An EM algorithm to improve the estimation of the probability of clonal relatedness of pairs of tumors in cancer patients Journal Article


Authors: Mauguen, A.; Seshan, V. E.; Ostrovnaya, I.; Begg, C. B.
Article Title: An EM algorithm to improve the estimation of the probability of clonal relatedness of pairs of tumors in cancer patients
Abstract: Background: We previously introduced a random-effects model to analyze a set of patients, each of which has two distinct tumors. The goal is to estimate the proportion of patients for which one of the tumors is a metastasis of the other, i.e. where the tumors are clonally related. Matches of mutations within a tumor pair provide the evidence for clonal relatedness. In this article, using simulations, we compare two estimation approaches that we considered for our model: use of a constrained quasi-Newton algorithm to maximize the likelihood conditional on the random effect, and an Expectation-Maximization algorithm where we further condition the random-effect distribution on the data. Results: In some specific settings, especially with sparse information, the estimation of the parameter of interest is at the boundary a non-negligible number of times using the first approach, while the EM algorithm gives more satisfactory estimates. This is of considerable importance for our application, since an estimate of either 0 or 1 for the proportion of cases that are clonal leads to individual probabilities being 0 or 1 in settings where the evidence is clearly not sufficient for such definitive probability estimates. Conclusions: The EM algorithm is a preferable approach for our clonality random-effect model. It is now the method implemented in our R package Clonality, making available an easy and fast way to estimate this model on a range of applications. © 2019 The Author(s).
Keywords: tumors; diseases; random effects model; parameter estimation; em algorithm; clonality; image segmentation; random processes; genetic algorithms; expectation-maximization algorithms; cancer; maximum principle; tumor mutation; random effect model; em algorithms; estimation approaches; quasi-newton algorithm; random-effect models
Journal Title: BMC Bioinformatics
Volume: 20
ISSN: 1471-2105
Publisher: Biomed Central Ltd  
Date Published: 2019-11-08
Start Page: 555
Language: English
DOI: 10.1186/s12859-019-3148-z
PUBMED: 31703552
PROVIDER: scopus
PMCID: PMC6839069
DOI/URL:
Notes: Source: Scopus
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MSK Authors
  1. Venkatraman Ennapadam Seshan
    336 Seshan
  2. Colin B Begg
    280 Begg
  3. Audrey   Mauguen
    67 Mauguen