bioRxivCancer BiologyPreprint
IRE1 activity regulates tumour and microenvironment cell lineage states while stratifying localised and metastatic prostate cancer.
Dimitrios DoultsinosiD, Ingrid TomljanovićiD, Eleftherios D. PilalisiD, Sophia M. AbusamraiD, Romuald ParmentieriD, I.E. Bridges, Sandy FigieliD, Yanis ZekriiD, Joanna HesteriD, Damien A. LeachiD, Charlotte Lynne BevaniD, Alastair D.G. LambiD, Wilbert T. ZwartiD, Aristotelis A. ChatziioannouiD, Clémentine Le MagneniD, Ian Geoffrey MillsiD
Abstract
Prostate cancer (PCa) is an androgen receptor (AR) driven, high-incidence disease significantly contributing to cancer mortality. PCa is in need of better risk stratification at diagnosis and treatment outcomes in patients at high risk of metastasis. The unfolded protein response (UPR) is an AR-dependent process. However, the impact of the UPR transducer IRE1 on AR-dependent biology and treatment resistance has not been defined. We use diverse pre-clinical models of stress response to describe IRE1 activity impact on multiple disease stages and demonstrate its involvement with poor prognosis (RB1 loss), and cell lineage determination (club phenotypes). Integrating clinical transcriptomic datasets, we chart IRE1 activity throughout PCa evolution by developing a PCa-specific, IRE1 activity gene set (IRE1_18) reflecting both tumoral and micro-environmental niches. IRE1_18 can determine tumoral identity, inform androgen deprivation treatment suitability, prognosticate localised and metastatic disease independently from AR activity, and guide IRE1 modulation as a novel combination therapeutic. Graphical Abstract created using Biorender.com O_FIG O_LINKSMALLFIG WIDTH=197 HEIGHT=200 SRC="FIGDIR/small/643042v3_ufig1.gif" ALT="Figure 1"> View larger version (49K): org.highwire.dtl.DTLVardef@18edd6aorg.highwire.dtl.DTLVardef@6a759eorg.highwire.dtl.DTLVardef@14ff3fborg.highwire.dtl.DTLVardef@1ae77d_HPS_FORMAT_FIGEXP M_FIG C_FIG
- organism: human
- assay: bulk RNA-seq
- research focus: cancer & tumor microenvironment
- research focus: differential expression
- research focus: protein structure prediction
- research focus: drug response / drug discovery
- approach: statistical model
- approach: genetic perturbation / CRISPR
- Citations
- 0
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