Biologically based risk estimation for radiation induced chronic myeloid leukemia
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Biologically-Based Risk Estimation for Radiation-Induced Chronic Myeloid Leukemia. Radiation Carcinogenesis: Applying Basic Science to Epidemiological Estimates of Low-Dose Risks. Overview. Bayesian methods and CML Linear-Quadratic-Exponential model Likelihood and prior data sets

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Biologically-Based Risk Estimation for Radiation-Induced Chronic Myeloid Leukemia

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Biologically based risk estimation for radiation induced chronic myeloid leukemia

Biologically-Based Risk Estimation for Radiation-Induced Chronic Myeloid Leukemia

Radiation Carcinogenesis: Applying Basic Science to Epidemiological Estimates of Low-Dose Risks


Overview

Overview

  • Bayesian methods and CML

  • Linear-Quadratic-Exponential model

  • Likelihood and prior data sets

  • Baseline LQE estimate of CML risk

  • Improved risk estimates based on BCR-to-ABL distances and CML target cell numbers

  • Net lifetime CML risk: Can it have a U-shaped low dose response?


Bayesian methods

Bayesian Methods

  • Priors+ likelihood estimates  posteriors

  • Posterior information equals prior plus likelihood information

  • Posterior means are information-weighted averages of prior and likelihood means

  • Posteriors are normal if the prior and likelihood estimates are normal

  • Priors act as soft constraints on the parameters

  • Priors and structures come from the same data


Chronic myeloid leukemia

Chronic Myeloid Leukemia

  • CML is homogeneous, prevalent, radiation-induced, and caused by BCR-ABL

  • The a2 intron of ABL is unusually large

  • Leukemic endpoints have rapid kinetics

  • White blood cells need fewer stages

  • Linear CML risk is not biologically-based

  • Linear-quadratic-exponential CML risk does have a biological basis


Linear risk model

Linear Risk Model

Using the BCR-ABL to CML

waiting time density

and the linear model

we maximized

the log-likelihood


Linear quadratic exponential model

Linear-Quadratic-Exponential Model

The LQE model is

where

Di and Dni are the gamma and neutron doses in gray

N is the number of CML target cells per adult

P(ba|T)is the probability of BCR-ABL given a translocation

This is a one-stage model of carcinogenesis.


Likelihood data

Likelihood Data

  • CML is practically absent in Nagasaki

  • High dose HF waiting times are too long

  • HM data is consistent with prior expectations


Biologically based risk estimation for radiation induced chronic myeloid leukemia

aage at diagnosis

bO = observed cases (E = expected background cases based on U.S. incidence rates)

ctsx = average of the times since exposure for the cases


Prior data sources

Prior Data: Sources

  • C1 and k: SEER data

  • kt : Patients irradiated for BGD

  • k, k and kn : CAFC and MRA assays

  • / and n/: Lymphocyte dicentric yields

  • C2 : Depends on , kt, N, and P(ba|T)

    • N: SEER and translocation age structure data

    • P(ba|T): BCR and ABL intron sizes, the genome size


Parameter estimates

Parameter Estimates


Cml risk estimates

CML Risk Estimates

  • Linear model

    • R = 0.0075 Gy-1 and Q = 0.0158 Gy-1

  • LQE posterior model

    • R = 0.0022 Gy-1 and Q = 0.0042 Gy-1

The lifetime excess CML risk in the limit of low -ray doses

yields


Cml target cell numbers

CML Target Cell Numbers

  • A comparison of age responses for CML and total translocations suggests a CML target cell number of 2x108

  • 1012 nucleated marrow cells per adult and one LTC-IC per 105 marrow cells suggests 107 CML target cells

  • P(ba|T) = 2TablTbcr/2 may not hold


Biologically based risk estimation for radiation induced chronic myeloid leukemia

BCR-to-ABL 2D distances in lymphocytes

Kozubek et al. (1999) Chromosoma 108: 426-435


Theory of dual radiation action

Theory of Dual Radiation Action

  • P(ba|D)= probability of a BCR-ABL translocation per G0/G1 cell given a dose D

  • tD(r)dr = expected energy at r given an ionization event at the origin

  • = intra-track component + inter-track component

  • Sba(r) = the BCR-to-ABL distance probability density

  • g(r) = probability that two DSBs misrejoin if they are created r units apart

  • Y = 0.0058 DSBs per Mb per Gy;  = mass density

  • TBCR = 5.8 kbp; TABL = 300 kbp


Estimation of g r

Estimation of g(r)

din [.01, .025], dx in [.04, .05], d in [.05, .06]

G=35 DSB/Gy per cell

6.25 kev/um3 = 1 Gy

R = 3.7 um r0 = 0.24 m, p0 = 0.06


Biologically based risk estimation for radiation induced chronic myeloid leukemia


Dead band control of hsc levels

Dead-Band Control of HSC levels

  • Transplant doses of 10, 100, and 1000 CRU => CRU levels 1-20% or 15-60% normal Blood (1996) 88: 2852-2858

  • Broad variation in human HSC levels Stem Cells (1995) 13: 512-516

  • Low levels of HSCs in BMT patients Blood (1998) 91: 1959-1965


Biologically based risk estimation for radiation induced chronic myeloid leukemia

Figure 3: Hypersensitivity ratios in the literature (left panel) and the log-survival dose response for T98G human glioma cells (right panel). Figures from Joiner, M.C., Marples, B., Lambin, P., Short, S.C. and Turesson, I., Low-dose hypersensitivity: current status and possible mechanisms. Int J Radiat Oncol Biol Phys (2001) 49: 379-389.


Net lifetime cml risk

Net Lifetime CML Risk

The net lifetime excess risk of CML is

Letting Dn = 0 while D 0

We solved R0 = 0 for ks as a function of exposure age x.


Conclusions

Conclusions

  • Bayesian methods provide a natural framework for biologically based risk estimation

  • BCR-to-ABL distance data and knowledge of CML target cell numbers can be useful in a biologically based approach to CML risk estimation

  • Low dose hypersensitivity to killing might lead to a U-shaped low dose response if there is a dead-band in the control of target cell numbers


Acknowledgments

Acknowledgments

  • Rainer Sachs

  • David Hoel

  • NIH and DOE


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