Biology subjects

Vicent, S.

Publications and source records attributed to Vicent, S..

4 recordsLinked to original sources

bioRxiv2026

Concurrent AXL inhibition enhances RAS and ERK inhibitor efficacy in KRAS-mutant pancreatic and lung cancer

Resistance limits the clinical efficacy of RAS inhibitors. We applied chemical and genetic screens and identified the AXL receptor tyrosine kinase as a driver of resistance to RAS-ERK inhibition. We determined that combination treatment with the AXL inhibitor bemcentinib (AXLi) together with the RAS(ON) multi-selective tri-complex inhibitor RMC-7977 (RASi) or the ERK-selective inhibitor SCH772984 (ERKi) significantly enhanced growth suppression in human KRAS-mutant pancreatic and lung cancer models. Combined AXLi and RASi treatment of human KRAS-mutant pancreatic cell line-derived xenograft tumors synergistically suppressed ERK activation and MYC expression, and caused tumor regression. Analyses of immunocompetent mouse allograft pancreatic tumor models revealed a largely tumor cell-intrinsic response to inhibitor treatment. We identified an unexpected mechanism whereby KRAS inhibition upregulated the AXL ligand GAS6, activating AXL but inducing an AXL-dependent adaptive resistance mechanism wherein AXL antagonizes RASi efficacy. Our observations support concurrent AXL inhibition as a strategy to enhance RAS inhibitor clinical efficacy. STATEMENT OF SIGNIFICANCEOur findings identify AXL as a driver of resistance to RAS inhibitors, establishing a combination strategy to overcome resistance and enhance RAS inhibitor therapeutic efficacy in KRAS-mutant cancer by maximally inhibiting oncogenic RAS signaling.

cancer biology↗

bioRxiv2025

Quantitative evaluation of the role of the microenvironment in the phenotype, metabolism, and drug resistance of PDAC tumor organoids

Tumor organoids are powerful tools to study cancer. However, the clinical translation of organoid-based studies depends on how closely the organoid scaffolding material recapitulates the tumor extracellular matrix (ECM). We present a quantitative analysis of the effect of scaffold composition on the phenotype, tissue remodeling, metabolism, and drug resistance of pancreatic ductal adenocarcinoma (PDAC) organoids. We grew PDAC organoids within hydrogels made of Matrigel, collagen-I, or a mixture of collagen-I and Matrigel. Our results show that 1) PDAC organoids grown in Matrigel-only are phenotypically and metabolically similar to low-physiological two-dimensional PDAC cultures; 2) collagen-containing hydrogels, and especially so those grown in hydrogels of mixed collagen-Matrigel composition, accurately reproduce invasive tumors that underwent epithelial-to-mesenchymal transition (EMT) regarding phenotype, tissue remodeling, metabolic activity, and drug resistance. In summary, our work illustrates the importance of three-dimensional (3D) matrix composition for PDAC growth and postulates that organoids grown in collagen-Matrigel hydrogels are the most adequate for studying the biology of invasive PDAC tumors.

bioengineering↗

bioRxiv2024

Targeted KRAS(G12V) degradation elicits efficient and durable lung adenocarcinoma regression in vivo

Recent drug discovery breakthroughs led to the approval of KRASG12C inhibitors in lung adenocarcinoma (LUAD). Unfortunately, clinical responses remain limited due to rapid resistance onset. Proteolysis-targeting chimeras (PROTACs) have emerged as promising alternatives to traditional inhibition. However, there is limited mechanistic understanding of KRAS degradation in vivo. Here, we developed a preclinical LUAD mouse model and demonstrated that targeted oncogenic KRAS degradation induces rapid tumor regression. Transcriptional, histological, and immunophenotypic analyses revealed a substantial remodeling of the tumor microenvironment. Notably, disease relapse observed during long-term degrader treatment stems from proteolysis machinery dysregulation, indicating resistance mechanisms distinct from those reported upon KRAS inhibition. Our findings highlight the therapeutic potential of KRAS degradation in LUAD, offering insights into cell-intrinsic and extrinsic mechanisms driving durable antitumor responses and supporting further clinical exploration. SIGNIFICANCEGiven the short duration of the clinical responses to KRAS inhibitors, complementary therapies are a dire medical need. Our preclinical findings endorse KRAS degradation as a therapeutic alternative in LUAD, where cell-intrinsic and extrinsic mechanisms drive tumor regression and durable therapeutic responses.

cancer biology↗

bioRxiv2023

GeNNius: An ultrafast drug-target interaction inference method based on graph neural networks

Drug-target interaction (DTI) prediction is a relevant but challenging task in the drug repurposing field. In-silico approaches have drawn particular attention as they can reduce associated costs and time commitment of traditional methodologies. Yet, current state-of-the-art methods present several limitations: existing DTI prediction approaches are computationally expensive, thereby hindering the ability to use large networks and exploit available datasets and, the generalization to unseen datasets of DTI prediction methods remains unexplored, which could potentially improve the development processes of DTI inferring approaches in terms of accuracy and robustness. In this work, we introduce GO_SCPLOWEC_SCPLOWnnO_SCPLOWIUSC_SCPLOW (Graph Embedding Neural Network Interaction Uncovering System), a Graph Neural Network (GNN)-based method that outperforms state-of-the-art models in terms of both accuracy and time efficiency across a variety of datasets. We also demonstrated its prediction power to uncover new interactions by evaluating not previously known DTIs for each dataset. We further assessed the generalization capability of GO_SCPLOWEC_SCPLOWnnO_SCPLOWIUSC_SCPLOW by training and testing it on different datasets, showing that this framework can potentially improve the DTI prediction task by training on large datasets and testing on smaller ones. Finally, we investigated qualitatively the embeddings generated by GO_SCPLOWEC_SCPLOWnnO_SCPLOWIUSC_SCPLOW, revealing that the GNN encoder maintains biological information after the graph convolutions while diffusing this information through nodes, eventually distinguishing protein families in the node embedding space. Code Availabilityhttps://github.com/ubioinformat/GeNNius

bioinformatics↗