (NHS) 
 Number 34,137 24,290 14,732 8,325 7,022 
 Age, yearsa 46.5 (7.0) 46.8 (7.2) 46.8 (7.3) 46.7 (7.4) 47.1 (7.4) 
 White, % 96.4 98.0 98.3 98.4 98.2 
 Body mass index, kg/m² 24.0 (4.2) 24.0 (4.2) 24.1 (4.1) 24.2 (4.1) 24.4 (4.4) 
 Activity, METS-hours/week 12.5 (18.0) 14.2 (19.7) 15.2 (21.5) 15.3 (21.0) 16.2 (26.4) 
 Family history of colorectal cancer, % 7.9 7.8 7.9 7.9 7.7 
 Regular aspirin use (2 or more tablets/week), % 33.1 33.4 32.7 31.4 30.6 
 Past smoking, % 25.3 28.8 29.2 28.9 28.5 
 Current smoking, % 32.0 27.5 26.4 26.0 26.1 
 Multivitamin use, % 28.5 33.8 37.9 40.6 45.0 
 History of sigmoidoscopy/endoscopy, % 9.9 10.0 10.0 10.6 10.4 
 Postmenopausal status, % 45.0 44.0 44.2 43.9 44.8 
 Postmenopausal hormone use, % 18.3 18.5 18.8 19.3 19.2 
 Total energy intake, kcal/day 1,573 (513) 1,546 (484) 1,565 (518) 1,602 (481) 1,569 (497) 
 Dietary calcium intake, mg/day 457 (97) 691 (61) 883 (71) 1,078 (88) 1,376 (263) 
 Dairy calcium intake, mg/day 211 (94) 413 (97) 595 (113) 791 (132) 1,082 (287) 
 Supplemental calcium intakeb, mg/day 358 (434) 372 (426) 382 (424) 392 (433) 402 (456) 
 Alcohol, g/day 7.8 (12.5) 6.2 (9.6) 5.5 (8.8) 4.8 (8.1) 3.9 (7.2) 
 Total folate intake, μg/day 311 (228) 365 (236) 399 (253) 417.2 (262) 503 (504) 
 Total vitamin D, IU/day 238 (227) 309 (238) 378 (252) 451 (268) 606 (489) 
 Red meat, servings/week 3.2 (2.3) 2.4 (1.8) 2.1 (1.7) 1.9 (1.5) 1.5 (1.4) 
 Processed meat, servings/week 1.3 (1.9) 1.2 (1.8) 1.0 (1.6) 0.9 (1.6) 0.7 (1.2) 
 Total fat, g/day 73.6 (14.3) 69.9 (12.6) 66.9 (12.8) 64.9 (12.8) 61.9 (13.6) 
 Total fiber, g/day 15.4 (5.6) 17.4 (6.2) 18.1 (6.9) 17.9 (6.9) 17.6 (7.4) 
 ω-3 polyunsaturated fatty acids, g/day 0.2 (0.1) 0.2 (0.2) 0.2 (0.2) 0.2 (0.2) 0.2 (0.2) 
 ω-6 polyunsaturated fatty acids, g/day 6.4 (2.5) 6.3 (2.4) 6.1 (2.4) 6.0 (2.4) 5.9 (2.6) 
Men (HPFS) 
 Number 10,817 13,820 9,049 5,328 8,726 
 Age, yearsa 54.0 (9.6) 53.9 (9.7) 54.5 (9.9) 54.6 (9.9) 55.8 (9.8) 
 White, % 93.3 95.8 96.7 97.0 97.3 
 Body mass index, kg/m² 25.6 (3.4) 25.6 (3.3) 25.5 (3.2) 25.4 (3.3) 25.4 (3.3) 
 Activity, METS-hours/week 18.3 (27.3) 20.8 (28.5) 22.3 (31.7) 21.8 (31.5) 22.6 (30.5) 
 Family history of colorectal cancer, % 8.7 8.3 8.3 8.6 8.5 
 Regular aspirin use (2 or more tablets/week), % 26.9 28.9 30.1 31.0 31.3 
 Past smoking, % 43.5 42.9 40.9 40.5 39.6 
 Current smoking, % 12.4 9.7 8.2 8.9 8.1 
 Multivitamin use, % 50.6 57.5 62.0 67.3 74.2 
 History of sigmoidoscopy/endoscopy, % 24.0 26.2 26.9 26.7 27.1 
 Total energy intake, kcal/day 1,957 (638) 1,994 (605) 1,956 (632) 2,111 (631) 1,959 (583) 
 Dietary calcium intake, mg/day 500 (76) 683 (78) 845 (115) 982 (109) 1,180 (395) 
 Dairy calcium intake, mg/day 201 (76) 357 (98) 506 (136) 643 (206) 838 (409) 
 Supplemental calcium intake, mg/day 7 (22) 21 (55) 52 (103) 118 (180) 423 (550) 
 Alcohol, g/day 15.5 (19.3) 11.8 (14.9) 9.6 (13.3) 9.9 (14.2) 8.2 (12.1) 
 Total folate intake, μg/day 381 (210) 447 (227) 497 (251) 529 (287) 612 (363) 
 Total vitamin D, IU/day 272 (241) 338 (253) 407 (279) 488 (291) 637 (371) 
 Red meat, servings/week 2.2 (1.9) 1.9 (1.6) 1.6 (1.5) 1.7 (1.5) 1.4 (1.4) 
 Processed meat, servings/week 1.4 (2.0) 1.3 (1.8) 1.1 (1.8) 1.2 (1.9) 1.0 (1.7) 
 Total fat, g/day 73.5 (14.6) 72.2 (13.3) 70.2 (13.7) 70.6 (13.8) 68.6 (14.5) 
 Total fiber, g/day 19.1 (6.4) 21.1 (6.5) 22.3 (7.1) 21.6 (7.6) 21.8 (7.9) 
 ω-3 polyunsaturated fatty acids, g/day 0.3 (0.3) 0.3 (0.3) 0.3 (0.3) 0.3 (0.3) 0.3 (0.2) 
 ω-6 polyunsaturated fatty acids, g/day 12.2 (3.8) 12.0 (3.5) 11.6 (3.4) 11.5 (3.4) 10.9 (3.4) 

NOTE: Values are means (SD) or percentages and are standardized to the age distribution of the study population.

aValue is not age adjusted.

bCalcium supplement data of NHS were based on questionnaires returned in 1986.

Table 2.

Total calcium intake and risk of colorectal cancer according to densities of tumor-infiltrating T-cell subsets in the NHS (1980–2012) and HPFS (1986–2012)

