Heterogeneity in multistage carcinogenesis and mixture modeling

Carcinogenesis is commonly described as a multistage process, in which stem cells are transformed into cancer cells via a series of mutations. In this article, we consider extensions of the multistage carcinogenesis model by mixture modeling. This approach allows us to describe population heterogeneity in a biologically meaningful way. We focus on finite mixture models, for which we prove identifiability. These models are applied to human lung cancer data from several birth cohorts. Maximum likelihood estimation does not perform well in this application due to the heavy censoring in our data. We thus use analytic graduation instead. Very good fits are achieved for models that combine a small high risk group with a large group that is quasi immune

Systems biological and mechanistic modelling of radiation-induced cancer

This paper summarises the five presentations at the First International Workshop on Systems Radiation Biology that were concerned with mechanistic models for carcinogenesis. The mathematical description of various hypotheses about the carcinogenic process, and its comparison with available data is an example of systems biology. It promises better understanding of effects at the whole body level based on properties of cells and signalling mechanisms between them. Of these five presentations, three dealt with multistage carcinogenesis within the framework of stochastic multistage clonal expansion models, another presented a deterministic multistage model incorporating chromosomal aberrations and neoplastic transformation, and the last presented a model of DNA double-strand break repair pathways for second breast cancers following radiation therapy

The effects of particulate and ozone pollution on mortality in Moscow, Russia

The objectives of this study were (1) to evaluate how acute mortality responds to changes in particulate and ozone (O3) pollution levels, (2) to identify vulnerable population groups by age and cause of death, and (3) to address the problem of interaction between the effects of O3 and particulate pollution. Time-series of daily mortality counts, air pollution, and air temperature were obtained for the city of Moscow during a 3-year period (2003–2005). To estimate the pollution-mortality relationships, we used a log-linear model that controlled for potential confounding by daily air temperature and longer term trends. The effects of 10 μg/m3 increases in daily average measures of particulate matter ≤10 μm in aerodynamic diameter (PM10) and O3 were, respectively, (1) a 0.33% [95% confidence interval (CI) 0.09–0.57] and 1.09% (95% CI 0.71–1.47) increase in all-cause non-accidental mortality in Moscow; (2) a 0.66% (0.30–1.02) and 1.61% (1.01–2.21) increase in mortality from ischemic heart disease; (3) a 0.48% (0.02–0.94) and 1.28% (0.54–2.02) increase in mortality from cerebrovascular diseases. In the age group >75 years, mortality increments were consistently higher, typically by factor of 1.2 – 1.5, depending upon the cause of death. PM10-mortality relationships were significantly modified by O3 levels. On the days with O3 concentrations above the 90th percentile, PM10 risk for all-cause mortality was threefold greater and PM10 risk for cerebrovascular disease mortality was fourfold greater than the unadjusted risk estimate

Association between air pollution and asthma admission among children in Hong Kong

OBJECTIVE: To examine the association of air pollutants with hospital admission for childhood asthma in Hong Kong. METHODS: Data on hospital admissions for asthma, influenza and total hospital admissions in children aged ≤18 years at all Hospital Authority hospitals during 1997–2002 were obtained. Data on daily mean concentrations of particles with aerodynamic diameter <10 μm (i. e. PM(10)) and <2.5 μm (i. e. PM(2.5)), nitrogen dioxide (NO(2)), sulphur dioxide (SO(2)), and ozone (O(3)) and data on meteorological variables were associated with asthma hospital admissions using Poisson's regression with generalized additive models for correction of yearly trend, temperature, humidity, day-of-week effect, holiday, influenza admissions and total hospital admission. The possibility of a lag effect of each pollutant and the interaction of different pollutants were also examined. RESULTS: The association between asthma admission with change of NO(2), PM(10), PM(2.5) and O(3) levels remained significant after adjustment for multi-pollutants effect and confounding variables, with increase in asthma admission rate of 5.64% (3.21–8.14) at lag 3 for NO(2), 3.67% (1.52–5.86) at lag 4 for PM(10), 3.24% (0.93–5.60) at lag 4 for PM(2.5) and 2.63% (0.64–4.67) at lag 2 for O(3). Effect of SO(2) was lost after adjustment. CONCLUSION: Ambient levels of PM(10), PM(2.5), NO(2) and O(3) are associated with childhood asthma hospital admission in Hong Kong

