Tamoxifen use and risk of endometrial cancer in breast cancer patients: A systematic review and dose-response meta-analysis

Machine learning Nomogram for Predicting endometrial lesions after tamoxifen therapy in breast Cancer patients

Objective Endometrial lesions are a frequent complication following breast cancer, and current diagnostic tools have limitations. This study aims to develop a machine learning-based nomogram model for predicting the early detection of endometrial lesions in patients. The model is designed to assess risk and facilitate individualized treatment strategies for premenopausal breast cancer patients. Method A retrospective study was conducted on 224 patients who underwent diagnostic curettage post-tamoxifen (TAM) therapy between November 2012 and November 2023. These patients exhibited signs of endometrial abnormalities or symptoms such as colporrhagia. Clinical data were collected and analyzed using R software (version 4.3.2) to identify factors influencing the occurrence of endometrial lesions and evaluate their predictive values. Three machine learning methods were employed to develop a risk prediction model, and their performances were compared. The best-performing model was selected to construct a nomogram of endometrial lesions. Internal validation was conducted using the bootstrap method, and the model's accuracy and fit were assessed using the concordance index (C-index) and calibration curves. Results Independent risk factors for endometrial lesions included ultrasound characteristics, duration of TAM therapy, presence of colporrhagia, and endometrial thickness (P < 0.05). Among the machine learning methods compared, the LASSO regression integrated with a multifactorial logistic regression model demonstrated strong performance, with a concordance index (C-index) of 0.874 and effective calibration (mean absolute error of conformity: 0.014). This model achieved an accuracy of 0.853 and a precision of 0.917, with a training set AUC of 0.874 (95% CI: 0.794-0.831) and a test set AUC of 0.891 (95% CI: 0.777-1.000), closely aligning the predicted risk with the actual observed risk. Conclusion The developed prediction model is effective in evaluating endometrial lesions in premenopausal breast cancer patients. This model offers a theoretical foundation for improving clinical predictions and devising tailored treatment strategies for this patient group.

Endocrine therapy initiation among women diagnosed with ductal carcinoma in situ from 2001 to 2018

PURPOSE

Trials demonstrating benefits of tamoxifen for women with ductal carcinoma in situ (DCIS) were published > 20 years ago; yet subsequent uptake of endocrine therapy was low. We estimated endocrine therapy initiation in women with DCIS between 2001 and 2018 in a community setting, reflecting more recent years of diagnosis than previous studies.

METHODS

This retrospective cohort included adult females ≥ 20 years diagnosed with first primary DCIS between 2001 and 2018, followed through 2019, and enrolled in one of three U.S. integrated healthcare systems. We collected data on endocrine therapy dispensings (tamoxifen, aromatase inhibitors [AIs]) from electronic pharmacy records within 12 months after DCIS diagnosis. Using generalized linear models with a log link and Poisson distribution, we estimated endocrine therapy initiation rates over time and by patient, tumor (including estrogen receptor [ER] status), and treatment characteristics.

RESULTS

Among 2020 women with DCIS, 587 (29%) initiated endocrine therapy within 12 months after diagnosis (36% among 1208 women with ER-positive DCIS). Among women who used endocrine therapy, 506 (86%) initiated tamoxifen and 81 (14%) initiated AIs. Age-adjusted endocrine therapy initiation declined from 34 to 21% between 2001 and 2017; between 2015 and 2018, AI use increased from 8 to 35%. Women less likely to initiate endocrine therapy were ER-negative or had borderline/unknown or no ER test results, ≥ 65 years at diagnosis, Black, and received no radiotherapy.

CONCLUSION

One-third of women diagnosed with DCIS initiated endocrine therapy, and use decreased over time. Understanding why women eligible for endocrine therapy do not initiate is important to maximizing disease-free survival following DCIS diagnosis.

Spatio-temporal localization of P21-activated kinase in endometrial cancer

Endometrial cancer is the sixth most common gynecologic cancer, and has been reported as a malignancy arising due to the idiopathic effects of certain anticancer agents. Tamoxifen is the drug of choice in ER-positive breast cancer, and several studies have shown better disease-free survival in these patients. However, the long-term usage of tamoxifen has been associated with resistance and risk for endometrial malignancy. A direct mechanistic basis for tamoxifen-induced endometrial tumorigenesis is still unclear. Hyperactivation of PAK1 in endometrial cancer correlates with poor overall survival. The present study demonstrates that tamoxifen treatment induces nuclear localization of PAK1 in endometrial carcinoma cells. This nuclear transit is mediated through JAK2 phosphorylation of PAK1 and binding of β-PIX. In addition, a computational approach involving molecular modeling and simulation of phosphorylated and unphosphorylated forms of PAK1 was used to elucidate the dynamics of nuclear localization. Thus, PAK1 phosphorylation by JAK2 is a prerequisite for its nuclear localization and its tumorigenic effects on endometrial cancer cells.

