### DEVELOPMENT OF A RISK MODE L FOR CHRONIC LYMPHOCYTIC LEUKEMIA FOR NIOSH-IREP

January 5, 2010

This document was prepared by J. R. Trabalka and A. I. Apostoaei of SENES Oak Ridge, Inc., under NIOSH contract 200-2006-18097. This final version has been slightly edited by NIOSH during the internal review process.CLL Risk Model for NIOSH-IREP January 5, 2010 Final

2CLL Risk Model for NIOSH-IREP January 5, 2010 Final

3 TABLE OF CONTENTS

1.0 PURPOSE AND APPROACH ............................................................................................3 2.0 BACKGROUND .................................................................................................................4 3.0 CHARACTERISTICS, CLINICAL FE ATURES, AND ORIGINS OF CLL .....................6 3.1 What is CLL? ............................................................................................ ...............6 3.2 Origins and Etiology .................................................................................... ............6 3.3 Ch aracteristics and Relationship to SLL ................................................................. .8 3.4 Implications for Assigning a Target Organ for Dose Reconstruction .....................9 4.0 CLASSIFICATION AND DIAGNOSIS OF CLL ............................................................11 5.0 DEVELOPMENT OF A RISK MODEL FOR CLL .........................................................13 5.1 Selection of Risk Coefficients ...............................................................................13 5.2 Concerns Associated with Use of the Lymphoma and Multiple Myeloma Model ......................................................................................15 5.3 Handling of Risk Transfer Between Populations ...................................................19 5.4 Latency ...................................................................................................................20 5.4.1 Handling of Latency in the CLL Model ..................................................22 5.5 Sample Results from the Proposed CLL Model ....................................................24 6.0 DOSE RECONSTRUCTIONS FOR NU CLEAR ENERGY WORKERS FOR USE WITH THE CLL MODEL ...............................................................................26 6.1 Background ............................................................................................................26 6.2 Challenges in Estimating Meaningful Doses for Use with the IREP CLL Model .............................................................................................29 7.0 CONCLUSIONS.......................................................................................................... ......34 8.0 REFERENCES .......................................................................................................... ........35

ACKNOWLEDGEMENTS ...........................................................................................................45
APPENDIX A Age and Gender Dependence of CLL Incidence Rates in the U.S. and Japan ......46
APPENDIX B Additional Background on Etiology of CLL .........................................................52
APPENDIX C Assessment of Potential Latency for CLL, Lymphomas, and Multiple Myeloma ...................................................................................... ...........55CLL Risk Model for NIOSH-IREP January 5, 2010 Final

4 1. PURPOSE AND APPROACH

SENES Oak Ridge, Inc. was asked by the Offi ce of Compensation Analysis and Support (OCAS) of the National Institute of Occupational Safety and Hea lth (NIOSH) to investigate the possibility of developing a radiogenic risk model for chronic lymphocytic leukemia (CLL) for potential inclusion in the com puter program NIOSH-IREP. Adaptation of the risk model for the lymphoma and multiple myeloma grouping in NIOSH -IREP was considered to be an option for a
CLL risk model because CLL is now classified as a form of non-Hodgkin’s lymphoma (NHL). The lymphoma and multiple myeloma model in NIOSH-IREP is based primarily on Japanese atomic-bomb survivor data for non-Hodgkin’s Lymphoma (NHL), but also on data for Hodgkin’s disease (also a lymphoma) and multiple myeloma (ICD-9 codes 200

− 203) (Land et al. 2003; UNSCEAR 2008). This model has been used by the Veteran’s Administration (VA) under the atomic veterans compensation program to assess the probability of causation (PC) of CLL occurring in military personnel who were exposed to radiation during te sts or uses of atmos pheric nuclear weapons. Currently, the PC of CLL occurring in these milita ry personnel is estimated using the highest of the reconstructed doses to one of three orga ns: red bone marrow, spleen, or thymus. The rationale behind the VA’s approach is that since CLL is now consid ered to be a form of NHL,
red bone marrow is not the sole target organ (see more below). We reviewed key papers on the epidemiological , molecular, and clin ical basis of CLL, including, but not limited to those cited by Richardson et al. (2005), by John Boice (in his January 7, 2005, response to NIOSH/OCAS on reconsideration of CLL for purposes of compensation; Boice 2005), in the draft NI OSH Annotated Bibliography for CLL (NIOSH 2004), Silver et al. (2007), the CLL Issue of the British Journal of Haematology (Volume 135 , No. 5, 2007), and by the BEIR VII committee (N RC 2006). The latter included an additional paper (Hall et al. 1992) that exam ined dose-responses and latency fo r CLL in patients exposed to I-131 in diagnostic or therapeutic procedures. We also compiled information on sex and agespecific background incidence rates for CLL from the SEER registry for the U.S. population (SEER 2005) and from the IARC data bases for the Japanese populations (Parkin et al. 1997) (Appendix A). We also evaluated data potentially beari ng on the issue of la tency of radiogenic CLL from epidemiological studies.CLL Risk Model for NIOSH-IREP January 5, 2010 Final

5 The evaluated data was used to create a risk model for CLL by modifying the risk model for lymphoma and multiple myeloma obtained from an evaluation of the Japanese atomic-bomb survivor data from the Life-Span Study (LSS) cohort by Land et al. (2003) to include an extended latency period with greater uncertainty than that currently used in IREP. Alternatives to the risk transfer module used for lymphoma and multiple myeloma in IREP were considered but rejected because of limitations of extant epidemiol ogical studies. This risk model is proposed to be used on an interim basis, at least until a m odel based on DS02 data for incidence of NHL in
the LSS cohort becomes available.

2. BACKGROUND

The occurrence of lymphomas, and of CLL in particular, has not been convincingly linked to radiation exposure (see, e.g., IARC 2000; Pr eston et al. 1994; Boice 2002, 2005; NIOSH 2004; NRC 2006; Cardis et al. 2007; Schubauer-Beri gan et al. 2007; Silver et al. 2007; Hamblin 2008; UNSCEAR 2008; Vrijheid et al . 2008; El Ghissassi et al. 2009) . An inability to discern a statistically significant increased risk for CLL fr om radioepidemiological studies had led to the widespread conclusion that CLL is not radi ogenic (Boice 2002, 2005). However, it has been argued that a number of factors (including diagnostic misclassifi cation; see Sect. 4), coupled with limited follow-up periods in epidemiological studies and the possibility of an extended latency period for induction of CLL, have conf ounded previous attempts to determine whether CLL is radiogenic (Crowther 2004; Richardson 2 004; Richardson et al. 2005; Schubauer-Berigan et al. 2007; Silver et al. 2007; Hamblin 2008). In addition, others have argued that, despite the
current lack of evidence from epidemiological studies, mechanistic evidence from genetic and molecular studies indicates that CLL should, in principle, be inducible by radiation exposure (Crowther 2004; Richardson 2004; Richardson et al. 2005; Zablotska 2004; Rai 2007a). These arguments appear to be supported by a variety of new information, including the recent demonstration by Richardson et al. (2009) of a significant positive association between NHL and ionizing radiation in th e Japanese atomic bomb survivors based on extended follow-up. Suggestive but as yet inconclusive findings in a number of recent epidemiological studies
indicate that the evidence for C LL as a radiogenic disease is gr owing (see, e.g., Linet et al. 2005; Rericha et al. 2006; Schubauer-Berigan et al. 2007; Abramenko et al. 2008; Hamblin 2008;CLL Risk Model for NIOSH-IREP January 5, 2010 Final

6 Kesminiene et al. 2008; Romanenko et al. 2008). Although these stud ies do not currently provide the basis for a radiation risk model for CLL, and contrary findings (i.e., no significant association between radiation and CLL) have been reported from recent comprehensive studies of radiation workers (Vrijheid et al. 2008; Muirhead et al. 20 09), we agree with Hamb lin’s (2008) conclusion that “[i]rradiation may have been given a clean bi ll of health with respec t to CLL with less than adequate evidence.”
Data on cancer incidence in the LSS cohort from 1950 through 1987 (Preston et al. 1994) provided the basis for development of risk m odels for leukemia, lymphomas, and multiple myeloma used in IREP (Land et al. 2003). However, only 4 cases of CLL were reported in the exposed and unexposed groups combined; this numb er was too small to develop a meaningful
risk model for CLL.
The incidence of CLL in the Ja panese population is very low a nd CLL is primarily a disease of old age in both the U.S. and Japan (Appendix A). Using the age- and sex-specific incidence rates for CLL in the Japanese population as a w hole in Table A.1, combined with information on age at exposure, sex, vital status, and overall population size for the LSS cohort from Fig.1 in Preston et al. (1994), we estimated that about one case of CLL (0.76; 95% C.I. 0.60, 0.93) might have been expected in the LSS cohort over the 37-y period from 1950

− 1987, in the absence of radiation exposure. A similar estimate based on cancer incidence data for Hiroshima and Nagasaki in Table A.2 was slightly higher ( 1.2 cases; 95% C.I. 0.78, 1.76). Our estimate of the expected number of cases of CLL in unexposed pe rsons is thus about one-fourth of the number
observed in the LSS cohort as a whole. Although this comparison suggests that there could be a positive association between
radiation and incidence of CLL in the LSS cohort, despite the small number of cases involved, the published information on the LSS cohort doe s not provide data on radiation doses or characteristics of the exposed individuals (gen der, age at exposure, or attained age) who developed CLL. Thus, we are prevented from dr awing conclusions about the radiogenicity of CLL from this comparison. From an epidemiological perspective, we can only conclude that we currently do not have scientific evidence from the LSS data to say that CLL is radiogenic, not
that there is no risk of radiat ion-induced CLL in the LSS cohor t or in occupationally exposed persons (also see Zablotska 2004; Rai 2007a; Hamblin 2008).CLL Risk Model for NIOSH-IREP January 5, 2010 Final

7 3. CHARACTERISTICS, CLINICAL FEATURES, AND ORIGINS OF CLL

3.1 What is CLL?

Chronic lymphocytic leukemia is a diso rder of morphologically mature but less immunologically mature lymphocytes and is mani fested by progressive accumulation of these cells in the blood, bone marrow, and lymphatic tissues (Chiorazzi et al. 2005; NCI 2009b). It appears etiologically and clinically to be a lymphoma and thus differs from other forms of leukemia (Harris et al. 1999; Boi ce 2005; NCI 2009a). Despite its name, it is considered to be a form of NHL by the U.S. National Cancer In stitute (NCI 2009a) a nd the World Health Organization (Harri s et al. 1999). The NHLs are a heterogeneous group of lym phoproliferative maligna ncies with differing patterns of behavior and responses to treatment. They can be divided into two prognostic groups: the indolent lymphomas and the aggressive lymphomas. Indolen t types have a relatively good prognosis, with median survival times post-diag nosis as long as 10 year s (e.g., most cases of CLL), but they are usually not curable in advanced clinical stages. In c ontrast, the aggressive types of NHL have a shorter natural history, but a significant number of patients can be cured with intensive combination chemotherapy regimens (NCI 2009a).

3.2 Origins and Etiology

CLL is now viewed as a disease originating fr om antigen-stimulated, mature B lymphocytes, which either avoid death through th e intercession of external signa ls or die by apoptosis, only to be replenished by proliferating precursor cells (Keating et al . 2003; Stevenson and CaligarisCappio 2004; Chiorazzi et al. 2005; Rai 2007b; Zent 2007; Table 1). Although the weight of evidence points to disr uption of apoptosis (p rogrammed cell death) as an early initiating event in some lympho mas (e.g., Voutsadakis 2000), the genes responsible for the origin and progression of the majority of cases of CLL remain unknown (Voutsadakis 2000; Stilgenbauer et al. 2002; Dewa ld et al. 2003) and the current view is that an inherent apoptotic defect is not the initiating lesion in CLL (Tab le 1; also see Rai 2007b).CLL Risk Model for NIOSH-IREP January 5, 2010 Final

8 Table 1. Comparison of Historical and Current Views of CLL

Historical view Current view a
Clinically heterogeneous disease with homogeneous cellular origin Clinically heterogeneous disease originating from B lymphocytes that may differ in activation, maturation state, or cellular subgroup
Disease derived from naive B lymphocytes Disease derived from antigen-experienced B lymphocytes that differ in level of immunoglobulin V-gene mutation

Leukemic-cell accumulation occurs because of inherent apoptotic defect involving entire mass of leukemic cells. Inherent apoptotic defect involving entire mass of leukemic cells is un likely at the outset. Leukemic-cell accumulation occurs because of survival signals delivered to subgroup of cells from external environment through variety of receptors (e.g., B-cell receptors and chemokine and cytokine receptors) and their cell-bound and soluble ligands.

Disease of accumulation Disease of accumulation with a higher level of cell proliferation than previously recognized
Prognostic markers identify patients at various risk levels (e.g., low, intermediate, or high in Rai staging categories) with acknowledged heteroge neity in clinical outcomes among patients in low- and intermediate risk categories. New molecular and protein markers identify patients within low- and intermediate risk categories who follow different clinical

courses.

Therapy is based largely on clinical observations and trial-and-error methods. New findings provide clues to discrete targets for developing hypothesis-driven and effective therapeutic agents.

a The current view refers to understanding of CLL as it has evolved during the past 10 years (Source: Chiorazzi et al. 2005; Rai 2007b).

The developmental history of CLL (e.g., the stage in B-cell development in which malignant transformation occurred) is informative both for predicting the course of the disease and for defining the appropriate targ et organ for dose reconstr uctions (see Appendix B).CLL Risk Model for NIOSH-IREP January 5, 2010 Final

9 CLL cases can be divided into two gr oups, depending on the degree of somatic hypermutation of the immunoglobulin V H genes. Cases from the “unmutated” group (defined as having B-CLL cells with V H gene sequences with <2% differences from the germ line cells) have a much more aggressive disease course than those from the mutated group, and represent about 40% of the cases reported in most West ern countries (Chiorazzi et al. 2005; Hamblin 2008). Interestingly, however, gene-expression prof iling studies (Klein et al. 2001; Rosenwald et al. 2001) indicate that unmutated CLL cells do no t differ from mutated CLL cells in a large number of differentially expresse d genes, suggesting the possibility of a common origin for both.

3.3 Characteristics and Relationship to SLL

CLL results in a clonal expansion of matu re-appearing B lymphocytes involving lymph nodes and other lymphoid tissues with progressive infiltration of bone ma rrow and circulation in blood (Berkow 1992). The compartments in which C LL proliferates are termed pseudofollicles, vaguely nodular areas without mantles that are observed in lymph nodes and bone marrow (Stevenson and Caligaris-Cappio 2004). The pathol ogical features of the lymph node in which CLL develops are those of a small lymphocytic lymphoma (SLL) (Chiorazzi et al. 2005); SLL and CLL are characterized as a single disease entity in the primary extant classification of hematopoietic and lymphoid diseases used currently (see Section 4). As lymphocytes progressively infiltrate the bone marrow, abnorma l hematopoiesis leads to anemia and immune dysfunction develops, resulting in increased susceptibility to a va riety of infections (Berkow 1992; Keating et al. 2003). As noted earlier, not all cases of CLL are “indol ent;” they represent about 60% of the cases reported in most Western count ries (Chiorazzi et al. 2005; Ha mblin 2008). Even within this “indolent” group, those that are firs t diagnosed in later stages (e .g., stage IV in the Rai staging system or stage C in the Binet classification sy stem) progress much more rapidly toward a fatal outcome, because current therapies are less eff ective than for other aggressive forms of NHL (Keating et al. 2003). Patients with indolent (i.e., mutated) CLL (or SLL) often can survive for long periods with no or minimal therapy. Because these diseases are often asymptomatic, they are typically identified in individuals undergoi ng routine medical examinations or seeking treatment for otherCLL Risk Model for NIOSH-IREP January 5, 2010 Final

10 diseases (Berkow 1992). The median survival ti me for patients dying from B-cell CLL or its complications is now about 10 years (Ber kow 1992; Keating et al. 2003; NCI 2009b). Patients with these forms of lymphoma can di e as a result of (1) conversion to a more malignant form (such as large B-cell lymphoma or Hodgkin’s disease; Kyasa et al. 2004), (2) an increased rate of other malignanc ies (Kyasa et al. 2004), or (3) co mplications of treatment, rather than from the initially diagnosed form of the disease (Magrath 1992) . Those patients who experience progression of their CLL predominantly die of complications of the disease itself, primarily from infections (Keating et al. 2003). In a study of a well-defined CLL/SLL population of predominantly older male smokers, second malignancy was the most common cause of death (34%), followed by progressive CLL (18%) and inf ection (16%) (Kyasa et al. 2004). Because the majority of second malignancies in this gr oup were those of the lung and prostate, the contribution to mortality from second cancers suggested by these results is undoubtedly an
overestimate. CLL affects older persons primar ily, with 75% of CLL patients diagnosed at an average age of 60 in the U.S. population. It is about 2

− 3 times more common in men than in women (Keating et al. 2003; Dores et al. 2007; Appendix A).

