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Int J Cancer. 2018 Sep 15;143(6):1335-1347. doi: 10.1002/ijc.31536. Epub 2018 Apr 26.

Pre-diagnostic blood immune markers, incidence and progression of B-cell lymphoma and multiple myeloma: Univariate and functionally informed multivariate analyses.

Author information

1
Division of Environmental Epidemiology, Utrecht University, Institute for Risk Assessment Sciences, Utrecht, The Netherlands.
2
MRC-PHE Centre for Environment and Health, Department of Epidemiology and Biostatistics, Imperial College London, London, United Kingdom.
3
Immunology Department, Erasmus University Medical Center, Rotterdam, The Netherlands.
4
Laboratoire de Mathématiques et de leurs Applications, Université de Pau et des Pays de l'Adour, UMR CNRS, Pau, France.
5
ARC Centre of Excellence for Mathematical and Statistical Frontiers, Queensland University of Technology (QUT), Brisbane, Australia.
6
Department of Hematology, University Medical Center Utrecht, Utrecht, The Netherlands.
7
Department of Public Health and Clinical Medicine, and Department of Biobank Research, Umeå University, Umeå, Sweden.
8
National Hellenic Research Foundation, Institute of Biology, Medicinal Chemistry and Biotechnology, Athens, Greece.
9
Department of Radiation Sciences, Oncology, Umeå University, Umeå, Sweden.
10
The Institute for Cancer Research and Prevention, Florence, Italy.
11
Fondazione IRCCS-Instituto Nazionale dei Tumori, Milan, Italy.
12
Department of Clinical Medicine and Surgery, University of Naples Frederico II, Naples, Italy.
13
Piedmont Reference Centre for Epidemiology and Cancer Prevention (CPO Piemonte), Turin, Italy.
14
Cancer registry and Histopathology Unit, Azienda Ospedaliera 'Civile-M.P.Arezzo', Ragusa, Italy.
15
HuGeF Foundation, Torino, Italy.
16
INSERM, UMR1027, Université Toulouse III-Paul Sabatier, Toulouse, France.
17
Institute of Biology, Medicinal Chemistry and Biotechnology, National Hellenic Research Foundation, Athens, Greece.
18
Department of Toxicogenomics, Maastricht University, Maastricht, The Netherlands.
19
Division of Cancer, Department of Surgery and Cancer, Imperial College London, Institute of Reproductive and Developmental Biology (IRDB), Hammersmith Hospital, London, United Kingdom.
20
Division of Computational and Systems Medicine, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, United Kingdom.
21
Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, USA.
22
Nutrition Research, Department of Public Health and Clinical Medicine, and Department of Biobank Research, Umeå University, Umeå, Sweden.
23
Environmental Chemical Processes Laboratory, University of Crete, Heraklion, Greece.
24
ISGlobal, Centre for Research in Environmental Epidemiology (CREAL), Barcelona, Spain.
25
Istituto Superiore di Sanita, Rome, Italy.
26
National Institute for Health and Welfare, Kuopio, Finland.
27
University of Leeds, Leeds, United Kingdom.
28
Lund University, Lund, Sweden.
29
National Taiwan University, Taipei, Taiwan.

Abstract

Recent prospective studies have shown that dysregulation of the immune system may precede the development of B-cell lymphomas (BCL) in immunocompetent individuals. However, to date, the studies were restricted to a few immune markers, which were considered separately. Using a nested case-control study within two European prospective cohorts, we measured plasma levels of 28 immune markers in samples collected a median of 6 years before diagnosis (range 2.01-15.97) in 268 incident cases of BCL (including multiple myeloma [MM]) and matched controls. Linear mixed models and partial least square analyses were used to analyze the association between levels of immune marker and the incidence of BCL and its main histological subtypes and to investigate potential biomarkers predictive of the time to diagnosis. Linear mixed model analyses identified associations linking lower levels of fibroblast growth factor-2 (FGF-2 p = 7.2 × 10-4 ) and transforming growth factor alpha (TGF-α, p = 6.5 × 10-5 ) and BCL incidence. Analyses stratified by histological subtypes identified inverse associations for MM subtype including FGF-2 (p = 7.8 × 10-7 ), TGF-α (p = 4.08 × 10-5 ), fractalkine (p = 1.12 × 10-3 ), monocyte chemotactic protein-3 (p = 1.36 × 10-4 ), macrophage inflammatory protein 1-alpha (p = 4.6 × 10-4 ) and vascular endothelial growth factor (p = 4.23 × 10-5 ). Our results also provided marginal support for already reported associations between chemokines and diffuse large BCL (DLBCL) and cytokines and chronic lymphocytic leukemia (CLL). Case-only analyses showed that Granulocyte-macrophage colony stimulating factor levels were consistently higher closer to diagnosis, which provides further evidence of its role in tumor progression. In conclusion, our study suggests a role of growth-factors in the incidence of MM and of chemokine and cytokine regulation in DLBCL and CLL.

KEYWORDS:

cytokine; lymphoma; mixed-effect modeling; multiple myeloma; multivariate models; prospective cohort; time to diagnosis

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