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Comparison between steroid binding to membrane progesterone receptor alpha (mPRalpha) and to nuclear progesterone receptor: correlation with physicochemical properties assessed by comparative molecular field analysis and identification of mPRalpha-specific agonists.

ABSTRACT: Recent results showing that the binding characteristics of 33 steroids for human membrane progesterone receptor alpha (hu-mPRalpha) differ from those for the nuclear progesterone receptor (nPR) suggest that hu-mPRalpha-specific agonists can be identified for investigating its physiological functions. The binding affinities of an additional 21 steroids for hu-mPRalpha were determined to explore the structure-activity relationships in more detail and to identify potent, specific mPRalpha agonists. Four synthetic progesterone derivatives with methyl or methylene groups on positions 18 or 19, 18a-methylprogesterone (18-CH(3)P4, Org OE 64-0), 13-ethenyl-18-norprogesterone (18-CH(2)P4, Org 33663-0), 19a-methylprogesterone (19-CH(3)P4, Org OD 13-0) and 10-ethenyl-19-norprogesterone (19-CH(2)P4, Org OD 02-0), showed similar or higher affinities than progesterone for hu-mPRalpha and displayed mPRalpha agonist activities in G-protein and MAP kinase activation assays. All four steroids also bound to the nPR in cytosolic fractions of MCF-7 cells. However, two compounds, 19-CH(2)P4 and 19-CH(3)P4, showed no nPR agonist activity in a nPR reporter assay and therefore are selective mPRalpha agonists suitable for physiological investigations. The structure-binding relationships of the combined series of 54 steroids for hu-mPRalpha deviated strikingly from those of a published set of 60 3-keto or 3-desoxy steroids for nPR. Close correlations were observed between the receptor binding affinities of the steroids and their physicochemical properties calculated by comparative molecular field analysis (CoMFA) for both hu-mPRalpha and nPR. A comparison of the CoMFA field graphs for the two receptors revealed several differences in the structural features required for binding to hu-mPRalpha and nPR which could be exploited to develop additional mPR-specific ligands.

SUBMITTER: Kelder J

PROVIDER: S-EPMC2858063 | biostudies-literature | 2010 Apr

REPOSITORIES: biostudies-literature

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Comparison between steroid binding to membrane progesterone receptor alpha (mPRalpha) and to nuclear progesterone receptor: correlation with physicochemical properties assessed by comparative molecular field analysis and identification of mPRalpha-specific agonists.

Kelder Jan J Azevedo Rita R Pang Yefei Y de Vlieg Jacob J Dong Jing J Thomas Peter P

Steroids 20100122 4-5

Recent results showing that the binding characteristics of 33 steroids for human membrane progesterone receptor alpha (hu-mPRalpha) differ from those for the nuclear progesterone receptor (nPR) suggest that hu-mPRalpha-specific agonists can be identified for investigating its physiological functions. The binding affinities of an additional 21 steroids for hu-mPRalpha were determined to explore the structure-activity relationships in more detail and to identify potent, specific mPRalpha agonists. ...[more]

PMID: 20096719

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Project description:Major roadblocks to developing effective progesterone receptor (PR)-targeted therapies in breast cancer include the lack of highly-specific PR modulators, a poor understanding of the pro- or anti-tumorigenic networks for PR isoforms and ligands, and an incomplete understanding of the cross talk between PR and estrogen receptor (ER) signaling. Through genomic analyses of xenografts treated with various clinically-relevant ER and PR-targeting drugs, we describe how the activation or inhibition of PR differentially reprograms estrogen signaling, resulting in the segregation of transcriptomes into separate PR agonist and antagonist-mediated groups. These findings address an ongoing controversy regarding the clinical utility of PR agonists and antagonists, alone or in combination with tamoxifen, for breast cancer management. Additionally, the two PR isoforms PRA and PRB, bind distinct but overlapping genomic sites and interact with different sets of co-regulators to differentially modulate estrogen signaling to be either pro- or anti-tumorigenic. Of the two isoforms, PRA inhibited gene expression and ER chromatin binding significantly more than PRB. Differential gene expression was observed in PRA and PRB-rich patient tumors and PRA-rich gene signatures had poorer survival outcomes. In support of antiprogestin responsiveness of PRA-rich tumors, gene signatures associated with PR antagonists, but not PR agonists, predicted better survival outcomes. The better patient survival associated with PR antagonists versus PR agonists treatments was further reflected in the higher in vivo anti-tumor activity of therapies that combine tamoxifen with PR antagonists and modulators. This study suggests that distinguishing common effects observed due to concomitant interaction of another receptor with its ligand (agonist or antagonist), from unique isoform and ligand-specific effects will inform the development of biomarkers for patient selection and translation of PR-targeted therapies to the clinic.

Project description:IntroductionManual interpretation of immunohistochemistry (IHC) is a subjective, time-consuming and variable process, with an inherent intra-observer and inter-observer variability. Automated image analysis approaches offer the possibility of developing rapid, uniform indicators of IHC staining. In the present article we describe the development of a novel approach for automatically quantifying oestrogen receptor (ER) and progesterone receptor (PR) protein expression assessed by IHC in primary breast cancer.MethodsTwo cohorts of breast cancer patients (n = 743) were used in the study. Digital images of breast cancer tissue microarrays were captured using the Aperio ScanScope XT slide scanner (Aperio Technologies, Vista, CA, USA). Image analysis algorithms were developed using MatLab 7 (MathWorks, Apple Hill Drive, MA, USA). A fully automated nuclear algorithm was developed to discriminate tumour from normal tissue and to quantify ER and PR expression in both cohorts. Random forest clustering was employed to identify optimum thresholds for survival analysis.ResultsThe accuracy of the nuclear algorithm was initially confirmed by a histopathologist, who validated the output in 18 representative images. In these 18 samples, an excellent correlation was evident between the results obtained by manual and automated analysis (Spearman's rho = 0.9, P < 0.001). Optimum thresholds for survival analysis were identified using random forest clustering. This revealed 7% positive tumour cells as the optimum threshold for the ER and 5% positive tumour cells for the PR. Moreover, a 7% cutoff level for the ER predicted a better response to tamoxifen than the currently used 10% threshold. Finally, linear regression was employed to demonstrate a more homogeneous pattern of expression for the ER (R = 0.860) than for the PR (R = 0.681).ConclusionsIn summary, we present data on the automated quantification of the ER and the PR in 743 primary breast tumours using a novel unsupervised image analysis algorithm. This novel approach provides a useful tool for the quantification of biomarkers on tissue specimens, as well as for objective identification of appropriate cutoff thresholds for biomarker positivity. It also offers the potential to identify proteins with a homogeneous pattern of expression.

Dataset Information

Comparison between steroid binding to membrane progesterone receptor alpha (mPRalpha) and to nuclear progesterone receptor: correlation with physicochemical properties assessed by comparative molecular field analysis and identification of mPRalpha-specific agonists.

Publications

Comparison between steroid binding to membrane progesterone receptor alpha (mPRalpha) and to nuclear progesterone receptor: correlation with physicochemical properties assessed by comparative molecular field analysis and identification of mPRalpha-specific agonists.

Similar Datasets

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