Markov Random Fields in Cancer Mutation Dependencies

Abstract

Even though a large amount of evidence would suggest that PP2A serine/threonine protein phosphatase acts as a tumour suppressor the genomics data to support this claim is limited. We fit a sparse binary Markov random field with individual sample's total mutational frequency as an additional covariate to model the dependencies between the mutations occurring in the PP2A encoding genes. We utilize the data from recent large scale cancer genomics studies, where the whole genome from a human tumour biopsy has been analysed.

Our results show a complex network of interactions between the occurrence of mutations in our twenty examined genes. According to our analysis the mutations occurring in the genes PPP2R1A, PPP2R3A, and PPP2R2B are identified as the key mutations. These genes form the core of the network of conditional dependency between the mutations in the investigated twenty genes. Additionally, we note that the mutations occurring in PPP2R4 seem to be more influential in samples with higher number of total mutations.

The mutations occurring in the set of genes suggested by our results has been shown to contribute to the transformation of human cells. We conclude that our evidence further supports the claim that PP2A acts as a tumour suppressor and restoring PP2A activity is an appealing therapeutic strategy.