Detectable clonal mosaicism and its relationship to aging and cancer

Kevin B Jacobs1,2, Meredith Yeager1,2, Weiyin Zhou1,2, Sholom Wacholder1, Zhaoming Wang1,2, Benjamin Rodriguez-Santiago3-5, Amy Hutchinson1,2, Xiang Deng1,2, Chenwei Liu1,2, Marie-Josephe Horner1, Michael Cullen1,2, Caroline G Epstein1, Laurie Burdett1,2, Michael C Dean6, Nilanjan Chatterjee1, Joshua Sampson1, Charles C Chung1, Joseph Kovaks1, Susan M Gapstur7, Victoria L Stevens7, Lauren T Teras7, Mia M Gaudet7, Demetrius Albanes1, Stephanie J Weinstein1, Jarmo Virtamo8, Philip R Taylor1, Neal D Freedman1, Christian C Abnet1, Alisa M Goldstein1, Nan Hu1, Kai Yu1, Jian-Min Yuan9,10, Linda Liao1, Ti Ding11, You-Lin Qiao12, Yu-Tang Gao13, Woon-Puay Koh14, Yong-Bing Xiang13, Ze-Zhong Tang11, Jin-Hu Fan12, Melinda C Aldrich15,16, Christopher Amos17, William J Blot16,18, Cathryn H Bock19,20, Elizabeth M Gillanders21, Curtis C Harris22, Christopher A Haiman23,24, Brian E Henderson23,24, Laurence N Kolonel25, Loic Le Marchand25, Lorna H McNeill26,27, Benjamin A Rybicki28, Ann G Schwartz19,20, Lisa B Signorello16,18,29, Margaret R Spitz30, John K Wiencke31, Margaret Wrensch31, Xifeng Wu17, Krista A Zanetti21,22, Regina G Ziegler1, Jonine D Figueroa1, Montserrat Garcia-Closas1,32, Nuria Malats33, Gaelle Marenne33, Ludmila Prokunina-Olsson1, Dalsu Baris1, Molly Schwenn34, Alison Johnson35, Maria Teresa Landi1, Lynn Goldin1, Dario Consonni36,37, Pier Alberto Bertazzi36,37, Melissa Rotunno1, Preetha Rajaraman1, Ulrika Andersson38, Laura E Beane Freeman1, Christine D Berg39, Julie E Buring40,41, Mary A Butler42, Tania Carreon42, Maria Feychting43, Anders Ahlbom43, J Michael Gaziano41,44,45, Graham G Giles46,47, Goran Hallmans48, Susan E Hankinson49, Patricia Hartge1, Roger Henriksson38,50, Peter D Inskip1, Christoffer Johansen51, Annelie Landgren1, Roberta McKean-Cowdin23, Dominique S Michaud52,53, Beatrice S Melin38, Ulrike Peters54,55, Avima M Ruder42, Howard D Sesso41, Gianluca Severi46,47, Xiao-Ou Shu29, Kala Visvanathan56, Emily White54,55, Alicja Wolk57, Anne Zeleniuch-Jacquotte58, Wei Zheng29, Debra T Silverman1, Manolis Kogevinas59-62, Juan R Gonzalez60,63,64, Olaya Villa3-5, Donghui Li65, Eric J Duell66, Harvey A Risch67, Sara H Olson68, Charles Kooperberg54, Brian M Wolpin49,69, Li Jiao70, Manal Hassan65, William Wheeler71, Alan A Arslan72-74, H Bas Bueno-de-Mesquita75,76, Charles S Fuchs49,69, Steven Gallinger77, Myron D Gross78, Elizabeth A Holly79, Alison P Klein80, Andrea LaCroix54, Margaret T Mandelson54,81, Gloria Petersen82, Marie-Christine Boutron-Ruault83, Paige M Bracci79, Federico Canzian84, Kenneth Chang85, Michelle Cotterchio86, Edward L Giovannucci49,87,88, Michael Goggins79,89,90, Judith A Hoffman Bolton56, Mazda Jenab91, Kay-Tee Khaw92, Vittorio Krogh93, Robert C Kurtz94, Robert R McWilliams95, Julie B Mendelsohn1, Kari G Rabe82, Elio Riboli52, Anne Tjønneland51, Geoffrey S Tobias1, Dimitrios Trichopoulos87,96, Joanne W Elena39, Herbert Yu67, Laufey Amundadottir1, Rachael Z Stolzenberg-Solomon1, Peter Kraft49,87, Fredrick Schumacher23, Daniel Stram23, Sharon A Savage1, Lisa Mirabello1, Irene Andrulis77,97, Jay Wunder76,97, Ana Patiño García98, Luis Sierrasesúmaga98, Donald A Barkauskas23, Richard G Gorlick99,100, Mark Purdue1, Wong-Ho Chow1, Lee E Moore1, Kendra L Schwartz101, Faith G Davis102, Ann W Hsing1, Sonja I Berndt1, Amanda Black1, Nicolas Wentzensen1, Louise A Brinton1, Jolanta Lissowska103, Beata Peplonska104, Katherine A McGlynn1, Michael B Cook1, Barry I Graubard1, Christian P Kratz1, Mark H Greene1, Ralph L Erickson105, David J Hunter49,87, Gilles Thomas106, Robert N Hoover1, Francisco X Real3,107, Joseph F Fraumeni1, Neil E Caporaso1, Margaret Tucker1, Nathaniel Rothman1, Luis A Pérez-Jurado3,4 ‡, Stephen J Chanock1 ‡*.
‡ L.A.P.J. and S.J.C. are co-last authors.
*Correspondence should be addressed to S.J.C. (chanocks@mail.nih.gov)

