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Figure S2. Male/Female EAC incidence

This file provides a list of 'static' CGI-gene pairs for which methylation is significantly correlated with gene expression (q-value ) in left colon (including rectum) and right colon

PDF - 1235K, Referred to as "Appendix". Supplementary Materials and Methods. Figure A1. Schematic diagram of MSEAC Model. Figure A2. Cell Proliferation Rates by Birth Cohort, Males. Figure A3. Cell Proliferation Rates by Birth Cohort, Females. Figure A4. Period and Cohort Effects as Applied to Conceptual Model Transitions. Figure A5. Period Effect Coefficient Values as a Function of Calendar Year for Males. Figure A6. Cohort Effect Coefficient Values as a Function of Birth Year for Males. Figure A7. Model Structure UW-MISCAN Esophageal Adenocarcinoma Model. Figure A8. Increasing or Plateauing of the EAC Incidence Trend by Age Group. Figure A9. The EAC incidence rates stratified by birth cohorts are shown for all males (A) and all females (B). Figure A10. The EAC incidence rates by 10 years birth cohorts for all females.

Cancer screening and early detection efforts have been partially successful in reducing incidence and mortality, but many improvements are needed. Although current medical practice is informed by epidemiologic studies and experts, the decisions for guidelines are ultimately ad hoc. We propose here that quantitative optimization of protocols can potentially increase screening success and reduce overdiagnosis. Mathematical modeling of the stochastic process of cancer evolution can be used to derive and optimize the timing of clinical screens so that the probability is maximal that a patient is screened within a certain “window of opportunity” for intervention when early cancer development may be observable. Alternative to a strictly empirical approach or microsimulations of a multitude of possible scenarios, biologically based mechanistic modeling can be used for predicting when best to screen and begin adaptive surveillance. We introduce a methodology for optimizing screening, assessing potential risks, and quantifying associated costs to healthcare using multiscale models. As a case study in Barrett's esophagus, these methods were applied for a model of esophageal adenocarcinoma that was previously calibrated to U.S. cancer registry data. Optimal screening ages for patients with symptomatic gastroesophageal reflux disease were older (58 for men and 64 for women) than what is currently recommended (age > 50 years). These ages are in a cost-effective range to start screening and were independently validated by data used in current guidelines. Collectively, our framework captures critical aspects of cancer evolution within patients with Barrett's esophagus for a more personalized screening design.Significance:This study demonstrates how mathematical modeling of cancer evolution can be used to optimize screening regimes, with the added potential to improve surveillance regimes.

Background: U.S. esophageal adenocarcinoma (EAC) incidence increased over 5-fold between 1975 and 2009. Symptomatic gastroesophageal reflux disease (sGERD) elevates the risk for EAC. However, a simple calculation suggests that changes in sGERD prevalence can explain at most approximately 16% of this trend. Importantly, a mechanistic understanding of the influence of sGERD and other factors (OF) on EAC is lacking.Methods: A multiscale model was developed to estimate temporal trends for sGERD and OF, and their mechanistic role during carcinogenesis. Model calibration was to Surveillance, Epidemiology, and End Results (SEER) incidence and age-dependent sGERD data using maximum likelihood and Markov chain Monte Carlo (MCMC) methods.Results: Among men, 77.8% [95% credibility interval (CI), 64.9%–85.6%] of the incidence trend is attributable to OF, 13.4% (95% CI, 11.4%–17.3%) to sGERD, and 8.8% (95% CI, 4.2%–13.7%) to sGERD–OF interactions. Among women, 32.6% (95% CI, 27.0%–39.9%) of the trend is attributable to OF, 13.6% (95% CI, 12.5%–15.9%) to sGERD, and 47.4% (95% CI, 30.7%–64.6%) to interactions. The predicted trends were compared with historical trends for obesity, smoking, and proton pump inhibitor use. Interestingly, predicted OF cohort trends correlated most highly with median body mass index (BMI) at age 50 (r = 0.988 for men; r = 0.998 for women).Conclusions: sGERD and OF mechanistically increase premalignant cell promotion, which increases EAC risk exponentially with exposure duration.Impact: Surveillance should target individuals with long-duration sGERD and OF exposures. Cancer Epidemiol Biomarkers Prev; 24(7); 1012–23. ©2015 AACR.

Many normal tissues undergo age-related drift in DNA methylation, providing a quantitative measure of tissue age. Here, we identify and validate 781 CpG islands (CGI) that undergo significant methylomic drift in 232 normal colorectal tissues and show that these CGI continue to drift in neoplasia while retaining significant correlations across samples. However, compared with normal colon, this drift advanced (∼3–4-fold) faster in neoplasia, consistent with increased cell proliferation during neoplastic progression. The observed drift patterns were broadly consistent with modeled adenoma-to-carcinoma sojourn time distributions from colorectal cancer incidence data. These results support the hypothesis that, beginning with the founder premalignant cell, cancer precursors frequently sojourn for decades before turning into cancer, implying that the founder cell typically arises early in life. At least 77% to 89% of the observed drift variance in distal and rectal tumors was explained by stochastic variability associated with neoplastic progression, whereas only 55% of the variance was explained for proximal tumors. However, gene–CGI pairs in the proximal colon that underwent drift were significantly and primarily negatively correlated with cancer gene expression, suggesting that methylomic drift participates in the clonal evolution of colorectal cancer. Methylomic drift advanced in colorectal neoplasia, consistent with extended sojourn time distributions, which accounts for a significant fraction of epigenetic heterogeneity in colorectal cancer. Importantly, these estimated long-duration premalignant sojourn times suggest that early dietary and lifestyle interventions may be more effective than later changes in reducing colorectal cancer incidence.Significance:These findings present age-related methylomic drift in colorectal neoplasia as evidence that premalignant cells can persist for decades before becoming cancerous.See related commentary by Sapienza, p. 437

