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Activity Number: 361 - SPEED: Biometrics - Methods and Application, Part 2
Type: Contributed
Date/Time: Tuesday, July 30, 2019 : 11:35 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract #307766
Title: To EM or Not to EM: Updated Estimation of the Probability of Clonal Relatedness of Pairs of Tumors in Cancer Patients
Author(s): Audrey Mauguen* and Venkatraman E. Seshan and Irina Ostrovnaya and Colin Begg
Companies: Memorial Sloan Kettering Cancer Center and MSKCC and MSKCC and Memorial Sloan Kettering Cancer Center
Keywords: Cancer; Clonality; EM algorithm; Tumor mutation; Parameter estimation; Random effect model

In the last release of the R package Clonality, we added a random-effects model to analyze a set of patients with 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. Using simulations, we compare two estimation approaches that we considered for our package: 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. We show that in specific settings 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 in breast cancer, since an estimate of 0 or 1 for the proportion of clonal cases leads to individual probabilities of 0 or 1 in a setting where the evidence is clearly not sufficient for such definitive predictions.

Authors who are presenting talks have a * after their name.

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