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BMC Cancer
Published in: BMC Cancer 1/2019

Open Access 01-12-2019 | Research article

How many samples are needed to infer truly clonal mutations from heterogenous tumours?

Authors: Luka Opasic, Da Zhou, Benjamin Werner, David Dingli, Arne Traulsen

Published in: BMC Cancer | Issue 1/2019

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Abstract

Background

Modern cancer treatment strategies aim to target tumour specific genetic (or epigenetic) alterations. Treatment response improves if these alterations are clonal, i.e. present in all cancer cells within tumours. However, the identification of truly clonal alterations is impaired by the tremendous intra-tumour genetic heterogeneity and unavoidable sampling biases.

Methods

Here, we investigate the underlying causes of these spatial sampling biases and how the distribution and sizes of biopsies in sampling protocols can be optimised to minimize such biases.

Results

We find that in the ideal case, less than a handful of samples can be enough to infer truly clonal mutations. The frequency of the largest sub-clone at diagnosis is the main factor determining the accuracy of truncal mutation estimation in structured tumours. If the first sub-clone is dominating the tumour, higher spatial dispersion of samples and larger sample size can increase the accuracy of the estimation. In such an improved sampling scheme, fewer samples will enable the detection of truly clonal alterations with the same probability.

Conclusions

Taking spatial tumour structure into account will decrease the probability to misclassify a sub-clonal mutation as clonal and promises better informed treatment decisions.
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Metadata
Title
How many samples are needed to infer truly clonal mutations from heterogenous tumours?
Authors
Luka Opasic
Da Zhou
Benjamin Werner
David Dingli
Arne Traulsen
Publication date
01-12-2019
Publisher
BioMed Central
Published in
BMC Cancer / Issue 1/2019
Electronic ISSN: 1471-2407
DOI
https://doi.org/10.1186/s12885-019-5597-1

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