A randomized controlled trial of metformin in women with components of metabolic syndrome: intervention feasibility and effects on adiposity and breast density

MRI-based Deep Learning Models for Preoperative Breast Volume and Density Assessment Assisting Breast Reconstruction

BACKGROUND

The volume of the implant is the most critical element of breast reconstruction, so it is necessary to accurately assess the preoperative volume of the healthy and affected breasts and select the appropriate implant for placement. Accurate and automated methods for quantitative assessment of breast volume can optimize breast reconstruction surgery and assist physicians in clinical decision making. The aim of this study was to develop an artificial intelligence model for automated segmentation of the breast and measurement of volume.

MATERIAL AND METHODS

A total of 249 subjects undergoing breast reconstruction surgery were enrolled in this study. Subjects underwent preoperative breast MRI, and the breast region manually outlined by the imaging physician served as the gold standard for volume measurement by the automated segmentation model. In this study, we developed three automated algorithms for automatic segmentation of breast regions, including a simple alignment model, an alignment dynamic encoding model, and a deep learning model. The volumetric agreement between the three automated segmentation algorithms and the breast regions manually segmented by imaging physicians was evaluated by calculating the mean square error (MSE) and intragroup correlation coefficient (ICC), and the reproducibility of the automated segmentation of the breast regions was assessed by the test-retest step.

RESULTS

The three breast automated segmentation models developed in this study (simple registration model, dynamic programming model, and deep learning model) showed strong ICC with manual segmentation of the breast region, with MSEs of 1.124, 0.693, and 0.781, and ICCs of 0.975 (95% CI, 0.869-0.991), 0.986 (95% CI, 0.967-0.996), and 0.983 (95% CI, 0.961-0.992), respectively. Regarding the test-retest results of breast volume, the dynamic programming model performed the best with an MSE of 0.370 and an ICC of 0.993 (95% CI, 0.982-0.997), followed by the deep learning algorithm with an MSE of 0.741 and an ICC of 0.983 (95% CI, 0.956-0.993), and the simple registration algorithm with an MSE of 0.763 and an ICC of 0.982 (95% CI, 0.949-0.993). The reproducibility of the breast region segmented by the three automated algorithms was higher than that of manual segmentation by different radiologists.

CONCLUSION

The three automated breast segmentation algorithms developed in this study generate accurate and reliable breast regions, enable highly reproducible breast region segmentation and automated volume measurements, and provide a valuable tool for surgical selection of appropriate prostheses.

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Statin use as a moderator on the association between metformin and breast cancer risk in women with type 2 diabetes mellitus

INTRODUCTION

Metformin and statins are considered as potential agents for prevention of breast cancer, however, existing evidence does not uniformly substantiate this claim, and the data is scarce concerning their interaction in relation to breast cancer risk. This study aims to investigate whether the effect of metformin on breast cancer incidence varied by statin use among women with type 2 diabetes mellitus (T2DM).

METHODS

This study included women with T2DM, without a history of cancers, and followed up for more than one year from the Zwolle Outpatient Diabetes project Integrating Available Care (ZODIAC) for the period 1998-2014. The dataset was structured using a person-time approach, where the cumulative medication usage was annually updated for each person. The extended Cox proportional hazards models were employed, reporting adjusted hazard ratios (HR) with 95% confidence intervals (CI).

RESULTS

During a median follow-up of 5 years, 515 of 29,498 women received a breast cancer diagnosis. Each additional year of metformin or statins use corresponded to a decrease in breast cancer incidence, while the magnitude attenuated over time. Noteworthily, statin use modified the effect of metformin on breast cancer incidence. For instance, after 5 years of follow-up, one-year increase of metformin use among women who used statins for 3 years was linked to a substantially reduced breast cancer risk (HR, 95% CI: 0.88, 0.84-0.93), however, there was no significant decrease in risk for those non-statins users (HR, 95% CI: 0.96, 0.89-1.04).

CONCLUSIONS

Extending metformin or statin usage by one year conferred breast cancer protection in women with T2DM. Enhanced protective effect of metformin was observed among those who also use statins. These results suggest the potential of combined metformin and statin therapy as promising breast cancer prevention strategies.