Total calcium intake (mg/d)
<600600–799800–9991,000–1,199≥1200PtrendaPheterogeneityb
Total colorectal cancer 
 Person-years (n = 3,663,039) 617,339 889,849 809,364 596,191 750,297   
 No. cases (n = 736) 116 207 176 121 116   
 Age-adjusted HR (95% CI) 1 (ref) 1.10 (0.87–1.38) 1.00 (0.79–1.27) 0.94 (0.72–1.21) 0.68 (0.53–0.89) 0.0002  
 Multivariable HR (95% CI)c 1 (ref) 1.12 (0.89–1.42) 1.04 (0.80–1.34) 1.01 (0.76–1.34) 0.80 (0.60–1.08) 0.04  
CD3+ 
 Low 
  No. cases (n = 347) 64 103 73 58 49   
  Age-adjusted HR (95% CI) 1 (ref) 1.01 (0.74–1.39) 0.78 (0.56–1.10) 0.85 (0.59–1.22) 0.55 (0.38–0.80) 0.0004 0.34 
  Multivariable HR (95% CI)c 1 (ref) 1.02 (0.74–1.40) 0.80 (0.56–1.14) 0.89 (0.61–1.31) 0.63 (0.42–0.94) 0.01 0.30 
 High 
  No. cases (n = 350) 48 98 91 56 57   
  Age-adjusted HR (95% CI) 1 (ref) 1.22 (0.86–1.73) 1.20 (0.84–1.71) 1.00 (0.67–1.48) 0.76 (0.52–1.13) 0.03  
  Multivariable HR (95% CI)c 1 (ref) 1.24 (0.88–1.77) 1.24 (0.86–1.78) 1.07 (0.71–1.61) 0.89 (0.58–1.35) 0.20  
CD8+ 
 Low 
  No. cases (n = 339) 59 93 86 55 46   
  Age-adjusted HR (95% CI) 1 (ref) 0.95 (0.68–1.32) 0.91 (0.65–1.27) 0.77 (0.53–1.11) 0.48 (0.33–0.72) <0.0001 0.06 
  Multivariable HR (95% CI)§ 1 (ref) 0.95 (0.68–1.33) 0.92 (0.65–1.30) 0.80 (0.54–1.18) 0.55 (0.36–0.84) 0.002 0.06 
 High 
  No. cases (n = 344) 47 104 79 57 57   
  Age-adjusted HR (95% CI) 1 (ref) 1.38 (0.97–1.95) 1.16 (0.80–1.67) 1.17 (0.79–1.74) 0.90 (0.60–1.33) 0.14  
  Multivariable HR (95% CI)c 1 (ref) 1.40 (0.98–1.98) 1.18 (0.81–1.72) 1.24 (0.82–1.87) 1.02 (0.67–1.55) 0.47  
CD45RO+ 
 Low 
  No. cases (n = 348) 65 98 80 57 48   
  Age-adjusted HR (95% CI) 1 (ref) 0.91 (0.66–1.24) 0.81 (0.58–1.13) 0.79 (0.55–1.13) 0.50 (0.35–0.74) 0.0002 0.11 
  Multivariable HR (95% CI)c 1 (ref) 0.92 (0.67–1.27) 0.84 (0.59–1.18) 0.84 (0.57–1.23) 0.58 (0.39–0.87) 0.006 0.09 
 High 
  No. cases (n = 359) 47 101 89 60 62   
  Age-adjusted HR (95% CI) 1 (ref) 1.35 (0.96–1.92) 1.25 (0.87–1.79) 1.14 (0.77–1.68) 0.89 (0.61–1.31) 0.12  
  Multivariable HR (95% CI)c 1 (ref) 1.38 (0.97–1.96) 1.29 (0.89–1.86) 1.22 (0.81–1.82) 1.04 (0.69–1.58) 0.54  
FOXP3+ 
 Low 
  No. cases (n = 336) 61 89 89 55 42   
  Age-adjusted HR (95% CI) 1 (ref) 0.91 (0.65–1.26) 0.98 (0.70–1.36) 0.81 (0.56–1.17) 0.47 (0.32–0.71) 0.0001 0.04 
  Multivariable HR (95% CI)c 1 (ref) 0.92 (0.66–1.29) 1.01 (0.72–1.43) 0.87 (0.59–1.28) 0.56 (0.36–0.85) 0.006 0.04 
 High 
  No. cases (n = 337) 45 95 74 59 64   
  Age-adjusted HR (95% CI) 1 (ref) 1.30 (0.91–1.85) 1.05 (0.72–1.53) 1.16 (0.78–1.72) 0.94 (0.64–1.39) 0.29  
  Multivariable HR (95% CI)c 1 (ref) 1.32 (0.92–1.89) 1.07 (0.73–1.59) 1.23 (0.81–1.86) 1.10 (0.72–1.67) 0.87  
Total calcium intake (mg/d)
<600600–799800–9991,000–1,199≥1200PtrendaPheterogeneityb
Total colorectal cancer 
 Person-years (n = 3,663,039) 617,339 889,849 809,364 596,191 750,297   
 No. cases (n = 736) 116 207 176 121 116   
 Age-adjusted HR (95% CI) 1 (ref) 1.10 (0.87–1.38) 1.00 (0.79–1.27) 0.94 (0.72–1.21) 0.68 (0.53–0.89) 0.0002  
 Multivariable HR (95% CI)c 1 (ref) 1.12 (0.89–1.42) 1.04 (0.80–1.34) 1.01 (0.76–1.34) 0.80 (0.60–1.08) 0.04  
CD3+ 
 Low 
  No. cases (n = 347) 64 103 73 58 49   
  Age-adjusted HR (95% CI) 1 (ref) 1.01 (0.74–1.39) 0.78 (0.56–1.10) 0.85 (0.59–1.22) 0.55 (0.38–0.80) 0.0004 0.34 
  Multivariable HR (95% CI)c 1 (ref) 1.02 (0.74–1.40) 0.80 (0.56–1.14) 0.89 (0.61–1.31) 0.63 (0.42–0.94) 0.01 0.30 
 High 
  No. cases (n = 350) 48 98 91 56 57   
  Age-adjusted HR (95% CI) 1 (ref) 1.22 (0.86–1.73) 1.20 (0.84–1.71) 1.00 (0.67–1.48) 0.76 (0.52–1.13) 0.03  
  Multivariable HR (95% CI)c 1 (ref) 1.24 (0.88–1.77) 1.24 (0.86–1.78) 1.07 (0.71–1.61) 0.89 (0.58–1.35) 0.20  
CD8+ 
 Low 
  No. cases (n = 339) 59 93 86 55 46   
  Age-adjusted HR (95% CI) 1 (ref) 0.95 (0.68–1.32) 0.91 (0.65–1.27) 0.77 (0.53–1.11) 0.48 (0.33–0.72) <0.0001 0.06 
  Multivariable HR (95% CI)§ 1 (ref) 0.95 (0.68–1.33) 0.92 (0.65–1.30) 0.80 (0.54–1.18) 0.55 (0.36–0.84) 0.002 0.06 
 High 
  No. cases (n = 344) 47 104 79 57 57   
  Age-adjusted HR (95% CI) 1 (ref) 1.38 (0.97–1.95) 1.16 (0.80–1.67) 1.17 (0.79–1.74) 0.90 (0.60–1.33) 0.14  
  Multivariable HR (95% CI)c 1 (ref) 1.40 (0.98–1.98) 1.18 (0.81–1.72) 1.24 (0.82–1.87) 1.02 (0.67–1.55) 0.47  
CD45RO+ 
 Low 
  No. cases (n = 348) 65 98 80 57 48   
  Age-adjusted HR (95% CI) 1 (ref) 0.91 (0.66–1.24) 0.81 (0.58–1.13) 0.79 (0.55–1.13) 0.50 (0.35–0.74) 0.0002 0.11 
  Multivariable HR (95% CI)c 1 (ref) 0.92 (0.67–1.27) 0.84 (0.59–1.18) 0.84 (0.57–1.23) 0.58 (0.39–0.87) 0.006 0.09 
 High 
  No. cases (n = 359) 47 101 89 60 62   
  Age-adjusted HR (95% CI) 1 (ref) 1.35 (0.96–1.92) 1.25 (0.87–1.79) 1.14 (0.77–1.68) 0.89 (0.61–1.31) 0.12  
  Multivariable HR (95% CI)c 1 (ref) 1.38 (0.97–1.96) 1.29 (0.89–1.86) 1.22 (0.81–1.82) 1.04 (0.69–1.58) 0.54  
FOXP3+ 
 Low 
  No. cases (n = 336) 61 89 89 55 42   
  Age-adjusted HR (95% CI) 1 (ref) 0.91 (0.65–1.26) 0.98 (0.70–1.36) 0.81 (0.56–1.17) 0.47 (0.32–0.71) 0.0001 0.04 
  Multivariable HR (95% CI)c 1 (ref) 0.92 (0.66–1.29) 1.01 (0.72–1.43) 0.87 (0.59–1.28) 0.56 (0.36–0.85) 0.006 0.04 
 High 
  No. cases (n = 337) 45 95 74 59 64   
  Age-adjusted HR (95% CI) 1 (ref) 1.30 (0.91–1.85) 1.05 (0.72–1.53) 1.16 (0.78–1.72) 0.94 (0.64–1.39) 0.29  
  Multivariable HR (95% CI)c 1 (ref) 1.32 (0.92–1.89) 1.07 (0.73–1.59) 1.23 (0.81–1.86) 1.10 (0.72–1.67) 0.87  

NOTE: Duplication-method Cox proportional cause-specific hazards regression for competing risks data was used to compute HRs and 95% CIs.

All analyses were stratified by age (in month), year of questionnaire return, and sex.

aLinear trend test using the median intake of each category.

bThe likelihood ratio test was used to test for the heterogeneity of the association between total calcium intake and colorectal cancer risk by densities of tumor-infiltrating T-cell subsets.

cMultivariable HRs were adjusted for age (in month), race (Caucasian vs. non-Caucasian), adult body mass index (<25, 25–<27.5, 27.5–<30, or ≥30 kg/m2), smoking (0, 1–10, or >10 pack-years), history of colorectal cancer in a parent or sibling (yes or no), history of sigmoidoscopy/colonoscopy (yes or no), physical activity (<3, 3–<27, or ≥27 MET-hours/week), regular aspirin use (yes or no), alcohol consumption (0–<5, 5–<15, or ≥15 g/d), energy-adjusted total intake of folate, vitamin D, red meat, and processed meat (all in tertiles).

Our primary hypothesis testing was the heterogeneity test on the subtype-specific associations (statistical linear trends) of calcium intake with risk of colorectal cancer subtypes classified by densities of tumor-infiltrating T cells. Considering multiple hypothesis testing for our four primary hypotheses associated with four immunity variables (i.e., densities of CD3+ cells, CD8+ cells, CD45RO+ cells, and FOXP3+ cells), we adjusted α level to 0.01 (≈0.05/4) by Bonferroni correction. All other analyses including evaluations of individual HRs and evaluations of a statistical linear trend in a specific stratum represent secondary analyses. We examined the statistical significance of the differences in association according to cancer subtypes using the likelihood ratio test that compared the model fit that allowed separate associations by different tumor immunity status with the model fit that assumed a common effect (30). Trend tests were conducted using the median of each category of total calcium intake as a continuous variable. To maximize statistical power, we combined the results from the two cohorts because we did not observe any significant heterogeneity between sex (Pheterogeneity for sex = 0.16).

