Natural History Model Research Articles

Characterization of patients with Duchenne muscular dystrophy across previously developed health states.

Project HERCULES has developed a natural history model (NHM) of disease progression in Duchenne muscular dystrophy (DMD) that comprises eight ordered health states (two ambulatory states, one transfer state indicating increased caregiver burden in which patients cannot walk/run 10m or rise from floor but can still support their own weight, and five non-ambulatory states). The current study used data from nine sources (clinical trial placebo arms, one real-world dataset, and three natural history datasets) to further characterize patients with DMD according to these health states. The study included 1,173 patients across 5,306‬ visits. Patients were on average older and exhibited worse ambulatory, pulmonary, upper limb, and cardiac functions with each successive health state. Mean±SE ages increased monotonically across health states, starting with 8.47±0.07 for early ambulatory, 10.86±0.13 for late ambulatory, 11.65±0.35 for transfer state, and ranging from 13.17±0.32 to 16.84±0.37 for the non-ambulatory states. North Star Ambulatory Assessment (NSAA) total score, which measures motor function and ranges from 34 (best) to 0 (worst), was 23.7 (interquartile range [IQR]: 20-30) for early ambulatory patients, 12.7 (IQR: 9-16) for late ambulatory patients, and 3.9 (IQR: 2-5) for transfer patients. Pulmonary function as measured by mean±SE of forced vital capacity percent predicted (FVC%p) was 94.5±0.8 for early ambulatory, 89.1±1.4 for late ambulatory, and 80.2±2.8 for transfer states, and decreased from 77.2±1.7 to 20.6±1.6 across the five non-ambulatory health states. In summary, these findings further characterize health states and their interpretation in economic modeling and decision-making in DMD management.

Stratification of Barrett's esophagus surveillance based on p53 immunohistochemistry: a cost-effectiveness analysis by an international collaborative group.

Surveillance of nondysplastic Barrett's esophagus (NDBE) is recommended to identify progression to dysplasia; however, the most cost-effective strategy remains unclear. Mutation of TP53 or aberrant expression of p53 have been associated with the development of dysplasia in BE. We sought to determine if surveillance intervals for BE could be stratified based on p53 expression. A Markov model was developed for NDBE. Patients with NDBE underwent p53 immunohistochemistry (IHC) and those with abnormal p53 expression underwent surveillance endoscopy at 1 year, while patients with normal p53 expression underwent surveillance in 3 years. Patients with dysplasia underwent endoscopic therapy and surveillance. On base-case analysis, the strategy of stratifying surveillance based on abnormal p53 IHC was cost-effective relative to conventional surveillance and a natural history model, with an incremental cost-effectiveness ratio (ICER) of $8258 for p53 IHC-based surveillance. Both the conventional and p53-stratified surveillance strategies dominated the natural history model. On probabilistic sensitivity analysis, the p53 IHC strategy ($28 652; 16.78 quality-adjusted life years [QALYs]) was more cost-effective than conventional surveillance ($25 679; 16.17 QALYs) with a net monetary benefit of $306 873 compared with conventional surveillance ($297 642), with an ICER <$50 000 in 96% of iterations. The p53-stratification strategy was associated with a 14% reduction in the overall endoscopy burden and a 59% increase in dysplasia detection. A surveillance strategy for BE based on abnormal p53 IHC is cost-effective relative to a conventional surveillance strategy and is likely to be associated with higher rates of dysplasia diagnosis.

P4.04D.08 Natural History Model for Second Primary Lung Cancer Among Lung Cancer Survivors: Utilizing the U.S. SEER Cancer Registries

P4.04D.08 Natural History Model for Second Primary Lung Cancer Among Lung Cancer Survivors: Utilizing the U.S. SEER Cancer Registries

Breast density and risk of breast cancer: masking and detection bias.

