Diet-Related Metabolomic Signature of Long-Term Breast Cancer Risk

Published OnlineFirst November 25, 2019; DOI: 10.1158/1055-9965.EPI-19-0900

CANCER EPIDEMIOLOGY, BIOMARKERS & PREVENTION | RESEARCH ARTICLE

Diet-Related Metabolomic Signature of Long-Term Breast Cancer Risk Using Penalized Regression: An Exploratory Study in the SU.VI.MAX Cohort Lucie Lecuyer 1,Celine Dalle2,3, Sophie Lefevre-Arbogast4, Pierre Micheau2, Bernard Lyan3, Adrien Rossary5, Aicha Demidem5,Melanie Petera3, Marie Lagree6, Delphine Centeno3, Pilar Galan1, Serge Hercberg1,7, Cecilia Samieri4, Nada Assi8, Pietro Ferrari8, Vivian Viallon8,Melanie Deschasaux1, Valentin Partula1, Bernard Srour1, Paule Latino-Martel1, Emmanuelle Kesse-Guyot1, Nathalie Druesne-Pecollo1, Marie-Paule Vasson5,9,Stephanie Durand3, Estelle Pujos-Guillot3, Claudine Manach2, and Mathilde Touvier1

ABSTRACT ◥ Background: Diet has been recognized as a modifiable risk factor breast cancer risk discriminant ions. A lower level of piperine (a for breast cancer. Highlighting predictive diet-related biomarkers compound from pepper) and higher levels of acetyltributylcitrate would be of great public health relevance to identify at-risk subjects. (an alternative plasticizer to phthalates), pregnene-triol sulfate (a The aim of this exploratory study was to select diet-related meta- steroid sulfate), and 2-amino-4-cyano butanoic acid (a metabolite bolites discriminating women at higher risk of breast cancer using linked to microbiota metabolism) were observed in plasma from untargeted metabolomics. women who subsequently developed breast cancer. This metabo- Methods: Baseline plasma samples of 200 incident breast cancer lomic signature was related to several dietary exposures such as a cases and matched controls, from a nested case–control study “Western” dietary pattern and higher alcohol and coffee intakes. within the Supplementation en Vitamines et Mineraux Antioxy- Conclusions: Our study suggested a diet-related plasma meta- dants (SU.VI.MAX) cohort, were analyzed by untargeted LC-MS. bolic signature involving exogenous, steroid metabolites, and Diet-related metabolites were identified by partial correlation with microbiota-related compounds associated with long-term breast dietary exposures, and best predictors of breast cancer risk were cancer risk that should be confirmed in large-scale independent then selected by Elastic Net penalized regression. The selection studies. stability was assessed using bootstrap resampling. Impact: These results could help to identify healthy women at Results: 595 ions were selected as candidate diet–related meta- higher risk of breast cancer and improve the understanding of bolites. Fourteen of them were selected by Elastic Net regression as nutrition and health relationship.

been suggested for nonstarchy vegetables [estrogen receptor–negative Introduction (ER ) breast cancer; ref. 3], dairy products (among premenopausal Cancer is a multifactorial disease (1). Although a large part of women; ref. 3), foods containing carotenoids (3), foods high in cancers are explained by intrinsic factors, between 30% and 50% of all calcium (3), or dietary fiber (5). In contrast, increased risk has been cancer cases are estimated to be preventable (2). In France in 2015, over suggested to be associated with some types of lipids such as plasma 30% of diagnosed incident breast cancers, the first female cancer in the levels of trans-fatty acids produced by industrial processing (ER world in terms of incidence (3), are attributable to nutrition-related breast cancer; ref. 6) and saturated fatty acids intake (7). Diet impacts factors, including alcohol, physical inactivity contributing to excess both the endogenous metabolome and the food metabolome (i.e., the adiposity and weight status (4). Beyond the three latter factors, a metabolites derived from ingested foods and their subsequent metab- protective role of other dietary factors in breast carcinogenesis has olism in the human body; ref. 8). The identification of a set of

1Center of Research of Epidemiology and StatisticS (CRESS), French National on Cancer, Section of Nutrition and Metabolism, Nutritional Methodology and Institute of Health and Medical Research (INSERM) U1153, French National Biostatistics Group, Lyon, France. 9Anticancer Center Jean-Perrin, CHU Institute for Agricultural Research (INRA) U1125, French National Conservatory Clermont-Ferrand, France. of Arts and Crafts (CNAM), Paris 13 University, Nutritional Epidemiology Note: Supplementary data for this article are available at Cancer Epidemiology, Research Team (EREN), Bobigny, France. 2Clermont Auvergne University, INRA, Biomarkers & Prevention Online (http://cebp.aacrjournals.org/). UMR 1019, Human Nutrition Unit (UNH), CRNH Auvergne, Micronutriments et 3 Sante cardiovasculaire (MicroCard), Clermont-Ferrand, France. Clermont Corresponding Author: Lucie Lecuyer, CRESS, Inserm—U1153 team 3/INRA Auvergne University, INRA, UNH, Plateforme d'Exploration du Metabolisme, U1125/Paris 13 University, EREN, Inserm 74 rue Marcel Cachin, Bobigny Cedex 4 MetaboHUB Clermont, Clermont-Ferrand, France. University of Bordeaux, 93000, France. Phone: 331-4838-8954; Fax: 331-4838-8931; E-mail: Inserm, Bordeaux Population Health Research Center, UMR 1219, Bordeaux, [email protected] France. 5Clermont Auvergne University, INRA, UMR 1019, Human Nutrition Unit (UNH), CRNH Auvergne, Cellular Micro-Environment, Immunomodulation and Cancer Epidemiol Biomarkers Prev 2020;XX:XX–XX Nutrition (ECREIN), Clermont-Ferrand, France. 6Clermont Auvergne University, Institut de Chimie de Clermont-Ferrand, Plateforme d'Exploration du doi: 10.1158/1055-9965.EPI-19-0900 Metabolisme, MetaboHUB-Clermont, Aubiere, France. 7Public Health Depart- ment, Avicenne Hospital, Bobigny, France. 8International Agency for Research 2019 American Association for Cancer Research.

