Marie Breeur1, Pietro Ferrari1, Laure Dossus1, Mazda Jenab1, Mattias Johansson2, Sabina Rinaldi1, Ruth C Travis3, Mathilde His1, Tim J Key3, Julie A Schmidt3,4, Kim Overvad5, Anne Tjønneland6, Cecilie Kyrø6, Joseph A Rothwell7, Nasser Laouali7, Gianluca Severi7, Rudolf Kaaks8, Verena Katzke8, Matthias B Schulze9, Fabian Eichelmann9,10, Domenico Palli11, Sara Grioni12, Salvatore Panico13, Rosario Tumino14, Carlotta Sacerdote15, Bas Bueno-de-Mesquita16, Karina Standahl Olsen17, Torkjel Manning Sandanger17, Therese Haugdahl Nøst17, J Ramón Quirós18, Catalina Bonet19, Miguel Rodríguez Barranco20,21,22, María-Dolores Chirlaque22,23, Eva Ardanaz22,24,25, Malte Sandsveden26, Jonas Manjer27, Linda Vidman28, Matilda Rentoft28, David Muller29, Kostas Tsilidis29, Alicia K Heath29, Hector Keun30, Jerzy Adamski31,32,33, Pekka Keski-Rahkonen1, Augustin Scalbert1, Marc J Gunter1, Vivian Viallon34. 1. Nutrition and Metabolism Branch, International Agency for Research on Cancer, NME Branch, 69372 CEDEX 08, Lyon, France. 2. Genetics Branch, International Agency for Research on Cancer, 69372 CEDEX 08, Lyon, France. 3. Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, OX3 7LF, UK. 4. Department of Clinical Epidemiology, Department of Clinical Medicine, Aarhus University Hospital and Aarhus University, DK-8200, Aarhus N, Denmark. 5. Department of Public Health, Aarhus University, DK-8000, Aarhus C, Denmark. 6. Danish Cancer Society Research Center Diet, Genes and Environment Nutrition and Biomarkers, DK-2100, Copenhagen, Denmark. 7. Université Paris-Saclay, UVSQ, Inserm, CESP U1018, "Exposome and Heredity" team, Gustave Roussy, 94800, Villejuif, France. 8. Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany. 9. Department of Molecular Epidemiology, German Institute of Human Nutrition, 14558, Nuthetal, Germany. 10. German Center for Diabetes Research (DZD), 85764, Neuherberg, Germany. 11. Institute of Cancer Research, Prevention and Clinical Network (ISPRO), 50139, Florence, Italy. 12. Epidemiology and Prevention Unit, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, 20133, Milan, Italy. 13. Dipartimento di Medicina Clinica e Chirurgia, Federico II University, 80131, Naples, Italy. 14. Hyblean Association for Epidemiological Research, AIRE-ONLUS, 97100, Ragusa, Italy. 15. Unit of Cancer Epidemiology Città della Salute e della Scienza University-Hospital, 10126, Turin, Italy. 16. Centre for Nutrition, Prevention and Health Services, National Institute for Public Health and the Environment (RIVM), PO Box 1, 3720, BA, Bilthoven, The Netherlands. 17. Department of Community Medicine, UiT The Arctic University of Norway, N-9037, Tromsø, Norway. 18. Public Health Directorate, 33006, Oviedo, Asturias, Spain. 19. Unit of Nutrition and Cancer, Cancer Epidemiology Research Program, Catalan Institute of Oncology (ICO), Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, 08908, Barcelona, Spain. 20. Escuela Andaluza de Salud Pública (EASP), 18011, Granada, Spain. 21. Instituto de Investigación Biosanitaria ibs. GRANADA, 18012, Granada, Spain. 22. Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), 28029, Madrid, Spain. 23. Department of Epidemiology, Regional Health Council, IMIB-Arrixaca, Murcia University, 30003, Murcia, Spain. 24. Navarra Public Health Institute, 31003, Pamplona, Spain. 25. IdiSNA, Navarra Institute for Health Research, 31008, Pamplona, Spain. 26. Department of Clinical Sciences Malmö Lund University, SE-214 28, Malmö, Sweden. 27. Departement of Surgery, Skåne University Hospital Malmö, Lund University, SE-214 28, Malmö, Sweden. 28. Department of Radiation Sciences, Oncology Umeå University, SE-901 87, Umeå, Sweden. 29. Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, W2 1PG, UK. 30. Department of Surgery and Cancer, Cancer Metabolism and Systems Toxicology Group, Division of Cancer, Imperial College London, London, SW7 2AZ, UK. 31. Institute of Experimental Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764, Neuherberg, Germany. 32. Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117597, Singapore. 33. Institute of Biochemistry, Faculty of Medicine, University of Ljubljana, 1000, Ljubljana, Slovenia. 34. Nutrition and Metabolism Branch, International Agency for Research on Cancer, NME Branch, 69372 CEDEX 08, Lyon, France. viallonv@iarc.who.int.