1	Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
2	Core Genotyping Facility, SAIC-Frederick, Inc., NCI-Frederick, Frederick, Maryland, USA
3	Departament de Ciències Experimentals i de la Salut, Universitat Pompeu Fabra, E-08003 Barcelona, Spain
4	Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), E-08003 Barcelona, Spain
5	Quantitative Genomic Medicine Laboratory, qGenomics, E-08003 Barcelona, Spain
6	Laboratory of Experimental Immunology, Center for Cancer Research, NCI- Frederick, Frederick, MD, USA
7	Epidemiology Research Program, American Cancer Society, Atlanta, Georgia, USA
8	Department of Chronic Disease Prevention, National Institute for Health and Welfare, Helsinki, Finland
9	Department of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN, USA
10	University of Pittsburgh Cancer Institute, Pittsburgh, PA, USA
11	Shanxi Cancer Hospital, Taiyuan, Shanxi, People’s Republic of China
12	Department of Epidemiology, Cancer Institute (Hospital), Chinese Academy of Medical Sciences, Beijing, People’s Republic of China
13	Shanghai Cancer Institute, Shanghai, People’s Republic of China
14	Saw Swee Hock School of Public Health, National University of Singapore, Singapore
15	Department of Thoracic Surgery, Vanderbilt University School of Medicine, Nashville, Tennessee, USA
16	Division of Epidemiology in the Department of Medicine, Vanderbilt Epidemiology Center,Nashville, Tennessee,USA
17	Department of Epidemiology, Division of Cancer Prevention and Population Sciences, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
18	International Epidemiology Institute, Rockville, Maryland 20850, USA
19	Karmanos Cancer Institute,  Wayne State University, Detroit, Michigan, USA
20	Department of Oncology, Wayne State University School of Medicine, Detroit, Michigan, USA
21	Division of Cancer Control and Population Sciences, National Cancer Institute, Bethesda, Maryland 20892, USA
22	Laboratory of Human Carcinogenesis, Center for Cancer Research, National Cancer Institute, Bethesda, Maryland 20892, USA
23	Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, California 90033, USA
24	Department of Pathology , Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, California 90033, USA
25	Epidemiology Program, University of Hawaii Cancer Center, Honolulu, Hawaii 96813, USA
26	Department of Health Disparities Research, Division of OVP , Cancer Prevention and Population Sciences, The University of Texas MD Anderson Cancer Center, Houston, Texas , USA
27	Center for Community Implementation and Dissemination Research , Duncan Family Institute, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
28	Department of Public Health Sciences, Henry Ford Hospital, Detroit, Michigan 48202, USA
29	Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, Tennessee 37203,USA
30	Dan L Duncan Cancer Center, Baylor College of Medicine, Houston, TX, USA
31	Department of Neurological Surgery, University of California San Francisco , San Francisco, California 94158, USA
32	Division of Genetics and Epidemiology, Institute of Cancer Research, London, UK
33	Genetics and Molecular Epidemiology Group, Centro Nacional de Investigaciones Oncológicas (CNIO), Madrid, Spain
34	Maine Cancer Registry, Augusta, Maine, USA
35	Vermont Cancer Registry, Burlington, Vermont, USA
36	Department of Occupational and Environmental Health , University of Milan, Milan, 20122, Italy 
37	Unit of Epidemiology, Fondazione Istituto di Ricevero e Cura a Carattere Scientifico (IRCCS), Ospedale Maggiore Policlinico, Mangiagalli e Regina Elena, Milan, 20122, Italy 
38	Department of Radiation Sciences, Oncology, Umeå University, Umeå, Sweden
39	Clinical and Translational Epidemiology Branch, Division of Cancer Control and Population Sciences, National Cancer Institute, Bethesda, MD, USA
40	Department of Ambulatory Care and Prevention, Harvard Medical School, Boston, MA, USA
41	Division of Preventive Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
42	National Institute for Occupational Safety and Health, Centers for Disease Control and Prevention, Cincinnati, OH, USA
43	Division of Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
44	Division of Aging, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
45	Massachusetts Veteran's Epidemiology, Research and Information Center, Geriatric Research Education and Clinical Center, VA Boston Healthcare System, Boston, MA, USA
46	Cancer Epidemiology Centre, The Cancer Council of Victoria, Melbourne, Australia
47	Centre for Molecular, Environmental, Genetic, and Analytic Epidemiology, University of Melbourne, Melbourne, Australia
48	Department of Public Health and Clinical Medicine, Umeå University, Umeå, Sweden
49	Channing Laboratory, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
50	Department of Oncology, Karolinska University Hospital, SE-171 76 Stockholm, Sweden