In secondary analyses, we examined the associations between calcium intake and colorectal cancer risk according to the densities of tumor-infiltrated T cells by sex, tumor location, and source of calcium intake. We also explored time-lagged analysis (2) using 8-year time latency. To account for potential confounding by tumor MSI status, we further evaluated these associations jointly by tumor-infiltrated T cells and MSI status. Lastly, we assessed the associations stratified by tumor CASR status because we speculated that CASR may partially mediate the potential effect of calcium on colorectal cancer immunoprevention (9). All analyses were performed using the SAS software (SAS Institute, Version 9.2).

Use of standardized official symbols

We use HUGO-approved official symbols (or root symbols) for genes and gene products, including CASR, CD3, CD8, FOXP3, IL6, IL23, LTA, and PTPRC, all of which are described at www.genenames.org. The official symbols are italicized to differentiate from nonitalicized colloquial names that are used along with the official symbols. This format enables readers to familiarize the official symbols for genes and gene products together with common colloquial names.

During up to 32 years of follow-up of 136,249 participants (88,509 women and 47,740 men) in these prospective cohorts, we identified 3,079 colorectal adenocarcinoma cases. Among cases with available tissue specimens, we could assess T-cell infiltration in the tumor microenvironment for 736 cases (472 women and 264 men). The included colorectal cancer cases with immunity data were comparable to all eligible patients with colorectal cancer without immunity data (Supplementary Table S1). Participants with lower total calcium intake were more likely to be current smokers, consumed more alcohol, and tended to have higher intake of red meat, processed meat, and fat, but less vitamin D and folate (Table 1).

As shown in Table 2, we found that higher calcium intake appeared to be associated with a lower risk of colorectal carcinomas containing low densities of CD8+ cells (Ptrend = 0.002) but not with risk of carcinoma containing high densities of CD8+ cells (Ptrend = 0.47), although the difference was not statistically significant (Pheterogeneity = 0.06, with the adjusted α of 0.01 by Bonferroni correction). Specifically, compared with calcium intake of <600 mg/day, calcium intake of ≥1,200 mg/day was associated with a multivariable HR of 0.55 (95% CI, 0.36–0.84) for CD8+ T-cell–low tumors and of 1.02 (95% CI, 0.67–1.55) for CD8+ T-cell–high tumors. Similarly, the corresponding HRs (95% CIs) for low versus high T-cell tumors were 0.63 (0.42–0.94; Ptrend = 0.01) and 0.89 (0.58–1.35; Ptrend = 0.20) for CD3+ (Pheterogeneity = 0.30); 0.58 (0.39–0.87; Ptrend = 0.006) and 1.04 (0.69–1.58; Ptrend = 0.54) for CD45RO+ (Pheterogeneity = 0.09); and 0.56 (0.36–0.85; Ptrend = 0.006) and 1.10 (0.72–1.67; Ptrend = 0.47) for FOXP3+ (Pheterogeneity = 0.04), although the differences by subtypes defined by T-cell density were not statistically significant for any of the T cells examined.

Although statistical power was generally limited, the stronger inverse associations of calcium intake with tumors infiltrated with low densities of T cells but not high generally appeared consistent regardless of sex (Supplementary Tables S2 and S3), source of calcium intake (Table 3), tumor location (Supplementary Table S4), tumor MSI status (Supplementary Table S5), and time-lagged analyses (Supplementary Table S6). Interestingly, the potential differential associations appeared slightly stronger in CASR-positive tumors (Supplementary Table S7).

Table 3.

Intake of dietary calcium, dairy calcium, and calcium supplement and risk of colorectal cancer according to densities of tumor-infiltrating T-cell subsets in the NHS (1980–2012) and HPFS (1986–2012)

