Metaplastic Breast Carcinoma Revisited; Subtypes Determine Outcomes: Comprehensive Pathologic, Clinical, and Molecular Review

Novel models based on machine learning to predict the prognosis of metaplastic breast cancer

BACKGROUND

Metaplastic breast cancer (MBC) is a rare and highly aggressive histological subtype of breast cancer. There remains a significant lack of precise predictive models available for use in clinical practice.

METHODS

This study utilized patient data from the SEER database (2010-2018) for data analysis. We utilized prognostic factors to develop a novel machine learning model (CatBoost) for predicting patient survival rates. Simultaneously, our hospital's cohort of MBC patients was utilized to validate our model. We compared the benefits of radiotherapy among the three groups of patients.

RESULTS

The CatBoost model we developed exhibits high accuracy and correctness, making it the best-performing model for predicting survival outcomes in patients with MBC (1-year AUC = 0.833, 3-year AUC = 0.806; 5-year AUC = 0.810). Furthermore, the CatBoost model maintains strong performance in an external independent dataset, with AUC values of 0.937 for 1-year survival, 0.907 for 3-year survival, and 0.890 for 5-year survival, respectively. Radiotherapy is more suitable for patients undergoing breast-conserving surgery with M0 stage [group1: (OS:HR = 0.499, 95%CI 0.320-0.777 p < 0.001; BCSS: HR = 0.519, 95%CI 0.290-0.929 p = 0.008)] and those with T3-4/N2-3M0 stage undergoing mastectomy [group2: (OS:HR = 0.595, 95%CI 0.437-0.810 p < 0.001; BCSS: HR = 0.607, 95%CI 0.427-0.862 p = 0.003)], compared to patients with stage T1-2/N0-1M0 undergoing mastectomy [group3: (OS:HR = 1.090, 95%CI 0.673-1.750 p = 0.730; BCSS: HR = 1.909, 95%CI 1.036-3.515 p = 0.038)].

CONCLUSION

We developed three machine learning prognostic models to predict survival rates in patients with MBC. Radiotherapy is considered more appropriate for patients who have undergone breast-conserving surgery with M0 stage as well as those in stage T3-4/N2-3M0 undergoing mastectomy.

Machine learning-guided synthesis of nanomaterials for breast cancer therapy

Breast cancer is a common malignant tumor, which mostly occurs in female population and is caused by excessive proliferation of breast epithelial cells. Breast cancer can cause nipple discharge, breast lumps and other symptoms, but these symptoms lack certain specificity and are easily confused with other diseases, thus affecting the early treatment of the disease. Once the tumor progresses to the advanced stage, distant metastasis can occur, leading to dysfunction of the affected organs, and even threatening the patients' lives. In this study, we synthesized high drug-loading gel particles and applied them to control the release of insoluble drugs. This method is simple to prepare, cost-effective, and validates their potential in breast cancer therapy. We first characterized the morphology and physicochemical properties of gel loaded with newly synthesized compound 1 by scanning electron microscopy (SEM), Fourier-transform infrared spectroscopy (FT-IR), and thermal gravimetric analysis (TGA). Using newly synthesized insoluble compound 1 as a model drug, its efficacy in treating breast cancer was investigated. The results showed that hydrogel@compound 1 was able to significantly inhibit the proliferation, migration and invasion of breast cancer cells. Additionally, we utilized machine learning to screen three structurally similar compounds, which showed promising therapeutic effects, providing a new approach for the development of novel small-molecule drugs.

Diagnosis and Management of Desmoid Fibromatosis of the Breast

Desmoid fibromatosis of the breast (also known as desmoid tumor of the breast) is a rare entity infrequently encountered by oncologists and surgeons caring for patients with breast disease. The current body of literature is highly reliant on case series and extrapolations from other sites of desmoid tumor-related disease. Much remains unclear regarding the pathological origins, natural history, and response to treatment of this condition. Traditional treatment strategies have centered on surgical resection, which may result in significantly disfiguring cosmetic and functional outcomes, frequent need for re-operation, and associated morbidity. There are limited data to support the superiority of upfront surgical resection when compared to medical therapy or watchful waiting strategies. Current treatment guidelines for desmoid tumors do not focus on the breast as a site of disease and are purposefully ambiguous due to the paucity of evidence available. We aim to review the literature concerning desmoid fibromatosis of the breast and propose an algorithm for current evidence-based management of this rare disease in the context of our experience with this pathology at a high-volume quaternary referral center.

Translational Aspects in Metaplastic Breast Carcinoma

Breast cancer is the most common cancer among women. Metaplastic breast carcinoma (MpBC) is a rare, heterogeneous group of invasive breast carcinomas, which are classified as predominantly triple-negative breast carcinomas (TNBCs; HR-negative/HER2-negative). Histologically, MpBC is classified into six subtypes. Two of these are considered low-grade and the others are high-grade. MpBCs seem to be more aggressive, less responsive to neoadjuvant chemotherapy, and have higher rates of chemoresistance than other TNBCs. MpBCs have a lower survival rate than expected for TNBCs. MpBC treatment represents a challenge, leading to a thorough exploration of the tumor immune microenvironment, which has recently opened the possibility of new therapeutic strategies. The epithelial-mesenchymal transition in MpBC is characterized by the loss of intercellular adhesion, downregulation of epithelial markers, underexpression of genes with biological epithelial functions, upregulation of mesenchymal markers, overexpression of genes with biological mesenchymal functions, acquisition of fibroblast-like (spindle) morphology, cytoskeleton reorganization, increased motility, invasiveness, and metastatic capabilities. This article reviews and summarizes the current knowledge and translational aspects of MpBC.

Triple-negative breast cancer: from none to multiple therapeutic targets in two decades

Triple-negative breast cancers (TNBCs) are more likely to occur in younger patients and have a poor prognosis. They are highly heterogeneous tumors consisting of different molecular subtypes. The only common characteristic among them is the absence of targets for endocrine therapy and human epidermal growth factor receptor 2 (HER2) blockade. In the past two decades, there has been an increased understanding of these tumors from a molecular perspective, leading to their stratification according to new therapeutic strategies. TNBC has ushered breast carcinomas into the era of immunotherapy. The higher frequency of germline BRCA mutations in these tumors enables targeting this repair defect by drugs like PARP inhibitors, resulting in synthetic lethality in neoplastic cells. Additionally, we have the identification of new molecules to which this generation of smart drugs, such as antibody-drug conjugates (ADCs), are directed. In this review, we will discuss the trajectory of this knowledge in a systematic manner, presenting the molecular bases, therapeutic possibilities, and biomarkers.