3.4 Implications for Assigning a Target Organ for Dose Reconstruction

The evidence that CLL cells emerge from a population of an tigen-selected, mature, immunocompetent B-lymphocytes that have carr ied out typical functions of normal B-cells currently seems compelling (Keating et al. 2003; Stevenson and Ca ligaris-Cappio 2004; Chiorazzi et al. 2005; Caligaris-Cappio and Ghia 2007; Ghia et al. 2007 ; Linet et al. 2007; Rai2007b; Appendix B). Activ ated B cells leave the lymph node (o r other site of activation) in the efferent lymph, return to the blood and seed the spleen, bone marrow, lymph nodes, mucosal associated lymphatic tissue, Pe yer’s patches, tonsils, and/or sites of inflammation (Anderson 2002). Thus, these cells could potentially undergo transformation to CLL clones anywhere in the haematopoietic or lymphatic systems.CLL Risk Model for NIOSH-IREP January 5, 2010 Final

11 It therefore follows that the bone marrow cannot be designated as the sole target organ for induction of CLL by radiation e xposure, as is often assumed. 1 It would appear that the defining lesion for CLL could be initiated anywhere the ma ture B-lymphocyte precursor might be located, i.e., potentially in most tissues of the body, including the bone ma rrow, but also in the lymph nodes, spleen, and mucosa-associated lymphoid tissues. 2 Thus, the number of potential target organs/tissues needs to be expanded well beyon d the three sites (red bon e marrow, spleen, or thymus) that have been used in es timating PC for CLL (see Section 5.2). There are several reasons why the bone marrow cannot be excluded as a potential target organ, however. First, after encountering antigen, human equivalents of B-2 cells 3 migrate to solid lymphoid organs to undergo the germinal ce nter reaction, where they can become memory cells, which often migrate to the bone marrow, lying dormant until a repeat antigen exposure triggers a similar migration to the lymphoid follicles (Chiora zzi 2007). In addition, some T-celldependent B-cells that become memory or plasma cells via the germinal center reaction migrate to the bone marrow where they may persist for many months (Angelin-Duclos et al. 2000) or even for a person’s lifetime (McHeyzer-Williams and McHeyzer-Williams 2005). Long-lived plasma cells preferentially home to the bone marrow (McHeyzer-Williams and McHeyzer-

1 NHL, Hodgkin’s lymphoma, and multiple myeloma also orig inate from mature B cells (Stevenson et al. 1998), for which the bone marrow appears not to be the most appropriate target organ. For example, the precursor cell in multiple myeloma is thought to arise from the action of one or more exogenous stimuli while the B-cell is resident in a lymph node (Bakkus et al. 19 94). The precursor then circulates to the bone marrow where it establishes itself by adhering to the stromal cells and inhibiting osteoblastic activity and osteocalcin production (Teoh and Anderson 1997). 2 The immune system operates like a single organ but its lymphatic components are located in every part of the body other than the central nervous system. Except for lymphocytes, which are present in most tissues, lymphatic tissue is present in the red bone marrow and the thymus (primary lymphoid organs) and the secondary lymphoid organs: lymph nodes, spleen, mucus membranes (including tens of thousands of isolated lymphoid follicles in the large and small intestines; Brandtzaeg and Johansen 2005; Brandtzaeg et al. 2008), tonsils , adenoids, Peyer’s patches, and the vermiform appendix. Integration of this system is effect ed through lymphocyte recirc ulation, a process by which lymphocytes leave the blood, cross tissues, and return to the blood via efferent lymph. Approximately 1

− 2% of the lymphocyte pool recirculates each hour, optimizing the opportunities for antigen-specific lymphocytes to come into contact with antigen in secondary lymphoid tissues (Anderson 2002; Bowers and Hunt 2007). As a result, the total circulating pool of lymphocytes in blood is turned over about 50 times a day (Pabst 1988; Westermann and Pabst 1992). In normal subjects, about 70

− 80% of blood lymphocytes are T cells and 10

− 20% are B cells (Westermann and Pabst 1992; Mellors 2002). Howeve r, T-lymphocytes circulate more readily than B-lymphocytes, whose movements are more restricted both inside and outside the confines of the blood and lymphatic systems. Unlike the B cells, which tend to remain in lymphoid tissue, T cells constantly recirculate between the peripheral blood and lymph in their search for antigen. In addition, the migratory capabilities of specific B lymphocytes may be quite different, depending on the B cell subset or lineage from wh ich they derive (Young et al. 1997; Andrade et al. 1998; Montoya et al. 1999; Young 1999; Chiorazzi 2007). 3 B-1 and B-2 cell lineages have been described in the mouse, but this type of lineage discrimination is not as clear in humans (Meffre et al .; 2004; Chio razzi 2007).CLL Risk Model for NIOSH-IREP January 5, 2010 Final

12 Williams 2005), and these authors consider such cells to belong to the memory cell compartment. 4 Second, an inherited familial predisposition to CLL has been documented (Keating et al. 2003; Chiorazzi et al. 2005; Linet et al. 2007; Rai 2007a) an d genetic differences in immunoglobulin genes considered re levant to CLL are observed in populations at low risk for developing CLL (e.g., persons of far East Asian ancestry; Keating et al. 2003). Low incidence rates for CLL persist in individuals migrating to the U.S. from Asian countries and in their descendants (Fig. A.1; Dores et al. 2007; Linet et al. 2007), sugges ting that a genetic factor may
be involved in the reduced leve ls of CLL observed in Asian popul ations. Thus, it is conceivable that irradiation could produce genetic changes in bone marrow st em cells that could result in increased susceptibility to devel opment of CLL that might only be manifested at later stages of lymphocyte development. A counter-argument is as follows: One of the reasons CLL appears non-radiogenic is that only those (relatively fe w) persons with an inherited predisposition to
CLL 5 are affected by radiation expos ure, and that radiation acts pr imarily as a weak promoter, e.g., on a B-cell in a later stage of development, ra ther than as an initia tor, acting directly on a bone marrow stem cell. Schubauer-Berigan et al. (2007) concluded that a 10-y latency period for radiogenic CLL (also see Sect. 5.3) would indicate that radiation ac ts primarily as a promoter for CLL development. Thus, because of current uncer tainty about both the lymphocyte precursor for CLL and the role of genetic background in i nduction of radiogenic CLL, the bone marrow should
be included as one of the potential target organs fo r development of radiogenic CLL.

4. CLASSIFICATION AND DIAGNOSIS OF CLL

As pointed out earlier, diagnostic misclassif ication is one of th e factors that have confounded previous attempts to determine whether CLL is radiogenic. Although effective diagnosis is now a routine matte r for CLL (NCI 2009b), diagnostic classification has long been marked by a lack of consistency. Because of th e typically old age at onset, many patients die

4 Rai (2007c) suggested that the B cell leading to CLL was transformed at a stage just prior to that which would produce a plasma cell (the last and final stage of a normal lymphocyte’s differentiation). This stage would include most memory cells but would rule out the long-lived pl asma cells identified by McHeyzer-Williams and McHeyzerWilliams (2005). 5 Potentially associated with mutations in the ATM gene in some cases (see Caligaris-Cappio 2000).CLL Risk Model for NIOSH-IREP January 5, 2010 Final

13 with these diseases, but not as a consequence of them. Thus, death ce rtificate information provides a very poor indication of the prevalen ce of CLL/SLL (Richardson et al. 2005). B-cell CLL and small lymphocytic lymphoma (S LL) are currently classified as a single disease entity in the World Health Organization modification of the Revised European American Lymphoma (REAL) Classificati on (Harris et al. 1999; NCI 2009a ). In this classification, lymphomas and lymphoid leukemias of the same cell type are considered one disease with different clinical presenta tions or stages, i.e., with the princi pal distinction between the two being the extent of solid tissue or peripheral blood involvement, respectivel y (Harris et al. 1999; Richardson et al. 2005). Put another way, B-cell CLL and SL L may simply be different manifestations of the same disease (NCI 2009a). Although CLL/SLL patients in some locations may be seen by different physicians based on th eir presentation [e.g., patients with peripheral blood involvement (CLL) are typica lly seen by hematologists and those with tissue involvement (SLL) are seen by oncologists], there is a consen sus that the two diseases are biologically the same (Harris et al. 1999). They are closely affiliated with hairy cell leukemia (HCL), another slowly progressing disease of mature B cells. It appears that the pr evalence of cases that present as “SLL” may be much lower than that of CLL (Dores et al. 2007 ); it reportedly made up <10% of CLL/SLL cases in one major study (Kyasa et al. 2004). All of th ese forms now fall into the broader category of NHL, as noted earlier (Harris et al. 1999; NCI 2009a). Although the modified REAL classification has been adopted by the World Health Organization and CLL is considered to be an indo lent form of NHL by the NCI (2009a), it is not the only system in use currently. CLL is classi fied as a leukemia and both SLL and HCL are classified as lymphomas in the International Cl assification of Diseases (ICD)-9-CM 2001 system (AMA 2001). Papers on radioepidemiological studies invol ving CLL referenced by Richardson et al. (2005) and Boice (2005) indicate that most of the previous studies (i.e., pre-1996) were conducted using the ICD-8 system as the primary classification scheme. However, investigators
often combined several ICD codes to create th eir own definitions of CLL or other types of leukemia. For example, Weiss et al. (1995) lumped CLL per se (i.e., as then defined to include diseases of both B- and T-cell origin; ICD- 9 code 204.1) with unspecified lymphocytic leukemias (ICD-9 code 207.8), while HCL (a ly mphoma in the ICD-9 scheme; code 202.4) wasCLL Risk Model for NIOSH-IREP January 5, 2010 Final

14 lumped with acute myeloid leukemia. Epidemio logical studies of the Japanese atomic-bomb survivors have utilized previous iterations of the ICD classifica tion system (Preston et al. 1994; Thompson et al. 1994), and the most recent studies were based on the ICD-9 system (Preston et al. 2004). Other chronic leukemic patterns that ha ve been categorized as “CLL” in clinical practice include: prolymphocytic leukemia, HCL, lymphoma leukemia, and Sézary syndrome (Berkow 1992). Thus, the effects of changing defin itions, which likely increased the potential for misdiagnoses or mischaracterizations of CLL, mu st be carefully considered in interpreting the results of epidemiologi cal studies involving CLL. 6

5. DEVELOPMENT OF A RISK MODEL FOR CLL

5.1 Selection of Risk Coefficients

Based on the information reviewed to date, and on the risk models currently developed for IREP, the risk model in IREP for NHL, Hodgki n’s disease (lymphoma), and multiple myeloma (lymphoma and multiple myeloma grouping; ICD-9 codes 200

− 203) (Preston et al. 1994; Land et al. 2003) could serve as the ba sis for a CLL risk model. This model is based on the incidence of 117 cancers of these types observed through the year 1987 in the Japanese A-bomb survivors exposed to doses ≥ 0.01 Sv: 76 cases of NHL, 10 cases of Hodgkin’s lymphoma, and 31 cases of multiple myeloma (Preston et al. 1994; UNS CEAR 2008; Land et al. 2003; Table 2). Although the risk model in IREP is based on da ta for all three types of cancers, UNSCEAR has derived risk estimates for each cancer type individually (Table 2). Point estimates for the ERRs for males for NHL and multiple myeloma were positive, while those for females were negative. Data for Hodgkin’s lymphoma were not extensive enough to attempt analyses by gender or age at exposure. The central value of the point estimate of the ERR for males in the model for the lymphoma and multiple myeloma grouping used in IREP was also positive, while that for females was

6 A T-cell form of CLL reportedly made up about 5% of CLL cases diagnosed in the past. Although T-cell CLL and prolymphocytic leukemia are grouped together under peripheral T-cell and NK-cell neoplasms in the updated REAL/WHO classification, it appears that CLL is currently considered a disease of excl usively B-cell origin (see Chiorazzi et al. 2005; Ch iorazzi 2007; Rai 2007b, Rai 2007c; Zent 2007; NCI 2009a ; NCI 2009b ). Further, Rai (2007b) indicates that the ratio of T cells to B cells in peripheral blood is reversed in CLL, with over 90% of circulating lymphocytes being B-CLL cells.CLL Risk Model for NIOSH-IREP January 5, 2010 Final

15

Table 2. Risk estimates for incidence of non-Hodgkin’s lymphoma, Hodgkin’s lymphoma, and multiple myeloma in the Japanes e atomic-bomb survivors at doses ≥ 0.01 Sv

Cancer site Internal grouping Number of cases Average ERR at 1 Sv a Source Non-Hodgkin’s lymphoma Gender Male 41 0.44 (

− 0.16, 1.42) Table 41 in UNSCEAR (2008), based on data in Preston et al. (1994) Female 35

− 0.22 (<

− 0.22, 0.40)
Age at exposure <20 y 17 0.45 (<0, 2.16)

20

− 40 y 34

− 0.12 (<

− 0.12, 0.73)

>40y 25 0.09 (<0, 1.04)

Time since exposure 12

− 15 y 7 0.33 (<0, 2.14)

15

− 30 y 34 0.33 (<0, 1.44)

>30 y 35

− 0.22 (<

− 0.22, 0.45)

All 76 0.08 (<0, 0.62)
Hodgkin’s lymphoma All 10 0.43 (-1.6, 3.5) Table 42 in UNSCEAR (2008), based on data in Preston et al. (1994) Multiple myeloma Gender Male 12 0.17 Table 43 in UNSCEAR (2008), based on data in Preston et al. (1994) Female 18

− 0.28
Age at exposure <20 y 4 1.07
>20y 26 0.09

All 30 0.20 (<-0.2, 1.7)
Multiple myeloma All 31 b 0.26 (0, 1.85) c Land (2000)

a 90% C.I. in parentheses, except where noted. b Includes one additional case of multiple myeloma not originally included in the analysis by Preston et al. (1994).

c 95% C.I. in parentheses.CLL Risk Model for NIOSH-IREP January 5, 2010 Final

16 negative. For the IREP model, it was assumed that the ERRs for the two sexes were the same, although there reportedly was suggestive evid ence that they differed (p = 0.09). The age parameters used were derived from an analysis of the incidence of so lid cancers other than digestive cancers, female stomach cancer, liver can cer, female breast can cer, thyroid cancer, and non-melanoma skin cancer. The common age parame ters derived from this analysis were reportedly used for the lymphoma and multiple myeloma model because there was little evidence of departure from them in the grouped da ta for the latter (Land et al. 2003). The risk model for lymphoma and multiple myeloma in IREP is defined as follows:

• For exposure ages e ≥ 30 and attained ages a ≥ 50, the ERR at 1 Sv = 0.178 (95% C.I. <0, 0.9465);

• For exposure ages e <30 and attained ages a <50, the ERR at 1 Sv is multiplied by a modifier
with a geometric mean = exp{

− 0.05255 f ( e )

− 1.626 g ( a )}, and a geometric standard
deviation = exp{[0.0003261 f ( e ) 2

− 0.014594 f ( e ) g ( a ) + 0.5648 g ( a ) 2 ] 1⁄2 }, where 3⁄4 f ( e ) =

− 15 for ages at exposure ≤ 15, increasing monotonically as (e-30) from the value at age 15 to a value of 0 at age 30; after age at exposure 30, it remains equal to zero. 3⁄4 g ( a ) = ln(a/50) for attained ages <50, and 0 for attained ages ≥ 50.
The risk models in NIOSH-IREP are not used to perform risk calculations for persons who are <15 y old. The variation of ERR at 1 Sv with attain ed age is shown in Fig. 1 for selected ages at time of exposure. For low acute and fractionated exposures and fo r chronic exposures, the dose and dose rate reduction factor (DDREF) for solid tumors is ap plied to these risk estimates. The DDREF is phased in at doses <0.2 Gy for acute and frac tionated acute exposures (Land et al. 2003).

