Response to Review Comments on the Draft Report
Development of a CLL Risk Model for NIOSH

- IREP 1

December 1, 2009

The reviewers of the draft report were : David Richardson, Ph.D., Department of Epidemiology, School of Public Health, Universi ty of No rth Carolina at Chapel H ill Mary Schubauer

- Berigan, Ph.D., Division of Surveillance, Hazard Evaluation and Field Studies, NIOSH, Cincinnati, Ohio Richard Wakeford, Ph.D., Dalton Nuclear Institute, University of Manchester, UK Lydia Zablotska, Ph.D., Depart ment of Epidemiology and Biostatistics, School of Medicine, University of California San Francisco.

Note: The r eviewers‘ opinions do not necessarily represent those of their organization or university. Affiliations are provided as information only.

The r eview comments have been grouped into eight general categories:
Ne ed to include ne wer epidemiological findings Potential radiogenicity of chronic lymphocytic leukemia ( CLL ) CLL incidence rates in the U.S. and Japanese populations Appropriateness of current IREP lymphoma and multiple myeloma model for CLL
Alternative models Appropriateness of e xtended latency for CLL Background information on CLL, including implications of reclassification as an NHL Internal dosimetry for CLL Specific editorial/technical sug gestions

Need to Include Newer Epidemiological Findings

One reviewer ( Mary Schubauer

- Berigan) noted that the list of key references on page 3 of our CLL risk model report appeared to be ―slightly outdated

. ‖ Although Silver et al. (2007) and publications from the 2007 workshop on CLL that were published in the British Journal of

1 Responses were prepared by J. R. Trabalka and A. I. Apostoaei of SENES Oak Ridge, Inc. , under contract 200

- 2006

- 180972

Haematology were cited in our draft report, she suggested adding them to the list of key references on page 3. We have done so. Lydia Zablotska and David Richardson each identifi ed other r ecent epidemiological studies
that they felt merited inclusion and that could influence development of a CLL risk model. The

most e xtensive listing was provided in Zablotska ‘s review

. The papers by Linet et al. (2005) and Rericha et al. (2006) on CLL in radiological technologists and Czech uranium miners, respectively, were reviewed by Silver et al. (2007) and Schubauer

- Berigan et al. (2007)
, both of
which we cited

. The studies by Rericha et al. (2006) and Abramenko et al. (2008) were reviewed
in the paper by Hamblin (2008), which we also cited, and the latter two papers were cited in our draft report on internal dosimetry for assessment of radiogenic risks from CLL (Apostoaei and Trabalka 2009). Our report was drafted in 2007, and amended only sl ightly in January 2009, primarily to reflect information obtained during work on the draft CLL dosimetry report. We had previously accumulated and reviewed all but one of the papers cited by Zablotska and Richardson. However, there was no pressing need to include them in the report because we had already noted that CLL was potentially radiogenic and none of them provided risk models that were appropriate substitutes for the lymphoma and multiple myeloma models in IREP (see below). All
of the relevant epidem iological paper s identified by Zablotska and Richardson have now be en cited in the revised report.

Potential Radiogenicity of CLL

T he reviewers did not disagree with our basic conclusion, namely that CLL could be radiogenic, and that
, from an epidemiol ogical perspective, we can only conclude that we currently do not have solid scientific evidence of a well

- defined dose

- response from the LSS
data
, but not that there is no risk of CLL due to occupational radiation exposure

. The BEIR VII committee (NRC 2 006) conducted an exhaustive review of radio

epidemiological studies. They pointed out that the variability inherent in all epidemiological studies requires that the entire body of relevant literature be evaluated in order to assess possible
associations b etween radiation and cancer induction, be they positive or negative. On this basis, it is the current judgment of all expert groups, including the BEIR VII committee, UNSCEAR,3

and IARC, that the occurrence of lymphomas, and of CLL in particular, has not be en convincingly linked to radiation exposure (also see Hamblin 2008; El Ghissassi et al. 2009)

. These conclusions are supported by the results of recent comprehensive studies of CLL in occupational workers (Cardis et al. 2007; Vrijheid et al. 2008; Muirhea d et al. 2009). However, Zablotska counter ed that studies of CLL incidence in Czech uranium miners (i.e., Rericha et al. 2006) and in radiological technologists (both reviewed in Silver et al. 2007 and
Schubauer

- Berigan et al. 2007) and three recent case

- control studies of Chornobyl (Chernobyl)
remediation workers (one of which was the aforementioned study by Abramenko et al. 2008) indicate that CLL is not only associated with radiation exposure (also see Richardson‘s comments in paragraph 3 on page 7 of his review ) but also that its risk profile may be similar to that of other types of leukemia. Relying on one study by Abramenko et al. (2008), Zablotska also suggests that cases of CLL in radiation workers occurred at younger ages, were more advanced
when identified clinically, and showed faster progression. T hese last conclusions are inconsi stent with the findings from other occupational studies cited above and previous studies of medically exposed individuals , which had a similar level of follow

- up (see F ig. 2 in the draft report; Inskip et al. 1993; Weiss et al. 1995; Wick et al. 1999)

. Both Silver et al. (2007) and Schub auer

- Berigan et al. (2007) concluded that the case both for and against CLL as a radiogenic disease is still to be proven. The papers by Rericha et al. (2006)
and Abramenko et al. (2008) specifically were evaluated by Hamblin (2008), who concluded that these ―new data‖

... certainly do not establish that CLL may be caused or even made worse by ionizing radiation.‖ Thus, the burden of proof f or a different perspective on CLL appears to fall
on the results from the other two studies of CLL in Chornobyl remediation workers (Kesminiene et al. 2008; Romanenko et al. 2008)

