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Boskova, V.

Publications and source records attributed to Boskova, V..

2 recordsLinked to original sources

Exploring the Anti-Tumor Effects of Brusatol in Aggressive B-Cell Lymphomas

Aggressive B-cell lymphomas are the most common lymphoid malignancies in adults, with an increasing incidence. Diffuse large B-cell lymphoma (DLBCL) is the most prevalent and highly heterogeneous type, often exhibiting poor responses to standard immuno-chemotherapy. One third of patients experience primary refractory disease or relapse, particularly those with high c-MYC and BCL-2 expression levels, which drive aggressiveness and resistance to treatment. Therefore, the development of new therapies is urgently needed. This study explores the anti-tumor properties of brusatol, both as single agent and in combination with the BCL-2 inhibitor, venetoclax, for the treatment of aggressive lymphoma cells. Brusatol inhibited lymphoma cell growth in a concentration-dependent manner in various cell lines. Further analysis revealed that brusatol is able to efficiently induce cell death in the lymphoma cells in vitro, as well as in patient-derived samples. Importantly, the effect of brusatol was strongly reduced in non-malignant lymphoid cells compared to lymphoma cells. We discovered that lymphoma cell lines highly sensitive to brusatol have elevated c-MYC expression. Brusatol treatment rapidly reduced protein expression of c-MYC and MCL-1. Protein biosynthesis analysis confirmed that brusatol induced translational inhibition in aggressive lymphoma cells. Furthermore, the combination of brusatol and venetoclax synergistically enhanced lymphoma cell death, particularly in samples with low c-MYC protein levels. Our findings suggest that brusatol has significant potential for the development of novel therapeutic strategies for aggressive lymphomas, especially for cases with high c-MYC content. Additionally, its combination with venetoclax represents a promising approach to improve treatment outcomes.

cancer biology↗

Deep learning from phylogenies to uncover the transmission dynamics of epidemics

Widely applicable, accurate and fast inference methods in phylodynamics are needed to fully profit from the richness of genetic data in uncovering the dynamics of epidemics. Standard methods, including maximum-likelihood and Bayesian approaches, generally rely on complex mathematical formulae and approximations, and do not scale with dataset size. We develop a likelihood-free, simulation-based approach, which combines deep learning with (1) a large set of summary statistics measured on phylogenies or (2) a complete and compact representation of trees, which avoids potential limitations of summary statistics and applies to any phylodynamics model. Our method enables both model selection and estimation of epidemiological parameters from very large phylogenies. We demonstrate its speed and accuracy on simulated data, where it performs better than the state-of-the-art methods. To illustrate its applicability, we assess the dynamics induced by superspreading individuals in an HIV dataset of men-having-sex-with-men in Zurich. Our tool PhyloDeep is available on github.com/evolbioinfo/phylodeep.

bioinformatics↗