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metabolites that are both (i) influenced by diet and on which it is possible to act through dietary interventions, and (ii) related to increased or decreased breast cancer risk would open new perspectives in terms of prevention and potentially provide insights on the underlying mechanisms. High-throughput technologies allow the exploration of thousands of molecules resulting from complex system-wide biological interac- tions. An in-depth untargeted investigation that is not hypothesis- driven holds promise for the discovery of new biomarkers. In comparison with approaches focused on a restricted number of a priori– selected biomarkers, untargeted metabolomics allows highlighting combinations of metabolites (potentially acting with synergistic and antagonist effects) associated with disease risk that could allow a better sensitivity and specificity of predictive models (9). This application to the nutrition field may highlight breast cancer–related metabolomic signatures, combining exogenous metabolites resulting directly from dietary exposure and endogenous or microbial metabolites, and therefore reflecting the impact of diet on host and its microbiota metabolism. So far, to our knowledge, only one previous study used nutritional semiuntargeted metabolomics to examine diet-related serum metabolite associations with long-term breast cancer risk (10). The latter highlighted 22 diet-related metabolites, mainly related to alcohol, vitamin E, and animal fat intakes, associated with breast cancer risk. However, this study was conducted in postmenopausal women only, thus these results cannot be generalized in premenopausal women. Moreover, the unknown compounds were not considered in statistical analyses, limiting the potential discoveries of new biomarkers. The aim of our study was to select a small subset of diet-related metabolites that best predicted long-term breast cancer risk using up- to-date statistical method, particularly adapted to large-scale omic data (Elastic Net regressions) by analyzing untargeted mass spectrometry metabolomic data.

Figure 1. Materials and Methods Participant flowchart, selection of the SU.VI.MAX study. Study population This study involved participants from the Supplementation en Vitamines et Mineraux Antioxydants (SU.VI.MAX) prospective Baseline data collection and case ascertainment cohort (clinicaltrials.gov; NCT00272428), which initially aimed to Baseline data collection and case ascertainment are described in investigate the effect of a daily antioxidant supplementation in nutri- Supplementary Methods. Briefly, at enrollment, participants were tional doses on the incidence of cardiovascular diseases and cancers. invited to fulfill self-administered questionnaires about sociodemo- This population-based, double-blinded, placebo-controlled, random- graphic characteristics, smoking status, medication use, health ized trial was conducted over 8 years, and observational follow-up of status and family history of cancer, and underwent anthropometric health events was subsequently maintained during 5 years (13 years of measurements as well as a fasting blood draw. During the trial follow-up). The study design and methods have been previously phase, participants were asked to complete computerized 24-hour detailed (11, 12). 13,017 participants were recruited in 1994–1995 dietary records every 2 months, including >990 food items (14). and were invited to provide their written informed consent. The trial Dietary habits at baseline were estimated by averaging intakes was approved by the Ethics Committee for Studies with Human from all dietary records collected during the first 2 years of Subjects of Paris-Cochin Hospital (CCPPRB 706/2364) and the participation in the SU.VI.MAX study. Self-reported health events “Commission Nationale de l'Informatique et des Libertes” (CNIL were reviewed by a physician expert committee. Pathological 334641/907094), and was conducted according to the Declaration of reports were used to validate cases, and cancers were classified Helsinki guidelines. This work focused on a nested case–control using the International Chronic Diseases Classification, 10th Revi- study, including SU.VI.MAX participants with a first incident sion, Clinical Modification (15). invasive breast cancer diagnosed after baseline (N ¼ 215) and matched with controls (1:1 ratio, using the density sampling Metabolomic analyses method;ref.13)forthefollowingcharacteristics at baseline: age, Untargeted metabolomics (vs. targeted) was performed to discover menopausal status, body mass index (BMI), intervention group of new biomarkers and new potential metabolites associated with breast the trial, smoking status, season of blood draw. Details about the cancer risk. Untargeted metabolomics was performed on plasma nested case–control study are presented in Supplementary Methods samples (N ¼ 430) following a slightly modified version of the and the flowchart is presented in Fig. 1. procedure described by Pereira and colleagues (16). Profiling was

