The use of the Gail model, body mass index and SNPs to predict breast cancer among women with abnormal (BI-RADS 4) mammograms

Cellular and Exosomal MicroRNAs: Emerging Clinical Relevance as Targets for Breast Cancer Diagnosis and Prognosis

Breast cancer accounts for the highest cancer cases globally, with 12% of occurrences progressing to metastatic breast cancer with a low survival rate and limited effective early intervention strategies augmented by late diagnosis. Moreover, a low concentration of prognostic and predictive markers hinders disease monitoring. Circulating and exosomal microRNAs (miRNAs) have recently shown a considerable interplay in breast cancer, standing out as effective diagnostic and prognostic markers. The primary functions are as gene regulatory agents at the genetic and epigenetic levels. An array of dysregulated miRNAs stimulates cancer-promoting mechanisms, activating oncogenes and controlling tumor-suppressing genes and mechanisms. Exosomes are vastly studied extracellular vesicles, carrying, and transporting cargo, including noncoding RNAs with premier roles in oncogenesis. Translocation of miRNAs from the circulation to exosomes, with RNA-binding proteins in stress-induced conditions, has shown significant cooperation in function to promote breast cancer. This review examines cellular and exosomal miRNA biogenesis and loading, the clinical implications of their dysregulation, their function in diagnosis, prognosis, and prediction of breast cancer, and in regulating cancer signaling pathways. The influence of cellular and exosomal miRNAs presents clinical significance on breast cancer diagnosis, subtyping, staging, prediction, and disease monitoring during treatment, hence a potent marker for breast cancer.

Efficacy of Single-Agent Chemotherapy in Endocrine Therapy-Refractory Metastatic Invasive Lobular Carcinoma

BACKGROUND

Hormone receptor (HR)-positive, HER2-negative metastatic invasive lobular breast cancer (mILC) is distinct from invasive ductal cancer (IDC) in clinicopathologic and molecular characteristics, impacting its response to systemic therapy. While endocrine therapy (ET) combined with targeted therapies has shown efficacy in ET-sensitive mILC, data on chemotherapy in ET-refractory mILC remain limited. We investigated the efficacy of single-agent capecitabine (CAP) versus taxanes (TAX) in ET-refractory HR+ HER2-negative patients with mILC.

MATERIALS AND METHODS

Using data from the MD Anderson prospectively collected breast cancer database, we identified patients with HR+ HER2-negative mILC who received prior ET and first-time chemotherapy in the metastatic setting. We compared outcomes between 173 CAP-treated and 96 TAX-treated patients.

RESULTS

CAP-treated patients had significantly better median progression-free survival (PFS) than TAX-treated patients (8.8 vs 5.0 months, HR 0.63, P < .001). Overall survival (OS) did not differ significantly between the groups (42.7 vs 36.6 months for CAP vs TAX, respectively, HR 0.84, P = .241). Multivariate analyses for PFS and OS revealed better outcomes in subjects with fewer metastatic sites and those exposed to more lines of ET. Additionally, Black patients showed worse OS outcomes compared to White patients (HR 2.46; P = .001).

CONCLUSION

In ET-refractory HR+ HER2-negative mILC, single-agent CAP demonstrated superior PFS compared to TAX. Our findings highlight the potential benefit of CAP in this patient subset, warranting further investigation through prospective trials.

Exploring Tumor-Promoting Qualities of Cancer-Associated Fibroblasts and Innovative Drug Discovery Strategies With Emphasis on Thymoquinone

Tumor epithelial development and chemoresistance are highly promoted by the tumor microenvironment (TME), which is mostly made up of the cancer stroma. This is due to several causes. Cancer-associated fibroblasts (CAFs) stand out among them as being essential for the promotion of tumors. Understanding the fibroblastic population within a single tumor is made more challenging by the undeniable heterogeneity within it, even though particular stromal alterations are still up for debate. Numerous chemical signals released by tumors improve the connections between heterotypic fibroblasts and CAFs, promoting the spread of cancer. It becomes essential to have a thorough understanding of this complex microenvironment to effectively prevent solid tumor growth. Important new insights into the role of CAFs in the TME have been revealed by recent studies. The objective of this review is to carefully investigate the relationship between CAFs in tumors and plant secondary metabolites, with a focus on thymoquinone (TQ). The literature published between 2010 and 2023 was searched in PubMed and Google Scholar with keywords such as TQ, TME, cancer-associated fibroblasts, mechanism of action, and flavonoids. The results showed a wealth of data substantiating the activity of plant secondary metabolites, particularly TQ's involvement in blocking CAF operations. Scrutinized research also clarified the wider effect of flavonoids on pathways related to cancer. The present study highlights the complex dynamics of the TME and emphasizes the critical role of CAFs. It also examines the possible interventions provided by secondary metabolites found in plants, with TQ playing a vital role in regulating CAF function based on recent literature.

Validating a model for predicting breast cancer and nonbreast cancer death in women aged 55 years and older

BACKGROUND

To support mammography screening decision making, we developed a competing-risk model to estimate 5-year breast cancer risk and 10-year nonbreast cancer death for women aged 55 years and older using Nurses' Health Study data and examined model performance in the Black Women's Health Study (BWHS). Here, we examine model performance in predicting 10-year outcomes in the BWHS, Women's Health Initiative-Extension Study (WHI-ES), and Multiethnic Cohort (MEC) and compare model performance to existing breast cancer prediction models.

METHODS

We used competing-risk regression and Royston and Altman methods for validating survival models to calculate our model's calibration and discrimination (C index) in BWHS (n = 17 380), WHI-ES (n = 106 894), and MEC (n = 49 668). The Nurses' Health Study development cohort (n = 48 102) regression coefficients were applied to the validation cohorts. We compared our model's performance with breast cancer risk assessment tool (Gail) and International Breast Cancer Intervention Study (IBIS) models by computing breast cancer risk estimates and C statistics.

RESULTS

When predicting 10-year breast cancer risk, our model's C index was 0.569 in BWHS, 0.572 in WHI-ES, and 0.576 in MEC. The Gail model's C statistic was 0.554 in BWHS, 0.564 in WHI-ES, and 0.551 in MEC; IBIS's C statistic was 0.547 in BWHS, 0.552 in WHI-ES, and 0.562 in MEC. The Gail model underpredicted breast cancer risk in WHI-ES; IBIS underpredicted breast cancer risk in WHI-ES and in MEC but overpredicted breast cancer risk in BWHS. Our model calibrated well. Our model's C index for predicting 10-year nonbreast cancer death was 0.760 in WHI-ES and 0.763 in MEC.

CONCLUSIONS

Our competing-risk model performs as well as existing breast cancer prediction models in diverse cohorts and predicts nonbreast cancer death. We are developing a website to disseminate our model.

Effect of testosterone therapy on breast tissue composition and mammographic breast density in trans masculine individuals

Objective

Determine the association between TT and breast tissue composition and breast tissue density in trans masculine individuals (TMIs).

Design

This is a cross-sectional study.

Setting

TMIs (n=444) underwent chest-contouring surgeries to treat their gender dysphoria between 2013 and 2019 at an urban medical center.

Participants

Of the 444 TMIs, 425 had pathology images analyzed by our deep-learning algorithm to extract breast tissue composition. A subset of 42/444 TMIs had mammography prior to surgery; mammography files were available for 25/42 TMIs and analyzed using a breast density software, LIBRA.

Main Outcomes and Measures

The first outcome was the association of duration of TT and breast tissue composition assessed by pathologists (categories of lobular atrophy and stromal composition) or by our algorithm (% epithelium, % fibrous stroma, and % fat). The second outcome is the association of TT and breast density as assessed by a radiologist (categorical variable) or by LIBRA (percent density, absolute dense area, and absolute non-dense area).

