Sulindac, a Nonselective NSAID, Reduces Breast Density in Postmenopausal Women with Breast Cancer Treated with Aromatase Inhibitors

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.

NO LEVEL ASSIGNED

This journal requires that authors assign a level of evidence to each submission to which Evidence-Based Medicine rankings are applicable. This excludes Review Articles, Book Reviews, and manuscripts that concern Basic Science, Animal Studies, Cadaver Studies, and Experimental Studies. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Interplay between Estrogen, Kynurenine, and AHR Pathways: An immunosuppressive axis with therapeutic potential for breast cancer treatment

Breast cancer is one of the most common malignancies among women worldwide. Estrogen exposure via endogenous and exogenous sources during a lifetime, together with environmental exposure to estrogenic compounds, represent the most significant risk factor for breast cancer development. As breast tumors establish, multiple pathways are deregulated. Among them is the aryl hydrocarbon receptor (AHR) signaling pathway. AHR, a ligand-activated transcription factor associated with the metabolism of polycyclic aromatic hydrocarbons and estrogens, is overexpressed in breast cancer. Furthermore, AHR and estrogen receptor (ER) cross-talk pathways have been observed. Additionally, the Tryptophan (Trp) catabolizing enzymes indolamine-2,3-dioxygenase (IDO) and tryptophan-2,3-dioxygenase (TDO) are overexpressed in breast cancer. IDO/TDO catalyzes the formation of Kynurenine (KYN) and other tryptophan-derived metabolites, which are ligands of AHR. Once KYN activates AHR, it stimulates the expression of the IDO enzyme, increases the level of KYN, and activates non-canonical pathways to control inflammation and immunosuppression in breast tumors. The interplay between E2, AHR, and IDO/TDO/KYN pathways and their impact on the immune system represents an immunosuppressive axis on breast cancer. The potential modulation of the immunosuppressive E2-AHR-IDO/TDO/KYN axis has aroused great expectations in oncotherapy. The present article will review the mechanisms implicated in generating the immunosuppressive axis E2-AHR-IDO/TDO/KYN in breast cancer and the current state of knowledge as a potential therapeutic target.

Sulfoxides in medicine

In the review, current status of sulfoxides on the pharmaceutical market is discussed. In the first part of the article, natural sulfoxides will be described with a special focus on sulforaphane and amanitin, a mushroom toxin which has been developed as payload in antibody drug conjugates in the possible cancer treatment. Controversies associated with the medical use of dimethylsulfoxide are briefly described in the next section. In the part devoted to PPIs, the benefits of using pure enantiomers (chiral switch) are discussed. An interesting approach, repositioning of drugs is exemplified by new possible applications of modafinil and sulindac. The review is concluded by presentation of cenicriviroc and adezmapimod, both with the status of promising drug candidates.

Targeting cancer-related inflammation with non-steroidal anti-inflammatory drugs: Perspectives in pharmacogenomics

Inflammatory processes are essential for innate immunity and contribute to carcinogenesis in various malignancies, such as colorectal cancer, esophageal cancer and lung cancer. Pharmacotherapies targeting inflammation have the potential to reduce the risk of carcinogenesis and improve therapeutic efficacy of existing anti-cancer treatment. Non-steroidal anti-inflammatory drugs (NSAIDs), comprising a variety of structurally different chemicals that can inhibit cyclooxygenase (COX) enzymes and other COX-independent pathways, are originally used to treat inflammatory diseases, but their preventive and therapeutic potential for cancers have also attracted researchers' attention. Pharmacogenomic variability, including distinct genetic characteristics among different patients, can significantly affect pharmacokinetics and effectiveness of NSAIDs, which might determine the preventive or therapeutic success for cancer patients. Hence, a more comprehensive understanding in pharmacogenomic characteristics of NSAIDs and cancer-related inflammation would provide new insights into this appealing strategy. In this review, the up-to-date advances in clinical and experimental researches targeting cancer-related inflammation with NSAIDs are presented, and the potential of pharmacogenomics are discussed as well.

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.