Background: The incidence of esophageal adenocarcinoma (EAC) has increased five-fold in the United States since 1975. The aim of our study was to estimate future U.S. EAC incidence and mortality and to shed light on the potential drivers in the disease process that are conduits for the dramatic increase in EAC incidence.Methods: A consortium of three research groups calibrated independent mathematical models to clinical and epidemiologic data including EAC incidence from the Surveillance, Epidemiology, and End Results (SEER 9) registry from 1975 to 2010. We then used a comparative modeling approach to project EAC incidence and mortality to year 2030.Results: Importantly, all three models identified birth cohort trends affecting cancer progression as a major driver of the observed increases in EAC incidence and mortality. All models predict that incidence and mortality rates will continue to increase until 2030 but with a plateauing trend for recent male cohorts. The predicted ranges of incidence and mortality rates (cases per 100,000 person years) in 2030 are 8.4 to 10.1 and 5.4 to 7.4, respectively, for males, and 1.3 to 1.8 and 0.9 to 1.2 for females. Estimates of cumulative cause-specific EAC deaths between both sexes for years 2011 to 2030 range between 142,300 and 186,298, almost double the number of deaths in the past 20 years.Conclusions: Through comparative modeling, the projected increases in EAC cases and deaths represent a critical public health concern that warrants attention from cancer control planners to prepare potential interventions.Impact: Quantifying this burden of disease will aid health policy makers to plan appropriate cancer control measures. Cancer Epidemiol Biomarkers Prev; 23(6); 997–1006. ©2014 AACR.

Derivation of adenoma to carcinoma sojourn times, with table S1 showing estimated methylation drift rate parameters and table S2 showing estimated sojourn times for three tissue age models.

Cancer arises through a multistage process, but it is not fully clear how this process influences the age-specific incidence curve. Studies of colorectal and pancreatic cancer using the multistage clonal expansion (MSCE) model have identified two phases of the incidence curves. One phase is linear, beginning about age of 60 years, suggesting that at least two rare rate-limiting mutations occur before clonal expansion of premalignant cells. A second phase is exponential, seen in early-onset cancers occurring before the age of 60 years that are associated with premalignant clonal expansion. Here, we extend the MSCE model to include clonal expansion of malignant cells, an advance that permits study of the effects of tumor growth and extinction on the incidence of colorectal, gastric, pancreatic, and esophageal adenocarcinomas in the digestive tract. After adjusting the age-specific incidence for birth-cohort and calendar-year trends, we found that initiating mutations and premalignant cell kinetics can explain the primary features of the incidence curve. However, we also found that the incidence data of these cancers harbored information on the kinetics of malignant clonal expansion before clinical detection, including tumor growth rates and extinction probabilities on three characteristic time scales for tumor progression. In addition, the data harbored information on the mean sojourn times for premalignant clones until occurrence of either the first malignant cell or the first persistent (surviving) malignant clone. Finally, the data also harbored information on the mean sojourn time of persistent malignant clones to the time of diagnosis. In conclusion, cancer incidence curves can harbor significant information about hidden processes of tumor initiation, premalignant clonal expansion, and malignant transformation, and even some limited information on tumor growth before clinical detection. Cancer Res; 73(3); 1086–96. ©2012 AACR.

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Background & Aims: Guidelines suggest endoscopic screening for esophageal adenocarcinoma (EAC) among individuals with symptoms of gastroesophageal reflux disease (GERD) and additional risk factors. We aimed to determine at what age to perform screening and whether sex and race should influence the decision. Methods: We conducted comparative cost-effectiveness analyses using 3 independent simulation models. For each combination of sex and race (White/Black, 100,000 individuals each), we considered 41 screening strategies, including one-time or repeated screening. The optimal strategy was that with the highest effectiveness and an incremental cost-effectiveness ratio

The incidence of cancer, adjusted for secular trends, is directly related to age, and advanced chronologic age is one of the most significant risk factors for cancer. Organismal aging is associated with changes at the molecular, cellular, and tissue levels and is affected by both genetic and environmental factors. The specific mechanisms through which these age-associated molecular changes contribute to the increased risk of aging-related disease, such as cancer, are incompletely understood. DNA methylation, a prominent epigenetic mark, also changes over a lifetime as part of an “epigenetic aging” process. Here, we give an update and review of epigenetic aging, in particular, the phenomena of epigenetic drift and epigenetic clock, with regard to its implication in cancer etiology. We discuss the discovery of the DNA methylation–based biomarkers for biological tissue age and the construction of various epigenetic age estimators for human clinical outcomes and health/life span. Recent studies in various types of cancer point to the significance of epigenetic aging in tumorigenesis and its potential use for cancer risk prediction. Future studies are needed to assess the potential clinical impact of strategies focused on lowering cancer risk by preventing premature aging or promoting healthy aging.

Patterns of cancer incidence, viewed over extended time periods, reveal important aspects of multistage carcinogenesis. Here we show how a multistage clonal expansion (MSCE) model for cancer can be harnessed to identify biological processes that shape the surprisingly dynamic and disparate incidence patterns of esophageal squamous cell carcinoma (ESCC) in the US population. While the dramatic rise in esophageal adenocarcinoma (EAC) in the US has been largely attributed to reflux related increases in the prevalence of Barrett’s esophagus (BE), the premalignant field in which most EAC are thought to arise, only scant evidence exists for field cancerization contributing to ESCC. Our analyses of incidence patterns suggest that ESCC is associated with a premalignant field that may develop very early in life. Although the risk of ESCC, which is substantially higher in Blacks than Whites, is generally assumed to be associated with late-childhood and adult exposures to carcinogens, such as from tobacco smoking, alcohol consumption and various industrial exposures, the temporal trends we identify for ESCC suggest an onset distribution of field-defects before age 10, most strongly among Blacks. These trends differ significantly in shape and strength from field-defect trends that we estimate for US Whites. Moreover, the rates of ESCC-predisposing field-defects predicted by the model for cohorts of black children are decreasing for more recent birth cohorts (for Blacks born after 1940). These results point to a potential etiologic role of factors acting early in life, perhaps related to nutritional deficiencies, in the development of ESCC and its predisposing field-defect. Such factors may explain some of the striking racial differences seen in ESCC incidence patterns over time in the US., Author summary We used a cell-level carcinogenesis model to analyze incidence patterns of esophageal squamous cell carcinoma (ESCC) in the US. We found an important role of an esophageal field-defect that is predicted to occur predominantly in childhood and predisposes to ESCC in adult life. Age-specific ESCC incidence patterns are also known to differ considerably between Blacks and Whites, and between males and females in the US, but the model consistently predicts early-childhood field-defects in all four groups. The estimated historical field-defect trends appear consistent with possible early childhood nutritional deficiencies.