Results

Length of TT was associated with increasing degrees of lobular atrophy ( p <0.001) but not fibrous content ( p =0.821) when assessed by the pathologists. Every six months of TT was associated with decreased amounts of both epithelium (exp(β)=0.97, 95% CI 0.95-0.98, adj p =0.005) and stroma (exp(β)=0.99, 95% CI 0.98-1.00, adj p =0.051), but not fat (exp(β)=1.01, 95%CI 0.98-1.05, p =0.394) in fully adjusted models. There was no association between TT and radiologist's breast density assessment ( p =0.575) or LIBRA measurements ( p >0.05).

Conclusions

TT decreases breast epithelium and fibrous stroma, thus potentially reducing the breast cancer risk of TMIs. Further studies are warranted to elucidate the effect of TT on breast density and breast cancer risk.

Summary Box

Very little is known about the effect of gender-affirming testosterone therapy on cancer risks, such as breast cancer.Epidemiological studies had different conclusions about the association between testosterone and breast cancer in cisgender women (positive association) and trans masculine individuals (inverse association).More laboratory-based research are needed to understand the effect of testosterone on breast cancer risk in the understudied trans masculine population.Our study provides quantitative histological evidence to support prior epidemiological reports that testosterone may reduce breast cancer risk in trans masculine individuals.

Breast cancer prediction model based on clinical and biochemical characteristics: clinical data from patients with benign and malignant breast tumors from a single center in South China

OBJECTIVE

Breast cancer is the most prevalent cancer and is second leading cause of death from malignancy among women worldwide. In addition to tumor factors, the host characteristics of tumors have been paid more and more attention by the medical community. This study aimed to develop a breast cancer prediction model for the Chinese population using clinical and biochemical characteristics.

METHODS

This is a retrospective study. From 2012 to 2021, we selected 19,751 patients with breast diseases from the Guangdong Hospital of Traditional Chinese Medicine, which included 5660 patients with breast cancer and 14,091 patients with benign breast diseases-75% of patients were randomly assigned to the training group and 25% to the test group using a total of 34 clinical and biochemical characteristics. Significant clinical signs were investigated, and logistic regression with recursive feature elimination (RFE) model was used to develop a prediction model for distinguishing benign from malignant breast diseases. The prediction model's accuracy, precision, sensitivity, specificity, and area under the ROC curve (AUC) were calculated.

RESULTS

Clinical statistics demonstrated that the prediction model comprised 19 clinical characteristics had statistical separability in both the training group and the test group, as well as good sensitivity and prediction.

CONCLUSIONS

This model based on biochemical parameters demonstrates a significant predictive effect for breast cancer and may be useful as a reference for invasive tissue biopsy in patients undergoing BI-RADS 3 and 4A breast imaging.

A Deep Learning Decision Support Tool to Improve Risk Stratification and Reduce Unnecessary Biopsies in BI-RADS 4 Mammograms

Purpose

To evaluate the performance of a biopsy decision support algorithmic model, the intelligent-augmented breast cancer risk calculator (iBRISK), on a multicenter patient dataset.

Materials and Methods

iBRISK was previously developed by applying deep learning to clinical risk factors and mammographic descriptors from 9700 patient records at the primary institution and validated using another 1078 patients. All patients were seen from March 2006 to December 2016. In this multicenter study, iBRISK was further assessed on an independent, retrospective dataset (January 2015-June 2019) from three major health care institutions in Texas, with Breast Imaging Reporting and Data System (BI-RADS) category 4 lesions. Data were dichotomized and trichotomized to measure precision in risk stratification and probability of malignancy (POM) estimation. iBRISK score was also evaluated as a continuous predictor of malignancy, and cost savings analysis was performed.

Results

The iBRISK model's accuracy was 89.5%, area under the receiver operating characteristic curve (AUC) was 0.93 (95% CI: 0.92, 0.95), sensitivity was 100%, and specificity was 81%. A total of 4209 women (median age, 56 years [IQR, 45-65 years]) were included in the multicenter dataset. Only two of 1228 patients (0.16%) in the "low" POM group had malignant lesions, while in the "high" POM group, the malignancy rate was 85.9%. iBRISK score as a continuous predictor of malignancy yielded an AUC of 0.97 (95% CI: 0.97, 0.98). Estimated potential cost savings were more than $420 million.

Conclusion

iBRISK demonstrated high sensitivity in the malignancy prediction of BI-RADS 4 lesions. iBRISK may safely obviate biopsies in up to 50% of patients in low or moderate POM groups and reduce biopsy-associated costs.Keywords: Mammography, Breast, Oncology, Biopsy/Needle Aspiration, Radiomics, Precision Mammography, AI-augmented Biopsy Decision Support Tool, Breast Cancer Risk Calculator, BI-RADS 4 Mammography Risk Stratification, Overbiopsy Reduction, Probability of Malignancy (POM) Assessment, Biopsy-based Positive Predictive Value (PPV3) Supplemental material is available for this article. Published under a CC BY 4.0 license.See also the commentary by McDonald and Conant in this issue.

Estrogens and the risk of breast cancer: A narrative review of literature

In female mammals, the development and regulation of the reproductive system and non-reproductive system are significantly influenced by estrogens (oestrogens). In addition, lipid metabolism is another physiological role of estrogens. Estrogens act through different types of receptors to introduce signals to the target cell by affecting many estrogen response elements. Breast cancer is considered mostly a hormone-dependent disease. Approximately 70% of breast cancers express progesterone receptors and/or estrogen receptors, and they are a good marker for cancer prognosis. This review will discuss estrogen metabolism and the interaction of estrogen metabolites with breast cancer. The carcinogenic role of estrogen is discussed in light of both conventional and atypical cancers susceptible to hormones, such as prostate, endometrial, and lung cancer, as we examine how estrogen contributes to the formation and activation of breast cancer. In addition, this review will discuss other factors that can be associated with estrogen-driven breast cancer.

Houttuynia cordata Thunb. Hexane fraction induces MDA-MB-231 cell apoptosis via caspases, ER stress, cell cycle arrest and attenuated Akt/ERK signaling

Houttuynia cordata Thunb. (HCT) is a perennial plant used in traditional Thai medicine for many centuries. This study aimed to investigate the antiproliferative effect of the hexane fraction, which has not been explored before. HCT ethanol extract (crude extract) was sequentially fractionated to obtain a hexane (H) fraction. GC-MS was used to determine the phytochemicals. The H fraction consisted of lipids, mainly α-linolenic acid and some terpenoids. MTT assay was used to determine the cytotoxic effects of H fraction in MCF-7, MDA-MB-231, NIH3T3 and PBMCs. The mode of cell death and cell cycle analysis were determined by flow cytometry. The mechanisms of cell death were defined by mitochondrial transmembrane potential (MTP) reduction and activation of caspase-3, -8 and -9. The expression levels of the Bcl-2 family, cell cycle-related, endoplasmic reticulum (ER) stress-associated proteins; and Akt/ERK signaling molecules were investigated by immunoblotting. The H fraction was toxic to MDA-MB-231 more than MCF-7 cells but not to NIH3T3 and PBMCs. The growth of MDA-MB-231 cells was inhibited through apoptosis. MTP was disrupted whereas caspase-3, -8 and -9 were activated. The expression of pro-apoptotic Bax and Bak was upregulated, while Bid and anti-apoptotic Bcl-xL proteins were downregulated. Cyclin D1 and CDK4 levels were downregulated. The cell cycle was arrested at G1. Moreover, GRP78 and CHOP elevation indicated ER stress-mediated pathway. The expression ratio of pAkt/Akt and pERK/ERK were reduced. Taken together, the molecular mechanisms of MDA-MB-231 cell apoptosis were via intrinsic/extrinsic pathways, cell cycle arrest, ER stress and abrogation of Akt/ERK survival pathways. According to the most current research, the H fraction may be used as an adjuvant in the BC treatment; however, before the anticancer strategy can be applied to patients, it is important to determine each active compound's effects in cell lines and in vivo when compared with a combined mixture.

