ArticleBMC cancer2019
How many samples are needed to infer truly clonal mutations from heterogenous tumours?
Article in BMC cancer, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- A guide to understanding tumour evolution through the lens of population genetics.Nature reviews. Cancer · 2026Review
- Agent-Based Simulations of Lung Tumor Evolution Suggest That Ongoing Cell Competition Drives Realistic Clonal Expansions.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Efficient mathematical methodology to determine multistep mutant burden in spatially growing cell populations.PNAS nexus · 2025Article
- Islands of genomic stability in the face of genetically unstable metastatic cancer.bioRxiv : the preprint server for biology · 2024Article
- Article
- Mutation divergence over space in tumour expansion.Journal of the Royal Society, Interface · 2023Article
- cfSNV: a software tool for the sensitive detection of somatic mutations from cell-free DNA.Nature protocols · 2023Review
- Agent-based methods facilitate integrative science in cancer.Trends in cell biology · 2023Review
- The role of migration in mutant dynamics in fragmented populations.Journal of evolutionary biology · 2023Article
- Article
- What's next in cancer immunotherapy? - The promise and challenges of neoantigen vaccination.Oncoimmunology · 2022Review
- Multiscale heterogeneity in gastric adenocarcinoma evolution is an obstacle to precision medicine.Genome medicine · 2021Article
- Indication-specific tumor evolution and its impact on neoantigen targeting and biomarkers for individualized cancer immunotherapies.Journal for immunotherapy of cancer · 2021Article
- Elements and evolutionary determinants of genomic divergence between paired primary and metastatic tumors.PLoS computational biology · 2021Article
- A practical guide to cancer subclonal reconstruction from DNA sequencing.Nature methods · 2021Review
- The impact of phenotypic heterogeneity of tumour cells on treatment and relapse dynamics.PLoS computational biology · 2021Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundModern 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.
methodsHere, 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.
resultsWe 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.
conclusionsTaking 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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