Neutral tumor evolution

Question

1. What have the authors contributed in "Neutral tumor evolution?" ?

2. What are the main assumptions that underlie the analysis?

3. Why are different subclone % values obtained across simulations?

4. What was the result of the dN/dS analysis?

Accepted Answer

The Francis Crick Institute, London, United Kingdom ; Wellcome Trust Sanger Institute, Cambridge, UK ; European Molecular Biology Laboratory, European Bioinformatics Institute ( EBML ), Cambridge, United UK ; Department of Life Sciences, Imperial College London, London UK ; Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge UK ; Molecular and Medical Genetics, Oregon Health & Science University, Portland, OR, USA ; Donnelly Centre, University Of Toronto and Vector Institute, Toronto, Canada, Department of Informatics and Centre for Cancer Biomedicine, University

Accepted Answer

the deterministic model of tumor growth described by Williams et al. relies on strong biological assumptions, among which are synchronous cell divisions, constant cell death and constant mutation and division rates.

Accepted Answer

Because of the stochastic nature of branching processes, different subclone % values are obtained across simulations from the same advsubclone values.

Accepted Answer

The authors ran dN/dS again on 100 of these 192-gene sets and found that 4 and 5 out of 100 gene lists yielded 95% confidence intervals of dN/dS >1 for subclonal mutations in neutral and non-neutral tumours, respectively.

Accepted Answer

With higher P(cell death), the first divisions are more likely to lead to thedeath of all cells and the tumor quickly stops growing.

Accepted Answer

To assess this, the authors simulated simple tumor growth in which the authors explicitly model one subclonal expansion with a selective advantage, i.e. increasing its division rate λ and/or the mutation rate µ of the subclone (Supplementary Methods).

Accepted Answer

This project was enabled through access to the MRC eMedLab Medical Bioinformatics infrastructure, supported by the Medical Research Council (grant number MR/L016311/1).

Accepted Answer

Applying this rationale to real cancer data from The Cancer Genome Atlas (TCGA), the test proposed by Williams et al. did not reject the null model, that is neutrality, in about one third of the cases and the authors concluded that these tumors are neutrally evolving.

Accepted Answer

The authors then plotted the ROC using the R package ROCR version 1.0-7 and calculated the false positive rate and the true positive rate assuming the R2 = 0.98 threshold used by Williams et al.1Detection of selection in neutral and non-neutral tumors - dN/dSThe authors ran their analyses on the data from The Cancer Genome Atlas, usingCaVeMan4,5 single nucleotide variant calls, and ASCAT3 copy number calls, as described by Martincorena et al.6Grouping variants into clonal and subclonal categories

Accepted Answer

The authors describe their implementation of the approach by Williams et al.1 to infer the most likely evolutionary path after the emergence of the most recent common ancestor (MRCA), i.e. neutral vs. non-neutral evolution.

Neutral tumor evolution

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Most frequently asked questions

1. What have the authors contributed in "Neutral tumor evolution?" ?

2. What are the main assumptions that underlie the analysis?

3. Why are different subclone % values obtained across simulations?

4. What was the result of the dN/dS analysis?

5. What is the effect of P(cell death) on the tumor?

6. What is the way to assess the effect of neutrality on tumor growth?

7. What was the funding for this project?

8. What is the reason for the rejection of the null model?

9. What is the ROC for the dN/dSThe authors simulations?

10. What is the likely path after the emergence of the recent common ancestor?

Figures

Citations

Reconstructing the molecular life history of gliomas.

Reconstructing the Molecular Life History of Gliomas

Estimating growth patterns and driver effects in tumor evolution from individual samples

References

The clonal evolution of tumor cell populations

Simple methods for estimating the numbers of synonymous and nonsynonymous nucleotide substitutions.

Clonal evolution in cancer

A codon-based model of nucleotide substitution for protein-coding DNA sequences.

COSMIC: somatic cancer genetics at high-resolution.