original articles

Annals of Oncology 21. National Cancer Institute sponsored study of classifications of non-Hodgkin’s lymphomas: summary and description of a working formulation for clinical usage. The Non-Hodgkin’s Lymphoma Pathologic Classification Project. Cancer 1982; 49: 2112–2135. 22. Piccaluga PP, Califano A, Klein U et al. Gene expression analysis provides a potential rationale for revising the histological grading of follicular lymphomas. Haematologica 2008; 93: 1033–1038. 23. Ott G, Katzenberger T, Lohr A et al. Cytomorphologic, immunohistochemical, and cytogenetic profiles of follicular lymphoma: 2 types of follicular lymphoma grade 3. Blood 2002; 99: 3806–3812. 24. Leich E, Hoster E, Wartenberg M et al. Similar clinical features in follicular lymphomas with and without breaks in the BCL2 locus. Leukemia 2015; 30: 854–860.

25. Oschlies I, Salaverria I, Mahn F et al. Pediatric follicular lymphoma—a clinicopathological study of a population-based series of patients treated within the NHLBFM (Berlin-Frankfurt-Munster) multicenter trials. Haematologica 2009; 29: 253–259. 26. Salaverria I, Philipp C, Oschlies I et al. Translocations activating IRF4 identify a subtype of germinal center-derived B-cell lymphoma affecting predominantly children and young adults. Blood 2011; 118: 139–147. 27. Pastore A, Jurinovic V, Kridel R et al. Integration of gene mutations in risk prognostication for patients receiving first-line immunochemotherapy for follicular lymphoma: a retrospective analysis of a prospective clinical trial and validation in a population-based registry. Lancet Oncol 2015; 16: 1111–1122.

Annals of Oncology 27: 1329–1336, 2016 doi:10.1093/annonc/mdw172 Published online 23 May 2016

Oral health and risk of colorectal cancer: results from three cohort studies and a meta-analysis H. G. Ren1,2, H. N. Luu1,3, H. Cai1, Y. B. Xiang4, M. Steinwandel5, Y. T. Gao4, M. Hargreaves6, W. Zheng1, W. J. Blot1,5, J. R. Long1 & X. O. Shu1* 1

Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, USA; 2Institution of Hematology, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; 3Department of Epidemiology and Biostatistics, College of Public Health, University of South Florida, Tampa, USA; 4Department of Epidemiology, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiaotong, University School of Medicine, Shanghai, China; 5International Epidemiology Institute, Rockville; 6Department of Internal Medicine, Meharry Medical College, Nashville, USA

Received 10 February 2016; revised 30 March 2016; accepted 31 March 2016

Background: While studies have shown that poor oral health status may increase the risk of cancer, evidence of a specific association with the risk of colorectal cancer (CRC) is inconclusive. We evaluated the association between oral health and CRC risk using data from three large cohorts: the Shanghai Men’s Health Study (SMHS), the Shanghai Women’s Health Study (SWHS), and the Southern Community Cohort Study (SCCS), and carried out a meta-analysis of results from other relevant published studies. Patients and methods: This study applied a nested case–control study design and included 825 cases/3298 controls from the SMHS/SWHS and 238 cases/2258 controls from the SCCS. The association between oral health status (i.e. tooth loss/tooth decay) and CRC risk was assessed using conditional logistic regression models. A meta-analysis was carried out based on results from the present study and three published studies. Results: We found that tooth loss was not associated with increased risk of CRC. ORs and respective 95% CIs associated with loss of 1–5, 6–10, and >10 teeth compared with those with full teeth are 0.87 (0.69–1.10), 0.93 (0.70–1.24), and 0.85 (0.66–1.11) among SMHS/SWHS participants; and 1.13 (0.72–1.79), 0.87 (0.52–1.43), and 1.00 (0.63–1.58) for those with loss of 1–4, 5–10, and >10 teeth among SCCS participants. Data regarding tooth decay were available in the SCCS, but were not associated with CRC risk. Meta-analysis confirmed the null association between tooth loss/ periodontal disease and CRC risk (OR 1.05, 95% CI 0.86–1.29). Conclusion: In this analysis of three cohorts and a meta-analysis, we found no evidence supporting an association between oral health and CRC risk. Key words: oral health, tooth loss, tooth decay, periodontal disease, colorectal cancer risk

*Correspondence to: Dr Xiao-Ou Shu, Vanderbilt Epidemiology Center, Vanderbilt University School of Medicine, 2525 West End Avenue, Suite 600 (IMPH), Nashville, TN 37203-1738, USA. Tel: +1-615-936-0713; E-mail: [email protected]