 Dietary calcium intake (mg/d)   
 <600 600–749 750–899 ≥900 Ptrenda Pheterogeneityb 
Total colorectal cancer 
 Person-years (n = 3,663,039) 1,006,115 1,018,393 769,809 868,723   
 No. cases (n = 736) 205 210 160 161   
 Age-adjusted HR (95% CI) 1 (ref) 0.97 (0.80–1.17) 0.98 (0.79–1.20) 0.86 (0.69–1.05) 0.15  
 Multivariable HR (95% CI) c 1 (ref) 1.00 (0.82–1.23) 1.04 (0.83–1.30) 0.96 (0.76–1.21) 0.74  
CD3+ 
 Low 
  No. cases (n = 347) 110 97 62 78   
  Age-adjusted HR (95% CI) 1 (ref) 0.84 (0.64–1.11) 0.74 (0.54–1.01) 0.80 (0.60–1.08) 0.12 0.78 
  Multivariable HR (95% CI)c 1 (ref) 0.87 (0.66–1.15) 0.77 (0.56–1.06) 0.88 (0.64–1.20) 0.36 0.74 
 High 
  No. cases (n = 350) 90 102 87 71   
  Age-adjusted HR (95% CI) 1 (ref) 1.05 (0.79–1.39) 1.16 (0.86–1.56) 0.82 (0.60–1.12) 0.25  
  Multivariable HR (95% CI)c 1 (ref) 1.08 (0.81–1.45) 1.23 (0.90–1.67) 0.90 (0.65–1.26) 0.63  
CD8+ 
 Low 
  No. cases (n = 339) 101 100 72 66   
  Age-adjusted HR (95% CI) 1 (ref) 0.94 (0.71–1.25) 0.91 (0.67–1.23) 0.74 (0.54–1.01) 0.06 0.36 
  Multivariable HR (95% CI)c 1 (ref) 0.97 (0.73–1.28) 0.94 (0.69–1.29) 0.80 (0.58–1.12) 0.20 0.36 
 High 
  No. cases (n = 344) 91 98 77 78   
  Age-adjusted HR (95% CI) 1 (ref) 1.01 (0.76–1.35) 1.05 (0.77–1.43) 0.91 (0.67–1.24) 0.53  
  Multivariable HR (95% CI)c 1 (ref) 1.04 (0.78–1.39) 1.10 (0.80–1.51) 0.99 (0.71–1.37) 0.93  
CD45RO+ 
 Low       
  No. cases (n = 348) 106 94 66 82   
  Age-adjusted HR (95% CI) 1 (ref) 0.82 (0.62–1.09) 0.77 (0.56–1.05) 0.81 (0.60–1.09) 0.18 0.62 
  Multivariable HR (95% CI)c 1 (ref) 0.85 (0.64–1.13) 0.82 (0.59–1.12) 0.90 (0.66–1.23) 0.52 0.57 
 High 
  No. cases (n = 359) 93 108 83 75   
  Age-adjusted HR (95% CI) 1 (ref) 1.11 (0.84–1.47) 1.13 (0.84–1.52) 0.91 (0.67–1.24) 0.51  
  Multivariable HR (95% CI)c 1 (ref) 1.15 (0.87–1.53) 1.20 (0.88–1.64) 1.03 (0.74–1.42) 0.91  
FOXP3+ 
 Low 
  No. cases (n = 336) 104 89 62 81   
  Age-adjusted HR (95% CI) 1 (ref) 0.81 (0.61–1.08) 0.75 (0.55–1.04) 0.84 (0.62–1.12) 0.22 0.57 
  Multivariable HR (95% CI)c 1 (ref) 0.85 (0.64–1.14) 0.81 (0.59–1.12) 0.94 (0.68–1.28) 0.64 0.59 
 High 
  No. cases (n = 337) 82 102 85 68   
  Age-adjusted HR (95% CI) 1 (ref) 1.18 (0.88–1.58) 1.30 (0.95–1.76) 0.93 (0.67–1.28) 0.68  
  Multivariable HR (95% CI)c 1 (ref) 1.23 (0.91–1.65) 1.38 (1.01–1.90) 1.03 (0.73–1.45) 0.82  
 Dairy calcium intake (mg/d)   
 0–299 300–499 500–699 ≥700   
Total colorectal cancer 
 Person-years (n = 3,663,039) 1,045,066 1,372,061 749,951 495,962   
 Cases, no. (n = 736) 223 266 149 98   
 Age-adjusted HR (95% CI) 1 (ref) 0.90 (0.75–1.07) 0.90 (0.73–1.11) 0.87 (0.69–1.11) 0.25  
 Multivariable HR (95%CI)c 1 (ref) 0.92 (0.76–1.10) 0.95 (0.76–1.19) 0.97 (0.75–1.26) 0.83  
CD3+ 
 Low 
  No. cases (n = 347) 111 126 64 46   
  Age-adjusted HR (95% CI) 1 (ref) 0.87 (0.67–1.12) 0.80 (0.59–1.09) 0.84 (0.59–1.19) 0.24 0.99 
  Multivariable HR (95% CI)c 1 (ref) 0.88 (0.68–1.14) 0.84 (0.61–1.15) 0.92 (0.64–1.32) 0.52 0.98 
 High 
  No. cases (n = 350) 107 124 76 43   
  Age-adjusted HR (95% CI) 1 (ref) 0.86 (0.66–1.11) 0.93 (0.70–1.26) 0.78 (0.55–1.11) 0.24  
  Multivariable HR (95% CI)c 1 (ref) 0.88 (0.68–1.14) 0.98 (0.72–1.33) 0.86 (0.59–1.25) 0.54  
CD8+ 
 Low 
  No. cases (n = 339) 100 131 71 37   
  Age-adjusted HR (95% CI) 1 (ref) 0.99 (0.76–1.28) 0.95 (0.70–1.29) 0.76 (0.52–1.10) 0.17 0.61 
  Multivariable HR (95% CI)c 1 (ref) 1.00 (0.77–1.30) 0.99 (0.72–1.36) 0.82 (0.55–1.23) 0.40 0.62 
 High 
  No. cases (n = 344) 109 118 66 51   
  Age-adjusted HR (95% CI) 1 (ref) 0.81 (0.62–1.05) 0.82 (0.60–1.12) 0.91 (0.65–1.28) 0.50  
  Multivariable HR (95% CI)c 1 (ref) 0.83 (0.63–1.08) 0.85 (0.62–1.17) 1.00 (0.70–1.42) 0.84  
CD45RO+ 
 Low 
  No. cases (n = 348) 113 119 67 49   
  Age-adjusted HR (95% CI) 1 (ref) 0.81 (0.62–1.05) 0.81 (0.60–1.10) 0.86 (0.61–1.21) 0.35 0.80 
  Multivariable HR (95% CI)c 1 (ref) 0.82 (0.63–1.07) 0.85 (0.62–1.16) 0.95 (0.66–1.35) 0.71 0.72 
 High 
  No. cases (n = 359) 104 132 77 46   
  Age-adjusted HR (95% CI) 1 (ref) 0.93 (0.72–1.21) 0.99 (0.74–1.33) 0.88 (0.62–1.25) 0.56  
  Multivariable HR (95% CI)c 1 (ref) 0.96 (0.74–1.25) 1.05 (0.77–1.43) 0.99 (0.69–1.44) 0.92  
FOXP3+ 
 Low 
  No. cases (n = 336) 114 114 66 42   
  Age-adjusted HR (95% CI) 1 (ref) 0.75 (0.58–0.98) 0.78 (0.58–1.06) 0.72 (0.50–1.02) 0.06 0.05 
  Multivariable HR (95% CI)c 1 (ref) 0.77 (0.59–1.01) 0.83 0.61–1.14) 0.79 (0.54–1.15) 0.21 0.05 
 High 
  No. cases (n = 337) 85 130 74 48   
  Age-adjusted HR (95% CI) 1 (ref) 1.15 (0.87–1.51) 1.17 (0.85–1.60) 1.15 (0.80–1.64) 0.41  
  Multivariable HR (95% CI)c 1 (ref) 1.18 (0.89–1.55) 1.22 (0.88–1.69) 1.27 (0.88–1.86) 0.19  
 Calcium supplement (mg/d)   
 0–199 200–299 300–499 ≥500   
Total colorectal cancer 
 Person-years (n = 3,663,039) 2,505,441 321,408 428,893 407,297   
 No. cases (n = 736) 514 80 91 51   
 Age-adjusted HR (95% CI) 1 (ref) 1.14 (0.89–1.45) 0.99 (0.78–1.25) 0.56 (0.42–0.75) 0.001  
 Multivariable HR (95%CI)c 1 (ref) 1.22 (0.95–1.56) 1.10 (0.87–1.39) 0.67 (0.49–0.90) 0.09  
CD3+ 
 Low 
  No. cases (n = 347) 251 32 41 23   
  Age-adjusted HR (95% CI) 1 (ref) 0.93 (0.64–1.36) 0.93 (0.66–1.30) 0.52 (0.34–0.80) 0.006 0.52 
  Multivariable HR (95% CI)c 1 (ref) 0.99 (0.68–1.45) 1.03 (0.73–1.46) 0.62 (0.40–0.96) 0.07 0.49 
 High 
  No. cases (n = 350) 237 43 48 22   
  Age-adjusted HR (95% CI) 1 (ref) 1.32 (0.94–1.84) 1.12 (0.81–1.55) 0.51 (0.33–0.80) 0.05  
  Multivariable HR (95% CI)c 1 (ref) 1.43 (1.02–2.00) 1.25 (0.90–1.74) 0.62 (0.39–0.97) 0.37  
CD8+ 
 Low 
  No. cases (n = 339) 233 39 47 20   
  Age-adjusted HR (95% CI) 1 (ref) 1.09 (0.77–1.54) 0.99 (0.72–1.37) 0.43 (0.27–0.68) 0.003 0.39 
  Multivariable HR (95% CI)c 1 (ref) 1.16 (0.82–1.65) 1.10 (0.79–1.53) 0.51 (0.32–0.81) 0.05 0.38 
 High 
  No. cases (n = 344) 244 37 39 24   
  Age-adjusted HR (95% CI) 1 (ref) 1.21 (0.85–1.72) 0.99 (0.70–1.41) 0.61 (0.40–0.94) 0.07  
  Multivariable HR (95% CI)c 1 (ref) 1.30 (0.91–1.86) 1.10 (0.77–1.57) 0.73 (0.47–1.13) 0.42  
CD45RO+ 
 Low 
  No. cases (n = 348) 258 34 37 19   
  Age-adjusted HR (95% CI) 1 (ref) 1.01 (0.70–1.46) 0.85 (0.60–1.22) 0.43 (0.27–0.69) 0.0008 0.12 
  Multivariable HR (95% CI)c 1 (ref) 1.08 (0.75–1.56) 0.94 (0.65–1.34) 0.51 (0.31–0.82) 0.01 0.11 
 High 
  No. cases (n = 359) 237 44 49 29   
  Age-adjusted HR (95% CI) 1 (ref) 1.27 (0.92–1.78) 1.07 (0.78–1.48) 0.65 (0.44–0.97) 0.15  
  Multivariable HR (95% CI)c 1 (ref) 1.36 (0.97–1.90) 1.19 (0.86–1.65) 0.77 (0.52–1.16) 0.67  
FOXP3+ 
 Low 
  No. cases (n = 336) 238 35 41 22   
  Age-adjusted HR (95% CI) 1 (ref) 1.09 (0.76–1.57) 0.96 (0.68–1.36) 0.53 (0.34–0.82) 0.01 0.63 
  Multivariable HR (95% CI)c 1 (ref) 1.18 (0.82–1.70) 1.08 (0.76–1.53) 0.63 (0.40–1.00) 0.15 0.64 
 High 
  No. cases (n = 337) 226 44 42 25   
  Age-adjusted HR (95% CI) 1 (ref) 1.35 (0.97–1.88) 0.99 (0.70–1.39) 0.59 (0.39–0.90) 0.06  
  Multivariable HR (95% CI)c 1 (ref) 1.44 (1.03–2.02) 1.10 (0.78–1.55) 0.71 (0.46–1.09) 0.41  
