Correction: CBL0137 and NKG2A blockade: a novel immuno-oncology combination therapy for Myc-overexpressing triple-negative breast cancers
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Breast cancer: pathogenesis and treatments
Breast cancer, characterized by unique epidemiological patterns and significant heterogeneity, remains one of the leading causes of malignancy-related deaths in women. The increasingly nuanced molecular subtypes of breast cancer have enhanced the comprehension and precision treatment of this disease. The mechanisms of tumorigenesis and progression of breast cancer have been central to scientific research, with investigations spanning various perspectives such as tumor stemness, intra-tumoral microbiota, and circadian rhythms. Technological advancements, particularly those integrated with artificial intelligence, have significantly improved the accuracy of breast cancer detection and diagnosis. The emergence of novel therapeutic concepts and drugs represents a paradigm shift towards personalized medicine. Evidence suggests that optimal diagnosis and treatment models tailored to individual patient risk and expected subtypes are crucial, supporting the era of precision oncology for breast cancer. Despite the rapid advancements in oncology and the increasing emphasis on the clinical precision treatment of breast cancer, a comprehensive update and summary of the panoramic knowledge related to this disease are needed. In this review, we provide a thorough overview of the global status of breast cancer, including its epidemiology, risk factors, pathophysiology, and molecular subtyping. Additionally, we elaborate on the latest research into mechanisms contributing to breast cancer progression, emerging treatment strategies, and long-term patient management. This review offers valuable insights into the latest advancements in Breast Cancer Research, thereby facilitating future progress in both basic research and clinical application.
Reversible downregulation of MYC in a spheroid model of metastatic epithelial ovarian cancer
Upon detachment from the primary tumour, epithelial ovarian cancer cells can form multicellular aggregates, also referred to as spheroids, that have the capacity to establish metastases at distant sites. These structures exhibit numerous adaptations that may facilitate metastatic transit and promote tumorigenic potential. One such adaptation is the acquisition of dormancy, characterized by decreased proliferation and molecular features of quiescence. One of the most frequently dysregulated genes in cancer is MYC, which encodes a transcription factor that promotes cell proliferation. In this study, we demonstrate that MYC protein abundance and associated gene expression is significantly decreased in EOC spheroids compared to adherent cells. This downregulation occurs rapidly upon cell detachment and is proteasome-dependent. Moreover, MYC protein abundance and associated gene expression is restored upon spheroid reattachment to an adherent culture surface. Overall, our findings suggest that suppression of MYC activity is a common feature of EOC spheroids and may contribute to the reversible acquisition of dormancy.
Unraveling complexity and leveraging opportunities in uncommon breast cancer subtypes
Special histologic subtypes of breast cancer (BC) exhibit unique phenotypes and molecular profiles with diagnostic and therapeutic implications, often differing in behavior and clinical trajectory from common BC forms. Novel methodologies, such as artificial intelligence may improve classification. Genetic predisposition plays roles in a subset of cases. Uncommon BC presentations like male, inflammatory and pregnancy-related BC pose challenges. Emerging therapeutic strategies targeting genetic alterations or immune microenvironment are being explored.
A single-cell network approach to decode metabolic regulation in gynecologic and breast cancers
Cancer metabolism is characterized by significant heterogeneity, presenting challenges for treatment efficacy and patient outcomes. Understanding this heterogeneity and its regulatory mechanisms at single-cell resolution is crucial for developing personalized therapeutic strategies. In this study, we employed a single-cell network approach to characterize malignant heterogeneity in gynecologic and breast cancers, focusing on the transcriptional regulatory mechanisms driving metabolic alterations. By leveraging single-cell RNA sequencing (scRNA-seq) data, we assessed the metabolic pathway activities and inferred cancer-specific protein-protein interactomes (PPI) and gene regulatory networks (GRNs). We explored the crosstalk between these networks to identify key alterations in metabolic regulation. Clustering cells by metabolic pathways revealed tumor heterogeneity across cancers, highlighting variations in oxidative phosphorylation, glycolysis, cholesterol, fatty acid, hormone, amino acid, and redox metabolism. Our analysis identified metabolic modules associated with these pathways, along with their key transcriptional regulators. These findings provide insights into the complex interplay between metabolic rewiring and transcriptional regulation in gynecologic and breast cancers, paving the way for potential targeted therapeutic strategies in precision oncology. Furthermore, this pipeline for dissecting coregulatory metabolic networks can be broadly applied to decipher metabolic regulation in any disease at single-cell resolution.
Personalized prediction of anticancer potential of non-oncology drugs through learning from genome derived molecular pathways
Advances in cancer genomics have significantly expanded our understanding of cancer biology. However, the high cost of drug development limits our ability to translate this knowledge into precise treatments. Approved non-oncology drugs, comprising a large repository of chemical entities, offer a promising avenue for repurposing in cancer therapy. Herein we present CHANCE, a supervised machine learning model designed to predict the anticancer activities of non-oncology drugs for specific patients by simultaneously considering personalized coding and non-coding mutations. Utilizing protein–protein interaction networks, CHANCE harmonizes multilevel mutation annotations and integrates pharmacological information across different drugs into a single model. We systematically benchmarked the performance of CHANCE and show its predictions are better than previous model and highly interpretable. Applying CHANCE to approximately 5000 cancer samples indicated that >30% might respond to at least one non-oncology drug, with 11% non-oncology drugs predicted to have anticancer activities. Moreover, CHANCE predictions suggested an association between SMAD7 mutations and aspirin treatment response. Experimental validation using tumor cells derived from seven patients with pancreatic or esophageal cancer confirmed the potential anticancer activity of at least one non-oncology drug for five of these patients. To summarize, CHANCE offers a personalized and interpretable approach, serving as a valuable tool for mining non-oncology drugs in the precision oncology era.
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