Dataset Information

Expression of androgen receptor and its regulatory molecule Lin28 in non-luminal subtype breast cancer.

ABSTRACT: Androgen receptor (AR) was associated with favourable outcome in luminal breast cancer. However, the role of AR in non-luminal breast cancer remains inconclusive. The aim of the present study was to evaluate the clinical significance of the AR and its regulatory pathway in non-luminal subtypes of breast cancer. In total, 284 breast cancer patients were recruited from January 2007 to January 2016. Tissue microarrays were constructed from archival paraffin blocks and assessed for AR and its regulatory molecule, Lin28, by immunohistochemistry. The association between AR and Lin28 expression and clinicopathological parameters was analyzed. Results showed that AR and Lin28 were co-expressed. No association between these proteins and clinicopathological parameters, and survival outcome was found. However, a higher proportion of the patients with AR and Lin28 expression were observed in HER2 subtype. In conclusion, Lin28 may be a novel marker for prognosis and targeted for treatment in HER2 subtype breast cancer.

SUBMITTER: Sa-Nguanraksa D

PROVIDER: S-EPMC7201307 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

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Expression of androgen receptor and its regulatory molecule Lin28 in non-luminal subtype breast cancer.

Sa-Nguanraksa Doonyapat D Pongthong Wanee W Samarnthai Norasate N Mitpakdi Kwanlada K Chuangsuwanich Tuenjai T Limjindaporn Thawornchai T Kulprom Anchalee A O-Charoenrat Pornchai P

Molecular and clinical oncology 20200409 6

Androgen receptor (AR) was associated with favourable outcome in luminal breast cancer. However, the role of AR in non-luminal breast cancer remains inconclusive. The aim of the present study was to evaluate the clinical significance of the AR and its regulatory pathway in non-luminal subtypes of breast cancer. In total, 284 breast cancer patients were recruited from January 2007 to January 2016. Tissue microarrays were constructed from archival paraffin blocks and assessed for AR and its regula ...[more]

PMID: 32382417

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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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Dataset Information

Expression of androgen receptor and its regulatory molecule Lin28 in non-luminal subtype breast cancer.

Publications

Expression of androgen receptor and its regulatory molecule Lin28 in non-luminal subtype breast cancer.

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