5.2 Concerns Associated with Use of the Lymphoma and Multiple Myeloma Model

Application of the lymphoma and multiple myel oma risk model as the basis for CLL is subject to a number of issues and caveats. The fi rst issue is whether a ri sk model for CLL should be based on data for NHL alone, i.e., by r eanalyzing the data after excluding Hodgkins lymphoma and multiple myeloma. There are major differences in etiology and effects of Hodgkins lymphoma and multiple myeloma co mpared to NHL (NCI 2009a). One potential argument in favor of inclusion of multiple myeloma is that, like CLL, it too is a disease of oldCLL Risk Model for NIOSH-IREP January 5, 2010 Final

17 

### 0.0 0.5 1.0 1.5

0 10 20 30 40 50 60 70 80 90 100 

## Age at exposure 18 Age at exposure 25 Age at exposure 30ERR/Sv



## Attained Age (years)
Fig. 1. Variation of the ERR at 1 Sv with attain ed age for selected ages at exposure in the lymphoma and multiple myeloma m odel in IREP, which is used as a surrogate for a risk model for CLL in this report.

age (Preston et al. 1994), and some aspects of it s causation may be similar to some types of CLL (Lonial 2005). However, the median survival time for patients diagnosed with multiple myeloma is about three years (George and Sadovs ky 1999), as compared to ~10 years for CLL. Arguments for inclusion of Hodgkins lym phoma seem less compelling, even though the estimated median survival post- diagnosis is about the same as that for CLL (~10 y; Grund 2004; NCI 2009c). Use of data for NHL alone reportedly results in a similarly low risk coefficient, comparable to that for the grouping, but with somewhat tig hter confidence limits th an if the other two diseases are included (see Table 2 and text de scribing the model for the lymphoma and multipleCLL Risk Model for NIOSH-IREP January 5, 2010 Final

18 myeloma grouping). One concern is that, alt hough both indolent and aggressive forms of lymphoma are included in the definition of NHL, when CLL is excluded (as in the Japanese atomic bomb survivor data) more cases of NHL may fall into the “aggressive” category than for CLL or for an NHL grouping that includes CLL (NCI 2009a, 2009b). Another is that the grouping of cancers in the current “NHL” data set is incomplete. It excludes, in addition to CLL, HCL, and possibly other conditions (discussed earl ier) that are included in the current definition of NHL by the NCI (2009a). These types of cancers are included in a grouping called “other leukemias” by Preston et al . (2004) (see Appendix B). Thus, the possibility exists th at even NHL as defined in th e questionable ICD classification system used in analyses of dose-responses in the LSS cohort might not be a good surrogate for CLL. However, since some, perhaps many, cases of CLL in former nuclear weapons workers covered under the EEOICPA were undoubtedly di agnosed in the past using a variety of classification schemes, use of a blanket category such as NHL (or the entire lymphoma plus multiple myeloma grouping) to develop a risk model for CLL may be the only interim option at present. Killing of highly radiosensitive B-lymphocytes in persons who received acute doses ≥ 1 Gy (e.g., some members of the LSS cohort and medi cally exposed groups) (M ettler and Upton 1995; Waselenko et al. 2004) would have significantly reduced the pool of CLL (and NHL) precursors in which malignancies could have been initiated, resulting in a potential depression of the doseresponse as a “cell-killing” effect. The ERR/Sv reported by Richardson et al. (2009) for NHL mortality in the LSS cohort when survivor dos es were limited to <0.5 Sv (4.24; 90% C.I. 0.83, 9.76) was about four times greater than the ERR/Sv based on the full dose range (1.12; 90% C.I. 0.26, 2.51). These results are suggestive of an effect of cell-killing at high er doses. Intriguingly, Schubauer-Berigan (2007) obtained a marginally significant dose-response (ERR/10 mSv: 0.20; 95% C.I. -0.035, 0.96) in a study of mortality from CLL in a case-c ontrol study involving
workers from six U.S. nuclear facilities when workers with doses >0.1 Sv were excluded. However, this observation could simply be a resu lt of data selection and/or the small numbers of individuals with higher doses. Development of a completely new risk model, e.g., based solely on incidence of NHL or some plausible combination of NHL with a lympho ma considered similar to CLL, such as HCL,
that has not heretofore been included in the NHL category in previous analyses of data for theCLL Risk Model for NIOSH-IREP January 5, 2010 Final

19 LSS cohort, would require: (1) full access to re levant DS02 dose-response data currently being analyzed by the Radiation Effects Research F oundation (RERF), (2) an independent Poisson regression analysis of these data leading to a unique risk model a nd associated parameter set, and (3) access to a specialized software package (e.g., AMFIT module of the software package EPICURE; Preston et al. 1998) for such an anal ysis. Alternatively, the NCI staff members who developed the current risk models in IREP coul d be asked to perform the required analyses. One obvious disadvantage of performing such analys es at the current time is that extended information on the LSS cohort, i.e., dose respon ses for lymphoma and leukemia incidence based on the new DS02 dosimetry system are yet to be pub lished and thus are not generally available.
Another issue is that the risk model in IREP was based on Japanese atomic-bomb survivor data that were analyzed assuming that the bone marrow dose was the appropriate reference (Preston et al. 1994). However, as discussed above, newer information on the origins of all of the diseases covered by the lymphoma plus multiple myeloma grouping indicate that use of a bone marrow dose is not appropriate, and that a whol e-body dose should be more representative of the exposure conditions of the precursors of these di seases if induced under conditions of uniform external exposure (as in the LSS cohort). We woul d expect that, all else being equal, use of a bone marrow dose could yield somewhat lower risk coefficients than use of a whole-body dose (perhaps ~5% lower; see dose adjustment factors given in Walsh et al. 2004). When and if these data are reanalyzed in future (i.e., after DS02-based dose-response information is made available), an appropriate surrogate for a whole- body dose should be used instead of the bone marrow dose alone, e.g., pe rhaps by an appropriate combination of the esophageal, colon, and bone marrow doses, because most of the lymphatic system, like the esophagus, lies close to the surface of the body, wh ile the circulating blood is more uniformly distributed the throughout the ti ssue volume. An alternative to a whole-body dose that could provide a more representative dose to the poten tial lymphocyte precurso rs of CLL would be a weighted average. It could be estimated by appl ying relative weights to doses to the different lymphocyte-containing organs/tissues based on di stributions of lymphocytes in the human body (e.g., Trepel 1974; Ganusov and De Boer 2007; Bl um and Pabst 2007). Unfortunately, however, these distributions, which are based partly on extrapolations of data on laboratory animals (mainly rodents) to humans, do not provide information for the individual categories of lymphocytes. This is problematic because T lymphocytes outnumber B lymphocytes by about 2CLL Risk Model for NIOSH-IREP January 5, 2010 Final

20 to 1 in the human body as a whole (Westerm ann and Pabst 1992; Young 1999; Mellors 2002), and constitute >95% of the lymphocytes in some tissues and organs (Westermann and Pabst 1992). Because lymphocytes, as well as the lymphatic or gans and tissues, are distributed widely in the human body, but the exact proportions of B- lymphocyte precursors for CLL (or ratios of B cells to T cells) within any of the regions are not known precisely, making su ch fine distinctions does not appear to be justified when estimati ng a whole-body dose at the present time (Section 6; also see Apostoaei and Trabalka 2009). 7

5.3 Handling of Risk Transfer between Populations

Because of the very large difference in baselin e risks for CLL between the Japanese A-bomb survivors and the U.S. population (Appendix A), the i ssue of risk transfer also required special
consideration. The risk transfer refers to whethe r a multiplicative or additive risk model for the Japanese population should be given higher we ight when applied to the U.S. population. In IREP, equal weights are given to th ese models (Land et al. 2003). One could argue, for example, that if the in teraction between CLL and radiation was more multiplicative than additive, a higher incidence might have been observed in epidemiological
studies of radiation workers and medically exposed groups. Ho wever, the NIOSH review of epidemiological studies related to CLL (Silver et al. 2007) reinforces the point that the follow-up periods in such studies were pr obably too limited to permit det ection of a dose-response for CLL because of an extended latency period (see Fig. 2). In addition, most studies were focused on mortality rather than incidence as an endpoint, which, as noted earlier, may have significantly
underestimated the o ccurrence of CLL. The potentially weak radiogenicity of C LL, combined with inadequate follow-up, inconsistent diagnosis and clas sification of CLL, and a questi onable endpoint (mortality) in

7 The distribution of lymphocytes in the human body given in ICRP Report 89 (ICRP 2002): 0.2% in blood, 7% in red marrow, 7% in lymphatic tissues, and about 86% elsewher e, suggests that most lymphocytes are located outside the bone marrow, circulatory, and lymphatic systems at any given time. However, this information was based on a now-discredited reference (see Trepel 1974). A more credib le distribution is as follows: 2% in blood, 10% in red marrow, 80+% in lymphatic tissues, and <8% in other tissues (Blum and Pabst 2006). In addition, much of the information on the lymph nodes in the 2002 ICRP report is derived from outdated material (dates of references ranging from 1885

− 1926) originally included in its 1975 report on reference man (ICRP Publication 23).CLL Risk Model for NIOSH-IREP January 5, 2010 Final

21 Fig. 2. Results from medical cohort studies and clinical trials suggesting a latency for incidence of radiogenic CLL of about 15 y or more, based on an analysis of mean follow-up time. Outcome is CLL mortality, except where noted. a Incidence studies. b Risk estimate for patients who received radiotherapy was lower than for patients who were not irradiated. Source: Silver et al. (2007).

epidemiological studies, appears to make any j udgment about the relativ e contributions of alternative representations of excess radiogenic risk inappropriate. Thus, it is concluded that the “uninformed” risk transfer module (as describe d on page 35 and in Fig. IV.G.1 of Land et al. 2003), which is used for the bulk of solid tumors in IREP, as well as lymphoma and multiple myeloma, is a more appropriate choice for the CLL model, based on current information.

5.4 Latency

Our review of the epidemiological data in dicated that the extended latency period (~20

− 30 years) for mortality from CLL suggested by Ri chardson et al. (2005) would not be applicable to a risk model for incidence of CLL (i.e., as in IREP). One reason is that the post-diagnosis survival times for CLL patients are highly uncertain, potentially ranging from a few months to a normal life expectancy (NCI 2008b). Another is that it is questiona ble whether existingCLL Risk Model for NIOSH-IREP January 5, 2010 Final

22 epidemiological data on CLL induction by radiati on are capable of providi ng useful information on an extended latency period (Appendix C). Beyond the obvious concerns about potential for misdiagnoses and classification of CLL, all of the studies of medically expos ed persons that we examined are potentially limited by the (1) confounding effects of prior disease on CLL inci dence, (2) uncertainties in diagnosis and classification of CLL, (3) limited statistical power, particularly in one study of the consequences of diagnostic or therapeutic uses of 131 I for thyroid diseases because of the relatively low doses involved, and, to a lesser extent, (4) meani ngfulness of bone marrow doses for lymphoma
induction. If we subtract an estimated median post-diagnos is survival time of ~10 y from the estimate of Richardson et al., the resulting estimate of late ncy of ~15 y for incidence of CLL is consistent with that suggested by the analys is of Silver et al. (2007) ba sed on an examination of mean follow-up time in medical cohort studies, in which the risk of CLL “is elevated, though nonsignificantly, in almost all studies with more th an 15 years average follow-up” (see Fig. 2) and our own admittedly crude point estimate (see Appendix C). However, recent time window analyses with in epidemiological studies highlight the uncertainty in potential latency periods for both CLL and NHL. A case-control stud y of mortality from CLL in occupationally exposed persons by Schubauer-Berigan et al. (2007) yielded marginally significant estimates of risk with a time lag of ≥ 10 years when workers with doses >100 mSv were excluded, suggesting that a lag pe riod of 10 years was appropriate for this group. The study by Romanenko et al. ( 2008), which did not examine th e effects of the very large uncertainty in doses for Chornobyl remediation workers (see Kryuchkov et al. 2009), reported a
minimum deviance for the risk estimate for bot h CLL and non-CLL leukemia using a time lag of only 2 years. In contrast, although Richardson et al. (2009) obtained sign ificant dose-responses (p ≤ 0.05) for mortality from NHL in both the LSS c ohort and workers at the Savannah River Site using a time lag of 10 years, their study did not show evidence of a signi ficant risk until a period of 35+ years had elapsed following irradiation. “In each cohort, evidence of a dose-response association was primarily observed more than 35 years after irradiation. Su ch findings indicate a protracted induction and latency period” (Richardson et al. 2 009). Such contrasting results
indicate that the issue of a suitable estimate of latency for modeling risks of radiogenic CLL or
NHL is far from resolved.CLL Risk Model for NIOSH-IREP January 5, 2010 Final

23 The results of our evaluation of information fr om medical cohort studie s bearing on latency of multiple myeloma and NHL are similar to th ose for CLL (Appendix C), i.e., an uncertainty band of ±5 y around a point estimate of latency for CLL or NHL of ~10

− 15 y. Because of current uncertainty in the value for the central es timate of a latency period, we have elected to use a point estimate of 10 y with an uncertainty ba nd of ±5 y. The characteristics of the resulting latency distribution are desc ribed in the next section.

5.4.1 Handling of Latency in the CLL Model

As with other neoplasms in the IREP model, the risk of CLL should be very low for short times after exposure due the exis tence of a minimum latency peri od (here assumed to be 5 y). For longer times after exposure the magnitude of risk is adjusted by a factor which is phased in using an S-shaped function, as described on pages 95

− 96 of Land et al. (2003). For the CLL risk model, the midpoint of the S-shaped function fo r latency was set at 10 y. At the midpoint, the ERR/Sv is half the maximum ERR/Sv, which is attained at about 20 y. Figure 3 shows the ERR at 1 Sv for CLL adjusted by an S-shaped functio n with a mid-point of 10 y, to account for the effect of the minimum latency period. Because precise knowledge about the onset of radiogenic CLL is lacking, an additional random linear translation of the S-shaped curve is introduced by letting the midpoint vary around the nominal value of 10 y. The uncertain midpoint is assumed to be distributed according to a triangular distribution with minimum 5 y, mode 10 y, and maximum 15 y. The effect of this uncertainty is an increase of assigned share fo r malignancies diagnosed within a shorter time following exposure (Fig. 4). The midpoint of the S-shaped latency function used in IREP for the bulk of solid tumors, including the lymphoma and multiple myeloma grouping, is 7.5 years, and reaches 0.01 and 0.99 at 4 y and 11 y, respectively. The addition of a triangular distribution (with extremes at 5 and 10 years) about the midpoint exte nds the points at which the 99 th percentile of the latency adjustment reaches 0.01 and 0.99 to approximately 1.5 y and 9 y, respectively (Land et al. 2003). In contrast, the latency adjustment represented by the central curve in Fig. 4 reaches 0.99 of the maximum at 17 y after exposure. When the un certainty in the mid-point of the S-shaped function is included, the point at which the 99 th percentile of the latenc y adjustment reaches 0.99CLL Risk Model for NIOSH-IREP January 5, 2010 Final

24 0.0 0.5 1.0 1.5

0 10 20 30 40 50 60 70 80 90 100 Age at exposure18 Age at exposure 25 Age at exposure 30ERR/Sv

Attained Age (years) Fig. 3. Risk model for CLL, adjusted for the eff ect of minimum latency period, depicted as the variation of the ERR at 1 Sv with attained ag e, for ages selected ages at exposure.

is extended to 12-13 y. As a result, the 99 th percentile of PC for CLL should be based on values for latency of about 12-13 years. The differences between the revised latency adjustment proposed for the CLL risk model and the existing latency adjustment in IREP are introduced by the assumption of a slightly longer but more uncertain minimum latency period (10 y with a range of 5 to 15 y for the CLL model versus 7.5 y with a range of 5 to 10 y used in IREP). Despite the differences in the two distributions, 99 th percentile estimates of PC made using th e revised distribution should be rather similar to those made with the existing latency dist ribution in IREP, all else being equal, with the revised distribution expect ed to produce lower 99 th percentiles of PC at 5 to 20 y after exposure, but possibly producing higher 99 th percentile of PC at 2

− 5 years after exposure —the latter effect being caused by the larger uncerta inty in the revised latency adjustment proposed for the CLL
model.CLL Risk Model for NIOSH-IREP January 5, 2010 Final

25 0.0 0.2 0.4 0.6 0.8 1.0

0 5 10 15 20 25 30Latency adjustment (unitless)

Time after exposure (years) 

## CLL

Fig.4. Adjustment factor accounting for the unc ertainty in the minimum latency period.

5.5 Sample Results from the Proposed CLL Model

Representative estimates of PC from the C LL model are given in Table 3 for an acute exposure of 1 Sv to high-energy gamma radiation. Although results are restri cted to exposures to males, the 99 th percentile of PC for females is similar to that obtained for males because the same risk coefficients are used and because the uppe r bound of PC is given by the multiplicative risk projection to the U.S. population, provided that th e Japan to U.S ratios of age-adjusted incidence rates in males and females are significantly lowe r than 1 (Appendix A). As shown in Table 3, the
PC exceeds 50% at the 99 th percentile for times since exposure greater than 10 y for men aged 20 y at the time of exposure, and for times since ex posure between 10 and 20 y for men aged 25 y at the time of exposure. For age at exposure 20, the 95 th percentile of PC also exceeds 50% for cancers diagnosed 15 and 20 y after exposure. Do ses higher than 1 Sv will be required to produce 99 th percentile values of PC that equal or exceed a value of 50% for older ages at time of exposure or for older ages at time of diagnosis wh en age at exposure is gr eater or equal to 25 y.CLL Risk Model for NIOSH-IREP January 5, 2010 Final

26 Table 3. Probability of causation (PC in %) of CLL in males acutely exposed to 1 Sv of high-energy gamma radiation
Age at exposure (y) PC a (percentile) Time since exposure (y)

5 10 15 20 25 ≥ 30

20 50 th 0.80 7.84 13.8 12.2 10.4 8.88

95 th 16.7 48.4 54.2 50.1 45.4 41.3

99 th 35.0 65.0 67.6 63.2 58.6 54.6

25 50 th 0.46 4.88 9.07 8.16 6.99 6.99

95 th 9.91 35.4 41.9 38.7 34.8 34.8

99 th 22.5 51.0 55.3 51.3 47.2 47.2

30 50 th 0.28 3.09 5.98 5.48 5.49 5.49

95 th 6.04 24.9 31.2 28.9 28.9 28.9

99 th 14.4 38.3 43.3 40.1 40.2 40.2

35 50 th 0.23 2.56 5.10 5.48 5.49 5.49

95 th 4.85 21.2 27.5 28.9 28.9 28.9

99 th 11.5 33.4 38.6 40.1 40.2 40.2

40 50 th 0.19 2.16 5.10 5.48 5.49 5.49

95 th 4.01 18.4 27.5 28.9 28.9 28.9

99 th 9.50 29.5 38.6 40.1 40.2 40.2

a PC were obtained using a modified version of NIOSH-IREP v.5.6 in which the "Lymphoma and Multiple Myeloma (200-203)" model was adapted to represent CLL. The PC values were obtained based on 30 runs with 2000 iterations each.

For testing purposes, the proposed CLL model was also applied to seventeen claimants selected by NIOSH/OCAS ( Development of a CLL Risk Model for NIOSH-IREP

− Revised
Addendum , dated December 10, 2009). These claima nts had diseases with ICD-9 codes 202.2x (Sezary's disease) or 202.4x (hairy cell leukemia), wh ich have been described in the literature as clinically resembling CLL. The PC values at the 99 th percentile obtained using the proposed CLLCLL Risk Model for NIOSH-IREP January 5, 2010 Final

27 model were compared to the PC values at the 99 th percentile obtained using the "Lymphoma and Multiple Myeloma (200-203)" model in NIOSH-IR EP v.5.6. All PC values for these claimants were lower than 50% regardless of the model used. The PC values produced by the proposed CLL model are always lower (sometimes significan tly lower) than the values obtained using the current Lymphoma and Multiple Myeloma model in NIOSH-IREP. The reason for the lower PC
values at the 99 th percentile is the longer latenc y used in the proposed CLL model.