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conducted using high performance liquid chromatography/mass spec- tary Tables S2 and S3. Two statistical analyses were independently troscopy with the Metabolic profiler platform (Bruker Daltonique). To performed: Correlation analysis was used to select ions related with monitor the analytical system stability, at the beginning of each diet (first step) whereas Elastic Net regression was used for the sequence, blank sample was injected three times to equilibrate selection of breast cancer risk–related ions (second step). In the the column, followed by a QC sample (pooled participants plasma current broad exploratory approach, dietary factors (either new or samples). Then, one QC sample was also injected after each set of already suggested) were considered altogether in the same analysis 12 participants plasma samples. All samples (blank, QC, and plasma and metabolites that were the best breast cancer risk predictors were samples) were analyzed using the same analytical method. Details selected. on chemicals and reagents, biological samples preparation, metabolic fi pro ling, raw data extraction, quality controls, and metabolite iden- Identification of ions potentially associated with diet fi ti cation are available in Supplementary Methods and Supplement- First, we estimated correlations between the 1,218 ions (both ary Table S1. Data were processed under the Galaxy web-based positive and negative modes) and dietary exposures using partial fl fl platform Work ow4Metabolomics (https://work ow4metabolomics. Spearman correlations adjusted for potential confounding factors fi org/) using rst XCMS module for peak detection, followed by quality specifically associated with diet, thatis,forage,BMI,menopausal checks and signal drift correction to yield a data matrix containing status, smoking status, season at time of blood draw, number of variables (retention times, masses) and peak intensities that were 24-hour dietary records and mean of daily energy intake during the corrected for batch effects. After a fast overview of all chromato- 2 years following the blood draw. Given that ions were tested grams, some individuals were excluded due to problems during individually in the models, no normalization was previously per- sample preparation, multiple ions with null intensity or pollution formed. In this exploratory study, the aim was to collect a maximum on chromatograms (few serum collecting tubes were contaminated of candidate ions potentially associated with diet in this first step, with a PEG-like materials), leaving 200 cases and 200 controls thus, all ions associated with diet at the threshold of P < 0.05 and samples for both positive and negative mode analyses. Highly correlation >|0.15| were selected for further analysis. Benjamini– correlated ions (correlation threshold at 0.9) from the same metab- Hochberg (BH) correction (20) was only tested before this selection olite within the same retention time cluster were removed using the (on 1,218 ions). metabolite correlation analysis (MCA) Galaxy module. After these processing steps, 528 and 690 ions (detected in positive and negative ionization modes, respectively) from plasma metabolome remained Subset selection of diet-related breast cancer risk discriminant in datasets for statistical analyses. This metabolomic discovery ions approach allows semiquantitative measurements, representing rel- Among the ions potentially associated with diet highlighted in ative ion intensities (no absolute concentration). However, linearity the first step, the best subset of predictors of breast cancer risk were between ion intensities and their concentration level was previously selected by using penalized logistic regression thanks to the opti- validated and matrix effect was previously studied (16). In partic- mization of the a and l parameters (no statistical test was per- ular, Pearson correlation was checked between ion intensities and formed when using this procedure). As metabolomic data are highly sample dilutions. correlated, we used the Elastic Net method (21) implemented in the R glmnet package. This method allows variable selection by forcing Statistical analysis the coefficients of the less predictive variables to be exactly zero. Participants' baseline characteristics were compared between cases Our model considered all diet-related ions simultaneously and and controls using conditional logistic regressions. the following confounding factors specifically associated with breast To cover different aspects of diet, dietary exposure was assessed cancer risk were included as unpenalized explanatory variables: using 4 complementary methods. First, two dietary scores were Age, BMI, season, menopausal status, smoking status, height, computed: the mPNNS-GS (modified “Program National Nutrition physical activity, education level, alcohol intake, use of hormone Sante—Guideline Score”;ref.17),reflecting adherence to 2001 replacement therapy for menopause, number of children and family French nutritional recommendations and including 12 compo- history of breast cancer at blood draw (baseline), and intervention nents, eight referred to food-serving recommendations and four group of the initial SU.VI.MAX trial after dietary data collection. As referred to moderation in consumption; and the DQI-I (Diet in this second step all diet-relatedionswereconsideredsimulta- Quality Index-International; ref. 18), including four components neously, intensities of these ions were previously unit-variance (variety, adequacy, moderation, and overall balance) with adapted scaled. The Elastic Net penalization a and l parameters (a ¼ 0 cutoff values corresponding to French recommendations (19); implies no variable selection; a ¼ 1isequivalenttoLASSO higher scores representing a higher dietary quality. Then, we regression; l defines the strength of regularization) were optimized computed the average daily intake of 74 specific food groups (g/day) by using 5-fold cross-validation repeated 100 times, to account for by considering several parameters such as the level of 24-hour the variance of cross-validation. Details on these steps are given in recalls, the food groups of official French dietary guidelines and the footnote of Fig. 2. current knowledge about nutritional biomarkers of dietary con- Spearman correlation matrix of the selected metabolites was sumption.Finally,aprincipalcomponent analysis (PCA) with computed. After Elastic Net selection, biological plausibility and varimax rotated factors by orthogonal transformation was per- plots of (ion intensity) (dietary exposure) were check. To formed. This PCA highlighted two dietary patterns representing determine the stability of the selected ions, we applied a Bootstrap a “Western diet” (mainly characterized by higher intakes of alcohol, resampling Elastic Net method (22) by repeating the selection bread, processed and red meat, animal fat, cheese, and potatoes) and process 1,000 times on re-sampled data and recording the percent- a “Healthy diet” (with higher intakes of fruits, vegetables, whole age of times where each ion was selected. Direction of the associa- grain, yoghurt, and vegetable oil). Details of these dietary exposures tions (OR) for the selected ions was estimated using logistic computation are presented in Supplementary Methods, Supplemen- regression with all the selected ions in the same model and adjusted

