HetFHMM: A novel approach to infer tumor heterogeneity using factorial Hidden Markov model

TL;DR

HetFHMM is a new factorial Hidden Markov Model-based method that accurately infers tumor heterogeneity by predicting clone-specific genotypes and cellular prevalence, outperforming existing approaches.

Contribution

The paper introduces HetFHMM, a novel factorial Hidden Markov Model that improves tumor heterogeneity inference by considering multiple mutations and accurately predicting clone-specific genotypes.

Findings

HetFHMM outperforms recent methods in simulated data tests.

It accurately predicts clone-specific genotypes.

It effectively infers cellular prevalence in tumor samples.

Abstract

Cancer arises from successive rounds of mutations which generate tumor cells with different genomic variation i.e. clones. For drug responsiveness and therapeutics, it is necessary to identify the clones in tumor sample accurately. Many methods are developed to infer tumor heterogeneity by either computing cellular prevalence and tumor phylogeny or predicting genotype of mutations. All methods suffer some problems e.g. inaccurate computation of clonal frequencies, discarding clone specific genotypes etc. In the paper, we propose a method, called- HetFHMM to infer tumor heterogeneity by predicting clone specific genotypes and cellular prevalence. To infer clone specific genotype, we consider the presence of multiple mutations at any genomic location. We also tested our model on different simulated data. The results shows that HetFHMM outperforms recent methods which infer tumor heterogeneity. Therefore, HetFHMM is a novel approach in tumor heterogeneity research area.