Risk of endometrial malignancy in women treated for breast cancer: the BLUSH prediction model - evidence from a comprehensive multicentric retrospective cohort study

OBJECTIVE

This study aimed to evaluate characteristics of endometrial surveillance in women treated for breast cancer to build a clinical prediction model.

DESIGN

A multicentric retrospective cohort study was conducted at two tertiary-care university hospitals from January 2020 to June 2023. Perimenopausal and postmenopausal women treated for breast cancer were categorized into two groups: patients with and without diagnosis of endometrial malignancy (endometrial carcinoma) or premalignancy (atypical endometrial hyperplasia). Characteristics of breast cancer and ultrasonographic and hysteroscopic examinations were compared. A prediction model for endometrial malignancy was built using logistic regression. Predictive accuracy was assessed using the receiver operating characteristic (ROC) curve and goodness of fit using the Hosmer-Lemeshow test.

RESULTS

One hundred and thirty-two (28 with premalignancy or malignancy and 104 without malignancy) women were analyzed. A nomogram was produced for prediction model development utilizing the presence and duration in months of abnormal uterine (BL)eeding, ultrasound (US) vascular pattern and echogenicity and (H)ysteroscopic appearance of endometrium (BLUSH) as determined by logistic regression. Sensitivity and specificity were 79.17% and 95.19%, respectively, with an area under ROC curve of 0.965, indicating good accuracy. Good goodness of fit and prediction stability were indicated by the calibration curve and Hosmer-Lemeshow test (χ2 = 26.36; p = 0.999).

CONCLUSIONS

Breast cancer survivors undergoing endometrial surveillance might benefit from a potentially useful prediction model based on hysteroscopic appearance, ultrasonographic uniformity of endometrium, Doppler flow and presence of abnormal uterine bleeding.

Second primary non-breast cancers in young breast cancer survivors

PURPOSE

We evaluated the incidence, timing, and risk factors for second primary non-breast cancers (SPNBC) among young breast cancer (BC) survivors.

METHODS

This study included participants of the Young Women's BC Study (YWS) who were diagnosed with stage 0-III BC between 2006 and 2016 and age 40 or younger at diagnosis (N = 1,230). Patient characteristics, treatment information, and clinical events were collected via serial surveys. Tumor and treatment data were obtained from medical record review. Five- and 10-year risks of SPNBCs were estimated via the cumulative incidence function, considering death, metastasis, or second primary BC as competing events. Fine and Gray subdistribution hazard models estimated subdistribution hazard ratios (sHRs) and 95% confidence intervals (CI) for SPNBC risk based on risk factors including demographics, germline genetics, primary BC characteristics, and treatments.

RESULTS

Among 1,230 women, over a median follow-up of 10.1 years, 47 patients (4%) developed an SPNBC. Types of malignancy included melanoma (n = 10), thyroid (n = 10), ovarian (n = 4), sarcoma (n = 4), uterine (n = 3), rectal (n = 3), bladder (n = 2), cervical (n = 2), head/neck (n = 2), lung (n = 2), lymphoma (n = 2), pancreatic (n = 2), and renal (n = 1). Five and 10-year cumulative incidence were 1.4% and 3.2%, respectively. Median time between primary BC and SPNBC was 7.3 years. No patient factors, primary tumor characteristics, or treatments were statistically significantly associated with SPNBC in univariable or multivariable models.

CONCLUSION

In this population, five-year cumulative incidence was higher than that reported among healthy women under 50 years of age, highlighting the importance of long-term surveillance for new non-breast cancers in young adult BC survivors.

StackER: a novel SMILES-based stacked approach for the accelerated and efficient discovery of ERα and ERβ antagonists

The role of estrogen receptors (ERs) in breast cancer is of great importance in both clinical practice and scientific exploration. However, around 15-30% of those affected do not see benefits from the usual treatments owing to the innate resistance mechanisms, while 30-40% will gain resistance through treatments. In order to address this problem and facilitate community-wide efforts, machine learning (ML)-based approaches are considered one of the most cost-effective and large-scale identification methods. Herein, we propose a new SMILES-based stacked approach, termed StackER, for the accelerated and efficient identification of ERα and ERβ inhibitors. In StackER, we first established an up-to-date dataset consisting of 1,996 and 1,207 compounds for ERα and ERβ, respectively. Using the up-to-date dataset, StackER explored a wide range of different SMILES-based feature descriptors and ML algorithms in order to generate probabilistic features (PFs). Finally, the selected PFs derived from the two-step feature selection strategy were used for the development of an efficient stacked model. Both cross-validation and independent tests showed that StackER surpassed several conventional ML classifiers and the existing method in precisely predicting ERα and ERβ inhibitors. Remarkably, StackER achieved MCC values of 0.829-0.847 and 0.712-0.786 in terms of the cross-validation and independent tests, respectively, which were 5.92-8.29 and 1.59-3.45% higher than the existing method. In addition, StackER was applied to determine useful features for being ERα and ERβ inhibitors and identify FDA-approved drugs as potential ERα inhibitors in efforts to facilitate drug repurposing. This innovative stacked method is anticipated to facilitate community-wide efforts in efficiently narrowing down ER inhibitor screening.