Background and Aims: Current guidelines recommend surveillance for patients with nondysplastic Barrett's esophagus (NDBE) but do not include a recommended age for discontinuing surveillance. This study aimed to determine the optimal age for last surveillance of NDBE patients stratified by sex and level of comorbidity. Methods: We used 3 independently developed models to simulate patients diagnosed with NDBE, varying in age, sex, and comorbidity level (no, mild, moderate, and severe). All patients had received regular surveillance until their current age. We calculated incremental costs and quality-adjusted life-years (QALYs) gained from 1 additional endoscopic surveillance at the current age versus not performing surveillance at that age. We determined the optimal age to end surveillance as the age at which incremental cost-effectiveness ratio of 1 more surveillance was just less than the willingness-to-pay threshold of $100,000/QALY. Results: The benefit of having 1 more surveillance endoscopy strongly depended on age, sex, and comorbidity. For men with NDBE and severe comorbidity, 1 additional surveillance at age 80 years provided 4 more QALYs per 1000 patients with BE at an additional cost of $1.2 million, whereas for women with severe comorbidity the benefit at that age was 7 QALYs at a cost of $1.3 million. For men with no, mild, moderate, and severe comorbidity, the optimal ages of last surveillance were 81, 80, 77, and 73 years, respectively. For women, these ages were younger: 75, 73, 73, and 69 years, respectively. Conclusions: Our comparative modeling analysis illustrates the importance of considering comorbidity status and sex when deciding on the age to discontinue surveillance in patients with NDBE.

The U.S. Preventive Services Task Force (USPSTF) recently updated their national lung screening guidelines and recommended low-dose computed tomography (LDCT) for lung cancer (LC) screening through age 80. However, the risk of overdiagnosis among older populations is a concern. Using four comparative models from the Cancer Intervention and Surveillance Modeling Network, we evaluate the overdiagnosis of the screening program recommended by USPSTF in the U.S. 1950 birth cohort. We estimate the number of LC deaths averted by screening (D) per overdiagnosed case (O), yielding the ratio D/O, to quantify the trade-off between the harms and benefits of LDCT. We analyze 576 hypothetical screening strategies that vary by age, smoking, and screening frequency and evaluate efficient screening strategies that maximize the D/O ratio and other metrics including D and life-years gained (LYG) per overdiagnosed case. The estimated D/O ratio for the USPSTF screening program is 2.85 (model range: 1.5–4.5) in the 1950 birth cohort, implying LDCT can prevent ∼3 LC deaths per overdiagnosed case. This D/O ratio increases by 22% when the program stops screening at an earlier age 75 instead of 80. Efficiency frontier analysis shows that while the most efficient screening strategies that maximize the mortality reduction (D) irrespective of overdiagnosis screen through age 80, screening strategies that stop at age 75 versus 80 produce greater efficiency in increasing life-years gained per overdiagnosed case. Given the risk of overdiagnosis with LC screening, the stopping age of screening merits further consideration when balancing benefits and harms.

Cancer screening and early detection efforts have been partially successful in reducing incidence and mortality but many improvements are needed. Although current medical practice is mostly informed by epidemiological studies, the decisions for guidelines are ultimately made ad hoc. We propose that quantitative optimization of protocols can potentially increase screening success and reduce overdiagnosis. Mathematical modeling of the stochastic process of cancer evolution can be used to derive and to optimize the timing of clinical screens so that the probability is maximal that a patient is screened within a certain “window of opportunity” for intervention when early cancer development may be observable. Alternative to a strictly empirical approach, or microsimulations of a multitude of possible scenarios, biologically-based mechanistic modeling can be used for predicting when best to screen and begin adaptive surveillance. We introduce a methodology for optimizing screening, assessing potential risks, and quantifying associated costs to healthcare using multiscale models. As a case study in Barrett’s esophagus (BE), we applied our methods for a model of esophageal adenocarcinoma (EAC) that was previously calibrated to US cancer registry data. We found optimal screening ages for patients with symptomatic gastroesophageal reflux disease to be older (58 for men, 64 for women) than what is currently recommended (age > 50 years). These ages are in a cost-effective range to start screening and were independently validated by data used in current guidelines. Our framework captures critical aspects of cancer evolution within BE patients for a more personalized screening design.SignificanceOur study demonstrates how mathematical modeling of cancer evolution can be used to optimize screening regimes. Surveillance regimes could also be improved if they were based on these models.Graphical Abstract

Background & Aims: Endoscopic treatment is recommended for patients with Barrett's esophagus (BE) with high-grade dysplasia, yet clinical management recommendations are inconsistent for patients with BE without dysplasia (NDBE) or with low-grade dysplasia (LGD). We used a comparative modeling analysis to identify optimal management strategies for these patients. Methods: We used 3 independent population-based models to simulate cohorts of 60-year-old individuals with BE in the United States. We followed up each cohort until death without surveillance and treatment (natural disease progression), compared with 78 different strategies of management for patients with NDBE or LGD. We determined the optimal strategy using cost-effectiveness analyses, at a willingness-to-pay threshold of $100,000 per quality-adjusted life-year (QALY). Results: In the 3 models, the average cumulative incidence of esophageal adenocarcinoma was 111 cases, with costs totaling $5.7 million per 1000 men with BE. Surveillance and treatment of men with BE prevented 23% to 75% of cases of esophageal adenocarcinoma, but increased costs to $6.2 to $17.3 million per 1000 men with BE. The optimal strategy was surveillance every 3 years for men with NDBE and treatment of LGD after confirmation by repeat endoscopy (incremental cost-effectiveness ratio, $53,044/QALY). The average results for women were consistent with the results for men for LGD management, but the optimal surveillance interval for women with NDBE was 5 years (incremental cost-effectiveness ratio, $36,045/QALY). Conclusions: Based on analyses from 3 population-based models, the optimal management strategy for patient with BE and LGD is endoscopic eradication, but only after LGD is confirmed by a repeat endoscopy. The optimal strategy for patients with NDBE is endoscopic surveillance, using a 3-year interval for men and a 5-year interval for women.

