Bayesian Machine Learning Enables Identification of Transcriptional Network Disruptions Associated with Drug-Resistant Prostate Cancer.

Journal: Cancer research
PMID: 36779846

Abstract

UNLABELLED: Survival rates of patients with metastatic castration-resistant prostate cancer (mCRPC) are low due to lack of response or acquired resistance to available therapies, such as abiraterone (Abi). A better understanding of the underlying molecular mechanisms is needed to identify effective targets to overcome resistance. Given the complexity of the transcriptional dynamics in cells, differential gene expression analysis of bulk transcriptomics data cannot provide sufficient detailed insights into resistance mechanisms. Incorporating network structures could overcome this limitation to provide a global and functional perspective of Abi resistance in mCRPC. Here, we developed TraRe, a computational method using sparse Bayesian models to examine phenotypically driven transcriptional mechanistic differences at three distinct levels: transcriptional networks, specific regulons, and individual transcription factors (TF). TraRe was applied to transcriptomic data from 46 patients with mCRPC with Abi-response clinical data and uncovered abrogated immune response transcriptional modules that showed strong differential regulation in Abi-responsive compared with Abi-resistant patients. These modules were replicated in an independent mCRPC study. Furthermore, key rewiring predictions and their associated TFs were experimentally validated in two prostate cancer cell lines with different Abi-resistance features. Among them, ELK3, MXD1, and MYB played a differential role in cell survival in Abi-sensitive and Abi-resistant cells. Moreover, ELK3 regulated cell migration capacity, which could have a direct impact on mCRPC. Collectively, these findings shed light on the underlying transcriptional mechanisms driving Abi response, demonstrating that TraRe is a promising tool for generating novel hypotheses based on identified transcriptional network disruptions.

Authors

  • Charles Blatti
    NCSA, University of Illinois at Urbana-Champaign, Champaign, Illinois.
  • Jesús de la Fuente
    TECNUN School of Engineering, University of Navarra, Navarra, Spain.
  • Huanyao Gao
    Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, Minnesota.
  • Irene Marín-Goñi
    Computational Biology Program, CIMA University of Navarra, Navarra, Spain.
  • Zikun Chen
    Department of Gastroenterology, Shanghai Tenth People's Hospital, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China.
  • Sihai D Zhao
    Department of Statistics, University of Illinois at Urbana-Champaign, Champaign, Illinois.
  • Winston Tan
    Department of Internal Medicine, Mayo Clinic, Rochester, Minnesota.
  • Richard Weinshilboum
    Department of Pharmacology, Mayo Clinic, Rochester, MN, USA.
  • Krishna R Kalari
    All authors: Mayo Clinic, Rochester, MN.
  • Liewei Wang
    All authors: Mayo Clinic, Rochester, MN.
  • Mikel Hernaez
    Computational Biology Program, CIMA University of Navarra, Navarra, Spain.

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