Metastasis remains the leading cause (90%) of cancer-related mortality, especially in metastatic triple-negative breast cancer (TNBC). Improved understanding of molecular drivers in the metastatic cascade is crucial, to find accurate prognostic markers for invasiveness after chemotherapy treatment. Current breast cancer chemotherapy treatments include doxorubicin and paclitaxel, inducing various effects, such as the unfolded protein response (UPR). The key regulator of the UPR is the 78-kDa glucose-regulated protein (GRP78), which is associated with metastatic disease, although, its expression level in the context of invasiveness is still controversial. We evaluate doxorubicin effects on TNBC cells, identifying GRP78 subpopulations linked to invasiveness. Specifically, we evaluate the motility and invasiveness of GRP78 positive vs. negative cell subpopulations by two different assays: the in vitro Boyden chamber migration assay and our innovative, rapid (2-3 h) clinically relevant, mechanobiology-based invasiveness assay. We validate chemotherapy-induced increase in the subpopulation of cell-surface GRP78(+) in two human, metastatic TNBC cell lines: MDA-MB-231 and MDA-MB-468. The GRP78(+) cell subpopulation exhibits reduced invasiveness and metastatic potential, as compared to whole-population control and to the GRP78(-) cell subpopulation, which are both highly invasive. Thus, using our innovative, clinically relevant assay, we rapidly (on clinical timescale) validate that GRP78(-) cells are likely linked with invasiveness, yet also demonstrate that combination of the GRP78(+) and GRP78(-) cells could increase the overall metastatic potential. Our results and approach could provide patient-personalized predictive marker for the expected benefits of chemotherapy in TNBC patients and potentially reveal non-responders to chemotherapy while also allowing evaluation of the clinical risk for metastasis.
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http://dx.doi.org/10.1007/s10439-024-03673-z | DOI Listing |
East Mediterr Health J
December 2024
Department of Radiology, King Abdulaziz University, Jeddah, Saudi Arabia.
Background: Breast cancer is often thought to occur at a younger age among Arab women based on the mean or median age at diagnosis, or the proportion of women diagnosed with breast cancer at a young age.
Objective: To compare age-specific breast cancer incidence rates among women from selected Arab countries with selected high- and middle-income countries.
Methods: We examined population-based, age-specific, national or regional breast cancer incidence data for 2008-2012 and 2013-2017 from Australia, Brazil, Canada, Germany, Japan, United Kingdom, and United States of America, and compared them with data from Algeria, Bahrain, Jordan, Kuwait, Morocco, Qatar, and Saudi Arabia.
Pharm Dev Technol
January 2025
Department of Pharmacy, School of Chemistry and Chemical Engineering, Liaoning Normal University, Dalian 116029, China.
In this paper, the pH-sensitive targeting functional material NGR-poly(2-ethyl-2-oxazoline)-cholesteryl methyl carbonate (NGR-PEtOz-CHMC, NPC) modified quercetin (QUE) liposomes (NPC-QUE-L) was constructed. The structure of NPC was confirmed by infrared spectroscopy (IR) and nuclear magnetic resonance hydrogen spectrum (H-NMR). Pharmacokinetic results showed that the accumulation of QUE in plasma of the NPC-QUE-L group was 1.
View Article and Find Full Text PDFJ Med Econ
January 2025
UNESCO-TWAS, The World Academy of Sciences, Trieste, Italy.
Aim: Dynamic cancer control is a current health system priority, yet methods for achieving it are lacking. This study aims to review the application of system dynamics modeling (SDM) on cancer control and evaluate the research quality.
Methods: Articles were searched in PubMed, Web of Science, and Scopus from the inception of the study to November 15th, 2023.
Int J Surg
January 2025
Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen, China.
Detection of biomarkers of breast cancer incurs additional costs and tissue burden. We propose a deep learning-based algorithm (BBMIL) to predict classical biomarkers, immunotherapy-associated gene signatures, and prognosis-associated subtypes directly from hematoxylin and eosin stained histopathology images. BBMIL showed the best performance among comparative algorithms on the prediction of classical biomarkers, immunotherapy related gene signatures, and subtypes.
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