51	Institute of Cancer Epidemiology, Danish Cancer Society, Copenhagen, Denmark
52	Division of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
53	Department of Epidemiology, Division of Biology and Medicine, Brown University, Providence, RI, USA
54	Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
55	Department of Epidemiology, University of Washington,  Seattle, WA, USA
56	Department of Epidemiology,  Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA 
57	Division of Nutritional Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
58	Division of Epidemiology, Department of Environmental Medicine, NYU School of Medicine, New York, NY  
59	National School of Public Health, Athens, Greece
60	Municipal Institute of Medical Research, Barcelona, Spain
61	Unidad de Genética, Facultad de Ciencias Experimentales y de la Salud, Universitat Pompeu Fabra, Barcelona, Spain
62	Universidad de Oviedo, Oviedo, Spain
63	Centre for Research in Environmental Epidemiology (CREAL), Barcelona, Spain. 
64	Institut Municipal d’Investigació Mèdica (IMIM), Barcelona, Spain 
65	Department of Gastrointestinal Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
66	Catalan Institute of Oncology (ICO), Institut d'Investigació Biomèdica de Bellvitge (IDIBELL ), Barcelona, Spain
67	Yale University School of Public Health, New Haven, CT, USA
68	Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, New York, NY, USA
69	Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
70	Department of Medicine, Baylor College of Medicine, Houston, TX, USA
71	Information Management Services, Silver Spring, MD, USA
72	Department  of Obstetrics and Gynecology, New York University School of Medicine, New York, NY, USA
73	Department of Environmental Medicine, New York University School of Medicine; New York, NY, USA
74	New York University Cancer Institute, New York, NY, USA
75	Department of Gastroenterology and Hepatology, University Medical Center Utrecht, Utrecht, The Netherlands
76	National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands 
77	Fred. A. Litwin Centre for Cancer Genetics, Samuel Lunenfeld Research Institute, Toronto, Ontario, Canada
78	Department of Laboratory Medicine and Pathology, School of Medicine, University of Minnesota, Minneapolis, MN, USA
79	Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, USA
80	Department of Oncology, the Johns Hopkins University School of Medicine, Baltimore, MD, USA 
81	Group Health Center for Health Studies, Seattle, WA, USA
82	Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
83	Institut National de la Santé et de la Recherche Médicale (INSERM), Paris-Sud University, Institut Gustave-Roussy, Villejuif, France
84	Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
85	Comprehensive Digestive Disease Center, University of California, Irvine Medical Center, Orange, CA, USA
86	Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
87	Department of  Epidemiology, Harvard School of Public Health, Boston, MA, USA
88	Department of Nutrition, Harvard School of Public Health, Boston, MA, USA
89	Department of Medicine, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
90	Department of Pathology, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
91	International Agency for Research on Cancer (IARC/WHO), Lyon, France
92	Department of Public Health and Primary Care, Clinical Gerontology, Addenbrooke's Hospital, University of Cambridge, Cambridge, UK
93	Nutritional Epidemiology Unit, Fondazione Istituto Di Ricovero e Cura a Carattere Scientifico (IRCCS), Istituto Nazionale dei Tumori, Milan, Italy
94	Department of Medicine, Memorial Sloan-Kettering Cancer Center, New York, NY, USA
95	Division of Medical Oncology, Mayo Clinic, Rochester, MN, USA
96	Bureau of Epidemiologic Research, Academy of Athens, Athens, Greece
97	Mount Sinai Hospital Christopher Sharp Centre for Surgery and Oncology, Toronto, Ontario, Canada
98	Clínica Universidad de Navarra, E31080 Pamplona, Spain
99	Department of Molecular Pharmacology , Albert Einstein College of Medicine of Yeshiva University, Bronx, NY, USA
100	Department of Pediatrics, Albert Einstein College of Medicine of Yeshiva University, Bronx, NY, USA
101	Department of Family Medicine and Public Health Sciences, Wayne State University, MI, USA
102	Division of Epidemiology and Biostatistics, School of Public Health, University of Illinois at Chicago, Chicago, Illinois, USA
103	Maria Sklodowska-Curie Memorial Cancer Center and Institute of Oncology, Warsaw, Poland
104	Nofer Institute of Occupational Medicine, Lodz, Poland
105	Walter Reed Army Institute of Research, Silver Spring, MD, USA
106	Synergie-Lyon-Cancer, Universite Lyon 1, Centre Leon Berard, Lyon, France
107	Epithelial Carcinogenesis Group, Centro Nacional de Investigaciones Oncológicas (CNIO), Madrid, Spain

