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Cancer Epidemiol Biomarkers Prev. 2015 Mar;24(3):538-45. doi: 10.1158/1055-9965.EPI-14-1107. Epub 2014 Dec 26.

Lag times between lymphoproliferative disorder and clinical diagnosis of chronic lymphocytic leukemia: a prospective analysis using plasma soluble CD23.

Author information

1
Division of Cancer Epidemiology, German Cancer Research Center Heidelberg, Heidelberg, Germany. r.kaaks@dkfz-heidelberg.de.
2
Division of Cancer Epidemiology, German Cancer Research Center Heidelberg, Heidelberg, Germany.
3
Center for Chronic Immunodeficiency, University Medical Center Freiburg, Freiburg, Germany.
4
Division of Epigenomics and Cancer Risk Factors, German Cancer Research Center, Heidelberg, Germany.
5
Division of Cancer Epidemiology, German Cancer Research Center Heidelberg, Heidelberg, Germany. Institute of Laboratory Medicine, Clinical Chemistry and Molecular Diagnostics, University Leipzig, Leipzig, Germany. Leipzig Research Center for Civilization Diseases (LIFE), University Leipzig, Leipzig, Germany.
6
Department of Radiation Sciences, Oncology, Umeå University, Umeå, Sweden.
7
Department of Gastroenterology and Nutrition, Malmö, Skåne University Hospital, Lund University, Lund, Sweden.
8
Hellenic Health Foundation, Athens, Greece. Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts. Bureau of Epidemiologic Research, Academy of Athens, Athens, Greece.
9
Hellenic Health Foundation, Athens, Greece. Bureau of Epidemiologic Research, Academy of Athens, Athens, Greece.
10
Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts. Bureau of Epidemiologic Research, Academy of Athens, Athens, Greece. Department of Hygiene, Epidemiology, and Medical Statistics, University of Athens Medical School, Goudi, Athens, Greece.
11
Dipartimento di Medicina Clinica e Chirurgia, Federico II, University, Naples, Italy.
12
Cancer Registry and Histopathology Unit, "Civic-M.P.Arezzo" Hospital, ASP Ragusa, Italy.
13
Molecular and Nutritional Epidemiology Unit, Cancer Research and Prevention Institute-ISPO, Florence, Italy.
14
Epidemiology and Prevention Unit, Fondazione IRCCS Istituto Nazionale dei Tumori Via Venezian, Milano, Italy.
15
Department of Epidemiology, German Institute of Human Nutrition (DIfE) Potsdam-Rehbrüucke, Nuthetal, Germany.
16
Section of Genetics, International Agency for Research on Cancer (IARC), Lyon, France.
17
Infections and Cancer Epidemiology Group, International Agency for Research on Cancer, Lyon, France.
18
Centre d'Immunologie de Marseille-Luminy, Aix-Marseille Université, Marseille, France.
19
Unit of Infections and Cancer (UNIC), IDIBELL, Institut Català d' Oncologia, L' Hospitalet de Llobregat, Barcelona, Spain. Epidemiología y Salud Pública (CIBERESP), Barcelona, Spain.
20
Epidemiología y Salud Pública (CIBERESP), Barcelona, Spain. Escuela Andaluza de Salud Pública Instituto de Investigación Biosanitaria de Granada (Granada.ibs), Granada, Spain.
21
Epidemiología y Salud Pública (CIBERESP), Barcelona, Spain. Department of Epidemiology, Murcia Regional Health Council, Murcia, Spain.
22
Epidemiología y Salud Pública (CIBERESP), Barcelona, Spain. Navarre Public Health Institute, Pamplona, Spain.
23
Translational Research Laboratory and Unit of Nutrition, Environment, and Cancer, Cancer Epidemiology Research Program, Catalan Institute of Oncology (ICO), Biomedical Research Institute (IDIBELL), Barcelona, Spain.
24
Section for Epidemiology, Department of Public Health, Aarhus University, Aarhus, Denmark.
25
Danish Cancer Society Research Center, Copenhagen, Denmark.
26
Department of Community Medicine, Faculty of Health Sciences, UiT The Arctic University of Norway, Tromsø, Norway. Department of Research, Cancer Registry of Norway, Oslo, Norway. Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden. Samfundet Folkhälsan, Helsinki, Finland.
27
National Institute for Public Health and the Environment (RIVM), Bilthoven, the Netherlands. Department of Gastroenterology and Hepatology, University Medical Centre, Utrecht, the Netherlands. School of Public Health, Imperial College, London, United Kingdom.
28
Division of Environmental Epidemiology, Institute for Risk Assessment Sciences (IRAS), Utrecht University, Utrecht, the Netherlands.
29
School of Public Health, Imperial College, London, United Kingdom. Department of Epidemiology, Julius Center for Health Sciences and Primary Care, University Medical Center, Utrecht, the Netherlands.
30
School of Public Health, Imperial College, London, United Kingdom. Human Genetic Foundation (HuGeF), Turin, Italy. MRC-HPA Centre for Environment and Health, Department of Epidemiology and Biostatistics, Imperial College London, London, United Kingdom.
31
MRC-HPA Centre for Environment and Health, Department of Epidemiology and Biostatistics, Imperial College London, London, United Kingdom.
32
School of Clinical Medicine, University of Cambridge, Cambridge, United Kingdom.
33
Cancer Epidemiology Unit, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, United Kingdom.
34
School of Public Health, Imperial College, London, United Kingdom.

Abstract

BACKGROUND:

Chronic lymphocytic leukemia (CLL) is a chronic disease that often progresses slowly from a precursor stage, monoclonal B-cell lymphocytosis (MBL), and that can remain undiagnosed for a long time.

METHODS:

Within the European Prospective Investigation into Cancer cohort, we measured prediagnostic plasma sCD23 for 179 individuals who eventually were diagnosed with CLL and an equal number of matched control subjects who remained free of cancer.

RESULTS:

In a very large proportion of CLL patients' plasma sCD23 was clearly elevated 7 or more years before diagnosis. Considering sCD23 as a disease predictor, the area under the ROC curve (AUROC) was 0.95 [95% confidence interval (CI), 0.90-1.00] for CLL diagnosed within 0.1 to 2.7 years after blood measurement, 0.90 (95% CI, 0.86-0.95) for diagnosis within 2.8 to 7.3 years, and 0.76 (95% CI, 0.65-0.86) for CLL diagnosed between 7.4 and 12.5 years. Even at a 7.4-year and longer time interval, elevated plasma sCD23 could predict a later clinical diagnosis of CLL with 100% specificity at >45% sensitivity.

CONCLUSIONS:

Our findings provide unique documentation for the very long latency times during which measurable B-cell lymphoproliferative disorder exists before the clinical manifestation of CLL.

IMPACT:

Our findings have relevance for the interpretation of prospective epidemiologic studies on the causes of CLL in terms of reverse causation bias. The lag times indicate a time frame within which an early detection of CLL would be theoretically possible. Cancer Epidemiol Biomarkers Prev; 24(3); 538-45. ©2014 AACR.

PMID:
25542829
DOI:
10.1158/1055-9965.EPI-14-1107
[Indexed for MEDLINE]
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