Poster Presentation 47th Lorne Genome Conference 2026

Revealing Master Transcription Factor Networks Driving Neuroendocrine-like Reprogramming in Prostate Cancer (133441)

Tianjun TZ Zhang 1 2 , Suzanne SM Maiolo 1 2 , Natalie NL Lister 3 , Lisa LB Butler 4 5 , Nora NL Liu 1 2 , Jose JP Polo 1 2
  1. Cancer Epigenetics Program, South Australian immunoGENomics Cancer Institute, University of Adelaide, Adelaide, South Australia, Australia
  2. Adelaide Centre for Epigenetics, School of Biomedicine, University of Adelaide, Adelaide, South Australia, Australia
  3. Prostate Cancer Research Group Biomedicine Discovery Institute Department of Anatomy and Developmental Biology, Monash University, melbroune, VIC, Australia
  4. Resistance Prevention Program, South Australian immunoGENomics Cancer Institute, University of Adelaide, Adelaide, South Australia, Australia
  5. South Australian Health and Medical Research Institute, Adelaide, South Australia, Australia

Neuroendocrine prostate cancer (NEPC) is an aggressive form of prostate cancer with a poor prognosis and high resistance to therapy [1]. The median survival for patients with NEPC is seven months, and the 5-year survival is less than 1% [2-3]. Clinical observations have noted the emergence of NEPC, often following androgen deprivation and hormonal therapies. Recent research analysing patient samples with single-cell transcriptomics suggests that NEPC may originate through a cell reprogramming process from prostate adenocarcinoma (PAC), the major subtype of prostate cancer [4-6]. However, the underlying mechanism of this NE-reprogramming process remains unclear, preventing further advances in NEPC treatment.

To investigate the transcription factors (TFs) involved in the reprogramming of PAC into NEPC, we utilized the in silico tool Mogrify to assess the transcriptional impact of various TFs on NEPC progression. This analysis was conducted across multiple models of NEPC progression. We then evaluated several predicted TFs and TF combinations using cell line models at transcriptomic level. Also, to novel transcription factors in PAC development, we developed a high-throughput screening approach that operates at both bulk and single-cell levels to identify TFs potentially driving prostate cancer development based on cellular phenotypic changes. Additionally, we developed 3D organoid models from LNCaP cells and patient-derived tissue to capture the complexity of tumour architecture. By combining these models alongside publicly data, we have identified master TFs involved in NE reprogramming.

In summary, we are developing complex NE models and high throughput screening method to uncover the molecular events underpinning NEPC progression and to identify key transcriptional factor networks. Furthermore, by integrating in vitro models with clinical single-cell data, we aim to reveal novel mechanisms behind NEPC development and persistence. Ultimately, this work seeks to identify new therapeutic targets to reduce mortality and improve treatment outcomes for NEPC patients.