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Figure 2. Percentage of the ions' selection frequency 40% over 1,000 bootstraps via Elastic Net regression. Elastic Net regression model considered all diet-related ions simultaneously, and the following confounding factors were included as unpenalized explanatory variables: age (continuous), BMI (continuous), season (a priori–deﬁned periods: October–November/December– January–February/March–April–May), menopausal status (pre/postmenopause status), smoking status (current, former, and nonsmokers), height (continuous), physical activity (low, moderate, intense), education level (primary, secondary, superior), alcohol intake (continuous), use of hormone replacement therapy for menopause, number of children and family history of breast cancer at blood draw (baseline), and intervention group of the initial SU.VI.MAX trial (placebo/supplemented). The Elastic Net parameters a and l were optimized by using 5-fold cross-validation (assigning the case/control pairs in the same fold) repeated 100 times, to account for the variance of cross-validation. After a had been optimized over a grid of 0.5 to 0.9 (a ¼ 0 implies no variable selection; a ¼ 1 is equivalent to LASSO regression) to get enough sparsity and still control for multicollinearity, l (deﬁning the strength of regularization) were also optimized by minimizing the mean deviance. Over the cross-validations of the Elastic Net regression models, the most frequent optimal a value was 0.5 and the corresponding average optimum l value was 0.09. To determine the stability of ions selected on the original dataset, we applied a bootstrap resampling Elastic Net method (22) by repeating the selection process 1,000 times on resampled data and recording the percentage of times where each ion was selected (using the optimized parameter a ¼ 0.5).

for the confounding factors cited above. Although P values are Because of the untargeted approach, only ions of interest were generatedbythismodel,theycannotbeusedforinferencegiventhe annotated according to the procedure described in Supplementary prior Elastic Net-based selection. Complementary analyses were Methods. As proposed by Sumner and colleagues (23), the metabolites performed using logistic regression models for each selected ion were classified according to levels of confidence in the identification adjusted for the same confounding factors to check the associations process: identified (level 1), putatively annotated (level 2), putatively between each individual ion and breast cancer risk. characterized compound classes (level 3), and unknown compound A flow chart of the different statistical steps is shown in Fig. 3. (level 4).

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Figure 3. Flowchart of statistical analyses and summary of results.

Analyses were performed using SAS (v9.3, Cary, NC) and R (v3.5.2) (r ¼ 0.22); ATBC was positively associated with coffee intake (r ¼ software. 0.17); 2-amino-cyano-butanoic acid was negatively associated with cake and biscuits intakes (r ¼0.16) and pregnene-triol sulfate was positively associated with alcoholic drinks intakes (r ¼ 0.17). The Results unknown compounds were associated with several dietary expo- Baseline characteristics of breast cancer cases and controls in the sures such as pasta and cereals, salty products, processed meat, study population are summarized in Table 1. Among the 1,218 tomatoes, citrus fruit, and pressed cooked cheese intakes (see detected ions, 595 were selected as candidate diet-related ions and Supplementary Table S4). Table 2 displays the associations between were considered in the penalized logistic regression analyses (associa- the 14 selected ions and breast cancer risk from adjusted logistic tions between these ions and dietary exposure and FDR values are regression, including the 14 ions altogether or one ion at a time. presented in Supplementary Table S4, N ¼ 1,085). Lower levels of piperine and 6 unknown compounds (M335T864, Fourteen ions resulted from the Elastic Net penalized regression. M364T125, M166T144, M415T1344, M475T122, and M587T121) Among these, 2 were identified with a high level of confidence and higher levels of 2-amino-4-cyano butanoic acid, ATBC, preg- in annotation: piperine (M286T989) and acetyltributylcitrate (ATBC; nene-triol sulfate and 4 unknown compounds (M192T181, M425T1158), two were putatively annotated: 2-amino-cyano- M265T186, M97T134, and M201T1091) were found in plasma butanoic acid (M153T116) and pregnene-triol sulfate (M413T967) from women who have subsequently developed breast cancer and the other were unknown compounds (M192T181, M265T186, during follow-up. M335T864, M364T125, M97T134, M166T144, M201T1091, Concerning the stability of the selected 14 ions on original data, their M415T1344, M475T122, and M587T121). Identification details of selection frequencies were 50% on the 1,000 bootstraps except for the 14 ions selected by Elastic net are presented in Supplementary 2-amino-cyano-butanoic acid (27%). It was even >70% for piperine Methods. The Spearman correlation matrix of these ions is provided in (M286T989), ATBC (M425T1158) and pregnene-triol sulfate Supplementary Table S5. 2-amino-cyano-butanoic acid was highly (M413T967) and 8 unknown compounds (see Fig. 2 presenting the correlated (P < 0.0001) with 3 unknown compounds, including 2 frequency of selection of ions 40% over 1,000 bootstraps). Further NaCOOH adducts. investigation was carried out to annotate ions with selection frequen- In particular, piperine was positively associated with alcoholic cies >70% but not selected on the original dataset (i.e., M114T115, drinks intake (r ¼ 0.19) and with a “Western” dietary pattern M498T1040, M230T171, M423T120, M454T1064, M196T953,

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Table 1. Baseline characteristics of breast cancer cases and controlsa, SU.VI.MAX cohort, France (1994–2007).