Abstract
BACKGROUND: Epidemiological studies of associations between metabolites and cancer risk have typically focused on specific cancer types separately. Here, we designed a multivariate pan-cancer analysis to identify metabolites potentially associated with multiple cancer types, while also allowing the investigation of cancer type-specific associations. METHODS: We analysed targeted metabolomics data available for 5828 matched case-control pairs from cancer-specific case-control studies on breast, colorectal, endometrial, gallbladder, kidney, localized and advanced prostate cancer, and hepatocellular carcinoma nested within the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort. From pre-diagnostic blood levels of an initial set of 117 metabolites, 33 cluster representatives of strongly correlated metabolites and 17 single metabolites were derived by hierarchical clustering. The mutually adjusted associations of the resulting 50 metabolites with cancer risk were examined in penalized conditional logistic regression models adjusted for body mass index, using the data-shared lasso penalty. RESULTS: Out of the 50 studied metabolites, (i) six were inversely associated with the risk of most cancer types: glutamine, butyrylcarnitine, lysophosphatidylcholine a C18:2, and three clusters of phosphatidylcholines (PCs); (ii) three were positively associated with most cancer types: proline, decanoylcarnitine, and one cluster of PCs; and (iii) 10 were specifically associated with particular cancer types, including histidine that was inversely associated with colorectal cancer risk and one cluster of sphingomyelins that was inversely associated with risk of hepatocellular carcinoma and positively with endometrial cancer risk. CONCLUSIONS: These results could provide novel insights for the identification of pathways for cancer development, in particular those shared across different cancer types.
BACKGROUND: Epidemiological studies of associations between metabolites and cancer risk have typically focused on specific cancer types separately. Here, we designed a multivariate pan-cancer analysis to identify metabolites potentially associated with multiple cancer types, while also allowing the investigation of cancer type-specific associations. METHODS: We analysed targeted metabolomics data available for 5828 matched case-control pairs from cancer-specific case-control studies on breast, colorectal, endometrial, gallbladder, kidney, localized and advanced prostate cancer, and hepatocellular carcinoma nested within the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort. From pre-diagnostic blood levels of an initial set of 117 metabolites, 33 cluster representatives of strongly correlated metabolites and 17 single metabolites were derived by hierarchical clustering. The mutually adjusted associations of the resulting 50 metabolites with cancer risk were examined in penalized conditional logistic regression models adjusted for body mass index, using the data-shared lasso penalty. RESULTS: Out of the 50 studied metabolites, (i) six were inversely associated with the risk of most cancer types: glutamine, butyrylcarnitine, lysophosphatidylcholine a C18:2, and three clusters of phosphatidylcholines (PCs); (ii) three were positively associated with most cancer types: proline, decanoylcarnitine, and one cluster of PCs; and (iii) 10 were specifically associated with particular cancer types, including histidine that was inversely associated with colorectal cancer risk and one cluster of sphingomyelins that was inversely associated with risk of hepatocellular carcinoma and positively with endometrial cancer risk. CONCLUSIONS: These results could provide novel insights for the identification of pathways for cancer development, in particular those shared across different cancer types.
Authors: Magdalena Stepien; Pekka Keski-Rahkonen; Agneta Kiss; Nivonirina Robinot; Talita Duarte-Salles; Neil Murphy; Gabriel Perlemuter; Vivian Viallon; Anne Tjønneland; Agnetha Linn Rostgaard-Hansen; Christina C Dahm; Kim Overvad; Marie-Christine Boutron-Ruault; Francesca Romana Mancini; Yahya Mahamat-Saleh; Krasimira Aleksandrova; Rudolf Kaaks; Tilman Kühn; Antonia Trichopoulou; Anna Karakatsani; Salvatore Panico; Rosario Tumino; Domenico Palli; Giovanna Tagliabue; Alessio Naccarati; Roel C H Vermeulen; Hendrik Bastiaan Bueno-de-Mesquita; Elisabete Weiderpass; Guri Skeie; Jose Ramón Quirós; Eva Ardanaz; Olatz Mokoroa; Núria Sala; Maria-Jose Sánchez; José María Huerta; Anna Winkvist; Sophia Harlid; Bodil Ohlsson; Klas Sjöberg; Julie A Schmidt; Nick Wareham; Kay-Tee Khaw; Pietro Ferrari; Joseph A Rothwell; Marc Gunter; Elio Riboli; Augustin Scalbert; Mazda Jenab Journal: Int J Cancer Date: 2020-08-28 Impact factor: 7.396
Authors: Tuo Deng; Christopher J Lyon; Stephen Bergin; Michael A Caligiuri; Willa A Hsueh Journal: Annu Rev Pathol Date: 2016-05-23 Impact factor: 23.472
Authors: Ilaria Elia; Dorien Broekaert; Stefan Christen; Ruben Boon; Enrico Radaelli; Martin F Orth; Catherine Verfaillie; Thomas G P Grünewald; Sarah-Maria Fendt Journal: Nat Commun Date: 2017-05-11 Impact factor: 14.919
Authors: Nelly C Muñoz-Esparza; M Luz Latorre-Moratalla; Oriol Comas-Basté; Natalia Toro-Funes; M Teresa Veciana-Nogués; M Carmen Vidal-Carou Journal: Front Nutr Date: 2019-07-11