Breast density is associated with risk of breast cancer (BC) diagnosis, impacting risk prediction tools and patient notification policies. Density affects mammography sensitivity and may influence screening intensity. Therefore, the observed association between density and BC diagnosis may not reflect the relationship between density and disease risk. We investigate the association between breast density and BC risk using data sourced from 33,542 women in the Breast Cancer Surveillance Consortium, 2000-2018. We estimated mammogram sensitivity and rates of screening mammography among dense (BI-RADS c, d) and non-dense (BI-RADS a, b) breasts. We used Kaplan-Meier estimates to summarize the relative risks of BC diagnosis (RRdx) by density and fit a natural history model to estimate the relative risks of BC onset (RRonset) given density-specific sensitivities. RRdx for dense versus non-dense breasts was 1.80 (95% CI 1.46 to 2.57). Based on estimated screening sensitivities of 0.88 and .78 for non-dense and dense breasts, respectively, RRonset was 1.73 (95% CI 1.43 to 2.25). Sensitivity analyses suggested higher breast density is robustly associated with increased risk of BC onset, similar in magnitude to the increased risk of BC diagnosis. These finding support laws requiring notifications to women with dense breasts of their increased BC risk.

Modelling long-term outcomes for patients with systemic lupus erythematosus

BackgroundNew treatments for systemic lupus erythematosus (SLE) aim to improve tolerability and disease activity control over standard of care (SoC) treatment. SoC typically includes daily glucocorticoid (GC) which carries a risk of organ damage over time. This study sought to develop natural history models to identify predictors of long-term outcomes with current SoC SLE treatment. MethodsGeneralized linear and parametric accelerated failure time survival models (GLM) and parametric accelerated failure time (AFT) survival models were designed to identify predictors of disease activity, flare rate, GC use, organ damage, and mortality beyond the first year of treatment in patients with SLE. Models were run using a longitudinal retrospective analysis of prospectively collected Toronto Lupus Cohort (TLC) study data, collected between 1997 and 2020. Covariates of clinical and statistical significance were selected by bivariate- then multi-variate regression to find the model of best fit. FindingsOf the 1255 subjects included, 89 % were female 89 % and 65 % Caucasian. Mean follow-up was 10·5 years. At first visit, 51 % of patients had moderate-to-severe disease activity (SLEDAI-2 K score ≥ 6). Mean organ damage scores gradually increased over the years following diagnosis. Median survival of the cohort was ∼35 years from study entry. In the GLM models, SLEDAI-2 K yearly average, and average GC dose were key for predicting change in SLEDAI-2 K, GC use/ dose, and flare (any/rate). Together, adjusted mean SLEDAI-2 K and GC dose were shown to be predictors of mortality and damage in at least 9 of 12 organ systems considered. InterpretationThese comprehensive, longitudinal, predictive models show that disease activity and GC use are significant predictors of organ damage and mortality in a patient population with predominantly moderate to severe SLE. This deepens understanding of SLE natural history and underscores the need for new treatment approaches that reduce disease activity and GC use with an aim to improve long-term SLE outcomes. FundingThis study was funded by AstraZeneca.

The Impact of Model Assumptions on Personalized Lung Cancer Screening Recommendations.

Recommendations regarding personalized lung cancer screening are being informed by natural-history modeling. Therefore, understanding how differences in model assumptions affect model-based personalized screening recommendations is essential. Five Cancer Intervention and Surveillance Modeling Network (CISNET) models were evaluated. Lung cancer incidence, mortality, and stage distributions were compared across 4 theoretical scenarios to assess model assumptions regarding 1) sojourn times, 2) stage-specific sensitivities, and 3) screening-induced lung cancer mortality reductions. Analyses were stratified by sex and smoking behavior. Most cancers had sojourn times <5 y (model range [MR]; lowest to highest value across models: 83.5%-98.7% of cancers). However, cancer aggressiveness still varied across models, as demonstrated by differences in proportions of cancers with sojourn times <2 y (MR: 42.5%-64.6%) and 2 to 4 y (MR: 28.8%-43.6%). Stage-specific sensitivity varied, particularly for stage I (MR: 31.3%-91.5%). Screening reduced stage IV incidence in most models for 1 y postscreening; increased sensitivity prolonged this period to 2 to 5 y. Screening-induced lung cancer mortality reductions among lung cancers detected at screening ranged widely (MR: 14.6%-48.9%), demonstrating variations in modeled treatment effectiveness of screen-detected cases. All models assumed longer sojourn times and greater screening-induced lung cancer mortality reductions for women. Models assuming differences in cancer epidemiology by smoking behaviors assumed shorter sojourn times and lower screening-induced lung cancer mortality reductions for heavy smokers. Model-based personalized screening recommendations are primarily driven by assumptions regarding sojourn times (favoring longer intervals for groups more likely to develop less aggressive cancers), sensitivity (higher sensitivities favoring longer intervals), and screening-induced mortality reductions (greater reductions favoring shorter intervals). Models suggest longer screening intervals may be feasible and benefits may be greater for women and light smokers. Natural-history models are increasingly used to inform lung cancer screening, but causes for variations between models are difficult to assess.This is the first evaluation of these causes and their impact on personalized screening recommendations through easily interpretable metrics.Models vary regarding sojourn times, stage-specific sensitivities, and screening-induced lung cancer mortality reductions.Model outcomes were similar in predicting greater screening benefits for women and potentially light smokers. Longer screening intervals may be feasible for women and light smokers.

