Epigenomic Tensor Predicts Disease Subtypes and Reveals Constrained Tumor Evolution [ATAC-seq]

Understanding the epigenomic evolution and specificity of disease subtypes from complex patient data remains a major biomedical problem. We here present DeCET (Decomposition and Classification of Epigenomic Tensors), an integrative computational approach for simultaneously analyzing hierarchical heterogeneous data, to identify robust epigenomic differences between tissue types, differentiation states, and disease subtypes. Applying DeCET to our own data from 21 uterine benign tumor (leiomyoma) patients identifies distinct epigenomic features discriminating normal myometrium and leiomyoma subtypes. Leiomyomas possess preponderant alterations in distal enhancers and long-range histone modifications confined to chromatin contact domains that constrain the evolution of pathological epigenomes. Moreover, we demonstrate the power and advantage of DeCET on multiple publicly available epigenomic datasets representing different cancers and cellular states. Epigenomic features extracted by DeCET can thus help improve our understanding of disease states, cellular development, and differentiation, thereby facilitating future therapeutic, diagnostic and prognostic strategies. Overall design: ATAC-seq was used to assess chromatin accessibility in matched leiomyoma and healthy myometrium tissue samples from 8 patients. Basecalls were performed using RTA v.2.4.11 on Nextseq instrument. Sequenced reads were adapter trimmed using TrimGalore v0.4.4 with parameters --illumina --stringency 13 --paired. Trimmed reads were aligned to the hg19 genome using bowtie2 v2.3.2 with parameters --end-to-end --sensitive --score-min L,-1.5,-0.3 --no-unal. Aligned reads were filtered using samtools v1.7 view with parameters -b -f 2 -F 2828 -F 1024 -q13. Peaks were called from the filtered alignment files using MACS2 v2.1.1 with parameters -f BAMPE -g hs -B --keep-dup 1.