Effects of the Co-Administration of Morphine and Lipopolysaccharide on Toll-Like Receptor-4/Nuclear Factor Kappa β Signaling Pathway of MDA-MB-231 Breast Cancer Cells

Background

The Toll-like receptor 4 (TLR4) gene promotes migration in adenocarcinoma cells. Morphine is an agonist for TLR4 that has a dual role in cancer development. The promoter or inhibitor role of morphine in cancer progression remains controversial. This study aims to evaluate the effects of morphine on the TLR4, myeloid differentiation primary response protein 88-dependent (MyD88), and nuclear factor-kappa B (NF-κB) expressions in the human MDA-MB-231 breast cancer cell line.

Materials and Methods

The cells were examined after 24 hours of incubation with morphine using the Boyden chamber system. TLR4, MyD88, and NF-κB mRNA expressions were assessed using quantitative real-time polymerase chain reaction (RT-PCR). The concentration of interleukin-2 beta was also measured using the ELISA assay.

Results

According to the findings, three doses of morphine (0.25, 1.25, and 0.025 μM) increased the expression of the TLR4 and NF-κB genes, whereas no significant change was observed in the mRNA expression of MyD88. Furthermore, treatment with morphine and lipopolysaccharide (LPS) significantly decreased the expression of TLR4, MyD88, and NF-κB. However, no significant change was observed in interleukin 2 beta concentration.

Conclusions

These findings confirmed the excitatory effects of morphine on TRL4 expression and the MYD88 signaling pathway in vitro.

Deep learning based registration of serial whole-slide histopathology images in different stains

For routine pathology diagnosis and imaging-based biomedical research, Whole-slide image (WSI) analyses have been largely limited to a 2D tissue image space. For a more definitive tissue representation to support fine-resolution spatial and integrative analyses, it is critical to extend such tissue-based investigations to a 3D tissue space with spatially aligned serial tissue WSIs in different stains, such as Hematoxylin and Eosin (H&E) and Immunohistochemistry (IHC) biomarkers. However, such WSI registration is technically challenged by the overwhelming image scale, the complex histology structure change, and the significant difference in tissue appearances in different stains. The goal of this study is to register serial sections from multi-stain histopathology whole-slide image blocks. We propose a novel translation-based deep learning registration network CGNReg that spatially aligns serial WSIs stained in H&E and by IHC biomarkers without prior deformation information for the model training. First, synthetic IHC images are produced from H&E slides through a robust image synthesis algorithm. Next, the synthetic and the real IHC images are registered through a Fully Convolutional Network with multi-scaled deformable vector fields and a joint loss optimization. We perform the registration at the full image resolution, retaining the tissue details in the results. Evaluated with a dataset of 76 breast cancer patients with 1 H&E and 2 IHC serial WSIs for each patient, CGNReg presents promising performance as compared with multiple state-of-the-art systems in our evaluation. Our results suggest that CGNReg can produce promising registration results with serial WSIs in different stains, enabling integrative 3D tissue-based biomedical investigations.

Advanced treatment strategies in breast cancer: A comprehensive mechanistic review

Breast cancer is a destructive lump type that affects women globally. Despite the availability of multi-directional therapeutic strategies, advanced stages of breast cancer are difficult to treat and impose major healthcare burdens. This situation reinforces the need to identify new potential therapeutic compounds with better clinical features. In this context, different treatment methods were included such as Endocrine therapy, chemotherapy, Radiation therapy, antimicrobial peptide-dependent growth inhibitor, liposome-based drug delivery, antibiotics used as a co-medication, photothermal, immunotherapy, and nano drug delivery systems such as Bombyx mori natural protein sericin and its mediated nanoparticles are promising biomedical agents. They have been tested as an anticancer agent against various malignancies in pre-clinical settings. The biocompatible and restricted breakdown properties of silk sericin and sericin-conjugated nanoparticles made them perfect contenders for a nanoscale drug-delivery system.

Rapid identification of breast cancer subtypes using micro-FTIR and machine learning methods

Breast cancer (BC) molecular subtypes diagnosis involves improving clinical uptake by Fourier transform infrared (FTIR) spectroscopic imaging, which is a non-destructive and powerful technique, enabling label free extraction of biochemical information towards prognostic stratification and evaluation of cell functionality. However, methods of measurements of samples demand a long time to achieve high quality images, making its clinical use impractical because of the data acquisition speed, poor signal to noise ratio, and deficiency of optimized computational framework procedures. To address those challenges, machine learning (ML) tools can facilitate obtaining an accurate classification of BC subtypes with high actionability and accuracy. Here, we propose a ML-algorithm-based method to distinguish computationally BC cell lines. The method is developed by coupling the K-neighbors classifier (KNN) with neighborhood components analysis (NCA), and hence, the NCA-KNN method enables to identify BC subtypes without increasing model size as well as adding additional computational parameters. By incorporating FTIR imaging data, we show that classification accuracy, specificity, and sensitivity improve, respectively, 97.5%, 96.3%, and 98.2%, even at very low co-added scans and short acquisition times. Moreover, a clear distinctive accuracy (up to 9 %) difference of our proposed method (NCA-KNN) was obtained in comparison with the second best supervised support vector machine model. Our results suggest a key diagnostic NCA-KNN method for BC subtypes classification that may translate to advancement of its consolidation in subtype-associated therapeutics.

Cancer situation in China: what does the China cancer map indicate from the first national death survey to the latest cancer registration?

BACKGROUND

Over the past four decades, the Chinese government has conducted three surveys on the distribution of causes of death and built cancer registration. In order to shine a new light on better cancer prevention strategies in China, we evaluated the profile of cancer mortality over the forty years and analyzed the policies that have been implemented.

METHODS

We described spatial and temporal changes in both cancer mortality and the ranking of major cancer types in China based on the data collected from three national surveys during 1973-1975, 1990-1992, 2004-2005, and the latest cancer registration data published by National Central Cancer Registry of China. The mortality data were compared after conversion to age-standardized mortality rates based on the world standard population (Segi's population). The geographical distribution characteristics were explored by marking hot spots of different cancers on the map of China.

RESULTS

From 1973 to 2016, China witnessed an evident decrease in mortality rate of stomach, esophageal, and cervical cancer, while a gradual increase was recorded in lung, colorectal, and female breast cancer. A slight decrease of mortality rate has been observed in liver cancer since 2004. Lung and liver cancer, however, have become the top two leading causes of cancer death for the last twenty years. From the three national surveys, similar profiles of leading causes of cancer death were observed among both urban and rural areas. Lower mortality rates from esophageal and stomach cancer, however, have been demonstrated in urban than in rural areas. Rural areas had similar mortality rates of the five leading causes of cancer death with the small urban areas in 1973-1975. Additionally, rural areas in 2016 also had approximate mortality rates of the five leading causes with urban areas in 2004-2005. Moreover, stomach, esophageal, and liver cancer showed specific geographical distributions. Although mortality rates have decreased at most of the hotspots of these cancers, they were still higher than the national average levels during the same time periods.