© The Author 2016. Published by Oxford University Press on behalf of the European Society for Medical Oncology. All rights reserved. For permissions, please email: [email protected]

original articles introduction Colorectal cancer (CRC) is the third most common cancer in both men and women worldwide, with an estimated 132 700 and 376 300 new cases, respectively, occurring in the United States in 2015 [1] and in China each year [2]. Inflammatory bowel disease is one established risk factor for CRC [3]. Several other known or suspected risk factors for CRC, such as smoking, dietary fat intake, obesity, and physical inactivity, are known to be associated with systemic inflammation [4], suggesting that inflammation plays a major role in CRC development. Periodontal disease caused by bacterial infections may increase the risk of cancer by the prolonged release of inflammatory mediators [5] and by an increase in the generating of carcinogens (e.g. nitrosamines) [6]. In vitro and animal studies have shown that poor oral health may increase the risk of various cancers [7, 8], including CRC [9]. Multiple tooth loss may be an indicator of periodontal disease, and the number of missing teeth can be viewed as an index of lifetime accumulation of poor oral health, particularly among individuals from a low socioeconomic background (e.g. the vast majority of our US study participants) or populations where preventive dental care is not a standard practice (e.g. the participants of our Chinese study). Recently, it has been reported that Fusobacterium nucleatum, one of the predominant subgingival microbial species found in chronic periodontitis, presented in overabundance in colorectal carcinoma tissues [9]. Studies have shown that Fusobacterium adhesion A of the F. nucleatum stimulates human CRC cell growth. Fusobacterium nucleatum was also shown to induce tumor multiplicity and selectively recruit tumor-infiltrating myeloid cells to promote tumorigenesis in ApcMin/+ mice [8]. These results suggest a possible direct role of the poor oral health-related microbiome in the pathophysiology of CRC. Another potential mechanism connecting oral health and CRC is the correlation between the oral and gut microbiomes. Several studies [10–12] and the Human Microbiome Project have shown the overlap between oral and intestinal microbiomes, and that the oral and gut microbiome community types can predict each other. These results suggest that oral health, via its association with the oral microbiome, may serve as a surrogate measurement of gut microbes and be indirectly associated with CRC risk. Few human studies have evaluated oral health for its relationship to CRC risk, and results from them have been mixed [13–16]. To systematically and comprehensively evaluate the association between oral health and the risk of CRC, we carried out a case–control study nested in three large cohort studies and carried out a meta-analysis to summarize results from the literature and the current study.

methods Our nested case–control study used resources from a US cohort (the Southern Community Cohort Study, SCCS) and Chinese cohort studies (the Shanghai Men’s Health Study, SMHS, and the Shanghai Women’s Health Study, SWHS). Study design and methodologies of these cohort studies have been described previously [17–19]. Briefly, the SCCS recruited ∼86 000 participants between 2002 and 2009 from 12 southeastern states of the United States. Approximately 86% of them were recruited from community health centers (CHCs), institutions providing basic health care and preventative

 | Ren et al.

Annals of Oncology services in underserved areas, resulting in a cohort that includes a large number of individuals of low income and educational status. The remaining 14% of the cohort was recruited through mail-based general population sampling. The SMHS and SWHS recruited 61 480 men and 74 741 women from nine communities in Shanghai, China from 2001 to 2006 and 1996 to 2000, respectively. Participants in the SCCS, SWHS, and SMHS provided written informed consent, and the institutional review boards of all participating institutions approved the study protocols. Only incident CRC cases occurring after study enrollment were included in the present study. In the SWHS and SMHS, CRC cases were matched with controls in a ratio of 1 : 4 based on age (±2 years) and enrollment in the same interview calendar years. In the SMHS, there was one case with only two controls. In the SCCS, 10 controls were matched with each CRC case based on age (±5 years), recruitment method (CHC or general population), sex, race, recruitment site (site and/or state of enrollment), and smoking status (never, former, and current). In the SCCS, a complete set of 10 controls could not be found for two cases, which resulted in one case having only seven matched controls and the other case, eight matched controls. In total, 309 incident CRC cases and 3085 controls from the SCCS, and 878 cases and 3510 controls from the SMHS/SWHS were selected for the present study. We excluded participants from the study who had no tooth loss data (SCCS: 17 cases and 130 controls; and 157 controls matched with the 17 cases; SWHS/SMHS: none), or had CRC diagnosed within 2 years after enrollment (to minimize the influence of lifestyle or oral health changes related to undiagnosed CRC) and their respective controls (SMHS/SWHS: cases = 53, controls = 212; SCCS: cases = 54, controls = 540). The final study dataset included 825 CRC cases and 3298 controls from the SMHS/SWHS and 238 cases and 2258 controls from the SCCS. In the SMHS/SWHS, participants were asked about the number of teeth they had lost, using the following categories: ‘none’, ‘1–5 teeth’, ‘6–10 teeth’, or ‘>10 teeth’. In the SCCS, participants were asked the following questions related to oral health: (i) about how many adult teeth have you lost in your lifetime due to tooth decay or gum disease, categorized into ‘none’, ‘1–4’, ‘5– 10’, ‘>10 but not all’, and ‘all of them’ and (ii) how many decayed teeth or cavities do you currently have that have not been treated, categorized into ‘none’, ‘1–2’, ‘3–5’, ‘>5’, and ‘no teeth’. In the SWHS and SMHS, new CRC cases were identified via linkage with the Shanghai Cancer registry. All possible matches were manually checked and were verified through home visits. Medical charts from diagnostic hospitals were reviewed to obtain information on the date and pathologic diagnosis of respective cancer cases [18, 19]. In the SCCS, incident cases of CRC were identified through linkage with 12 state cancer registries and from National Death Index mortality records [17]. CRC and its subsites were defined according to ICD-9 codes (153–154) [20] in the SMHS/SWHS and ICD-10 codes (C18-C21) in the SCCS [21]. In-person interview or mailed surveys (only for non-CHC SCCS) were conducted to collect information on demographics, dietary intake, physical activity, weight, height, smoking and drinking habits, family history of cancer, and other exposures. We estimated body mass index (BMI) as weight in kilograms divided by height in meters squared. Physical activity levels were estimated by multiplying the energy expenditure in metabolic equivalent tasks (METs) measured in hours per week of each activity by hours spent on the activity and summing the values of all activities in METS-hour/ week (MET-h/w) [22].