Kinetics of cancer: a method to test hypotheses of genetic causation

BACKGROUND: Mouse studies have recently compared the age-onset patterns of cancer between different genotypes. Genes associated with earlier onset are tentatively assigned a causal role in carcinogenesis. These standard analyses ignore the great amount of information about kinetics contained in age-onset curves. We present a method for analyzing kinetics that measures quantitatively the causal role of candidate genes in cancer progression. We use our method to demonstrate a clear association between somatic mutation rates of different DNA mismatch repair (MMR) genotypes and the kinetics of cancer progression. METHODS: Most experimental studies report age-onset curves as the fraction diagnosed with tumors at each age for each group. We use such data to estimate smoothed survival curves, then measure incidence rates at each age by the slope of the fitted curve divided by the fraction of mice that remain undiagnosed for tumors at that age. With the estimated incidence curves, we compare between different genotypes the median age of cancer onset and the acceleration of cancer, which is the rate of increase in incidence with age. RESULTS: The direction of change in somatic mutation rate between MMR genotypes predicts the direction of change in the acceleration of cancer onset in all 7 cases (p ˜ 0.008), with the same result for the association between mutation rate and the median age of onset. CONCLUSION: Many animal experiments compare qualitatively the onset curves for different genotypes. If such experiments were designed to analyze kinetics, the research could move to the next stage in which the mechanistic consequences of particular genetic pathways are related to the dynamics of carcinogenesis. The data we analyzed here were not collected to test mechanistic and quantitative hypotheses about kinetics. Even so, a simple reanalysis revealed significant insights about how DNA repair genotypes affect separately the age of onset and the acceleration of cancer. Our method of comparing genotypes provides good statistical tests even with small samples for each genotype

A Heuristic Solution of the Identifiability Problem of the Age-Period-Cohort Analysis of Cancer Occurrence: Lung Cancer Example

Background: The Age–Period–Cohort (APC) analysis is aimed at estimating the following effects on disease incidence: (i) the age of the subject at the time of disease diagnosis; (ii) the time period, when the disease occurred; and (iii) the date of birth of the subject. These effects can help in evaluating the biological events leading to the disease, in estimating the influence of distinct risk factors on disease occurrence, and in the development of new strategies for disease prevention and treatment. Methodology/Principal Findings: We developed a novel approach for estimating the APC effects on disease incidence rates in the frame of the Log-Linear Age-Period-Cohort (LLAPC) model. Since the APC effects are linearly interdependent and cannot be uniquely estimated, solving this identifiability problem requires setting four redundant parameters within a set of unknown parameters. By setting three parameters (one of the time-period and the birth-cohort effects and the corresponding age effect) to zero, we reduced this problem to the problem of determining one redundant parameter and, used as such, the effect of the time-period adjacent to the anchored time period. By varying this identification parameter, a family of estimates of the APC effects can be obtained. Using a heuristic assumption that the differences between the adjacent birth-cohort effects are small, we developed a numerical method for determining the optimal value of the identification parameter, by which a unique set of all APC effects is determined and the identifiability problem is solved

Adjusting a cancer mortality-prediction model for disease status-related eligibility criteria

<p>Abstract</p> <p>Background</p> <p>Volunteering participants in disease studies tend to be healthier than the general population partially due to specific enrollment criteria. Using modeling to accurately predict outcomes of cohort studies enrolling volunteers requires adjusting for the bias introduced in this way. Here we propose a new method to account for the effect of a specific form of healthy volunteer bias resulting from imposing disease status-related eligibility criteria, on disease-specific mortality, by explicitly modeling the length of the time interval between the moment when the subject becomes ineligible for the study, and the outcome.</p> <p>Methods</p> <p>Using survival time data from 1190 newly diagnosed lung cancer patients at MD Anderson Cancer Center, we model the time from clinical lung cancer diagnosis to death using an exponential distribution to approximate the length of this interval for a study where lung cancer death serves as the outcome. Incorporating this interval into our previously developed lung cancer risk model, we adjust for the effect of disease status-related eligibility criteria in predicting the number of lung cancer deaths in the control arm of CARET. The effect of the adjustment using the MD Anderson-derived approximation is compared to that based on SEER data.</p> <p>Results</p> <p>Using the adjustment developed in conjunction with our existing lung cancer model, we are able to accurately predict the number of lung cancer deaths observed in the control arm of CARET.</p> <p>Conclusions</p> <p>The resulting adjustment was accurate in predicting the lower rates of disease observed in the early years while still maintaining reasonable prediction ability in the later years of the trial. This method could be used to adjust for, or predict the duration and relative effect of any possible biases related to disease-specific eligibility criteria in modeling studies of volunteer-based cohorts.</p