Natural history and growth prediction model of pancreatic serous cystic neoplasms

ObjectiveSerous cystic neoplasms (SCN) are benign pancreatic cystic neoplasms that may require resection based on local complications and rate of growth. We aimed to develop a predictive model for the growth curve of SCNs to aid in the clinical decision making of determining need for surgical resection. MethodsUtilizing a prospectively maintained pancreatic cyst database from a single institution, patients with SCNs were identified. Diagnosis confirmation included imaging, cyst aspiration, pathology, or expert opinion. Cyst size diameter was measured by radiology or surgery. Patients with interval imaging ≥3 months from diagnosis were included. Flexible restricted cubic splines were utilized for modeling of non-linearities in time and previous measurements. Model fitting and analysis were performed using R (V3.50, Vienna, Austria) with the rms package. ResultsAmong 203 eligible patients from 1998 to 2021, the mean initial cyst size was 31 mm (range 5–160 mm), with a mean follow-up of 72 months (range 3–266 months). The model effectively captured the non-linear relationship between cyst size and time, with both time and previous cyst size (not initial cyst size) significantly predicting current cyst growth (p < 0.01). The root mean square error for overall prediction was 10.74. Validation through bootstrapping demonstrated consistent performance, particularly for shorter follow-up intervals. ConclusionSCNs typically have a similar growth rate regardless of initial size. An accurate predictive model can be used to identify rapidly growing outliers that may warrant surgical intervention, and this free model (https://riskcalc.org/SerousCystadenomaSize/) can be incorporated in the electronic medical record.

The Natural History of SARS-CoV-2-Incurred Disease: From Infection to Long COVID

The coronavirus SARS-CoV-2 is the causative pathogen of the COVID-19 pandemic that has been causing global upheaval since 2019. The widespread administration of vaccines has partially deterred the spread of SARS-CoV-2, yet the virus is mutating its genome to reduce its antigenicity and evade the human herd immunity. It seems that SARS-CoV-2 will co-exist with the human population for many decades to come. While most infected individuals only experience mild to moderate symptoms, some develop severe pulmonary and systemic disease that can result in hospitalization or even death. The natural history model of SARS-CoV-2 infection has been proposed which includes three sequential stages: the early infection stage, pulmonary stage, and hyper-inflammatory stage. Recently, it has been observed that many people who recovered from an acute infection still experience persistent symptoms for weeks or months, a condition known as long COVID. Furthermore, some COVID-19 patients display escalated rates of both macro- and micro-thrombosis due to endotheliopathy. Hence, we added the thrombosis and convalescent stages to the natural history model, encompassing the entire period from early infection to long COVID. The early infection stage is characterized by symptomatic or asymptomatic elevation of viral titers. Some patients progress to the pulmonary stage characterized by opacities in chest X-rays and computed tomography. The thrombosis stage is characterized by heightened rates of pulmonary thrombosis and consistently elevated D-dimer levels. The hyper-inflammatory stage is characterized by storms of cytokines, such as IL-6, IL-17, and interferons, which is a systemic effect. In the convalescent stage, some people recover completely, while others suffer from long COVID with persistent symptoms such as fatigue, shortness of breath, or brain fog. The natural history model of SARS-CoV-2 infection can be used to elucidate treatment and care.