statistical analysis Cases and control differences in baseline sociodemographic characteristics and suggested risk factors were examined using a χ 2 test for categorical variables and t-test for continuous variables. We used a conditional logistic regression model to determine the association between oral health status (i.e. tooth loss/tooth decay—SCCS and tooth loss—SMHS/SWHS) and the risk

Volume 27 | No. 7 | July 2016

original articles

Annals of Oncology of CRC in the three cohorts. The tooth loss variable was grouped into four categories: none, 1–5, 6–10, and >10 in the SMHS/SWHS and 1–4, 5–10, and >10 in the SCCS. Tooth decay information was available in the SCCS only and grouped into five categories as follows: none, 1–2, 3–5, >5, and dentures only. Different covariates were included in the logistic regression analyses for the SCCS and SMHS/SWHS data to account for population-specific confounders. In the SCCS, occupation, income, education, and fruit and sweet beverage consumption were included in our conditional logistic regression models. In the SMHS/SWHS, income, education, BMI, exercise, smoking (never, former, and current), red meat consumption, and fruit intake were adjusted. We further carried out stratified analysis by cancer type (colon and rectum), adjusted for similar covariates. All statistical analyses were conducted using SAS, version 9.4 (SAS Institute, Inc.). All tests were two-sided, and P < 0.05 was considered statistically significant.

the associations between oral health and CRC using the following key words: periodontal diseases OR dental caries OR tooth decay OR tooth loss, AND colorectal neoplasms OR CRC OR colon cancer OR rectal cancer OR colonic neoplasms. The search was completed on 1 May 2015. We limited our search to population-based studies and those in the English language. Relevant study information was extracted from the publications. Combined OR and 95% CI were used to measure the impact of tooth loss and periodontal disease on the risk of CRC. The heterogeneity across studies was evaluated by the Q test and I 2 statistics. The meta-analysis, applying the random-effect model, was carried out using STATA 12.0 software (StataCorp, College Station, TX).


characteristics of study participants

PubMed, Embase, and Web of Science repositories were searched systematically to identify all available cohort and case–control studies that examined

Sociodemographic characteristics of the study participants are presented in Table 1. In the SCCS, controls were more likely


Table 1. Sociodemographic characteristics of study participants Characteristic

Number (%) Age (years)a 40–49 50–59 60–69 70–79 Educational level College Incomeb Low Lower middle Middle Upper middle High Smoking statusc Never Former Current BMI (mean ± SD) Exercise MET (mean ± SD) Caloric intake (mean ± SD) Saturate fat intake (mean ± SD) Total fiber intake (mean ± SD) Family history of any cancer Family history of CRC



825 (20.0) 59.2 ± 9.2 152 (18.4) 239 (29.0) 332 (40.2) 102 (12.4)

3298 (80.0) 59.5 ± 9.1 684 (20.7) 939 (28.5) 1282 (38.9) 393 (11.9)

452 (55.4) 212 (26.0) 152 (18.6)

1687 (51.7) 886 (27.2) 689 (21.1)

118 (14.3)

440 (13.4)

653 (79.1)

2546 (77.2)

54 (6.6)

310 (9.4)

524 (63.5) 70 (8.5) 231 (28.0) 24.5 ± 3.5 12.2 ± 6.1 1798 ± 441 9.2 ± 4.8 11.1 ± 4.0 256 (31.0) 30 (3.6)