Breast cancer cases (N ¼ 200) Controls (N ¼ 200) Pb

Age at baseline (y) 48.8 5.8 48.7 5.9 0.4 BMI (kg/m2) 23.1 3.9 23.5 4.2 0.08 Not applicable <18.5 kg/m2 (underweight) 8 (4) 6 (3) 18.5–<25 kg/m2 (normal weight) 143 (71.5) 145 (72.5) 25 kg/m2 (overweight) 49 (24.5) 49 (24.5) Height (cm) 162.9 6.1 160.9 5.9 0.001 Intervention group Not applicable Placebo 99 (49.5) 99 (49.5) Antioxidants 101 (50.5) 101 (50.5) Smoking status Not applicable Never and former 159 (79.5) 159 (79.5) Current smoker 41 (20.5) 41 (20.5) Physical activity 0.8 Irregular 64 (32) 58 (29) <1 h/d walking equivalent 63 (31.5) 67 (33.5) 1 h/d walking equivalent 73 (36.5) 75 (37.5) Educational level 0.1 Primary 35 (17.5) 43 (21.5) Secondary 75 (37.5) 86 (43) Superior 90 (45) 71 (35.5) Number of biological children 1.9 1.2 2 1.2 0.3 Hormonal treatment for menopause (yes) 69 (34.5) 70 (35) 0.9 Menopausal status at baseline Not applicable Premenopausal 127 (63.5) 127 (63.5) Postmenopausal 73 (36.5) 73 (36.5) Menopausal status at diagnosis Not applicable Premenopausal 77 (38.5) 77 (38.5) Postmenopausal 123 (61.5) 123 (61.5) Family history of breast cancerc (yes) 34 (17) 21 (10.5) 0.05 Alcohol intake (g/day) 10.8 11.2 11.6 13.3 0.5 Month of blood draw Not applicable March–April–May 74 (37) 74 (37) October–November 30 (15) 31 (15.5) December–January–February 96 (48) 95 (47.5)

Abbreviation: BMI, body mass index. aValues are means SDs or n (%). bP value for the comparison between breast cancer cases and controls using conditional logistic regression. Not applicable for matching factors except for more precise variables (e.g., age and BMI). cAmong ﬁrst-degree female relatives.

M485T1437, M491T120, and M581T123); however, none of them associations with individual food as citrus and coffee appear much were identified. The associations of these ions with dietary exposures higher compared with other dietary exposures and could match to are available in Supplementary Table S4 and with breast cancer risk metabolites from direct consumption of these foods. For instance, (from adjusted logistic regression) in Supplementary Table S6. several correlations between ions and coffee intake were over 0.4, A summary of the results is shown in Fig. 3. including one at 0.54 that seems higher than some studies based on FFQ as the one of Guertin and colleagues (25) despite their use of serum that is more concentrated in metabolites than plasma (26). Few Discussion studies have investigated the associations between diet-related meta- This exploratory study used untargeted metabolomics coupled with bolomics signatures and cancer risk. They observed associations multivariable penalized regression to screen for a limited set of ions between metabolites and, on the one hand, nutritional exposures (in potentially associated with various dietary exposures and maximized particular coffee, alcohol, fibers, vitamin E and fried foods intake, BMI, breast cancer risk discrimination. physical activity), and on the other hand, risk of HCC (27–29), Comparison with literature remains difficult due to the large colorectal cancer (30, 31), and only two dealing with breast can- diversity of study designs, type of biofluid used or statistical analyses cer (10, 32). In particular, most of these studies found metabolites performed. Nevertheless, the magnitude of the dietary associations related to alcohol intake, in particular, associated with breast can- highlighted in our study seems to be comparable with similar studies cer (10) or HCC (27–29) risks. In our study, among the 14 selected based on Food Frequency Questionnaires (FFQ) as in Playdon and ions; 3 were positively associated to alcohol intake (M286T989, colleagues (10) and seems relatively low for dietary patterns and M335T864, M413T967), including piperine and pregnene-triol sul- nutritional scores as in Playdon and colleagues (24). However, some fate. Moreover, the ion M230T171, frequently selected during the

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Table 2. Direction of the 14 selected ions variations from adjusted logistic regression modelsa—SU.VI.MAX cohort, France (1994–2007).

Mass/retention Mode of All selected ions together Selected ions one by one Ions (annotationb) time detection OR (95% CI) Pc OR (95% CI) Pc

M153T116 (level 2: 2-amino-cyano-butanoic acid) 153.0162/1.93 ESI Positive 1.23 (0.93–1.62) 0.1 1.08 (1.03–1.14) 0.002 M192T181 (level 4: molecular formula; C9H6O4N) 192.0305/3.01 ESI Positive 1.37 (1.05–1.79) 0.02 1.08 (1.03–1.13) 0.003 M265T186 (level 4: unknown) 265.3932/3.1 ESI Positive 1.28 (0.93–1.76) 0.1 1.09 (1.04–1.14) 0.0008 M286T989 (level 1: piperine) 286.143/16.48 ESI Positive 0.76 (0.58–0.99) 0.04 0.94 (0.89–0.99) 0.01 M335T864 (level 4: unknown) 335.2404/14.4 ESI Positive 0.77 (0.61–0.99) 0.04 0.94 (0.89–0.98) 0.009 M364T125 (level 4: NaCOOH adduct) 363.9292/2.09 ESI Positive 0.74 (0.58–0.94) 0.01 0.93 (0.89–0.98) 0.007 M425T1158 (level 1: ATBC) 425.2172/19.31 ESI Positive 1.39 (1.08–1.79) 0.01 1.07 (1.02–1.12) 0.006 M97T134 (level 4: NaCOOH adduct) 96.9218/2.24 ESI Positive 1.23 (0.94–1.60) 0.1 1.10 (1.04–1.15) 0.0003 M166T144 (level 4: unknown) 166.0387/2.4 ESI Negative 0.80 (0.62–1.03) 0.09 0.94 (0.89–0.98) 0.01 M201T1091 (level 4: unknown) 201.149/18.18 ESI Negative 1.21 (0.94–1.57) 0.1 1.10 (1.04–1.15) 0.0003 M413T967 (level 2: pregnene-triol sulfate) 413.2002/16.11 ESI Negative 1.40 (1.08–1.84) 0.01 1.08 (1.03–1.14) 0.004 M415T1344 (level 4: unknown) 415.2078/22.4 ESI Negative 0.83 (0.64–1.07) 0.2 0.92 (0.87–0.96) 0.0006 M475T122 (level 4: unknown) 474.7265/2.04 ESI Negative 0.89 (0.70–1.14) 0.4 0.93 (0.88–0.97) 0.003 M587T121 (level 4: unknown) 586.6522/2.02 ESI Negative 0.69 (0.54–0.89) 0.004 0.93 (0.89–0.98) 0.005

