Interactions between BRD4S, LOXL2, and MED1 drive cell cycle transcription in triple‐negative breast cancer
Dec 1, 2023·,,,,,,,,,,,
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Laura Pascual-Reguant
Queralt Serra-Camprubí
Debayan Datta
Damiano Cianferoni
Savvas Kourtis
Antoni Gañez-Zapater
Chiara Cannatá
Lorena Espinar
Jessica Querol
Laura García-López
Sara Musa-Afaneh
Maria Guirola
Anestis Gkanogiannis
Andrea Miró Canturri
Marta Guzman
Olga Rodríguez
Andrea Herencia-Ropero
Joaquin Arribas
Violeta Serra
Luis Serrano
Tian V Tian
Sandra Peiró
Sara Sdelci
Abstract
Triple‐negative breast cancer (TNBC) often develops resistance to single‐agent treatment, which can be circumvented using targeted combinatorial approaches. Here, we demonstrate that the simultaneous inhibition of LOXL2 and BRD4 synergistically limits TNBC proliferation in vitro and in vivo. Mechanistically, LOXL2 interacts in the nucleus with the short isoform of BRD4 (BRD4S), MED1, and the cell cycle transcriptional regulator B‐MyB. These interactions sustain the formation of BRD4 and MED1 nuclear transcriptional foci and control cell cycle progression at the gene expression level. The pharmacological co‐inhibition of LOXL2 and BRD4 reduces BRD4 nuclear foci, BRD4‐MED1 colocalization, and the transcription of cell cycle genes, thus suppressing TNBC cell proliferation. Targeting the interaction between BRD4S and LOXL2 could be a starting point for the development of new anticancer strategies for the treatment of TNBC.
Type
Publication
EMBO Molecular Medicine, 15(12), e18459
Authors
Senior AI/ML and genomics practitioner
Senior AI/ML and genomics practitioner with ~15 years building open-source, production-grade tools for large-scale biological data. Maintainer of multiple Bioconductor packages (fastreeR, metabinR, jvecfor), and author of agentic, LLM-driven tooling that runs reproducible bioinformatics workflows from natural-language requests.
Broad multi-omics background spanning genome assembly and annotation, population genomics, large-scale NGS and functional-genomics analysis, and metagenomics, backed by reproducible HPC software and end-to-end program leadership. Currently focused on bringing modern AI — embeddings, deep learning, and LLM-based agents — to making complex omics datasets faster and easier to interrogate.