2201 (66.7) 263 (8.0) 833 (25.3) 24.1 ± 3.2 12.3 ± 6.7 1759 ± 442 9.1 ± 4.7 11.1 ± 4.2 941 (28.5) 85 (2.6)

SCCS Cases


238 (9.5) 56.8 ± 8.8 53 (22.3) 93 (39.1) 69 (29.0) 23 (9.6)

2258 (90.5) 56.5 ± 8.6 525 (23.3) 906 (40.1) 632 (28.0) 195 (8.6)


73 (31.7) 79 (33.2) 67 (28.1) 19 (8.0)

622 (27.5) 802 (35.5) 654 (29.0) 180 (8.0)



128 (54.5) 31 (13.2) 45 (19.1) 26 (11.1) 5 (2.1)

1126 (50.9) 457 (20.6) 310 (14.0) 239 (10.8) 82 (3.7)



86 (36.2) 76 (31.9) 76 (31.9) 30.8 ± 6.9 20.7 ± 16.5 2266 ± 1319 26.1 ± 17.9 20.7 ± 13.6 130 (58.0) 24 (11.2)

814 (36.1) 719 (31.8) 725 (32.1) 30.7 ± 7.3 20.1 ± 17.0 2280 ± 1188 26.5 ± 16.1 20.5 ± 11.4 1168 (54.6) 168 (8.1)




10 Per category increment

(n = 501) 80 184 79 158

(n = 2002) 293 771 296 642

Rectum cancer risk None 1–5 6–10 >10 Per category increment

(n = 324) 66 120 44 94

(n = 1296) 252 498 171 375

SCCS Teeth lost Overall CRC risk None 1–4 5–10 >10 Per category increment

(n = 238) 31 67 44 96

(n = 2258) 308 570 493 887

Colon cancer risk None 1–4 5–10 >10 Per category increment

(n = 172) 20 53 34 65

(n = 1626) 217 406 357 646

Rectum cancer risk None 1–4 5–10 >10 Per category increment

(n = 56) 10 12 8 26

(n = 533) 80 139 114 200

(n = 236) 91 50 31 12 52

(n = 2214) 956 447 260 151 400

Tooth decay Overall CRC risk None 1–2 3–5 >5 Only dentures Per category increment

OR (95% CI)a

OR (95% CI)b

Ref. 0.89 (0.71–1.12) 0.98 (0.74–1.29) 0.92 (0.71–1.18) 0.99 (0.91–1.07) P = 0.79

Ref. 0.88 (0.70–1.11) 0.92 (0.69–1.23) 0.81 (0.63–1.06) 0.95 (0.87–1.03) P = 0.19

Ref. 0.87 (0.64–1.18) 0.97 (0.67–1.40) 0.89 (0.64–1.24) 0.99 (0.89–1.09) P = 0.77

Ref. 0.88 (0.64–1.20) 0.95 (0.65–1.38) 0.83 (0.58–1.18) 0.96 (0.86–1.06) P = 0.41

Ref. 0.92 (0.65–1.29) 0.98 (0.63–1.53) 0.96 (0.64–1.42) 1.00 (0.88–1.13) P = 0.95

Ref. 0.89 (0.63–1.27) 0.85 (0.54–1.36) 0.80 (0.53–1.21) 0.93 (0.81–1.06) P = 0.29

OR (95% CI)a

OR (95% CI)c

Ref. 1.15 (0.74–1.81) 0.88 (0.54–1.44) 1.05 (0.67–1.63) 0.99 (0.87–1.13) P = 0.85

Ref. 1.13 (0.72–1.79) 0.87 (0.52–1.43) 1.00 (0.63–1.58) 0.97 (0.85–1.16) P = 0.69

Ref. 1.40 (0.81–2.40) 1.02 (0.57–1.85) 1.05 (0.61–1.80) 0.95 (0.82–1.11) P = 0.53

Ref. 1.32 (0.75–2.30) 0.98 (0.54–1.80) 0.99 (0.56–1.73) 0.94 (0.80–1.11) P = 0.47

Ref. 0.70 (0.29–1.68) 0.58 (0.21–1.55) 1.07 (0.47–2.44) 1.08 (0.82–1.41) P = 0.61

Ref. 0.67 (0.27–1.67) 0.53 (0.19–1.45) 0.98 (0.41–2.34) 1.05 (0.78–1.39) P = 0.76

Ref. 1.17 (0.81–1.69) 1.26 (0.81–1.96) 0.84 (0.45–1.58) 1.36 (0.93–1.98) 1.06 (0.97–1.16) P = 0.20

Ref. 1.21 (0.83–1.77) 1.28 (0.81–2.02) 0.78 (0.41–1.49) 1.30 (0.87–1.93) 1.05 (0.95–1.15) P = 0.35 Continued

 | Ren et al.