Optimizing Combination Therapies with Existing and Future CML Drugs

Small-molecule inhibitors imatinib, dasatinib and nilotinib have been developed to treat Chromic Myeloid Leukemia (CML). The existence of a triple-cross-resistant mutation, T315I, has been a challenging problem, which can be overcome by finding new inhibitors. Many new compounds active against T315I mutants are now at different stages of development. In this paper we develop an algorithm which can weigh different combination treatment protocols according to their cross-resistance properties, and find the protocols with the highest probability of treatment success. This algorithm also takes into account drug toxicity by minimizing the number of drugs used, and their concentration. Although our methodology is based on a stochastic model of CML microevolution, the algorithm itself does not require measurements of any parameters (such as mutation rates, or division/death rates of cells), and can be used by medical professionals without a mathematical background. For illustration, we apply this algorithm to the mutation data obtained in [1], [2]

Evaluating the Number of Stages in Development of Squamous Cell and Adenocarcinomas across Cancer Sites Using Human Population-Based Cancer Modeling

BACKGROUND: Adenocarcinomas (ACs) and squamous cell carcinomas (SCCs) differ by clinical and molecular characteristics. We evaluated the characteristics of carcinogenesis by modeling the age patterns of incidence rates of ACs and SCCs of various organs to test whether these characteristics differed between cancer subtypes. METHODOLOGY/PRINCIPAL FINDINGS: Histotype-specific incidence rates of 14 ACs and 12 SCCs from the SEER Registry (1973-2003) were analyzed by fitting several biologically motivated models to observed age patterns. A frailty model with the Weibull baseline was applied to each age pattern to provide the best fit for the majority of cancers. For each cancer, model parameters describing the underlying mechanisms of carcinogenesis including the number of stages occurring during an individual's life and leading to cancer (m-stages) were estimated. For sensitivity analysis, the age-period-cohort model was incorporated into the carcinogenesis model to test the stability of the estimates. For the majority of studied cancers, the numbers of m-stages were similar within each group (i.e., AC and SCC). When cancers of the same organs were compared (i.e., lung, esophagus, and cervix uteri), the number of m-stages were more strongly associated with the AC/SCC subtype than with the organ: 9.79±0.09, 9.93±0.19 and 8.80±0.10 for lung, esophagus, and cervical ACs, compared to 11.41±0.10, 12.86±0.34 and 12.01±0.51 for SCCs of the respective organs (p<0.05 between subtypes). Most SCCs had more than ten m-stages while ACs had fewer than ten m-stages. The sensitivity analyses of the model parameters demonstrated the stability of the obtained estimates. CONCLUSIONS/SIGNIFICANCE: A model containing parameters capable of representing the number of stages of cancer development occurring during individual's life was applied to the large population data on incidence of ACs and SCCs. The model revealed that the number of m-stages differed by cancer subtype being more strongly associated with ACs/SCCs histotype than with organ/site

Relationship between ozone and temperature during the 2003 heat wave in France: consequences for health data analysis

BACKGROUND: PAPRICA is a research program designed to estimate the impact on the health of patients with chronic respiratory insufficiency of a prevention strategy based on notification of ozone pollution. The first year of this study was conducted during the 2003 heat wave, and high temperatures were therefore considered as a confounding factor in the data analysis. The aim of the present study was to assess the relationship between ozone and temperature in order to propose a methodology to distinguish between the effects of ozone and temperature on the impact of a prevention strategy with regard to ozone pollution. METHODS: Multivariate analyses were used to identify associated climate and ozone pollution profiles. This descriptive method is of great value to highlight the complexity of interactions between these parameters. RESULTS: Ozone concentration and temperature were strongly correlated, but the health impact of ozone pollution alone will be evaluated by focusing on situations characterized by ozone concentrations above 110 μg/m(3)/8h (air quality guidelines to protect human health defined by the French legislation) and temperatures lower than 26°C, below the discomfort threshold. CONCLUSION: The precise relationship between ambient ozone concentration and temperature identified during the PAPRICA 2003 study period will be used in analysing the PAPRICA health data