Optimal screening and surveillance regimes for early diagnosis of cancer and precision medicine using mathematical modelling

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The main rationale for cancer screening is that detecting disease early offers the opportunity to change its prognosis. Compared with symptomatic cancers, the lifetime prognosis is often greatly improved for patients found to have precancerous lesions or small cancers that are detected at an early stage. Therefore, clinicians focus on identifying patients with precancerous change on initial tests (screens), and then after this initial screening may advise these patients to undergo long-term, periodic screening by returning to the clinic at certain intervals for regular examinations (surveillance screens) throughout the course of their lives. However, many precancerous changes will never progress to cancer in the lifetime of the patient. Thus, many patients who undergo regular surveillance will never be diagnosed with cancer in their lifetimes. Overall, many current approaches for prevention by screening and surveillance programs have achieved minimal success in reducing cancer deaths at a high cost to healthcare services, and thus paradoxically yield both under-diagnosis due to inadequate screening and over-diagnosis due to ineffective patient stratification in surveillance protocols. Herein lies the balancing act performed during risk stratification - identify who is most `at risk' of progressing to cancer and suggest effective surveillance and intervention strategies for the high risk groups. With this motivation, the overall public health goal of the mathematical modelling presented in this research plan is to help improve the efficacy of screening and surveillance for precancerous and cancer lesions. Although clinical endpoints like cancer incidence and prevalence of pre-cancerous change are reported on a population level, many important biological processes on smaller physical and temporal scales occur with significant differences between patients during disease progression from normal tissue to incident cancer. Due to the biological and clinical nature of screening patients at various times during their lives, such details at tissue and cell levels provide vital information to determine screening outcomes obtained by different modalities and protocols. This research plan will use many such levels of data in inventive and rigorous ways to improve personalized healthcare. Within the MRC priority area Precision Medicine and Diagnostics, mathematical modelling of cancer formation can be used to derive and to optimise the timing of clinical screens so that an individual is screened within a certain "window of opportunity" for intervention when early cancer development may be observed. By using data from epidemiological studies with long-term patient follow-up, current empirical approaches can aid in screening design and may inform cost-effectiveness analyses to compare proposed screening and intervention strategies. However, mechanistic modelling that incorporates a greater level of biological understanding and detail for how and when normal tissues progress to cancer can be used for a more refined screening design than typically implemented in population screening studies.The aims of this Health Data Research UK research plan are1) To use mathematical modelling to inform optimal cancer screening recommendations for a population,2) To perform patient risk stratification by identifying who is low risk versus who is high risk in order to make personalised surveillance regimes that are more effective than "one-size-fits-all" approaches, 3) To build tools that will assist in precision medicine such as clearly portraying future cancer risk to a patient in visuals aids.