6. DOSE RECONSTRUCTIONS FO R NUCLEAR ENERGY WORKERS FOR USE WITH TH E CLL MODEL

6.1 Background

As discussed earlier, estimates of external doses for use in modeling th e radiological risk of CLL appear to be well represented by whole-body doses , but not by bone marrow doses. For purposes of dose reconstruction made in conj unction with risk es timates based on IREP, information from existing personal dosimeter or film badge records for doses from penetrating radiation should probably suffice in most instances.
For internal exposures or for non-uniform external exposures to former radiation workers, however, the situation is much more complex. The ta rget organ or tissue is the irradiated site in which a normal stem or stem-like cell undergoe s transformation to a cancerous clone. The
leukemias per se (as distinguished from diseases such as CLL and HCL) are monoclonal diseases that arise from hematopoietic stem and progeni tor cells located in the red bone marrow. Thus,
the red bone marrow is considered both the sole target organ for radiogenic leukemias and the site in which the pathol ogical effects of the leukemic clones develop. For CLL, the precursor cell is a mature, like ly antigen-experienced ly mphocyte of currently uncertain lineage or subtype (among the know n subsets of normal B cells) and of unknown location in the body when it was transformed to a B-CLL cell (by radiation or some other stimulus). Thus, the appropriate target organ is unclear (Schubauer-Berigan et al. 2007). The residence times of potential lym phocyte precursors at given locat ions within the body prior to transformation, which normally would be used to estimate the potential dose received, and their effective lifespan, which should limit the time duri ng which they could have been irradiated, areCLL Risk Model for NIOSH-IREP January 5, 2010 Final

28 also essentially unknowable. Under these conditions , the risk from radiation exposure cannot be
estimated from radiation doses reconstructed using a conventional “tar get organ” approach. Questions that immediately aris e include: If the dose to a mature B lymphocyte determines the biological response to radiation exposure, but that B cell potentially could have arisen at any time during the life of an individual diagnosed w ith CLL, does the term “radiation dose” as it is normally used in conjunction with a specific popul ation of cells within a target organ or tissue have any meaning? How does one define “age at exposure” or “time since exposure” under such
conditions? To what extent does irradiation of stem and progen itor cells of the lymphocytes in
the bone marrow influence later development of CLL, ev en though the event that transforms the
normal B cell to a CLL clone occurs at a later time and most probably in a different location? Is it possible to develop an aggregated estimate of internal dose incorporating appropriate uncertainties to cover the range of behaviors, locales inhabited, residence times, and lifespan typical of the half-dozen types of mature B cells that are cu rrently considered to be potential CLL precursors? To what extent does such an es timate have to be tailored to account for the route of entry of a radionuclide and internal kinetics and partitioni ng specific to a given radionuclide? Given the paucity of relevant information, would such an aggregated estimate be a case of “garbage in,” “garbage out?” Under ideal conditions, dose estimates would be made considering th e potential routes of entry (e.g., inhalation vs wounds) and radionuc lide-specific patterns of transport and accumulation. For inhaled plutonium, for example, highest doses might accrue to the lymph nodes associated with the lung. However, transfers via the circulatory system to other organs and tissues, including the bone marrow, from the lung, ex trathoracic regions of the respiratory tract, and their associated lymph nodes will result in ad ditional doses to circulating lymphocytes while in blood and to circulating and sedentary lymphoc ytes while transiting or residing in the bone marrow and lymphatic system. Since a fraction of B lymphocytes spend some of their time outside the blood, lymph, bone marrow, and lymphatic tissues per se , plutonium taken up by organs a nd tissues such as liver and skeleton could represent another im portant source of exposure. For example, the liver contains a large resident and migratory population of lymp hocytes and macrophages that provide immune surveillance against foreign antigen (Lalor and Adams 2002). Unfortunately, the exact proportions of the pools of th e half-dozen CLL precursors—or of the total pool of BCLL Risk Model for NIOSH-IREP January 5, 2010 Final

29 lymphocytes—in specific regions of the human b ody, such as the liver, are not currently known. Since mass distributions of lymphocytes are based primarily on information that does not distinguish among B, T, or natu ral killer cells, and as such ar e dominated by the more abundant T cells, performing a mass balance for B cells carri es with it high uncertainty and limits the use of data in some of the publis hed sources (e.g., ICRP 1994, 2002). The situation for CLL is analogous to that for NHL, in which radiation could have interacted with the lymphocyte precursors anywhere in th e lymphatic or circulatory systems, and then
formed the cancerous lesion elsewhere. Because the site of origin for NHL could not be determined with any confidence—and data on the inventories and distribu tions of lymphocytes in the human body are quite uncertain, the dose to the organ/tissue receiving the highest dose is used in dose reconstructions for NHL (ORAU 20 06). For both B-cell and T-cell lymphomas, the thoracic lymph nodes were selected as the target organ because the dose to these tissues from exposure via inhalation of insolubl e radioactive material is always higher than the dose to other organs/tissues. Thus, a similar approach could be applied to CLL if a more scientifically acceptable alternative ca nnot be developed. For radionuclides that are more uniformly di stributed in the body, such as tritium and 137 Cs, estimates of whole-body doses based on concentra tions and the effectiv e half-lives of the
radioactive materials should be sufficient. For other radionuclides with non-uniform deposition patterns in the body (e.g., 131 I), doses to components of the lymphatic system and to the circulating blood would have to be estimated on a case-by-case basis, as for plutonium. How to aggregate doses to individual compone nts of the lymphatic and circulatory systems for purposes of developing the i nput to IREP is an issue that would have to be evaluated carefully. One potential way to accomplish this is to estimate dose rates to circulating lymphocytes based on the estimated fraction of the time that they spend while in blood or lymph and in tissues outside and inside the lympha tic and hematopoietic systems, e.g., based on
residence times of and dose rates from radionuclides within these compartments or on dose rates to lymphatic components or bone marrow from nearby organs of accumulation (such as the thyroid, which can irradiate near by lymph nodes following uptake of 131 I). 8

8 Lymphocytes as a group spend about 0.5 h in the bloodstream, after which ~45% move into the spleen, where they spend several h, ~42% get into the lymph nodes, where they spend about 12 h, and ~10% cross the endothelial cells lining the capillaries and move into other body tissues. Deta ils on the fractional distribu tions of B, T, and natural killer lymphocytes in each of thes e fluxes are currently lacking.CLL Risk Model for NIOSH-IREP January 5, 2010 Final

30 ICRP dose models all contain a blood compartmen t that can be used to calculate dose rates for lymphocytes in circulating blood and organs and tissues of accumulation, but it appears that information on transfers to the lymphatic comp artments is very limited, except for inhaled materials, for which information on transfers to the lymph nodes associated with the lungs and extrathoracic regions of the respiratory tr act is available (ICRP 1994). Thus, obtaining meaningful dose estimates to use as input for IREP for former radiation workers could be a significant challenge, depending on the nature of the exposure and the specific radionuclides
involved. If, as expected, this challenge proves to t oo daunting for some combinations of exposure type and radionuclide, an alternative would be to estimate the maximum dose to a lymphocyte in any component of the circulatory, lymphatic, or blood-forming systems or in any other organ or tissue through which they circul ate or in which they reside—a s is currently done using the thoracic lymph nodes as the target organ for NHL. However, there are al so significant challenges associated with making such an estimate and se rious questions about whether use of such an estimate would even be justified—particularly in the case of CLL, for which an association with radiation exposure is still in doubt. We will us e the example of inhaled plutonium again to illustrate the associated technical questions and concerns.

6.2 Challenges in Estimating Meaningful Doses for Use with the IREP CLL Model

Cells in the thoracic lymph nodes are exp ected to experience the highest dose rate following inhalation of plutonium (ICRP 1994). Under th ese conditions, is it reasonable to use a
conservative estimate of dose referenced to th e thoracic lymph nodes to estimate the probability of causation of CLL using IREP? Th e argument in favor of this a pproach is that uncertainties about the nature of the CLL precursor, including its homing/migratory behavior, cannot rule out the possibility that such a cell c ould have spent most of its life time in the thoracic lymph nodes. If one gives the benefit of the doubt to the claimant, as has been done for other B-cell lymphomas (ORAU 2006), shouldn’ t the organ potentially recei ving the highest dose be the target organ for dose reconstruction? The counter-argument is that a conservative es timate may be difficult to justify on technical grounds, given the weak evidence for radiogenicity of CLL. Even if a lymphocyte is periodicallyCLL Risk Model for NIOSH-IREP January 5, 2010 Final

31 irradiated by α particles in these lymph nodes at a hi gh dose rate while it is passing through or temporarily resident in the lymph node, the pr obability that it will receive a significant dose from plutonium could be quite low because some lymphocytes can spend a significant part of their time outside the lymphatic system (e.g., while circul ating in blood) or could be localized for long periods in lymphoid tissues remote from the thoracic lymph nodes.
As an alternative to an assu mption of total residence in th e thoracic lymph nodes, one could attempt to estimate a dose to a lymphocyte precu rsor for CLL based on the ratio of the mass (volume) of the thoracic lymph nodes to those of the circulating blood, lymphatic system, bone marrow, or the body mass through which lymphocytes circulate. The mass of the thoracic lymph nodes in adults (15 g) is about 2

− 2.5% of that of the mass of th e lymphatic system as a whole (600

− 730 g, depending on ge nder) and about 0.025

− 0.04% of the lean body mass (ICRP 1994, 2002). Thus, the probability that a lymphocyte might be in the t horacic lymph nodes at any given point in time could range from about 2% for th e hypothetical case of a B-lymphocyte restricted to but circulating within the lymphatic system alone and as low as about 0.03% for the highly unlikely case of a B-lymphocyte circulating freel y into and out of extra-lymphoid organs and
tissues. In the case of a B-lymphocyte able to circ ulate freely through only the blood and lymphatic systems (e.g., antigen-naïve mature B cells, some types of memory cells), the probability would be reduced by about an order of magnit ude, i.e., from about 2% to about 0.2

− 0.3%, when compared to that of a B-lymphocyte restricted to the lymphatic system alone because of the extra volume (and mass) contributed by the blood (4.50

− 6.03 L; specific gravity about 1.05; ICRP 2002). 9 Multiplying these probabilities by the expect ed dose rate in the thoracic lymph nodes and the estimated lifetime of the lymphocyte precurs ors for CLL could yield an estimate of dose because some lymphocytes spend only part of their time at any one place, e ither inside or outside the lymphatic system. However, as previously discussed, ther e are large uncertainties involving the characterization of the lymphoc yte precursor for CLL/SLL, th e degree to which precursors circulate or are localized in the blood and lympha tic systems or other organs and tissues at the point in time when they undergo transformation to become CLL clones, and the nature of post-

9 A large fraction of B lymphocytes in the peripheral blood do not recirculate, but tend to remain in the blood (Young 1999). Thus, the cases described are oversimplifications of what actually happens in the human body.CLL Risk Model for NIOSH-IREP January 5, 2010 Final

32 transformation transfers of the clones to regions where the disease manifests itself (e.g., bone marrow, lymphatic components, or other organs and tissues). All of these factors affect our ability to estimate the probability that the dose as sociated with a given locale is representative of that which produced the ensuing cancerous lesion. For example, the percentage of total lymphoc ytes represented by B ly mphocytes within the various body compartments ranges from ≤ 1% in skin, thymus, and the epithelium of the lung or digestive tract, to ≥ 50% in the spleen, tonsils, red bone marrow, and greater omentum. There is
also a wide variation in the di stributions of specific types of lymphocytes within different microenvironments of lymphoid or gans or tissues (Westermann and Pabst 1992). There are 5 or more subsets of B cells, including several di fferent types of memory cells, in human blood (Klein et al. 1998; van Zelm et al. 2007). Likewise, there are 5

− 7 different subtypes of B cells, including several different types of memory cells, in human tonsil s (Liu et al. 1997; van Zelm et al. 2007). About 50

− 90% of the intraepithelial lymphocytes in the palatine tonsils are B cells, while the majority of the B cells in the crypt ep ithelium are mature memory B cells (Nave et al. 2001). The extent to which lymphocytes are localized within the lymphatic sy stem, and even within a given lymph node, thus depends on the type of lymphocyte (e.g., B- or T- cell) and its specific origin (see footnotes and associated text in Appendix B) and its experience with antigen, including involvement of the ge rminal center reaction. Some memory B-cells tend to remain in the lymph nodes while activated T-cells usually leave the lymph nodes via the efferent lymph and circulate more freely. However, T-cell-depende nt B-cells that become memory cells via the germinal center reaction often migrate to sites other than th e lymph nodes, including the bone marrow and antigen-draining sites in mucosal asso ciated lymphatic tissue, Peyer’s patches, the subepithelial region of the tonsil s, and splenic marginal zones, where they may persist for a considerable period of time (months or more ) (Liu and Arpin 1997; Paramithiotis and Cooper 1997; Hay and Andrade 1998; Angelin-Du clos et al. 2000; Chiorazzi 2007). Thus, how often lymphocytes might be exposed to plutonium α particles in the thoracic lymph nodes, for example, could vary signifi cantly depending on the type of lymphocyteCLL Risk Model for NIOSH-IREP January 5, 2010 Final

33 precursor, the level of “antigen experience,” and wh ether it was activated w ith T-cell help in the germinal center. 10 In addition, the doses from plutonium α particles to each of the lymphocyte precursors for CLL in the groups identified above would depe nd on their effective lif espan. Estimated mean lifespan for lymphocytes is about 4

− 6 months, but individual lifetim es can range from a few days
to several months for some B cells and from se veral years to a human lifetime for memory cells (Chiorazzi 2007; Rai 2007c). Ma rginal zone B cells are predominantly memory cells, and most are long-lived, with some expected to survive as long as their host (B oyd 2007). The lifespan is again determined by the type of lymphocyte in volved, its temporal pattern of exposure to antigen, and nature of involvement of the germ inal center reaction (Ange lin-Duclos et al. 2000; Chiorazzi 2007). Uncertainty about the influence of each of these factors on a given lymphocyte and about the lymphocyte precursor for CLL makes it difficult to choose which estimate of lifespan would be most appropriate to apply in a dose reconstruc tion effort. For example, active suppression of terminal differentiation gives the memory lymphocy te a self-renewing capa bility like that of stem cells in other organ systems (Fearon et al. 2001) and thus makes it a seemingly good candidate for a CLL precursor. Under these cond itions, an assumption of a lymphocyte lifespan as long as a human lifetime might be a technically defensible choi ce. Unfortunately, there is not yet a consensus on whether the memory cell is th e most likely precursor for CLL (Keating et al. 2003; Stevenson and Caligaris-Cappio 2004; Chior azzi et al. 2005; Hervé et al. 2005; CaligarisCappio and Ghia 2007; Chiorazzi 2007; Ghia et al. 2007; Zent 2007). Even if agreement could be reached on this point, uncertainties of the t ypes described in the previous paragraph limit the estimation of a meaningful dose for use in IREP with the proposed CLL model. Finally, only those lymphatic cells or CLL precursors (see f ootnote 10) located within the thoracic lymph nodes would be exposed at a high dos e rate over the entire lifetime of an exposed person. Although the structural ce lls of the lymph nodes themselves are not expected to be precursors for CLL, radiogenic changes in th ese cells, including induc tion of senescence, 11 could

10 A possible but seemingly low probability scenario could involve a mature B-lymphocyte (e.g., a memory cell) that localized in one of the thoracic lymph nodes for an entire human lifetime. Unfortunately, it is not currently possible to estimate the probability of such a scenario based on current limited information. 11 It is more likely that such changes would result from buildup of senescent cells as part of the aging process rather than as an effect of radiation exposure. This could expl ain in part why CLL is primarily a disease of old age.CLL Risk Model for NIOSH-IREP January 5, 2010 Final

34 potentially affect the tissue microenvironment in a manner that would promote development of CLL, e.g., by rescuing CLL cells from apoptosis or supporting the expansion of CLL clones (Table 1; Stevenson and Caligaris -Cappio 2004; Chiorazzi et al. 2005). 12, 13 However, even under such assumptions, the dose rate to this restricted part of the lymphatic system (i.e., the thoracic lymph nodes) is not nece ssarily meaningful for estimating the risk of developing CLL (or other types of NHL, for that matter). The reas on is that a substantial body of research indicates that the mass of the whole orga n or tissue is the appropriate reference to be considered in calculating radiation dose and estim ating radiation risk. The problem in the case of CLL is to ascertain just what constitutes the “whole organ or tissu e” (see footnote 2). The frequency of chromosome aberrations and induction of liver cancer was not affected by spatial inhomogeneity in the dose delivered to Chinese hamster liver cells by plutonium α particles (Brooks et al. 1974, 1983). Based on a wi de range of information, including results
from studies of the effects of va riations in spatial di stribution of doses fr om plutonium deposited in the lungs of dogs, hamsters, and rats (Bair et al. 1974), the NCRP (1 975) concluded that “particulate plutonium in the l ung is no greater hazard than the same amount of plutonium more uniformly distributed throughout the lung.” Brooks (2003) has recently concluded that bystander effects make the organ or tissue respond as a whol e and thus indicate that radiation effects may be related to organized responses at the level of the organ or tissue rather than to alterations induced in single cells. Thus, it is not clear that the dose within an individual component of the lymphatic system, as opposed to an estimate of the dose to the entire “system”—assuming a meaningful definition of the term is possibl e for CLL—would necessarily be a meaningful quantity with respect to induction of CLL (or other types of NHL).

