Similarities in the Age-Specific Incidence of Colon and Testicular Cancers

Colon cancers are thought to be an inevitable result of aging, while testicular cancers are thought to develop in only a small fraction of men, beginning in utero. These models of carcinogenesis are, in part, based upon age-specific incidence data. The specific incidence for colon cancer appears to monotonically increase with age, while that of testicular cancer increases to a maximum value at about 35 years of age, then declines to nearly zero by the age of 80. We hypothesized that the age-specific incidence for these two cancers is similar; the apparent difference is caused by a longer development time for colon cancer and the lack of age-specific incidence data for people over 84 years of age. Here we show that a single distribution can describe the age-specific incidence of both colon carcinoma and testicular cancer. Furthermore, this distribution predicts that the specific incidence of colon cancer should reach a maximum at about age 90 and then decrease. Data on the incidence of colon carcinoma for women aged 85–99, acquired from SEER and the US Census, is consistent with this prediction. We conclude that the age specific data for testicular cancers and colon cancers is similar, suggesting that the underlying process leading to the development of these two forms of cancer may be similar.     

Multistage models and the incidence of cancer in the cohort of atomic bomb survivors

The analyses in this paper show that a number of biologically based models describe cancer incidence among the A-bomb survivors equally well. However, these different models can predict very different temporal patterns of risk after irradiation. No evidence was found to support the previous claim of Pierce and Mendelsohn that excess cancer risks for the solid tumors depend only upon attained age and not on age at exposure or time since exposure. Although the A-bomb survivor cohort is the largest epidemiological data set for the study of radiation and cancer, it is not large enough to discriminate among various possible carcinogenic mechanisms. Unfortunately for hypothesis generation, the data appear to be consistent with a number of different mechanistic interpretations of the role of radiation in carcinogenesis.     

A method for determining weights for excess relative risk and excess absolute risk when applied in the calculation of lifetime risk of cancer from radiation exposure

Radiation-related risks of cancer can be transported from one population to another population at risk, for the purpose of calculating lifetime risks from radiation exposure. Transfer via excess relative risks (ERR) or excess absolute risks (EAR) or a mixture of both (i.e., from the life span study (LSS) of Japanese atomic bomb survivors) has been done in the past based on qualitative weighting. Consequently, the values of the weights applied and the method of application of the weights (i.e., as additive or geometric weighted means) have varied both between reports produced at different times by the same regulatory body and also between reports produced at similar times by different regulatory bodies. Since the gender and age patterns are often markedly different between EAR and ERR models, it is useful to have an evidence-based method for determining the relative goodness of fit of such models to the data. This paper identifies a method, using Akaike model weights, which could aid expert judgment and be applied to help to achieve consistency of approach and quantitative evidence-based results in future health risk assessments. The results of applying this method to recent LSS cancer incidence models are that the relative EAR weighting by cancer solid cancer site, on a scale of 0–1, is zero for breast and colon, 0.02 for all solid, 0.03 for lung, 0.08 for liver, 0.15 for thyroid, 0.18 for bladder and 0.93 for stomach. The EAR weighting for female breast cancer increases from 0 to 0.3, if a generally observed change in the trend between female age-specific breast cancer incidence rates and attained age, associated with menopause, is accounted for in the EAR model. Application of this method to preferred models from a study of multi-model inference from many models fitted to the LSS leukemia mortality data, results in an EAR weighting of 0. From these results it can be seen that lifetime risk transfer is most highly weighted by EAR only for stomach cancer. However, the generalization and interpretation of radiation effect estimates based on the LSS cancer data, when projected to other populations, are particularly uncertain if considerable differences exist between site-specific baseline rates in the LSS and the other populations of interest. Definitive conclusions, regarding the appropriate method for transporting cancer risks, are limited by a lack of knowledge in several areas including unknown factors and uncertainties in biological mechanisms and genetic and environmental risk factors for carcinogenesis; uncertainties in radiation dosimetry; and insufficient statistical power and/or incomplete follow-up in data from radio-epidemiological studies.     