Many normal tissues undergo age-related drift in DNA methylation, providing a quantitative measure of tissue age. Here, we identify and validate 781 CpG islands (CGI) that undergo significant methylomic drift in 232 normal colorectal tissues and show that these CGI continue to drift in neoplasia while retaining significant correlations across samples. However, compared with normal colon, this drift advanced (∼3–4-fold) faster in neoplasia, consistent with increased cell proliferation during neoplastic progression. The observed drift patterns were broadly consistent with modeled adenoma-to-carcinoma sojourn time distributions from colorectal cancer incidence data. These results support the hypothesis that, beginning with the founder premalignant cell, cancer precursors frequently sojourn for decades before turning into cancer, implying that the founder cell typically arises early in life. At least 77% to 89% of the observed drift variance in distal and rectal tumors was explained by stochastic variability associated with neoplastic progression, whereas only 55% of the variance was explained for proximal tumors. However, gene–CGI pairs in the proximal colon that underwent drift were significantly and primarily negatively correlated with cancer gene expression, suggesting that methylomic drift participates in the clonal evolution of colorectal cancer. Methylomic drift advanced in colorectal neoplasia, consistent with extended sojourn time distributions, which accounts for a significant fraction of epigenetic heterogeneity in colorectal cancer. Importantly, these estimated long-duration premalignant sojourn times suggest that early dietary and lifestyle interventions may be more effective than later changes in reducing colorectal cancer incidence. Significance: These findings present age-related methylomic drift in colorectal neoplasia as evidence that premalignant cells can persist for decades before becoming cancerous. See related commentary by Sapienza, p. 437

Screening colonoscopy and flexible sigmoidoscopy (FSG) reduce the risk of colorectal cancer (CRC), but the magnitude and duration of protection, particularly against right-sided cancer, remain uncertain. We computed the incremental benefit of colonoscopy over FSG using a validated mathematical model, which reflects colorectal neoplasia growth characteristics while allowing uncertainty in endoscopic detection and removal of adenomas.We calibrated models of CRC incidence within a multistage clonal expansion framework to data from: (1) San Francisco-Oakland SEER registry (reference population) and (2) FSG long-term follow-up data from 50,757 individuals after a negative FSG in the Kaiser Permanente system. We compared the residual CRC risks after FSG with full-length colonoscopy.Our model mirrors trial data with 10-year CRC risk reductions after FSG screening at age 50 years of approximately one-third; the optimal age for a 'once-only' FSG exam was between ages 50 and 60 years; and the greater benefit was for men compared with women. There were considerable incremental gains in reduction in CRC risk by colonoscopy compared with FSG with the greatest benefit for screening colonoscopy at age 50 years. These results held up against lowering the right-sided adenoma detection sensitivity by 30%, as well as reducing the curative efficacy of polypectomy throughout the colon.Mathematical modeling of CRC screening, which takes account of important aspects of tumor biology, demonstrates superior risk reductions by colonoscopy over FSG. Our predictions provide further rationale for recommending screening colonoscopy in average-risk populations before the age of 60.

Background: The incidence of esophageal adenocarcinoma (EAC) has increased five-fold in the United States since 1975. The aim of our study was to estimate future U.S. EAC incidence and mortality and to shed light on the potential drivers in the disease process that are conduits for the dramatic increase in EAC incidence. Methods: A consortium of three research groups calibrated independent mathematical models to clinical and epidemiologic data including EAC incidence from the Surveillance, Epidemiology, and End Results (SEER 9) registry from 1975 to 2010. We then used a comparative modeling approach to project EAC incidence and mortality to year 2030. Results: Importantly, all three models identified birth cohort trends affecting cancer progression as a major driver of the observed increases in EAC incidence and mortality. All models predict that incidence and mortality rates will continue to increase until 2030 but with a plateauing trend for recent male cohorts. The predicted ranges of incidence and mortality rates (cases per 100,000 person years) in 2030 are 8.4 to 10.1 and 5.4 to 7.4, respectively, for males, and 1.3 to 1.8 and 0.9 to 1.2 for females. Estimates of cumulative cause-specific EAC deaths between both sexes for years 2011 to 2030 range between 142,300 and 186,298, almost double the number of deaths in the past 20 years. Conclusions: Through comparative modeling, the projected increases in EAC cases and deaths represent a critical public health concern that warrants attention from cancer control planners to prepare potential interventions. Impact: Quantifying this burden of disease will aid health policy makers to plan appropriate cancer control measures. Cancer Epidemiol Biomarkers Prev; 23(6); 997–1006. ©2014 AACR.

BACKGROUND The National Lung Screening Trial (NLST) demonstrated that low-dose computed tomography screening is an effective way of reducing lung cancer (LC) mortality. However, optimal screening strategies have not been determined to date and it is uncertain whether lighter smokers than those examined in the NLST may also benefit from screening. To address these questions, it is necessary to first develop LC natural history models that can reproduce NLST outcomes and simulate screening programs at the population level. METHODS Five independent LC screening models were developed using common inputs and calibration targets derived from the NLST and the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial (PLCO). Imputation of missing information regarding smoking, histology, and stage of disease for a small percentage of individuals and diagnosed LCs in both trials was performed. Models were calibrated to LC incidence, mortality, or both outcomes simultaneously. RESULTS Initially, all models were calibrated to the NLST and validated against PLCO. Models were found to validate well against individuals in PLCO who would have been eligible for the NLST. However, all models required further calibration to PLCO to adequately capture LC outcomes in PLCO never-smokers and light smokers. Final versions of all models produced incidence and mortality outcomes in the presence and absence of screening that were consistent with both trials. CONCLUSIONS The authors developed 5 distinct LC screening simulation models based on the evidence in the NLST and PLCO. The results of their analyses demonstrated that the NLST and PLCO have produced consistent results. The resulting models can be important tools to generate additional evidence to determine the effectiveness of lung cancer screening strategies using low-dose computed tomography.