12 All experts contacted by NIOSH/OCAS (Chiorazzi 2007; Kay 2007; Rai 2007; Zent 2007) agreed that CLL cells are sustained and stimulated to prolifer ate by external signals from other cell types (stromal cells in bone marrow, T cells in lymph nodes, and nurse-like cells in pe ripheral blood—or perhaps elsewhere; see footnote 13 ) or soluble factors (cytokines, chemokines, and/or antigens). CLL B cells seem to need close or intimate contact with the cell types just mentioned and are drawn to them by chemokines. A lthough there is extensive current research in this area, including that by one of the experts (Kay), it is not possible to assess which signals are most important or how many are needed to sustain a CLL clone. Chiorazzi and Kay seem to agree that a multiplicity of such signals is probably needed. Kay also pointed out that it is not currently known whether genetic or cytogenetic alterations in these other cell types play a role in the etiology of CLL. 13 Nurse-like cells were first recognized in situ in the thymus, where they form characteristic complexes with immature T cells and play an important role in T-cell maturation and differentiation. Mature counterparts of bloodderived nurse-like cells are likely to play a role in protecting CLL B cells from apoptosis in distinct lymphoid (and non-lymphoid) microenvironments rather than in the bloodstream (Burger et al. 2000).CLL Risk Model for NIOSH-IREP January 5, 2010 Final

35 It currently appears that reconstructing inte rnal doses or external non-uniform doses for claimants with CLL (and related cancers) represen ts a major technical challenge given current uncertainties of the types just discussed. Effec tive implementation of the CLL model in cases in
which claimants received non-uniform internal or external exposures hinges on whether meaningful estimates of dose can be reconstructe d and appears to require a significant research effort, outside the scope of this report. An analysis of several appr oaches to determine a meaningful dose from non-uniform exposures for the purpose of assessing the risk of CLL (Apostoaei and Trabalka 2009) has recently been completed.

7. CONCLUSIONS

Based on the most recent information on CLL inci dence reported in epidemiological studies
and the latest estimates of ER R/Sv for NHL mortality in male atomic-bomb survivors reported by Richardson et al. (2009), it is concluded th at the IREP lymphoma and multiple myeloma model should be used for CLL, but only as an interim solution because of the concerns
enumerated in Section 5.2. Whereas neither the earlier analysis of mo rtality from malignant lymphoma in the LSS cohort by Pierce et al. (1996), based on follow-up through 1990, nor the UNSCEAR analysis of LSS data on CLL incide nce, based on follow-up through 1987, showed a significant dose-response in males or females, the updated analysis by Richardson et al. (2009), which was based on 10 years of additional follo w-up, now shows a highly significant response for mortality from NHL in males. Thus, we thi nk that the DS02 data for lymphoma incidence, which will have an even longer follow-up when published, could show significantly higher risks
for both males and females. Therefore, a high priority will be assigned to development of a new NHL model to cover CLL, and other diseases now in cluded in the revised definition of NHL, after the RERF-NCI team publishes the new LSS data. Reanalysis of th e Japanese A-bomb survivor data for NHL (or, barring that, for the lymphoma and multiple myel oma grouping), using an appropriate surrogate for whole-body dose, and with a focus on de riving age-at-exposure and attained-age dependencies that are independent of those derived for solid tumors , could potentially result in significant changes to the risk coeffi cients in the proposed CLL model.CLL Risk Model for NIOSH-IREP January 5, 2010 Final

36 8. REFERENCES

Abramenko, I., Bilous, N., Chumak, A., Davidova, E., Kryachok, I., Martina, Z., Nechaev, S., Dyagil, I., Bazyka, D. and Bebeshko, V. 2008. Chronic lymphocytic leukemia patients exposed to ionizing radiation due to the Chernobyl NPP accident—with focus on immunoglobulin heavy chain analysis. Leuk. Res. 32 : 535

− 545.
Anderson, A. O. 2002. Lymphocyte Recirculation and Homing in Immunity

. (http://www.geocities.com/Cape Canaveral/Hangar/1962/page2.html ), January 1, 2002.
Andrade, W. N., Johnston, M. G., and Hay, J. B. 1998. The relationship of blood lymphocytes to the recirculating lymphocyte pool. Blood 91 :1653

− 1661.
Angelin-Duclos, C., Cattoretti, G., Lin, K. -I., and Calame, K. 2000. Commitment of B lymphocytes to a plasma cell fate is a ssociated with Blimp-1 expression in vivo. J. Immunol. 165 : 5462

− 5471.
Apostoaei A.I. and Trabalka J.R. 2009. Review , synthesis, and applic ation of information on the human lymphatic system to radiation dos imetry for chronic lymphocytic leukemia. Draft report; SENES Oak Ridge, Inc., Oak Ridge, TN.
Bair, W. J., Richmond, C. R ., and Wachholz, B. W. 1974. A Radiobiological Assessment of the Spatial Distribution of Radiation dose from Inhaled Plutonium. U.S. Atomic Energy Commission (AEC) Report WASH-1320, Se ptember 1974. AEC, Washington, DC.
Bakkus, M. H., Van Riet, I., Van Camp, B., and Thielemans, K. 1994. Evidence that the clonogenic cell in multiple myeloma originates from a pre-switched but somatically mutated cell. Br. J. Haematol. 87 : 68

− 74.
Berkow, R. 1992. Chronic lymphocytic leukemia (CLL), pp. 1241

− 1243 in The Merck Manual of Diagnosis and Therapy , 16 th edition, R. Berkow (editor- in chief). Merck Research Laboratories, Rahway, NJ.
Boice, J. D., Jr. 2002. Radiation-induced leukemia, pp. 152

− 169 in Leukemia, 7 th edition, E. S. Henderson, T. A. Lister, and M. F. Greaves (editors). Saunders, Philadelphia, PA.
Boice, J. D., Jr. 2005. Reconsideration of ch ronic lymphocytic leukemia for purposes of compensation. Report submitted to NIOSH/OCAS, January 7, 2005.
Boice, J. D., Jr., Day, N. E., Andersen, A., Brint on, L. A., Brown, R., Choi, N. W., Clarke, E. A., Coleman, M. P., Curtis, R. E., Flannery, J. T., Hakama, M., Hakulinen, T., Howe, G. R., Jensen, O. M., Kleinerman, R. A., Magnin, D., Magnus, K., Makela, K., Malker, B., Miller, A. B., Nelson, N., Patterson, C. C., Pettersson, F. , Pompe-Kirn, V., Primic-Zakelj, M., Prior, P., Ravnihar, B., Skeet, R. G., Skjerven, J. E., Smith, P. G., Sok, M., Spengler, R. F., Storm, H. H., Stovall, M., Tomkins, G. W. O ., and Wall, C. 1985. Second cancers followingCLL Risk Model for NIOSH-IREP January 5, 2010 Final

37 radiation treatment for cervical cancer. An international coll aboration among cancer registries. J. Natl Cancer I. 74 :955

− 975.
Boice, J. D., Jr., Blettner, M., Kleinerman, R. A., Stovall, M., Molone y, W. C., Engholm, G., Austin, D.F., Bosch, A., Cookfair, D. L., Krementz, E. T., Latourette, H. B., Peters, L. J., Schulz, M. D., Lundell, M., Pettersson, F., Stor m, H. H., Bell, C. M. J., Coleman, M. P., Fraser, P., Palmer, M., Prior, P., Choi, N. W., Hislop, T. G., Koch, M., Robb, D., Robson, D., Spengler, R. F., von Fournier, D., Frisc hkorn, R., Lochmüller, H., Pompe-Kirn, V., Rimpela, A., Kjørstad, K., Pejovic, M. H ., Sigurdsson, K., Pisani, P., Kucera, H., and Hutchison, G. B. 1987. Radiation dose and leukemia risk in patients treated for cancer of the cervix. J. Natl Cancer I. 79 :1295

− 1311.
Blum, K. S., and Pabst, R. 2007. Lymphocyte number s and subsets in the human blood: Do they mirror the situation in all organs? Immunol. Lett. 108 :45

− 51.
Bowers, W., and Hunt, R. 2007. Immunology – Chap ter Nine: Cells Involved in Immune Responses. Microbiology and Immunology On-Line, (http://pathmicro.med.sc.e du/bowers/immune%20cells.htm ), last modified: April 5, 2007. University of South Carolina, Columbia, SC.
Boyd, D. 2007. Marginal Zone B Cell (Monocytoid B cell, MALT B cell), (http://www.healthsystem.virginia.edu/ hematology/HessEDD/immune%20cells.htm ), last modified: June 19, 2007. Division of Hemato logy/Oncology, University of Virginia, Charlottesville, VA.
Braig, M., Lee, S., Loddenkemper, C., Rudolph, C., Peters, A. H. F. M., Schlegelberger, B., Stein, H., Dörken, B., Jenuwein, T., and Scmitt, C. A. 2005. Oncogene-induced senescence as an initial barrier in lymphoma development. Nature 436 :660

− 665.
Brandtzaeg, P., and Johansen, F.-E. 2005. Mucosa l B cells: phenotypic characteristics, transcriptional regulation , and homing properties. Immunol. Rev. 206 :32

− 63.
Brandtzaeg, P., Kiyono, H., Pabst, R., and Russell , M. W. 2008. Terminology: nomenclature of mucosa-associated lymphoid tissue. Mucosal Immunol. 1 :31

− 37.
Brooks, A. L. 2003. Developing a scientific basis for radiation risk estimate s: Goal of the DOE Low Dose Research Program. Health Phys. 85 :85–93.
Brooks, A. L., Retherford, J. C., a nd McClellan, R. O. 1974. Effect of 239 PuO 2 particle number and size on the frequency and distribution of ch romosome aberrations in the liver of the Chinese hamster. Radiat. Res. 59 :693

− 709.
Brooks, A. L., Benjamin, S. A., Hahn, F. F., Browns tein, D. G., Griffith, M. W., and McClellan, R. O. 1983. The induction of liver tumors by 239 PuO 2 citrate or 239 PuO 2 particles in the Chinese hamster. Radiat. Res. 96 :135

− 151.CLL Risk Model for NIOSH-IREP January 5, 2010 Final

38 Brown, A. R. 1992. Immunological func tions of splenic B-lymphocytes. Crit. Rev. Immunol. 11 :395

− 417.
Burger, J. A., Tsukada, N., Burger, M., Zvaifler , N. J., Dell’Aquila, M., and Kipps, T. J. 2000. Blood-derived nurse-like cells protect chro nic lymphocytic leukemia B cells from spontaneous apoptosis through stromal cell

− derived factor-1. Blood 96 :2655

− 2663.
Caligaris-Cappio, F. 2000. Biology of chronic lymphocytic leukemia. Rev. Clin. Exp. Hematol. 4 :5

− 21.
Caligaris-Cappio, F., and Ghia, P. 2007. The norma l counterpart to the chronic lymphocytic leukemia B cell. Best Pract. Res. Clin. Haematol. 20 :385

− 397.
Cardis, E., Vrijheid, M., Blettner, M., Gilbert, E., Hakama, M., Hill, C., Howe, G., Kaldor, J., Muirhead, C. R., Schubauer-Berigan, M., Yoshim ura, T., Bermann, F., Cowper, G., Fix, J., Hacker, C., Heinmiller, B., Marshall, M., Thierry-Chef, I., Utteback, D., Ahn, Y.-O., Amoros, E., Ashmore, P., Auvinen, A., Bae, J.-M., Bernar, J., Biau, A., Combalot, E., Deboodt, P., Diez Sacristan, A., Eklöf, M., Enge ls, H., Engholm, G., Gulis, G., Habib, R. R., Holan, K., Hyvonen, H., Kerekes, A., Kurtinaitis, J., Malker, H., Martuzzi, M., Mastaukas, A., Monnet, A., Moser, M., Pearce, M. S., Rich ardson, D. B., Rodriguez-Artalejo, F., Rogel, A., Tardy, H., Telle-Lamberton, M., Turai, I., Usel, M., and Veress, K. 2007. The 15- country collaborative study of can cer risk among radiation work ers in the nuclear industry: estimates of radiation-related cancer risks. Radiat. Res

. 167 :396

− 416.
Chiorazzi, N. 2007. Comments on questions a bout chronic lymphocytic leukemia. Report submitted to NIOSH/OCAS, August 18, 2007.
Chiorazzi, N., Rai, K. R., and Ferrarini, M. 2005. Mechanisms of disease: Chronic lymphocytic leukemia. N. Engl. J. Med. 352 :804

− 815.
Crespo, M., Bosch, F., Villamor, N., Bellosillo, B., Colomer, D., Rozman, M., Marcé, S., LópezGuillermo, A., Campo, E., and Montserrat, E. 2003. ZAP-70 expression as a surrogate for immunoglobulin-variable-region mutations in chronic lymphocytic leukemia. N. Engl. J. Med. 348 :1764

− 1775.
Crowther, M. A. 2004. Untitled report s ubmitted to NIOSH/OCAS, November 2004.
Dewald, G. W., Brockman, S. R., Paternoster, S. F., Bone, Nancy, D., O’Fallon, J. R., Allmer, C., James, C. D., Jelinek, D. F., Tschumper, R. C., Hanson, C. A., Pruthi, R. K., Witzig, T. E., Call, T. G., and Kay, N. E. 2003. Chro mosome anomalies detected by interphase in situ hybridization: correlatio n with significant biological featur es of B-cell chronic lymphocytic leukemia. Br. J. Haematol. 121 :287

− 295.
Dores, G. M., Anderson, W. F., Curtis, R. E ., Landgren, O., Ostroumova, E., Bluhm, E. C.,CLL Risk Model for NIOSH-IREP January 5, 2010 Final

39 Rabkin, C. S., Devesa, S. S., and Linet, M. S. 2007. Chronic lymphocytic leukemia and small lymphocytic leukemia: overvie w of the descriptive epidemiology. Brit. J. Haematol. 139 :809

− 819.
El Ghissassi, F., Baan, R., Straif, K., Grosse, Y., Secretan, B., Bouvard, V., Benbrahim-Tallaa, Guha, N., Freeman, C., Galichet, L., a nd Cogliano, V. 2009. A review of human carcinogens—Part D: radiation. Lancet Oncol. 28 :751

− 752.
Fearon, D. T., Manders, P., and Wagner, S. D. 2001. Arrested differentia tion, the self-renewing memory lymphocyte, and vaccination. Science 293 : 248

− 250.
Ganusov, V. V., and De Boer, R. J. 2007. Do most lymphocytes in humans really reside in the gut? Trends Immunol. 28 :514

− 518.
George, E. D., and Sadovsky, R. 1999. Multiple myeloma: Recognition and management. American Family Physician (http://www.aafp.org/afp/990401ap/1885.html ), April 1, 1999.
Ghia, P., Scielzo, C., Frenquelli, M., Muzio, M ., and Caligaris-Cappio, F. 2007. From normal to clonal B cells: Chronic lymphocytic leukemia (C LL) at the crossroad between neoplasia and autoimmunity. Autoimmun. Rev. 7 :127

− 131.
Grund, S. 2004. Medical Encyclopedia: Hodgkin’s lymphoma . Medline Plus (http://www.nlm.nih.gov/medlineplu s/print/ency/a rticle/000580.htm ), May 3, 2004.
Hall, P., Boice, J. D., Jr., Berg, G., Bjelkengre n, G., Ericsson, U.-B., Hall quist, A., Lidberg, M., Lundell, G., Mattsson, A., Tennvall, J., Wi klund, K., and Holm, L.-E. 1992. Leukaemia incidence after i odine-131 exposure. Lancet 340 :1

− 4.
Hamblin, T. J. 2008. Have we been wrong about ionizing radiation and chronic lymphocytic leukemia. Leuk. Res. 32 :523

− 525.
Harris, N. L., Jaffe, E. S., Diebold, J., Fla ndrin, G., Muller-Hermelink, H. K., Vardiman, J., Lister, T. A., and Bloomfield, C. D. 1999. Wo rld Health Organization classification of neoplastic diseases of the hematopoietic a nd lymphoid tissues: Repor t of the clinical advisory committee meeting

─ Airlie House, Virginia, November 1997. J. Clin. Oncol. 17 : 3835

− 3849.
Hay, J. B., and Andrade, W. N. 1998. Lymphoc yte recirculation, exercise, and immune responses. Can. J. Physiol. Pharmacol. 76 :490

− 496.
Hervé, M., Xu, K., Ng, Y.-S., Wardemann, H., Albe siano, E., Messmer, B. T., Chiorazzi, N., and Meffre, E. 2005. Unmutated and mutated chronic lymphocytic leukemias derive from selfreactive B cell precursors despite expr essing different antibody reactivity. J. Clin. Invest. 115 :1636

− 1643.
(IARC) International Agency for Research on Ca ncer. 2000. Ionizing Radiati on, Part I: X- andCLL Risk Model for NIOSH-IREP January 5, 2010 Final

40 gamma ( γ ) –radiation and neutrons. IARC Monogra phs on the Evaluation of Carcinogenic Risk to Humans, Volume 75. IARC, Lyon, France.
(ICRP) International Commission on Radiological Protection. 1994. Human Respiratory Tract Model for Radiological Prot ection, ICRP Publication 60. Ann. ICRP 24 (1–3). Pergamon Press, Oxford.
(ICRP) International Commission on Radiological Protection. 2002. Basic Anatomical and Physiological Data for Use in Radiological Pr otection: Reference Values, ICRP Publication 89. Ann. ICRP 32 (3–4). Pergamon Press, Oxford.
Inskip, P. D., Kleinerman, R. A., Stovall, M., Cookfair, D. L., Hadjimic hael, O., Moloney, W. C., Monson, R. C., Thompson, W. T., Wactawski- Wende, J., Wagoner, J. K., and Boice, J. D., Jr. 1993. Radiat. Res. 135 :108