Modeling progression in radiation-induced lung adenocarcinomas

Quantitative multistage carcinogenesis models are used in radiobiology to estimate cancer risks and latency periods (time from exposure to clinical cancer). Steps such as initiation, promotion and transformation have been modeled in detail. However, progression, a later step during which malignant cells can develop into clinical symptomatic cancer, has often been approximated simply as a fixed lag time. This approach discounts important stochastic mechanisms in progression and evidence on the high prevalence of dormant tumors. Modeling progression more accurately is therefore important for risk assessment. Unlike models of earlier steps, progression models can readily utilize not only experimental and epidemiological data but also clinical data such as the results of modern screening and imaging. Here, a stochastic progression model is presented. We describe, with minimal parameterization: the initial growth or extinction of a malignant clone after formation of a malignant cell; the likely dormancy caused, for example, by nutrient and oxygen deprivation; and possible escape from dormancy resulting in a clinical cancer. It is shown, using cohort simulations with parameters appropriate for lung adenocarcinomas, that incorporating such processes can dramatically lengthen predicted latency periods. Such long latency periods together with data on timing of radiation-induced cancers suggest that radiation may influence progression itself.     

Organoids in recapitulating tumorigenesis driven by risk factors: Current trends and future perspectives

Environmental and exogenous/ endogenous factors, in a setting of individual genetic predisposition, contribute to the cancer development. Over the years, epidemical evidence increasingly highlights the correlations of multiple cancer incentives and genetic alterations with cancer incidence. Unraveling the pivotal carcinogenesis events prompted by particular risk factors remarkably advances early surveillance and oncogenesis intervening. Traditional cell-based models and animal-based models are unrealistic and unreliable for translational study, respectively ascribing to the limited tumor heterogeneity and species-related variation. Organoid emerged as a fidelity model that well preserves the properties of its origin. With inherent quality of holistic perspective, organoid is therefore ideally suited for delineating the carcinogenesis under risk exposure, in favor of understanding pathogen-host interactions and alleviating cancer initiation. In this review, we have summarized the organoid model-based evidence that identified or validated carcinogenic risks, mainly including diet, aging, microbial infection, and chemical exposure. In addition, we envisioned the exciting prospect of organoid model in screening promising treatment and/or prevention during tumorigenesis. As a robust 3D in vitro system, organoid has been widespread applied in basial and clinical cancer research, which may elucidate crucial mechanisms of oncogenesis and develop novel targeting strategies.     

Age Distribution of Cancer: The Incidence Turnover at Old Age

In recent years data on cancer incidence in the USA, the Netherlands, and in Hong Kong indicate a flattening and perhaps a turnover at advanced age, but no model has been successful in fitting this data and thus providing clues to the underlying biology. In this work we assume these data are reliable and free from bias. We find that a Beta distribution fits SEER age-specific cancer incidence data for all adult cancers extremely well, and its interpretation as a model leads to the possibility that there is a beneficial cancer extinction process that becomes important at elevated age. Particularly evident from the data is the apparent remarkable uniformity of adult cancers peaking in incidence at about the same age, including cancers in other countries. Possible biological mechanisms include increasing apoptosis and cell senescence with age. Further, the model suggests that cancer is not inevitable at advanced age, but reaches a maximum cumulative probability of affliction with any cancer of about 70% for men and 53% for women in the US, and much smaller values for individual cancers.     

Effects of measured susceptibility genes on cancer risk in family studies

Numerous family studies have been performed to assess the associations between cancer incidence and genetic and non-genetic risk factors and to quantitatively evaluate the cancer risk attributable to these factors. However, mathematical models that account for a measured hereditary susceptibility gene have not been fully explored in family studies. In this report, we proposed statistical approaches to precisely model a measured susceptibility gene fitted to family data and simultaneously determine the combined effects of individual risk factors and their interactions. Our approaches are structured for age-specific risk models based on Cox proportional hazards regression methods. They are useful for analyses of families and extended pedigrees in which measured risk genotypes are segregated within the family and are robust even when the genotypes are available only in some members of a family. We exemplified these methods by analyzing six extended pedigrees ascertained through soft-tissue sarcoma patients with p53 germ-line mutations. Our analyses showed that germ-line p53 mutations and sex had significant interaction effects on cancer risk. Our proposed methods in family studies are accurate and robust for assessing age-specific cancer risk attributable to a measured hereditary susceptibility gene, providing valuable inferences for genetic counseling and clinical management.     