Volume 27 | No. 7 | July 2016

original articles

Annals of Oncology Table 2. Continued SWHS/SMHS



Colon cancer risk None 1–2 3–5 >5 Only dentures Per category increment

(n = 170) 71 36 19 5 39

(n = 1590) 700 314 186 102 288

Rectum cancer risk None 1–2 3–5 >5 Only dentures Per category increment

(n = 56) 17 11 10 7 11

(n = 527) 217 114 59 45 92

OR (95% CI)a

OR (95% CI)b

Ref. 1.10 (0.72–1.69) 0.99 (0.57–1.69) 0.48 (0.19–1.21) 1.31 (0.85–2.02) 1.03 (0.93–1.15) P = 0.54

Ref. 1.13 (0.72–1.76) 0.99 (0.56–1.74) 0.43 (0.17–1.12) 1.28 (0.80–2.04) 1.03 (0.92–1.15) P = 0.67

Ref. 1.34 (0.59–3.04) 2.40 (0.99–5.78) 2.17 (0.83–5.66) 1.52 (0.67–3.49) 1.15 (0.96–1.38) P = 0.13

Ref. 1.30 (0.55–3.05) 2.31 (0.91–5.85) 1.94 (0.71–5.28) 1.45 (0.61–3.43) 1.13 (0.94–1.37) P = 0.20


Conditional logistic analysis with no adjustment. In SMHS/SWHS, models were adjusted for income, education, BMI, exercise, smoking (never, former, and current), and red meat and fruit consumption. c In SCCS, models were adjusted for occupation, income, education, and fruit and sweet beverage consumption. CRC, colorectal cancer; BMI, body mass index (calculated as weight in kilograms divided by height in meters squared); SCCS, Southern Community Cohort Study; SMHS, Shanghai Men’s Health Study; SWHS, Shanghai Women’s Health Study. b

than cases to be in low or lower-middle income groups (71.5% versus 67.7%, P = 0.02). Participants with CRC in the SWHS/ SMHS were more likely than controls to have higher BMI and caloric intake (P < 0.0001 and P = 0.03, separately).

association of oral health with CRC Tooth loss was not associated with increased risk of CRC in either the US or Chinese studies [ORs and respective 95% CIs of CRC for 1–5, 6–10, and >10 lost teeth in comparison with none: 0.88 (0.70–1.11), 0.93 (0.69–1.23), and 0.81 (0.63–1.06) in the SMHS/SWHS and 1.13 (0.72–1.79), 0.87 (0.52–1.43), and 1.00 (0.63–1.58) for those with loss of 1–4, 5–10, and >10 teeth in the SCCS; respectively). The ORs and respective 95% CIs between tooth decay and the risk of CRC in the SCCS were 1.21 (0.83– 1.77), 1.28 (0.81–2.02), 0.78 (0.41–1.49), and 1.30 (0.87–1.93) for 1–2, 3–5, >5 lost teeth, and dentures only, respectively, in comparison with no lost teeth (Table 2). Further analyses, carried out by assigning the categorical variables to an ordinal value and treating them as continuous variables in the regression model, showed the same null association. Furthermore, we found that the association between CRC and tooth loss (in the three cohorts) and tooth decay (in the SCCS) did not vary by gender (data not shown) or cancer site (colon or rectum; Table 2). We also found no effect modification from total energy intake, BMI, or exercise (data not shown).

articles that described five studies [13–16] (i.e. two investigating the association between tooth loss and CRC [13, 14] and three others examining the association between periodontal disease and CRC risk/death [14–16]) were included in the meta-analysis (see supplementary Figure S1, available at Annals of Oncology online for the flow chart for the meta-analysis study selection process). The characteristics of the included studies are given in Table 3. Indicators of poor oral health were not significantly associated with risk of CRC when data from three of the published studies and our current study were meta-analyzed. No significant heterogeneity was found. The pooled ORs and respective 95% CIs for the association between total tooth loss (uppermost versus no tooth loss) or periodontal diseases (with versus without disease status) and CRC risk were 1.00 (0.997–1.01) and 1.05 (0.86–1.29) (Figure 1). There was no heterogeneity among studies in overall analyses for periodontal disease (P = 0.10). To examine the sensitivity of the present meta-analysis, we repeated the meta-analysis sequentially, excluding each individual study. The results remained largely unchanged, with OR (95% CI) changing from 1.07 (0.85–1.35, when removing Hujoel’s study, P = 0.046), 1.10 (0.78–1.56, when removing Michaud’s study, P = 0.047), 1.01 (0.90–1.14, when removing Ahn’s study, P = 0.326), 1.02 (0.79–1.31, when removing SCCS, P = 0.078) to 1.09 (0.95–1.25, when removing SMHS/SWHS, P = 0.157). In a bias test using a funnel plot and Begg’s test, no significant publication bias was found (P = 0.31).