Abstract (169)
In an analysis of 31,717 cancer cases and 26,136 cancer-free controls drawn from 13 genome-wide association studies (GWAS), we observed large chromosomal abnormalities in a subset of clones from DNA obtained from blood or buccal samples.  Mosaic chromosomal abnormalities, either aneuploidy or copy-neutral loss of heterozygosity, of size >2 Mb were observed in autosomes of 517 individuals (0.89%) with abnormal cell proportions between 7% and 95%. In cancer-free individuals, the frequency increased with age; 0.23% under 50 and 1.91% between 75 and 79 (p=4.8×10-8). Mosaic abnormalities were more frequent in individuals with cancer (0.97% versus 0.74% in cancer-free individuals, OR=1.25, p=0.016), with a stronger association in cases who had DNA collected prior to diagnosis or treatment (OR=1.45, p=0.0005). Detectable clonal mosaicism was more frequent in individuals diagnosed with leukemia when DNA was collected at least one year prior to diagnosis compared to cancer-free individuals (OR=35.4, p=3.8×10-11). These findings underscore the importance of the role and time-dependent nature of somatic events in the etiology of cancer and other late-onset diseases.
Introduction 
Classically, genetic mosaicism is defined as the co-existence of cells with two or more distinct karyotypes within an individual that results from a post-zygotic event during development and can occur in both somatic and germline cells1,2. Errors in chromosomal duplication and subsequent transmission to daughter cells may lead to aneuploidy, the gain or loss of chromosomes or segments of chromosomes, and reciprocal gain and loss events manifesting in copy-neutral loss of heterozygosity (cnloh) or uniparental disomy. Somatic mosaicism has been established as a cause of miscarriage, birth defects, developmental delay, and cancer3-9. Because mosaicism can be benign or may manifest with diverse clinical phenotypes, no accurate estimates of its frequency exist for the general population3,6. On rare occasions the propensity to develop chromosomal abnormalities is inherited and leads to multiple phenotypic abnormalities including cancer predisposition as reported in families with mutations in BUB1B and CEP57 10,11. Recently, two groups have identified somatic mosaic mutations in IDH1 and IDH2 in tumors of individuals with Ollier disease and Maffucci syndrome12,13 while another group has characterized somatic mosaicism of HRAS in an individual with urothelial cancer and epidermal nevus14. Recent work in a population of twins has suggested that the occurrence of de novo structural variants is related to age and may be related to reduction in blood cell clonality15.  In this report, we define mosaic chromosomal abnormalities broadly: the presence of large structural genomic events resulting in alteration of copy number or loss of heterozygosity in distinct and detectable subpopulations of cells regardless of the clonal or developmental origin of the subpopulations. We report only autosomal chromosomal abnormalities, as the analysis of the sex chromosomes presents distinct technical and interpretative challenges.
Recently, we reported on 1,991 individuals from the Spanish Bladder Cancer/EPICURO population-based case-control study in which we had performed a GWAS of adult-onset bladder cancer using DNA obtained from blood or buccal samples16. The SNP array data generated for the GWAS was subsequently used to detect clonal mosaic abnormalities in the autosomes in 1.7% of study subjects, suggesting a higher frequency in adults than previously suspected. Even though somatic mosaicism has been implicated in several cancers, this study did not reveal a significant difference in frequency between cases and controls. A computational algorithm was used to detect 42 large mosaic events involving two or more distinct clones in DNA extracted from blood or buccal samples and we experimentally validated the findings using multiplex ligation-dependent probe amplification (MLPA) and microsatellite analysis (as well as fluorescent in situ hybridization in a subset), establishing the robustness of the software detection method. A similar proportion of cells carrying each event was found in 5 of 6 events (in four individuals with bladder cancer in whom three had one event and one individual with three separate events) in which it was possible to examine more than one tissue (whole blood and bladder mucosa), suggesting an early embryonic origin of the somatic mutation leading to the observed mosaic chromosomal abnormalities16. 

