BackgroundAntiangiogenic therapy is one of the most significant advances in anticancer treatment. The benefits of antiangiogenic therapies of late-stage cancers have been investigated but are still too limited.MethodsWe used an ovarian cancer model to test the effect of short-term bevacizumab treatment on metastasis as measured by bioluminescence. Western blotting and CD34-PAS dual staining were performed to assess hypoxia-inducible transcription factor-1α (HIF-1α) expression and vasculogenic mimicry(VM) formation. Cell viability was examined by a CCK8 assay.ResultsBevacizumab demonstrated antitumor effects in models of ovarian cancer, but also accelerated metastasis together, with marked hypoxia and VM formation in mice receiving short-term therapy. Bevacizumab treatment did not affect SKOV3 cell viability and the amount of VM in three-dimensional culture.ConclusionThese results suggest that antiangiogenic therapy may potentially influence the progression of metastatic disease, which has been linked to the hypoxic response and VM formation.
DNA-encoded library
(DEL) is an efficient high-throughput screening
technology platform in drug discovery and is also gaining momentum
in academic research. Today, the majority of DELs are assembled and
encoded with double-stranded DNA tags (dsDELs) and has been selected
against numerous biological targets; however, dsDELs are not amendable
to some of the recently developed selection methods, such as the cross-linking-based
selection against immobilized targets and live-cell-based selections,
which require DELs encoded with single-stranded DNAs (ssDELs). Herein,
we present a simple method to convert dsDELs to ssDELs using exonuclease
digestion without library redesign and resynthesis. We show that dsDELs
could be efficiently converted to ssDELs and used for affinity-based
selections either with purified proteins or on live cells.
Dynamic combinatorial libraries (DCLs) is a powerful tool for ligand discovery in biomedical research; however, the application of DCLs has been hampered by their low diversity. Recently, the concept of DNA encoding has been employed in DCLs to create DNA‐encoded dynamic libraries (DEDLs); however, all current DEDLs are limited to fragment identification, and a challenging process of fragment linking is required after selection. We report an anchor‐directed DEDL approach that can identify full ligand structures from large‐scale DEDLs. This method is also able to convert unbiased libraries into focused ones targeting specific protein classes. We demonstrated this method by selecting DEDLs against five proteins, and novel inhibitors were identified for all targets. Notably, several selective BD1/BD2 inhibitors were identified from the selections against bromodomain 4 (BRD4), an important anti‐cancer drug target. This work may provide a broadly applicable method for inhibitor discovery.
DNA-encoded library (DEL) is a powerful ligand discovery technology that has been widely adopted in the pharmaceutical industry. DEL selections are typically performed with a purified protein target immobilized on a matrix or in solution phase. Recently, DELs have also been used to interrogate the targets in the complex biological environment, such as membrane proteins on live cells. However, due to the complex landscape of the cell surface, the selection inevitably involves significant nonspecific interactions, and the selection data are much noisier than the ones with purified proteins, making reliable hit identification highly challenging. Researchers have developed several approaches to denoise DEL datasets, but it remains unclear whether they are suitable for cell-based DEL selections. Here, we report the proof-of-principle of a new machine-learning (ML)-based approach to process cell-based DEL selection datasets by using a Maximum A Posteriori (MAP) estimation loss function, a probabilistic framework that can account for and quantify uncertainties of noisy data. We applied the approach to a DEL selection dataset, where a library of 7,721,415 compounds was selected against a purified carbonic anhydrase 2 (CA-2) and a cell line expressing the membrane protein carbonic anhydrase 12 (CA-12). The extended-connectivity fingerprint (ECFP)-based regression model using the MAP loss function was able to identify true binders and also reliable structure−activity relationship (SAR) from the noisy cell-based selection datasets. In addition, the regularized enrichment metric (known as MAP enrichment) could also be calculated directly without involving the specific machine-learning model, effectively suppressing low-confidence outliers and enhancing the signal-to-noise ratio. Future applications of this method will focus on de novo ligand discovery from cell-based DEL selections.
Background: Antiangiogenic therapy is one of the most significant advances in anticancer treatment. The benefits of antiangiogenic therapies of late-stage cancers have been investigated but are still too limited.
Methods:We used an ovarian cancer model to test the effect of short-term bevacizumab treatment on metastasis as measured by bioluminescence. Western blotting and CD34-PAS dual staining were performed to assess hypoxiainducible transcription factor-1α (HIF-1α) expression and vasculogenic mimicry(VM) formation. Cell viability was examined by a CCK8 assay.Results: Bevacizumab demonstrated antitumor effects in models of ovarian cancer, but also accelerated metastasis together, with marked hypoxia and VM formation in mice receiving short-term therapy. Bevacizumab treatment did not affect SKOV3 cell viability and the amount of VM in three-dimensional culture. Conclusion: These results suggest that antiangiogenic therapy may potentially influence the progression of metastatic disease, which has been linked to the hypoxic response and VM formation.
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