We identified 275 unique records in PubMed, Web of Science, and Embase based on our primary search key words. Among them, 271 were excluded based on a review of their abstracts because they were not population-based. The four remaining

In the current analysis of 1063 CRC cases and 5556 controls from three cohort studies, two conducted among Chinese men and women in the SMHS and SWHS, and the other carried out among black and white men and women in the southeastern

Volume 27 | No. 7 | July 2016

doi:10.1093/annonc/mdw172 | 

original articles

Annals of Oncology

Table 3. Characteristics of studies included in the meta-analysis Study

Source of participants

Follow-up No. of subjects duration (case/control or (years) event/cohort members)

Hujoel et al. NHANES I, 21 [16] United States Michaud HPFS, 16 et al., [14] United States

Ahn et al., [15]

NHANES III, United States


98/11 328 1043/47 332

39 (death with periodontal disease)/ 12 605

Exposure of interest and definition of reference (group and dental status)

Periodontal disease (subjects/controls)

Odds ratio or relative risk (95% CI)

Covariates adjusted

Periodontal No (n = 3962) disease (yes/no) Yes (n = 2092) Periodontal No periodontal disease (yes/no) disease (n = 40 512 (828 CRC)) Periodontal disease [n = 7863 (215 CRC)]

1.00 (Ref.) 0.91(0.49–1.70) 1.00 (Ref.) 1.05 (0.90–1.23)

Age and gender

Periodontal No (n = 10 400) disease (yes/no) Yes (n = 1826 moderate periodontitis, n = 379 severe periodontitis)

1.0 (Ref.) 3.58 (1.15–11.16)

Age, race, physical activity, history of diabetes, alcohol, BMI, geographic location, height, calcium intake, total caloric intake, red meat intake, fruit and vegetable intake, vitamin D score, smoking history and pack-years Age and sex and after further adjustment for smoking, education, race/ethnicity and BMI

All three studies were cohorts based in the United States. BMI, body mass index; HPFS, Health Professionals Follow-up Study; NHANES, National Health and Nutrition Examination Survey.


OR (95% Cl)

Weight (%)

Hujoel (2003)

0.91 (0.49, 1.70)


Michaud (2008)

1.05 (0.90, 1.23)


Ahn (2012)

3.58 (1.15, 11.16)



1.21 (0.89, 1.63)



0.87 (0.70, 1.09)


Overall (I 2 = 50.9%, P = 0.086)

1.05 (0.86, 1.29)


Note: Weights are from random effects analysis




Figure 1. Forest plot of oral health associated with colorectal cancer in overall analysis.

United States in the SCCS, we did not find a significant association between tooth loss (all three cohorts) or tooth decay (SCCS only) and the risk of CRC.

 | Ren et al.

The null results found in our cohort analyses are generally in line with the results of previous studies. In a hospital-based case–control study involving 662 cases and 1324 age- and sex-

Volume 27 | No. 7 | July 2016

original articles

Annals of Oncology

matched controls [13], ORs and 95% CIs were 1.22 (0.97–1.52), 1.11 (0.82–1.50), and 0.92 (0.56–1.51) for those subjects with 9– 20 teeth, 1–8 teeth, and no teeth, compared with those having >20 teeth. This study [13] was not included in our meta-analysis due to the lack of binary exposure information that was used in the meta-analysis. Similarly, the analysis conducted in the Health Professionals Follow-Up Study [14] also found no significant association between tooth loss and CRC, with ORs and 95% CIs being 0.93 (0.78–1.12) and 1.10 (0.87–1.37) for participants with 17–24 teeth and 0–16 teeth compared with those with >24 teeth. This study also found no significant association between periodontal disease and CRC risk, with OR (95% CI) being 1.05 (0.90–1.23) [14]. In a study of 11 328 adults, including 2092 with periodontitis, 2603 with gingivitis, 3962 without any teeth, and 2671 with healthy periodontium, using data from the National Health and Nutrition Examination Survey, no association was observed between periodontitis, gingivitis, or edentulism and CRC, with respective ORs and 95% CIs being 0.91 (0.49–1.70), 1.27 (0.69–2.34), and 1.07 (0.62–1.84) [13]. However, our results are not in complete agreement with a report from the National Health and Nutrition Examination Survey III [15]. This study followed a cohort of 12 605 individuals for an average of 6 years, and found that periodontal disease was associated with increased CRC mortality (relative risk of 3.58, 95% CI 1.15–11.16). Because mortality is more prone to the influence of socioeconomic status, which is also associated with oral health, residual confounding is possible. Our meta-analysis, which included all but one of these published studies, and our nested case–control studies revealed consistently null results. Given that the upper bounds of the 95% CI for the OR of CRC associated with high tooth loss in the two Shanghai cohorts, the SCCS, and the meta-analysis, were, respectively, 1.18, 1.58, and 1.01, any large increase in risk of this cancer due to poor oral health can be ruled out. Our study included a large sample size across multiple races/ ethnicities and a comprehensive evaluation that also took into consideration possible modifications from diet, socioeconomic characteristics, and other lifestyle factors. However, several limitations of the study should also be acknowledged. First, the statistical power for some stratified analyses is low, resulting in non-precise point estimates. Second, information on oral health was self-reported and potentially prone to recall errors. In addition, information on tooth loss was missing for 4.3% of SCCS participants. Because oral health information was collected at the baseline survey before CRC occurrence, the exposure misclassification is likely to be non-differential, i.e. independent of case–control status, and thus may bias the results toward null. The limited information available on oral health and heterogeneity in exposure assessment are also drawbacks to our study. Finally, our study is limited by the lack of measurements of oral microbiome composition and of chronic systematic inflammation, prohibiting us from directly investigating potential mechanisms that have been suggested to link oral health and CRC risk. In conclusion, data from three large cohorts and a meta-analysis did not support the hypothesis that oral health is associated with the development of CRC. Future study on this topic should include objective and direct measurements of oral health and may consider incorporating microbiome profiling, evaluation of