− 124.
Kay, N. E. 2007. Review: Evidence for the intera ction of CLL leukemic B cells with stromal tissue and its consequences for the leukemic B cell. Report submitted to NIOSH/OCAS, July 17, 2007. Keating, M. J., Chiorazzi, N., Messmer, B., Damle, R. N., Allen, S. L., Rai, K. R., Ferrrarini, M., and Kipps, T. J. 2003. Biology and treatmen t of chronic lymphocytic leukemia. Hematology 2003 :154

− 175.
Kesminiene, A., Evrard, A.-S., Ivanov, V. K., Mala khova, I., Kurtinaitis, J ., Stengrevics, A., Tekkel, M., Anspaugh, L. R., Bouville, A., Ch ekin, S., Chumak, V. V., Drozdovitch, D., Gapanovitch, V., Golovanov, I., Hubert, P., Ilic hev, S. V., Khait, S. E., Kryuchkov, V. P., Maceika, E., Maksyoutov, M., Mirkhaidorov, A. K., Polyakov, S., Shchukina, N., Tenet, V., Tserakhovitch, T. I., Tsykalo, A., Tukov, A. R., and Cardis, E. 2008. Risk of hematological malignancies among Chernobyl liquidators. Radiat. Res

. 170 :721

− 735.
Klein, U., Goosens, T., Fischer, M., Kanzler, H. , Braeuniger, A., Rajewsky, K., and Küppers, R. 1998. Somatic hypermutation in normal and transformed human cells. Immunol. Rev. 162 :261

− 280.
Klein, U., Tu, Y., Stolovitsky, G. A., Mattioli, M., Cattoretti, G., Hu sson, H., Freedman, A., Inghirami, G., Cro, L., Baldini, L., Neri, A ., Califano, A., and Dalla-Favera, R. 2001. Gene expression profiling of B cell chronic lym phocytic leukemia reveals a hommogeneous phenotype related to memory cells. J. Exp. Med. 194 : 1625

− 1638.
Kraal, G. 1992. Cells in the marginal zone of the spleen. Int. Rev. Cytol. 132 :31

− 74.
Kryuchkov, V., Chumak, V., Maceika, E., Anspaugh, L. R., Cardis, E., Bakhanova, E., Golovanov, I., Drozdovitch, V., Luckyanov, N., Kesminiene, A., Voilleque, P., and Bouville, A. 2009. RADRUE method for reconstruction of external photon doses for Chernobyl liquidators in epidemiological studies. Health Phys. 97 , 275–298.
Kyasa, M. J., Hazlett, L., Parrish, R. S., Schi chman, S. A., and Zent, C. S. 2004. Veterans withCLL Risk Model for NIOSH-IREP January 5, 2010 Final

41 chronic lymphocytic leukemia/small lymphocy tic leukemia (CLL/SLL) have a markedly increased rate of second malignancy, whic h is the most common cause of death. Leuk. Lymphoma 45 : 507

− 513.
Land, C. E. 2000. Unpublished data evalua ted during development of IREP.
Land, C. E., Gilbert, E. S., Smith, J. M., Hoffman, F. O., Apostoaei, I., Thomas, B., and Kocher, D. C. 2003. Report of the NCI-CDC Worki ng Group to Revise the 1985 NIH Radioepidemiological Tables. NIH Publication No. 03-5387. U.S. Department of Health and Human Services, National Instit utes of Health, National Canc er Institute, Bethesda, MD.
Linet, M. S., Schubauer-Berigan, M. K., Weisenburger, D. D., Rich ardson, D. B., Landgren, O., Blair, A., Silver, S. R., Field, R. W., Ca ldwell, G., Hatch, M., and Dores, G. M. 2007. Chronic lymphocytic leukemia: an overview of aetiology in light of recent developments in classification and pathogenesis. Brit. J. Haematol. 139 :672

− 686.
Linet, M. S., Freedman, D. M., Mohan, A. K., Doody, M. M., Ron, E., Mabuchi, K., Alexander, B. H., Sigurdson, A., and Hauptman, M. 2005. In cidence of hematopoietic malignancies in U.S. radiologic technologists. Occup. Environ. Med. 62 :861

− 867.
Liu, Y.-J., and Arpin, C. 1997. Germ inal center development. Immunol. Rev. 156 :111

− 126.
Lonial, S. 2005. Introduction to Myeloma , Multiple Myeloma Research Foundation (http://www.multiplemyeloma.org/about_myeloma/ ), September 9, 2005.
Magrath, I. 1992. Molecular ba sis of lymphomagenesis. Cancer Res. (Suppl.) 52 :5529s

− 5540s.
Marti, G., Abbasi, F., Raveche, E., Rawstron, A. C., Ghia, P., Aurran, T., Caporaso, N., Shim, Y. K., and Vogt, R.F. 2007. Overview of monoclonal B-cell lymphocytosis. Brit. J. Haematol. 139 :701

− 708.
McHeyzer-Williams, L. J., and McHeyzer-Williams , M. G. 2005. Antigen-specific memory cell development. Annu. Rev. Immunol. 23 :487

− 513.
Meffre, E., Schaefer, A., Wardemann, H., Wils on, P., Davis, E., and Nussenzweig, M. C. 2004. Surrogate light chain expressing human periphe ral B cells produce self-reactive antibodies. J. Exp. Med. 199 :145

− 150.
Mellors, R. C. 2002. Immunopathology. (http://edcenter.med.cornell.edu/CU MC/PathNotes/Immunopathology/Immuno_01.html ), last modified: August 27, 2002. Cornell Univ ersity Medical Center, Ithaca, NY.
Montoya, M. C., Holtmann, K., Snapp, K. R., Borges , E., Sánchez-Madrid, F., Luscinsckas, F. W., Kansas, G., Vestweber, D., and de La ndázuri, M. O. 1999. Memory B lymphocytes from secondary lymphoid organs interact w ith E-selectin through a novel glycoprotein ligand. J. Clin. Invest. 103 :1317

− 1327.CLL Risk Model for NIOSH-IREP January 5, 2010 Final

42 Muirhead, C. R., O’Hagan, J. A., Haylock, R. G. E., Phillipson, M. A., Willcock, T., Berridge, G. L. C., and Zhang, W. 2009. Mortality a nd cancer incidence following occupational radiation exposure: third anal ysis of the National Regist ry for Radiation Workers. Brit. J. Cancer 100 :206–212.
Nave, H., Gebert, A., and Pabst, R. 2001. Morp hology and immunology of the human palatine tonsil. Anat. Embryol. 204 :367

− 373.
NCI (National Cancer Institute). 2009a. Adu lt Non-Hodgkin Lymphoma (PDQ®): Treatment, Health Professional Versi on, http://www.cancer.gov/cancertop ics/pdq/treatment/adult-nonhodgkins/healthprofessional/allpages/print , last modified: August 28, 2009. NCI, U.S. National Institutes of Health, Bethesda, MD.
NCI (National Cancer Institute). 2009b. Chroni c Lymphocytic Leukemia (PDQ®): Treatment, Health Professional Version, http://www.cancer.gov/cancertopi cs/pdq/treatment/CLL/healthpr ofessional/a llpages/print , last modified: May 22, 2009. NCI, U.S. Nationa l Institutes of Hea lth, Bethesda, MD.
NCI (National Cancer Institute). 2009c. A dult Hodgkin Lymphoma (PDQ®): Treatment, Health Professional Version, http://www.cancer.gov/cancertopi cs/pdq/treatment/adu lthodgkins/healthprofessional/allpage s/print , last modified: May 15, 2009. NCI, U.S. Nati onal Institutes of Health, Bethesda, MD.
NCRP (National Council on Radiati on Protection and Measurements). 1975. Alpha-emitting Particles in Lungs. NCRP Report 46. NCRP, Washington, DC.
NIOSH (National Institute for Occ upational Safety and Health). 2004. Chronic Lymphocytic Leukemia: Annotated Bibliography , July 21, 2004 draft. NIOSH, Health-Related Energy Research Branch, Cincinnati, OH.
NRC (National Research Council) , Committee to Assess Health Risks from Exposure to Low Levels of Ionizing Radiation. 2006. Health Risks from Exposure to Low Levels of Ionizing Radiation, BEIR VII

− Phase 2. The National Academies Press, Washington, DC.
ORAU (Oak Ridge Associated Universities). 2006. Selection for Internal and External Dosimetry Target Organs for Ly mphatic/Hematopoietic Cancers , Office of Compensation Analysis and Support Technical Informati on Bulletin 12, Rev. 1. ORAU, Oak Ridge, TN.
Pabst, R. 1988. The spleen in lymphocyte migration. Immunol. Today 9 :43

− 45.
Paramithiotis, E., and Cooper, M. D. 1997. Memory B lymphocytes migrate to bone marrow in humans. Proc. Natl. Acad. Sci. USA 94 :208

− 212.
Parkin D.M., Whelan S.L., Ferlay J., Raym ond L., Young J (eds.). 1997. Cancer in five continents, Volume VII. IARC Scien tific Publication No. 143, Lyon, France.CLL Risk Model for NIOSH-IREP January 5, 2010 Final

43 Pillai, S., Cariappa, A., and Moran, S. T. 2005. Marginal zone B cells. Annu. Rev. Immunol. 23 : 161

− 196.
Preston, D. L., Kusumi, S., Tom onaga, M., Izumi, S., Ron, E., Ku ramoto, A., Kamada, N., Dohy, H., Matsui, T., Nonaka, H., Thompson, D. E., Soda, M., and Mabuchi, K. 1994. Cancer incidence in atomic bomb survivors. Part I II. Leukemia, lymphoma, and multiple myeloma, 1950–1987. Radiat. Res. 137 (Suppl.), S68–S97.
Preston, D. L., Lubin, J. H., Pierce, D. A., and McConney, M. E. 1998. EPICURE Version 2.10. Hirosoft International Corporation, Seattle, WA.
Preston, D. L., Pierce, D. A., Shimizu, Y., Cullings, H. M., Fujita, S., Funamoto, S., and Kodama, K. 2004. Effect of recent changes in atomic bomb survivor dosimetry on cancer mortality risk estimates. Radiat. Res. 162 :377–389.
Rai, K. R. 2007a. Re: NIOSH inquiry. Report submitted to NIOSH/OCAS, May 8, 2007.
Rai, K. R. 2007b. Pathophysiologic mechanisms of chronic lymphocytic leukemia and their application to therapy. Exp. Haematol. 35 :134

− 136.
Rai, K. R. 2007c. Re: Your review on CLL fo r NIOSH. Report submitted to NIOSH/OCAS, June 8, 2007.
Rawstron, A. C., Green, M. J., Kuzmicki, A., Kenne dy, B., Fenton, J. A. L., Evans, P. A. S., O’Connor, S. J. M., Richards, S. J., Morga n, G. J., Jack, A. S., and Peter Hillmen. 2002. Monoclonal B lymphocytes with the characte ristics of “indolent” chronic lymphocytic leukemia are present in 3.5% of adults with no rmal blood counts: revelations from the B-cell receptor. Blood 100 :635

− 639.
Rawstron, A. C., Bennett, F., and Hillmen, P. 200 7. The biological and c linical relationship between CD5+23+ monoclonal B-cell lymphocyt osis and chronic lymphocytic leukemia. Brit. J. Haematol. 139 :701

− 708.
Rericha, V., Kulich, M., Reicha, R., Shore, D. L., and Sandler, D. P. 2006. Incidence of leukemia, lymphoma, and multiple myeloma in Czech uranium miners: a case-cohort study. Environ. Health Persp. 114 :818

− 822.
Richardson, D. B. 2004. Chronic Lymphocytic Leuke mia: Reconsideration of Exclusion from Eligibility for Compensation Under EEO ICPA. Report submitted to NIOSH/OCAS, November 2004.
Richardson, D. B., Wing, S., Schroeder, J ., Schmitz-Feuerhake, I., and Hoffmann, W. 2005. Ionizing radiation and chr onic lymphocytic leukemia. Environ. Health Persp. 113 :1

− 5.
Richardson, D. B., Sugiyama, H., Wing, S., Sakata, R ., Grant, E., Shimizu, Y., Nishi, N., Geyer,CLL Risk Model for NIOSH-IREP January 5, 2010 Final

44 S., Soda, M., Suyama, A., Kasagi, F., and K odama, K. 2009. Positive associations between ionizing radiation and lym phoma mortality among men. Am. J. Epidemiol. 169 :969

− 976.
Romanenko, A. Ye., Finch, S. C., Hatch, M., Lubi n, J. H., Bebeshko, V. G., Bazyka, D. A., Gudzenko, N., Dyagil, I. S., Reiss, R. F., Bouville, A., Chumak, V. V., Trotsiuk, N. K., Babkina, N. G., Belyayev, Y., Masnyk, I., Ron, E., Howe, G. R., and Zablotska, L. B. 2008. The Ukrainian-American study of leukemia and related disorders among Chornobyl cleanup workers from the Ukraine: III. Radiation risks. Radiat. Res

. 170 :711

− 720.
Rosenwald, A., Alizadeh, A. A ., Widhopf, G., Simon, R., Davis, R. E., Yu, X., Yang, L., Pickeral, O. K., Rassenti, L. Z., Powell, J. Bo tstein, D., Byrd, J. C., Grever, M. R., Cheson, B. D., Chiorazzi, N., Wilson, W. H., Kipps, T. J., Brown, P. O., and Staudt, L. M. 2001. Relation of gene expression phenotype to immunoglobulin mutation genotype in B cell chronic lymphocytic leukemia. J. Exp. Med. 194 :1639

− 1647.
Schubauer-Berigan, M. K., Daniel s, R. D., Fleming, D. A., Markey, A. M., Couch, J. R., Ahrenholz, S. H., Burphy, J. S., Ande rson, J. L., and Tseng, C.-Y. 2007. Chronic lymphocytic leukemia and radiation: findings am ong workers at five U. S. nuclear facilities and a review of the recent literature. Brit. J. Haematol. 139 :799

− 808.
(SEER) Surveillance, Epidemiology, and End Results Program. 2005. (www.seer.cancer.gov) SEER*Stat Database: Inciden ce - SEER 13 Regs Public-U se, Nov 2004 Sub for Expanded Races (1992-2002), National Cancer Institute, DCCPS, Surveillance Research Program, Cancer Statistics Branch, released Apr il 2005, based on the November 2004 submission.
Silver, S. R., Hiratzka, S. L., Schubauer-Berig an, M. K., and Daniel s, R. D. 2007. Chronic lymphocytic leukemia radiogeni city: a system atic review. Cancer Causes Control 18 : 1077

− 1093.
Stevenson, F. K., and Caligaris-Cappio, F. 2004. Chronic lymphocytic leukemia: revelations from the B-cell receptor. Blood 103 :4389

− 4395.
Stevenson, F., Sahota, S., Zhu, D., Ottensmeier, C., Chapman, C., Oscier, D., and Hamblin, T. 1998. Insight into the origin and clonal history of B-cell tumors as re vealed by analysis of immunoglobulin variable region genes. Immunol. Rev. 162 :247–259.
Stilgenbauer, S., Bullinger, L., Lichter, P., Döhner, H., and the German CLL Study Group. 2002. Genetics of chronic lymphocytic leukemia: genomic aberrations and V h gene mutation status in pathogenesis and clinical course. Leukemia 16 :993

− 1007. Teoh, G., and Anderson, K. C. 1997. Interaction of tumor and host cells with adhesion and extracellular matrix molecules in th e development of multiple myeloma. Haematol. Oncol. Clin. North Am. 11 :27

− 42.
Thompson, D. E., Mabuchi, K., Ron, E., Soda, M., Tokunaga, M., Ochikubo, S., Sugimoto, S., Ikeda, T., Terasaki, M., Izumi, S., and Pres ton, D. L. 1994. Cancer occurrence in atomic bomb survivors. Part II: Solid tumors, 1958–1987. Radiat. Res. 137 , S17–S67.CLL Risk Model for NIOSH-IREP January 5, 2010 Final

45 Tierens, A., Delabie, J., Mich iels, L., Vandenberghe, P. and De Wolf-Peeters. 2000. Marginalzone B cells in the human lymph node and sp leen show somatic hypermutation and display clonal expansion. Blood 96 :1612

− 1614.
Trepel, F. 1974. Number and distribution of lymphocytes in man. A critical analysis. Klin. Wschr. 52 :511

− 515.
(UNSCEAR) United Nations Scientific Committ ee on the Effects of Atomic Radiation. 2008. Effects of Ionizing Radiation, Vo lume I, Annex A, Epidemiolo gical Studies of Radiation and Cancer. United Nations, New York.
Van Zelm, M. C., Szczepanski, T., van der Burg, M., and van Dongen, J. J. M. 2007. Replication history of B lymphocytes reveals homeostatic proliferation and ex tensive antigen-induced cell expansion. J. Exp. Med. 204 :645

− 655. Voutsadakis, I. A. 2000. Apoptosis a nd the pathogenesis of lymphoma. Acta Oncol. 39 : 151

− 156.
Vrijheid, M., Cardis, E., Ashmore, P., Auvinen, A., Gilbert, E., Blettner, M., Gilbert, E., Habib, R. R., Malker, H., Muirhead, C. R., Richar dson, D. B., Rogel, A., Schubauer-Berigan, M., Tardy, H., and Telle-Lamberton. 2008. Ionizing ra diation and risk of chronic lymphocytic leukemia in the 15-country of nuclear industry workers. Radiat. Res

. 170 :661

− 665.
Walsh, L., Rühm, W., and Kellerer, A. M. 2004. Ca ncer risk estimates for gamma-rays with regard to organ-specific doses. Part II: site sp ecific solid cancers. Radiat. Environ. Biophys. 43 :225

− 231.
Weiss, H. A., Darby, C., and Doll, R. C. 1994. Cancer mortality following x-ray treatment for ankylosing spondylitis. Int. J. Cancer 59 :327

− 338.
Weiss, H. A., Darby, C., Fearn, T., and Doll, R. C. 1995. Leukemia mortality after x-ray treatment for ankylosing spondylitis. Radiat. Res. 142 :1

− 11.
Westermann, J., and Pabst, R. 1992. Distribution of lymphocyte subsets and natural kill er cells in the human body. Clin. Investig. 70 :539

− 544.
Zablotska, L. B. 2004. Comments on the Ar guments for Covering Chronic Lymphocytic Leukemia Under the Energy Employees Occupa tional Illness Compensation Program Act (EEOICPA) advanced by its stakeholders in the “Chronic Lymphocytic Leukemia: Reconsideration of Exclusi on from Eligibility for Comp ensation under EEOICPA.” Report submitted to NIOSH/OCAS, December 2004.
Young, A. J. 1999. The physiology of lymphocyte recirculation through the single lymph node in vivo

. Seminars Immunol. 11 :73

− 83.
Young, A. J., Marston, W. L., Dessing, M., Dudl er, L., and Hein, W. R. 1997. DistinctCLL Risk Model for NIOSH-IREP January 5, 2010 Final

46 recirculating and non-recirculating pools in th e peripheral blood are defined by ccordinated expression of CD21 and L-selectin. Blood 90 :4865

− 4875. Zent, C. S. 2007. OCAS NIOSH Report on CLL. Report submitted to NIOSH/OCAS, April 30, 2007.

