Abstract
Cancers are composed of genetically distinct subpopulations of malignant cells. By sequencing DNA from cancer tissue samples, we can characterize the somatic mutations specific to each population and build clone trees describing the evolutionary relationships between these populations. These trees reveal critical points in disease development and inform treatment. Both liquid cancers and solid tumours can be profiled with this approach.
Pairtree is a new method for constructing clone trees using DNA sequencing data from one or more bulk samples of an individual cancer. It uses Bayesian inference to compute posterior distributions over the evolutionary relationships between every pair of identified subpopulations, then uses these distributions in a Markov Chain Monte Carlo algorithm to perform efficient inference of the posterior distribution over clone trees. Pairtree also uses the pairwise relationships to detect mutations that violate the infinite sites assumption. Unlike previous methods, Pairtree can perform clone tree reconstructions using as many as 100 samples per cancer that reveal 30 or more cell subpopulations. On simulated data, Pairtree is the only method whose performance reliably improves when provided with additional bulk samples from a cancer. On 14 B-progenitor acute lymphoblastic leukemias with up to 90 samples from each cancer, Pairtree was the only method that could reproduce or improve upon expert-derived clone tree reconstructions. By scaling to more challenging problems, Pairtree supports new biomedical research applications that can improve our understanding of the natural history of cancer, as well as better illustrate the interplay between cancer, host, and therapeutic interventions.
Significance Clone trees describe the evolutionary history of a cancer and can provide insights into how the disease changed through time (e.g., between diagnosis and relapse). Pairtree uses DNA sequencing data from many samples of the same cancer to reconstruct a cancer’s evolutionary history with much greater detail and accuracy than previously possible.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
Conflicts of interest, The authors declare no potential conflicts of interest.
* We have incorporated CALDER as one of the methods that we compare Pairtree to, and have updated all the relevant figures and text. * We have produced two new supplemental figures where we isolate our low depth simulations and show that Pairtree performs as well, or better, on the lower depth simulated datasets as on higher-depth ones. * We have added the "tree error" measure developed by Raphael lab as one of the metrics we use to compare algorithms and added a supplemental figure incorporating it. * We have added a section to the main text describing our ISA violation detection evaluations . * We have added discussion regarding the incorporation of alternative models for cancer evolution with Pairtree. This discussion paragraph is supported by three new sections in the supplemental materials where we prove that Pairtree's subclonal frequency likelihood is convex, argue that the mode is a good approximation to the marginal likelihood, and explain how various alternative models of cancer evolution may impact these two properties.
