Cancer cells adapt their metabolic processes to drive macromolecular biosynthesis for rapid cell growth and proliferation. RNA interference (RNAi)-based loss-of-function screening has proven powerful for the identification of new and interesting cancer targets, and recent studies have used this technology in vivo to identify novel tumour suppressor genes. Here we developed a method for identifying novel cancer targets via negative-selection RNAi screening using a human breast cancer xenograft model at an orthotopic site in the mouse. Using this method, we screened a set of metabolic genes associated with aggressive breast cancer and stemness to identify those required for in vivo tumorigenesis. Among the genes identified, phosphoglycerate dehydrogenase (PHGDH) is in a genomic region of recurrent copy number gain in breast cancer and PHGDH protein levels are elevated in 70% of oestrogen receptor (ER)-negative breast cancers. PHGDH catalyses the first step in the serine biosynthesis pathway, and breast cancer cells with high PHGDH expression have increased serine synthesis flux. Suppression of PHGDH in cell lines with elevated PHGDH expression, but not in those without, causes a strong decrease in cell proliferation and a reduction in serine synthesis. We find that PHGDH suppression does not affect intracellular serine levels, but causes a drop in the levels of α-ketoglutarate, another output of the pathway and a tricarboxylic acid (TCA) cycle intermediate. In cells with high PHGDH expression, the serine synthesis pathway contributes approximately 50% of the total anaplerotic flux of glutamine into the TCA cycle. These results reveal that certain breast cancers are dependent upon increased serine pathway flux caused by PHGDH overexpression and demonstrate the utility of in vivo negative-selection RNAi screens for finding potential anticancer targets.
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http://dx.doi.org/10.1038/nature10350 | DOI Listing |
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.
View Article and Find Full Text PDFInt J Gen Med
December 2024
Department of Thyroid and Breast Surgery, Quzhou People's Hospital, Quzhou, 324000, People's Republic of China.
Objective: This study aims to demonstrate the impact of sarcopenia on the prognosis of early breast cancer and its role in early multimodal intervention.
Methods: The clinical data of patients (n=285) subjected to chemotherapy for early-stage breast cancer diagnosed pathologically between January 1, 2016, and December 31, 2020, in our hospital were retrospectively analyzed. Accordingly, the recruited subjects were divided into sarcopenia (n=85) and non-sarcopenia (n=200) groups according to CT diagnosis correlating with single-factor and multifactorial logistic regression analyses.
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