Progesterone receptors in normal breast development and breast cancer

Giulianelli, Sebastián; Lamb, Caroline A.; Lanari, Claudia

doi:10.1042/ebc20200163

Cited by 16 publications

(14 citation statements)

References 185 publications

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“…The role of PR in BC growth and response to therapy has gained much attention in the past years ( 5 , 17 ). PR can modulate ERα transactivation of target genes, with genes like MYC or CCND1 containing regions regulated by ERα and PR, where both receptors physically interact to drive gene expression ( 18 , 19 ). Thus, targeting PR stands as an alternative to treat specific subtypes of antiestrogen-resistant BC ( 5 , 7 ).…”

Section: Introductionmentioning

confidence: 99%

Metabolic Adaptations in an Endocrine-Related Breast Cancer Mouse Model Unveil Potential Markers of Tumor Response to Hormonal Therapy

et al. 2022

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Breast cancer (BC) is the most common type of cancer in women and, in most cases, it is hormone-dependent (HD), thus relying on ovarian hormone activation of intracellular receptors to stimulate tumor growth. Endocrine therapy (ET) aimed at preventing hormone receptor activation is the primary treatment strategy, however, about half of the patients, develop resistance in time. This involves the development of hormone independent tumors that initially are ET-responsive (HI), which may subsequently become resistant (HIR). The mechanisms that promote the conversion of HI to HIR tumors are varied and not completely understood. The aim of this work was to characterize the metabolic adaptations accompanying this conversion through the analysis of the polar metabolomes of tumor tissue and non-compromised mammary gland from mice implanted subcutaneously with HD, HI and HIR tumors from a medroxyprogesterone acetate (MPA)-induced BC mouse model. This was carried out by nuclear magnetic resonance (NMR) spectroscopy of tissue polar extracts and data mining through multivariate and univariate statistical analysis. Initial results unveiled marked changes between global tumor profiles and non-compromised mammary gland tissues, as expected. More importantly, specific metabolic signatures were found to accompany progression from HD, through HI and to HIR tumors, impacting on amino acids, nucleotides, membrane percursors and metabolites related to oxidative stress protection mechanisms. For each transition, sets of polar metabolites are advanced as potential markers of progression, including acquisition of resistance to ET. Putative biochemical interpretation of such signatures are proposed and discussed.

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Section: Introductionmentioning

confidence: 99%

Metabolic Adaptations in an Endocrine-Related Breast Cancer Mouse Model Unveil Potential Markers of Tumor Response to Hormonal Therapy

et al. 2022

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“…The reason may be that the expression level of ER and PR is related to the degree of differentiation of breast cancer. The expression of ER and PR in breast cancer indicates that breast cancer cells still retain the characteristics of hormone dependent growth, and the degree of cell differentiation is high, whereas ER and PR negative breast cancer cells malignant degree is high, the proliferation of hyperplasia more exuberant, and therefore more sensitive to chemotherapy drugs that block cell growth and reproduction [31,32]. For survival prognosis, this study found that the level of ER expression had no signi cant effect on the disease-free survival and overall survival.…”

Section: Discussmentioning

confidence: 99%

The influence factors of clinical efficacy and prognosis of breast cancer patients treated with neoadjuvant chemotherapy

Tang

Zhang

2023

Preprint

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Objective To investigate predictive indicators of clinical efficacy and prognosis of breast cancer patients treated with neoadjuvant chemotherapy. Methods 290 female patients with breast cancer who treated with neoadjuvant chemotherapy were enrolled in this study. The collected data including tumor position, tumor size, cTNM staging, LNR, pathological molecular types(including ER, PR, Her-2, Ki-67 and so on), neoadjuvant chemotherapy regimens, pathological complete response rate, postoperative treatment and survival time. Results Neoadjuvant chemotherapy curative effect was related to pausimenia, T staging, N staging, ER/PR and Ki-67(P < 0.05), N staging (OR = 0.041, 95% CI = 0.012 ~ 0.134) and Ki-67 (OR = 9.340, 95% CI = 1.770 ~ 49.286) were independent risk factors of pCR. Recurrence of metastases was related to T staging, LNR, Ki-67, ET regimen, postoperative chemotherapy and postoperation radiotherapy(P < 0.05), T staging (OR = 1.731, 95%CI = 1.244 ~ 2.408), LNR (OR = 2.726, 95%CI = 1.430 ~ 5.197) and Ki-67 (OR = 3.107, 95%CI = 1.395 ~ 6.920) were independent risk factors of recurrence of metastases. Patients with low Ki-67 expression, low LNR, T1 staging and T2 staging and N0/N1/N2 staging had a better prognosis. Conclusion Clinical efficacy and prognosis of breast cancer patients treated with neoadjuvant chemotherapy were related to cTNM staging, LNR, ER/PR and Ki-67.

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“…The St. Gallen International Breast Conference proposed estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor type 2 (HER2) protein, and the Ki-67 proliferation index as the main receptor indicators for the molecular subtype of breast cancer [25]. The above factors are strongly associated with the prognosis and outcome of breast cancer patients [26][27][28][29][30]. According to this, each patient underwent a surgical excision to obtain the tumor's biological marker status evaluation via immunohistochemical (IHC) staining, namely the estrogen receptor (ER), the progesterone receptor (PR), the human epidermal growth factor receptor type 2 (HER2) protein, and the Ki-67 proliferation index.…”

Section: Subtype Labelingmentioning

confidence: 99%

Deep learning-based system for automatic prediction of triple-negative breast cancer from ultrasound images

Boulenger

Luo

Zhang

et al. 2022

Med Biol Eng Comput

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To develop a deep-learning system for the automatic identification of triple-negative breast cancer (TNBC) solely from ultrasound images. A total of 145 patients and 831 images were retrospectively enrolled at Peking Union College Hospital from April 2018 to March 2019. Ultrasound images and clinical information were collected accordingly. Molecular subtypes were determined from immunohistochemical (IHC) results. A CNN with VGG-based architecture was then used to predict TNBC. The model’s performance was evaluated using randomized k-fold stratified cross-validation. A t-SNE analysis and saliency maps were used for model visualization. TNBC was identified in 16 of 145 (11.03%) patients. One hundred fifteen (80%) patients, 15 (10%) patients, and 15 (10%) patients formed the train, validation, and test set respectively. The deep learning system exhibits good efficacy, with an AUC of 0.86 (95% CI: 0.64, 0.95), an accuracy of 85%, a sensitivity of 86%, a specificity of 86%, and an F1-score of 0.74. In addition, the internal representation features learned by the model showed clear differentiation across molecular subtype groups. Such a deep learning system can automatically predict triple-negative breast cancer preoperatively and accurately. It may help to get to more precise and comprehensive management. Graphical Abstract

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Progesterone receptors in normal breast development and breast cancer

Cited by 16 publications

References 185 publications

Metabolic Adaptations in an Endocrine-Related Breast Cancer Mouse Model Unveil Potential Markers of Tumor Response to Hormonal Therapy

Metabolic Adaptations in an Endocrine-Related Breast Cancer Mouse Model Unveil Potential Markers of Tumor Response to Hormonal Therapy

The influence factors of clinical efficacy and prognosis of breast cancer patients treated with neoadjuvant chemotherapy

Deep learning-based system for automatic prediction of triple-negative breast cancer from ultrasound images

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