Identification of the α2 chain of interleukin‐13 receptor as a potential biomarker for predicting castration resistance of prostate cancer using patient‐derived xenograft models

Abstract

Background Several treatment strategies use upfront chemotherapy or androgen receptor axis-targeting therapies for metastatic prostate cancer. However, there are no useful biomarkers for selecting appropriate patients who urgently require these treatments. Methods Novel patient-derived xenograft (PDX) castration-sensitive and -resistant models were established and gene expression patterns were comprehensively compared. The function of a gene highly expressed in the castration-resistant models was evaluated by its overexpression in LNCaP prostate cancer cells. Protein expression in the tumors and serum of patients was examined by immunohistochemistry and ELISA, and correlations with castration resistance were analyzed. Results Expression of the α2 chain of interleukin-13 receptor (IL13Rα2) was higher in castration-resistant PDX tumors. LNCaP cells overexpressing IL13Rα2 acquired castration resistance in vitro and in vivo. In tissue samples, IL13Rα2 expression levels were significantly associated with castration-resistant progression (p < 0.05). In serum samples, IL13Rα2 levels could be measured in 5 of 28 (18%) castration-resistant prostate cancer patients. Conclusion IL13Rα2 was highly expressed in castration-resistant prostate cancer PDX models and was associated with the castration resistance of prostate cancer cells. It might be a potential tissue and serum biomarker for predicting castration resistance in prostate cancer patients.Citation: Nagai T, Terada N, Fujii M, Nagata Y, Nakahara K, Mukai S, Okasho K, Kamiyama Y, Akamatsu S, Kobayashi T, Iida K, Denawa M, Hagiwara M, Inoue T, Ogawa O, Kamoto T. Identification of the α2 chain of interleukin-13 receptor as a potential biomarker for predicting castration resistance of prostate cancer using patient-derived xenograft models. Cancer Rep (Hoboken). 2023 Feb;6(2):e1701. doi: 10.1002/cnr2.1701. Epub 2022 Aug 9. PMID: 36806727; PMCID: PMC9939991