Biology subjects

Pineda-Lucena, A.

Publications and source records attributed to Pineda-Lucena, A..

4 recordsLinked to original sources

bioRxiv2026

Rational engineering of sdAb-based CAR T cells targeting BCMA enhances antitumor efficacy and persistence in Multiple Myeloma

BCMA-directed CAR T therapies have transformed the treatment of relapsed/refractory multiple myeloma (MM), yet durable responses remain limited by insufficient persistence and functional exhaustion. The contribution of antigen-binding domain properties to CAR T cell performance is not fully defined. Here, we report the rational discovery and engineering of BCMA-targeting single-domain antibodies (sdAbs) and their incorporation into second-generation CAR T cells. We generated sdAbs recognizing distinct BCMA epitopes with diverse binding kinetics. Comprehensive biophysical and functional characterization identified sdAb5 as a lead candidate with balanced binding kinetics and a non-overlapping epitope relative to clinically approved constructs. sdAb-based CAR T cells exhibited potent antigen-specific cytotoxicity, minimal tonic signaling, and preserved IL-2 production without excessive inflammatory activation. In xenograft models, sdAb5-based CAR T cells induced durable tumor control, enhanced persistence, and improved responses to tumor rechallenge compared with ide-cel and cilta-cel constructs. Notably, this superior functionality correlated with balanced binding kinetics rather than maximal affinity. Single-cell transcriptomic analyses corroborated these findings, revealing a restrained inflammatory activation in sdAb5-based CAR T cells with preservation of transcriptional plasticity. Collectively, these findings support antigen-binding optimization as a key determinant of CAR T-cell durability and identify sdAb5 as a candidate for next-generation BCMA-directed therapies in MM.

bioengineering↗

bioRxiv2025

Histone methyl-transferase G9a inhibition boosts the efficacy of immune checkpoint inhibitors in experimental hepatocellular carcinoma

Background and AimsImmune checkpoint inhibitors (ICI) have revolutionized cancer therapy. Yet, their efficacy in hepatocellular carcinoma (HCC) remains limited, partly due to tumor-intrinsic mechanisms of immune evasion. This study focused on the identification of potential epigenetic drivers of immune resistance in HCC evaluating the therapeutic potential of targeting the histone methyltransferase G9a (EHMT2). Approach and ResultsWe analyzed G9a expression across multiple human HCC cohorts and found that elevated G9a levels were inversely correlated with the most relevant immune-related gene expression signatures predictive of ICI responsiveness. Using HCC cell lines and orthotopic models implemented in immunocompetent mice, we assessed the effects of pharmacologic inhibition of G9a with two innovative epigenetic inhibitors, CM272 and EZM8266. G9a blockade enhanced tumor cell immunogenicity by restoring IFN{gamma} responsiveness, increasing MHC-I surface expression, and promoting chemokine-mediated (CXCL10) recruitment of T cells. Mechanistically, G9a inhibition induced a viral mimicry response through derepressing endogenous retroviral elements and the accumulation of cytosolic double-stranded RNA. In vivo, G9a inhibition synergized with anti-PD-1 therapy to suppress tumor growth, significantly enhancing CD8 T cell infiltration. Notably, in a clinically-relevant post-hepatectomy HCC recurrence model, the combination therapy overcame immune resistance. ConclusionsG9a functions as a central epigenetic barrier to antitumor immunity in HCC. Pharmacologic G9a inhibition reprograms the tumor microenvironment, enhances immunogenicity, and sensitizes tumors to ICIs. These findings provide strong preclinical rationale for integrating G9a-targeted therapies with immunotherapy, particularly in perioperative settings.

pharmacology and toxicology↗

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↗

bioRxiv2022

gMCStool: automated network-based tool to search for metabolic vulnerabilities in cancer

The development of computational tools for the systematic prediction of metabolic vulnerabilities of cancer cells constitutes a central question in systems biology. Here, we present gMCStool, a freely accessible and online tool that allows us to carry out this task in a simple, efficient and intuitive environment. gMCStool exploits the concept of genetic Minimal Cut Sets (gMCSs), a theoretical approach to synthetic lethality based on genome-scale metabolic networks, including a unique database of thousands of synthetic lethals computed from Human1, the most recent metabolic reconstruction of human cells. Based on RNA-seq data, gMCStool extends and improves our previously developed algorithms to predict, visualize and analyze metabolic essential genes in cancer, demonstrating a superior performance than competing algorithms in both accuracy and computational performance. A detailed illustration of gMCStool is presented for multiple myeloma (MM), an incurable hematological malignancy. gMCStool could identify a synthetic lethal that explains the dependency on CTP Synthase 1 (CTPS1) in a sub-group of MM patients. We provide in vitro experimental evidence that supports this hypothesis, which opens a new research area to treat MM.

systems biology↗