Dose response problems in carcinogenesis.

The estimation of risks from exposure to carcinogens is an important problem from the viewpoint of protection of human health. It also poses some very difficult dose-response problems. Two dose-response models may fit experimental data about equally well and yet predict responses that differ by many orders of magnitude at low doses. Mechanisms of carcinogenesis are not sufficiently understood so that the shape of the dose-response curve at low doses can be satisfactorily predicted. Mathematical theories of carcinogenesis and statistical procedures can be of use with dose-reponse problems such as this and, in addition, can lead to a better understanding of the mechanisms of carcinogenesis. In this paper, mathematical dose-response models of carcinogenesis are considered as well as various proposed dose-response procedures for estimating carcinogenic risks at low doses. Areas are suggested in which further work may be useful. These areas include experimental design problems, statistical procedures for use with time-to-occurrence data, and mathematical models that incorporate such biological features as pharmacokinetics of carcinogens, synergistic effects, DNA repair, susceptible subpopulations, and immune reactions.     

Age-specific acceleration of cancer

One of the great challenges of cancer research is to explain the epidemiological patterns of cancer incidence based on the molecular processes that lead to uncontrolled cellular proliferation. The epidemiological data demonstrate that the age-specific incidence of many cancers increases in an approximately linear way with age when plotted on a log-log scale, with different slopes for different cancers. However, those epidemiological data also show that cancers of various tissues depart from log-log linearity in particular ways. Here, I illustrate those departures from log-log linearity by introducing plots of the age-specific acceleration of cancer. I then develop a very general model of cancer progression, which I use to explain the observed differences between tissues in age-specific acceleration. In one application of the model, I show that the spectacular rise and fall in age-specific acceleration observed in prostate cancer may be explained by multiple rounds of clonal expansion. In a second application, I demonstrate that the steady decline in age-specific acceleration of breast cancer may occur because precancerous mutations accumulate in many cellular lineages.     

A statistical model for the identification of genes governing the incidence of cancer with age

The cancer incidence increases with age. This epidemiological pattern of cancer incidence can be attributed to molecular and cellular processes of individual subjects. Also, the incidence of cancer with ages can be controlled by genes. Here we present a dynamic statistical model for explaining the epidemiological pattern of cancer incidence based on individual genes that regulate cancer formation and progression. We incorporate the mathematical equations of age-specific cancer incidence into a framework for functional mapping aimed at identifying quantitative trait loci (QTLs) for dynamic changes of a complex trait. The mathematical parameters that specify differences in the curve of cancer incidence among QTL genotypes are estimated within the context of maximum likelihood. The model provides testable quantitative hypotheses about the initiation and duration of genetic expression for QTLs involved in cancer progression. Computer simulation was used to examine the statistical behavior of the model. The model can be used as a tool for explaining the epidemiological pattern of cancer incidence.     

Multiple imputation for estimating the risk of developing dementia and its impact on survival

Dementia, Alzheimer's disease in particular, is one of the major causes of disability and decreased quality of life among the elderly and a leading obstacle to successful aging. Given the profound impact on public health, much research has focused on the age-specific risk of developing dementia and the impact on survival. Early work has discussed various methods of estimating age-specific incidence of dementia, among which the illness-death model is popular for modeling disease progression. In this article we use multiple imputation to fit multi-state models for survival data with interval censoring and left truncation. This approach allows semi-Markov models in which survival after dementia depends on onset age. Such models can be used to estimate the cumulative risk of developing dementia in the presence of the competing risk of dementia-free death. Simulations are carried out to examine the performance of the proposed method. Data from the Honolulu Asia Aging Study are analyzed to estimate the age-specific and cumulative risks of dementia and to examine the effect of major risk factors on dementia onset and death.     

