Exploring Tumor Evolution in Pediatric CNS Tumors: Evaluation of EVOFLUX-Derived Parameters and Posterior Summary Statistics from DNA Methylation Data

Abstract

In this thesis, the EVOFLUX framework was investigated as a method for inferring evolutionary features from DNA methylation data. The DNA methylation data was taken from pediatric central nervous systemCNS tumor samples. EVOFLUX was originally developed for lymphoid malignancies and uses fluctuating CpG sites (fCpGs) to estimate parameters describing tumor evolution. The framework was applied to pediatric CNS tumor samples from Barntumörbanken. The fCpG selection and filtering procedure was evaluated for the tumor samples, after which EVOFLUX was used to infer parameters including tumor growth rate, tumor age, methylation switching rates, and effective population size. The resulting posterior distributions were summarized using the median, median absolute deviation (MAD), skewness, and kurtosis. The inferred parameters and summary statistics were compared between age groups and tumor types and were evaluated in relation to time to first relapse. The analyses showed that the age of the patients were associated with differences of the inferred parameters. And that this is needed to take into account when doing analyses. Summary statistics describing the shape of the posterior distributions were evaluated and showed to also have importance meaning that using only the median of the posterior distributions would leave out possibly important information. The results indicate that EVOFLUX can be used for analysis of evolutionary features in pediatric CNS tumor methylation data and that statistics describing shape of the posterior distributions can provide further insights. However, the survival analyses were limited by small sample sizes and median-based cutoffs. Larger cohorts and additional clinical data are needed to validate these findings and determine whether EVOFLUX-derived features can be useful for relapse prediction.