Abbreviation: CI, confidence interval. aThe principal logistic regression model considered all the 14 selected ions at the same time. Logistic regression models from complementary analyses considered separately the selected ions (one model for each of the 14 ions). All these models were adjusted for age (continuous), BMI (continuous), season (a priori–defined periods: October–November/December–January–February/March–April–May), menopausal status (pre/postmenopause status), smoking status (current, former and nonsmokers), height (continuous), physical activity (low, moderate, intense), education level (primary, secondary, superior), alcohol intake (continuous), use of hormone replacement therapy for menopause, number of children and family history of breast cancer at blood draw (baseline), and intervention group of the initial SU,VI,MAX trial (placebo/supplemented). Tests for linear trend were performed using the continuous variables. ORs were presented for a 1 SD increaseofthe continuous variable (semiquantification). bLevels of confidence for every identification were given accordingly to Sumner and colleagues (23): level 1, formally identified compound (confirmed with analysis of authentic standard); level 2, putatively identified compound (based upon spectral similarity with public/commercial spectral libraries or reference compound in the literature and/or physicochemical properties); level 3, putatively characterized compound classes; and level 4, unknown compound. Among the unknown compounds, two ions were NaCOOH adducts, but not all of them. The parent ions of the two ion adducts resulted from Elastic Net regression were not detected because data were acquired in positive and negative ion modes with a scan range from 50 to 1,000 mass-to-charge ratio (m/z). To observe the parent ions it would be necessary to acquire data with a lower mass range but this is not achievable with this kind of instrument. cAlthough P values are generated by this model, they cannot be used for inference given the prior Elastic Net–based selection. bootstrap resampling but not selected on the original dataset, was also association with alcoholic drinks. The association with alcohol intake associated with alcoholic drinks intake. Compared with the literature, could also be explained by higher alkaloid solubility in ethanol (41). some associations were newly identified in this study (such as the Consistent with our findings, Playdon and colleagues (10) found a positive association between ATBC, coffee intake and breast cancer positive association between piperine and liquor intake and a risk), whereas several others were not replicated. These differences decreased risk of breast cancer. Direct association was also found across studies are probably explained by differences in analytical between piperine and wine intake in blood of female Twins (42). technics, study design, and statistical approaches, as well as study However, the origin of circulating piperine is not restricted to population with heterogeneous underlying diets and cancer sites. Western-type foods and can also be the results of adding black pepper In this study, piperine, an exogenous active alkaloid with no into food (e.g., for salad or fish seasoning). Unfortunately, the level of endogenous origin reported so far, was highlighted as potential detail of the SU.VI.MAX dietary questionnaire did not allow us to predictor of breast cancer risk (inverse association), with high stability estimate pepper intake. across penalized models (selection frequencies >90% on the 1,000 ATBC is an alternative plasticizer to phthalates (43) commonly used bootstraps). Piperine is contained in black and long pepper (33) and is in polyvinyl resins and permitted as a food additive and food contact used as feed additive in animal feed, in particular for poultry (34). The substance (44). Migration of ATBC from food packaging material into human exposure to piperine via this latter source has been estimated at food has been observed for cheese, wrapped cake, microwaved soup, 0.93 mg/metabolic body weight per day (34). Several animal or cellular and microwaved peanut-containing cookies (44, 45) and its leaching studies suggested a promising spectrum of properties for piperine such rate from medical equipment was found 10 times faster than the potent as anti-oxidant (35, 36), anti-inflammatory (33, 35, 36), immuno- endocrine disruptor di-2-ethylhexyl phthalate (44, 46, 47). In our modulatory, bioavailability and absorption promoter for many active study, we found a positive association between plasma ATBC and molecules (33, 37), antiasthmatic, anticonvulsing, antimutagenic, coffee intake. This association should be confirmed in independent antimycobacterial, and anticancer activities (ref. 33; chemopreventive human observational studies and in vivo or in vitro animal interven- properties, including inhibition of angiogenesis and increased cell tion studies; however, it may reflect contaminant migration from apoptosis), especially in breast cancer models (38–40). In our study, plastic cup into coffee. Milk added in coffee may facilitate this piperine was in particular positively associated with alcohol intake and migration because one study suggested that ATBC is prone to migrate with a “Western” dietary pattern. These associations are probably into protein liquids, such as aqueous skim milk solution (48). Its explained by the fact that either several foods (e.g., processed meat, increase in plasma from women who have developed breast cancer sauces, industrial cheese, poultry) containing piperine as feed additive could also come from other exposure that we were unable to detect in or via pepper are either part of a “Western” diet or consumed in this study. Recent studies found potential biological activity of ATBC