. However, n one of the studies of Chornobyl remediation workers provided risk coefficients for CLL alone that were significantly different from zero at the 95% level of confidence. The BEIR VII committee concluded that studies of Chornobyl remediators did not provide reliable
risk estimates because of difficulties in follow

- up and the lack of validated dose estimates (NRC 2006). For the majority of these wo r kers, individual doses were often monitored inadequately or were not monitored at all (Kryuchkov et al. 2009). T he risk estimate in the study by Romanenko et al. (2008) does not reflect effects of the very large dosimetric uncertainty
, and th e study by Kesminiene et al. (2008) did not examine the effect of dosimetric uncertainty on the dose

-4

response for CLL in isolation from other forms of leukemia

. Hamblin (2008) suggested that t he
differences in clinical characteristics and mutational status reported in the study by Abramenko et al. (2008) were probably explain ed by ―less assiduous screening‖ of the control populations
, because the incidence of CLL with unmutated IgVH genes (indi cating a more aggressive form of the disease) in remediation workers was not significantly greater than that in the controls, and both were much higher than the figure of 40% of cases reported in most We stern countries

. The results reported in Chornobyl r emediation workers, including a minimum deviance for
both CLL and non

- CLL leukemia using a latency of 2 years in one study (Romanenko et al. 2008), are seemingly diametrically opposed to those reported in the majority of epidemiological
studies and deserve considerable additional follow

- up before being accepted as valid representations of the radiogenicity of CLL. Nonetheless, the reviewers‘ points are well taken. Based on an evaluation of all of the newer information, a new paragraph was inserted in Secti on 2, as follows: Suggestive but as yet inconclusive findings in a number of recent epidemiological studies indicate that the evidence for CLL as a radiogenic disease is growing (see, e.g., Linet et al. 2005; Rericha et al. 2006; Schubauer

- Berigan et al. 2 007; Abramenko et al. 2008; Hamblin 2008; Kesminiene et al. 2008; Romanenko et al. 2008). Although these studies do not currently provide the basis for a radiation risk model for CLL, and contrary findings (i.e., no significant association between radiatio n and CLL) have been reported from recent comprehensive studies of radiation workers (Vrijheid et al. 2008; Muirhead et al. 2009), we fully agree with Hamblin’s (2008) conclusion that “[i]rradiation may have been given a clean bill of health with respect t o CLL with less than adequate evidence.”

Finally, we have modified the text in the last two paragraphs of Section 2 in an attempt to
address wh at may have been a source of confusion for two reviewers (Richardson and Zablotska) about the information on po tential radiogenicity of CLL that we tried to extract from the limited information available to us from the LSS data. Since our purpose was not to conduct an epidemiological study for CLL in the LSS cohort and since the breakdown of cases between the

expos ed and non

- exposed members of the cohort is unavailable (total was only 4 ! ) , we could not

carry out a formal calculation of standardized incidence ratios ( SIRs ) for CLL and associated
statistical confidence intervals , as suggested by Richardson

. Thus , our point was that because the5

expec ted background incidence of CLL in the LSS cohort was so low (~1 case), the available information did not allow us to conclude that CLL was not radiogenic in the LSS cohort despite
the low number of cases observed

. Because t he material in these two paragraphs now seem s superfluous, we delete d most of it.

CLL Incidence Rates in the U.S. and Japanese Populations

We have revise d the calculations based on the incidence rates presented in Appendix A to
reflect the more appropria te 1950−1987 time frame covered by the LSS data for CLL and have
added 95% confidence limits to our estimates of the background incidence of CLL in the Japanese population s as recommended by both Richardson and Zablotska. However, neither of
these revisions h ad a significant impact on the estimated baseline incidence of CLL.
Zablotska recommended that we revise our data for the baseline incidence of CLL in Japan to reflect the updated information available in IARC Vol. VIII (Parkin et al. 2003). However, these
data are less relevant for the period of CLL ascertainment in the LSS cohort (1950−1987)
, because the updated information covers a different (i.e., more recent) period

. No action is needed in response to this comment. Referring to our analysis of CLL incidence in the U.S. population, Zablotska said that rates can be affected by re porting delay, and that it would be advisable to combine incidence rates of CLL and SLL, citing the study of Dores et al. (2007), who describe the effects of reporting delay and provide age

- adjusted rates of CLL + SLL that are about twice the incidence rat es presented in Fig. A.1 in our report. The purpose for including the CLL incidence rates in the U.S. and Japan in our report was to highlight that fact that Asians who live in the U.S. retain a background incidence that is similar
to that in Asia but whi ch is significantly lower than in other population sub

- groups who live in the U.S. The differences suggest a strong influence of genetic background as opposed to environmental effects on the incidence of CLL. This conclusion is fully supported by the data provided by Dores et al. , which account for reporting delays and the effects of combining CLL
with SLL. We arrived at the same conclusions as Dores et al. (2007) because d elays in reporting of CLL

cases affect all populations and sub

- groups in Japan and th e U.S. Thus, the issue of potential6

effects of reporting delays on CLL studies , raised by Zablotska based on the paper by Dores et al. (2007) , is not germane to comparisons between non

- Asian and Asian populations in the U.S. (Fig. A.1) or between non

- expos ed and exposed populations in Japan. The paper by Dores et al. (2007) also confirms that the differences between the Asian and non

- Asian populations exist whether one uses data for CLL alone or for CLL + SLL. Thus, we recommend keeping Fig. A.1
, which comp ares age

- dependent rates of CLL incidence in the Japanese population and in different ethnic groups in the U.S. population. However, we will include information from the paper by Dores et al. (2007) on effects of reporting delays and of inclusion of SLL in the
discu ssion of information on baseline risks of CLL in Appendix A.

