A computational modelling approach for deriving biomarkers to predict cancer risk in premalignant disease
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 by tissue
sampling; and (iv) the spatial pattern of clonal expansions is a
particularly prognostic measure. This study demonstrates how the use of a
mechanistic framework provided by computational modelling can diminish
empirical constraints on biomarker development.
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