Results

In this report, we extend our analysis of clonal mosaic abnormalities in the autosomes of 57,853 individuals (including those previously published16). We tested 31,717 cancer cases and 26,136 cancer free controls for evidence of mosaic abnormalities using genome-wide SNP array data generated as part of 13 distinct cancer GWAS drawn from 48 epidemiological case-control and case-cohort studies (Supplementary Table 1). DNA samples were extracted from blood or buccal samples using a variety of collection and extraction techniques and genotyped using one or more Infinium Human SNP arrays from Illumina Inc. (including versions of Hap300, Hap240, Hap550, Hap610, Hap660, Hap1, Omni Express, and Omni1). Genotype clusters were empirically estimated in 45 batches to optimize accuracy while minimizing potential batch effects (Online Methods). 
Detection of clonal mosaic events was based on assessment of allelic imbalance and copy number changes. We used the B-allele frequency (BAF) measurement, derived from the ratio of probe values relative to the locations of the estimated genotype-specific clusters, for initial segmentation using the Mosaic Alteration Detection (MAD) algorithm implemented in GADA-R with modifications17,18. The BAF and log2 relative probe intensity ratio (LRR), which provides data on copy number, were used to classify each event as copy-altering (gain or loss) or neutral (reciprocal gain and loss resulting in loss of heterozygosity, LOH) and to assign the proportions of abnormal (p) and normal (1-p) cells. Mosaic proportions were required to deviate from levels expected from constitutional (non-mosaic) changes in order to exclude homozygous chromosomal segments inherited identical by descent and non-mosaic instances of trisomy, monosomy and uniparental disomy. A minimum event size threshold was set to detect only clonal mosaic events greater than 2 Mbps to minimize the false discovery of constitutional copy number variants. Copy-neutral LOH and copy-loss events could be detected for mosaic proportions between 7% and 95% (Figure 1) with sensitivity that was affected by the signal-to-noise ratio characteristic of each microarray assay and sample quality. There was reduced sensitivity to distinguish between copy-neutral LOH and copy-loss events for mosaic proportions less than 15% across the autosomes. The magnitude of BAF differences for single-copy gain events was 1/3 of the magnitude of copy-neutral LOH or copy-loss events, reducing the sensitivity for calling copy-gain events. As a result, single copy gain events could only be reliably detected for mosaic proportions between 22% and 88%, with ambiguity in distinguishing copy-gain from copy-neutral LOH for mosaic proportions of less than 20%.  Since DNA was obtained for the purpose of performing a GWAS, it was not possible to further explore the developmental and clonal characteristics of mosaic events detected in these individuals (e.g. by studying DNA from fractionated blood and other tissue types, determining cell composition of buccal samples, or effect of DNA collection and extraction methods on detection and accuracy of the estimation of mosaic proportions).
We observed 681 mosaic segments of size greater than 2 Mb on 641 autosomal chromosomes in 517 individuals for an overall frequency of individuals with mosaicism of 0.87% (Tables 1 and 2). Supplementary Table 2 includes the count of mosaic events by chromosome and event type. The most frequent type of event observed was copy-neutral LOH (48.2%), while copy-gains and copy-losses were observed for 15.1% and 34.8% of mosaic events, respectively (Table 1). A small proportion (1.9%) of mosaic chromosomes were complex, harboring more than one type of event. 18.7% of mosaic chromosomal events spanned the entire chromosome, including 62 complete trisomies, predominantly in chromosomes 8, 12 and 15 (Supplementary Table 2). 47.9% of mosaic chromosomal events began at a telomere and extended across some portion of the chromosomal arm (Table 1 and Figure 2). The majority of telomeric events were mosaic copy-neutral LOH (85.7%), most frequently on 9p (Supplementary Table 2). The remainder of mosaic chromosomal events were interstitial (31.5%) spanning neither telomere nor centromere, while an additional small proportion (1.8%) spanned the centromere or had more complex structure (e.g. distinct events involving both telomeres, but not the whole chromosome). The majority of interstitial events were mosaic copy-loss (91.6%), which was most frequently observed within specific regions of chromosomes 13q and 20q (Figure 2 and Supplementary Table 2). We observed 69 individuals (46 cancer cases and 23 cancer-free individuals) with clonal mosaic events on multiple chromosomes. The distribution of the number of clonal mosaic chromosomal events per individual is shown in Supplementary Table 3. Among cancer-free individuals, the greatest number observed was 5 mosaic chromosomal events, whereas six individuals with cancer had greater than 5 events, including two individuals with gastric cancer who each had 20. A list of mosaic events with phenotype data is available as Supplementary Data.
The strongest predictor of mosaic autosomal abnormalities was age at DNA collection. We examined the effect of aging on the frequency of mosaicism across all studies, which were predominantly individuals over the age of 50. The frequency of cancer-free individuals with detectable clonal mosaic events increased with age, from 0.23% for those under 50 to 1.91% (p=4.8×10-8) for those between the ages of 75 and 79, and with slightly higher frequencies for individuals with cancer (Figure 3). In the early onset cancers (under age 40), which constituted less than 5% of analyzed cases (e.g., testicular cancer and osteogenic sarcoma), we did not observe an increase in mosaic abnormalities. Further studies are needed to investigate the relationship between mosaic abnormalities and cancer in children and young adults, particularly because of the strong association between mosaicism and many forms of developmental disorders. There was no apparent relationship between age at DNA collection and the number, size of mosaic events, or the proportion of abnormal cells (Supplementary Figures 1 and 2). 
We regressed the presence of detectable clonal mosaicism in 26,136 cancer-free individuals on age at DNA collection (in 5 year intervals), sex (male versus female), DNA source (buccal cells  versus blood), smoking (ever versus never) and admixture coefficients for African and East Asian ancestry in a logistic model to determine the additional factors that influenced frequency of detectable clonal mosaicism. The source of DNA was known for 87% of individuals, of whom 19% were derived from buccal cells and the remainder from blood. DNA source was not significantly associated with mosaicism (OR=0.83, 0.55-1.26 95% confidence interval (CI), p=0.39). By admixture analysis, 75% of subjects were determined to be of European ancestry, 9% of African ancestry and 16% of East Asian ancestry. Although power was limited, we observed that cancer-free individuals with African admixture were at a lower risk of being mosaic (OR=0.43, 0.20-0.92 95% CI, p=0.03), but not in those with East Asian admixture (OR=0.60, 0.32-1.15 95% CI, p=0.12). We did not observe an association between smoking (ever/never) and frequency of mosaic abnormalities (OR=1.04, 0.75-1.44 95% CI, p=0.81).
