Premium
Identification of Key Transcription Factor Target Interactions That Regulate Prostate Cancer Metastasis
Author(s) -
Sharma Nitya,
Pellegrini Kathryn L.,
Giuste Felipe O.,
Ouellet Veronique,
Trudel Dominique,
MesMasson AnneMarie,
Saad Fred,
Osunkoya Adeboye O.,
Petros John,
Moreno Carlos S.
Publication year - 2017
Publication title -
the faseb journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.709
H-Index - 277
eISSN - 1530-6860
pISSN - 0892-6638
DOI - 10.1096/fasebj.31.1_supplement.980.3
Subject(s) - prostate cancer , metastasis , androgen deprivation therapy , transcription factor , prostatectomy , prostate , medicine , oncology , cancer research , disease , ets1 , cancer , gene , biology , genetics
Prostate cancer remains the most commonly diagnosed cancer in U.S. males, and ranks second in mortality with over 28,000 deaths per year. The standard of care for patients with recurrent, aggressive prostate cancer is androgen deprivation therapy (ADT), but the benefits from ADT are typically short‐lived. Recurrent disease following ADT treatment is termed castration‐resistant prostate cancer (CRPC), and is generally incurable after progression to metastatic disease. Therefore, understanding the mechanisms underlying CRPC and subsequent progression to metastatic disease is critical. To gain insights into how transcriptional networks change in response to ADT and lead to metastasis, we have identified the relationships between transcription factors and corresponding gene targets in matched pre‐ADT and post‐ADT tissue samples, as well as matched primary and metastatic lesions. First, we generated gene expression data by sequencing RNA from 24 formalin‐fixed paraffin‐embedded patient‐matched pre‐ADT needle core biopsies and corresponding post‐ADT radical prostatectomy prostate cancer samples. For generating metastatic networks, we used publicly available expression data from matched primary prostate and metastatic tumors. Next, we integrated mRNA expression, protein‐protein interaction, and DNA binding motif data using the PANDA algorithm to reverse engineer transcriptional networks. We identified key transcription factors with significant gains or losses of interactions with target genes specifically in metastatic networks. We also identified putative novel key prostate cancer specific transcription factor interactions that share multiple gene targets, such as ETS1 and GATA2, possibly revealing relationships that directly contribute to increased metastatic potential. By comparing changes in post‐ADT and metastatic transcriptional networks, we may identify critical transcription factor‐target gene interactions that are essential for the progression of CRPC to metastatic disease. This study may thus provide insights into novel therapeutic approaches for treatment of CRPC and prevention of metastasis, as well as prognosis of patients with poor outcomes. Support or Funding Information Movember Foundation