Understanding the Natural History of Chronic Hepatitis D: Proposal of a Model for Cost-Effectiveness Studies.

As new therapeutic options become available, better understanding the potential impact of emerging therapies on clinical outcomes of hepatits D virus (HDV) is critical. The aim of this study was to develop a natural history model for patients with hepatitis D virus. We developed a model (decision tree followed by a Markov cohortmodel) in adults with chronic HDV infection to assess the natural history and impact of novel treatments on disease progression versus best supportive care (BSC). The model time horizon was over a lifetime (up to 100 years of age); state transitions and health states were defined by responder status. Patients in fibrosis stages 0 through 4 received treatment; decompensated patients were not treated. Response was defined as the combined response endpoint of achievement of HDV-RNA undetectability/≥2-log10 decline and alanine aminotransferase normalization; response rates of 50% and 75% were explored. Health events associated with advanced liver disease were modeled as the number of events per 10,000 patients. Scenario analyses of early treatment, alternate treatment response, and no fibrosis regression for treatment responders were also explored. The model was able to reflect disease progression similarly to published natural history studies for patients with HBV/HDV infection. In a hypothetical cohort of patients reflecting a population enrolled in a recent clinical trial, fewer advanced liver disease events were observed with a novel HDV treatment versus BSC. Fewer liver-related deaths were observed under 50% and 75% response (900 and 1,358 fewer deaths, respectively, per 10,000 patients). Scenario analyses showed consistently fewer advanced liver disease events with HDV treatment compared with BSC, with greater reductions observed with earlier treatment. This HDV disease progression model replicated findings from natural history studies. Furthermore, it found that a hypothetical HDV treatment results in better clinical outcomes for patients versus BSC, with greater benefit observed when starting treatment early. This validated natural history model for HBV/HDV infection can serve as a foundation for future clinical and economic analyses of novel HDV treatments that can support healthcare stakeholders in the management of patients with chronic HDV.

Natural History and Prognostic Model of Untreated Papillary Thyroid Cancer: A SEER Database Analysis.

This investigation leveraged the SEER database to delve into the progression patterns of PTC when left untreated. Furthermore, it aimed to devise and authenticate a nomogram for prognosis prediction for such patients. We extracted data from the SEER database, focusing on PTC-diagnosed individuals from 2004-2020. To discern disease progression intervals, median survival times across stages were gauged, and the disease progression time was estimated by subtracting the median survival time of a more severe stage from its preceding stage. Prognostic determinants in the training set were pinpointed using both univariate and multivariate Cox regression. Using these determinants, a prognostic nomogram was crafted. In untreated PTC patients, those in stages I and II had a favorable prognosis, with 10-year overall survival rates of 86.34% and 66.03%, respectively. Patients in stages III and IV had a relatively poorer prognosis. The median survival time of stage III, stage IVA, stage IVB and stage IVC patients was 108months, 43 months, 20 months and 8 months, respectively. The deduced progression intervals from stages III-IVC were 65, 23, and 12 months. In the training set, age, tumor stage, gender, and marital status were identified as independent risk factors influencing the prognosis of untreated PTC, and a nomogram was constructed using these variables. In the absence of treatment intervention, early-stage PTC progressed slowly with an overall favorable prognosis. However, in mid to advanced-stage PTC, as tumor stage increased, disease progression accelerated, and prognosis gradually worsened. Age, tumor stage, marital status, and gender were independent risk factors influencing the prognosis of untreated PTC, and the nomogram based on these factors demonstrated good prognostic capability.

