Cellular identity relies on cell-type-specific gene expression controlled at the 
transcriptional level by cis-regulatory elements (CREs). CREs are unevenly 
distributed across the genome, giving rise to individual CREs and clusters of 
CREs (COREs). Technical and biological features hinder CORE identification. We 
addressed these issues by developing an unsupervised machine learning approach 
termed clustering of genomic regions analysis method (CREAM). CREAM automates 
CORE detection from chromatin accessibility profiles that are enriched in CREs 
strongly bound by master transcription regulators, proximal to highly expressed 
and essential genes, and discriminating cell identity. Although COREs share 
similarities with super-enhancers, we highlight differences in terms of the 
genomic distribution and structure of these cis-regulatory units. We further 
show the enhanced value of COREs over super-enhancers to identify master 
transcription regulators, highly expressed and essential genes defining cell 
identity. COREs enrich at topologically associated domain (TAD) boundaries. They 
are also preferentially bound by the chromatin looping factors CTCF and cohesin, 
in contrast to super-enhancers, forming clusters of CTCF and cohesin binding 
regions and defining homotypic clusters of transcription regulator binding 
regions (HCTs). Finally, we show the clinical utility of CREAM to identify COREs 
across chromatin accessibility profiles to stratify more than 400 tumor samples 
according to their cancer type and to delineate cancer type-specific active 
biological pathways. Collectively, our results support the utility of CREAM to 
delineate COREs underlying, with greater accuracy than individual CREs or 
super-enhancers, the cell-type-specific biological underpinning across a wide 
range of normal and cancer cell types.