Proposed Risk Model

(1) Appropriateness of Current IREP Lymphoma and Multiple Myeloma Model for CLL

Two reviewers ( Schubauer

- Berigan and Richard Wakeford) agreed with the proposa l to use the proposed lymphoma and multiple myeloma model for estimating the PC of CLL claims
, and as discussed in the draft report, the VA has set a precedent by using this same model under the atomic veterans compensation program to PC of CLL occurring i n military personnel who were exposed to radiation during tests or uses of atmospheric nuclear weapons. The other two reviewers ( Zablotska and Richardson ) did not agree with the use of the lymphoma and multiple myeloma model as a surrogate for CLL. They al so objected to our use of an extended latency with this model

. Our response to their concerns about latency is covered in a
later section devoted to this issue

. However, it sho uld be recognized that questions about latency affect judgments about the approp riateness of the risk model, and the two issues cannot be completely separated (see section entitled Appropriateness of Extended Latency for CLL below). Zablotska‘s questions about appropriate target organs are also covered in a separate section
below

. Bec ause our discussion of risk transport between populations (Section 5.2) was confusing
to her and to Schubauer

- Berigan, and we recommended adopting the status quo in IREP, we
have deleted the problematical text and included some of the points she made as ju stification for
our choice (see response to Schubauer

- Berigan‘s specific comment # 12 on page 16)

.7

Schubauer

- Berigan‘s comments about discontinuing the use of a DDREF in IREP are duly noted, but they may have been based on a misconception, namely that th e DDREF distribution currently in use always reduces the probability of causation (PC) for a claimant. This is not the

case because values from the lower end of the DDREF distribution (i.e., <1) drive PC estimates at the 99th percentile, the decision crite rion used in evaluating compensation claims. In addition,
the technical basis for the risk estimates in the 15

- country study to which she referred in support

o f her argument has been challenged because of concerns about data for the Canadian cohort (see
Wa keford 2005; UNSCEAR 2008; Jacob et al. 2009). Further , the more recent results from the epidemiological study of the U.K. cohort by Muirhead et al. (2009) , which were based on cancer
incidence data , support the use of a DDREF similar in magnitude to that currently used in IREP. Thus, although we agree that the DDREF distributions in IREP should be reevaluated, based on the full range of information that has become available since they were developed, we don‘t currently see a justification for deleting them in their entirety

. Zablotska raised issues similar to those we have already discussed regarding the inclusion of multiple myeloma cases in the CLL model . She also suggested that the characteristics of radiogenic CLL might be different than those in an un exposed population , a conclusion that was countered by Hamblin‘s review of the paper by Abramenko et al. (2008).
She also suggest ed , again based on the responses in the Chornobyl clean

- up workers
, that the risks of CLL may be comparable to the risks of n on

- CLL leukemia, and that consideration should be given to using current leukemia models to predict the risks of CLL , a conclusion with
which we strongly disagree

. Richardson argued that because ― CLL is a disease of older ages and very rarely occurs at

ag es <50 years,‖ that the modeled variation in age at exposure in the IREP lymphoma and multiple myeloma model is of little consequence. He suggested that, for simplicity, that the
attained

- age effect could be eliminated. In addition, he stated that since fe w workers were exposed at ages <18 years and the age

- at

- exposure influence is modest over the 18−30 y ear age range, a plausible model may behave as a time

- constant ERR/Sv of 0.18 : the point estimate for a person who is ≥30 years old at time of exposure and attained age ≥50 years

. While we agree that the variation of ERR/Sv with age at exposure b etween ages 18

- 3 0 may be considered modest, it is not negligible. A model like that proposed by Richardson would penalize those workers who were exposed at younger ages or workers who might develop CLL8

within a relatively short time after exposure. The current model in IREP with a fully described

age

- at

- exposure and attained

- age dependency does not pose any computational challenges. Thus, we believe that simplifying the model in IREP as proposed by Richardson is unnecessary.
Richardson then pointed to an apparent discrepancy , stating that th e value of ERR/Sv from the IREP model for lymphoma and multiple myeloma incidence in both men and women is
low er than estimates of ERR/Sv modeled by his team for NHL mortality in men in the LSS cohort (see Ri chardson et al. 2009). We do not think that this was an appropriate comparison.
The rationale and approach used by Land et al. to develop the model currently used in IREP

to e stimate ERR/Sv for lymphoma and multiple myeloma was described in Section 5.1 of the draft report: ―[As for NHL or multiple myeloma individually, t]he central value of the point estimate of the ERR for males in the model for the lymphoma and multiple myel oma grouping used in IREP was also positive, while that for females was negative. For the IREP model, it was

assume d that the ERRs for the two gender s were the same, although there reportedly was suggestive evidence that they differed (p = 0.09). The age p arameters used were derived from an analysis of the incidence of solid cancers other than digestive cancers, female stomach cancer, liver cancer, female breast cancer, thyroid cancer, and non

- melanoma skin cancer. The common age parameters derived from thi s analysis were reportedly used for the lymphoma and multiple myeloma model because there was little evidence of departure from them in the grouped data for the latter.‖ It should be noted that the decision to pool these dat a was driven in large part by th e recommendation of the NAS/NRC that individual models not be developed for those cases where
fewer than 50 cancers were observed in the LSS cohort. Counter to Richardson‘s assertion that the number of cases of NHL in the LSS data is adequate to support a separate analysis of the dose

- response for CLL, i t is clear from Table 2 in the draft report that the NAS/NRC criteria would argue against separate models for NHL, Hodgkins lymphoma, or multiple myeloma or for males and females for NHL alone.
We recently discovered that the UN SCEAR has reanalyzed the data for NHL alone
, significantly lowering the estimates of ERR/Sv in the process (see hi

