Biology subjects

Sanchez-Arevalo Lobo, V. J.

Publications and source records attributed to Sanchez-Arevalo Lobo, V. J..

4 recordsLinked to original sources

bioRxiv2026

Revisiting AXL as a therapeutic target in soft tissue sarcoma: dubermatinib and bemcentinib effects are largely independent of AXL inhibition

Soft tissue sarcomas (STS) are a heterogeneous group of mesenchymal malignancies with generally poor prognosis, raising a continued interest in novel therapeutic targets. The TAM-family receptor tyrosine kinase AXL has been proposed as a therapeutic target across several cancer types, including STS. Here we revisit the clinical relevance of AXL in STS by combining in silico analyses with functional and transcriptomic characterisation of two representative AXL inhibitors, dubermatinib and bemcentinib, in the HT-1080 and SK-LMS-1 STS-derived cell models. STS ranked among the tumour types with the highest median AXL mRNA expression across The Cancer Genome Atlas, yet AXL expression was not associated with patient survival. Both dubermatinib and bemcentinib reduced cell viability and induced apoptosis but, unexpectedly, shRNA-mediated AXL knockdown failed to reproduce the antiproliferative effect of either inhibitor and did not modify cellular sensitivity to drugs treatment. Transcriptomic profiling after AXL genetic knockdown showed weak correlation with drug-induced transcriptional changes, with the large majority of drug-regulated genes being AXL-independent and largely specific to each cell line. Our data indicate that although dubermatinib and bemcentinib show clear antitumour activity in STS models, this activity is largely independent from AXL inhibition, suggesting the rationale for AXL-directed therapy may be reconsidered.

cancer biology↗

bioRxiv2025

Deep Learning based deconvolution methods: a systematic review

Within this systematic review we examine the role of Artificial Intelligence (AI) and Deep Learning (DL) in the development of cellular deconvolution tools, with an special focus on their application to the analysis of transcriptomics data from RNA sequencing. We emphasize the critical importance of high-quality reference profiles for enhancing the accuracy of the discussed deconvolution methods, which is essential to determine cellular compositions in complex biological samples. To ensure the robustness of our work, we have applied a rigorous selection process following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. Through the review process, we have identified several key research gaps, highlighting the necessity for standardized methodologies and the improvement of the interpretability of the models. Overall, we present a comprehensive, up to date overview of the different methodologies, datasets, and findings associated with DL-driven deconvolution tools, paving the way for future research and emphasizing the value of collaboration between computational and biological sciences.

bioinformatics↗

bioRxiv2024

ELOVL6 as a Therapeutic Target: Disrupting c-MYC-Driven Lipid Metabolism to Enhance Chemotherapy in Pancreatic Cancer

Pancreatic ductal adenocarcinoma (PDAC) is a devastating disease, marked by a survival rate of only 12%. Consequently, the exploration of novel therapeutic strategies becomes a critical clinical imperative. Among the genetic alterations contributing to PDAC, c-MYC overexpression arises due to upstream mutations, amplifications, and copy number alterations. c-MYC serves as a key regulator in the tumors metabolic reprogramming, playing a pivotal role in proliferation, migration, and metastasis. This study delves into the investigation of the role of the elongase ELOVL6 in c-MYC-induced cell transformation and its potential as a therapeutic target in PDAC. Here, we demonstrate that c-MYC regulates lipid elongation to promote cell transformation, offering a new avenue for therapeutic intervention. Initially, we show the direct regulation of ELOVLs expression by c-MYC in various PDAC mouse models and cell lines, elucidating its upregulation during transformation and tumor progression. Genetic or chemical inhibition of ELOVL6 results in decreased proliferation and migration, accompanied by alterations in fatty acid elongation. These changes in fatty acid composition led to modifications in membrane rigidity, permeability, and thickness, which collectively affect micropinocytosis and macropinocytosis. Importantly, we observe an increase in Abraxane uptake and a synergistic effect when combined with ELOVL6 interference in vitro. In vivo validation demonstrates that ELOVL6 inhibition significantly reduces tumor growth and enhances the response to Abraxane, thereby increasing overall survival. Altogether, these results position ELOVL6 as a promising therapeutic target in the treatment of PDAC.

cancer biology↗

bioRxiv2024

Pymportx: Facilitating Next-Generation Transcriptomics Analysis in Python

The efficient importation of quantified gene expression data is pivotal in transcriptomics. Historically, the R package Tximport addressed this need by enabling seamless data integration from various quantification tools. However, the Python community lacked a corresponding tool, restricting cross-platform bioinformatics interoperability. We introduce Pymportx, a Python adaptation of Tximport, which replicates and extends the original packages functionalities. Pymportx maintains the integrity and accuracy of gene expression data while improving processing speed and integration within the Python ecosystem. It supports new data formats and includes tools for enhanced data exploration and analysis. Available under the MIT license, Pymportx integrates smoothly with Pythons bioinformatics tools, facilitating a unified and efficient workflow across the R and Python ecosystems. This advancement not only broadens access to Pythons extensive toolset but also fosters interdisciplinary collaboration and the development of cutting-edge bioinformatics analyses. Availability and ImplementationPymportx is released as an open-source software under the MIT license. The source code is available on GitHub at https://github.com/victorsanchezarevalo/Pymportx.

bioinformatics↗