Volume 27 | No. 7 | July 2016

biomarkers of systematic inflammation, and measurement of immune responses to chronic inflammation.

acknowledgements We thank the research members and participants of the Shanghai Men’s Health Study (SMHS), the Shanghai Women’s Health Study (SWHS), and the Southern Community Cohort Study (SCCS). We also thank Nan Kennedy for editing this manuscript.

funding This work was supported by grants from the US National Cancer Institute: R37 CA070867 (Principal Investigator: WZ), R01 CA082729, UM1 CA173640, and R25 CA160056 (Principal Investigator: X-OS), and R01 CA092447 (Principal Investigators: WJB and WZ).

disclosure The authors have declared no conflicts of interest.

references 1. Siegel RL, Miller KD, Jemal A. Cancer statistics, 2015. CA Cancer J Clin 2015; 65 (1): 5–29. 2. Chen W, Zheng R, Baade PD et al. Cancer statistics in China, 2015. CA Cancer J Clin 2016; 66(2): 115–132. 3. Beaugerie L, Itzkowitz SH. Cancers complicating inflammatory bowel disease. N Engl J Med 2015; 372(15): 1441–1452. 4. Corrêa Lima MP, Gomes-da-Silva MHG. Colorectal cancer: lifestyle and dietary factors. Nutr Hosp 2005; 20(4): 235–241. 5. Correa P. Bacterial infections as a cause of cancer. J Natl Cancer Inst 2003; 95 (7): E3. 6. Shapiro KB, Hotchkiss JH, Roe DA. Quantitative relationship between oral nitratereducing activity and the endogenous formation of N-nitrosoamino acids in humans. Food Chem Toxicol Int J Publ Br Ind Biol Res Assoc 1991; 29(11): 751–755. 7. Soory M. Oxidative stress induced mechanisms in the progression of periodontal diseases and cancer: a common approach to redox homeostasis? Cancers 2010; 2(2): 670–692. 8. Kostic AD, Chun E, Robertson L et al. Fusobacterium nucleatum potentiates intestinal tumorigenesis and modulates the tumor-immune microenvironment. Cell Host Microbe 2013; 14(2): 207–215. 9. Ray K. Colorectal cancer: Fusobacterium nucleatum found in colon cancer tissue— could an infection cause colorectal cancer? Nat Rev Gastroenterol Hepatol 2011; 8 (12): 662. 10. Seville LA, Patterson AJ, Scott KP et al. Distribution of tetracycline and erythromycin resistance genes among human oral and fecal metagenomic DNA. Microb Drug Resist Larchmt N 2009; 15(3): 159–166. 11. Dal Bello F, Hertel C. Oral cavity as natural reservoir for intestinal lactobacilli. Syst Appl Microbiol 2006; 29(1): 69–76. 12. Maukonen J, Mättö J, Suihko M-L, Saarela M. Intra-individual diversity and similarity of salivary and faecal microbiota. J Med Microbiol 2008; 57(Pt 12): 1560–1568. 13. Hiraki A, Matsuo K, Suzuki T et al. Teeth loss and risk of cancer at 14 common sites in Japanese. Cancer Epidemiol Biomark Prev 2008; 17(5): 1222–1227. 14. Michaud DS, Liu Y, Meyer M et al. Periodontal disease, tooth loss, and cancer risk in male health professionals: a prospective cohort study. Lancet Oncol 2008; 9(6): 550–558.

doi:10.1093/annonc/mdw172 | 

original articles 15. Ahn J, Segers S, Hayes RB. Periodontal disease, Porphyromonas gingivalis serum antibody levels and orodigestive cancer mortality. Carcinogenesis 2012; 33(5): 1055–1058. 16. Hujoel PP, Drangsholt M, Spiekerman C, Weiss NS. An exploration of the periodontitis—cancer association. Ann Epidemiol 2003; 13(5): 312–316. 17. Signorello LB, Hargreaves MK, Blot WJ. The Southern Community Cohort Study: investigating health disparities. J Health Care Poor Underserved 2010; 21(1 Suppl): 26–37. 18. Shu X-O, Li H, Yang G et al. Cohort profile: the Shanghai Men’s Health Study. Int J Epidemiol 2015; 44(3): 810–818.

