Integrative Analysis of Deep Sequencing Data Identifies Estrogen Receptor Early Response Genes and Links ATAD3B to Poor Survival in Breast Cancer

Identification of responsive genes to an extra-cellular cue enables characterization of pathophysiologically crucial biological processes. Deep sequencing technologies provide a powerful means to identify responsive genes, which creates a need for computational methods able to analyze dynamic and multi-level deep sequencing data. To answer this need we introduce here a data-driven algorithm, SPINLONG, which is designed to search for genes that match the user-defined hypotheses or models. SPINLONG is applicable to various experimental setups measuring several molecular markers in parallel. To demonstrate the SPINLONG approach, we analyzed ChIP-seq data reporting PolII, estrogen receptor alpha (ER alpha), H3K4me3 and H2A.Z occupancy at five time points in the MCF-7 breast cancer cell line after estradiol stimulus. We obtained 777 ER alpha early responsive genes and compared the biological functions of the genes having ER alpha binding within 20 kb of the transcription start site (TSS) to genes without such binding site. Our results show that the non-genomic action of ER alpha via the MAPK pathway, instead of direct ER alpha binding, may be responsible for early cell responses to ER alpha activation. Our results also indicate that the ER alpha responsive genes triggered by the genomic pathway are transcribed faster than those without ER alpha binding sites. The survival analysis of the 777 ER alpha responsive genes with 150 primary breast cancer tumors and in two independent validation cohorts indicated the ATAD3B gene, which does not have ER alpha binding site within 20 kb of its TSS, to be significantly associated with poor patient survival.