Biology subjects

Martinez-Rubio, A.

Publications and source records attributed to Martinez-Rubio, A..

3 recordsLinked to original sources

bioRxiv2026

Reverse molecular pharmacology identifies the non-canonical axis of IRAK as a chemoresistance factor in neuroblastoma

Owing to chemoresistance, the prognosis of relapsed neuroblastoma is dismal with less than 10% of patients surviving after 5 years. We developed a reverse molecular pharmacology approach that is based on high-throughput drug screening coupled with chemo-informatic and transcriptomic analyses. This led to the identification of IRAK1 as a key chemoresistance factor in neuroblastoma. By performing functional and pharmacological drug combination screens targeting IRAK1, we revealed a synergy between IRAK1 inhibition/silencing and BET, EGFR and mTOR inhibitors as well as microtubule-targeting agents. The synergistic combination of microtubule-targeting agent, vincristine and IRAK inhibitors was then confirmed in tumor spheroids, patient-derived tumoroids and a syngeneic orthotopic mouse model. Mechanistically, IRAK inhibition potentiated the pro-apoptotic and cell cycle arrest properties of vincristine via a pathway involving the PIDDosome complex rather than its canonical MyDDosome axis. Altogether, this study represents a proof-of-concept of our reverse molecular pharmacology approach to quickly develop biology-guided drug combinations, that could be applied to any other human diseases.

cancer biology↗

bioRxiv2025

Automatic computational classification of bone marrow cells for B cell pediatric leukemia using UMAP

B Acute Lymphoblastic Leukemia (B-ALL) accounts for approximately 80% of pediatric leukemia cases. Despite treatment advances, 15-20% of children experience relapse, highlighting the need of improved monitoring of patients and novel strategies leading to successful therapies. Flow Cytometry is an essential technique for measuring residual disease and guiding treatment. However, traditional manual gating limits its efficiency. In recent years, computational tools have been integrated to enhance these clinical processes but many mathematical techniques are underexploited. Particularly, Uniform Manifold Approximation and Projection (UMAP), together with Machine Learning, provide promising approaches for analyzing large datasets. Mathematical tools and artificial intelligence offer new perspectives on these health problems, beyond the usual approach in biomedicine. We have exploited 234 samples from 75 B-ALL patients to develop an artificial intelligence-based algorithm that can improve patient classification and therapy decisions in different patient cohorts. This implies an advancement on the routine manual analysis of the disease progression, as we identify key subpopulations automatically, distinguishing patients bone marrow regeneration patterns, thus improving the prediction and prognosis of the disease. MSC Classification: 68-04, 68T09, 90C90, 92B05 Abbreviations: A list of abbreviations used throughout this work can be found in Appendix A.

cancer biology↗

bioRxiv2020

A mesoscopic simulator to uncover heterogeneity and evolutionary dynamics in tumors

Increasingly complex in-silico modeling approaches offer a way to simultaneously access cancerous processes at different spatio-temporal scales. High-level models, such as those based on partial differential equations, are computationally affordable and allow large tumor sizes and long temporal windows to be studied, but miss the discrete nature of many key underlying cellular processes. Individual-based approaches provide a much more detailed description of tumors, but have difficulties when trying to handle full-sized real cancers. Thus, there exists a trade-off between the integration of macroscopic and microscopic information, now widely available, and the ability to attain clinical tumor sizes. In this paper we put forward a stochastic mesoscopic simulation framework that incorporates key cellular processes during tumor progression while keeping computational costs to a minimum. Our framework captures a physical scale that allows both the incorporation of microscopic information, tracking the spatio-temporal emergence of tumor heterogeneity and the underlying evolutionary dynamics, and the reconstruction of clinically sized tumors from high-resolution medical imaging data, with the additional benefit of low computational cost. We illustrate the functionality of our modeling approach for the case of glioblastoma, a paradigm of tumor heterogeneity that remains extremely challenging in the clinical setting. Author summaryComputer simulation based on mathematical models provides a way to improve the understanding of complex processes in oncology. In this paper we develop a stochastic mesoscopic simulation approach that incorporates key cellular processes while keeping computational costs to a minimum. Our methodology captures the development of tumor heterogeneity and the underlying evolutionary dynamics. The physical scale considered allows microscopic information to be included, tracking the spatio-temporal evolution of tumor heterogeneity and reconstructing clinically sized tumors from high-resolution medical imaging data, with a low computational cost. We illustrate the functionality of the modeling approach for the case of glioblastoma, an epitome of heterogeneity in tumors.

systems biology↗