Breast Cancer Mortality Trends and Predictions to 2030 and Its Attributable Risk Factors in East and South Asian Countries

Temporal trends of breast cancer burden in the Western Pacific Region from 1990 to 2044: Implications from the Global Burden of Disease Study 2019

INTRODUCTION

Breast cancer (BC) is a malignant disease that occurs worldwide and poses serious health burden.

OBJECTIVES

To assess the prevalence of BC burden in the Western Pacific region (WPR) from 1990 to 2019, and to predict trends from 2020 to 2044. To analyze the driving factors and put forward the region-oriented improvement.

METHODS

Based on the Global Burden of Disease Study 2019, BC cases, deaths, disability-adjusted life years (DALYs) cases, age-standardized incidence rate (ASIR), age-standardized death rate (ASDR), and age-standardized DALYs rate in WPR from 1990 to 2019 was obtained and analysed. Age-period-cohort (APC) model was used to analyze age, period, and cohort effects in BC, and Bayesian APC (BAPC) was used to predict trends over the next 25 years.

RESULTS

In conclusion, BC incidence and deaths in the WPR have increased rapidly over the past 30 years and are expected to continue to increase between 2020 and 2044. Among behavioral and metabolic factors, high body-mass index was the main risk factor for BC mortality in middle-income countries, whereas alcohol use was the main risk factor in Japan. Age is a key factor in the development of BC, with 40 years being the critical point. Incidence trends coincide with the course of economic development.

CONCLUSIONS

The BC burden remains an essential public health issue in the WPR and will increase substantially in the future. More efforts should be made in middle-income countries to prompt the health behavior and minimize the burden of BC because these nations accounts for the majority of BC burden in the WPR.

Rates of breast reconstruction uptake and attitudes toward breast cancer and survivorship among south asians: A literature review

Our aim in this review was to ascertain rates of breast reconstruction among South Asian patients and identify attitudes towards breast cancer, survivorship, and breast reconstruction. Mastectomy rates for South Asian patients ranged from 52% to 77% and reconstruction following mastectomy varied from 0% to 14%. A negative perception of cancer, fears of social isolation, and taboos around breasts can prevent South Asian women from receiving surgical care after a breast cancer diagnosis.

Conditional generative adversarial network driven radiomic prediction of mutation status based on magnetic resonance imaging of breast cancer

BACKGROUND

Breast Cancer (BC) is a highly heterogeneous and complex disease. Personalized treatment options require the integration of multi-omic data and consideration of phenotypic variability. Radiogenomics aims to merge medical images with genomic measurements but encounter challenges due to unpaired data consisting of imaging, genomic, or clinical outcome data. In this study, we propose the utilization of a well-trained conditional generative adversarial network (cGAN) to address the unpaired data issue in radiogenomic analysis of BC. The generated images will then be used to predict the mutations status of key driver genes and BC subtypes.

METHODS

We integrated the paired MRI and multi-omic (mRNA gene expression, DNA methylation, and copy number variation) profiles of 61 BC patients from The Cancer Imaging Archive (TCIA) and The Cancer Genome Atlas (TCGA). To facilitate this integration, we employed a Bayesian Tensor Factorization approach to factorize the multi-omic data into 17 latent features. Subsequently, a cGAN model was trained based on the matched side-view patient MRIs and their corresponding latent features to predict MRIs for BC patients who lack MRIs. Model performance was evaluated by calculating the distance between real and generated images using the Fréchet Inception Distance (FID) metric. BC subtype and mutation status of driver genes were obtained from the cBioPortal platform, where 3 genes were selected based on the number of mutated patients. A convolutional neural network (CNN) was constructed and trained using the generated MRIs for mutation status prediction. Receiver operating characteristic area under curve (ROC-AUC) and precision-recall area under curve (PR-AUC) were used to evaluate the performance of the CNN models for mutation status prediction. Precision, recall and F1 score were used to evaluate the performance of the CNN model in subtype classification.