- lit ed data in revised Table 2 below). Although the point estimate for NHL incidence in males is still positive, it is less than half the previous estimate and is not significantly different from zero even at the 90% level of9

confidence. When the cases of NHL in males and females are considered together, the p ooled risk estimate (ERR/Sv 0.08 ; see Table 2 ) is even smaller t han that for the IREP lymphoma and
Table 2. Risk estimates for incidence of non - Hodgkin’s lymphoma, Hodgkin’s lymphoma, and multiple myeloma in the Japanese 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 sinc e 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. (19 94) 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 Al l 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).10 c 95% C.I. in parentheses.

multiple myeloma model (0.178). As is the case for the individual estimates of ERR/Sv for both males and females, it is not significantly different from zero at the 90% level of confidence

. Thus, in light of the latest analysis by UNSCEAR, many of the comments and
recommendations made b y Richardson and Zablotska may no longer be relevant. Because the last data set for NHL in the LSS cohort was defined by the ICD

- 9 classification rather than the most current classification adopted by the WHO and the NCI, it still does not include CLL, HC L, Sezary‘s disease, and a variety of other conditions now included under the NHL umbrella, which, if included, could conceivably lower the ERR/Sv even further. Finally, in her specific comment No. 2, Schubauer

- Berigan noted that IARC recently
excluded CL L along with NHL and multiple myeloma from its list of cancer types having sufficient evidence of radiogenicity (see El Ghissassi et al. 2009). [Hodgkins lymphoma was also excluded by IARC, and, it, along with CLL, is excluded from the U.K. compensation pr ogram, per Richard Wakeford.] She concluded that consistent treatment of these three types of cancers within a compensation program made sense from a scientific and epidemiological perspective, if
the sole barrier to inclusion of CLL has been the lack of an available dose

- response model from the LSS cohort. Richard Wakeford made essentially the same point in his review.

(2) Alternative Models Richardson contends that we argued for use of the current lymphoma and multiple myeloma model and against developm ent of a new risk model based on NHL alone out of convenience. In point of fact, we simply pointed out what we thought it would take to conduct such an analysis, and he apparently does not disagree with our enumeration of the steps required, which were detailed in Section 5.1 of the draft report. He considered these steps to be modest obstacles to proper conduct of the EEOICPA program. However, he did not acknowledge a major disadvantage of performing such analyses at the current time, namely that exten ded information on the LSS cohort, i.e., dose responses for lymphoma and leukemia incidence that are based on DS02 dosimetry and significantly extended
follow

- up , are yet to be publi shed

.11

Zablotska ―strongly advocate[ed] ‖ that NCI (specifically Charles La nd) be asked to develop a
new risk model from the DS02

- based LSS data when they become available, by combining the NHL and CLL cases and by ―careful consideration of the newly published Chornobyl studies.‖ Wakeford also suggested that it might be prudent to await the upcoming analysis of the
DS02

- based data for lymphatic and hematopoietic cancers by the staff from the RERF and NCI before making any decisions about CLL and the applicability of the current lymphoma and multiple myeloma model.
As a fa ll

- back position, Zablotska recommended adoption of ―the NHL

- alone model‖ rather than use of the lymphoma and multiple myeloma model. Her recommendatio ns in this regard
echo those of David Richardson. Unfortunately, no such model currently exists , as discussed abo ve, and t here are good arguments against reanalysis of the existing NHL data set because of the relatively small
numbers of cases availa ble and the lower revised risk estimates for NHL developed by
UNSCEAR . The existing LSS data for NHL were also based on an older disease classification system and are thus in complete for reasons previously given. It seems unlikely that the disadvantages of using data based on an outmoded classification system could be fully overcome or that the results would be materially d ifferent from those based on the current IREP model for lymphoma and multiple myeloma. The decision to proceed with an existing model
, that is acknowledged at the outset to be imperfect
, or to await development of a more refined model based on the new DS0 2

- based LSS data is a policy decision and not a technical one (see discussion of unresolved issues and caveats in Section 5.1 of the draft report)

. Nonetheless , b ased on the newer information on CLL incidence in recent epidem iological
studies and the incr eased estimates of E RR/Sv for NHL mortality in mal e atomic

- bomb survivors reported by Richardson et al. (2009)
, we agree that the IREP lymphoma and multiple myeloma
model should be used only as an interim solution
, and that a high priority be assigned to
d evelop ment of a new CLL model based on the updated DS02 data on NHL (including CLL and
other diseases now included in the revised definition of NHL ) after the RERF

- NCI team publishes the new LSS data. In retrospect, we should have strengthened the point th at the lymphoma and multiple myeloma model was only a surrogate for CLL, and should be replaced
as soon as new data from the LSS cohort became available.12

Whereas neither the earlier analysis of mortality 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 incidence based on follow

- up through 1987 show ed a significant dose

response in males or females, the updated analysis of mortality from NHL by Richardson et al. (200 9), which was based on 10 years of additional follow

- up, now shows a highly significant

r esponse in males. Thus, it is conceivable that the DS02 data for lymphoma incidence, which will have an even longer follow

- up when published
, could show significantly h igher risks for
both males and females. Another concern that appears to have been overlooked is the potential for confounding of
dose

- responses for CLL and lymphomas associated with a ― cell

- killing ‖ effect. Because of the very high radiosensitivity of lymp hocytes, especially B lymphocytes, a n artificially lower ERR/Gy could have resulted from studies of acute exposures some of which were delivered at high doses. T he effect could have been manifested at lower doses than for solid tumors or
leukemia

. Signifi cant depressions in human lymphocyte populations have been observed in blood and other lymphoid tissues at doses ≥ 0.25 Gy. Killing of highly radiosensitive B

- lymphocytes in persons who received acute doses ≥ 1 Gy (e.g., some members of the LSS cohort and me dically exposed groups) (Mettler and Upton 1995; Waselenko et al. 2004) would have drastical ly reduced the pool of CLL (and NHL) precursors in which malignancies could have been initiated, potentially resulting in a depression of the dose

- response if analy zed over the full dose range (up to 4 Sv)

. Such ―cell

- killing‖ effects have been observed for other types of malignancies , but occur at higher doses consistent with the lower sensitivity of the precursor cells (e.g., from solid tissues)

. The ERR/Sv reporte d by Richardson et al. (2009) for NHL mortality in the LSS cohort when
survivor doses 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 a re suggestive of an effect of cell

- killing at higher doses. Intriguingly, Schubauer

- Berigan et al. (2007) obtained a marginally significant dose

- response (ERR/10 mSv: 0.20; 95% C.I.