CONCLUSIONS

Building up a strong primary public health system especially among rural areas may be one critical step to reduce cancer burden in China.

Sensory nerves enhance triple-negative breast cancer invasion and metastasis via the axon guidance molecule PlexinB3

In breast cancer, nerve presence has been correlated with more invasive disease and worse prognosis, yet the mechanisms by which different types of peripheral nerves drive tumor progression remain poorly understood. In this study, we identified sensory nerves as more abundant in human triple-negative breast cancer (TNBC) tumors. Co-injection of sensory neurons isolated from the dorsal root ganglia (DRG) of adult female mice with human TNBC cells in immunocompromised mice increased the number of lung metastases. Direct in vitro co-culture of human TNBC cells with the dorsal root ganglia (DRG) of adult female mice revealed that TNBC cells adhere to sensory neuron fibers leading to an increase in migration speed. Species-specific RNA sequencing revealed that co-culture of TNBC cells with sensory nerves upregulates the expression of genes associated with cell migration and adhesion in cancer cells. We demonstrated that lack of the semaphorin receptor PlexinB3 in cancer cells attenuate their adhesion to and migration on sensory nerves. Together, our results identify a mechanism by which nerves contribute to breast cancer migration and metastasis by inducing a shift in TNBC cell gene expression and support the rationale for disrupting neuron-cancer cell interactions to target metastasis.

Classification of breast cancer using a manta-ray foraging optimized transfer learning framework

Due to its high prevalence and wide dissemination, breast cancer is a particularly dangerous disease. Breast cancer survival chances can be improved by early detection and diagnosis. For medical image analyzers, diagnosing is tough, time-consuming, routine, and repetitive. Medical image analysis could be a useful method for detecting such a disease. Recently, artificial intelligence technology has been utilized to help radiologists identify breast cancer more rapidly and reliably. Convolutional neural networks, among other technologies, are promising medical image recognition and classification tools. This study proposes a framework for automatic and reliable breast cancer classification based on histological and ultrasound data. The system is built on CNN and employs transfer learning technology and metaheuristic optimization. The Manta Ray Foraging Optimization (MRFO) approach is deployed to improve the framework's adaptability. Using the Breast Cancer Dataset (two classes) and the Breast Ultrasound Dataset (three-classes), eight modern pre-trained CNN architectures are examined to apply the transfer learning technique. The framework uses MRFO to improve the performance of CNN architectures by optimizing their hyperparameters. Extensive experiments have recorded performance parameters, including accuracy, AUC, precision, F1-score, sensitivity, dice, recall, IoU, and cosine similarity. The proposed framework scored 97.73% on histopathological data and 99.01% on ultrasound data in terms of accuracy. The experimental results show that the proposed framework is superior to other state-of-the-art approaches in the literature review.

Prediction of Breast Cancer using Machine Learning Approaches

BACKGROUND

Breast cancer is considered one of the most common cancers in women caused by various clinical, lifestyle, social, and economic factors. Machine learning has the potential to predict breast cancer based on features hidden in data.

OBJECTIVE

This study aimed to predict breast cancer using different machine-learning approaches applying demographic, laboratory, and mammographic data.

MATERIAL AND METHODS

In this analytical study, the database, including 5,178 independent records, 25% of which belonged to breast cancer patients with 24 attributes in each record was obtained from Motamed cancer institute (ACECR), Tehran, Iran. The database contained 5,178 independent records, 25% of which belonged to breast cancer patients containing 24 attributes in each record. The random forest (RF), neural network (MLP), gradient boosting trees (GBT), and genetic algorithms (GA) were used in this study. Models were initially trained with demographic and laboratory features (20 features). The models were then trained with all demographic, laboratory, and mammographic features (24 features) to measure the effectiveness of mammography features in predicting breast cancer.

RESULTS

RF presented higher performance compared to other techniques (accuracy 80%, sensitivity 95%, specificity 80%, and the area under the curve (AUC) 0.56). Gradient boosting (AUC=0.59) showed a stronger performance compared to the neural network.

CONCLUSION

Combining multiple risk factors in modeling for breast cancer prediction could help the early diagnosis of the disease with necessary care plans. Collection, storage, and management of different data and intelligent systems based on multiple factors for predicting breast cancer are effective in disease management.

Targeting a Tumor-Specific Epitope on Podocalyxin Increases Survival in Human Tumor Preclinical Models

Podocalyxin (Podxl) is a CD34-related cell surface sialomucin that is normally highly expressed by adult vascular endothelia and kidney podocytes where it plays a key role in blocking adhesion. Importantly, it is also frequently upregulated on a wide array of human tumors and its expression often correlates with poor prognosis. We previously showed that, in xenograft studies, Podxl plays a key role in metastatic disease by making tumor initiating cells more mobile and invasive. Recently, we developed a novel antibody, PODO447, which shows exquisite specificity for a tumor-restricted glycoform of Podxl but does not react with Podxl expressed by normal adult tissue. Here we utilized an array of glycosylation defective cell lines to further define the PODO447 reactive epitope and reveal it as an O-linked core 1 glycan presented in the context of the Podxl peptide backbone. Further, we show that when coupled to monomethyl auristatin E (MMAE) toxic payload, PODO447 functions as a highly specific and effective antibody drug conjugate (ADC) in killing ovarian, pancreatic, glioblastoma and leukemia cell lines in vitro. Finally, we demonstrate PODO447-ADCs are highly effective in targeting human pancreatic and ovarian tumors in xenografted NSG and Nude mouse models. These data reveal PODO447-ADCs as exquisitely tumor-specific and highly efficacious immunotherapeutic reagents for the targeting of human tumors. Thus, PODO447 exhibits the appropriate characteristics for further development as a targeted clinical immunotherapy.

miR-647 inhibits hepatocellular carcinoma cell progression by targeting protein tyrosine phosphatase receptor type F

Hepatocellular carcinoma (HCC) is a kind of malignant tumor derived from hepatocytes and hepatobiliary cells, and its occurrence is prevalent worldwide. Although medical technology is developing rapidly, the therapeutic efficacy of HCC is still poor. Emerging evidence manifests that microRNAs (miRNAs) play a crucial role in various cancers and have been regarded as cancer suppressor gene. However, the regulatory mechanisms mediated by miR-647 involved in HCC remain unclear. Hence, to clarify the regulatory mechanisms mediated by miR-647 in HCC, we studied the independent effects of miR-647 and explored protein tyrosine phosphatase receptor type F (PTPRF) in the constructed HCC cell line (HCV-huh7.5). Thereafter, we used dual-luciferase gene reporting and Western blot to investigate the relationship between PTPRF and miR-647. Furthermore, we studied the mechanism of miR-647 on PTPRF in HCV-huh7.5. We found that miR-647 could not only promote the proliferation and invasion of HCV-huh7.5 cells but also facilitate cell migration, while PTPRF has the opposite effect. Besides, the results of cell function experiment implied that the overexpression of miR-647 or inhibition of PTPFRF remarkably influenced the Erk signaling pathway, which could regulate cell proliferation, migration, and invasion. In addition, the dual luciferase reporting identified PTPRF as a direct target of miR-647. We further demonstrated that miR-647 inhibitor or PTPRF knockdown administration boosted HCV-huh7.5 cell proliferation, migration, and invasion by targeting PTPRF.These findings provided clues for the mechanism of miR-647 in promoting the biology of HCV-huh7.5 cells by inhibiting the expression level of PTPRF.