No Abstract Available

It is currently not known whether most lung cancers detected by computerized tomography (CT) screening are aggressive and likely to be fatal if left untreated, or if a sizable fraction are indolent and unlikely to cause death during the natural lifetime of the individual. We developed a longitudinal biologically-based model of the relationship between individual smoking histories and the probability for lung cancer incidence, CT screen detection, lung cancer mortality, and other-cause mortality. The longitudinal model relates these different outcomes to an underlying lung cancer disease pathway and an effective other-cause mortality pathway, which are both influenced by the individual smoking history. The longitudinal analysis provides additional information over that available if these outcomes were analyzed separately, including testing if the number of CT detected and histologically-confirmed lung cancers is consistent with the expected number of lung cancers “in the pipeline”. We assume indolent nodules undergo Gompertz growth and are detectable by CT, but do not grow large enough to contribute significantly to symptom-based lung cancer incidence or mortality. Likelihood-based model calibration was done jointly to data from 6,878 heavy smokers without asbestos exposure in the control (placebo) arm of the Carotene and Retinol Efficacy Trial (CARET); and to 3,642 heavy smokers with comparable smoking histories in the Pittsburgh Lung Screening Study (PLuSS), a single-arm prospective trial of low-dose spiral CT screening for diagnosis of lung cancer. Model calibration was checked using data from two other single-arm prospective CT screening trials, the New York University Lung Cancer Biomarker Center (NYU) (n=1,021), and Moffitt Cancer Center (Moffitt) cohorts (n=677). In the PLuSS cohort, we estimate that at the end of year 2, after the baseline and first annual CT exam, that 33.0 (26.9, 36.9)% of diagnosed lung cancers among females and 7.0 (4.9, 11.7)% among males were overdiagnosed due to being indolent cancers. At the end of the PLuSS study, with maximum follow-up of 5.8 years, we estimate that due to early detection by CT and limited follow-up, an additional 2.2 (2.0, 2.4) % of all diagnosed cancers among females and 7.1 (6.7, 8.0) % among males would not have been diagnosed in the absence of CT screening. We also find a higher apparent cure rate for lung cancer among CARET females than males, consistent with the larger indolent fraction of CT detected and histologically confirmed lung cancers among PLuSS females. This suggests that there are significant gender differences in the aggressiveness of lung cancer. Females may have an inherently higher proportion of indolent lung cancers than males, or aggressive lung cancers may be brought into check by the immune system more frequently among females than males.

Sophisticated modeling techniques can be powerful tools to help us understand the effects of cancer control interventions on population trends in cancer incidence and mortality. Readers of journal articles are however rarely supplied with modeling details. Six modeling groups collaborated as part of the National Cancer Institute’s Cancer Intervention and Surveillance Modeling Network (CISNET) to investigate the contribution of US tobacco control efforts towards reducing lung cancer deaths over the period 1975 to 2000. The models included in this monograph were developed independently and use distinct, complementary approaches towards modeling the natural history of lung cancer. The models used the same data for inputs and agreed on the design of the analysis and the outcome measures. This article highlights aspects of the models that are most relevant to similarities of or differences between the results. Structured comparisons can increase the transparency of these complex models.

As a member of the Cancer Intervention and Surveillance Modeling Network (CISNET), the lung cancer group at Fred Hutchinson Cancer Research Center (FHCRC) developed a model for evaluating US lung cancer mortality trends and the impact of changing tobacco consumption. Model components include a biologically-based two-stage clonal expansion (TSCE) model; a smoking simulator to generate smoking histories and other cause mortality; and adjustments for period and birth-cohort to improve calibration to US lung cancer mortality. The TSCE model was first calibrated to five substantial cohorts: British doctors’, American Cancer Society CPS-I and CPS-II, Health Professionals’ Follow-Up Study (HPFS), and Nurses’ Health Study (NHS). The NHS and HPFS cohorts included the most detailed smoking histories and were chosen to represent the effects of smoking on US lung cancer mortality. The calibrated TSCE model and smoking simulator were used to simulate US lung cancer mortality. Further adjustments were necessary to account for unknown factors. This provided excellent fits between simulated and observed US lung cancer mortality for ages 30–84 and calendar years 1975–2000. The FHCRC lung cancer model may be used to study the effects of public health information on US lung cancer trends and the impact of tobacco control policy. For example, we estimated that over 500,000 males and 200,000 females avoided lung cancer death between 1975–2000 due to increasing awareness since the mid-1950’s of the harmful effects of smoking. We estimated that 1.1 million male and 0.6 million female lung cancer deaths were avoidable if smokers quit smoking in 1965.

textabstractBackground Considerable effort has been expended on tobacco control strategies in the United States since the mid-1950s. However, we have little quantitative information on how changes in smoking behaviors have impacted lung cancer mortality. We quantified the cumulative impact of changes in smoking behaviors that started in the mid-1950s on lung cancer mortality in the United States over the period 1975-2000. Methods A consortium of six groups of investigators used common inputs consisting of simulated cohort-wise smoking histories for the birth cohorts of 1890 through 1970 and independent models to estimate the number of US lung cancer deaths averted during 1975-2000 as a result of changes in smoking behavior that began in the mid-1950s. We also estimated the number of deaths that could have been averted had tobacco control been completely effective in eliminating smoking after the Surgeon General's first report on Smoking and Health in 1964.Results Approximately 795851 US lung cancer deaths were averted during the period 1975-2000: 552574 among men and 243 277 among women. In the year 2000 alone, approximately 70218 lung cancer deaths were averted: 44135 among men and 26083 among women. However, these numbers are estimated to represent approximately 32% of lung cancer deaths that could have potentially been averted during the period 1975-2000, 38% of the lung cancer deaths that could have been averted in 1991-2000, and 44% of lung cancer deaths that could have been averted in 2000. Conclusion s Our Results reflect the cumulative impact of changes in smoking behavior since the 1950s. Despite a large impact of changing smoking behaviors on lung cancer deaths, lung cancer remains a major public health problem. Continued efforts at tobacco control are critical to further reduce the burden of this disease.