 Dietary calcium intake (mg/d)   
 <600 600–749 750–899 ≥900 Ptrenda Pheterogeneityb 
Total colorectal cancer 
 Person-years (n = 3,663,039) 1,006,115 1,018,393 769,809 868,723   
 No. cases (n = 736) 205 210 160 161   
 Age-adjusted HR (95% CI) 1 (ref) 0.97 (0.80–1.17) 0.98 (0.79–1.20) 0.86 (0.69–1.05) 0.15  
 Multivariable HR (95% CI) c 1 (ref) 1.00 (0.82–1.23) 1.04 (0.83–1.30) 0.96 (0.76–1.21) 0.74  
CD3+ 
 Low 
  No. cases (n = 347) 110 97 62 78   
  Age-adjusted HR (95% CI) 1 (ref) 0.84 (0.64–1.11) 0.74 (0.54–1.01) 0.80 (0.60–1.08) 0.12 0.78 
  Multivariable HR (95% CI)c 1 (ref) 0.87 (0.66–1.15) 0.77 (0.56–1.06) 0.88 (0.64–1.20) 0.36 0.74 
 High 
  No. cases (n = 350) 90 102 87 71   
  Age-adjusted HR (95% CI) 1 (ref) 1.05 (0.79–1.39) 1.16 (0.86–1.56) 0.82 (0.60–1.12) 0.25  
  Multivariable HR (95% CI)c 1 (ref) 1.08 (0.81–1.45) 1.23 (0.90–1.67) 0.90 (0.65–1.26) 0.63  
CD8+ 
 Low 
  No. cases (n = 339) 101 100 72 66   
  Age-adjusted HR (95% CI) 1 (ref) 0.94 (0.71–1.25) 0.91 (0.67–1.23) 0.74 (0.54–1.01) 0.06 0.36 
  Multivariable HR (95% CI)c 1 (ref) 0.97 (0.73–1.28) 0.94 (0.69–1.29) 0.80 (0.58–1.12) 0.20 0.36 
 High 
  No. cases (n = 344) 91 98 77 78   
  Age-adjusted HR (95% CI) 1 (ref) 1.01 (0.76–1.35) 1.05 (0.77–1.43) 0.91 (0.67–1.24) 0.53  
  Multivariable HR (95% CI)c 1 (ref) 1.04 (0.78–1.39) 1.10 (0.80–1.51) 0.99 (0.71–1.37) 0.93  
CD45RO+ 
 Low       
  No. cases (n = 348) 106 94 66 82   
  Age-adjusted HR (95% CI) 1 (ref) 0.82 (0.62–1.09) 0.77 (0.56–1.05) 0.81 (0.60–1.09) 0.18 0.62 
  Multivariable HR (95% CI)c 1 (ref) 0.85 (0.64–1.13) 0.82 (0.59–1.12) 0.90 (0.66–1.23) 0.52 0.57 
 High 
  No. cases (n = 359) 93 108 83 75   
  Age-adjusted HR (95% CI) 1 (ref) 1.11 (0.84–1.47) 1.13 (0.84–1.52) 0.91 (0.67–1.24) 0.51  
  Multivariable HR (95% CI)c 1 (ref) 1.15 (0.87–1.53) 1.20 (0.88–1.64) 1.03 (0.74–1.42) 0.91  
FOXP3+ 
 Low 
  No. cases (n = 336) 104 89 62 81   
  Age-adjusted HR (95% CI) 1 (ref) 0.81 (0.61–1.08) 0.75 (0.55–1.04) 0.84 (0.62–1.12) 0.22 0.57 
  Multivariable HR (95% CI)c 1 (ref) 0.85 (0.64–1.14) 0.81 (0.59–1.12) 0.94 (0.68–1.28) 0.64 0.59 
 High 
  No. cases (n = 337) 82 102 85 68   
  Age-adjusted HR (95% CI) 1 (ref) 1.18 (0.88–1.58) 1.30 (0.95–1.76) 0.93 (0.67–1.28) 0.68  
  Multivariable HR (95% CI)c 1 (ref) 1.23 (0.91–1.65) 1.38 (1.01–1.90) 1.03 (0.73–1.45) 0.82  
 Dairy calcium intake (mg/d)   
 0–299 300–499 500–699 ≥700   
Total colorectal cancer 
 Person-years (n = 3,663,039) 1,045,066 1,372,061 749,951 495,962   
 Cases, no. (n = 736) 223 266 149 98   
 Age-adjusted HR (95% CI) 1 (ref) 0.90 (0.75–1.07) 0.90 (0.73–1.11) 0.87 (0.69–1.11) 0.25  
 Multivariable HR (95%CI)c 1 (ref) 0.92 (0.76–1.10) 0.95 (0.76–1.19) 0.97 (0.75–1.26) 0.83  
CD3+ 
 Low 
  No. cases (n = 347) 111 126 64 46   
  Age-adjusted HR (95% CI) 1 (ref) 0.87 (0.67–1.12) 0.80 (0.59–1.09) 0.84 (0.59–1.19) 0.24 0.99 
  Multivariable HR (95% CI)c 1 (ref) 0.88 (0.68–1.14) 0.84 (0.61–1.15) 0.92 (0.64–1.32) 0.52 0.98 
 High 
  No. cases (n = 350) 107 124 76 43   
  Age-adjusted HR (95% CI) 1 (ref) 0.86 (0.66–1.11) 0.93 (0.70–1.26) 0.78 (0.55–1.11) 0.24  
  Multivariable HR (95% CI)c 1 (ref) 0.88 (0.68–1.14) 0.98 (0.72–1.33) 0.86 (0.59–1.25) 0.54  
CD8+ 
 Low 
  No. cases (n = 339) 100 131 71 37   
  Age-adjusted HR (95% CI) 1 (ref) 0.99 (0.76–1.28) 0.95 (0.70–1.29) 0.76 (0.52–1.10) 0.17 0.61 
  Multivariable HR (95% CI)c 1 (ref) 1.00 (0.77–1.30) 0.99 (0.72–1.36) 0.82 (0.55–1.23) 0.40 0.62 
 High 
  No. cases (n = 344) 109 118 66 51   
  Age-adjusted HR (95% CI) 1 (ref) 0.81 (0.62–1.05) 0.82 (0.60–1.12) 0.91 (0.65–1.28) 0.50  
  Multivariable HR (95% CI)c 1 (ref) 0.83 (0.63–1.08) 0.85 (0.62–1.17) 1.00 (0.70–1.42) 0.84  
CD45RO+ 
 Low 
  No. cases (n = 348) 113 119 67 49   
  Age-adjusted HR (95% CI) 1 (ref) 0.81 (0.62–1.05) 0.81 (0.60–1.10) 0.86 (0.61–1.21) 0.35 0.80 
  Multivariable HR (95% CI)c 1 (ref) 0.82 (0.63–1.07) 0.85 (0.62–1.16) 0.95 (0.66–1.35) 0.71 0.72 
 High 
  No. cases (n = 359) 104 132 77 46   
  Age-adjusted HR (95% CI) 1 (ref) 0.93 (0.72–1.21) 0.99 (0.74–1.33) 0.88 (0.62–1.25) 0.56  
  Multivariable HR (95% CI)c 1 (ref) 0.96 (0.74–1.25) 1.05 (0.77–1.43) 0.99 (0.69–1.44) 0.92  
FOXP3+ 
 Low 
  No. cases (n = 336) 114 114 66 42   
  Age-adjusted HR (95% CI) 1 (ref) 0.75 (0.58–0.98) 0.78 (0.58–1.06) 0.72 (0.50–1.02) 0.06 0.05 
  Multivariable HR (95% CI)c 1 (ref) 0.77 (0.59–1.01) 0.83 0.61–1.14) 0.79 (0.54–1.15) 0.21 0.05 
 High 
  No. cases (n = 337) 85 130 74 48   
  Age-adjusted HR (95% CI) 1 (ref) 1.15 (0.87–1.51) 1.17 (0.85–1.60) 1.15 (0.80–1.64) 0.41  
  Multivariable HR (95% CI)c 1 (ref) 1.18 (0.89–1.55) 1.22 (0.88–1.69) 1.27 (0.88–1.86) 0.19  
 Calcium supplement (mg/d)   
 0–199 200–299 300–499 ≥500   
Total colorectal cancer 
 Person-years (n = 3,663,039) 2,505,441 321,408 428,893 407,297   
 No. cases (n = 736) 514 80 91 51   
 Age-adjusted HR (95% CI) 1 (ref) 1.14 (0.89–1.45) 0.99 (0.78–1.25) 0.56 (0.42–0.75) 0.001  
 Multivariable HR (95%CI)c 1 (ref) 1.22 (0.95–1.56) 1.10 (0.87–1.39) 0.67 (0.49–0.90) 0.09  
CD3+ 
 Low 
  No. cases (n = 347) 251 32 41 23   
  Age-adjusted HR (95% CI) 1 (ref) 0.93 (0.64–1.36) 0.93 (0.66–1.30) 0.52 (0.34–0.80) 0.006 0.52 
  Multivariable HR (95% CI)c 1 (ref) 0.99 (0.68–1.45) 1.03 (0.73–1.46) 0.62 (0.40–0.96) 0.07 0.49 
 High 
  No. cases (n = 350) 237 43 48 22   
  Age-adjusted HR (95% CI) 1 (ref) 1.32 (0.94–1.84) 1.12 (0.81–1.55) 0.51 (0.33–0.80) 0.05  
  Multivariable HR (95% CI)c 1 (ref) 1.43 (1.02–2.00) 1.25 (0.90–1.74) 0.62 (0.39–0.97) 0.37  
CD8+ 
 Low 
  No. cases (n = 339) 233 39 47 20   
  Age-adjusted HR (95% CI) 1 (ref) 1.09 (0.77–1.54) 0.99 (0.72–1.37) 0.43 (0.27–0.68) 0.003 0.39 
  Multivariable HR (95% CI)c 1 (ref) 1.16 (0.82–1.65) 1.10 (0.79–1.53) 0.51 (0.32–0.81) 0.05 0.38 
 High 
  No. cases (n = 344) 244 37 39 24   
  Age-adjusted HR (95% CI) 1 (ref) 1.21 (0.85–1.72) 0.99 (0.70–1.41) 0.61 (0.40–0.94) 0.07  
  Multivariable HR (95% CI)c 1 (ref) 1.30 (0.91–1.86) 1.10 (0.77–1.57) 0.73 (0.47–1.13) 0.42  
CD45RO+ 
 Low 
  No. cases (n = 348) 258 34 37 19   
  Age-adjusted HR (95% CI) 1 (ref) 1.01 (0.70–1.46) 0.85 (0.60–1.22) 0.43 (0.27–0.69) 0.0008 0.12 
  Multivariable HR (95% CI)c 1 (ref) 1.08 (0.75–1.56) 0.94 (0.65–1.34) 0.51 (0.31–0.82) 0.01 0.11 
 High 
  No. cases (n = 359) 237 44 49 29   
  Age-adjusted HR (95% CI) 1 (ref) 1.27 (0.92–1.78) 1.07 (0.78–1.48) 0.65 (0.44–0.97) 0.15  
  Multivariable HR (95% CI)c 1 (ref) 1.36 (0.97–1.90) 1.19 (0.86–1.65) 0.77 (0.52–1.16) 0.67  
FOXP3+ 
 Low 
  No. cases (n = 336) 238 35 41 22   
  Age-adjusted HR (95% CI) 1 (ref) 1.09 (0.76–1.57) 0.96 (0.68–1.36) 0.53 (0.34–0.82) 0.01 0.63 
  Multivariable HR (95% CI)c 1 (ref) 1.18 (0.82–1.70) 1.08 (0.76–1.53) 0.63 (0.40–1.00) 0.15 0.64 
 High 
  No. cases (n = 337) 226 44 42 25   
  Age-adjusted HR (95% CI) 1 (ref) 1.35 (0.97–1.88) 0.99 (0.70–1.39) 0.59 (0.39–0.90) 0.06  
  Multivariable HR (95% CI)c 1 (ref) 1.44 (1.03–2.02) 1.10 (0.78–1.55) 0.71 (0.46–1.09) 0.41  

