Do cells repair precancerous lesions induced by radiation?

Yakovlev, Andrei; Müller, W.; Павлова, Л. А.; Polig, E.

doi:10.1016/s0025-5564(97)00013-8

Cited by 14 publications

(4 citation statements)

References 33 publications

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“…Significance of the set Ef stems from the fact that, for O<_t<_T, Yakovlev and Polig (1996), Boucher and Yakovlev (1997), Boucher et al (1998) and Tsodikov and Mtiller (1998) used Y-P model for analysis of real time-to-tumor data that resulted from animal experiments. In particular, Y-P model has explained successfully the inverse dose-rate effect in radiation carcinogenesis (Yakovlev et al, 1977;Yakovlev and Polig, 1996) and allowed estimation of the proportion of initiated cells, which are killed by urethane in mice (Boucher and Yakovlev, 1997;Boucher et al, 1998). The promotion time distribution was assumed to belong to the gamma family with PDF given by Eq.…”

Section: Z Ai[f(t Ri-1) F(t Ri)] + Anf(t Rn-) I--1 Since the Functiomentioning

confidence: 99%

Identification problem for stochastic models with application to carcinogenesis, cancer detection and radiation biology

Hanin

2002

Discrete Dynamics in Nature and Society

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A general framework for solving identification problem for a broad class of deterministic and stochastic models is discussed. This methodology allows for a unified approach to studying identifiability of various stochastic models arising in biology and medicine including models of spontaneous and induced carcinogenesis, tumor progression and detection, and randomized hit and target models of irradiated cell survival. A variety of known results on parameter identification for stochastic models is reviewed and several new results are presented with an emphasis on rigorous mathematical development.

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Section: Z Ai[f(t Ri-1) F(t Ri)] + Anf(t Rn-) I--1 Since the Functiomentioning

confidence: 99%

Identification problem for stochastic models with application to carcinogenesis, cancer detection and radiation biology

Hanin

2002

Discrete Dynamics in Nature and Society

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“…The main reason for our choice is that this distribution has been successfully used in similar context: incorporating the gamma distribution for the time of tumor promotion, i.e. the time it takes for an initiated cell to undergo malignant transformation, into a stochastic model of tumor latency yields an excellent fit to experimental data on radiation and chemical carcinogenesis [1,2,18].…”

Section: The Simulation Modelmentioning

confidence: 99%

Preferred Sequences of Genetic Events in Carcinogenesis: Quantitative Aspects of the Problem

Szabó

Yakovlev

2001

J. Biol. Syst.

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In this paper we discuss some natural limitations in quantitative inference about the frequency, correlation and ordering of genetic events occurring in the course of tumor development. We consider a simple, yet frequently used experimental design, under which independent tumors are examined once for the presence/absence of specific mutations of interest. The most typical factors that affect the inference on the chronological order of genetic events are: a possible dependence of mutation rates, the sampling bias that arises from the observation process and small sample sizes. Our results clearly indicate that just these three factors alone may dramatically distort the outcome of data analysis, thereby leading to estimates of limited utility as an underpinning for mechanistic models of carcinogenesis.

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“…The phenomenon of cell death plays a key role in the dose-rate effects observed in carcinogenesis studies (7,8). The model by Yakovlev and Polig provides a good description of various dose-rate effects in radiation and chemical carcinogenesis (9,10) while keeping parametric assumptions to a minimum as far as the dependence of its parameters on dose rate is concerned. In the present paper, the potentialities of the Yakovlev-Polig model are exploited to estimate the contribution of cell death to the development of multiple pulmonary adenomas in mice receiving different schedules of urethane.…”

Section: Sectionmentioning

confidence: 99%

“…To this end, the function f(u) in Eq. 3 was assumed to be a ⌫ distribution density with scale parameter ␣ and shape parameter ␤ because this distribution, quite simple as it is, provides a good fit to various experimental and clinical data when incorporated in stochastic models of tumor latency (9,10,14,15). The formulas: ϭ ␣͞␤ and ϭ ͌ ␣͞␤ can be used to calculate the mean and SD of the promotion time, respectively.…”

Section: The Expected Number Of Tumors Developed By the Time T Is Givmentioning

confidence: 99%

Estimating the probability of initiated cell death before tumor induction

Boucher

Yakovlev²

1997

Proc. Natl. Acad. Sci. U.S.A.

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The effects of cell toxicity are known to be inherent in carcinogenesis induced by radiation or chemical carcinogens. The event of cell death precludes tumor induction from occurring. A long standing problem is to estimate the proportion of initiated cells that die before tumor induction. No experimental techniques are currently available for directly gauging the rate of cell death over extended periods of time. The obstacle can be surmounted by newly developed theoretical methods of carcinogenesis modeling. In this paper, we apply such methods to published data on multiple lung tumors in mice receiving different schedules of urethane. Bioassays of this type play an important role in testing environmental chemicals for carcinogenic activity. Our estimates for urethane-induced carcinogenesis show that, unexpectedly, many initiated cells die early in the course of tumor promotion. We present numerical estimates for the probability of initiated cell death for different schedules (and doses) of urethane administration. Section 1As with many processes of carcinogenesis, the process of cell killing induced by a carcinogen is difficult to observe directly, and inferences rely on mathematical models. In a recent study (1), Moolgavkar et al. reported a statistical analysis of experimental data on enzyme-altered (ATPase-deficient) liver foci in rats induced by diethylnitrosamine. It is generally believed that these foci represent clones of initiated cells, and at least some of them eventually transform into malignant tumors. The authors used a two-stage birth-death-mutation model of carcinogenesis, usually referred to as the MoolgavkarVenzon-Knudson model (2, 3), to estimate the extinction probability for clones of altered cells in initiation-promotion experiments. In the absence of a promoter, the estimated extinction probability was higher than 0.99, and its value tended to decrease when two different promoters were applied; this effect can be attributed to stimulation of cell proliferation.The Moolgavkar-Venzon-Knudson model introduces the clonal expansion of initiated cells as the basic mechanism of tumor promotion. It is assumed that clonal growth begins immediately after initiation and can mathematically be described as a birth-and-death stochastic process. Under this model, the probability of a clone to become extinct is a natural summary characteristic of the susceptibility of initiated cells to death in the course of tumor development. However, there is experimental evidence that single initiated cells persist over an extended time period after the administration of a carcinogen. Using another biochemical marker (the placental form glutathione S-transferase) of cell initiation, Satoh et al. (4) have shown that single putative initiated cells (the placental form glutathione S-transferase-positive hepatocytes) and mini-foci consisting of such cells (2-10 cells) predominate in the preneoplastic liver within the first 12 weeks after a single exposure to diethylnitrosamine. Within 1 week after exposure, the e...

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Do cells repair precancerous lesions induced by radiation?

Cited by 14 publications

References 33 publications

Identification problem for stochastic models with application to carcinogenesis, cancer detection and radiation biology

Identification problem for stochastic models with application to carcinogenesis, cancer detection and radiation biology

Preferred Sequences of Genetic Events in Carcinogenesis: Quantitative Aspects of the Problem

Estimating the probability of initiated cell death before tumor induction

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