Harnessing SeN to develop novel fluorescent probes for visualizing the variation of endogenous hypobromous acid (HOBr) during the administration of an immunotherapeutic agent

By means of the formation of SeN, the ABT-Se and NDI-Se were developed to detect and visualize endogenous hypobromous acid (HOBr) in live cells. Specifically, the upregulation of HOBr was monitored by NDI-Se during the administration of an immunotherapeutic agent.

Evidence and assessment of parenchymal patterns of ultrasonography for breast cancer detection among Chinese women: a cross-sectional study

Background

Screening of breast cancer in asymptomatic women is important to evaluate for early diagnosis. In China ultrasound is a more frequently used method than mammography for the detection of breast cancer. The objectives of the study were to provide evidence and assessment of parenchymal patterns of ultrasonography for breast cancer detection among Chinese women.

Methods

Breast ultrasound examinations including the parenchymatous pattern of cytopathological confirmed breast cancer (n = 541) and age-matched cytopathological not confirmed breast cancer (n = 849) women were retrospectively reviewed by seven sonographer physicians. According to compositions of ducts, the thickness of the breast, diameter of ducts, fat lobules, and fibro glandular tissues, the breast parenchymatous pattern was categorized into heterogeneous (high percentage of fatty tissues), ductal (the inner diameters of ducts > 50% of the thick mass of the breast), mixed (the inner diameters of ducts was 50% of the thick mass of the breast), and fibrous categories (a dense classification of the breast).

Results

Heterogeneous (p < 0.0001, OR = 3.972) and fibrous categories (p < 0.0001, OR = 2.702) were higher among women who have cytopathological confirmed breast cancer than those who have not cytopathological confirmed breast cancer. The heterogeneous category was high-risk ultrasonographic examination category followed by the fibrous category. Agreements between sonographer physicians for categories of ultrasonic examinations were fair to good (Cohen’s k = 0.591).

Conclusions

Breast cancer risk in Chinese asymptomatic women differ according to the ultrasonographic breast parenchymal pattern.

Level of Evidence: III.

Technical efficacy stage: 2.

Reducing False-Positive Screening MRI Rate in Women with Extremely Dense Breasts Using Prediction Models Based on Data from the DENSE Trial

Background High breast density increases breast cancer risk and lowers mammographic sensitivity. Supplemental MRI screening improves cancer detection but increases the number of false-positive screenings. Thus, methods to distinguish true-positive MRI screening results from false-positive ones are needed. Purpose To build prediction models based on clinical characteristics and MRI findings to reduce the rate of false-positive screening MRI findings in women with extremely dense breasts. Materials and Methods Clinical characteristics and MRI findings in Dutch breast cancer screening participants (age range, 50-75 years) with positive first-round MRI screening results (Breast Imaging Reporting and Data System 3, 4, or 5) after a normal screening mammography with extremely dense breasts (Volpara density category 4) were prospectively collected within the randomized controlled Dense Tissue and Early Breast Neoplasm Screening (DENSE) trial from December 2011 through November 2015. In this secondary analysis, prediction models were built using multivariable logistic regression analysis to distinguish true-positive MRI screening findings from false-positive ones. Results Among 454 women (median age, 52 years; interquartile range, 50-57 years) with a positive MRI result in a first supplemental MRI screening round, 79 were diagnosed with breast cancer (true-positive findings), and 375 had false-positive MRI results. The full prediction model (area under the receiver operating characteristics curve [AUC], 0.88; 95% CI: 0.84, 0.92), based on all collected clinical characteristics and MRI findings, could have prevented 45.5% (95% CI: 39.6, 51.5) of false-positive recalls and 21.3% (95% CI: 15.7, 28.3) of benign biopsies without missing any cancers. The model solely based on readily available MRI findings and age had a comparable performance (AUC, 0.84; 95% CI: 0.79, 0.88; P = .15) and could have prevented 35.5% (95% CI: 30.4, 41.1) of false-positive MRI screening results and 13.0% (95% CI: 8.8, 18.6) of benign biopsies. Conclusion Prediction models based on clinical characteristics and MRI findings may be useful to reduce the false-positive first-round screening MRI rate and benign biopsy rate in women with extremely dense breasts. Clinical trial registration no. NCT01315015 © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Imbriaco in this issue.

Automated Breast Ultrasound: Technical Aspects, Impact on Breast Screening, and Future Perspectives

Purpose of Review

Automated breast ultrasound (ABUS) is a three-dimensional imaging technique, used as a supplemental screening tool in women with dense breasts. This review considers the technical aspects, pitfalls, and the use of ABUS in screening and clinical practice, together with new developments and future perspectives.

Recent Findings

ABUS has been approved in the USA and Europe as a screening tool for asymptomatic women with dense breasts in addition to mammography. Supplemental US screening has high sensitivity for cancer detection, especially early-stage invasive cancers, and reduces the frequency of interval cancers. ABUS has similar diagnostic performance to handheld ultrasound (HHUS) and is designed to overcome the drawbacks of operator dependence and poor reproducibility. Concerns with ABUS, like HHUS, include relatively high recall rates and lengthy reading time when compared to mammography. ABUS is a new technique with unique features; therefore, adequate training is required to improve detection and reduce false positives. Computer-aided detection may reduce reading times and improve cancer detection. Other potential applications of ABUS include local staging, treatment response evaluation, breast density assessment, and integration of radiomics.

Summary

ABUS provides an efficient, reproducible, and comprehensive supplemental imaging technique in breast screening. Developments with computer-aided detection may improve the sensitivity and specificity as well as radiologist confidence and reduce reading times, making this modality acceptable in large volume screening centers.

Discriminatory Accuracy of the Gail Model for Breast Cancer Risk Assessment among Iranian Women

Background:

The Gail model is the most well-known tool for breast cancer risk assessment worldwide. Although it was validated in various Western populations, inconsistent results were reported from Asian populations. We used data from a large case-control study and evaluated the discriminatory accuracy of the Gail model for breast cancer risk assessment among the Iranian female population.

Methods:

We used data from 942 breast cancer patients and 975 healthy controls at the Cancer Institute of Iran, Tehran, Iran, in 2016. We refitted the Gail model to our case-control data (the IR-Gail model). We compared the discriminatory power of the IR-Gail with the original Gail model, using ROC curve analyses and estimation of the area under the ROC curve (AUC).

Results:

Except for the history of biopsies that showed an extremely high relative risk (OR=9.1), the observed ORs were similar to the estimates observed in Gail’s study. Incidence rates of breast cancer were extremely lower in Iran than in the USA, leading to a lower average absolute risk among the Iranian population (2.78, ±SD 2.45). The AUC was significantly improved after refitting the model, but it remained modest (0.636 vs. 0.627, ΔAUC = 0.009, bootstrapped P=0.008). We reported that the cut-point of 1.67 suggested in the Gail study did not discriminate between breast cancer patients and controls among the Iranian female population.

Conclusion:

Although the coefficients from the local study improved the discriminatory accuracy of the model, it remained modest. Cohort studies are warranted to evaluate the validity of the model for Iranian women.

Prediction for Breast Cancer in BI-RADS Category 4 Lesion Categorized by Age and Breast Composition of Women in Songklanagarind Hospital

Background:

Older age and dense breast are the important risk factors for breast cancer. The ACR BI-RADS lexicon 5^th edition does not mention how patient age and breast density may affect the category assessment. The aim of this study was to investigate whether patient age and breast density influence the positive predictive value (PPV) of mammographic and ultrasonographic findings categorized as BI-RADS category 4 and subcategories 4a, 4b, and 4c among female patients.