- 0.035, 0.96) in a case

- control study of mortality from CLL involving work ers from six U.S. nuclear
facilities when workers with doses >0.1 Sv were excluded. In the latter case, however, the ERR13

estimate translates to an ERR/Sv of 20, i.e., high enough to suggest a potential artifact, a
common concern about such studies.
W e thi nk that the resul ts obtained over the lower dose range from the study by Richardson et al. (2009) provide additional support for our recommendation to implement the IREP lymphoma and multiple myeloma model for CLL only on an interim basis. Dose

- responses f or CLL and lymphomas appear to be less well

- understood than formerly thought.

Appropriateness of Extended Latency for CLL

Two reviewers (Schubauer

- Berigan and Wakeford) agreed with our proposal to use the IREP lymphoma model with an extended latency per iod and two (Richardson and Zablotska) did not. Based on the information available in 2007 when the model report was initially drafted, it was being argued that an extended latency p eriod for induction of CLL was a major factor confounding previous attemp ts to determine whether CLL was radiogenic (Crowther 2004; Richardson 2004; Richardson et al. 2005; Schubauer

- Berigan et al. 2007; Silver et al. 2007; also

see Hamblin 2008). The available information pointed to a central estimate of latency (15 years)
tha t was twice as long as that used in IREP for other cancers, alb eit with a very high uncertainty (see Figs . 1 and 2 in the draft report). The midpoint of the S

- shaped latency function used in IREP for the bulk of solid tumors, including the lymphoma and mul tiple myeloma grouping, is 7 .5 years, and reaches 0.99 at 11 years, a full 6 years beyond the point in time when data from the LSS cohort become available. The addition of a triangular distribution (with extremes at 5 and 10 years) about the midpoint

exten ds the points at which the latency correction reaches 0.01 and 0.99 to 1.5 and 9 years, respectively. In contrast, the triangular distribution we recommended had extremes at 10 years and 20 years. Using the distribution we recommended, the 99 th percentile estimate of PC for CLL in former atomic energy workers are based on values for latency sampled from near the upper bound of the leftmost curve in the latency distribution in Fig. 2. In this region of the distribution, the latency
correction reaches 0.99 be tween 17 and 18 years. Thus, PC estimates at the 99 th percentile we re driven by values of latency that are about 4

− 5 years long er than in the latency distribution that is currently used in IREP

.14

Application of an extended latency appeared to be justifiable, given clinical information indicating a long lag between initiation and diagnosis of CLL, the lack of evidence of radiogenicity of CLL in 2007
, and questions about whether the modeled estimates of ERR/Sv for lymphomas and multiple myelomas might indicate a higher risk than that for CLL becau se of
differences in the diseases, including the relative distributions of indolent and aggressive forms

. However, the growing evidence for radiogenicity of CLL and recent information from the study of lymphoma mortality in the LSS cohort by Richardson et al. (2009) suggest that an extended latency may not be justified

. B oth Ri chardson and Zablotska challenge d the used of an extended latency for CLL, even with explicit consideration of its uncertainty as in Fig. 2, albeit for slightly different reasons. [Zabl otska appears to suggest that we assume a point estimate of 15 years fo r latency (see bullet at the bottom of page 8 in her review) , as opposed to the wide distribution shown in Fig. 2.]
Zablotska contended that at ―the time when all solid cancers are assigned a 10

- year latency period, it is hard to defend the notion that CLL is different fro m other cancers.‖ However, NHL, Hodgkins disease, and multiple myeloma are hematopoietic cancers, and our review of information on the etiology of CLL indicates that it may be quite unique

— and unquestionably
different from that of ―solid‖ cancers

. She noted that ― a strategy of separating CLL from other radiation

- related leukemias has been challenged recently and that o ne should be cautious about introducing new  ̳exceptional‘

appr oaches to modeling CLL risks.‖ However, the revised approach she suggests is driven by the studies of CLL in Chornobyl workers, which have a number of limitations, as discussed earlier.
I t is clear that the etiology of CLL, which is now classified as an NHL, is very different from that of the ―true‖ leukemias. W e al so note that the most recent review by IARC still excludes CLL, NHL, and multiple myeloma from its list of cancer types having sufficient evidence of radiogenicity (El Ghissassi et al. 2009).
Zablotska also suggested that a 10

- year lag has become a standa rd in CLL/NHL studies in
the last decade , noting that Richardson et al. (2009) found significant increases in the risk of mortality from NHL in men in the LSS cohort under both 5

- and 10

- year lag assumptions, based on DS02 data that cover the period from 1 950

− 2000

. In addition, she cited an observation of a
minimum deviance for both CLL and non

- CLL leukemia in one study of Chornobyl remediation workers (Romanenko et al. 2008) using a latency of only 2 years, a value that we questioned15

earlier in the section on Potenti al Radiogenicity of CLL

. However, studies of CLL by Schubauer

Berigan (2009) and of NHL by Richardson et al. (2009) devoted considerable effort to investigation of the effects of varying lag times and extended time s since exposure on the
ERR/Sv , su ggesting that there is as yet no standard for latency in epidemiological studies of
either CLL or NHL

. In the study by Richardson et al. (2009), a better fit (P = 0.02) was obtained using a 10