NOTE: Duplication-method Cox proportional cause-specific hazards regression for competing risks data was used to compute HRs and 95% CIs.

All analyses were stratified by age (in month), year of questionnaire return and sex.

aLinear trend test using the median intake of each category.

bThe likelihood ratio test was used to test for the heterogeneity of the association between total calcium intake and colorectal cancer risk by densities of tumor-infiltrating T-cell subsets.

cMultivariable HRs were adjusted for age (in month), race (Caucasian vs. non-Caucasian), adult body mass index (<25, 25–<27.5, 27.5–<30, or ≥30 kg/m2), smoking (0, 1–10, or >10 pack-years), history of colorectal cancer in a parent or sibling (yes or no), history of sigmoidoscopy/colonoscopy (yes or no), physical activity (<3, 3–<27, or ≥27 MET-hours/week), regular aspirin use (yes or no), alcohol consumption (0–<5, 5–<15, or ≥15 g/d), energy-adjusted total intake of folate, vitamin D, red meat, and processed meat (all in tertiles).

In these two large prospective cohort studies, we found that higher calcium intake appeared to be primarily associated with lower risk of colorectal cancer infiltrated with low, but not high, densities of T cells regardless of the type of T cell examined, although the differences in the associations by subtype were not statistically significant for any of the T cells examined. These suggestive differential associations generally persisted regardless of sex, source of calcium intake, tumor location, and tumor MSI status. Our findings suggest a possible role of calcium in colorectal cancer immunoprevention (32) through modulation of T cells.

The role of immunity in cancer development and progression is becoming increasingly recognized (33–36). In this study, we investigated whether the potential anticancer effect of calcium on colorectal cancer differs by immune status in the tumor microenvironment. The observed differential associations by tumor immunity status suggest potential crosstalk between calcium intake and host immunity in affecting colorectal carcinogenesis. In the immune system, calcium is essential for diverse cellular functions including proliferation, differentiation, and effector function (37). Changes in the flux of calcium ions (Ca2+) through Ca2+ channels in lymphocyte membranes play an important role in the regulation of T-cell function and immunity (13, 14, 38). Of note, dysregulated Ca2+ responses are critical for T-cell–mediated autoimmunity and inflammation including inflammatory bowel disease (38, 39), a risk factor for colorectal cancer (15). In line with experimental studies showing a potential effect of calcium on immunity, clinical trials have shown that supplementation with calcium reduces several tumor-promoting inflammation biomarkers (15–17). Furthermore, a recent human crossover trial (18) showed that consumption of a Western-style diet (characterized by low calcium and vitamin D) modestly upregulated genes (e.g., HLA class genes), which are involved in inflammation and immune response. In contrast, supplementation of calcium (but not vitamin D) to Western-style diet reversed these deleterious effects, and upregulated genes in the anti-inflammatory interferon signaling and the IL23 pathways (18).

It is also possible that calcium exerts its immunomodulatory effect partially via CASR. The CASR, a calcium-binding G protein–coupled receptor, is expressed in the entire intestinal epithelium and plays a key role in the preservation of gut microbiota and immune homeostasis (40–42). The CASR is also functionally expressed in human T lymphocytes (43). Evidence shows that intestinal epithelial CASR deficiency enhances permeability of the epithelial barrier, leading to the translocation and dissemination of luminal bacteria and activation of local and systemic innate and adaptive proinflammatory immune responses (44). In addition, calcium may promote T lymphocyte function through activation of CASR to secrete cytokines including IL6 and LTA (TNF-β; ref. 43), which may play important roles in immune defense, as well as systemic inflammatory response. Collectively, our data support that calcium exerts its immunomodulatory effect partially via CASR, as the differential associations we observed by immunity status appeared slightly stronger in CASR-positive tumors than in CASR-negative tumors (see Supplementary Table S7). However, the exact mechanisms underlying these differential associations remain unclear. We emphasize that our study remains hypothesis generating and requires confirmation from independent studies.

Our study also suggests a different role of host immunity in mediating the effect of calcium and vitamin D in colorectal cancer chemoprevention because we previously found that the inverse association for plasma 25(OH)D was stronger for risk of colorectal cancer subtypes with intense immune reactions (35). Consistently, the aforementioned human crossover trial found that supplementing the Western-style diet with 1,25(OH)2D3 upregulated genes involved in immune response and inflammation pathways, whereas calcium supplementation largely abrogated these changes (18).

Recent studies showed that MSI-high colorectal cancers were sensitive to immune checkpoint blockade (45, 46), indicating an important interplay between MSI status and immune cells. MSI-high tumors have frame shift mutations in coding sequences throughout the genome, which may elicit intense and more diverse immune responses and improve cancer survival (47, 48). In this study, however, the observed differential associations appeared to be independent of MSI status. This suggests that MSI status is not the sole determinant of tumor immune response because the levels of T-cell infiltrates overlap considerably between MSI-high and non-MSI–high tumors, although are, on average, higher in MSI-high cancers (10).

Our current study has several strengths, including prospective cohort design, high follow-up rates, validated colorectal cancer outcomes, and the use of repeated measures of calcium and other covariates during follow-up of the cohorts. The integration of tumor immunology analyses into the framework of molecular pathologic epidemiology is an emerging research area (49, 50), which enabled us to better understand etiologic heterogeneity according to tumor molecular and immune features. However, several limitations should be noted. First, despite the overall large sample size of the cohorts, we had a limited number of cases with tumor tissue data on T-cell infiltration for the secondary analyses by anatomic subsites, sources of calcium intake, tumor MSI, or CASR status. Second, the inclusion of cancer cases with available tissue specimen may introduce potential selection bias. However, cases that provided tumor tissue were comparable with all eligible cases with regard to a number of demographic, dietary, and lifestyle factors. Third, because most of participants in our study are Caucasian U.S. health professionals, the generalizability of our findings to the general population is limited. However, little heterogeneity across diverse populations has been suggested in the association between calcium intake and risks of colorectal cancer (3). Lastly, we cannot rule out residual confounding although we have adjusted for a wide range of known risk factors for colorectal cancer.

In summary, we found inverse associations between calcium intake and risk of colorectal cancers with low densities of T-cell infiltration, but not with risk of colorectal cancers with high densities of T-cell infiltration, although the differences by subtypes defined by T-cell density were not statistically significant for any of the T cells examined. Our results suggest a possible immunomodulatory effect of calcium in colorectal carcinogenesis. Future studies are warranted to confirm our findings and elucidate the underlying mechanisms for colorectal cancer immunoprevention by calcium.

K. Ng reports receiving commercial research grant from Pharmavite, Genentech, Tarrex Biopharma, and Gilead and is a consultant/advisory board member for Bayer, Seattle Genetics, and Tarrex. W.S. Garrett reports receiving speakers bureau honoraria from Merck, Janssen, and Pfizer and is a consultant/advisory board member for BiomX, Kintai Therapeutics, and Evelo Biosciences. M. Giannakis is a consultant/advisory board member for AstraZeneca. C.S. Fuchs is a consultant/advisory board member for Merck, Entrinsic Health, CytomX, Taiho Pharmaceutical, Sanofi, Eli Lilly, and Unum Therapeutics. No potential conflicts of interest were disclosed by the other authors.