Pharmacological Small Molecules against Prostate Cancer by Enhancing Function of Death Receptor 5

Death receptor 5 (DR5) is a membrane protein that mediates exogenous apoptosis. Based on its function, it is considered to be a target for the treatment of cancers including prostate cancer. It is encouraging to note that a number of drugs targeting DR5 are now progressing to different stages of clinical trial studies. We collected 38 active compounds that could produce anti-prostate-cancer effects by modulating DR5, 28 of which were natural compounds and 10 of which were synthetic compounds. In addition, 6 clinically used chemotherapeutic agents have also been shown to promote DR5 expression and thus exert apoptosis-inducing effects in prostate cancer cells. These compounds promote the expression of DR5, thereby enhancing its function in inducing apoptosis. When these compounds were used in combination with the natural ligand of DR5, the number of apoptotic cells was significantly increased. These compounds are all promising for development as anti-prostate-cancer drugs, while most of these compounds are currently being evaluated for their anti-prostate-cancer effects at the cellular level and in animal studies. A great deal of more in-depth research is needed to evaluate whether they can be developed as drugs. We collected literature reports on small molecules against prostate cancer through modulation of DR5 to understand the current dynamics in this field and to evaluate the prospects of small molecules against prostate cancer through modulation of DR5.

Molecular dynamics simulation and docking analysis of NF-κB protein binding with sulindac acid

It is of interest to document the Molecular Dynamics Simulation and docking analysis of NF-κB target with sulindac sodium in combating COVID-19 for further consideration. Sulindac is a nonsteroidal anti-inflammatory drug (NSAID) of the arylalkanoic acid class that is marketed by Merck under the brand name Clinoril. We show the binding features of sulindac sodium with NF-κB that can be useful in drug repurposing in COVID-19 therapy.

NSAIDs: Old Acquaintance in the Pipeline for Cancer Treatment and Prevention─Structural Modulation, Mechanisms of Action, and Bright Future

The limitations of current chemotherapeutic drugs are still a major issue in cancer treatment. Thus, targeted multimodal therapeutic approaches need to be strategically developed to successfully control tumor growth and prevent metastatic burden. Inflammation has long been recognized as a hallmark of cancer and plays a key role in the tumorigenesis and progression of the disease. Several epidemiological, clinical, and preclinical studies have shown that traditional nonsteroidal anti-inflammatory drugs (NSAIDs) exhibit anticancer activities. This Perspective reports the most recent outcomes for the treatment and prevention of different types of cancers for several NSAIDs alone or in combination with current chemotherapeutic drugs. Furthermore, an extensive review of the most promising structural modifications is reported, such as phospho, H₂S, and NO releasing-, selenium-, metal complex-, and natural product-NSAIDs, among others. We also provide a perspective about the new strategies used to obtain more efficient NSAID- or NSAID derivative- formulations for targeted delivery.

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

PURPOSE

Obesity is a known risk factor for post-menopausal breast cancer and may increase risk for triple negative breast cancer in premenopausal women. Intervention strategies are clearly needed to reduce obesity-associated breast cancer risk.

METHODS

We conducted a Phase II double-blind, randomized, placebo-controlled trial of metformin in overweight/obese premenopausal women with components of metabolic syndrome to assess the potential of metformin for primary breast cancer prevention. Eligible participants were randomized to receive metformin (850 mg BID, n = 76) or placebo (n = 75) for 12 months. Outcomes included breast density, assessed by fat/water MRI with change in percent breast density as the primary endpoint, anthropometric measures, and intervention feasibility.

RESULTS

Seventy-six percent in the metformin arm and 83% in the placebo arm (p = 0.182) completed the 12-month intervention. Adherence to study agent was high with more than 80% of participants taking ≥ 80% assigned pills. The most common adverse events reported in the metformin arm were gastrointestinal in nature and subsided over time. Compared to placebo, metformin intervention led to a significant reduction in waist circumference (p < 0.001) and waist-to-hip ratio (p = 0.019). Compared to placebo, metformin did not change percent breast density and dense breast volume but led to a numerical but not significant decrease in non-dense breast volume (p = 0.070).

CONCLUSION

We conclude that metformin intervention resulted in favorable changes in anthropometric measures of adiposity and a borderline decrease in non-dense breast volume in women with metabolic dysregulation. More research is needed to understand the impact of metformin on breast cancer risk reduction.

TRIAL REGISTRATION

ClinicalTrials.gov NCT02028221. Registered January 7, 2014, https://clinicaltrials.gov/ct2/show/NCT02028221.