Brain metastasis is an ominous complication of cancer, yet most cancer cells that infiltrate the brain die of unknown causes. Here, we identify plasmin from the reactive brain stroma as a defense against metastatic invasion, and plasminogen activator (PA) inhibitory serpins in cancer cells as a shield against this defense. Plasmin suppresses brain metastasis in two ways: by converting membrane-bound astrocytic FasL into a paracrine death signal for cancer cells, and by inactivating the axon pathfinding molecule L1CAM, which metastatic cells express for spreading along brain capillaries and for metastatic outgrowth. Brain metastatic cells from lung cancer and breast cancer express high levels of anti-PA serpins, including neuroserpin and serpin B2, to prevent plasmin generation and its metastasis-suppressive effects. By protecting cancer cells from death signals and fostering vascular co-option, anti-PA serpins provide a unifying mechanism for the initiation of brain metastasis in lung and breast cancers.
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http://dx.doi.org/10.1016/j.cell.2014.01.040 | DOI Listing |
Sci Adv
January 2025
Molecular Pharmacology Program, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA.
A major limiting factor in the success of chimeric antigen receptor (CAR) T cell therapy for the treatment of solid tumors is targeting tumor antigens also found on normal tissues. CAR T cells against GD2 induced rapid, fatal neurotoxicity because of CAR recognition of GD2 normal mouse brain tissue. To improve the selectivity of the CAR T cell, we engineered a synthetic Notch receptor that selectively expresses the CAR upon binding to P-selectin, a cell adhesion protein overexpressed in tumor neovasculature.
View Article and Find Full Text PDFCancer J
January 2025
Department of Radiation Oncology, Miami Cancer Institute, Baptist Health South Florida, Miami, FL.
There is major interest in deintensifying therapy for isocitrate dehydrogenase-mutant low-grade gliomas, including with single-agent cytostatic isocitrate dehydrogenase inhibitors. These efforts need head-to-head comparisons with proven modalities, such as chemoradiotherapy. Ongoing clinical trials now group tumors by intrinsic molecular subtype, rather than classic clinical risk factors.
View Article and Find Full Text PDFCancer J
January 2025
From the Division of Neuro-Oncology, Department of Neurology and the Herbert Irving Comprehensive Cancer Center, Columbia University Vagelos College of Physicians & Surgeons and NewYork-Presbyterian, New York, NY.
The term "low-grade glioma" historically refers to adult diffuse gliomas that exhibit a less aggressive course than the more common high-grade gliomas. In the current molecular era, "low-grade" refers to World Health Organization central nervous system grade 2 gliomas almost always with an isocitrate dehydrogenase (IDH) mutation (astrocytomas and oligodendrogliomas). The term "lower-grade gliomas" has emerged encompassing grades 2 and 3 IDH-mutant astrocytomas and oligodendrogliomas, to acknowledge that histological grade is not as important a prognostic factor as molecular features, and distinguishing them from grade 4 glioblastomas, which lack an IDH mutation.
View Article and Find Full Text PDFCancer J
January 2025
From the Department of Radiation Oncology, Ohio State University Comprehensive Cancer Center, Columbus, OH.
There has been a significant paradigm shift in the clinical management of lower-grade glioma patients given the recent updates to the 2021 World Health Organization classification along with long-term results from randomized phase III clinical trials. As a result, we are now better able to diagnose and assign patients to the most appropriate treatment course. This review provides a comprehensive summary of the most robust and reliable molecular biomarkers for adult lower-grade gliomas and discusses current challenges facing this patient population that future correlative biology studies combined with advancements in technologies could help overcome.
View Article and Find Full Text PDFNeuroinformatics
January 2025
Department of Information Technology, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Ramapuram, Chennai, 600089, India.
Brain tumours are one of the most deadly and noticeable types of cancer, affecting both children and adults. One of the major drawbacks in brain tumour identification is the late diagnosis and high cost of brain tumour-detecting devices. Most existing approaches use ML algorithms to address problems, but they have drawbacks such as low accuracy, high loss, and high computing cost.
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