Takuya Moriyama
moriyama@hgc.jp
Human Genome Center, The Institute of Medical Science, The University of Tokyo
Tokyo, Japan
Acquired somatic mutations have a large effect on cancer evolution, and mutation profiles from multi-regional tumor sequencing data sets give helpful information to understand the tumor evolutionary process or the intratumoral heterogeneity. For better understanding of the intratumoral heterogeneity, it is important to detect subclonal mutations with lower variant allele frequencies. Therefore, researchers have developed mutation calling methods that are suitable for multi-regional tumor data sets.
Here, we introduce a Bayesian method termed MultiMuC for accurate detection of somatic mutations in multi-regional tumor sequence data sets. To improve detection performance, our method is based on the assumption of mutation sharing: if we can predict at least one tumor region has the mutation, then we can be more confident to detect a mutation in more tumor regions by lowering the original threshold of detection. We find two drawbacks in existing methods for leveraging the assumption of mutation sharing. First, existing methods do not consider the probability of the ‘’No-TP(True Positive)'' case: even if we could detect mutation candidates in multiple regions, no true mutations exist, unfortunately. Second, existing methods cannot leverage scores from other state-of-the-art mutation calling methods, e.g., Strelka2 and NeuSomatic, for a single-regional tumor. We overcome the first drawback through evaluating the probability of the No-TP case. Next, we solve the second drawback by the idea of Bayes-factor-based model construction that enables flexible integration of probability-based mutation call scores as building blocks of a Bayesian statistical model.