A computational modelling approach for deriving biomarkers to predict cancer risk in premalignant disease

BioRxiv : the Preprint Server for Biology
Andrew DhawanAlexander G Fletcher

Abstract

The lack of effective biomarkers for predicting cancer risk in premalignant disease is a major clinical problem. There is a near-limitless list of candidate biomarkers and it remains unclear how best to sample the tissue in space and time. Practical constraints mean that only a few of these candidate biomarker strategies can be evaluated empirically and there is no framework to determine which of the plethora of possibilities is the most promising. Here we have sought to solve this problem by developing a theoretical platform for in silico biomarker development. We construct a simple computational model of carcinogenesis in premalignant disease and use the model to evaluate an extensive list of tissue sampling strategies and different molecular measures of these samples. Our model predicts that: (i) taking more biopsies improves prognostication, but with diminishing returns for each additional biopsy; (ii) longitudinally-collected biopsies provide only marginally more prognostic information than a single biopsy collected at the latest possible time-point; (iii) measurements of clonal diversity are more prognostic than measurements of the presence or absence of a particular abnormality and are particularly robust to confounding ...Continue Reading

Related Concepts

Biological Markers
Study
Precancerous Conditions
Anatomical Space Structure
Clone
Clonal Expansion
Biopsy
Malignant Neoplasms
Carcinogenesis
Congenital Abnormality

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