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on tissue growth (49) and a potential disruption of ovarian function in the available databases. However, sharing putatively annotated com- female mice due to exposure to ATBC at low-dosage–imitating human pounds or unknowns within the scientific community could be of great exposure (50). interest. Indeed, in case of absence of commercial standard, it could be Currently, alcohol intake is the only established dietary risk factor relevant if several studies and consortia shared the same hypothesis, to for breast cancer risk with strong evidence (3). Some underlying further synthetize the compound or isolate it from a biological media. mechanisms are already described; however, other remain to eluci- In case of too low signal abundance, as MS technologies are more and date (3). In our study, pregnene-triol sulfate, a steroid sulfate hormone more sensitive, unknowns could be elucidated with new instruments in belonging to progestin family, was positively associated with both the future. alcohol intake and breast cancer risk. Consistently with our results, a The major strengths of this study pertained to the very sensitive recent metabolomic study found positive correlations between alcohol untargeted MS metabolomic analysis, the prospective design and the intake and several serum steroids, notably pregnene-diol sulfate, choice of statistical design using Elastic Net penalized regression; a which were associated with an increased breast cancer risk (10). suitable method to high-dimensional correlated data. Penalized Moreover, increased levels of sex steroids seem strongly associated regression techniques were applied to control the variance of estimates with risk of postmenopausal breast cancers (51). Alcohol intake (that increases in the presence of many predictors or multicollinearity) may increase circulating levels of steroid hormone, which could by shrinking coefficients toward zero (59). Some penalized regression affect susceptibility to transform or promote cancer growth (52). techniques such as the Least Absolute Shrinkage and Selection Oper- An interventional study in postmenopausal women showed that ator (LASSO; ref. 59) and Elastic Net (21) simultaneously perform alcohol consumption increased serum level of dehydroepiandros- automatic variable selection by shrinking the irrelevant predictor terone (DHEA) sulfate (53), a precursor to androstenedione, tes- coefficients to exactly zero. The latter tends to better select important tosterone, and, ultimately, estrone and estradiol. Furthermore, the variables when high correlations are present as in metabolomic data. pregnene-triol sulfate found in our study, may come from the Several studies applied these methods in multiple fields (as youth 17-hydroxy-pregnenolone, which losing its side chain can produce violence, genetics, cardiovascular disease risk; refs. 22, 60–62); how- DHEA. Other factors could influence the level of sex steroids, such ever, to our knowledge, no previous study focusing on nutritional as BMI and lactation (3). Furthermore, it has been shown that metabolomics and cancer prevention used penalized techniques. ATBC strongly activated human and rat Steroid and Xenobiotic Nevertheless, several limitations should be acknowledged for this Receptor (SXR) and may alter metabolism of endogenous steroid study. First, despite the processes of cross validation, iterations and hormones (43). bootstraps, these results need to be validated through an independent In our study, increased plasma level of 2-amino-4-cyano study sample. Unbiased predictive performance could not be inves- butanoic acid was found in plasma from women who have tigated in this study due to the lack of an independent dataset. The subsequently developed a breast cancer during follow-up. This investigation of performance gain when adding a diet-related meta- metabolite, also called alpha-amino-gamma-cyanobutanoic acid, bolic signature to a model, including already known breast cancer risk is a non-proteinogenic alpha-amino acid (2-aminobutanoic acid) factors would be useful to improve discrimination of women at higher substituted at position 4 by a cyano group and an aliphatic nitrile. risk of breast cancer. However, to get a positive impact in terms of It may derive from butyrate (54) that is a short-chain fatty acid prevention, this signature must be modifiable following a change of synthesized by the fermentation of fibers by colon bacteria (55). diet. This issue, as well as several others (e.g., replication, quantifica- Butyrate has recently received growing attention for its beneficial tion) should be investigated before considering an application in public effects on intestinal homeostasis and energy metabolism. With its health. Second, associations may have been missed due to a lack of anti-inflammatory properties, butyrate improves intestinal barrier power, self-reporting bias or to the analytical protocol (LC-MS) that function and mucosal immunity (56). In our study, we found an did not allow detecting all categories of metabolites. Indeed, although inverse association between plasma 2-amino-4-cyano butanoic acid our analytical method was optimized to detect as many metabolites as and cake and biscuits intakes. To our knowledge, at this time no direct possible, because of the huge chemical diversity of compounds in blood association between plasma 2-amino-cyano butanoic acid and diet it is impossible to cover all metabolites using a single analytical exposure has been established. The production and effects of butyrate method, even with an untargeted approach. Thus, some metabolites appear to be related to diet, including the type of dietary fibers and fat of interest may have been not detected with our analytical conditions. consumed, respectively (57). Butyrate is present in various types of However, UPLC-MS is one of the most sensitive methodology avail- foods, including vanilla, oats, peanut, and several fruits (58). Having no able. Complementary GC-MS analyses could be useful to provide available data on the plasma butyrate level in our study, we cannot additional types of metabolites. Moreover, a larger sample size would conclude on a possible link between the observed variations and a have allowed a stratification of the population according to several disturbance of butyrate metabolism and therefore of the microbiota. parameters such as time prior to cancer diagnosis and menopausal Our results need to be replicated in other independent observational status. Third, the possibility of residual or unmeasured confounding studies, and intervention studies on animal models would provide a cannot be ruled out in this observational study. However, many better understanding of the origin of its variations and related to food. potential confounders were accounted for. Finally, our study was Its association with the risk of breast cancer and a potential causal based on a single blood draw, which limits the investigation of relationship could be investigated through cellular mechanistic studies. metabolomic profiles stability across time. Nevertheless, several stud- Unfortunately, despite additional analytical analyses (including ies have showed a good reproducibility of metabolomic measurements different fragmentation experiments using ultra high-resolution MS, for most of metabolites (63, 64). Several blood draws during follow-up fraction collection, pre concentration of the biological samples, H/D would allow a finer detection of metabolic pathways that are disrupted exchange and eventually additional analysis such as GC/MS), the other during carcinogenesis. metabolites selected by Elastic Net regression for breast cancer risk In conclusion, this exploratory prospective study identified a plasma analyses could not be identified, mainly because of limited signal diet–related metabolic signature of long-term breast cancer risk intensities, the lack of commercial standards and the incompleteness of involving exogenous, steroid and microbiota-related metabolites. The