There are many more strategies for early detection of cancer than can be evaluated with randomized trials. Consequently, model-projected outcomes under different strategies can be useful for developing cancer control policy provided that the projections are representative of the population. To project population-representative disease progression outcomes and to demonstrate their value in assessing competing early detection strategies, we implement a model linking prostate-specific antigen (PSA) levels and prostate cancer progression and calibrate it to disease incidence in the US population. PSA growth is linear on the logarithmic scale with a higher slope after disease onset and with random effects on intercepts and slopes; parameters are estimated using data from the Prostate Cancer Prevention Trial. Disease onset, metastatic spread, and clinical detection are governed by hazard functions that depend on age or PSA levels; parameters are estimated by comparing projected incidence under observed screening and biopsy patterns with incidence observed in the Surveillance, Epidemiology, and End Results registries. We demonstrate implications of the model for policy development by projecting early detections, overdiagnosis, and mean lead times for PSA cutoffs 4.0 and 2.5 ng/mL and for screening ages 50-74 or 50-84. The calibrated model validates well, quantifies the tradeoffs involved across policies, and indicates that PSA screening with cutoff 4.0 ng/mL and screening ages 50-74 performs best in terms of overdiagnosis per early detection. The model produces representative outcomes for selected PSA screening policies and is shown to be useful for informing the development of sound cancer control policy.

Background: U.S. esophageal adenocarcinoma (EAC) incidence increased over 5-fold between 1975 and 2009. Symptomatic gastroesophageal reflux disease (sGERD) elevates the risk for EAC. However, a simple calculation suggests that changes in sGERD prevalence can explain at most approximately 16% of this trend. Importantly, a mechanistic understanding of the influence of sGERD and other factors (OF) on EAC is lacking. Methods: A multiscale model was developed to estimate temporal trends for sGERD and OF, and their mechanistic role during carcinogenesis. Model calibration was to Surveillance, Epidemiology, and End Results (SEER) incidence and age-dependent sGERD data using maximum likelihood and Markov chain Monte Carlo (MCMC) methods. Results: Among men, 77.8% [95% credibility interval (CI), 64.9%–85.6%] of the incidence trend is attributable to OF, 13.4% (95% CI, 11.4%–17.3%) to sGERD, and 8.8% (95% CI, 4.2%–13.7%) to sGERD–OF interactions. Among women, 32.6% (95% CI, 27.0%–39.9%) of the trend is attributable to OF, 13.6% (95% CI, 12.5%–15.9%) to sGERD, and 47.4% (95% CI, 30.7%–64.6%) to interactions. The predicted trends were compared with historical trends for obesity, smoking, and proton pump inhibitor use. Interestingly, predicted OF cohort trends correlated most highly with median body mass index (BMI) at age 50 (r = 0.988 for men; r = 0.998 for women). Conclusions: sGERD and OF mechanistically increase premalignant cell promotion, which increases EAC risk exponentially with exposure duration. Impact: Surveillance should target individuals with long-duration sGERD and OF exposures. Cancer Epidemiol Biomarkers Prev; 24(7); 1012–23. ©2015 AACR.

Purpose To study radiation effects using a combined cell cycle and multistage clonal expansion model that includes processes of damage, repair, apoptosis, and mutation. The model includes endogenous and radiation induced damage causing progression of cells from normal, to damaged, to initiated, to initiated damage, to malignant status. We utilize complementary deterministic and stochastic versions of the model that share the same transition rates. The deterministic version is used to calibrate model rates for cell cycle progression, damage, checkpoint delay, repair, and apoptosis, and to implement tissue homeostasis. The stochastic version is used to predict the cancer hazard and survival. Results We calibrated transition rates in the deterministic version of the model to fit flow cytometry-based clonogenic survival data for Chinese hamster V79 cells and for HeLa × skin fibroblast human hybrid cells exposed to sparsely ionizing radiation during different phases of the cell cycle. We also calibrated repair and malignant transformation rates to fit neoplastic transformation data for HeLa × skin fibroblast human hybrid cells. We found that induced repair in G2 phase explained the low-dose hypersensitivity for survival in both cell lines, and a different induced repair process explained the neoplastic transformation data. Conclusion The shape of the induced repair curves for G2-phase survival and neoplastic transformation differ significantly, suggesting that these low-dose phenomena differ in regulation and, in fact, may be mechanistically unrelated.

We have a considerable amount of work ahead of us to determine the importance of the wealth of new information emerging in the fields of sub-cellular, cellular and tissue biology in order to improve the estimation of radiation risk at low dose and protracted dose-rate. In this paper, we suggest that there is a need to develop models of the specific health effects of interest (e.g., carcinogenesis in specific tissues), which embody as much of the mechanistic (i.e., biological) information as is deemed necessary. Although it is not realistic to expect that every radiation-induced process should or could be included, we can hope that the major factors that shape the time dependence of evolution of damage can be identified and quantified to the point where reasonable estimations of risk can be made. Regarding carcinogenesis in particular, the structure of the model itself plays a role in determining the relative importance of various processes. We use a specific form of a multi-stage carcinogenic model to illustrate this point. We show in a review of the application of this model to lung cancer incidence and mortality in two exposed populations that for both high- and low-LET radiation, there is evidence of an "inverse dose-rate" or protraction effect. This result could be of some considerable importance, because it would imply that risk from protracted exposure even to low-LET radiation might be greater than from acute exposure, an opinion not currently held in the radiation protection community. This model also allows prediction of the evolution of the risk over the lifetimes of the exposed individuals. One inference is that radiation-induced initiation (i.e., the first cellular carcinogenic event(s) occurring in normal tissue after the passage of the radiation) may not be the driving factor in the risk, but more important may be the effects of the radiation on already-initiated cells in the tissue. Although present throughout the length of the exposure, radiation-induced initiation appears to play a dominating role only very late in life, and only for those individuals who began their exposure early in life. These conclusions are very dependent, of course, on the hypotheses embodied in the initiation-promotion-conversion paradigm of carcinogenesis. We suggest that recently identified processes, such as the "bystander effect", might affect initiation, promotion, and malignant conversion in different ways. Finally, the manner in which the quality of radiation affects these processes must be understood in the context of the mixed high- and low-LET radiations that are found in the space environment. Important directions in critical experiment definition are suggested, including a renewed emphasis on well-designed animal experiments over extended periods of time.