Metformin and Breast Cancer: Current Findings and Future Perspectives from Preclinical and Clinical Studies

Over the last several decades, a growing body of research has investigated the potential to repurpose the anti-diabetic drug metformin for breast cancer prevention and/or treatment. Observational studies in the early 2000s demonstrated that patients with diabetes taking metformin had decreased cancer risk, providing the first evidence supporting the potential role of metformin as an anti-cancer agent. Despite substantial efforts, two decades later, the exact mechanisms and clinical efficacy of metformin for breast cancer remain ambiguous. Here, we have summarized key findings from studies examining the effect of metformin on breast cancer across the translational spectrum including in vitro, in vivo, and human studies. Importantly, we discuss critical factors that may help explain the significant heterogeneity in study outcomes, highlighting how metformin dose, underlying metabolic health, menopausal status, tumor subtype, membrane transporter expression, diet, and other factors may play a role in modulating metformin's anti-cancer effects. We hope that these insights will help with interpreting data from completed studies, improve the design of future studies, and aid in the identification of patient subsets with breast cancer or at high risk for the disease who are most likely to benefit from metformin treatment.

An Overview of Recent Findings that Shed Light on the Connection between Fat and Cancer

Obesity and cancer have been found to have a direct link in epidemiological studies. Obesity raises the risk of cancer and associated chronic disorders. Furthermore, an imbalance of adipokines, like leptins, plays a crucial role in neoplasm pathogenesis, cell migration, and thereby, cancer metastasis. Also, leptin increases human epidermal growth factor receptor 2 (HER2) protein levels through the STAT3-mediated (signal transducer and activator of transcription) upregulation of heat shock protein (Hsp90) in breast cancer cells. It has been noticed that insulin and insulin-like growth factors (IGFs) act as mitosis activators in the host and cancerous breast epithelial cells. The condition of hyperinsulinemia explains the positive association between colorectal cancer and obesity. Furthermore, in prostate cancer, an alteration in sex hormone levels, testosterone and dihydrotestosterone, has been reported to occur, along with increased oxidative stress, which is the actual cause of the tumors. Whereas, there have been two interconnected factors that play a crucial role in the psychological cycle concerned with lung cancer. The review article focuses on all the prospects of etiological mechanisms that have found linkage with obesity and breast, colon, lung, and prostate cancers. Furthermore, the article has also highlighted how these new insights into the processes occur and, due to which reasons, obesity contributes to tumorigenesis. This review provides a detailed discussion on the progression, which can assist in the development of new and innovative techniques to interfere in this process, and it has been supported with insights based on evidence literature on approved clinical treatments for obesity and cancer.

Metformin and cancer hallmarks: shedding new lights on therapeutic repurposing

Metformin is a well-known anti-diabetic drug that has been repurposed for several emerging applications, including as an anti-cancer agent. It boasts the distinct advantages of an excellent safety and tolerability profile and high cost-effectiveness at less than one US dollar per daily dose. Epidemiological evidence reveals that metformin reduces the risk of cancer and decreases cancer-related mortality in patients with diabetes; however, the exact mechanisms are not well understood. Energy metabolism may be central to the mechanism of action. Based on altering whole-body energy metabolism or cellular state, metformin's modes of action can be divided into two broad, non-mutually exclusive categories: "direct effects", which induce a direct effect on cancer cells, independent of blood glucose and insulin levels, and "indirect effects" that arise from systemic metabolic changes depending on blood glucose and insulin levels. In this review, we summarize an updated account of the current knowledge on metformin antitumor action, elaborate on the underlying mechanisms in terms of the hallmarks of cancer, and propose potential applications for repurposing metformin for cancer therapeutics.