Annals of Oncology 19. Zheng W, Chow W-H, Yang G et al. The Shanghai Women’s Health Study: rationale, study design, and baseline characteristics. Am J Epidemiol 2005; 162 (11): 1123–1131. 20. Zhang X, Giovannucci EL, Wu K et al. Associations of self-reported sleep duration and snoring with colorectal cancer risk in men and women. Sleep 2013; 36(5): 681–688. 21. International Statistical Classification of Diseases, Tenth Revision. Geneva, Switzerland: World Health Organization, 1992. 22. Muraki I, Imamura F, Manson JE et al. Fruit consumption and risk of type 2 diabetes: results from three prospective longitudinal cohort studies. BMJ 2013. doi:10.1136/bmj.f5001.

Annals of Oncology 27: 1336–1341, 2016 doi:10.1093/annonc/mdw152 Published online 6 April 2016

Comprehensive genomic profiling of anal squamous cell carcinoma reveals distinct genomically defined classes J. H. Chung1,†*, E. Sanford1,†‡, A. Johnson1, S. J. Klempner2, A. B. Schrock1, N. A. Palma1, R. L. Erlich1, G. M. Frampton1, Z. R. Chalmers1, J. Vergilio1, D. A. Rubinson3, J. X. Sun1, J. Chmielecki1, R. Yelensky1, J. H. Suh1, D. Lipson1, T. J. George, Jr4, J. A. Elvin1, P. J. Stephens1, V. A. Miller1, J. S. Ross1,5 & S. M. Ali1 1

Foundation Medicine, Cambridge; 2Division of Hematology-Oncology, University of California Irvine, Irvine; 3Department of Medical Oncology, Dana Farber Cancer Institute, Boston; 4Division of Hematology-Oncology, University of Florida, Gainesville; 5Department of Pathology and Laboratory Medicine, Albany Medical College, Albany, USA

Received 19 January 2016; revised 20 March 2016; accepted 22 March 2016

Background: Squamous cell cancers of the anal canal (ASCC) are increasing in frequency and lack effective therapies for advanced disease. Although an association with human papillomavirus (HPV) has been established, little is known about the molecular characterization of ASCC. A comprehensive genomic analysis of ASCC was undertaken to identify novel genomic alterations (GAs) that will inform therapeutic choices for patients with advanced disease. Patients and methods: Hybrid-capture-based next-generation sequencing of exons from 236 cancer-related genes and intronic regions from 19 genes commonly rearranged in cancer was performed on 70 patients with ASCC. HPV status was assessed by aligning tumor sequencing reads to HPV viral genomes. GAs were identified using an established algorithm and correlated with HPV status. Results: Sixty-one samples (87%) were HPV-positive. A mean of 3.5 GAs per sample was identified. Recurrent alterations in phosphoinositol-3-kinase pathway (PI3K/AKT/mTOR) genes including amplifications and homozygous deletions were present in 63% of cases. Clinically relevant GAs in genes involved in DNA repair, chromatin remodeling, or receptor tyrosine kinase signaling were observed in 30% of cases. Loss-of-function mutations in TP53 and CDKN2A were significantly enhanced in HPV-negative cases (P < 0.0001). Conclusions: This is the first comprehensive genomic analysis of ASCC, and the results suggest new therapeutic approaches. Differing genomic profiles between HPV-associated and HPV-negative ASCC warrants further investigation and may require novel therapeutic and preventive strategies. Key words: anal squamous cell carcinoma, comprehensive genomic profiling, HPV, PI3KCA, targeted therapy

*Correspondence to: Dr Jon H. Chung, Foundation Medicine, Inc., 150 Second St, 1st Floor, Cambridge, MA 02141, USA. Tel: +1-617-418-2200; Fax: +1-617-418-2290; E-mail: [email protected]

Both authors contributed equally to this work. Present address: Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.

© The Author 2016. Published by Oxford University Press on behalf of the European Society for Medical Oncology. All rights reserved. For permissions, please email: [email protected]

Oral health and risk of colorectal cancer: results from three cohort studies and a meta-analysis.

While studies have shown that poor oral health status may increase the risk of cancer, evidence of a specific association with the risk of colorectal ...
152KB Sizes 0 Downloads 8 Views