RESULTS

The FID of the images from the well-trained cGAN model based on the test set is 1.31. The CNN for TP53, PIK3CA, and CDH1 mutation prediction yielded ROC-AUC values 0.9508, 0.7515, and 0.8136 and PR-AUC are 0.9009, 0.7184, and 0.5007, respectively for the three genes. Multi-class subtype prediction achieved precision, recall and F1 scores of 0.8444, 0.8435 and 0.8336 respectively. The source code and related data implemented the algorithms can be found in the project GitHub at https://github.com/mattthuang/BC_RadiogenomicGAN .

CONCLUSION

Our study establishes cGAN as a viable tool for generating synthetic BC MRIs for mutation status prediction and subtype classification to better characterize the heterogeneity of BC in patients. The synthetic images also have the potential to significantly augment existing MRI data and circumvent issues surrounding data sharing and patient privacy for future BC machine learning studies.

Effect and mechanism of paclitaxel loaded on magnetic Fe3O4@mSiO2-NH2-FA nanocomposites to MCF-7 cells

Magnetic Fe₃O₄ nanoparticles were prepared via a simple hydrothermal method and utilized to load paclitaxel. The average particle size of Fe₃O₄ nanoparticles was found to be 20.2 ± 3.0 nm, and the calculated saturation magnetization reached 129.38 emu/g, verifying superparamagnetism of nanomaterials. The specific surface area and pore volume were 84.756 m²/g and 0.265 cm³/g, respectively. Subsequently, Fe₃O₄@mSiO₂ nanoparticles were successfully fabricated using the Fe₃O₄ nanoparticles as precursors with an average size of 27.81 nm. The relevant saturation magnetization, zeta potential, and specific surface area of Fe₃O₄@mSiO₂-NH₂-FA were respectively 76.3 emu/g, -14.1 mV, and 324.410 m²/g. The pore volume and average adsorption pore size were 0.369 cm³/g and 4.548 nm, respectively. Compared to free paclitaxel, the solubility and stability of nanoparticles loaded with paclitaxel were improved. The drug loading efficiency and drug load of the nanoformulation were 44.26 and 11.38%, respectively. The Fe₃O₄@mSiO₂-NH₂-FA nanocomposites were easy to construct with excellent active targeting performance, pH sensitivity, and sustained-release effect. The nanoformulation also showed good biocompatibility, where the cell viability remained at 73.8% when the concentration reached 1200 μg/mL. The nanoformulation induced cell death through apoptosis, as confirmed by AO/EB staining and flow cytometry. Western blotting results suggested that the nanoformulation could induce iron death by inhibiting Glutathione Peroxidase 4 (GPX4) activity or decreasing Ferritin Heavy Chain 1 (FTH1) expression. Subsequently, the expression of HIF-1α was upregulated owing to the accumulation of reactive oxygen species (ROS), thus affecting the expression of apoptosis-related proteins regulated by p53, inducing cell apoptosis.

Small breast epithelial mucin as a useful prognostic marker for breast cancer patients

This study aimed to evaluate the clinical utility of small breast epithelial mucin (SBEM) as a prognostic biomarker in an independent patient cohort. The paraffin-embedded tissues and clinicopathological data of 105 patients with breast cancer were collected, and the expression of SBEM in breast cancer samples was detected by immunohistochemical staining. The correlations between clinicopathological variables and the expression of SBEM were analyzed, and its significance as a prognostic indicator for breast cancer patients was determined. Immunohistochemical staining revealed that SBEM was expressed mostly in the cytomembrane and cytoplasm, with markedly increased SBEM expression (≥4 points on staining intensity) observed in 34 of 105 breast cancer tissues (32.4%). Elevated expression of SBEM was found to be significantly associated with larger tumor size (P = 0.002), more frequent lymph node metastasis (P = 0.029), advanced tumor node metastasis stage (P = 0.005), reduced expression of the progesterone receptor (PR) (P = 0.002), and a higher Ki-67 index (P = 0.006). Survival analysis indicated that patients with elevated SBEM expression had worse overall survival (OS) (5-year OS rate: 50.5 vs 93.9% for high and low SBEM expression, respectively, P < 0.001) and disease-free survival (DFS) (5-year DFS rate: 52.8 vs 81.7% for high and low SBEM expression, respectively, P = 0.001) rates than those with low expression of SBEM. Univariate and multivariate Cox analyses demonstrated that elevated expression of SBEM (hazard ratio [HR] = 1.994, 95% confidence interval [CI]: 1.008-3.945, P = 0.047), tumor size (HR = 2.318, 95% CI: 1.071-5.017, P = 0.033), and PR status (HR = 0.195, 95% CI: 0.055-0.694, P = 0.012) were independent predictors of OS in breast cancer patients. Elevated expression of SBEM was associated with both aggressive tumor characteristics and poor survival, indicating its potential as a useful prognostic biomarker for breast cancer patients.