ACKNOWLEDGEMENTS

This study was funded by NIOSH under cont ract number 200-2006-18097. We thank David Richardson, Mary Schubauer-Berigan, Richard Wakeford, and Lydia Zablotska for their constructive review comments and helpfu l suggestions for improving the report.CLL Risk Model for NIOSH-IREP January 5, 2010 Final

47 

### APPENDIX A

AGE AND GENDER DEPENDENCE OF CHRONIC LYMPHOCYTIC LEUKEMIA (CLL) INCIDENCE RATES IN U.S. AND JAPANESE POPULATIONS

This appendix summarizes age-dependent a nd age-adjusted incidence rates of CLL (ICD-9 204.1) in the United States (SEER 2005), in Japan, and in a combined population from Hiroshima and Nagasaki (from the IARC; Parkin et al. 1997). The age-adjusted rates are normalized to the world standard population, as is required for use in PC calculations in IREP. World-standardized cancer rates for the combined population from Hiroshima and Nagasaki are
used in NIOSH-IREP to model the transfer of ri sk from the LSS cohort to the U.S. population. The data in Tables A.1, A.2, A.3, and in Fig. A.1 show that there are large differences in background incidence rates of CLL in the U.S. and Japan. Incidence rates are more than an order of magnitude lower in the Japanese than in th e U.S. population. The rates for Asians and Pacific
Islanders who reside in the U.S. are also lower than for other popul ation subgroups and are similar to those of the Japanese population, sugge sting that the influen ce of genetic background may be stronger than that of environment on the incidence of CLL in Asians (see Section 3.4 in the main text). This conclusion holds not only for incidence rates of CLL alone, but also for rates of CLL + SLL. (Dores et al. 2007). The data in the tables represent snapshots in ti me of expected inciden ce rates. Incidence rates have been changing over time for a variety of reas ons. For example, Dores et al. (2007) showed that incidence rates compile d by the Surveillance, Epidemiology, and End Results (SEER) Program in the U.S. are affected by delays inreporting of CLL (but not of SLL). The ageadjusted rates reported by Dores et al. (2007) based on records covering 1993

− 2004 and standardized to the U.S. populat ion in the year 2000 (5.34 per 10 5 person-years for males and 2.70 per 10 5 person-years for females), which take this factor into account, are about 60% higher
than the incidence rates show n in Table A.1. However, onl y a small portion of the 60% difference is due to the delay in reporting. The majo rity of this difference is due to the fact the age-adjusted rates repo rted by Dores et al. (2007) were obt ained using the 2000 U.S. standardCLL Risk Model for NIOSH-IREP January 5, 2010 Final

48 population, while the age-adjusted ra tes included in this report we re based on the world standard population. Dores et al. (2007) showed that the incidence rate for SLL was about 1/3 that for CLL. They also reported what appeared to be differences in the gender and age-specif ic trends of SLL when compared to CLL. However, differences in met hods of detection of CLL and SLL and lack of information from epidemiological studies preven ted them from drawing firm conclusions about the meaning of their observations, i. e., without further investigation.CLL Risk Model for NIOSH-IREP January 5, 2010 Final

49 Table A.1. Incidence rates of CLL (ICD-9 204.1) in the United States based on SEER-13 data covering the 1992

− 2002 period. CLL incidence rates in the U.S. a [per 100,000 per year] Age group Male Females newborn 0 0.033 01-04 0 0.0082 05-09 0 0 10-14 0 0 15-19 0 0 20-24 0.0068 0.014 25-29 0.012 0.013 30-34 0.12 0.040 35-39 0.43 0.14 40-44 1.0 0.4 45-49 2.4 1.2 50-54 4.9 2.2 55-59 9.0 4.0 60-64 13.5 6.6 65-69 20.3 10.2 70-74 27.6 13.3 75-79 32.6 17.5 80-84 39.4 21.2 85+ 47.9 24.6Ageadjusted b 3.3 (0.0375) c 1.6 (0.0239) c

a SEER (2005) - SEER database: Incidence - SEER 13 Registries Public-Use, Nov 2004 Sub (1992-2002).

b Adjusted to the world standard population.

c Standard error in the age-adjusted rate.CLL Risk Model for NIOSH-IREP January 5, 2010 Final

50 Table A.2. Incidence rates of CLL (ICD-9 204.1) in the Japanese population. CLL incidence rates a [per 100,000 per year] Age group Male Females newborn 0 0 01-04 0 0 05-09 0 0 10-14 0 0 15-19 0 0.034 20-24 0 0.038 25-29 0.041 0 30-34 0.035 0 35-39 0.089 0 40-44 0.139 0 45-49 0.077 0.037 50-54 0.46 0 55-59 0.41 0.23 60-64 0.90 0.11 65-69 0.87 0.22 70-74 0.87 0.37 75-79 1.0 1.2 80-84 2.6 0.68 85+ 0 0 Age-adjusted b 0.13 (0.016) c 0.037 (0.0078) c

a Rates represent the combined population of all cancer registries in Japan – IARC Publication No. 143 (Parkin et al. 1997).

b Adjusted to the world standard population.

c Standard error in the age-adjusted rate.CLL Risk Model for NIOSH-IREP January 5, 2010 Final

51 Table A.3. Incidence rates of CLL (ICD-9 204.1) in the Hiroshima and Nagasaki population. CLL incidence rates a [per 100,000 per year] Age group Male Females newborn 0 0 01-04 0 0 05-09 0 0 10-14 0 0 15-19 0 0 20-24 0 0.23 25-29 0 0 30-34 0 0 35-39 0.19 0 40-44 0.56 0 45-49 0.48 0 50-54 0.52 0 55-59 0.27 0 60-64 0.61 0 65-69 0.84 0 70-74 2.94 0.40 75-79 0.80 0.51 80-84 0 2.32 85+ 5.11 0 Age-adjusted b 0.25 (0.055) c 0.043 (0.022) c

a Rates are for the combined population of Hiroshima and Nagasaki, Japan – IARC Publication No. 143 (Parkin et al. 1997).

b Adjusted to the world standard population.

c Standard error in the age-adjusted rate.CLL Risk Model for NIOSH-IREP January 5, 2010 Final

52 0 10 20 30 40 50 60 

### MALESCLL incidence rates (cases per 100,000 per yr)

0 10 20 30 40 50 6000-0405-0910-1415-1920-2425-2930-3435-3940-4445-4950-5455-5960-6465-6970-7475-7980-8485+ 

### FEMALES
US Population Whites (US) Blacks (US) American Indians / Alaska Native (US) Asian or Pacific Islanders (US) Japanese PopulationCLL incidence rates (cases per 100,000 per yr)

Age-group
Figure A.1 . Comparison of incidence rates of CLL in different groups in U.S. and Japanese populations. Incidence data for the US populatio n is obtained from SE ER (1992-2002 dataset). Incidence data for the Japanese population is obtained from IARC (Parkin et al. 1997).CLL Risk Model for NIOSH-IREP January 5, 2010 Final

53 

### APPENDIX B

ADDITIONAL INFORMATION BEARI NG ON ETIOLOGY OF CLL

Models for the progression of many solid cancer s from pre-malignant tumors to full-blown malignancies have been established, but the pr ecursor lesions for lymphomas, including lowgrade forms such as CLL, have yet to be identi fied (Dewald et al. 2003; Braig et al. 2005). As assays have become more sensitive for dete cting monoclonal B-CLL-lik e cells in peripheral blood, researchers have detected a monoclonal B-cell lymphocytosis (MBL) in about 3% of adults over 40 years of age with otherw ise normal blood counts (<5000 lymphocytes mm -3 ) (Rawstron et al. 2002), but a higher percentage ha s been reported in families with an increased incidence of CLL (Rawstron et al. 2007). Current ly however, the signific ance of these findings with respect to the etiology of CLL in the gene ral population is not fully clear, because only a small percentage of those with detected MBL show progression to CLL, requiring treatment, while in others the MBL may spontaneously disa ppear or simply remain stable (Marti et al. 2007; NCI 2009b). Molecular and cellular changes associated w ith the onset of CLL are being intensively investigated by research organi zations around the world. A diverse set of B-cell genetic and cell
surface markers has been identified and used to differentiate the indolent and aggressive subtypes of the disease (Stevenson et al. 1998; Stilgenbauer et al. 2002; Crespo et al. 2003; Dewald et al. 2003; Chiorazzi 2005; Chiorazzi 2007; Rai 2007b). It has been suggested that even B-cell CLL may not be a single dise ase because the clinical and gene tic information indicate that median survival time post-diagnosis can range fro m a few months to >20 years, depending on the genetic characteristics, mutation status of the V genes, and expression of surface proteins, such
as CD38 and ZAP70. Such characte ristics often appear to correla te with disease progression and survival time (e.g., Stilgenbauer et al. 2002; Cres po et al. 2003; Dewald et al. 2003; Chiorazzi 2005; NCI 2009b; Rai 2007b). B lymphocytes mature in the bone marrow a nd in the process rearrange immunoglobulin variable (V) genes to create th e code for an immunoglobulin molecu le that serves as the B-cellCLL Risk Model for NIOSH-IREP January 5, 2010 Final

54 receptor for antigen. When an antigen of adequate affinity engages the receptor, the cell typically enters a germinal center in lymph-node follicle s, where it rapidly divides and its V genes undergo somatic hypermutation (see Fig. 2 in Ch iorazzi et al. 2005). The somatic hypermutation process requires antigen stimul ation, B-cell activation, cooperati on of T lymphocytes and other cells, and, typically, the germinal center reaction (Keating et al . 2003). However, the process can
occur without the involvement of T cells and out side germinal centers, in the marginal zones around lymphoid follicles (see Fig. 2 in Chiorazzi et al. 2005). 14 Both processes can lead to the development of plasma (antibody-secreting) cells or memory
B cells. 15 The latter retain a “memory” of the specifi c antigen that can be used to mobilize the immune system faster if the body encounters the antigen later in life. Although memory cells without V gene mutations that derive from T-cell- independent stimulation ar e often referred to as “antigen-experienced” B cells, to distinguish them from memory cells with somatic mutations acquired inside germinal centers (Chiorazzi et al. 2003), both types of cells are obviously “antigen-experienced” and “antigen-stimulated” (Fig. 2 in Chio razzi et al. 2005; Table 1; see more below).
Those B-CLL cells that have been mutated must have come from immunologically competent cells, but, using this parameter as the sole definition of immune competence,
“unmutated” B-CLL cells could derive from im mature, naïve incompetent cells (i.e., possibly residing in the bone marrow). However, surface membrane phenotypic studies and measurements of telomeric shortening indicate that antigen stimu lation and response to it are prerequisites for the development of BCLL, even in those cases that do not exhibit V H gene mutations. B-CLL cells over-express activation markers and under-exp ress markers down-regulated by cell activation. Leukemic cells uniformly express CD27, identifying them as B cells that were triggered and had entered the memory (or plasma) cell pool. Telomere shorte ning occurs with each round of cell division.
Telomeres of B-CLL cells are much shorter th an those in normal B-cells, and those in

14 Marginal zone B cells are so named because they reside in the marginal zones of the spleen and other lymphoid organs/tissues and are thought by many investigators to develop in the marginal zone of the spleen (Brown 1992; Kraal 1992; Weller et al. 2004; Pillai et al. 2005). In humans , they circulate and are found in lymph nodes, mucosalassociated lymphoid tissue, Peyer’s patches, and tonsils (Tierens et al. 1999; Pillai et al. 2005; Boyd 2007). 15 Other potential CLL precursors are: mature B-cells residing in either the marginal zone or follicular mantle; the human equivalents of murine B-1 cells; V-preB + L + cells; and transitional B cells that routinely exit the marrow and circulate to solid lymphoid tissues (Chiorazzi et al. 2005; He rvé et al. 2005; Chiorazzi 20 07). The first three of these groupings include some types of memory cells.CLL Risk Model for NIOSH-IREP January 5, 2010 Final

55 “unmutated” B-cells are much shorte r than those in cells bearing V H gene mutations, suggesting
that the “unmutated” leukemic cells have a much more extensive history of cell division than those in mutated cells (Keating et al. 2003; Rai 2007b). As noted above, ra pid cell division takes place in the lymph-node follicle after antigen exposure. Whether they are mutated or not, current inform ation indicates that B-CLL cells have passed through developmental stages that are not asso ciated with the bone marrow, raising serious questions about its current design ation as the most appropriate ta rget organ for induction of CLL by radiation exposure.CLL Risk Model for NIOSH-IREP January 5, 2010 Final

56 

### APPENDIX C

ASSESSMENT OF POTENTIAL LATE NCY FOR INCIDENCE OF CLL, LYMPHOMAS, AND MULTIPLE MYELOMA

Our review of the epidemiological data indicate d that the extended latency period (20+ years) for mortality from CLL suggested by Richardson et al. (2005) would not be applicable to a risk model for incidence of CLL (i.e., as in IREP). On e reason is that the post-diagnosis survival time for CLL patients is highly uncertain, potentially ranging from a few months to a normal life expectancy (NCI 2009b). Another is that it is que stionable whether existin g epidemiological data on CLL induction by radiation ar e capable of providing useful information on an extended latency period (see Section 5.4 in main text). Four earlier studies in addition to that of Silver et al. (2007) potentially provide information on the latency period for radiogenic CLL:

• Mortality in women with benign gynecological disease (mean age 46.5 y) treated with radiotherapy (mean dose to bone marrow: 1.2 Gy) (Inskip et al. 1993) (Table C.1). Fourteeen CLL cases were coded to ICD-8 (code 204.1), but then were lumped with 3 lymphocytic leukemias, not otherwis e specified (code 204.9).

• Mortality in ankylosing spondylit is patients (83.5% male) treate d with radiotherapy (93.2% aged between 20

− 60 at first treatment; mean dose to bone marrow: 4.4 Gy) (Weiss et al. 1995) (Table C.2). CLL deaths prior to 1979 were coded to ICD-8. ICD-9 codes were grouped to correspond with subtyp es coded under ICD-8 (204.1, 207.8).

• Incidence in cervical cancer patients (mean age 52 y) treated with radiotherapy (mean dose to bone marrow: 7.5 Gy) (Boice et al. 1985) (Table C.1). Mean age at leukemia diagnosis was in the range 60

− 64 y for acute lymphocytic leukemia and acute or chronic myeloid leukemia, but was 67 y for CLL (Boice et al. 1987). CLL = chronic and unspecified lymphatic leukemia (ICD-7 code 204.0); direct correspon dences were made from ICD- 8 to ICD-7 codes (Boice et al. 1985).CLL Risk Model for NIOSH-IREP January 5, 2010 Final

57 Table C.1. Risks for CLL plus uns pecified lymphocytic leukemia by time since treatment in patients with cervical cancer (incidence) or benign gynecological disease (mortality) treated with radiotherapy

Time since treatment (y) Source 0

− 9 10

− 19 20

− 29 > 30 All years Observed 9 6 3 0 18 Boice et al. (1985) Expected 13.2 6.8 2.2 0.2 22.3

SIR 0.7 0.9 1.4 0.0 0.8

Observed 1 6 3 7 17 Inskip et al. (1993) Expected 1.08 2.06 2.90 3.19 9.23

SMR 0.9 2.9 1.0 2.2 (0.9

− 4.5) a 1.8 (1.0

− 2.9) a

a 95% C.I.

Table C.2. Risks for mortality from CLL plus unspecified ly mphocytic leukemia by time since treatment in ankylosi ng spondylitis patients treated with radiotherapy
Time since treatment (y) Source 0

− 4 5

− 14 15

− 24 > 25 All years Observed 0 1 0 6 7 Weiss et al. (1995) SMR 0.0 1.8 0.0 2.0 (0.7

− 4.3) a 1.4 (0.6

− 2.8) a Adjusted

SMR 0.0 2.8 0.0 1.6 Not given

a 95% C.I.