Overview: genes that predispose to cancer

Heredity and environment both operate in the origin of cancer. Dominantly heritable cancer is caused by 'cancer' genes that impart high relative risks but account for only a small part of the incidence of cancer; they are usually recessive in oncogenesis, mutation or loss of the second allele being necessary. Non-hereditary forms of cancer may involve the same genes. Other genes interact with environment in carcinogenesis; these may impart relatively small relative risks, but because their frequencies may be high, the attributable risks can be great, as probably is the case with lung cancer. The process of carcinogenesis is thought to involve 2 or more somatic genetic events in most cases. The genes whose germline mutations cause dominantly inherited cancer can also be mutated somatically to cause non-hereditary cancer. Other genes may influence the numbers of target cells, or the proliferation of once-hit stem cells, without being critical events on the path to cancer. However, such genes could greatly influence the incidence of a cancer. Other genes, such as that for Bloom's syndrome, may affect the rates at which first and second events occur. Finally, other genes may influence the occurrence of events critical for progression and metastasis, such as vascularization of a small tumor.     

Flexible dose-response models for Japanese atomic bomb survivor data: Bayesian estimation and prediction of cancer risk

Generalised absolute risk models were fitted to the latest Japanese atomic bomb survivor cancer incidence data using Bayesian Markov Chain Monte Carlo methods, taking account of random errors in the DS86 dose estimates. The resulting uncertainty distributions in the relative risk model parameters were used to derive uncertainties in population cancer risks for a current UK population. Because of evidence for irregularities in the low-dose dose response, flexible dose-response models were used, consisting of a linear-quadratic-exponential model, used to model the high-dose part of the dose response, together with piecewise-linear adjustments for the two lowest dose groups. Following an assumed administered dose of 0.001 Sv, lifetime leukaemia radiation-induced incidence risks were estimated to be 1.11 x 10(-2) Sv(-1) (95% Bayesian CI -0.61, 2.38) using this model. Following an assumed administered dose of 0.001 Sv, lifetime solid cancer radiation-induced incidence risks were calculated to be 7.28 x 10(-2) Sv(-1) (95% Bayesian CI -10.63, 22.10) using this model. Overall, cancer incidence risks predicted by Bayesian Markov Chain Monte Carlo methods are similar to those derived by classical likelihood-based methods and which form the basis of established estimates of radiation-induced cancer risk.     

Analysis of thyroid cancer data from the Ukraine after ’Chernobyl’ using a two-mutation carcinogenesis model

The thyroid cancer data of children in the northern regions of the Ukraine after the reactor accident at Chernobyl were combined with thyroid dose measurements in the same regions and analysed using a two- mutation carcinogenesis model. The best fit was obtained for radiation acting as an initiating agent, i.e. on the first mutation of the model. The observed relatively high increase of thyroid cancer incidence after 1990 in children exposed to radiation released after the reactor accident could be ascribed to the high thyroid doses and the relatively low background thyroid cancer incidence in children. The maximum annual incidence is predicted to occur fairly soon after the reactor accident, i.e. about 10 years. For adults, the predicted relative increase of annual thyroid cancers is much lower than for children younger than 20 years. The modelling results are used to derive risk estimates for radiation-induced thyroid cancer. These risk estimates are dependent on age at exposure, follow-up time and the background thyroid cancer incidence. The calculated excess absolute risk for a population of all ages is about one-third of that currently used by ICRP, but for children the calculated absolute risks are about a factor of 3 higher than derived in other epidemiological studies. The model results indicate that the excess absolute radiation risk per unit dose for children is about the same as or a little lower than that for adults. Received: 11 May 1999 / Accepted: 30 December 1999     

Does chemically induced hepatocyte proliferation predict liver carcinogenesis?

Cell proliferation has long been recognized as having an important role in chemically induced carcinogenesis. Based on findings that certain nongenotoxic chemical carcinogens induced cell proliferation in the same organ that had an increased incidence of tumors, it has been hypothesized that a chemically induced response of enhanced DNA synthesis and cellular division causes cancer by increasing the rate of spontaneous mutations. It was further suggested that there would be no increased human risk of cancer by non-DNA-reactive compounds at doses that do not cause a proliferative response. An evaluation of the literature on the relationship between chemically induced cell proliferation and liver carcinogenesis reveals that very few systematic cell proliferation studies have been conducted over periods of extended exposure, and in many cases the exposure concentrations were not similar to those used in the cancer studies. The proliferative response resulting from exposure to many nongenotoxic carcinogens is not well sustained, whereas the carcinogenic response by these chemicals often requires prolonged exposure. The available literature leads to the conclusion that quantitative correspondences between cellular proliferation and carcinogenic responses have not been demonstrated and do not support the hypothesis that chemically induced cell proliferation is the primary mechanism by which nongenotoxic chemicals cause liver cancer. Studies of liver carcinogenesis in two-stage models point out the need to better understand chemical effects on cell loss as well as on cell replication, and demonstrate that measurements of cell proliferation alone are not sufficient to elucidate mechanisms of tumor development.     