Treat All versus targeted strategies to select HBV-infected people for antiviral therapy in The Gambia, west Africa: a cost-effectiveness analysis

Global elimination of hepatitis B virus (HBV) requires expanded uptake of antiviral therapy, potentially by simplifying testing algorithms, especially in resource-limited countries. We evaluated the effectiveness, cost-effectiveness, and budget impact of three strategies that determine eligibility for anti-HBV treatment, as compared with the WHO 2015 treatment eligibility criteria, in The Gambia. We developed a microsimulation model of natural history using data from the Prevention of Liver Fibrosis and Cancer in Africa programme (known as PROLIFICA) in The Gambia, for an HBV-infected cohort of individuals aged 20 years. The algorithms included in the model were a conventional strategy using the European Association for the Study of the Liver (EASL) 2017 criteria, a simplified algorithm using hepatitis B e antigen and alanine aminotransferase (the Treatment Eligibility in Africa for the Hepatitis B Virus [TREAT-B] score), a Treat All approach for all HBV-infected individuals, and the WHO 2015 criteria. Outcomes to measure effectiveness were disability-adjusted life years (DALYs) and years of life saved (YLS), which were used to calculate incremental cost-effectiveness ratios (ICERs) with the WHO 2015 criteria as the base-case scenario. Costs were assessed from a modified social perspective. A budget impact analysis was also done. We tested the robustness of results with a range of sensitiviy analyses including probabilistic sensitivity analysis. Compared with the WHO criteria, TREAT-B resulted in 4877 DALYs averted and Treat All resulted in 9352 DALYs averted, whereas the EASL criteria led to an excess of 795 DALYs. TREAT-B was cost-saving, whereas the ICER for Treat All (US$2149 per DALY averted) was higher than the cost-effectiveness threshold for The Gambia (0·5 times the country's gross domestic product per capita: $352). These patterns did not change when YLS was the outcome. In a modelled cohort of 5000 adults (aged 20 years) with chronic HBV infection from The Gambia, the 5-year budget impact was $1·14 million for Treat All, $0·66 million for TREAT-B, $1·03 million for the WHO criteria, and $1·16 million for the EASL criteria. Probabilistic sensitivity analysis indicated that among the Treat All, EASL, and TREAT-B algorithms, Treat All would become the most preferred strategy only with a willingness-to-pay threshold exceeding approximately $72 000 per DALY averted or $110 000 per YLS. Although the Treat All strategy might be the most effective, it is unlikely to be cost-effective in The Gambia. A simplified strategy such as TREAT-B might be a cost-saving alternative. UK Research and Innovation (Medical Research Council). For the French translation of the abstract see Supplementary Materials section.

Review article: Emerging insights into the epidemiology, pathophysiology, diagnostic and therapeutic aspects of eosinophilic oesophagitis and other eosinophilic gastrointestinal diseases.

Eosinophilic gastrointestinal diseases (EGIDs) are chronic, immune-mediated disorders characterised clinically by gastrointestinal symptoms and histologically by a pathologic increase in eosinophil-predominant inflammation in the gastrointestinal tract, in the absence of secondary causes of eosinophilia. To highlight emerging insights and research efforts into the epidemiology, pathophysiology, diagnostic and therapeutic aspects of eosinophilic oesophagitis (EoE) and non-EoE EGIDs, and discuss key remaining knowledge gaps. We selected and reviewed original research, retrospective studies, case series, randomised controlled trials, and meta-analyses. Standardised nomenclature classifies EGIDs as EoE, eosinophilic gastritis (EoG), eosinophilic enteritis (EoN), and eosinophilic colitis (EoC). Incidence and prevalence of EoE are rising, emphasising the need to better understand how environmental risk factors and genetic features interact. Advances in understanding EoE pathophysiology have led to clinical trials of targeted therapy and the approval (in the United States) of dupilumab for EoE. Several therapies that are under investigation hope to satisfy both histologic and clinical targets. For non-EoE EGIDs, efforts are focused on better defining clinical and histopathologic disease determinants and natural history, as well as establishing new therapies. Unmet needs for research are dramatically different for EoE and non-EoE EGIDs. In EoE, non-invasive diagnostic tests, clinicopathologic models that determine the risk of disease progression and therapeutic failure, and novel biologic therapies are emerging. In contrast, in non-EoE EGIDs, epidemiologic trends, diagnostic histopathologic thresholds, and natural history models are still developing for these more rare disorders.

