Optimal screening schedules for prevention of metastatic cancer

Optimal Timing for Cancer Screening and Adaptive Surveillance Using Mathematical Modeling

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. GRAPHICAL ABSTRACT: http://cancerres.aacrjournals.org/content/canres/81/4/1123/F1.large.jpg.

Cancer recurrence times from a branching process model

As cancer advances, cells often spread from the primary tumor to other parts of the body and form metastases. This is the main cause of cancer related mortality. Here we investigate a conceptually simple model of metastasis formation where metastatic lesions are initiated at a rate which depends on the size of the primary tumor. The evolution of each metastasis is described as an independent branching process. We assume that the primary tumor is resected at a given size and study the earliest time at which any metastasis reaches a minimal detectable size. The parameters of our model are estimated independently for breast, colorectal, headneck, lung and prostate cancers. We use these estimates to compare predictions from our model with values reported in clinical literature. For some cancer types, we find a remarkably wide range of resection sizes such that metastases are very likely to be present, but none of them are detectable. Our model predicts that only very early resections can prevent recurrence, and that small delays in the time of surgery can significantly increase the recurrence probability.

The 2019 mathematical oncology roadmap

Whether the nom de guerre is Mathematical Oncology, Computational or Systems Biology, Theoretical Biology, Evolutionary Oncology, Bioinformatics, or simply Basic Science, there is no denying that mathematics continues to play an increasingly prominent role in cancer research. Mathematical Oncology-defined here simply as the use of mathematics in cancer research-complements and overlaps with a number of other fields that rely on mathematics as a core methodology. As a result, Mathematical Oncology has a broad scope, ranging from theoretical studies to clinical trials designed with mathematical models. This Roadmap differentiates Mathematical Oncology from related fields and demonstrates specific areas of focus within this unique field of research. The dominant theme of this Roadmap is the personalization of medicine through mathematics, modelling, and simulation. This is achieved through the use of patient-specific clinical data to: develop individualized screening strategies to detect cancer earlier; make predictions of response to therapy; design adaptive, patient-specific treatment plans to overcome therapy resistance; and establish domain-specific standards to share model predictions and to make models and simulations reproducible. The cover art for this Roadmap was chosen as an apt metaphor for the beautiful, strange, and evolving relationship between mathematics and cancer.

An evolutionary perspective on field cancerization

Tumorigenesis begins long before the growth of a clinically detectable lesion and, indeed, even before any of the usual morphological correlates of pre-malignancy are recognizable. Field cancerization, which is the replacement of the normal cell population by a cancer-primed cell population that may show no morphological change, is now recognized to underlie the development of many types of cancer, including the common carcinomas of the lung, colon, skin, prostate and bladder. Field cancerization is the consequence of the evolution of somatic cells in the body that results in cells that carry some but not all phenotypes required for malignancy. Here, we review the evidence of field cancerization across organs and examine the biological mechanisms that drive the evolutionary process that results in field creation. We discuss the clinical implications, principally, how measurements of the cancerized field could improve cancer risk prediction in patients with pre-malignant disease.

Evolution of Premalignant Disease

Where does cancer come from? Although the cell-of-origin is difficult to pinpoint, cancer clones harbor information about their clonal ancestries. In an effort to find cells before they evolve into a life-threatening cancer, physicians currently diagnose premalignant diseases at frequencies that substantially exceed those of clinical cancers. Cancer risk prediction relies on our ability to distinguish between which premalignant features will lead to cancer mortality and which are characteristic of inconsequential disease. Here, we review the evolution of cancer from premalignant disease, and discuss the concept that even phenotypically normal cell progenies inherently gain more malignant potential with age. We describe the hurdles of prognosticating cancer risk in premalignant disease by making reference to the underlying continuous and multivariate natures of genotypes and phenotypes and the particular challenge inherent in defining a cell lineage as "cancerized."

Classification in two-stage screening

Decision theory is applied to the problem of setting thresholds in medical screening when it is organised in two stages. In the first stage that involves a less expensive procedure that can be applied on a mass scale, an individual is classified as a negative or a likely positive. In the second stage, the likely positives are subjected to another test that classifies them as (definite) positives or negatives. The second-stage test is more accurate, but also more expensive and more involved, and so there are incentives to restrict its application. Robustness of the method with respect to the parameters, some of which have to be set by elicitation, is assessed by sensitivity analysis.