The funders had no role in design and conduct of the study; collection, management, analysis, and interpretation of the data; and preparation, review, or approval of the manuscript. The authors assume full responsibility for analyses and interpretation of these data. The content is solely the responsibility of the authors and does not necessarily represent the official views of NIH. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Conception and design: W. Yang, L. Liu, N. Keum, A.T. Chan, C.S. Fuchs, E.L. Giovannucci, S. Ogino, X. Zhang

Development of methodology: Z.R. Qian, M. Song, S. Ogino, X. Zhang

Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): L. Liu, Z.R. Qian, T. Hamada, K. Nosho, Y. Masugi, K. Kosumi, M. Giannakis, A.T. Chan, C.S. Fuchs, S. Ogino, X. Zhang

Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): W. Yang, L. Liu, N. Keum, Z.R. Qian, M. Song, Y. Cao, S.A. Smith-Warner, S. Zhang, K. Ng, K. Kosumi, Y. Ma, W.S. Garrett, J.A. Meyerhardt, E.L. Giovannucci, S. Ogino, X. Zhang

Writing, review, and/or revision of the manuscript: W. Yang, L. Liu, N. Keum, Z.R. Qian, J.A. Nowak, T. Hamada, M. Song, S.A. Smith-Warner, S. Zhang, Y. Masugi, K. Ng, Y. Ma, W.S. Garrett, M. Wang, H. Nan, J.A. Meyerhardt, A.T. Chan, C.S. Fuchs, K. Wu, E.L. Giovannucci, S. Ogino, X. Zhang

Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): L. Liu, Z.R. Qian, Y. Masugi, A.T. Chan

Study supervision: J.A. Nowak, A.T. Chan, C.S. Fuchs, R. Nishihara, S. Ogino, X. Zhang

We would like to thank the participants and staff of the Nurses' Health Study and Health Professionals Follow-up Study for their valuable contributions as well as the following state cancer registries for their help: AL, AZ, AR, CA, CO, CT, DE, FL, GA, ID, IL, IN, IA, KY, LA, ME, MD, MA, MI, NE, NH, NJ, NY, NC, ND, OH, OK, OR, PA, RI, SC, TN, TX, VA, WA, WY. This work was supported by the NIH (P01 CA87969 and UM1 CA186107, to M.J. Stampfer; P01 CA55075 and UM1 CA167552 to W.C. Willett; U01 CA167552 to W.C. Willett and L.A. Mucci; P50 CA127003, R01 CA118553, R01 CA169141, R01 CA137178, and K24 DK098311 to A.T. Chan; R01 CA205406 to K. Ng.; R01 CA151993 and R35 CA197735 to S. Ogino; K07 CA190673 to R. Nishihara; and R03 CA176717 and K07 CA188126 to X. Zhang); Nodal Award (to S. Ogino) from the Dana-Farber Harvard Cancer Center; Research supported by a Stand Up To Cancer Colorectal Cancer Dream Team Translational Research Grant (grant no.: SU2C-AACR-DT22-17) to C.S. Fuchs Stand Up To Cancer is a division of the Entertainment Industry Foundation. Research grants are administered by the American Association for Cancer Research, the Scientific Partner of SU2C; and by grants from The Project P Fund for Colorectal Cancer Research, The Friends of the Dana-Farber Cancer Institute, Bennett Family Fund, and the Entertainment Industry Foundation through National Colorectal Cancer Research Alliance. W. Yang and L. Liu were supported by scholarship grants from Chinese Scholarship Council. L. Liu was also supported by a fellowship grant from Huazhong University of Science and Technology. K. Kosumi was supported by the JSPS Overseas Research Fellowships grant from the Japan Society for the Promotion of Science (JP2017-775).