The promising therapeutic effects of metformin on metabolic reprogramming of cancer-associated fibroblasts in solid tumors

Tumor-infiltrated lymphocytes are exposed to many toxic metabolites and molecules in the tumor microenvironment (TME) that suppress their anti-tumor activity. Toxic metabolites, such as lactate and ketone bodies, are produced mainly by catabolic cancer-associated fibroblasts (CAFs) to feed anabolic cancer cells. These catabolic and anabolic cells make a metabolic compartment through which high-energy metabolites like lactate can be transferred via the monocarboxylate transporter channel 4. Moreover, a decrease in molecules, including caveolin-1, has been reported to cause deep metabolic changes in normal fibroblasts toward myofibroblast differentiation. In this context, metformin is a promising drug in cancer therapy due to its effect on oncogenic signal transduction pathways, leading to the inhibition of tumor proliferation and downregulation of key oncometabolites like lactate and succinate. The cross-feeding and metabolic coupling of CAFs and tumor cells are also affected by metformin. Therefore, the importance of metabolic reprogramming of stromal cells and also the pivotal effects of metformin on TME and oncometabolites signaling pathways have been reviewed in this study.

Two fully automated data-driven 3D whole-breast segmentation strategies in MRI for MR-based breast density using image registration and U-Net with a focus on reproducibility

Presence of higher breast density (BD) and persistence over time are risk factors for breast cancer. A quantitatively accurate and highly reproducible BD measure that relies on precise and reproducible whole-breast segmentation is desirable. In this study, we aimed to develop a highly reproducible and accurate whole-breast segmentation algorithm for the generation of reproducible BD measures. Three datasets of volunteers from two clinical trials were included. Breast MR images were acquired on 3 T Siemens Biograph mMR, Prisma, and Skyra using 3D Cartesian six-echo GRE sequences with a fat-water separation technique. Two whole-breast segmentation strategies, utilizing image registration and 3D U-Net, were developed. Manual segmentation was performed. A task-based analysis was performed: a previously developed MR-based BD measure, MagDensity, was calculated and assessed using automated and manual segmentation. The mean squared error (MSE) and intraclass correlation coefficient (ICC) between MagDensity were evaluated using the manual segmentation as a reference. The test-retest reproducibility of MagDensity derived from different breast segmentation methods was assessed using the difference between the test and retest measures (Δ2-1), MSE, and ICC. The results showed that MagDensity derived by the registration and deep learning segmentation methods exhibited high concordance with manual segmentation, with ICCs of 0.986 (95%CI: 0.974-0.993) and 0.983 (95%CI: 0.961-0.992), respectively. For test-retest analysis, MagDensity derived using the registration algorithm achieved the smallest MSE of 0.370 and highest ICC of 0.993 (95%CI: 0.982-0.997) when compared to other segmentation methods. In conclusion, the proposed registration and deep learning whole-breast segmentation methods are accurate and reliable for estimating BD. Both methods outperformed a previously developed algorithm and manual segmentation in the test-retest assessment, with the registration exhibiting superior performance for highly reproducible BD measurements.

Breast Density: Current Knowledge, Assessment Methods, and Clinical Implications

Breast density is an accepted independent risk factor for the future development of breast cancer, and greater breast density has the potential to mask malignancies on mammography, thus lowering the sensitivity of screening mammography. The risk associated with dense breast tissue has been shown to be modifiable with changes in breast density. Numerous studies have sought to identify factors that influence breast density, including age, genetic, racial/ethnic, prepubertal, adolescent, lifestyle, environmental, hormonal, and reproductive history factors. Qualitative, semiquantitative, and quantitative methods of breast density assessment have been developed, but to date there is no consensus assessment method or reference standard for breast density. Breast density has been incorporated into breast cancer risk models, and there is growing consciousness of the clinical implications of dense breast tissue in both the medical community and public arena. Efforts to improve breast cancer screening sensitivity for women with dense breasts have led to increased attention to supplemental screening methods in recent years, prompting the American College of Radiology to publish Appropriateness Criteria for supplemental screening based on breast density.

