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Liquid biopsy and mathematical modelling

An evolutionary model utilising serial blood samples from patients with advanced colorectal cancer treated with anti-EGFR therapies in a phase II trial could predict personalised waiting time for progression.

The claim comes in a paper in Cancer Discovery – a journal of the American Association for Cancer Research.

Study author Andrea Sottoriva said: “By combining frequent longitudinal sampling of cell-free DNA with mathematical modelling of tumour evolution, we were able to make statistical predictions of patients who were at risk of progression.

“We could also determine when a cancer was going to come back, on a patient-by-patient basis. This is the first time that quantitative forecasting of this sort has been successfully used in cancer.”

The model utilising CEA measurements was applied to six patients to predict time to clinical progression. Of these predictions, three were within 10% of progression time as measured by RECIST.

Predictions generated with high sensitivity cfDNA profiling allowed for the prediction of progression time several weeks in advance, compared with models utilising CEA.

Information garnered from the cfDNA, can be used to generate multiple models based on the predicted growth of individual subclones driven by different mutations.

Image credit | Science Photo Library 

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