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

1.
Bailey
RL
,
Dodd
KW
,
Goldman
JA
,
Gahche
JJ
,
Dwyer
JT
,
Moshfegh
AJ
, et al
Estimation of total usual calcium and vitamin D intakes in the United States
.
J Nutr
2010
;
140
:
817
22
.
2.
Zhang
X
,
Keum
N
,
Wu
K
,
Smith-Warner
SA
,
Ogino
S
,
Chan
AT
, et al
Calcium intake and colorectal cancer risk: Results from the nurses' health study and health professionals follow-up study
.
Int J Cancer
2016
;
139
:
2232
42
.
3.
Keum
N
,
Aune
D
,
Greenwood
DC
,
Ju
W
,
Giovannucci
EL
. 
Calcium intake and colorectal cancer risk: dose-response meta-analysis of prospective observational studies
.
Int J Cancer
2014
;
135
:
1940
8
.
4.
World Cancer Research Fund
/
American Institute for Cancer Research
. 
Continuous update project report summary. Food, Nutrition, Physical Activity, and the Prevention of Colorectal Cancer
; 
2011
. Available from: https://www.wcrf.org/sites/default/files/Colorectal-Cancer-2011-Report.pdf.
5.
Wactawski-Wende
J
,
Kotchen
JM
,
Anderson
GL
,
Assaf
AR
,
Brunner
RL
,
O'Sullivan
MJ
, et al
Calcium plus vitamin D supplementation and the risk of colorectal cancer
.
N Engl J Med
2006
;
354
:
684
96
.
6.
Baron
JA
,
Barry
EL
,
Mott
LA
,
Rees
JR
,
Sandler
RS
,
Snover
DC
, et al
A trial of calcium and vitamin D for the prevention of colorectal adenomas
.
N Engl J Med
2015
;
373
:
1519
30
.
7.
Institute of Medicine (US) Committee to Review Dietary Reference Intakes for Vitamin D and Calcium
.
Ross
AC
,
Taylor
CL
,
Yaktine
AL
,
Del Valle
HB
,
editors
.
Dietary reference intakes for calcium and vitamin D
,
Washington (DC)
:
National Academies Press
; 
2011
.
8.
Ogino
S
,
Chan
AT
,
Fuchs
CS
,
Giovannucci
E
. 
Molecular pathological epidemiology of colorectal neoplasia: an emerging transdisciplinary and interdisciplinary field
.
Gut
2011
;
60
:
397
411
.
9.
Yang
W
,
Liu
L
,
Masugi
Y
,
Qian
ZR
,
Nishihara
R
,
Keum
N
, et al
Calcium intake and risk of colorectal cancer according to expression status of calcium-sensing receptor (CASR)
.
Gut
2018
;
67
:
1475
83
.
10.
Nosho
K
,
Baba
Y
,
Tanaka
N
,
Shima
K
,
Hayashi
M
,
Meyerhardt
JA
, et al
Tumour-infiltrating T-cell subsets, molecular changes in colorectal cancer, and prognosis: cohort study and literature review
.
J Pathol
2010
;
222
:
350
66
.
11.
Salama
P
,
Phillips
M
,
Grieu
F
,
Morris
M
,
Zeps
N
,
Joseph
D
, et al
Tumor-infiltrating FOXP3+ T regulatory cells show strong prognostic significance in colorectal cancer
.
J Clin Oncol
2009
;
27
:
186
92
.
12.
Ohtani
H
. 
Focus on TILs: prognostic significance of tumor infiltrating lymphocytes in human colorectal cancer
.
Cancer Immun
2007
;
7
:
4
.
13.
Feske
S
,
Skolnik
EY
,
Prakriya
M
. 
Ion channels and transporters in lymphocyte function and immunity
.
Nat Rev Immunol
2012
;
12
:
532
47
.
14.
Monteith
GR
,
Prevarskaya
N
,
Roberts-Thomson
SJ
. 
The calcium-cancer signalling nexus
.
Nat Rev Cancer
2017
;
17
:
367
80
.
15.
Bostick
RM
. 
Effects of supplemental vitamin D and calcium on normal colon tissue and circulating biomarkers of risk for colorectal neoplasms
.
J Steroid Biochem Mol Biol
2015
;
148
:
86
95
.
16.
Fedirko
V
,
Bostick
RM
,
Long
Q
,
Flanders
WD
,
McCullough
ML
,
Sidelnikov
E
, et al
Effects of supplemental vitamin D and calcium on oxidative DNA damage marker in normal colorectal mucosa: a randomized clinical trial
.
Cancer Epidemiol Biomarkers Prev
2010
;
19
:
280
91
.
17.
Hopkins
MH
,
Owen
J
,
Ahearn
T
,
Fedirko
V
,
Flanders
WD
,
Jones
DP
, et al
Effects of supplemental vitamin D and calcium on biomarkers of inflammation in colorectal adenoma patients: a randomized, controlled clinical trial
.
Cancer Prev Res
2011
;
4
:
1645
54
.
18.
Protiva
P
,
Pendyala
S
,
Nelson
C
,
Augenlicht
LH
,
Lipkin
M
,
Holt
PR
. 
Calcium and 1,25-dihydroxyvitamin D3 modulate genes of immune and inflammatory pathways in the human colon: a human crossover trial
.
Am J Clin Nutr
2016
;
103
:
1224
31
.
19.
Colditz
GA
,
Hankinson
SE
. 
The Nurses' Health Study: lifestyle and health among women
.
Nat Rev Cancer
2005
;
5
:
388
96
.
20.
Giovannucci
E
,
Ascherio
A
,
Rimm
EB
,
Colditz
GA
,
Stampfer
MJ
,
Willett
WC
. 
Physical activity, obesity, and risk for colon cancer and adenoma in men
.
Ann Intern Med
1995
;
122
:
327
34
.
21.
Wu
K
,
Willett
WC
,
Fuchs
CS
,
Colditz
GA
,
Giovannucci
EL
. 
Calcium intake and risk of colon cancer in women and men
.
J Natl Cancer Inst
2002
;
94
:
437
46
.
22.
Rimm
EB
,
Giovannucci
EL
,
Stampfer
MJ
,
Colditz
GA
,
Litin
LB
,
Willett
WC
. 
Reproducibility and validity of an expanded self-administered semiquantitative food frequency questionnaire among male health professionals
.
Am J Epidemiol
1992
;
135
:
1114
26
.
23.
Willett
WC
,
Sampson
L
,
Stampfer
MJ
,
Rosner
B
,
Bain
C
,
Witschi
J
, et al
Reproducibility and validity of a semiquantitative food frequency questionnaire
.
Am J Epidemiol
1985
;
122
:
51
65
.
24.
Feskanich
D
,
Rimm
EB
,
Giovannucci
EL
,
Colditz
GA
,
Stampfer
MJ
,
Litin
LB
, et al
Reproducibility and validity of food intake measurements from a semiquantitative food frequency questionnaire
.
J Am Diet Assoc
1993
;
93
:
790
6
.
25.
Yamauchi
M
,
Morikawa
T
,
Kuchiba
A
,
Imamura
Y
,
Qian
ZR
,
Nishihara
R
, et al
Assessment of colorectal cancer molecular features along bowel subsites challenges the conception of distinct dichotomy of proximal versus distal colorectum
.
Gut
2012
;
61
:
847
54
.
26.
Yamauchi
M
,
Lochhead
P
,
Morikawa
T
,
Huttenhower
C
,
Chan
AT
,
Giovannucci
E
, et al
Colorectal cancer: a tale of two sides or a continuum?
Gut
2012
;
61
:
794
7
.
27.
Sherman
ME
,
Howatt
W
,
Blows
FM
,
Pharoah
P
,
Hewitt
SM
,
Garcia-Closas
M
. 
Molecular pathology in epidemiologic studies: a primer on key considerations
.
Cancer Epidemiol Biomarkers Prev
2010
;
19
:
966
72
.
28.
Chan
AT
,
Ogino
S
,
Fuchs
CS
. 
Aspirin and the risk of colorectal cancer in relation to the expression of COX-2
.
N Engl J Med
2007
;
356
:
2131
42
.
29.
Ogino
S
,
Brahmandam
M
,
Cantor
M
,
Namgyal
C
,
Kawasaki
T
,
Kirkner
G
, et al
Distinct molecular features of colorectal carcinoma with signet ring cell component and colorectal carcinoma with mucinous component
.
Mod Pathol
2006
;
19
:
59
68
.
30.
Wang
M
,
Spiegelman
D
,
Kuchiba
A
,
Lochhead
P
,
Kim
S
,
Chan
AT
, et al
Statistical methods for studying disease subtype heterogeneity
.
Stat Med
2016
;
35
:
782
800
.
31.
Willett
WC
,
Howe
GR
,
Kushi
LH
. 
Adjustment for total energy intake in epidemiologic studies
.
Am J Clin Nutr
1997
;
65
:
1220S
8S
.
32.
Kensler
TW
,
Spira
A
,
Garber
JE
,
Szabo
E
,
Lee
JJ
,
Dong
Z
, et al
Transforming cancer prevention through precision medicine and immune-oncology
.
Cancer Prev Res
2016
;
9
:
2
10
.
33.
Zitvogel
L
,
Pietrocola
F
,
Kroemer
G
. 
Nutrition, inflammation and cancer
.
Nat Immunol
2017
;
18
:
843
50
.
34.
Basile
D
,
Garattini
SK
,
Bonotto
M
,
Ongaro
E
,
Casagrande
M
,
Cattaneo
M
, et al
Immunotherapy for colorectal cancer: where are we heading?
Expert Opin Biol Ther
2017
;
17
:
709
21
.
35.
Song
M
,
Nishihara
R
,
Wang
M
,
Chan
AT
,
Qian
ZR
,
Inamura
K
, et al
Plasma 25-hydroxyvitamin D and colorectal cancer risk according to tumour immunity status
.
Gut
2016
;
65
:
296
304
.
36.
Cao
Y
,
Nishihara
R
,
Qian
ZR
,
Song
M
,
Mima
K
,
Inamura
K
, et al
Regular aspirin use associates with lower risk of colorectal cancers with low numbers of tumor-infiltrating lymphocytes
.
Gastroenterology
2016
;
151
:
879
92
.
37.
Oh-hora
M
,
Rao
A
. 
Calcium signaling in lymphocytes
.
Curr Opin Immunol
2008
;
20
:
250
8
.
38.
Feske
S
. 
Calcium signalling in lymphocyte activation and disease
.
Nat Rev Immunol
2007
;
7
:
690
702
.
39.
McCarl
CA
,
Khalil
S
,
Ma
J
,
Oh-hora
M
,
Yamashita
M
,
Roether
J
, et al
Store-operated Ca2+ entry through ORAI1 is critical for T cell-mediated autoimmunity and allograft rejection
.
J Immunol
2010
;
185
:
5845
58
.
40.
Owen
JL
,
Cheng
SX
,
Ge
Y
,
Sahay
B
,
Mohamadzadeh
M
. 
The role of the calcium-sensing receptor in gastrointestinal inflammation
.
Semin Cell Dev Biol
2016
;
49
:
44
51
.
41.
Jouret
F
,
Wu
J
,
Hull
M
,
Rajendran
V
,
Mayr
B
,
Schofl
C
, et al
Activation of the Ca2+-sensing receptor induces deposition of tight junction components to the epithelial cell plasma membrane
.
J Cell Sci
2013
;
126
:
5132
42
.
42.
MacLeod
RJ
. 
Extracellular calcium-sensing receptor/PTH knockout mice colons have increased Wnt/beta-catenin signaling, reduced non-canonical Wnt signaling, and increased susceptibility to azoxymethane-induced aberrant crypt foci
.
Lab Invest
2013
;
93
:
520
7
.
43.
Li
T
,
Sun
M
,
Yin
X
,
Wu
C
,
Wu
Q
,
Feng
S
, et al
Expression of the calcium sensing receptor in human peripheral blood T lymphocyte and its contribution to cytokine secretion through MAPKs or NF-kappaB pathways
.
Mol Immunol
2013
;
53
:
414
20
.
44.
Cheng
SX
,
Lightfoot
YL
,
Yang
T
,
Zadeh
M
,
Tang
L
,
Sahay
B
, et al
Epithelial CaSR deficiency alters intestinal integrity and promotes proinflammatory immune responses
.
FEBS Lett
2014
;
588
:
4158
66
.
45.
Le
DT
,
Uram
JN
,
Wang
H
,
Bartlett
BR
,
Kemberling
H
,
Eyring
AD
, et al
PD-1 blockade in tumors with mismatch-repair deficiency
.
N Engl J Med
2015
;
372
:
2509
20
.
46.
Le
DT
,
Durham
JN
,
Smith
KN
,
Wang
H
,
Bartlett
BR
,
Aulakh
LK
, et al
Mismatch repair deficiency predicts response of solid tumors to PD-1 blockade
.
Science
2017
;
357
:
409
13
.
47.
Mlecnik
B
,
Bindea
G
,
Angell
HK
,
Maby
P
,
Angelova
M
,
Tougeron
D
, et al
Integrative analyses of colorectal cancer show immunoscore is a stronger predictor of patient survival than microsatellite instability
.
Immunity
2016
;
44
:
698
711
.
48.
Rozek
LS
,
Schmit
SL
,
Greenson
JK
,
Tomsho
LP
,
Rennert
HS
,
Rennert
G
, et al
Tumor-infiltrating lymphocytes, Crohn's-like lymphoid reaction, and survival from colorectal cancer
.
J Natl Cancer Inst
2016
;
108
:
djw027
.
49.
Ogino
S
,
Nowak
JA
,
Hamada
T
,
Phipps
AI
,
Milner
DA
 Jr
, et al
Integrative analysis of exogenous, endogenous, tumour and immune factors for precision medicine
.
Gut
2018
;
67
:
1168
80
.
50.
Ogino
S
,
Nowak
JA
,
Hamada
T
,
Milner
DA
 Jr
,
Nishihara
R
. 
Insights into pathogenic interactions among environment, host, and tumor at the crossroads of molecular pathology and epidemiology
.
Annu Rev Pathol
2019
;
14
:
83
103
.

Supplementary data