Radiation carcinogenesis in experimental animals and its implications for radiation protection

Cancer induction is generally considered to be the most important somatic effect of low doses of ionizing radiation. It is therefore of great concern to assess the quantitative cancer risk of exposure to radiations of different quality and to obtain information on the dose-response relationships for carcinogenesis. Tissues in the human with a high sensitivity for cancer induction include the bone marrow, the lung, the thyroid and the breast in women. If the revised dosimetry estimates for the Japanese survivors of the atomic bomb explosions are correct, there is no useful data base left to derive r.b.e. values for human carcinogenesis. As a consequence, it will be necessary to rely on results obtained in biological systems, including experimental animals, for these estimates. With respect to radiation protection, the following aspects of experimental studies on radiation carcinogenesis are of relevance: Assessment of the nature of dose-response relationships. Determination of the relative biological effectiveness of radiations of different quality. Effects of fractionation or protraction of the dose on tumour development. For the analysis of tumour data in animals, specific approaches have to be applied which correct for competing risks. These methods include actuarial estimates, non-parametric models and analytical models. The dose-response curves for radiation-induced cancers in different tissues vary in shape. This is exemplified by studies on myeloid leukaemia in mice and mammary neoplasms in different rat strains. The results on radiation carcinogenesis in animal models clearly indicate that the highest r.b.e. values are observed for neutrons with energies between 0.5 and 1 MeV. On the basis of such results it might be concluded that the maximum quality factor of 10 for neutrons should be increased. Based on current evidence, an increase by a factor of 2 to 3 seems more realistic than a tenfold rise. The diversity of dose-response relationships point to different mechanisms involved in the induction of different tumours in various species and even in different strains of the same species.     

Temporal and regional trends of cancer mortality in West Germany

Age-specific and cumulative mortality rates are presented for different cancer sites from 1970 until 1988 for the 11 individual federal states of West Germany (FRG). Sex- and age-specific evaluations are performed and tempora and regional trends in mortality from different cancer sites are revealed. In the FRG there is no comprehensive cancer registry with national coverage for recording cancer patients of all ages (nationwide incidence rates are available only for childhood cancers). Therefore, in view of the lack of a nationwide cancer registry the importance of long-term cancer mortality studies for health policy is emphasized. Methodological aspects of certification regulations and classification of cancer sites are discussed.     

A reanalysis of liver cancer incidence in Danish patients administered thorotrast using a two-mutation carcinogenesis model

In recent years, a two-mutation carcinogenesis (TMC) model has been used to analyze epidemiological data and estimate the radiation risks at low doses for the organs affected. Here the TMC model was used to reanalyze the liver cancer incidence in the Danish population in general and in patients administered Thorotrast, and to estimate the radiation risks for the liver. The data for 807 patients for whom sufficient data on the injected volumes of Thorotrast were available were used in this reanalysis. These data were combined with data on liver cancer incidence in the Danish population as the baseline or background incidence. Because males and females show different baseline liver cancer incidences, separate fits were made for males and females. The fits showed that the radiation effect could be ascribed entirely to the radiation dependence of the first mutation rate of the TMC model, which was higher for females than for males. The second mutation rate was not significantly dependent on dose. The radiation risks for the liver were calculated on the basis of the model parameters. These risks for lifetime exposures are about the same for males and females and are between a factor of 2 and 10 higher than current estimates. The discrepancy between the model results and previous risk estimates probably arises because the model calculations give more complete lifetime radiation risk estimates. For short-term exposures of the liver to ionizing radiation, the maximum radiation-induced excess liver cancer risk per unit dose applies to exposures at the age of about 10; exposures at ages above 35 have a radiation effect of less than approximately 15% of this maximum.