 In 26,136 cancer-free controls and 23,093 cancer cases drawn from non-sex specific and non-hematological cancer sites (i.e. excluding 8,470 individuals with leukemia, lymphoma, multiple myeloma and cancers of the breast, endometrium, ovary, testis, and prostate), we observed a higher frequency of males with mosaic abnormalities than females. In cancer-free individuals, we observed mosaic events in 0.56% of females and 0.87% males (OR=1.35, 0.98-1.88 95% CI, p=0.07); for individuals with cancer we observed mosaic events in 0.79% of females and 1.21% of males (OR=1.48, 1.08-2.03 95% CI, p=0.015); and overall, 0.65% of females and 1.04% of males (OR=1.42, 1.14-1.80 95% CI, p=0.002) in logistic models adjusted for cancer diagnosis (if applicable), age at DNA collection, ancestry, DNA source and smoking. These differences could be due to a true sex-specific effect akin to sex-differential mutation and recombination rates19; however the complex and heterogeneous nature of the inclusion of individual studies and the differences in their entry and selection criteria could result in spurious associations. Although this observation was consistent across cancer types, it should be confirmed in additional studies better designed to address this question.
To determine the relationship between detectable mosaic autosomal abnormalities and non-hematological cancers, we regressed the presence of detectable clonal mosaicism on cancer diagnosis, age, sex, DNA source, smoking and ancestry in a logistic model. We observed a modest increase in cancer risk for mosaic individuals (OR=1.27, 1.05-1.52 95% CI, p=0.012) (Tables 2 & Supplementary Table 4). Notable associations were observed in stratified analyses of lung (OR=1.56, 1.18-2.08 95% CI, p=0.002) and kidney (OR=1.98, 1.27-3.06 95% CI, p=0.002) cancers, both tobacco-associated malignancies. However no cancer site-specific associations were observed for bladder, esophagus, stomach and pancreas cancers, which are also typically associated with tobacco use. There was no significant association in non-hematological cancer cases overall between smoking (ever/never) and frequency of mosaicism (OR=1.19, 0.92-1.54 95% CI, p=0.19) or when stratified by cancer site (results not shown).
In an analysis of the subset of 14,050 individuals with cancer for whom it was possible to determine that DNA was likely obtained before or at the time of diagnosis and prior to treatment with radiation or chemotherapy for a primary tumor (designated as “likely untreated”), we observed a stronger association between mosaic abnormalities and non-hematological cancer diagnosis (OR=1.45, 1.18-1.80 95% CI, p=0.0005). The associations for lung and kidney also increased in significance (Table 3). It is notable that the evidence for association with non-hematological cancer diminished in individuals who were potentially treated (OR=1.03, 0.81-1.30 95% CI, p=0.80). We had approached this analysis with the hypothesis that there could be an increased frequency in detectable clonal mosaicism in non-hematological cancers induced by chemotherapy or radiotherapy but were surprised to observe the frequency was reduced to virtually the same as in the cancer-free population. Although this attenuated effect could have many explanations (e.g., related to the diagnosis and treatment of a solid tumor leading to a decrease in populations of cells with mosaic alteration), we had a limited capacity to model and control for treatment-effects since many of the studies did not provide any treatment information or only provided incomplete, retrospective ascertainment of the specifics. Although many of the participating studies were prospectively ascertained cohorts, DNA collection often occurred after cancer diagnosis. Additional studies are needed in prospectively ascertained cohorts and longitudinal studies in which multiple DNA samples were collected prior to and after diagnosis in order to explore treatment and disease effects. 
For the 43 individuals with hematological cancers for whom DNA was obtained at least a year prior to diagnosis, the frequency of detectable clonal mosaicism was 15.8% for myeloid leukemia and 26.3% for lymphocytic leukemia (predominantly chronic lymphocytic leukemia, Table 2) compared to 0.75% in 26,136 cancer free controls (overall OR=35.4, 14.7-76.6 95% CI, Fisher exact p=3.8×10‑11). Of the 9 mosaic individuals with leukemia for whom DNA samples were collected prior to diagnosis, 5 were diagnosed with chronic lymphocytic leukemia (CLL) of which 4 had a mosaic deletion in a region of chromosome 13q14 previously described to be deleted in CLL20. DNA was obtained more than 5 years prior to diagnosis for 6 mosaic individuals, with the longest interval being 14 years, suggesting that detectable clonal mosaicism could be a marker of or precursor to hematological cancer or its precursors, i.e., monoclonal B cell lymphocytosis (MBL) for CLL and myelodysplastic syndrome for acute myelogenous leukemia. Recent work shows that the majority of MBL have mono- or bialleleic 13q14 abnormalities21. However, further studies will be needed, preferably with serial samples pre- and post- diagnosis sampling to investigate the predictive nature of detectable clonal mosaicism, especially involving regions of chromosome 13 and 20 with respect to leukemia risk20. 
	We further explored the 4 most recurrent altered regions (>20), which also harbor well known cancer genes (as noted in the COSMIC22 and Mitelman databases: http://cgap.nci.nih.gov/Chromosomes/Mitelman); these were on chromosomes 9p (cnloh), 13q (del), 14 (cnloh) and 20q (del) (Table 4). Notably, the most recurrent mosaic events were observed in cancer-free individuals as well as across multiple solid tumors. We observed a comparable frequency in non-hematologic cancer cases and cancer-free controls for all four regions. The recurrent segmental deletion of 13q14 was observed in 4 leukemia cases, but also in 18 individuals with solid tumors (8 with lung cancer and 5 with prostate cancer), and in 10 cancer-free individuals. This region includes the tumor suppressor gene DLEU7 (Deleted In Leukemia 7) and related genes, DLEU1 and DLEU2, the latter harboring two microRNAs within one of its introns (miR-15a and miR-16-1)23-25. The retinoblastoma gene, RB1 was also included within a subset harboring a mosaic deletion of 13q14. It cannot be ruled out that these individuals have either undiagnosed CLL or MBL. The 20q- was seen in two individuals with myeloid leukemia as has been described previously26 but also in cancer-free and individuals with solid tumors. 
	The accuracy of our software methods to detect clonal mosaic abnormalities was previously addressed and we were able to validate 100% of 42 events in 34 individuals from the Spanish Bladder Cancer Study using confirmatory cytogenetic assays16 (Supplementary Figure 3). We have also performed a comparison of mosaic events in samples from the EAGLE and PLCO lung cancer studies which were independently analyzed as part of the Gene, Environment Association studies consortium (GENEVA) report on mosaic events27.   83 mosaic events in individuals from the EAGLE and PLCO lung study were detected in common, 20 additional events of size less than 2 Mb and 8 events greater than 2 Mb were detected by GENEVA and not by our study, while we detected 20 additional events (size > 2 Mb) that were not detected by GENEVA. Although additional cytogenetic or molecular validation was not performed, neither method detected notable false-positive events based on manual review of the data. The concordance rate is 75% if considering events > 2 Mb (the cut-off for this analysis) or 63% if considering all events, both of which are considerably better than the 25-50% concordance rates observed across CNV detection methods28-30. Our method is more conservative in the size of events detected, while the GENEVA method is more conservative with respect to sample quality, but provides calls for smaller events when assay quality is sufficient. Better approaches are needed to characterize smaller size events accurately as either mosaic or constitutional and to estimate their frequency. Further improvements to data normalization, segmentation and event classification methods will also likely reduce false-negative rates. 	