Developing a Natural History Model for Duchenne Muscular Dystrophy.

The aim of this study was to pool multiple data sets to build a patient-centric, data-informed, natural history model (NHM) for Duchenne muscular dystrophy (DMD) to estimate disease trajectory across patient lifetime under current standard of care in future economic evaluations. The study was conducted as part of Project HERCULES, a multi-stakeholder collaboration to develop tools to support health technology assessments of new treatments for DMD. Health states were informed by a review of NHMs for DMD and input from clinicians, patients and caregivers, and defined using common outcomes in clinical trials and real-world practice. The primary source informing the NHM was the Critical Path Institute Duchenne Regulatory Science Consortium (D-RSC) database. This was supplemented with expert input obtained via an elicitation exercise, and a systematic literature review and meta-analysis of mortality data. The NHM includes ambulatory, transfer and non-ambulatory phases, which capture loss of ambulation, ability to weight bear and upper body and respiratory function, respectively. The NHM estimates patients spend approximately 9.5years in ambulatory states, 1.5 years in the transfer state and the remainder of their lives in non-ambulatory states. Median predicted survival is 34.8 years (95% CI 34.1-35.8). The model includes a detailed disease pathway for DMD, including the clinically and economically important transfer state. The NHM may be used to estimate the current trajectory of DMD in economic evaluations of new treatments, facilitating inclusion of a lifetime time horizon, and will help identify areas for further research.

Natural history of Duchenne muscular dystrophy in the United Kingdom: A descriptive study using the Clinical Practice Research Datalink.

Duchenne muscular dystrophy (DMD) is a rare, muscle-degenerative disease predominantly affecting males. Natural history models capture the full disease pathway under current care and combine with estimates of new interventions' effects to assess cost-effectiveness by health technology decision-makers. These models require mortality estimates throughout a patient's lifetime, but rare disease datasets typically contain relatively few patients with short follow-ups. Alternative (published) sources of mortality data may therefore be required. The Clinical Practice Research Datalink (CPRD) was evaluated as a source of mortality and natural history data for future economic evaluations of health technologies for DMD and rare diseases in general in the UK population. This retrospective longitudinal cohort study provides flexible parametric estimates of mortality rates and survival probabilities in the current UK DMD population through primary/secondary records in the CPRD since 1990. It also investigates clinically significant milestones such as corticosteroid use, spinal surgery, and cardiomyopathy in these patients. A total of 1121 male patients were included in the study, observed from 0.7 to 48.9 years. Median life expectancy was 25.64 years (95% confidence interval 24.73, 26.47), consistent with previous global estimates. This has improved to 26.47 (25.16, 27.89) years in patients born after 1990. The median ages at corticosteroid initiation, spinal surgery, ventilation, and cardiomyopathy diagnosis were 6.06 years (5.77, 6.29), 14.79 years (14.29, 15.09), 16.97 years (16.50, 18.31), and 15.26 years (14.22, 16.70), respectively. Estimates of mortality in UK-based DMD patients are age-specific in a uniquely large and nationally representative sample from the CPRD.

