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MYC regulation of glutamine--proline regulatory axis is key in luminal B breast cancer

Craze, Madeleine L, Cheung, Hayley, Jewa, Natasha, Coimbra, Nuno DM, Soria, Daniele, El-Ansari, Rokaya, Aleskandarany, Mohammed A, Cheng, Kiu Wai, Diez-Rodriguez, Maria, Nolan, Christopher C, and others. (2018) MYC regulation of glutamine--proline regulatory axis is key in luminal B breast cancer. British Journal of Cancer, 118 (2). pp. 258-265. ISSN 0007-0920. (doi:10.1038/bjc.2017.387)

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Abstract

Background: Altered cellular metabolism is a hallmark of cancer and some are reliant on glutamine for sustained proliferation and survival. We hypothesise that the glutamine–proline regulatory axis has a key role in breast cancer (BC) in the highly proliferative classes. Methods: Glutaminase (GLS), pyrroline-5-carboxylate synthetase (ALDH18A1), and pyrroline-5-carboxylate reductase 1 (PYCR1) were assessed at DNA/mRNA/protein levels in large, well-characterised cohorts. Results: Gain of PYCR1 copy number and high PYCR1 mRNA was associated with Luminal B tumours. High ALDH18A1 and high GLS protein expression was observed in the oestrogen receptor (ER)+/human epidermal growth factor receptor (HER2)– high proliferation class (Luminal B) compared with ER+/HER2– low proliferation class (Luminal A) (P=0.030 and P=0.022 respectively), however this was not observed with mRNA. Cluster analysis of the glutamine–proline regulatory axis genes revealed significant associations with molecular subtypes of BC and patient outcome independent of standard clinicopathological parameters (P=0.012). High protein expression of the glutamine–proline enzymes were all associated with high MYC protein in Luminal B tumours only (P<0.001). Conclusions: We provide comprehensive clinical data indicating that the glutamine–proline regulatory axis plays an important role in the aggressive subclass of luminal BC and is therefore a potential therapeutic target.

Item Type: Article
DOI/Identification number: 10.1038/bjc.2017.387
Divisions: Faculties > Sciences > School of Computing > Data Science
Depositing User: Daniele Soria
Date Deposited: 13 Oct 2019 17:29 UTC
Last Modified: 16 Jan 2020 10:11 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/77398 (The current URI for this page, for reference purposes)
Soria, Daniele: https://orcid.org/0000-0002-0164-8218
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