Network integration of epigenomic data: Leveraging the concept of master regulators in ER negative breast cancer

Document Type

Conference Proceeding

Publication Date


Publication Title

Cancer Res


Background: There has been relatively little advancement in changing the management of women with estrogen receptor (ER) negative breast cancer (BC), mainly due to a dearth of actionable therapeutic targets. Therefore, understanding the underlying biology of such a complex disease is necessary for bringing new therapeutic treatments to light. A key question in cancer genomics is how to distinguish 'driver' or essential alterations, which contribute to tumorigenesis, from functionally neutral or 'passenger' alterations that go along for the ride. The majority of published studies investigating driver genes have focused primarily on genomic mutations which have led to novel study designs (basket trials) where patients with a rare mutation, regardless of tumor histology, are matched to a drug expected to work through the mutated pathway. This dominant focus on mutations has overshadowed consideration of inclusion of epigenetic information. This study illustrates network integration of epigenomic data to prioritize ER negative specific methylated genes as potential epigenetic drivers of aggressive disease.

Methods: Causal Networks are small hierarchical networks of regulators whose activity can be modulated by the expression of downstream target genes to enhance understanding of the effect of upstream master regulators on disease or function. A master regulator is a gene or drug positioned as the central or master hub that has the ability to command or influence downstream events. Causal Network Analysis (CNA) was used to find networks that connect upstream master regulators with a 16 candidate methylation gene signature differentiating ER negative from ER positive BC. The 16 ER-negative specific gene methylation signature (AHNAK, ALPL, ANXA2R, CCND1, CIRBP, CPQ, DST, EGFR, ESR1, GPRC5B, HERC5, IL22RA2, MITF, OBSL1, POU3F3, RB1CC1) was identified via our drill-down approach starting from a discovery approach (Illumina 450k BeadChip) followed by expression verification, significant rankings in biological pathways (Ingenuity Pathway Analysis), confirmation by targeted sequencing using Illumina MiSeq, and additional filtering in 450K TCGA data sets.

Results: CNA software identified 4 hierarchical networks and their corresponding master regulatory molecules, diethylstilbestrol, transcription regulator SP1, MSH2, and 15-ketoprotaglandin E2. Diethylstilbestrol and SP1 had direct regulatory influence (depth level 1) to the candidate molecules ALPL, CCND1, EGFR, ESR1 and CCND1, CIRBP, EGFR, ESR1, respectively.

Conclusion: In this study, direct regulatory influence, noted for 5/16 candidate genes indicates additional rationale for further consideration and validation of ALPL, CCND1, CIRBP, EGFR, ESR1 as potential epigenetic driver targets in ER negative BC. As cancer therapies become increasingly more specific and begin to move past cytotoxic agents, determining the molecular features of a tumor that predict response to a given drug has become increasingly essential to match patients with optimal therapy. Currently epigenetic therapy in the form of hypomethylating agents (e.g: decitabine) exhibit clinical efficacy in patients with AML and MDS including those patients not responding to cytotoxic therapy.





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