Materials and Methods:

A retrospective study was conducted in Songklanagarind Hospital between January 1, 2016 and December 31, 2017 in female patients older than 18 years who had breast lesions categorized as BI-RADS category 4 and subcategories 4a, 4b, 4c. A total of 961 breast lesions consisted of 772 (80.33%) benign lesions and 189 (19.67%) malignant lesions. Categorization was done in each lesion based on age ranges of ≤35 years, >35 to 60 years, and >60 years and breast density according to mammographic breast composition. The PPV for each BI-RADS category was calculated based on the pathological diagnoses and were compared using the chi-square test.

Results:

The overall PPV in each subcategory was in the reference range. The PPV increased with increasing age: 4% vs. 22.63% vs. 36.67% for category 4 (p-value=0.01); 0% vs. 5.81% vs. 6.88% for subcategory 4a (p-value=0.002); 6.67% vs. 26.62% vs. 51.35% for subcategory 4b (p-value=0.001); and 33.33% vs. 76.92% vs. 81.82% for subcategory 4c (p-value=0.02). An association was not found between PPV and breast density.

Conclusion:

A significantly positive association was found between PPV and age in patients in BI-RADS subcategories 4a, 4b, and 4c. This study could not determine that mammographic breast composition according to the ACR BI-RADS 5th edition was associated with PPV due to improper sample distribution.

Assessment of the risk of developing breast cancer using the Gail model in Asian females: A systematic review

Introduction

Currently, the Breast Cancer Risk Assessment Tool (BCRAT), also known as the Gail model (GM) has been widely recognized and adapted for to study disparity in racial and ethnic groups in America including Asian and Pacific Islander American females. However, its applicability outside America remains uncertain due to diversity in epidemiology and risk factors of breast cancer in populations especially in Asian females. We sought to evaluate the performance of the GM to predict breast cancer risk in Asian countries.

Material and methods

This study identified articles published from 2010 by searching PubMed, MEDLINE, Scopus, Web of Science, Google Scholar and gray literature. The initial search terms were breast cancer, mammary, carcinoma, tumor, neoplasm, risk assessment tool, BCRAT, breast cancer prediction, Gail model, Asia, and Asian.

Results

The search yielded 20 articles, with 7 articles addressing the AUC and/or the expected (E) to observed (O) ratio of predicted breast cancer risk, representing the accuracy of the GM in the Asian population. One publication reported the sensitivity and specificity but no AUC. None of the studies were accepted as the standard for reporting prognostic models. Several studies reported good prognostic testing and likely developed a new model modifying the items in the instrument.

Conclusion

The results are not strong enough to develop breast cancer risk in the setting of Asian countries. Involving the breast cancer risk of the Asian population in developing a prognostic model with good statistical understanding is particularly important and can reduce flawed or biased models. Identifying the best methods to achieve well-suited prognostic models in the Asian population should be a priority.

AUNet: attention-guided dense-upsampling networks for breast mass segmentation in whole mammograms

Mammography is one of the most commonly applied tools for early breast cancer screening. Automatic segmentation of breast masses in mammograms is essential but challenging due to the low signal-to-noise ratio and the wide variety of mass shapes and sizes. Existing methods deal with these challenges mainly by extracting mass-centered image patches manually or automatically. However, manual patch extraction is time-consuming and automatic patch extraction brings errors that could not be compensated in the following segmentation step. In this study, we propose a novel attention-guided dense-upsampling network (AUNet) for accurate breast mass segmentation in whole mammograms directly. In AUNet, we employ an asymmetrical encoder-decoder structure and propose an effective upsampling block, attention-guided dense-upsampling block (AU block). Especially, the AU block is designed to have three merits. Firstly, it compensates the information loss of bilinear upsampling by dense upsampling. Secondly, it designs a more effective method to fuse high- and low-level features. Thirdly, it includes a channel-attention function to highlight rich-information channels. We evaluated the proposed method on two publicly available datasets, CBIS-DDSM and INbreast. Compared to three state-of-the-art fully convolutional networks, AUNet achieved the best performances with an average Dice similarity coefficient of 81.8% for CBIS-DDSM and 79.1% for INbreast.

Improving breast cancer risk prediction by using demographic risk factors, abnormality features on mammograms and genetic variants

The predictive capability of combining demographic risk factors, germline genetic variants, and mammogram abnormality features for breast cancer risk prediction is poorly understood. We evaluated the predictive performance of combinations of demographic risk factors, high risk single nucleotide polymorphisms (SNPs), and mammography features for women recommended for breast biopsy in a retrospective case-control study (n = 768) with four logistic regression models. The AUC of the baseline demographic features model was 0.580. Both genetic variants and mammography abnormality features augmented the performance of the baseline model: demographics + SNP (AUC =0.668), demographics + mammography (AUC =0.702). Finally, we found that the demographics + SNP + mammography model (AUC = 0.753) had the greatest predictive power, with a significant performance improvement over the other models. The combination of demographic risk factors, genetic variants and imaging features improves breast cancer risk prediction over prior methods utilizing only a subset of these features.

Role of Clinical and Imaging Risk Factors in Predicting Breast Cancer Diagnosis Among BI-RADS 4 Cases

PURPOSE

To analyze women with suspicious findings (assessed as Breast Imaging Reporting and Data System [BI-RADS] 4), examining the value of clinical and imaging predictors in predicting cancer diagnosis.

PATIENTS AND METHODS

A set of 2138 examinations (1978 women) given a BI-RADS 4 with matching pathology results were analyzed. Predictors such as patient demographics, clinical risk factors, and imaging-derived features such as BI-RADS assessment and qualitative breast density were considered. Independent predictors of breast cancer were determined by univariate analysis and multivariate logistic regression.

RESULTS

In univariate analysis, age, race, body mass index, age at first live birth, BI-RADS assessment, qualitative breast density, and risk triggers were found to be independent predictors. In multivariate analysis, age, BI-RADS score, breast density, race, presence of a lump, and number of risk triggers were the most predictive. An integrative logistic regression model achieved a performance of 0.84 cross-validated area under the curve. No variable was a constant independent predictor when stratifying the population on the basis of the BI-RADS score.

CONCLUSION

While BI-RADS assessment remains the strongest predictor of breast cancer, the inclusion of clinical risk factors such as age, breast density, presence of a lump, and number of risk triggers derived from guidelines improves the specificity of identifying individuals with imaging descriptors associated with BI-RADS 4A and 4B that are more likely to be diagnosed with breast cancer.

Assessment of performance of the Gail model for predicting breast cancer risk: a systematic review and meta-analysis with trial sequential analysis

BACKGROUND

The Gail model has been widely used and validated with conflicting results. The current study aims to evaluate the performance of different versions of the Gail model by means of systematic review and meta-analysis with trial sequential analysis (TSA).

METHODS

Three systematic review and meta-analyses were conducted. Pooled expected-to-observed (E/O) ratio and pooled area under the curve (AUC) were calculated using the DerSimonian and Laird random-effects model. Pooled sensitivity, specificity and diagnostic odds ratio were evaluated by bivariate mixed-effects model. TSA was also conducted to determine whether the evidence was sufficient and conclusive.

