Molecular profile of androgen-independent prostate cancer xenograft LuCaP 23.1

Abstract

After castration or therapeutic hormone deprivation, most cancer of the prostate (CaP) cells develop androgen-independent (AI) growth. In this work, we studied the effect of androgen depletion (castration) on the growth of experimental model LuCaP 23.1 xenograft. A total of 101 nude mice were implanted and analysed for their growth profile before experimental period 1 (11 weeks) and after castration experimental period 2 (15 weeks). For specific periods, tumors were harvested and assessed for molecular marker expression specific for CaP. Taking into account tumor dynamic growth, prior to castration we found 37 fast growing (FG) tumors (948.9 ± 76.9 mm 3) and 63 slow growing (SG) tumors (229.6 ± 18.4 mm 3). Real-time quantitative RT-PCR showed that in comparison to SGs, FGs contained elevated expression of epidermal growth factor receptor type 1 (HER1), urokinase plasminogen activator (uPA), thymidine phosphorylase (TP) and thymidilate synthase (TS) mRNAs expression and low levels of 5α-reductase 2 (5α-R2) mRNA. After castration all FG tumors progressed rapidly (by 5 weeks) to AI growth (FG-P). In SG castrated tumors, 66% of tumors showed retarded progression (by 12 weeks) to AI (SG-P), whereas 34% responded to castration (SG-R). Molecular analysis demonstrated distinct molecular profiles integrating different pathways associated with AI progression. The progressive tumors FG-P, and some tumors of SG-P subgroup, presented significantly high levels of HER1, epidermal growth factor receptor type 2 (HER2), TS, uPA, TP, tumor necrosis factor superfamily member 6 (FAS) and peptidylglycine α-amidating mono-oxygenase (PAM) mRNA all of which correlated with androgen receptor (AR) mRNA. The second subgroup of SG-P tumors showed a high expression of the anti-apoptotic gene Bcl-2. A third subgroup of SG-P tumors showed significant expression of hypoxia-related genes such as adrenomedullin (AM) after castration. LuCaP 23.1 xenograft represent a useful dynamic model to study pre-clinically new therapeutic molecules and evaluate non-randomized therapeutics protocols combining different target inhibition specific to each AI pathways.