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. Author manuscript; available in PMC: 2020 Mar 19.
Published in final edited form as: Cancer Prev Res (Phila). 2019 Feb 13;12(5):283–294. doi: 10.1158/1940-6207.CAPR-18-0279

Calcium Intake and Risk of Colorectal Cancer According to Tumor-infiltrating T Cells

Wanshui Yang 1,2, Li Liu 3,4,5, NaNa Keum 4,6, Zhi Rong Qian 3, Jonathan A Nowak 7,8, Tsuyoshi Hamada 3, Mingyang Song 4,9,10, Yin Cao 4,9,10,11, Katsuhiko Nosho 3, Stephanie A Smith-Warner 2,4, Sui Zhang 8, Yohei Masugi 3, Kimmie Ng 8, Keisuke Kosumi 3, Yanan Ma 12,13, Wendy S Garrett 14, Molin Wang 15, Hongmei Nan 16,17, Marios Giannakis 7,18, Jeffrey A Meyerhardt 7, Andrew T Chan 2,9,10,18, Charles S Fuchs 19,20,21, Reiko Nishihara 3,4,5,8,15,18, Kana Wu 4, Edward L Giovannucci 4,5, Shuji Ogino 3,5,8,18, Xuehong Zhang 2
PMCID: PMC7081141  NIHMSID: NIHMS1553394  PMID: 30760501

Abstract

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.

Introduction

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.

Materials and Methods

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 k 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 whichever came firstfollow-up (June1, 2012forthe NHS and January 31, 2012 for the HPFS), þ. 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)
<600 600–799 800–999 1,000–1,199 ≥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)
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NOTE: Values are means (SD) or percentages and are standardized to the age distribution of the study population.

a

Value is not age adjusted.

b

Calcium 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)
<600 600–799 800–999 1,000–1,199 ≥1200 Ptrenda Pheterogeneityb
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
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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.

a

Linear trend test using the median intake of each category.

b

The 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.

c

Multivariable 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.

Results

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 a 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
Open in a new tab

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.

a

Linear trend test using the median intake of each category.

b

The 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.

c

Multivariable 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).

Discussion

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+ 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 frameshift 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.

Supplementary Material

Calcium_Immunity_eSupplTables
Supplementary Figure 1

Acknowledgments

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).

Footnotes

Disclosure of Potential Conflicts of Interest

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.

Disclaimer

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.

Note: Supplementary data for this article are available at Cancer Prevention Research Online (http://cancerprevres.aacrjournals.org/).

References

  • 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. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 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. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 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. [DOI] [PubMed] [Google Scholar]
  • 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. [DOI] [PubMed] [Google Scholar]
  • 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. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 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. [PubMed] [Google Scholar]
  • 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. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 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. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 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. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 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. [DOI] [PubMed] [Google Scholar]
  • 12.Ohtani H. Focus on TILs: prognostic significance of tumor infiltrating lymphocytes in human colorectal cancer. Cancer Immun 2007;7:4. [PMC free article] [PubMed] [Google Scholar]
  • 13.Feske S, Skolnik EY, Prakriya M. Ion channels and transporters in lymphocyte function and immunity. Nat Rev Immunol 2012;12: 532–47. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Monteith GR, Prevarskaya N, Roberts-Thomson SJ. The calcium-cancer signalling nexus. Nat Rev Cancer 2017;17:367–80. [DOI] [PubMed] [Google Scholar]
  • 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. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 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. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 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. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 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. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Colditz GA, Hankinson SE. The Nurses’ Health Study: lifestyle and health among women. Nat Rev Cancer 2005;5:388–96. [DOI] [PubMed] [Google Scholar]
  • 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. [DOI] [PubMed] [Google Scholar]
  • 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. [DOI] [PubMed] [Google Scholar]
  • 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. [DOI] [PubMed] [Google Scholar]
  • 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. [DOI] [PubMed] [Google Scholar]
  • 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. [DOI] [PubMed] [Google Scholar]
  • 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. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 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. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 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. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 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. [DOI] [PubMed] [Google Scholar]
  • 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. [DOI] [PubMed] [Google Scholar]
  • 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. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Willett WC, Howe GR, Kushi LH. Adjustment for total energy intake in epidemiologic studies. Am J Clin Nutr 1997;65: 1220S–8S. [DOI] [PubMed] [Google Scholar]
  • 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. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Zitvogel L, Pietrocola F, Kroemer G. Nutrition, inflammation and cancer. Nat Immunol 2017;18:843–50. [DOI] [PubMed] [Google Scholar]
  • 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. [DOI] [PubMed] [Google Scholar]
  • 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. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 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. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Oh-hora M, Rao A. Calcium signaling in lymphocytes. Curr Opin Immunol 2008;20:250–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Feske S. Calcium signalling in lymphocyte activation and disease. Nat Rev Immunol 2007;7:690–702. [DOI] [PubMed] [Google Scholar]
  • 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. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 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. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 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. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 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. [DOI] [PubMed] [Google Scholar]
  • 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. [DOI] [PubMed] [Google Scholar]
  • 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. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 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. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 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. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47.Mlecnik B, Bindea G, Angell HK, Maby P, Angelova M, Tougeron D, et al. Integrative analyses of colorectal cancer show immune-score is a stronger predictor of patient survival than microsatellite instability. Immunity 2016;44:698–711. [DOI] [PubMed] [Google Scholar]
  • 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. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 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. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 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. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

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Supplementary Materials

Calcium_Immunity_eSupplTables
NIHMS1553394-supplement-Calcium_Immunity_eSupplTables.docx (118.2KB, docx)
Supplementary Figure 1
NIHMS1553394-supplement-Supplementary_Figure_1.pptx (51.3KB, pptx)

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