- year lag than with a 5

- year lag. They did not evaluate the use of longer lag periods. However, they observed the highest values of ERR/Sv for mortality in males from NHL (and the lowest P values) when time since exposure exceeded 36 years in both the LSS cohort and in Savannah
River Site workers. Time window analyse s showed that positive associations between radiation dose and lymphoma mortality among male atomic bomb survivors have only been observed since 1980
, i.e., 30 years after follow

- up began in 1950. ― In each cohort, evidence of a dose

- response association wa s primarily observed more than 35 years after irradiation. Such findings indicate a protract ed induction and latency period ‖ (Richardson et al. 2009). These findings suggest that the issue of latency for NHL is still not fully resolved.
Richardson ‘s own specific arguments against an extended latency for use with the lymphoma and multiple myeloma model

— or an alternative NHL

- based model , based on cancer incidence rather than cancer mortality, appear to be based on the fact that the age

- related parameters we re based on data for solid tumors rather than lymphoma and multiple myeloma . He contended that a
13

- year lag period , not far different from the central estimate of 15 year s that we employed in

our latency function
, is already inherent in the LSS data for s olid cancer incidence because
follow

- up did not begin until 1958. Because there is empirical data in the period beginning about
13 years after exposure, he argued that it would make sense to employ a model that gives full weight to the fitted ERR/Sv values during this period , i.e., to allow the age effects in the model to determine the outcome after 13 years

. However, as noted earlier, Land et al. (2003) elected to use age

- relate d parameters based on
solid tumors because there was little evidence that the patterns in data for the lymphoma and multiple myeloma grouping differed significantly, even though data on incidence of CLL, NHL, Hodgkins lymp homa, and multiple myeloma were available beginning in 1950, about 5 years16

after the bombings. In addition, t he 10

- year time lag he used in his study of lymphoma mortality in the LSS cohort extends a full 5 years into the region in which empirical data are available. Despite our serious reserva tions about some of the specific arguments made by Zablotska
and Richard son, we think that there is enough information currently to warrant reducing the latency period for use with the lymphoma and multiple myeloma model in IREP. Thus, the midpoint of the function shown in Fig. 2 of the draft report will be reduced from 15 yea rs to 10

years , while maintaining the uncertainty in the midpoint at ± 5 years

. This change would shift each point in the curves shown in Fig. 2 to the left by 5 years

. The net result should be that the
99 th p ercentile estimate of the latency correction wi ll typically be based on values for a time since exposure of about 12

− 13 years rather than at about 17

− 18 years , as in the distribution shown in Fig. 2

. T his change in latency is being made, even though there is still major uncertainty about the appropriate latency period for CLL and lymphomas that could justify a longer p eriod
, and about
the effects of assumptions about latency on risk estimates for CLL simulated with the lymphoma and multiple myeloma model

.

Background Information on CLL, Including Implications of Reclassification as an NHL

One reviewer (Schubauer

- Beri gan) recommended that the extensive discussion in Section 3
be moved to an appendix and retain a brief summary in the main text because she thought there was too much detail on the natural history of CLL and the important information was not readily

extrac table by the reader

. We think that most of the material is relevant to understanding why the bone marrow cannot be the sole target organ and why previous studies of CLL may have bee n uninformative. To address her concerns, however
, some of the material has been moved to an appendix, subheadings have been added
, and the material in S ection 3 has been reorganized

.

Internal Dosimetry for CLL The subject of internal dosimetry for CLL was sufficiently complex that a separate study was
undertaken to deal wit h the question of what constitutes an appropriate target organ for CLL (Apostoaei and Trabalka 2009). Zablotska did not have access to our CLL dosimetry report and17

thus she may not have fully appreciate d the complexity of this issue, even for whole

- body

ex ternal exposures (se e paragraph 3 on page 9 in her review)

. Similar concerns apply to NHL (see NIOSH/OCAS 2005; ORAU 2006).
For both CLL and NHL , and unlike the situation for most primary solid tumors, where the
tumor results from the interaction of radiat ion with immobile cells, radiation could have interacted with the lymphocyte precursors anywhere in the lymphatic or circu latory system, and
then formed the cancerous lesion elsewhere. Using NHL as the example, because the site of origin cannot be determin ed with any confidence

— and data on the inventories and distributions of lymphocytes in th e human body is quite uncertain , NIOSH modified the selection of target organs so that the dose to the organ/tissue receiving the highest dose is used in the dose

reco nstruction. For internal dose, the thoracic lymph nodes were selected because the dose to these tissues from exposure via inhalation of insoluble radioactive material is always higher than the dose to other organs/tissues. For external dose, the lungs were selected for B

- cell lymphomas and the thymus for T

- cell lymphomas. Although most epidemiological studies of CLL/NHL reportedly used either the whole

- body doses based on film badge readings or doses to bone marrow, neither approach gives an exact

measure of the organ/tissue weighted dose that would be considered representative for estimating radiogenic risk. For cases involving uniform external exposures to the whole body , the errors introduced by use of whole

- body doses or bone marrow doses would be quit e small, amounting to a few percent, as discussed in the draft report. However, the issue is thus not whether use of
whole

- body or bone marrow doses compromised past epidemiological studies but rather what dose is most appropriate for developing risk coeff icients for use in IREP.
For internal exposures, the uncertainties in dose s could amount to several orders of magnitude , depending on the radionuclide and route of entry

. To address one of Schubauer

Berigan‘s questions, the dose to the thoracic lymph node s could be used for dose reconstruction purposes, but could be considered overly conservative for CLL, for which the level of radiogenicity has not been firmly established. Thus, as noted above
, the issue of an appropriate approach for estimating internal doses for dose reconstruction was considered by NIOSH to be worthy of further investigation. However, because the draft report on this subject is still undergoing technical review, it is inappropriate to make the formal recommendations on how to18

estimate a n appropriate dose from internal exposure that she requested. We have moved the discussion on this topic from Appendix C (back) into the main text as she recommended.

