Breast cancer plasticity is restricted by a LATS1-NCOR1 repressive axis

Epigenetic mechanisms of cancer progression and therapy resistance in estrogen-receptor (ER+) breast cancer

Estrogen receptor-positive (ER+) breast cancer is the most frequent breast cancer subtype. Agents targeting the ER signaling pathway have been successful in reducing mortality from breast cancer for decades. However, mechanisms of resistance to these treatments arise, especially in the metastatic setting. Recently, it has been recognized that epigenetic dysregulation is a common feature that facilitates the acquisition of cancer hallmarks across cancer types, including ER+ breast cancer. Alterations in epigenetic regulators and transcription factors (TF) coupled with changes to the chromatin landscape have been found to orchestrate breast oncogenesis, metastasis, and the development of a resistant phenotype. Here, we review recent advances in our understanding of how the epigenome dictates breast cancer tumorigenesis and resistance to targeted therapies and discuss novel therapeutic interventions for overcoming resistance.

Subtype Transdifferentiation in Human Cancer: The Power of Tissue Plasticity in Tumor Progression

The classification of tumors into subtypes, characterized by phenotypes determined by specific differentiation pathways, aids diagnosis and directs therapy towards targeted approaches. However, with the advent and explosion of next-generation sequencing, cancer phenotypes are turning out to be far more heterogenous than initially thought, and the classification is continually being updated to include more subtypes. Tumors are indeed highly dynamic, and they can evolve and undergo various changes in their characteristics during disease progression. The picture becomes even more complex when the tumor responds to a therapy. In all these cases, cancer cells acquire the ability to transdifferentiate, changing subtype, and adapt to changing microenvironments. These modifications affect the tumor's growth rate, invasiveness, response to treatment, and overall clinical behavior. Studying tumor subtype transitions is crucial for understanding tumor evolution, predicting disease outcomes, and developing personalized treatment strategies. We discuss this emerging hallmark of cancer and the molecular mechanisms involved at the crossroads between tumor cells and their microenvironment, focusing on four different human cancers in which tissue plasticity causes a subtype switch: breast cancer, prostate cancer, glioblastoma, and pancreatic adenocarcinoma.

Estrogen Receptor Signaling in Breast Cancer

Estrogen receptor (ER) signaling is a critical regulator of cell proliferation, differentiation, and survival in breast cancer (BC) and other hormone-sensitive cancers. In this review, we explore the mechanism of ER-dependent downstream signaling in BC and the role of estrogens as growth factors necessary for cancer invasion and dissemination. The significance of the clinical implications of ER signaling in BC, including the potential of endocrine therapies that target estrogens' synthesis and ER-dependent signal transmission, such as aromatase inhibitors or selective estrogen receptor modulators, is discussed. As a consequence, the challenges associated with the resistance to these therapies resulting from acquired ER mutations and potential strategies to overcome them are the critical point for the new treatment strategies' development.

Real-time fluorescence imaging flow cytometry enabled by motion deblurring and deep learning algorithms

Fluorescence imaging flow cytometry (IFC) has been demonstrated as a crucial biomedical technique for analyzing specific cell subpopulations from heterogeneous cellular populations. However, the high-speed flow of fluorescent cells leads to motion blur in cell images, making it challenging to identify cell types from the raw images. In this study, we present a real-time single-cell imaging and classification system based on a fluorescence microscope and deep learning algorithm, which is able to directly identify cell types from motion-blur images. To obtain annotated datasets of blurred images for deep learning model training, we developed a motion deblurring algorithm for the reconstruction of blur-free images. To demonstrate the ability of this system, deblurred images of HeLa cells with various fluorescent labels and HeLa cells at different cell cycle stages were acquired. The trained ResNet achieved a high accuracy of 96.6% for single-cell classification of HeLa cells in three different mitotic stages, with a short processing time of only 2 ms. This technology provides a simple way to realize single-cell fluorescence IFC and real-time cell classification, offering significant potential in various biological and medical applications.