Susceptibility Genes Associated with Multiple Primary Cancers

With advancements in treatment and screening techniques, we have been witnessing an era where more cancer survivors harbor multiple primary cancers (MPCs), affecting approximately one in six patients. Identifying MPCs is crucial for tumor staging and subsequent treatment choices. However, the current clinicopathological criteria for clinical application are limited and insufficient, making it challenging to differentiate them from recurrences or metastases. The emergence of next-generation sequencing (NGS) technology has provided a genetic perspective for defining multiple primary cancers. Researchers have found that, when considering multiple tumor pairs, it is crucial not only to examine well-known essential mutations like MLH1/MSH2, EGFR, PTEN, BRCA1/2, CHEK2, and TP53 mutations but also to explore certain pleiotropic loci. Moreover, specific deleterious mutations may serve as regulatory factors in second cancer development following treatment. This review aims to discuss these susceptibility genes and provide an explanation of their functions based on the signaling pathway background. Additionally, the association network between genetic signatures and different tumor pairs will be summarized.

Enhancing Tamoxifen Therapy with α-Mangostin: Synergistic Antiproliferative Effects on Breast Cancer Cells and Potential Reduced Endometrial Impact

Breast cancer is the most prevalent neoplasia among women worldwide. For the estrogen receptor-positive (ER+) phenotype, tamoxifen is the standard hormonal therapy; however, it carries the risk of promoting endometrial carcinoma. Hence, we aimed to evaluate the antiproliferative effect of the phytochemical α-mangostin (AM) as a co-adjuvant alongside tamoxifen on breast cancer cells to improve its efficacy while reducing its adverse effects on endometrium. For this, ER+ breast cancer cells (MCF-7 and T-47D) and endometrial cells (N30) were treated with AM, 4-hydroxytamoxifen (4-OH-TMX), and their combination. Cell proliferation was evaluated using sulforhodamine B assay, and the pharmacological interaction was determined through the combination index and the dose reduction index calculation. The genes KCNH1, CCDN1, MKI67, and BIRC5 were amplified by real-time PCR as indicators of oncogenesis, cell cycle progression, cell proliferation, and apoptosis, respectively. Additionally, genes involved in ER signaling were analyzed. In breast cancer cells, the combination of AM with 4-OH-TMX showed a synergistic antiproliferative effect and favorable dose reduction. AM and 4-OH-TMX decreased KCNH1, CCND1, and BIRC5 gene expression. In endometrial cells, AM decreased MKI-67 gene expression, while it reverted the 4-OH-TMX-dependent CCND1 upregulation. This study establishes the benefits of incorporating AM as a co-adjuvant for first-line ER+ breast cancer therapy.

Cardioprotective effect of tamoxifen and raloxifene: Preventing proteoglycan synthesis by modulating non-canonical TGF-β signalling through NADPH oxidase and ERK phosphorylation

Atherosclerosis, a leading cause of cardiovascular disease, remains a significant global health concern. Tamoxifen and raloxifene, selective estrogen receptor modulators (SERMs), have demonstrated potential cardioprotective effects. However, the underlying molecular mechanisms by which these SERMs modulate Transforming Growth Factor-β (TGF-β) signaling in human vascular smooth muscle cells (VSMCs) remain largely unexplored. This study sought to investigate the impact of tamoxifen and raloxifene on TGF-β-induced CHSY1 expression and Smad2 linker region phosphorylation in VSMCs and to elucidate the role of reactive oxygen species (ROS), NADPH oxidase (NOX), and kinase pathways in mediating these effects. Employing a comprehensive experimental strategy, VSMCs were treated with TGF-β in the presence or absence of tamoxifen, raloxifene, and various pharmacological inhibitors. Subsequently, CHSY1 mRNA expression, Smad2C and Smad2L phosphorylation, ROS production, p47phox and ERK 1/2 phosphorylation were assessed. Our results revealed that tamoxifen and raloxifene significantly attenuated TGF-β-mediated CHSY1 mRNA expression and Smad2 linker region phosphorylation, without affecting the canonical TGF-β-Smad2C pathway. Furthermore, these compounds effectively inhibited ROS production, p47phox and ERK 1/2 phosphorylation, implicating the involvement of the TGF-β-NOX-ERK-Smad2L signaling cascade in their cardioprotective properties. This study provides a comprehensive understanding of the molecular mechanisms underlying the cardioprotective effects of tamoxifen and raloxifene in VSMCs, offering valuable insights for the development of targeted therapeutic strategies aimed at atherosclerosis prevention and the promotion of cardiovascular health.