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Importance of Breast Cancer Subtype in the Development of Androgen Receptor Directed Therapy.

ABSTRACT: The androgen receptor (AR) has re-emerged as a potential therapeutic target in breast cancer. This stems from recent progress made in preclinical models, that have recognized important differences in the effect of AR expression on patient outcomes among different breast cancer subtypes. In parallel, the clinical development of new generations of AR directed therapies for prostate cancer has begun to mature. The availability of these new agents has translated into new trials to treat breast cancer. It is critical that studies of the effect of AR expression and signaling in breast cancer be context and subtype specific in order to successfully target AR signalling as a therapeutic strategy for breast cancer. We will review developments in preclinical studies, and recent clinical trials targeting AR in breast cancer.

SUBMITTER: Lim E

PROVIDER: S-EPMC4026357 | biostudies-literature | 2014 Jun

REPOSITORIES: biostudies-literature

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Importance of Breast Cancer Subtype in the Development of Androgen Receptor Directed Therapy.

Lim Elgene E Ni Min M Cao Shiliang S Hazra Aditi A Tamimi Rulla M RM Brown Myles M

Current breast cancer reports 20140601 2

The androgen receptor (AR) has re-emerged as a potential therapeutic target in breast cancer. This stems from recent progress made in preclinical models, that have recognized important differences in the effect of AR expression on patient outcomes among different breast cancer subtypes. In parallel, the clinical development of new generations of AR directed therapies for prostate cancer has begun to mature. The availability of these new agents has translated into new trials to treat breast cance ...[more]

PMID: 24860642

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Project description:ObjectiveTo investigate whether radiomics features extracted from multi-parametric MRI combining machine learning approach can predict molecular subtype and androgen receptor (AR) expression of breast cancer in a non-invasive way.Materials and methodsPatients diagnosed with clinical T2-4 stage breast cancer from March 2016 to July 2020 were retrospectively enrolled. The molecular subtypes and AR expression in pre-treatment biopsy specimens were assessed. A total of 4,198 radiomics features were extracted from the pre-biopsy multi-parametric MRI (including dynamic contrast-enhancement T1-weighted images, fat-suppressed T2-weighted images, and apparent diffusion coefficient map) of each patient. We applied several feature selection strategies including the least absolute shrinkage and selection operator (LASSO), and recursive feature elimination (RFE), the maximum relevance minimum redundancy (mRMR), Boruta and Pearson correlation analysis, to select the most optimal features. We then built 120 diagnostic models using distinct classification algorithms and feature sets divided by MRI sequences and selection strategies to predict molecular subtype and AR expression of breast cancer in the testing dataset of leave-one-out cross-validation (LOOCV). The performances of binary classification models were assessed via the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). And the performances of multiclass classification models were assessed via AUC, overall accuracy, precision, recall rate, and F1-score.ResultsA total of 162 patients (mean age, 46.91 ± 10.08 years) were enrolled in this study; 30 were low-AR expression and 132 were high-AR expression. HR+/HER2- cancers were diagnosed in 56 cases (34.6%), HER2+ cancers in 81 cases (50.0%), and TNBC in 25 patients (15.4%). There was no significant difference in clinicopathologic characteristics between low-AR and high-AR groups (P > 0.05), except the menopausal status, ER, PR, HER2, and Ki-67 index (P = 0.043, <0.001, <0.001, 0.015, and 0.006, respectively). No significant difference in clinicopathologic characteristics was observed among three molecular subtypes except the AR status and Ki-67 (P = <0.001 and 0.012, respectively). The Multilayer Perceptron (MLP) showed the best performance in discriminating AR expression, with an AUC of 0.907 and an accuracy of 85.8% in the testing dataset. The highest performances were obtained for discriminating TNBC vs. non-TNBC (AUC: 0.965, accuracy: 92.6%), HER2+ vs. HER2- (AUC: 0.840, accuracy: 79.0%), and HR+/HER2- vs. others (AUC: 0.860, accuracy: 82.1%) using MLP as well. The micro-AUC of MLP multiclass classification model was 0.896, and the overall accuracy was 0.735.ConclusionsMulti-parametric MRI-based radiomics combining with machine learning approaches provide a promising method to predict the molecular subtype and AR expression of breast cancer non-invasively.

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Importance of Breast Cancer Subtype in the Development of Androgen Receptor Directed Therapy.

Publications

Importance of Breast Cancer Subtype in the Development of Androgen Receptor Directed Therapy.

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