RESULTS

Gail model 1 accurately predicted breast cancer risk in American women (pooled E/O = 1.03; 95% CI 0.76-1.40). The pooled E/O ratios of Caucasian-American Gail model 2 in American, European and Asian women were 0.98 (95% CI 0.91-1.06), 1.07 (95% CI 0.66-1.74) and 2.29 (95% CI 1.95-2.68), respectively. Additionally, Asian-American Gail model 2 overestimated the risk for Asian women about two times (pooled E/O = 1.82; 95% CI 1.31-2.51). TSA showed that evidence in Asian women was sufficient; nonetheless, the results in American and European women need further verification. The pooled AUCs for Gail model 1 in American and European women and Asian females were 0.55 (95% CI 0.53-0.56) and 0.75 (95% CI 0.63-0.88), respectively, and the pooled AUCs of Caucasian-American Gail model 2 for American, Asian and European females were 0.61 (95% CI 0.59-0.63), 0.55 (95% CI 0.52-0.58) and 0.58 (95% CI 0.55-0.62), respectively. The pooled sensitivity, specificity and diagnostic odds ratio of Gail model 1 were 0.63 (95% CI 0.27-0.89), 0.91 (95% CI 0.87-0.94) and 17.38 (95% CI 2.66-113.70), respectively, and the corresponding indexes of Gail model 2 were 0.35 (95% CI 0.17-0.59), 0.86 (95% CI 0.76-0.92) and 3.38 (95% CI 1.40-8.17), respectively.

CONCLUSIONS

The Gail model was more accurate in predicting the incidence of breast cancer in American and European females, while far less useful for individual-level risk prediction. Moreover, the Gail model may overestimate the risk in Asian women and the results were further validated by TSA, which is an addition to the three previous systematic review and meta-analyses.

TRIAL REGISTRATION

PROSPERO CRD42016047215 .

Exosomal microRNAs in giant panda (Ailuropoda melanoleuca) breast milk: potential maternal regulators for the development of newborn cubs

The physiological role of miRNAs is widely understood to include fine-tuning the post-transcriptional regulation of a wide array of biological processes. Extensive studies have indicated that exosomal miRNAs in the bodily fluids of various organisms can be transferred between living cells for the delivery of gene silencing signals. Here, we illustrated the expression characteristics of exosomal miRNAs in giant panda breast milk during distinct lactation periods and highlighted the enrichment of immune- and development-related endogenous miRNAs in colostral and mature giant panda milk. These miRNAs are stable, even under certain harsh conditions, via the protection of extracellular vesicles. These findings indicate that breast milk may facilitate the dietary intake of maternal miRNAs by infants for the regulation of postnatal development. We also detected exogenous plant miRNAs from the primary food source of the giant panda (bamboo) in the exosomes of giant panda breast milk that were associated with regulatory roles in basic metabolism and neuron development. This result suggested that dietary plant miRNAs are absorbed by host cells and subsequently secreted into bodily fluids as potential cross-kingdom regulators. In conclusion, exosomal miRNAs in giant panda breast milk may be crucial maternal regulators for the development of intrinsic 'slink' newborn cubs.

Our genes, our selves: hereditary breast cancer and biological citizenship in Norway

In this paper we explore the rise of 'the breast cancer gene' as a field of medical, cultural and personal knowledge. We address its significance in the Norwegian public health care system in relation to so-called biological citizenship in this particular national context. One of our main findings is that, despite its claims as a measure for health and disease prevention, gaining access to medical knowledge of BRCA 1/2 breast cancer gene mutations can also produce severe instability in the individuals and families affected. That is, although gene testing provides modern subjects with an opportunity to foresee their biological destiny and thereby become patients in waiting, it undoubtedly also comes with difficult existential dilemmas and choices, with implications that resonate beyond the individual and into different family and love relations. By elaborating on this finding we address the question of whether the empowerment slogan, which continues to be advocated through various health, BRCA and breast cancer discourses, reinforces a naïve or an idealized notion of the actively responsible patient: resourceful enough to seek out medical expertise and gain sufficient knowledge, on which to base informed decisions, thereby reducing the future risk of developing disease. In contrast to this ideal, our Norwegian informants tell a different story, in which there is no apparent heroic mastery of genetic fates, but rather a pragmatic attitude to dealing with a dire situation over which they have little control, despite having complied with medical advice through national guidelines and follow-up procedures for BRCA 1/2 carriers. In conclusion we claim that the sense of safety that gene testing and its associated medical solutions allegedly promise to provide proved illusory. Although BRCA-testing offers the potential for protection from adverse DNA-heritage, administered through possibilities for self-monitoring and self-management of the body, the feeling of 'being in good health' has hardly been reinforced by the emergence of gene technology.

Grilled, Barbecued, and Smoked Meat Intake and Survival Following Breast Cancer

Background

Grilled, barbecued, and smoked meat intake, a prevalent dietary source of polycyclic aromatic hydrocarbon (PAH) carcinogens, may increase the risk of incident breast cancer. However, no studies have examined whether intake of this PAH source influences survival after breast cancer.

Methods

We interviewed a population-based cohort of 1508 women diagnosed with first primary invasive or in situ breast cancer in 1996 and 1997 at baseline and again approximately five years later to assess grilled/barbecued and smoked meat intake. After a median of 17.6 years of follow-up, 597 deaths, of which 237 were breast cancer related, were identified. Multivariable Cox regression was used to estimate adjusted hazard ratios (HRs) and 95% confidence intervals (CIs) for mortality as related to prediagnosis intake, comparing high (above the median) to low intake, as well as postdiagnosis changes in intake, comparing every combination of pre-/postdiagnosis intake to low pre-/postdiagnosis intake. All statistical tests were two-sided.

Results

High prediagnosis grilled/barbecued and smoked meat intake was associated with increased risk of all-cause mortality (HR = 1.23, 95% CI = 1.03 to 1.46). Other associations were noted, but estimates were not statistically significant. These include high prediagnosis smoked beef/lamb/pork intake and increased all-cause (HR = 1.17, 95% CI = 0.99 to 1.38, P_trend = .10) and breast cancer-specific (HR = 1.23, 95% CI = 0.95 to 1.60, P_trend = .09) mortality. Also, among women with continued high grilled/barbecued and smoked meat intake after diagnosis, all-cause mortality risk was elevated 31% (HR = 1.31, 95% CI = 0.96 to 1.78). Further, breast cancer-specific mortality was decreased among women with any pre- and postdiagnosis intake of smoked poultry/fish (HR = 0.55, 95% CI = 0.31 to 0.97).

Conclusion

High intake of grilled/barbecued and smoked meat may increase mortality after breast cancer.

Projecting Individualized Absolute Invasive Breast Cancer Risk in US Hispanic Women

Background

There is no model to estimate absolute invasive breast cancer risk for Hispanic women.

Methods

The San Francisco Bay Area Breast Cancer Study (SFBCS) provided data on Hispanic breast cancer case patients (533 US-born, 553 foreign-born) and control participants (464 US-born, 947 foreign-born). These data yielded estimates of relative risk (RR) and attributable risk (AR) separately for US-born and foreign-born women. Nativity-specific absolute risks were estimated by combining RR and AR information with nativity-specific invasive breast cancer incidence and competing mortality rates from the California Cancer Registry and Surveillance, Epidemiology, and End Results program to develop the Hispanic risk model (HRM). In independent data, we assessed model calibration through observed/expected (O/E) ratios, and we estimated discriminatory accuracy with the area under the receiver operating characteristic curve (AUC) statistic.

Results

The US-born HRM included age at first full-term pregnancy, biopsy for benign breast disease, and family history of breast cancer; the foreign-born HRM also included age at menarche. The HRM estimated lower risks than the National Cancer Institute's Breast Cancer Risk Assessment Tool (BCRAT) for US-born Hispanic women, but higher risks in foreign-born women. In independent data from the Women's Health Initiative, the HRM was well calibrated for US-born women (observed/expected [O/E] ratio = 1.07, 95% confidence interval [CI] = 0.81 to 1.40), but seemed to overestimate risk in foreign-born women (O/E ratio = 0.66, 95% CI = 0.41 to 1.07). The AUC was 0.564 (95% CI = 0.485 to 0.644) for US-born and 0.625 (95% CI = 0.487 to 0.764) for foreign-born women.