Embedding the Community and Individuals in Disease Prevention

The primary prevention of non-communicable diseases is one of the most challenging and exciting aspects of medicine and primary care this century. For cancer, it is an urgent matter in light of the increasing burden of the disease among younger people and the higher frequency of more aggressive forms of the disease for all ages. Most chronic disorders result from the influence of the environment on the expression of genes within an individual. The environment at-large encompasses lifestyle (including nutrition), and chemical/physical and social exposures. In cancer, the interaction between the (epi)genetic makeup of an individual and a multiplicity of environmental risk and protecting factors is considered key to disease onset. Thus, like for precision therapy developed for patients, personalized or precision prevention is envisioned for individuals at risk. Prevention means identifying people at higher risk and intervening to reduce the risk. It requires biological markers of risk and non-aggressive preventive actions for the individual, but it also involves acting on the environment and the community. Social scientists are considering micro (individual/family), meso (community), and macro (country population) levels of care to illustrate that problems and solutions exist on different scales. Ideally, the design of interventions in prevention should integrate all these levels. In this perspective article, using the example of breast cancer, we are discussing challenges and possible solutions for a multidisciplinary community of scientists, primary health care practitioners and citizens to develop a holistic approach of primary prevention, keeping in mind equitable access to care.

De novo cholesterol biosynthesis: an additional therapeutic target for the treatment of postmenopausal breast cancer with excessive adipose tissue

The onset and development of breast cancer in postmenopausal women are associated with closely related individual-dependent factors, including weight gain and high levels of circulating androgens. Adipose tissue is the most peripheral site of aromatase enzyme synthesis; therefore, the excessive accumulation of visceral fat results in increased androgens aromatization and estradiol production that provides the microenvironment favorable to tumorigenesis in mammary epithelial cells expressing estrogen receptors (ERs). Moreover, to meet the increased requirement of cholesterol for cell membrane assembly and the production of steroid hormones to sustain their proliferation, ER-positive cells activate de novo cholesterol biosynthesis and subsequent steroidogenesis. Several approaches have been followed to neutralize the de novo cholesterol synthesis, including specific enzyme inhibitors, statins, and, more recently, metformin. Cumulating evidence indicated that inhibiting cholesterol biosynthesis by statins and metformin may be a promising therapeutic strategy to block breast cancer progression. Unlike antiestrogens and aromatase inhibitors (AIs) which compete for binding to ER and inhibit androgens aromatization, respectively, statins block the production of mevalonic acid by inhibiting the activity of 3-hydroxy-3-methylglutaryl-coenzyme A (HMG-CoA) reductase, and metformin hampers the activation of the sterol regulatory element-binding protein 2 (SREBP2) transcription factor, thus inhibiting the synthesis of several enzymes involved in cholesterol biosynthesis. Noteworthy, statins and metformin not only improve the prognosis of overweight patients with ER-positive cancer but also improve the prognosis of patients with triple-negative breast cancer, the aggressive tumor subtype that lacks, at present, specific therapy.

Nuclear Receptor-Mediated Metabolic Reprogramming and the Impact on HR+ Breast Cancer

Simple Summary

Breast cancer is the most commonly diagnosed and second leading cause of cancer-related deaths in women in the United States, with hormone receptor positive (HR+) tumors representing more than two-thirds of new cases. Recent evidence has indicated that dysregulation of multiple metabolic programs, which can be driven through nuclear receptor activity, is essential for tumor genesis, progression, therapeutic resistance and metastasis. This study will review the current advances in our understanding of the impact and implication of altered metabolic processes driven by nuclear receptors, including hormone-dependent signaling, on HR+ breast cancer.

Abstract

Metabolic reprogramming enables cancer cells to adapt to the changing microenvironment in order to maintain metabolic energy and to provide the necessary biological macromolecules required for cell growth and tumor progression. While changes in tumor metabolism have been long recognized as a hallmark of cancer, recent advances have begun to delineate the mechanisms that modulate metabolic pathways and the consequence of altered signaling on tumorigenesis. This is particularly evident in hormone receptor positive (HR+) breast cancers which account for approximately 70% of breast cancer cases. Emerging evidence indicates that HR+ breast tumors are dependent on multiple metabolic processes for tumor progression, metastasis, and therapeutic resistance and that changes in metabolic programs are driven, in part, by a number of key nuclear receptors including hormone-dependent signaling. In this review, we discuss the mechanisms and impact of hormone receptor mediated metabolic reprogramming on HR+ breast cancer genesis and progression as well as the therapeutic implications of these metabolic processes in this disease.