Discussion

	 Our study has important implications for the design and analysis of molecular epidemiology studies in cancer as well as the somatic characterization of cancer genomes, like The Cancer Genome Atlas31 and International Cancer Genome Consortium32. Investigators will need to carefully analyze samples used as exemplars of germline DNA for somatic alterations, such as detectable clonal mosaicism. Otherwise, comparisons between “germline” and tumor DNA may result in implausible somatic changes (e.g. large gains of heterozygosity) and it may be impossible to determine whether somatic events pre-date changes secondary to driver mutations. Since how to detect mosaic events with next generation sequencing technologies is neither routine nor well understood, for the near future it may be prudent to continue to utilize SNP microarrays for such analyses. Due to the increased frequency of detectable clonal mosaicism with age, this will be particularly important for the analysis of epithelial cancers, which characteristically occur in the older population. For future large-scale GWAS in prospective studies, it may be wise to consider analyzing the earliest, pre-diagnosis DNA samples and to consider time from collection to diagnosis in the analysis of longitudinally collected biospecimens.
	We have extended our initial observation_ENREF_15 that detectable clonal mosaicism of the autosomes is present in the population with surprising frequency and particularly in the aging genome.  A recent study of detectable clonal mosaicism in twins reported an increase in frequency with age and suggested that this reduction could lead to a less diverse blood cell population and immune system15. These emerging data raise a number of critical issues in mechanisms underlying the possible shift in the repertoire of clones with large structural abnormalities. Thus cells with abnormal karyotypes could have an early developmental origin in which a somatic event in a single stem cell progenitor during embryogenesis could become apparent when cellular diversity decreases with age and cell populations become increasingly oligoclonal. Higher rates of detectable clonal mosaicism in older cancer-free individuals could also be due to increased rates of somatic mutation or diminished capacity for genomic maintenance, such as with telomere attrition33 leading to proliferation of somatically altered cell populations. A survival bottleneck of cellular progenitors could also lead to observable mosaic alterations that were previously below the threshold of detection but subsequently expanded due to positive selection. Further work is required to begin to unravel the underlying mechanisms that result in mosaic abnormalities, particularly as it relates to how and when altered clones are created, tissue-specificity, and the timing and expansion of distinct populations of cells with age. Finally, these findings underscore the importance of considering the role and time-dependent nature of somatic events in the etiology of cancer as well as other late-onset diseases. 

Acknowledgments: Support for each contributing study is listed in the Supplementary Acknowledgement Section. We thank Cathy Laurie and Bruce Weir for constructive discussion and a comparison of methodology and results for the GENEVA study. The findings and conclusions in this report are those of the authors and do not necessarily represent the views of the National Cancer Institute, the National Institute for Occupational Safety and Health, or the Maryland Cancer Registry.

URLs cited:
http://cgap.nci.nih.gov/Chromosomes/Mitelman : Mitelman, F., Johansson, B. & Mertens, F. (eds.). Mitelman Database of Chromosome Aberrations and Gene Fusions in Cancer, (2011)

Figure Legends:
Figure 1. Characteristics of detectable clonal mosaic events 
Detectable clonal mosaic events plotted by proportion of abnormal cells (p) and Log R Ratio (LRR) for 681 events in 517 individuals. 

Figure 2. Circular genomic plot of detectable clonal mosaic events
Genomic location of detectable clonal mosaic events. Yellow region denotes events of copy neutral loss of heterozygosity. Blue region denotes copy gain events. Red region denotes copy loss events. Panel A includes events in cancer free controls. Panel B includes events in cancer cases.

Figure 3. Frequency of detectable clonal mosaic events by age and cancer status 
Analysis excluded 1,000 individuals with unknown age at DNA collection.

Supplementary Items

Supplementary Acknowledgements

Supplementary Table 1. Description and references for 48 participating studies

Supplementary Table 2. Count of detectable clonal mosaic events by chromosome 

Supplementary Table 3. Count of mosaic chromosomal events per individual. 

Supplementary Table 4.  Association between non-hematologic cancer and detectable clonal mosaicism adjusted for age, sex, ancestry, DNA source, and smoking

Supplementary Figure 1. Distribution of the size of events by age and event type

Supplementary Figure 2. Distribution of mosaic proportion by age and event type

Supplementary Figure 3. Variance of log2 intensity ratio (LRR) before and after normalization procedure for one cluster group of Illumina HumanHap660 assays.