• Incidence in patients (79% female; mean ag e 47 y) exposed to I-131 for diagnostic or therapeutic (hyperthyroidism or thyroid cancer) procedures (Hall et al. 1992) (Tables C.3 and C.4). The mean doses to bone marrow varied by exposure group as follows: diagnostic (mean 0.19 mGy), treatment for hyperthyroidism (48 mGy), and treatment for thyroid cancerCLL Risk Model for NIOSH-IREP January 5, 2010 Final

58 Table C.3. Incidence of CLL a plus unspecified lymphocy tic leukemia by time since treatment in patients exposed to I-131 in diagnostic or therapeutic procedures
Time since treatment (y) Source

2

− 9 10

− 19 > 20 All years Hall et al. (1992)

Observed 13 30 22 65

SIR b 0.71 (0.38

− 1.21) 1.22 (0.83

− 1.75) 1.26 (0.79

− 1.90) 1.08 (0.84

− 1.38)

a Hall et al. (1992) do not specify which cancer classification was used in diagnosing CLL; however, because of the time frame in which th e analyses were conduc ted, “CLL” may include unspecified lymphocytic leukemia (see text). b 95% C.I. given in parentheses.

Table C.4. Incidence of CLL a in relation to dose to bone marrow in patients exposed to I-131 in diagnostic or therapeutic procedures
Dose to bone marrow (and mean dose) (mGy) Source 0.00

− 0.01

(0.009) 0.02

− 0.10 (0.058) 0.11

− 10 (3.5) 11

− 100 (39) >100 (221) Hall et al. (1992) Observed 2 17 30 9 7 b

SIR c 0.53 (0.06

− 1.90) 1.15 (0.67

− 1.83) 1.22 (0.82

− 1.74) 0.62 (0.28

− 1.17) 3.17 (1.27

− 6.53) RR c 1.00 d 2.22 (0.52

− 9.63) 2.54 (0.61

− 10.6) 1.17 (0.25

− 5.41) 6.49 (1.34

− 31.3)

a Hall et al. (1992) do not specify which cancer classification was used in diagnosing CLL; however, because of the time frame in which th e analyses were conduc ted, “CLL” may include unspecified lymphocytic leukemia (see text). b 4 of the 7 “CLL” cases occurred in patients trea ted for thyroid cancers (4 in 802 patients) vs 3 cancers in 46,186 patients exposed to I-131 in diagnoses or treatment for hyperthyroidism.

c 95% C.I. given in parentheses. d Low-dose group was used as comparis on group in estimating relative risk.

(250 mGy). As might be expected, the highest in cidence rate for “CLL” was in the latter group (see footnote b to Table C.4). No CLL classification scheme was referenced. Beyond the obvious concerns about potential for misdiagnoses and classification of CLL, all of the studies are potentially limited by the (1 ) confounding effects of prior disease on CLL incidence, (2) uncertainties in diagnosis and clas sification of CLL, (3) limited statistical power, particularly in the last case, because of the low doses involved, and (4) meaningfulness of boneCLL Risk Model for NIOSH-IREP January 5, 2010 Final

59 marrow doses for lymphoma induction. However, the epidemiological studies of mortality cited by Richardson et al. (2005) as providing the best evidence for an ex tended latency period [studies of patients treated for benign gynecologi cal disorders (Inskip et al. 1993) or ankylosing spondylitis (Weiss et al. 1995)] ha ve another major drawback for our purposes (i.e., beyond the
fact that the numbers of “CLL” cases in the exposed groups are small and thus none of the results indicate an excess risk signifi cantly different from zero) (Tab les C.1 and C.2). Namely, the clinical and genetic informati on on CLL indicate that median su rvival time post-diagnosis can range anywhere from a few months to >20 years, depending on the genetic characteristics of the malignant cells involved , but with an estimated median survival time of ~10 y (e.g., Stilgenbauer et al. 2002; Crespo et al. 2003; Dewald et al. 2003). As Richardson et al. (2005) acknowledge, patients with CLL can live many years without developing significant symptoms, a nd often die with the disease, but from causes other than CLL (e.g., Kyasa et al. 2004). Thus, although the two epidemiological studies cited could contain evidence of an extended latency with re spect to mortality (assuming that CLL was the predominant cause of mortality), the results would s till have to be adjusted for estimated survival post-diagnosis to be useful for estimating a latency period for CLL incidence

. The evidence for an extended latent period is w eak in the results from the study by Inskip et al. (1993), where 41% of the cases of mortality at tributed to “CLL” were observed in the first 20 years, even if it is assumed that the grouped lymphocytic malignancies analyzed in the study were dominated by CLL (Table C.1). Although the tr end appears to be str onger in the data from the study by Weiss et al. (1995), where 86% of th e deaths occurred after 25 or more years had elapsed (Table C.2), the case for a significant e ffect is weakened by the very small numbers involved (7 total cases). If a median survival time post-diagnosis of 10 y is subtracted from the results for latency with respect to mortality in these two studies (i.e., a time since treatment of 20

− 29 y becomes
10

− 19 y), an estimate for the mean latency pe riod appears to fall in the range ~10

− 20 y (Table C.5). Even if the median survival time was sk ewed to a much lower value (say, 2 y), on the assumption that the “CLL” cases in these two stud ies were all more aggre ssive and thus led to earlier mortality than expected or that median survival time during the periods that the studies were conducted was much lower than reported, th e results, when combined with the “CLL”CLL Risk Model for NIOSH-IREP January 5, 2010 Final

60 Table C.5. Adjusted numbers of CLL cases in patients treated with radiotherapy as a function of treatment conditi on and time since treatment

Reason for radiotherapy Time since treatment (y)

0

− 9 10

− 19 ≥ 20

Cervical cancer 9 6 3
Benign gynecological disease 7 a 3 a 7 a
Ankylosing spondylitis 1 a 2 b 4 b

Thyroid diseases 13 30 22
Total including thyroid diseases 30 41 36
Total excluding thyroid diseases 17 11 14

a Adjusted by moving original time since trea tment range forward by one decade to reflect estimated time of incidence. b Moving original time since treatment range forward by one decade to estimate time of incidence results in 6 cases observed at ≥ 15 y. These were apportioned between the decade 10

− 19 y and all decades >20 y, as shown.

incidence data from the studies by Boice et al. (1985) and Hall et al. (1992), still l ead to the same conclusion: a mean latency ~10

− 20 y (see Tables C.1 and C.2).
The results on incidence of “CLL” from the studies by Boic e et al. (1985) of second cancers in cervical cancer patients and by Hall et al. (199 2) of patients exposed to I-131 in diagnostic or therapeutic procedures both suggestthat peak incidence occurs before 20 y has elapsed. Eightythree percent of “CLL” cases in cervical cancer patients and 66% of cases in patients exposed to I-131 were recorded within 20 years of treatment (Tables C.1 and C.3). Both of these studies have their drawbacks (effects of previous cancer in both, and relatively high doses in the first study, and very low doses and lower statistical po wer in the second case). On the other hand, the study by Hall et al. (1992) showed the greatest effect at the highest doses (T able C.4), albeit with large uncertainties and with perhaps the greatest potential for confounding (i.e., by thyroid cancer) . Taken as a whole, the limited data on the latency period for CLL induction by radiation exposure do not support the conten tion that the latency period is in excess of 20 years, butCLL Risk Model for NIOSH-IREP January 5, 2010 Final

61 instead indicate that the uncertainty associated w ith defining a latency period is quite large, even when the data for thyroid diseases are excluded (i .e., because radiation doses to this cohort were so low). However, even using our admittedly crude approach, it appears possible to make a reasonable case for a peak latency occurri ng prior to 20 y (28 total cases over 0

− 19 y vs 14 occurring at or beyond 20 y), if the assumption is made that the lymphocytic malignancies identified in the other three studies were pr edominantly made up of CLL alone (ICD-9 code 204.1). A question that immediately arises, however, is whether the latency pe riod for the grouping of lymphoma and multiple myeloma [which include NHL, Hodgkin’s disease (lymphoma), and multiple myeloma; ICD-9 codes 200

− 203)] selected as a surrogate for CLL is similar to that of CLL. If it is not, it is conceivable that the risk coefficients in the IREP risk model (which was
based on data on cancer incidence in individuals w ith a much wider age range than those treated with radiotherapy) c ould be affected. Data bearing on latency of the cancer types in the lymphoma and multiple myeloma grouping was available from three of the four ep idemiological studies discussed above (Tables C.6 and C.7). The data for incidence of NHL and multiple myeloma as a function of time since treatment do not indicate a prolonged latency peri od; 92% and 80% of cases, respectively, were diagnosed in the first 19 y post-treatment (Table C .6). The mortality data ar e somewhat divergent (Tables C.6 and C.7). While both studies of mo rtality suggest the possibility of an extended latency period for multiple myeloma, one (Weiss et al. 1994) indicates that the peak latency for both NHL and Hodgkin’s lymphoma occurs before 25 y has elapsed, the other (Inskip et al. 1993) shows no clear pattern for Hodgkin’s lymphoma (50% of cases diagnosed before and after 20 years have elapsed, respectively), and the data for NHL in Table C.6 are not comparable to
the data in Table C.7 because information on la tency for 10 cases of NHL with ICD-8 code 202
were not included in the former. Even with these limitations, when the mort ality data are adjusted by subtracting the estimated time from diagnosis to mortality following treatment (as was done for CLL) to estimate the incidence patte rns as a function of time and then are pooled with the incidence data (Table C.8), the results for both multiple my eloma and NHL are similar to those for CLL (particularly for the case where the data for thyr oid diseases were exclude d; see Table C.5). The results for Hodgkin’s lymphoma suggest a mean late ncy on the order of 10 y or less (Table C.8).CLL Risk Model for NIOSH-IREP January 5, 2010 Final

62 Table C.6. Numbers of cases of non-Hodg kin’s lymphoma, Hodgkin’s lymphoma, and multiple myeloma by time since treatment in patients with cervical cancer (incidence) or benign gynecological disease (mor tality) treated with radiotherapy

Cancer site (ICD-8 code) Time since treatment (y)

Source 0

− 9 10

− 19 20

− 29 ≥ 30 All years Non-Hodgkin’s lymphoma (200, 202) 34 26 5 0 65 Cervical cancer study (Boice et al. 1985) Multiple myeloma (203) 13 15 5 2 35

Non-Hodgkin’s lymphoma (200 only) 0 3 11 10 24 Benign gynecological disease study (Inskip et al. 1993) Hodgkin’s lymphoma (201) 2 3 2 3 10
Multiple myeloma (203) 1 1 8 4 14

Table C.7. Numbers of deaths from non-Hodgkin’s lymphoma, Hodgkin’s lymphoma, and multiple myeloma by time since treatment in ankylosing spondylitis patients treated with radiotherapy
Cancer site (ICD-9 code) Time since treatment (y) Source 5

− 24 a > 25 All years Non-Hodgkin’s lymphoma (200, 202, exc. 202.4) 23 14 37 Weiss et al. (1994) Hodgkin’s lymphoma (201) 11 2 13 Multiple myeloma (203, exc. 203.1, 238.6) 8 14 22

a Five-year latency period was assumed.

Since Hodgkin’s lymphoma contributed only 10 cases and NHL and multiple myeloma contributed 76 and 31, respectively, of the 117 cases in the combined set of data used to parameterize the Lymphoma and multiple myeloma model in IREP (our surrogate for CLL), the potential for confounding because of potential di fferences in latency when compared to CLL may not be a serious issue. However, the midpoi nt of the latency function used with theCLL Risk Model for NIOSH-IREP January 5, 2010 Final

63

Table C.8. Adjusted numbers of NHL, Hodgki n’s lymphoma, and multiple myeloma cases in patients treated with radiotherapy as a function of treatment condition and time since treatment

Cancer site Reason for radiotherapy Time since treatment (y)

0

− 9 10

− 19 20

− 29 ≥ 30 Non-Hodgkin’s lymphoma Cervical cancer 34 26 5 0
Benign gynecological disease a 1 b 7 b 8 b 8 b

Ankylosing spondylitis a 10 c 13 c 7 c 7 c Hodgkin’s lymphoma Benign gynecological disease 5 2 1 d 2 d

Ankylosing spondylitis a 7 e 4 e 1 e 1 e Multiple myeloma Cervical cancer 13 15 5 2
Benign gynecological disease a 1 f 2 f 7 f 4 f

Ankylosing spondylitis a 2 g 6 g 5 g 9 g Non-Hodgkin’s lymphoma All conditions 44 46 20 15 Hodgkin’s lymphoma All conditions h 12 6 2 3 Multiple myeloma All conditions 16 23 17 15

a Mortality studies. b Moving original time-since-treatment range fo rward by 5 y to estimate time of incidence results in 3 cases observed at 5

− 14 y, 11 cases observed at 15

− 24 y, and 10 cases observed at ≥ 25 y. These were apportioned between the decades 0

− 9 y, 10

− 19 y, 20

− 29 y and all decades ≥ 30 y, as shown.

c Moving original time since treatment range forward by one decade to estimate time of incidence results in 23 cases observed at 0

− 19 y and 14 at ≥ 20 y. These were apportioned between the decades 0

− 9 y, 10

− 19 y, 20

− 29 y, and all decades ≥ 30 y, as shown. d Moving original time since treatment range forward by one decade to estimate time of incidence results in 3 cases observed at ≥ 20 y. These were apportioned between the decade 20

− 29 y and all decades >30 y, as shown.

e Moving original time since treatment range forward by one decade to estimate time of incidence results in 11 cases observed at 0

− 14 y and 2 at ≥ 15 y. These were apportioned between the decades 0

− 9 y, 10

− 19 y, and all decades ≥ 20 y, as shown.CLL Risk Model for NIOSH-IREP January 5, 2010 Final

64 ______________________________Table C.8 (continued)_____________________________ f Moving original time since treatment range fo rward by 3 y to estimate time of incidence results in 1 case observed at 0

− 6 y, 1 case observed at 7

− 16 y, 8 cases observed at 17

− 26 y, and 4 cases observed at ≥ 27 y. These were apportioned between the decades 0

− 9 y, 10

− 19 y, 20

− 29 y, and all decades ≥ 30 y, as shown.
g Moving original time since treatment range fo rward by 3 y to estimate time of incidence results in 8 cases observed at 2

− 21 y and 14 at ≥ 22 y. These were apportioned between the decades 0

− 9 y, 10

− 19 y, 20

− 29 y, and all decades ≥ 30 y, as shown. h Data not available for beni gn gynecological disease study.

lymphoma and multiple myeloma mode l in IREP is the same as that for solid tumors, and thus some modification appears to be jus tified for its use with a CLL model. Another set of data that might provide information bearing on the issue of latency (or age dependencies on risks) is that for the Japane se atomic-bomb survivors (Table C.9). For individuals exposed at ages ≥ 40 (and at doses ≥ 0.01 Gy), the time-dependent incidence of NHL combined with Hodgkin’s lymphoma as is reason ably similar to that for CLL in radiotherapy patients (Table C.5), who were of similar ages when exposed. However, the data for individuals exposed at younger ages and data for incidence of multiple myeloma for all ages at exposure
indicate that most cases were diagnosed more than 25 y after the bombings. These observations could indicate a longer latency for lymphoma in individuals exposed at younger ages or at all ages at exposure for those diagnosed with multip le myeloma. However, because the patterns for individuals exposed at doses ≥ 0.01 Gy and at doses <0.01 Gy are so similar, these data probably
result from the increase in incidence of CLL w ith increasing age (see Appendix A), and could be interpreted to mean that radi ation exposure had little infl uence on development of their lymphomas. This is an issue that could be reexam ined at some point, perhaps in conjunction with the reanalysis of the LSS data when the DS02-based dose-response information becomes
available. The only published atomic-bomb survivor data that include CLL are compromised because CLL is combined with HCL, acute leukemias of unspecified type, and cases of myelodysplastic syndrome (Table C.10). The small numbers involved also limit interpretati on of the data, but, in general, the time-dependent incide nce of these “other” leukemias is very similar to that for NHL and Hodgkin’s lymphoma in Table C.9.CLL Risk Model for NIOSH-IREP January 5, 2010 Final

65 Table C.9. Number of lymphoma and multiple myeloma cases in Japanese atomic bomb survivors as a function of time since the bombings a

Cancer site Age at exposure (y) Exposure (Gy) Time since bombings (y)

5

− 7 8

− 12 13

− 24 25

− 32 NHL and Hodgkin’s lymphoma 0

− 19 ≥ 0.01 0 3 2 12

<0.01 0 0 9 11

20

− 39 ≥ 0.01 0 1 10 25

<0.01 3 2 10 25

≥ 40 ≥ 0.01 0 5 17 11

<0.01 1 6 17 1 Multiple myeloma 0

− 19 ≥ 0.01 0 0 2 3

<0.01 0 0 0 2

20

− 39 ≥ 0.01 0 1 2 6

<0.01 0 0 1 9

≥ 40 ≥ 0.01 0 0 5 11

<0.01 0 0 1 15

a Source: Table III in Preston et al. (1994).CLL Risk Model for NIOSH-IREP January 5, 2010 Final

66 C.10. Number of “other leukemia” cases in Japanese atomic bomb survivors from Hiroshima as a function of time since the bombings a

Age at exposure (y) Exposure (Gy) Time since bombings (y)

5

− 20 21

− 32

0

− 19 ≥ 0.01 1 1

<0.01 1 0

20

− 39 ≥ 0.01 0 4

<0.01 2 2

≥ 40 ≥ 0.01 1 1

<0.01 2 0

a Source: Table XI in Preston et al. (1994). “Other leukemia” cases include: 4 cases of CLL, 2 of HCL, 7 of acute leukemias of unspecified type , and 2 of myelodysplastic syndrome. They are based only on Hiroshima data because cases fr om Nagasaki were predominantly adult T-cell leukemia, for which no association with radiation exposure was found.