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.

We will briefly review the concept of multistage carcinogenesis and the mathematical models built on this idea, including a class of models recently developed to describe the pathogenesis of colon cancer. The simplest model that explicitly accounts for cell proliferation kinetics is the two-stage clonal expansion model. It has been applied to a number of experimental and epidemiological data sets and has generated several thought-provoking hypotheses, especially as they relate to the role of promotion in radiation-induced carcinogenesis. The model generates hazard functions (used to estimate age-specific cancer incidence) that are not always intuitive because of the stochastic nature of the clonal expansion process involved. However, the model does make specific predictions depending on whether initiation, promotion or malignant conversion is affected by a carcinogen. A recent analysis of a large cohort of radiation workers exemplifies this behaviour.

The application of the two-stage clonal expansion (TSCE) model to lung cancer mortality of two populations of miners (the Colorado Plateau miners and the Chinese tin miners) after protracted high-LET radiation exposure (alpha particles of radon-progeny decay) results in the conclusion that the carcinogenesis process in protracted exposures is dominated by the promotion of already-initiated cells. This means that the increase in tumor induction is caused mainly by the modification (i.e., increase) of the net cell proliferation of already-initiated cells during the time of exposure. We hypothesize that this phenomenon is caused by a “bystander effect” whereby normal cells being hit by alpha particles send mitogenic signals to surrounding cells. If the signals are received by (unhit but) already-initiated cells, which are less controlled by homeostasis than the normal cell population, the cells respond by increasing their net cell proliferation, thus increasing the probability of the emergence of a fully malignant cell. Thus, tissue homeostasis (or the lack thereof) plays an important role. Possible modulators at the cellular level include reactive oxygen species (ROS), transforming growth factor-beta (TGF-β) within the extracellular matrix and the subsequent up and/or down regulation of genes controlling mitogenic factors within the cell. This process of proliferative stimulation causes an “inverse dose-rate effect” which is more appropriately termed as a protraction effect to distinguish it from the dose-rate effects usually attributed to cellular repair and other mechanisms (e.g., a radiosensitive window in the cell cycle).

When applied to the Colorado Plateau miner population, the two-stage clonal expansion (TSCE) model of radiation carcinogenesis predicts that radiation-induced promotion dominates radiation-induced initiation. Thus, according to the model, at least for alpha-particle radiation from inhaled radon daughters, lung cancer induction over long periods of protracted irradiation appears to be dominated by radiation-induced modification of the proliferation kinetics of already-initiated cells rather than by direct radiation-induced initiation (i.e., mutation) of normal cells. We explore the possible consequences of this result for radiation exposures to space travelers on long missions. Still unknown is the LET dependence of this effect. Speculations of the cause of this phenomenon include the suggestion that modification of cell kinetics is caused by a "bystander" effect, i.e., the traversal of normal cells by alpha particles, followed by the signaling of these cells to nearby initiated cells which then modify their proliferation kinetics.

Barrett’s esophagus (BE) patients are routinely screened for high grade dysplasia (HGD) and esophageal adenocarcinoma (EAC) through endoscopic screening, during which multiple esophageal tissue samples are removed for histological analysis. We propose a computational method called the multistage clonal expansion for EAC (MSCE-EAC) screening model that is used for screening BE patients in silico to evaluate the effects of biopsy sampling, diagnostic sensitivity, and treatment on disease burden. Our framework seamlessly integrates relevant cell-level processes during EAC development with a spatial screening process to provide a clinically relevant model for detecting dysplastic and malignant clones within the crypt-structured BE tissue. With this computational approach, we retain spatio-temporal information about small, unobserved tissue lesions in BE that may remain undetected during biopsy-based screening but could be detected with high-resolution imaging. This allows evaluation of the efficacy and sensitivity of current screening protocols to detect neoplasia (dysplasia and early preclinical EAC) in the esophageal lining. We demonstrate the clinical utility of this model by predicting three important clinical outcomes: (1) the probability that small cancers are missed during biopsy-based screening, (2) the potential gains in neoplasia detection probabilities if screening occurred via high-resolution tomographic imaging, and (3) the efficacy of ablative treatments that result in the curative depletion of metaplastic and neoplastic cell populations in BE in terms of the long-term impact on reducing EAC incidence., Author Summary Endoscopic screening for detecting cancer and cancer precursors in Barrett’s esophagus (BE) is currently informed by repeated systematic biopsying of the metaplastic BE tissue. Here we present a comprehensive multiscale model of the natural history of esophageal adenocarcinoma (EAC), which describes the entire multistage process beginning with the conversion event of normal squamous esophageal tissue to BE metaplasia, the spatio-temporal formation of independent dysplastic and malignant clones at the cell level, and finally the appearance of symptomatic EAC in BE. This model lends itself to a systematic exploration of the efficacy and sensitivity of current biopsy-based screening methods to detect neoplasia in BE patients, as well as alternative screening techniques based on high-resolution imaging of the BE tissue. Moreover, the model can also be used to predict the impact of ablative treatments on the risk of occurrence or recurrence of dysplasia or cancer. Due to the lack of studies that attempt to explicitly model the physical and biological dimensions of the screening process itself, our computational model provides a unique, publicly-available tool to improve understanding of factors that limit the efficacy of current screening protocols for BE patients.