#2622 COST-EFFECTIVENESS OF SGLT2 INHIBITORS FOR THE TREATMENT OF TYPE 2 DIABETES AND ITS COMPLICATIONS

Abstract Background and Aims Diabetes and its complications (e.g. chronic kidney disease [CKD] and cardiovascular disease [CVD]) impose significant clinical and financial burden. Recent large-scale randomized controlled trials have demonstrated cardiorenal benefit of the anti-diabetic drug sodium glucose co-transporter 2 (SGLT2) inhibitors, and they are approved for the treatment of CKD and/or heart failure (HF) in many countries. In the present study, we assessed the economic effectiveness of SGLT2 inhibitors for patients with type 2 diabetes (T2DM) using a cost-analysis model that adopts CKD-dependent increases in the risk of developing CVD. Method We constructed a natural history model for T2DM patients incorporating CKD, end-stage renal disease, and CVD including myocardial infarction, HF, and stroke as diabetic complications. All diseases except for CKD were considered independent (i.e., the development of CKD increases the risk of CVD manifestation but a patient who develops one disease does not present with another disease). By using a population-based microsimulation, we estimated the 10-year medical costs in Japanese Yen (JPY) and outcomes (development of complications and quality-adjusted life years [QALY]) for patients who initiated antidiabetic therapy with SGLT2 inhibitors or conventional therapy. Probabilistic sensitivity analysis (PSA) was performed with 1,000,000 iterations. Results The total medical costs per person were JPY 1,193,000 versus JPY 1,160,000 and the QALYs were 8.13 versus 8.02 years for the SGLT2 inhibitor strategy versus the conventional strategy, respectively. As a result, the incremental cost-effectiveness ratio (ICER) was 302,000 JPY/QALY, well below the threshold value of JPY 5,000,000/QALY. The PSA revealed that the probability of ICER being below the threshold was 97.7%. Conclusion We demonstrated that the use of SGLT2 inhibitors was cost-effective compared to the conventional strategy for treating patients with T2DM, and these results suggest clinical and financial benefit of SGLT2 inhibitors that can holistically approach T2DM and its renal and cardiovascular complications.

Modelled mortality benefits of multi-cancer early detection screening in England

BackgroundScreening programmes utilising blood-based multi-cancer early detection (MCED) tests, which can detect a shared cancer signal from any site in the body with a single, low false-positive rate, could reduce cancer burden through early diagnosis.MethodsA natural history (‘interception’) model of cancer was previously used to characterise potential benefits of MCED screening (based on published performance of an MCED test). We built upon this using a two-population survival model to account for an increased risk of death from cfDNA-detectable cancers relative to cfDNA-non-detectable cancers. We developed another model allowing some cancers to metastasise directly from stage I, bypassing intermediate tumour stages. We used incidence and survival-by-stage data from the National Cancer Registration and Analysis Service in England to estimate longer-term benefits to a cohort screened between ages 50–79 years.ResultsEstimated late-stage and mortality reductions were robust to a range of assumptions. With the least favourable dwell (sojourn) time and cfDNA status hazard ratio assumptions, we estimated, among 100,000 screened individuals, 67 (17%) fewer cancer deaths per year corresponding to 2029 fewer deaths in those screened between ages 50–79 years.ConclusionRealising the potential benefits of MCED tests could substantially reduce late-stage cancer diagnoses and mortality.

The clonal heterogeneity of colon cancer with liver metastases

Background Colon cancer with liver metastases (CCLM) characterized by genetic heterogeneity is an evolutionary process leading to variations in response to selective pressure, but the underlying evolutionary models still remains unclear.Methods Total of 30 samples, including primary tumor and two to four matched liver metastases from 8 treatment-naïve patients with CCLM were collected, and subjected to whole-exome DNA sequencing. PyClone was used to calculate intra and inter-tumor heterogeneity, LICHeE was used to reconstruct the cancer phylogeny trees and investigate the subclonal composition.ResultsThe genetic differences were observed between primary and metastatic lesions, as well as among multiple metastases in all patients. The natural history models of colorectal cancer in each case were identified, including parallel, linear, and branching evolution. Liver metastases could originate from primary lesions or other metastases. Pathway and process enrichment analysis also showed obvious heterogeneity and enhancement of several molecular functions.ConclusionsOur data reveal the genetic and heterogeneity between primary and metastatic lesions, as well as among multiple metastases and provide genomic evidence for clonal heterogeneity for CCLM.

Abstract P4-03-03: Elevated Risk of Breast Cancer Diagnosis in Women with Dense Breasts reflects a similarly Elevated Risk of Breast Cancer Onset that is Robust to the Effect of Density on Mammography Sensitivity