Conclusions

The HRM is the first absolute risk model that is based entirely on data specific to Hispanic women by nativity. Further studies in Hispanic women are warranted to evaluate its validity.

Epigenetic regulation of glycosylation and the impact on chemo-resistance in breast and ovarian cancer

Glycosylation is one of the most fundamental posttranslational modifications in cellular biology and has been shown to be epigenetically regulated. Understanding this process is important as epigenetic therapies such as those using DNA methyltransferase inhibitors are undergoing clinical trials for the treatment of ovarian and breast cancer. Previous work has demonstrated that altered glycosylation patterns are associated with aggressive disease in women presenting with breast and ovarian cancer. Moreover, the tumor microenvironment of hypoxia results in globally altered DNA methylation and is associated with aggressive cancer phenotypes and chemo-resistance, a feature integral to many cancers. There is sparse knowledge on the impact of these therapies on glycosylation. Moreover, little is known about the efficacy of DNA methyltransferase inhibitors in hypoxic tumors. In this review, we interrogate the impact that hypoxia and epigenetic regulation has on cancer cell glycosylation in relation to resultant tumor cell aggressiveness and chemo-resistance.

MicroRNA-125b promotes invasion and metastasis of gastric cancer by targeting STARD13 and NEU1

MicroRNAs have been documented playing key roles in cancer development and progression. Here, we investigate the role of miR-125b in gastric cancer metastasis. We found that the expression of miR-125b was up-regulated in gastric cancer tissue specimens compared with their corresponding nontumorous tissues, and the up-regulated miR-125b level was significantly associated with TNM stage and lymph node-metastasis. Overexpression of miR-125b promoted gastric cancer cell migration and invasion in vitro and metastasis in vivo. STARD13 and NEU1 were identified as direct target genes of miR-125b by luciferase assays, and they were involved in the cell migration and invasion regulated by miR-125b in gastric cancer. Taken together, miR-125b functions as an oncogene in gastric cancer and represents a new potential therapeutic target for gastric cancer.

Incorporating Biomarkers in Studies of Chemoprevention

Despite Food and Drug Administration approval of tamoxifen and raloxifene for breast cancer risk reduction and endorsement by multiple agencies, uptake of these drugs for primary prevention in the United States is only 4% for risk eligible women likely to benefit from their use. Side effects coupled with incomplete efficacy and lack of a survival advantage are the likely reasons. This disappointing uptake, after the considerable effort and expense of large Phase III cancer incidence trials required for approval, suggests that a new paradigm is required. Current prevention research is focused on (1) refining risk prediction, (2) exploring behavioral and natural product interventions, and (3) utilizing novel translational trial designs for efficacy. Risk biomarkers will play a central role in refining risk estimates from traditional models and selecting cohorts for prevention trials. Modifiable risk markers called surrogate endpoint or response biomarkers will continue to be used in Phase I and II prevention trials to determine optimal dose or exposure and likely effectiveness from an intervention. The majority of Phase II trials will continue to assess benign breast tissue for response and mechanism of action biomarkers. Co-trials are those in which human and animal cohorts receive the same effective dose and the same tissue biomarkers are assessed for modulation due to the intervention, but then additional animals are allowed to progress to cancer development. These collaborations linking biomarker modulation and cancer prevention may obviate the need for cancer incidence trials for non-prescription interventions.

Advancing breast cancer survivorship among African-American women

Advances have occurred in breast cancer survivorship but, for many African-American women, challenges and gaps in relevant information remain. This article identifies opportunities to address disparities in breast cancer survival and quality of life, and thereby to increase breast cancer survivorship among African-American women. For breast cancer survivors, common side effects, lasting for long periods after cancer treatment, include fatigue, loss of strength, difficulty sleeping, and sexual dysfunction. For addressing physical and mental health concerns, a variety of interventions have been evaluated, including exercise and weight training, dietary interventions, yoga and mindfulness-based stress reduction, and support groups or group therapy. Obesity has been associated with breast cancer recurrence and poorer survival. Relative to white survivors, African-American breast cancer survivors are more likely to be obese and less likely to engage in physical activity, although exercise improves overall quality of life and cancer-related fatigue. Considerable information exists about the effectiveness of such interventions for alleviating distress and improving quality of life among breast cancer survivors, but few studies have focused specifically on African-American women with a breast cancer diagnosis. Studies have identified a number of personal factors that are associated with resilience, increased quality of life, and positive adaptation to a breast cancer diagnosis. There is a need for a better understanding of breast cancer survivorship among African-American women. Additional evaluations of interventions for improving the quality of life and survival of African-American breast cancer survivors are desirable.

Can the breast screening appointment be used to provide risk assessment and prevention advice?

Breast cancer risk is continuing to increase across all societies with rates in countries with traditionally lower risks catching up with the higher rates in the Western world. Although cure rates from breast cancer have continued to improve such that absolute numbers of breast cancer deaths have dropped in many countries despite rising incidence, only some of this can be ascribed to screening with mammography, and debates over the true value of population-based screening continue. As such, enthusiasm for risk-stratified screening is gaining momentum. Guidelines in a number of countries already suggest more frequent screening in certain higher-risk (particularly, familial) groups, but this could be extended to assessing risks across the population. A number of studies have assessed breast cancer risk by using risk algorithms such as the Gail model, Tyrer-Cuzick, and BOADICEA (Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm), but the real questions are when and where such an assessment should take place. Emerging evidence from the PROCAS (Predicting Risk Of Cancer At Screening) study is showing not only that it is feasible to undertake risk assessment at the population screening appointment but that this assessment could allow reduction of screening in lower-risk groups in many countries to 3-yearly screening by using mammographic density-adjusted breast cancer risk.

Associations between breast density and a panel of single nucleotide polymorphisms linked to breast cancer risk: a cohort study with digital mammography

Background

Breast density and single-nucleotide polymorphisms (SNPs) have both been associated with breast cancer risk. To determine the extent to which these two breast cancer risk factors are associated, we investigate the association between a panel of validated SNPs related to breast cancer and quantitative measures of mammographic density in a cohort of Caucasian and African-American women.

Methods

In this IRB-approved, HIPAA-compliant study, we analyzed a screening population of 639 women (250 African American and 389 Caucasian) who were tested with a validated panel assay of 12 SNPs previously associated to breast cancer risk. Each woman underwent digital mammography as part of routine screening and all were interpreted as negative. Both absolute and percent estimates of area and volumetric density were quantified on a per-woman basis using validated software. Associations between the number of risk alleles in each SNP and the density measures were assessed through a race-stratified linear regression analysis, adjusted for age, BMI, and Gail lifetime risk.

Results

The majority of SNPs were not found to be associated with any measure of breast density. SNP rs3817198 (in LSP1) was significantly associated with both absolute area (p = 0.004) and volumetric (p = 0.019) breast density in Caucasian women. In African-American women, SNPs rs3803662 (in TNRC9/TOX3) and rs4973768 (in NEK10) were significantly associated with absolute (p = 0.042) and percent (p = 0.028) volume density respectively.

Conclusions

The majority of SNPs investigated in our study were not found to be significantly associated with breast density, even when accounting for age, BMI, and Gail risk, suggesting that these two different risk factors contain potentially independent information regarding a woman’s risk to develop breast cancer. Additionally, the few statistically significant associations between breast density and SNPs were different for Caucasian versus African American women. Larger prospective studies are warranted to validate our findings and determine potential implications for breast cancer risk assessment.

Electronic supplementary material

The online version of this article (doi:10.1186/s12885-015-1159-3) contains supplementary material, which is available to authorized users.