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Tran, B. Q.

Publications and source records attributed to Tran, B. Q..

2 recordsLinked to original sources

The ASNS inhibitor ASX-173 potentiates L-asparaginase anticancer activity

Cancer cells reprogram metabolic pathways to meet increased energy and biosynthetic demands. Among those pathways, elevated asparagine metabolism regulated by asparagine synthetase (ASNS) has been linked to tumor progression in various cancers, driving cell proliferation, chemoresistance, and metastasis. ASNS inhibition represents a promising therapeutic strategy, but inhibitors have shown limited efficacy due to poor specificity and cell permeability. Through phenotypic screening, we identified ASX-173, a cell-permeable small molecule that inhibits ASNS at nanomolar concentrations. Biochemical and cellular assays confirm the specificity of ASX-173 activity and demonstrate its potentiation of the anti-cancer activity of L-asparaginase (ASNase), a key component of childhood acute lymphoblastic leukemia therapy. Mechanistically, the combination treatment disrupted nucleotide synthesis and induced cell cycle arrest and apoptosis. In a mouse model of acute myeloid leukemia, the combination significantly delayed the growth of OCI-AML2 xenografts. Analysis of data from The Cancer Genome Atlas (TCGA) revealed that ASNS mRNA expression is associated with poor survival in some cancer types and that ASNS protein levels are elevated in multiple solid tumors compared with the levels in normal tissues, suggesting possible broad utility of ASNS inhibition across the landscape of cancer. Together, these findings establish ASX-173 as a promising ASNS inhibitor and, for the first time, demonstrate a viable strategy to target ASNS therapeutically--an approach that has long remained elusive.

cancer biology↗

Enhanced longitudinal differential expression detection in proteomics with robust reproducibility optimization regression

Quantitative proteomics has matured into an established tool and longitudinal proteomic experiments have begun to emerge. However, no effective, simple-to-use differential expression method for longitudinal proteomics data has been released. Typically, such data is noisy, contains missing values, has only few time points and biological replicates. To address this need, we provide a comprehensive evaluation of several existing differential expression methods for high-throughput longitudinal omics data and introduce a new method, Robust longitudinal Differential Expression (RolDE). The methods were evaluated using nearly 2000 semi-simulated spike-in proteomic datasets and a large experimental dataset. The RolDE method performed overall best; it was most tolerant to missing values, displayed good reproducibility and was the top method in ranking the results in a biologically meaningful way. Furthermore, contrary to many approaches, the open source RolDE does not require prior knowledge concerning the types of differences searched, but can easily be applied even by non-experienced users.

bioinformatics↗