RESUMEN
B-cell lymphomas (BCL) is the most frequent hematological cancer in dogs. Treatment typically consists of chemotherapy, with CHOP-based protocols. However, outcome remains generally poor, urging the exploration of new therapeutic strategies with a targeted approach. Myc transcription factor plays a crucial role in regulating cellular processes, and its dysregulation is implicated in numerous human and canine malignancies, including canine BCL (cBCL). This study aims to evaluate the efficacy of indirectly inhibiting Myc in cBCL using BI2536 and MZ1 compounds in two in vitro models (CLBL-1 and KLR-1201). Both BI2536 and MZ1, alone and combined, affected cell viability in a significant concentration- and time-dependent manner. Western Blot revealed an upregulation of PLK1 expression in both cell lines upon treatment with BI2536, in association with a reduction in c-Myc protein levels. Conversely, MZ1 led to a decrease in its primary target, BRD4, along with a reduction in c-Myc. Furthermore, BI2536, both alone and in combination with MZ1, induced larger transcriptomic changes in cells compared to MZ1 alone, primarily affecting MYC target genes and genes involved in cell cycle regulation. These data underscore the potential role of Myc as therapeutic target in cBCL, providing a novel approach to indirectly modulate this molecule.
RESUMEN
Canine oral malignant melanoma (COMM) is the most common neoplasm in the oral cavity characterized by local invasiveness and high metastatic potential. Hypoxia represents a crucial feature of the solid tumor microenvironment promoting cancer progression and drug resistance. Hypoxia-inducible factor-1α (HIF-1α) and its downstream effectors, vascular endothelial growth factor A (VEGF-A), glucose transporter isoform 1 (GLUT1), C-X-C chemokine receptor type 4 (CXCR4), and carbonic anhydrase IX (CAIX), are the main regulators of the adaptive response to low oxygen availability. The prognostic value of these markers was evaluated in 36 COMMs using immunohistochemistry. In addition, the effects of cobalt chloride-mediated hypoxia were evaluated in 1 primary COMM cell line. HIF-1α expression was observed in the nucleus, and this localization correlated with the presence or enhanced expression of HIF-1α-regulated genes at the protein level. Multivariate analysis revealed that in dogs given chondroitin sulfate proteoglycan-4 (CSPG4) DNA vaccine, COMMs expressing HIF-1α, VEGF-A, and CXCR4 were associated with shorter disease-free intervals (DFI) compared with tumors that were negative for these markers (P = .03), suggesting hypoxia can influence immunotherapy response. Western blotting showed that, under chemically induced hypoxia, COMM cells accumulate HIF-1α and smaller amounts of CAIX. HIF-1α induction and stabilization triggered by hypoxia was corroborated by immunofluorescence, showing its nuclear translocation. These findings reinforce the role of an hypoxic microenvironment in tumor progression and patient outcome in COMM, as previously established in several human and canine cancers. In addition, hypoxic markers may represent promising prognostic markers, highlighting opportunities for their use in therapeutic strategies for COMMs.
RESUMEN
MOTIVATION: The prediction of reliable Drug-Target Interactions (DTIs) is a key task in computer-aided drug design and repurposing. Here, we present a new approach based on data fusion for DTI prediction built on top of the NXTfusion library, which generalizes the Matrix Factorization paradigm by extending it to the nonlinear inference over Entity-Relation graphs. RESULTS: We benchmarked our approach on five datasets and we compared our models against state-of-the-art methods. Our models outperform most of the existing methods and, simultaneously, retain the flexibility to predict both DTIs as binary classification and regression of the real-valued drug-target affinity, competing with models built explicitly for each task. Moreover, our findings suggest that the validation of DTI methods should be stricter than what has been proposed in some previous studies, focusing more on mimicking real-life DTI settings where predictions for previously unseen drugs, proteins, and drug-protein pairs are needed. These settings are exactly the context in which the benefit of integrating heterogeneous information with our Entity-Relation data fusion approach is the most evident. AVAILABILITY AND IMPLEMENTATION: All software and data are available at https://github.com/eugeniomazzone/CPI-NXTFusion and https://pypi.org/project/NXTfusion/.
