Abstract B1-40: Biological network analysis using an in vitro model of androgen-resistance in prostate cancer
Authors: Detchokul Sujitra , Elangovan Aparna , Davis Melissa J. , Macintyre Geoff , Crampin Edmund J. , Frauman Albert G. 
Published: May 2016

Abstract
The development of androgen resistance is a major limitation to androgen deprivation treatment in prostate cancer (PCa). We have developed an in vitro model of androgen-resistance using the androgen sensitive cell line LNCaP to characterize the phenotypic and transcriptomic changes occurring as androgen resistance develops. Our aim is to understand biological network profiles of transcriptomic changes during the transition to androgen-resistance and to validate these changes between our in vitro model and previously published clinical datasets (paired samples before and after androgen-deprivation therapy of patients with advanced PCa) (1). Methods: PCa cells, LNCaP, expressing mutated AR which are androgen-dependent (2,3), are used in the development of an androgen-resistant subline. Subline cells were established by prolonged cultures in media + 10% CS-FBS to mimic the clinical course of PCa. Cell proliferation, cell motility and invasion, morphology, AR expression were examined. RNA-sequencing was performed using the parental LNCaP cells and an androgen-resistant subline (LNCaP-AI) established by chronic exposure to the androgen-deprivation. Reads from cells and clinical samples (1), pre- vs. post-treatment, were processed through the same standard pipeline and quality control. Data outputs were analysed as differential expression [DEG] (EdgeR) (4) and top scoring protein-protein interaction (PPI) networks [PINA2 (5) and BioNet (6)]. Data outputs from the cell line model and clinical samples were compared. Results: LNCaP cells initially showed poor growth after prolonged exposure to androgen-deprived conditions but later adapted and started to grow well. After 24 weeks of androgen-deprivation, LNCaP-AI's growth was no longer responsive to addition of androgen [0.1 - 10 nM]. AR expression was not different in LNCaP and LNCaP-AI (P&gt;0.05). LNCaP-AI cells had increased proliferation and cell invasion compared to LNCaP. We identified key genes that overlap between our cell line and clinical RNAseq (1) datasets and analyzed the overlapping PPI network that showed the same pattern of behavior in both datasets. The network revealed several potential mechanisms and gene interactions that warrant further investigation, including cooperative behaviors of other nuclear receptors, TP63 mediated signalling pathway and Aryl hydrocarbon receptor transcriptional pathway. Conclusion: Cell line model of androgen-resistance will be used for further longitudinal study of the mechanism of castrate resistant prostate cancer (CRPC). Our approach allows for better characterization of biological processes of CRPC. Knowledge of the genetic profiles during transition to androgen resistance will improve our understanding of this common clinical scenario and may lead to biomarker discovery.
References:
1. Rajan P, Sudbery IM, Villasevil ME, Mui E, Fleming J, Davis M, et al. Next-generation sequencing of advanced prostate cancer treated with androgen-deprivation therapy. Eur Urol 2013;66(1):32-9.
2. Marques RB, van Weerden WM, Erkens-Schulze S, de Ridder CM, Bangma CH, Trapman J, et al. The human PC346 xenograft and cell line panel: A model system for prostate cancer progression. Eur Urol 2006;49(2):245-57.
3. Pienta KJ, Bradley D. Mechanisms underlying the development of androgen-independent prostate cancer. Clin Cancer Res 2006;12(6):1665-71.
4. Robinson MD, McCarthy DJ, Smyth GK. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 2010;26(1):139-40.
5. Cowley MJ, Pinese M, Kassahn KS, Waddell N, Pearson JV, Grimmond SM, et al. PINA v2.0: mining interactome modules. Nucleic Acids Res 2012;40(Database issue):D862-5.
6. Beisser D, Klau GW, Dandekar T, Müller T, Dittrich M. BioNet: an R-Package for the functional analysis of biological networks. Bioinformatics 2010;26(8):1129-30.
Citation Format: Sujitra Detchokul, Aparna Elangovan, Melissa J. Davis, Geoff Macintyre, Edmund J. Crampin, Albert G. Frauman. Biological network analysis using an in vitro model of androgen-resistance in prostate cancer. [abstract]. In: Proceedings of the AACR Special Conference on Computational and Systems Biology of Cancer; Feb 8-11 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 2):Abstract nr B1-40.