Updates on Triple-Negative Breast Cancer in Type 2 Diabetes Mellitus Patients: From Risk Factors to Diagnosis, Biomarkers and Therapy

Triple-negative breast cancer (TNBC) is usually the most malignant and aggressive mammary epithelial tumor characterized by the lack of expression for estrogen receptors and progesterone receptors, and the absence of epidermal growth factor receptor (HER)2 amplification. Corresponding to 15-20% of all breast cancers and well-known by its poor clinical outcome, this negative receptor expression deprives TNBC from targeted therapy and makes its management therapeutically challenging. Type 2 diabetes mellitus (T2DM) is the most common ageing metabolic disorder due to insulin deficiency or resistance resulting in hyperglycemia, hyperinsulinemia, and hyperlipidemia. Due to metabolic and hormonal imbalances, there are many interplays between both chronic disorders leading to increased risk of breast cancer, especially TNBC, diagnosed in T2DM patients. The purpose of this review is to provide up-to-date information related to epidemiology and clinicopathological features, risk factors, diagnosis, biomarkers, and current therapy/clinical trials for TNBC patients with T2DM compared to non-diabetic counterparts. Thus, in-depth investigation of the diabetic complications on TNBC onset, development, and progression and the discovery of biomarkers would improve TNBC management through early diagnosis, tailoring therapy for a better outcome of T2DM patients diagnosed with TNBC.

Evaluation of Lee-Carter model to breast cancer mortality prediction in China and Pakistan

Background

Precise breast cancer-related mortality forecasts are required for public health program and healthcare service planning. A number of stochastic model-based approaches for predicting mortality have been developed. The trends shown by mortality data from various diseases and countries are critical to the effectiveness of these models. This study illustrates the unconventional statistical method for estimating and predicting the mortality risk between the early-onset and screen-age/late-onset breast cancer population in China and Pakistan using the Lee-Carter model.

Methods

Longitudinal death data for female breast cancer from 1990 to 2019 obtained from the Global Burden of Disease study database were used to compare statistical approach between early-onset (age group, 25-49 years) and screen-age/late-onset (age group, 50-84 years) population. We evaluated the model performance both within (training period, 1990-2010) and outside (test period, 2011-2019) data forecast accuracy using the different error measures and graphical analysis. Finally, using the Lee-Carter model, we predicted the general index for the time period (2011 to 2030) and derived corresponding life expectancy at birth for the female breast cancer population using life tables.

Results

Study findings revealed that the Lee-Carter approach to predict breast cancer mortality rate outperformed in the screen-age/late-onset compared with that in the early-onset population in terms of goodness of fit and within and outside forecast accuracy check. Moreover, the trend in forecast error was decreasing gradually in the screen-age/late-onset compared with that in the early-onset breast cancer population in China and Pakistan. Furthermore, we observed that this approach had provided almost comparable results between the early-onset and screen-age/late-onset population in forecast accuracy for more varying mortality behavior over time like in Pakistan. Both the early-onset and screen-age/late-onset populations in Pakistan were expected to have an increase in breast cancer mortality by 2030. whereas, for China, it was expected to decrease in the early-onset population.

Conclusion

The Lee-Carter model can be used to estimate breast cancer mortality and so to project future life expectancy at birth, especially in the screen-age/late-onset population. As a result, it is suggested that this approach may be useful and convenient for predicting cancer-related mortality even when epidemiological and demographic disease data sets are limited. According to model predictions for breast cancer mortality, improved health facilities for disease diagnosis, control, and prevention are required to reduce the disease's future burden, particularly in less developed countries.

