1 / 26

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. Bayesian methods and CML Linear-Quadratic-Exponential model Likelihood and prior data sets

Télécharger la présentation

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

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Biologically-Based Risk Estimation for Radiation-Induced Chronic Myeloid Leukemia Radiation Carcinogenesis: Applying Basic Science to Epidemiological Estimates of Low-Dose Risks

  2. 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?

  3. 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

  4. 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

  5. Linear Risk Model Using the BCR-ABL to CML waiting time density and the linear model we maximized the log-likelihood

  6. 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.

  7. Likelihood Data • CML is practically absent in Nagasaki • High dose HF waiting times are too long • HM data is consistent with prior expectations

  8. 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

  9. 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

  10. Parameter Estimates

  11. 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

  12. 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

  13. BCR-to-ABL 2D distances in lymphocytes Kozubek et al. (1999) Chromosoma 108: 426-435

  14. 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

  15. 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

  16. 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

  17. 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.

  18. 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.

  19. 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

  20. Acknowledgments • Rainer Sachs • David Hoel • NIH and DOE

More Related