Abstract Dense breasts are associated with a higher risk of breast cancer diagnosis, which has impacted risk prediction tools and patient notification policies. However, given that mammography is less sensitive for women with dense breasts and these women may be subject to different confirmation testing pathways, the true association between breast density and cancer risk is unknown. We investigated the relationship between breast density and onset using a natural history model that accounts for differential sensitivity and rates of exams by breast density. Data consisted of Breast Cancer Surveillance Consortium mammogram and cancer outcomes among women aged 40-54 with a first digital mammogram between 2000-2018 (N=33,542). Of these, 15,092 had non-dense (almost entirely fatty or scattered fibroglandular densities) and18,450 had dense (heterogeneously dense or extremely dense) breasts. We estimated the empirical sensitivity of mammograms in dense and non-dense breasts (fraction of diagnosed cancers that were screen detected) and examined rates of mammograms by density. We estimated the relative risk of breast cancer diagnosis five years after the first exam using Kaplan Meier methods and the relative risk of breast cancer onset from a natural history model, assuming density-specific sensitivity was equal to the empirical sensitivity. Empirical sensitivity was .88 in women with non-dense and .78 in women with dense breasts. Mammogram utilization was somewhat higher in women with dense breasts (HR for subsequent mammograms 1.10 (95% CI [1.07, 1.12]). The relative risk of diagnosis for dense versus non-dense breasts was 1.80 (95% CI [1.46,2.57]); based on the natural history model the relative risk of onset was 1.73 [1.43,2.25]. The estimated relative risk of onset ranged from 1.67 to 2.03 under assumptions that the relative sensitivity of the screening episode for dense versus non-dense breasts was 1.0 to 0.4. In conclusion, the association of risk of breast cancer onset with breast density is robust to assumptions about the relative sensitivity in dense and non-dense breasts. Citation Format: Jane M. Lange, Charlotte Gard, Ellen O’meara, Ruth Etzioni. Elevated Risk of Breast Cancer Diagnosis in Women with Dense Breasts reflects a similarly Elevated Risk of Breast Cancer Onset that is Robust to the Effect of Density on Mammography Sensitivity [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P4-03-03.

Estimating stage-specific sensitivity for cancer screening tests.

When evaluating potential new cancer screening modalities, estimating sensitivity, especially for early-stage cases, is critical. There are methods to approximate stage-specific sensitivity in asymptomatic populations, both in the prospective (active screening) and retrospective (stored specimens) scenarios. We explored their validity via a simulation study. We fit natural history models to lung and ovarian cancer screening data that permitted estimation of stage-specific (early/late) true sensitivity, defined as the probability subjects screened in the given stage had positive tests. We then ran simulations, using the fitted models, of the prospective and retrospective scenarios. Prospective sensitivity by stage was estimated as screen-detected divided by screen-plus interval-detected cancers, where stage is defined as stage at detection. Retrospective sensitivity by stage was estimated based on cancers detected within specified windows before clinical diagnosis with stage defined as stage at clinical diagnosis. Stage-specific true sensitivities estimated by the lung cancer natural history model were 47% (early) and 63% (late). Simulation results for the prospective setting gave estimated sensitivities of 81% (early) versus 62% (late). In the retrospective scenario, early/late sensitivity estimates were 35%/57% (1-year window) and 27%/49% (2-year window). In the prospective scenario, most subjects with negative early-stage screens presented as other than early-stage interval cases. Results were similar for ovarian cancer, with estimated prospective sensitivity much greater than true sensitivity for early stage, 84% versus 25%. Existing methods for approximating stage-specific sensitivity in both prospective and retrospective scenarios are unsatisfactory; improvements are needed before they can be considered to be reliable.

Tumour Growth Models of Breast Cancer for Evaluating Early Detection-A Summary and a Simulation Study.

With the advent of nationwide mammography screening programmes, a number of natural history models of breast cancers have been developed and used to assess the effects of screening. The first half of this article provides an overview of a class of these models and describes how they can be used to study latent processes of tumour progression from observational data. The second half of the article describes a simulation study which applies a continuous growth model to illustrate how effects of extending the maximum age of the current Swedish screening programme from 74 to 80 can be evaluated. Compared to no screening, the current and extended programmes reduced breast cancer mortality by 18.5% and 21.7%, respectively. The proportion of screen-detected invasive cancers which were overdiagnosed was estimated to be 1.9% in the current programme and 2.9% in the extended programme. With the help of these breast cancer natural history models, we can better understand the latent processes, and better study the effects of breast cancer screening.