Prevalence, incidence, and mortality rates of breast cancer in Kazakhstan: data from the Unified National Electronic Health System, 2014-2019

Background

Although there are numerous sources of epidemiologic information on breast cancer in Kazakhstan, none of them have specifically examined the burden of this disease. Therefore, this article aims to provide an overview of the breast cancer prevalence, incidence, mortality, and distribution and changes over time in Kazakhstan based on nationwide large-scale healthcare data from the National Registry in order to encourage more research on the impact of various diseases at the regional and national levels.

Methods

The study cohort included all adult women older than 25 years who were diagnosed with breast cancer in any clinical setting of the Republic of Kazakhstan during the period of 2014-2019. The data were extracted from the Unified Nationwide Electronic Health System (UNEHS) to get an overview of descriptive statistics, incidence, prevalence, and mortality rate calculations and the Cox proportional hazards regression model. All survival functions and factors associated with mortality were tested for significance.

Results

The cohort population (n = 55,465) comprised subjects with the age at the diagnosis of breast cancer from 25 to 97 years, with a mean of 55.7 ± 12.0 years. The majority of the study population belonged to the age group 45-59 years, which is 44.8% of the cohort. The all-cause mortality rate of the cohort is 16%. The prevalence rate increased from 30.4 per 10,000 population in 2014 to 50.6 in 2019. The incidence rate varied from 4.5 per 10,000 population in 2015 to 7.3 in 2016. Mortality rates were stable and high in the senile age patients (75-89 years old). Breast cancer mortality was positively associated with women who had been diagnosed with diabetes, HR 1.2 (95% CI, 1.1-2.3), whereas it was negatively associated with arterial hypertension, HR 0.4 (95% CI, 0.4-0.5).

Conclusion

Overall, Kazakhstan is experiencing an increase in the incidence of breast cancer cases, but the mortality rate has started to decline. The switch to population mammography screening could reduce the breast cancer mortality rate. These findings should be utilized to help Kazakhstan determine what cancer control priorities should be utilized, including the need to implement efficient and affordable screening and prevention programs.

Lifestyle and Socioeconomic Transition and Health Consequences of Breast Cancer in the East Asia Region, From 1990 to 2019

Background

Due to its higher prevalence and heterogeneity, female breast cancer (BC) is the widest disease throughout the world. We sought to assess the epidemiological and sociodemographic transitions of BC and to identify the potential risk factors attributed to burden of BC in East Asia.

Methods

At the regional level of East Asia and at a national level of East Asian countries, we investigated the burden of the incidence of female BC, mortality, and disability-adjusted life years (DALYs) in 2019 and assessed the epidemiological, socioeconomic, and health-linked disparities in incidence of BC and mortality over a 30-year period. The changes in BC’s mortality and DALYs between 1990 and 2019, attributable to varying risk factors, were evaluated in different age groups.

Results

In 2019, the incidence of and mortality from and DALYs of BC were estimated to be 382,321 (95% UI: 303,308–477,173) incidence cases [age-standardized rate (ASR) of 35.69 per 100,000; 28.32–44.54], 98,162 (79,216–120,112) deaths (ASR of 9.12; 7.36–11.13), and 3,024,987 (2,477, 984–3,659,370) DALYs with an ASR of 282.15 (230.81–341.19) in 2019. It was also observed that out of four most representative locations of East Asia, two (China and Japan) showed more than 60% increase in age-standardized incidence rate between 1990 and 2019. While only Japan females showed a significant rise of 15.3% (95% UI: 2.3–28) in ASR of death and 12.6% (95% UI: 0.5–26.9) in ASR of DALYs between 1990 and 2019. Inclusively, 88 and 81% variations were explained in the incidence of BC and death due to change in sociodemographic index (SDI) in 2019, in East Asia. The highest positive percent changes in death and DALYs between 1990 and 2019 were attributable to high body mass index (BMI), high fasting plasma glucose (FPG), and alcohol consumption in East Asia.

Conclusion

The burden of death and disability from female BC is the result of multiple risk factors, mainly due to behavioral and metabolic risk factors. The increase of the incidence is related to the westernized lifestyle and diet habits and the improvement of screening and diagnosis techniques in the recent years, whereas the increase in DALYs is mainly attributed to high BMI, high FPG, alcohol use, and high diet in red meat.