Supplementary Data. Anonymized listing of coordinates for all detectable clonal mosaic events and example plots for each event type.

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Table 1. Count and frequency of mosaic chromosomal events by event type and location

		Mosaic Chromosome Count		Mosaic Chromosome Frequency (%)	
Event Location	gain	loss	cnloh	mixed	Total	 	gain	loss	cnloh	mixed	Total	
chromosome	62	11	42	5	120	 	9.7	1.7	6.6	0.8	18.7	
telomeric P	11	13	114	1	139		1.7	2.0	17.8	0.2	21.7	
telomeric Q	9	10	149	0	168		1.4	1.6	23.2	0.0	26.2	
interstitial	14	185	2	1	202		2.2	28.9	0.3	0.2	31.5	
span centromere	1	1	2	0	4		0.2	0.2	0.3	0.0	0.6	
complex	0	3	0	5	8	 	0.0	0.5	0.0	0.8	1.2	
 	Total	97	223	309	12	641	 	15.1	34.8	48.2	1.9	 	

Table 2. Count and frequency of individuals with detectable clonal mosaic events for cancer-free individuals and by cancer site.
Non-hematological cancers are listed by first cancer site and exclude anyone diagnosed with a hematological cancer, shown separately.
 	Mosaic Counts	 	Non-Mosaic Counts	 	Mosaic Frequency (%)	
Site of first cancer	Likely Untreated	Possibly Treated	Total	 	Likely Untreated	Possibly Treated	Total	 	Likely Untreated	Possibly Treated	Overall	
overall*	 	 	498	 	 	 	57,201	 	 	 	0.87	
cancer-free	 	 	194	 	 	 	25,942	 	 	 	0.75	
non-hematologic cancer	185	119	304	 	13,865	17,394	31,259	 	1.32	0.68	0.96	
bladder	37	6	43		2,240	973	3,213		1.62	0.61	1.32	
breast	4	8	12		1,060	1,753	2,813		0.38	0.45	0.42	
endometrium	3	6	9		247	624	871		1.20	0.95	1.02	
esophagus	1	6	7		53	1,855	1,908		1.85	0.32	0.37	
glioma	7	2	9		1,279	441	1,720		0.54	0.45	0.52	
kidney	21	3	24		1,241	325	1,566		1.66	0.91	1.51	
lung	73	26	99		4,647	2,605	7,252		1.55	0.99	1.35	
osteosarcoma	0	3	3		0	760	760			0.39	0.39	
ovary	1	3	4		260	283	543		0.38	1.05	0.73	
pancreas	2	29	31		379	3,513	3,892		0.52	0.82	0.79	
prostate	32	11	43		2,116	1,410	3,526		1.49	0.77	1.20	
stomach	2	13	15		99	2,194	2,293		1.98	0.59	0.65	
testis	2	0	2		144	503	647		1.37	0.00	0.31	
other sites	0	3	3	 	100	155	255	 	0.00	1.90	1.16	
hematologic cancer	9	9	18	 	34	62	96	 	20.93	12.68	15.79	
leukemia	9	8	17		34	11	45		20.93	42.11	27.42	
lymphocytic	5	4	9		14	5	19		26.32	44.44	32.14	
myeloid	3	4	7		16	5	21		15.79	44.44	25.00	
other/nos	1	0	1		4	1	5		20.00	0.00	16.67	
lymphoma	0	1	1		0	42	42			2.33	2.33	
multiple myeloma	0	0	0	 	0	9	9	 	 	0.00	0.00	
* overall total of cancer-free individuals and those with non-hematologic cancers

Table 3. Distribution and frequency of recurrent detectable clonal mosaic events
First cancer site	Mosaic Counts		Mosaic Frequency	
	13q (del)	20q (del)	9p (cnloh)	14q (cnloh)	other		13q (del)	20q (del)	9p (cnloh)	14q (cnloh)	other	
Cancer Free	10	30	22	6	126		5%	15%	11%	3%	64%	
Cancer DX	22	46	18	26	206		7%	14%	6%	8%	65%	
non-hematologic												
bladder	2	4	1	7	29		5%	9%	2%	16%	67%	
breast	1	1	0	1	10		8%	8%		8%	77%	
endometrium	0	1	2	1	5			11%	22%	11%	56%	
esophagus	0	1	1	1	4			14%	14%	14%	57%	
glioma	0	2	2	0	5			22%	22%		56%	
kidney	1	3	0	3	17		4%	13%		13%	71%	
lung	8	13	5	7	66		8%	13%	5%	7%	67%	
osteosarcoma	0	0	1	0	2				33%		67%	
ovary	0	2	0	0	3			40%		0%	60%	
pancreas	1	6	1	1	22		3%	19%	3%	3%	71%	
prostate	5	9	4	1	27		11%	20%	9%	2%	59%	
stomach	0	1	1	1	12			7%	7%	7%	80%	
testis	0	0	0	0	2						100%	
other sites	0	1	0	1	2			25%		25%	50%	
hematologic cancer												
leukemia	4	2	0	2	5		33%	17%		17%	31%	
lymphoma	0	0	0	0	1						100%	
Overall	32	76	40	32	332		6%	15%	8%	6%	65%