OF8 Cancer Epidemiol Biomarkers Prev; 2020 CANCER EPIDEMIOLOGY, BIOMARKERS &PREVENTION

Dietary Metabolomic Signatures of Breast Cancer Risk

hypotheses highlighted in this study should be further investigated in Administrative, technical, or material support (i.e., reporting or organizing data, future large-scale independent studies. In the future, such signatures constructing databases): L. Lecuyer, P. Micheau, E. Kesse-Guyot, E. Pujos-Guillot, could help to better understand the etiology of nutrition and breast M. Touvier fi Study supervision: P. Micheau, P. Galan, S. Hercberg, M. Touvier cancer and to identify key metabolites both associated to modi able Other (mass spectrometry analysis and metabolites identification): B. Lyan nutritional behavior and to breast cancer risk. Acknowledgments Disclosure of Potential Conflicts of Interest The authors thank Younes Esseddik, Frederic Coffinieres, Thi Hong Van No potential conflicts of interest were disclosed. Duong, Paul Flanzy, Regis Gatibelza, Jagatjit Mohinder, and Maithyly Sivapalan (computer scientists); Rachida Mehroug and Frederique Ferrat (logistic assis- Disclaimer tants); Nathalie Arnault, Veronique Gourlet, PhD, Fabien Szabo, PhD, Julien The funders had no role in the design, analysis, or writing of this article. Allegre, and Laurent Bourhis (data-manager/statisticians); and Cedric Agaesse (dietitian) for their technical contribution to the SU.VI.MAX study. We ’ also thank Nathalie Druesne-Pecollo, PhD (operational coordination) as well as Authors Contributions all participants of the SU.VI.MAX study. This work was conducted in the Conception and design: L. Lecuyer, P. Micheau, P. Galan, S. Hercberg, C. Manach, framework of the French network for Nutrition And Cancer Research (NACRe M. Touvier network), www.inra.fr/nacre, and received the NACRe Partnership Label. Meta- Development of methodology: L. Lecuyer, P. Micheau, C. Samieri, P. Ferrari, bolomic analysis was performed within the metaboHUB French infrastructure M. Touvier (ANR-INBS-0010). This work was supported by the French National Cancer Acquisition of data (provided animals, acquired and managed patients, provided Institute (grant number INCa_8085 for the project, and PhD grant number facilities, etc.): P. Micheau, A. Rossary, A. Demidem, M. Petera, M. Lagree, INCa_11323, to L. Lecuyer); the Federative Institute for Biomedical Research D. Centeno, P. Galan, S. Hercberg, E. Kesse-Guyot, S. Durand, M. Touvier IFRB Paris 13; and the Canceropole^ Ile-de-France/Region Ile de France (PhD Analysis and interpretation of data (e.g., statistical analysis, biostatistics, grant for M. Deschasaux). computational analysis): L. Lecuyer, C. Dalle, S. Lefevre-Arbogast, P. Micheau, B. Lyan, C. Samieri, N. Assi, V. Viallon, M. Deschasaux, V. Partula, B. Srour, E. Kesse- The costs of publication of this article were defrayed in part by the payment of page Guyot, N. Druesne-Pecollo, M.-P. Vasson, S. Durand, E. Pujos-Guillot, C. Manach, charges. This article must therefore be hereby marked advertisement in accordance M. Touvier with 18 U.S.C. Section 1734 solely to indicate this fact. Writing, review, and/or revision of the manuscript: L. Lecuyer, S. Lefevre-Arbogast, P. Micheau, A. Rossary, A. Demidem, M. Petera, P. Galan, C. Samieri, N. Assi, V. Viallon, M. Deschasaux, V. Partula, P. Latino-Martel, E. Kesse-Guyot, N. Druesne- Received July 29, 2019; revised October 3, 2019; accepted November 18, 2019; fi Pecollo, S. Durand, E. Pujos-Guillot, C. Manach, M. Touvier published rst November 25, 2019.

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OF10 Cancer Epidemiol Biomarkers Prev; 2020 CANCER EPIDEMIOLOGY, BIOMARKERS &PREVENTION

Diet-Related Metabolomic Signature of Long-Term Breast Cancer Risk Using Penalized Regression: An Exploratory Study in the SU.VI.MAX Cohort

Lucie Lécuyer, Céline Dalle, Sophie Lefevre-Arbogast, et al.

Cancer Epidemiol Biomarkers Prev Published OnlineFirst November 25, 2019.

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