Specific Editorial/Technical Suggestions by Zablotska and Schubauer

- Berigan

Lydia Zablo tska

First bulleted item on page 11 of her review was addressed earlier. Several mistakes in referencing various tables on pages 14−15 w ere alleg ed:  Tables 18, 19, and 20 in UNSCEAR (2000)  Table IV.C.2 in Land et al. (2003)

Response: We found no mistakes in our references . The reference to UNSCEAR (2000 ) was correct. Dr . Zablotska evidently wanted us to add the specific references to tables in UNSCEAR (2000) from which the data were obtained. We have added table references
, but these now refer
to UNSCEAR (2008), which provides revised risk estimates for NHL and more detailed information on the effects of age at exposure and time since exposure on the risk estimates. The reference to Land et al. (2003) she proposed was incorrect. The correct re ference is to Land (2000) (see T able 2).

She sugge sted that there were several direct quotes from Land et al. (2003) on p. 15 that were not properly referenced.

Response: There were no direct quotes from Land et al. (2003) on page 15.

Section 5 would benefit fro m a more structured approach with separate subtitles for each.

Response: We agree with her suggestion. T he material in Section 5 has been revised accordingly.

Figure 1 is missing Boivin et al. (1986) with 166 CLL cases.19

Response: There is no missing inform ation in the figure. It was obtained from the systematic review of material on the radiogenicity of CLL by Silver et al. (2007). They did not includ e data from this study in the figure , evide ntly because it did not merit inclusion

. They noted that ―[this] study of radiotherapy for various [primary cancers] had no dosimetry data, and only evaluated incident CLL risk as a check against study bias.‖

Her comment on the application of weights to different target organs on page 1 8 was addressed in the previous s ection

. In view of the recently published epidemiological studies, she disagreed with the statement on page 19 about ―w eek [sic] radiogenicity‖ of CLL, citing i mproved study methods and diagnostic procedures in recent studies as providing substantial evide nce of radiation

- related
risks of CLL.

Response: We disagree; see earlier discussion on this topic.

Mary Schubauer

- Berigan

The first three of her specific recommendation have been addressed previously.

4. She asked why there was no mention of T

- CLL, which comprises 5% of the cases.

Response: This was explained in footnote 7 of the draft report. CLL is now defined as an exclusively B

- cell disease i n the current NCI

- WHO disease classification

.

5. She requested that the implications of the discussion on B

- cell maturation on page 7 be made more explicit, namely to show that lymph nodes could be target organs for CLL.

Response: The material to which she refers is part of Section 3. T he location in which B

- cell maturation takes place is not necessarily the place where transformation to a CLL clone takes place, which could be almost anywhere in the hematopoietic or lymphatic systems because of20

post

- maturation migration. Thus, the lymph nodes are one of the potential locations where transformation could take place. We have added material to strengthen this point.

6. She pointed out an error in the fifth sentence in the first paragraph on page 11, where we misinterpreted a statement about the implications of the latency period that was made in
Schubauer

- Berigan et al. ( 2007), namely that we should have specified a period of 10 y.

Response: We have replaced the words ―an extended latency‖ to ―a 10

- y latency‖ in the offending sentence.

7. She thought that the second full paragraph on page 11 was repetitive and confusing, and also objected to th e wording in the first sentence, asking what we meant by: ―75% diagnosed at an average age of 60‖ and asking that it indicate a median age at diagnosis or other meaningful
fractile.

Response: Some of the material on the influence o f genetic factors on CLL incidence was repetitive. We have deleted part of it and moved the remainder to support the prior discussion
where this issue was addressed. To address her specific question, we have inserted the words ― of CLL patients ‖ after ―75%‖ in the first sentence. We think the use of ―an average age of 60,‖ as previously given, is reasonable.

8. She pointed out another misinterpretation in the first full paragraph on page 13, where we mistakenly attributed some of the problems in changes in CL L classification to changes in
classification of CLL in the series of ICD reports, when, in fact, the problem was that investigators inappropriately lum ped lymphoproliferative diseases with other ICD codes with
CLL.

Response: We have revised the material in this paragraph as follows (changes hi

- lit ed ) :

... However, investigators often combined several ICD codes to create their own definitions of CLL or other types of leukemia. For example, Weiss et al. (1995) lumped21

CLL per se (i.e., as then defined to incl ude 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 lymphoma in the ICD

- 9 scheme; code 202.4) was lumped with acute myeloid leukemia

... Other chronic leukemic patterns that have been categorized as “CLL” in clinical practice include: prolymphocytic leukemia, HCL, lymphoma leukemia, and S é zary syndrome (Berkow 1992). Thus, the effects of changing definitions , which likely increased the potential for misdiagnoses or mischaracteriza tions of CLL, must be carefully considered in interpreting the results of epidemiological studies involving CLL.

9. She recommended deleting the first full paragraph on page 14 because of uncertain relevance to the issue of target organ.

Response: Although we think t his material is germane to the issue of a target organ for CLL , it is not critical to this discussion. Thus, we have deleted it in order to reduce the length of Section
3.

10. She stated that we referenced a pooled estimate for NHL, Hodgkins lympho ma, and multiple myeloma on page 14 but did not include it in Table 2. She asked that we include it in the
table or in the text.

Response: She apparently misinterpreted the following sentence: ― Although the risk model in IREP is based on the pooled data s et, UNSCEAR has [only] derived risk estimates for each cancer type individually (Table 2). ‖ We have added the word ―only‖ to the cited sentence to prevent misunderstandings on the part of other readers.