Hazelton, W. D., Luebeck, E. G., Heidenreich, W. F. and Moolgavkar, S. H. Analysis of a Historical Cohort of Chinese Tin Miners with Arsenic, Radon, Cigarette Smoke, and Pipe Smoke Exposures Using the Biologically Based Two-Stage Clonal Expansion Model. Radiat. Res. 156, 78-94 (2001).The two-stage clonal expansion model is used to analyze lung cancer mortality in a cohort of Yunnan tin miners based on individual histories with multiple exposures to arsenic, radon, cigarette smoke, and pipe smoke. Advances in methodology include the use of nested dose-response models for the parameters of the two-stage clonal expansion model, calculation of attributable risks for all exposure combinations, use of both a fixed lag and a gamma distribution to represent the time between generation of the first malignant cell and death from lung cancer, and scaling of biological parameters allowed by parameter identifiability. The cohort consists of 12,011 males working for the Yunnan Tin Corporation, with complete exposure records, who were initially surveyed in 1976 and followed through 1988. Tobacco and arsenic dominate the attributable risk for lung cancer. Of 842 lung cancer deaths, 21.4% are attributable to tobacco alone, 19.7% to a combination of tobacco and arsenic, 15.8% to arsenic alone, 11% to a combination of arsenic and radon, 9.2% to a combination of tobacco and radon, 8.7% to combination of arsenic, tobacco and radon, 5.5% to radon alone, and 8.7% to background. The models indicate that arsenic, radon and tobacco increase cell division, death and malignant conversion of initiated cells, but with significant differences in net cell proliferation rates in response to the different exposures. Smoking a bamboo water pipe or a Chinese long-stem pipe appears to confer less risk than cigarette use, given equivalent tobacco consumption.

We analyzed the association between various indices of air pollution, pollens, and hospital admissions for chronic respiratory disease in King County, WA, over the period 1987–1995. Both air pollution and tree pollens were independently associated with hospital admissions. In single-pollutant models, among the gases, we found the strongest association between carbon monoxide and hospitalization. The association with sulfur dioxide was weaker, and there was no evidence of an association with ozone. We also found association of hospital admissions with PM10, and a suggestion of an association with an index of light scattering measured by nephelometry. In two-pollutant models, the effect of carbon monoxide remained stable, whereas the effect of particulate matter, measured either as PM10 or by nephelometry, was attenuated and became unstable. We examined also the association between air pollution, pollens, and hospital admissions in three broad age groups, 0–19 yr, 20–64 yr, and 65 yr and older. Although tre...

Background. The National Lung Screening Trial (NLST) demonstrated that in current and former smokers aged 55 to 74 years, with at least 30 pack-years of cigarette smoking history and who had quit smoking no more than 15 years ago, 3 annual computed tomography (CT) screens reduced lung cancer-specific mortality by 20% relative to 3 annual chest X-ray screens. We compared the benefits achievable with 576 lung cancer screening programs that varied CT screen number and frequency, ages of screening, and eligibility based on smoking. Methods and Findings: We used five independent microsimulation models with lung cancer natural history parameters previously calibrated to the NLST to simulate life histories of the US cohort born in 1950 under all 576 programs. 'Efficient' (within model) programs prevented the greatest number of lung cancer deaths, compared to no screening, for a given number of CT screens. Among 120 'consensus efficient (identified as efficient across models) programs, the average starting age was 55 years, the stopping age was 80 or 85 years, the average minimum pack-years was 27, and the maximum years since quitting was 20. Among consensus efficient programs, 11% to 40% of the cohort was screened, and 153 to 846 lung cancer deaths were averted per 100,000 people. In all models, annual screening based on age and smoking eligibility in NLST was not efficient; continuing screening to age 80 or 85 years was more efficient. Conclusions:Consensus results from five models identified a set of efficient screening programs that include annual CT lung cancer screening using criteria like NLST eligibility but extended to older ages. Guidelines for screening should also consider harms of screening and individual patient characteristics.

Cancer arises through a multistage process, but it is not fully clear how this process influences the age-specific incidence curve. Studies of colorectal and pancreatic cancer using the multistage clonal expansion (MSCE) model have identified two phases of the incidence curves. One phase is linear, beginning about age of 60 years, suggesting that at least two rare rate-limiting mutations occur before clonal expansion of premalignant cells. A second phase is exponential, seen in early-onset cancers occurring before the age of 60 years that are associated with premalignant clonal expansion. Here, we extend the MSCE model to include clonal expansion of malignant cells, an advance that permits study of the effects of tumor growth and extinction on the incidence of colorectal, gastric, pancreatic, and esophageal adenocarcinomas in the digestive tract. After adjusting the age-specific incidence for birth-cohort and calendar-year trends, we found that initiating mutations and premalignant cell kinetics can explain the primary features of the incidence curve. However, we also found that the incidence data of these cancers harbored information on the kinetics of malignant clonal expansion before clinical detection, including tumor growth rates and extinction probabilities on three characteristic time scales for tumor progression. In addition, the data harbored information on the mean sojourn times for premalignant clones until occurrence of either the first malignant cell or the first persistent (surviving) malignant clone. Finally, the data also harbored information on the mean sojourn time of persistent malignant clones to the time of diagnosis. In conclusion, cancer incidence curves can harbor significant information about hidden processes of tumor initiation, premalignant clonal expansion, and malignant transformation, and even some limited information on tumor growth before clinical detection. Cancer Res; 73(3); 1086–96. ©2012 AACR.

Sophisticated modeling techniques can be powerful tools to help us understand the effects of cancer control interventions on population trends in cancer incidence and mortality. Readers of journal articles are, however, rarely supplied with modeling details. Six modeling groups collaborated as part of the National Cancer Institute's Cancer Intervention and Surveillance Modeling Network (CISNET) to investigate the contribution of U.S. tobacco-control efforts toward reducing lung cancer deaths over the period 1975-2000. The six models included in this monograph were developed independently and use distinct, complementary approaches toward modeling the natural history of lung cancer. The models used the same data for inputs, and agreed on the design of the analysis and the outcome measures. This article highlights aspects of the models that are most relevant to similarities of or differences between the results. Structured comparisons can increase the transparency of these complex models.