11. She suggested that the effects of attained age and age at exposure in the IREP model would be more easily understandable if there were a verbal description of their net effect on page
15.

Response: This is a good suggestion. We plan to add a figure and text to do what she asked.22

12. She suggested that we eli minate the discussion about having considered

— and rejected

— an additive risk transport model. [Zablotska also reacted negatively to this discussion (see second paragraph on page 10 in her review).]

Response: Our recommendation was to adopt the current appr oach to risk transport in IREP, using a function which assigns equal probabilities to multiplicative and additive models. As noted on page 6, we have deleted the problematic text while including some of the points Zablotska made as justification for our ch oice of the current approach used in IREP

.

13. She thought that the discussion of latency on page 20 should mention the results of the time windows analysis in her recent paper (Schubauer

- Berigan et al, 2007), in which a significant
dose

- response for mortali ty fro m CLL was seen at doses <100 mSv but not when higher doses were included. The former suggested that a latency of 10 years was appropriate and that latency for an incidence study would be shorter still.

Response: We do not plan to add the detailed m aterial she suggested because the investigators did not provide a scientific justification for excluding the results associated with higher doses.
The result s at lower doses could represent a n artifact of data selection. It seems implausible that they coul d be explained by a cell

- killing effect, because the dose

- ranges invo lved (~100 mSv or less) are well below those where significant killing of lymphocytes has been observed. T he suggested latency period of 10 years is at odds with the results in Fig. 1 rep orted by Silver et al. (2007) and other statements in the paper she referenced. However, we will reference results from her paper, along with several other s, in the discussion associated with justifying an extension of the centroid of the latency function used with the lymphoma and multiple myeloma model from 7.5 years to 10 years.

14. She noted that the results in Table 3 appear to be percentages not probabilities, and recommends clarifying the labels.

Response: She is correct. We have revised the table leg end to reflect this. I f, as recommended,
we reduce the length of the lat ency period, as proposed above
, the PC calculations in Table 323

will also have to be rerun because these calculations were made with a risk model which
included the 15

- y latency function

.

15. This item was dealt with earlier in the section on Internal Dosimetry for CLL.

16. She suggested including information from the third column of Table III of Schubauer

Berigan et al. (2007) in Appendix B.

Response: We do not plan to do so, for re asons that were discussed in our response to item 13. These were the selected data to which we objected. However, we will reference her paper as part of the revised discussion on the latency period.

17. She stated that Tables B.9 and B.10 are not really rele vant for a discussion of latency without an appropriate denominator.

Response: We do not understand the comment. These tables have the same format as all of the other tables in the appendix, with numbers of cancers and time since exposure being the key
i nformation presented. We think that they are useful and should remain in the section.

18. This item was dealt with earlier in the section on Internal Dosimetry for CLL.

Summary and Conclusions

We very much appreciated the thoughtful and constructive comme nts of the reviewers. We think that the changes that we have made

— or have recommended

— in response to their

comments will significantly improve the approach to estimating risks of CLL as well as the clarity of the final report. We agree that evidence for CL L as a radiogenic disease is growing

. We reiterate that the absenc e of a well

- defined dose

- response for CLL in the LSS data does not
mean that there is no risk of CLL due to occupational radiation exposure

. Although we do not think that a suitable alterna tive to the lymphoma and multiple myeloma
risk model currently exists fo r estimating the PC of CLL
, we agree that the model proposed in24

our report should be used only as an interim solution, and that a high priority be assigned to development of a new CLL model based on the updated DS02 data on NHL (including CLL and other diseases now included in the revised definition of NHL) after the RERF

- NCI team publishes the new LSS data. We also think that there is enough information currently to warrant reducing th e midpoint of the proposed latency period for use with the lymphoma and multiple myeloma model from 15 years to 10 years, while maintaining the uncertainty in the midpoint at ± 5 years.

References Not Included in Draft Report or in Bibliographies Provide d by Reviewers

El Ghissassi, F., Baan, R., Straif, K., Grosse, Y., Secretan, B., Bouvard, V., Benbrahim

- Tallaa, Guha, N., Freeman, C., Galichet, L., and Cogliano, V. 2009. A review of human carcinogens

— Part D: radiation. Lancet Oncol. 28 :751−752.
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 f or Chernobyl liquidators in epidemiological studies. Health Phys. 97 , 275

– 298.
Mettler, F. A., and Upton, A. C. 1995. Medical Effects of Ionizing Radiation . W. B. Saunders Company, Philadelphia, PA.
NIOSH/OCAS. 2005. Summary of NIOSH‘s Re

- examination of Lymphoma Target Organ Selection, October 31, 2005.
ORAU (Oak Ridge Associated Universities). 2006. Selection for Internal and External Dosimetry Target Organs for Lymphatic/Hematopoietic Cancers , Office of Compensation Analysis and Support Technical I nformation Bulletin 12, Rev. 1. ORAU, Oak Ridge, TN.
Pierce, D. A., Shimizu, Y., Preston, D. L., Vaeth, M., and Mabuchi, K. 1996a. Studies of the mortality of atomic bomb survivors. Report 12, Part 2: Cancer. Radiat. Res. 146 , 1

– 27.
Waselenko, J. K., Ma cVittie, T. J., Blakely, W. F., Pesik, N., Wiley, A. L., Dickerson, W. E., Tsu, H., Confer, D. L., Coleman, C. N., Seed, T., Lowry, P., Armitage, J. O., and Dainiak, N. 2004. Medical management of the acute radiation syndrome: recommendations of the Strate gic Stockpile Radiation Working Group. Ann. Intern. Med. 1 40 :1037−1051.
Wick, R. R., Nekolla, E. A., Gossner, W., and Kellerer, A. M. 1999. Late effects in ankylosing spondylitis patients treated with 224Ra. Radiat. Res. 152 (6 Sup pl.):S8−S11.