Biology subjects

Puente, X. S.

Publications and source records attributed to Puente, X. S..

3 recordsLinked to original sources

bioRxiv2025

The costimulatory domain influences CD19 CAR-T cell resistance development in B-cell malignancies.

CD19-CAR-T-cells emerge as a major therapeutic option for relapsed/refractory B-cell-derived malignancies, however approximately half of patients eventually relapse. To identify resistance-driving factors, we repeatedly exposed B-cell lymphoma/B-cell acute lymphoblastic leukemia to 4-1BB/CD28-based CD19-CAR-T-cells in vitro. Generated models revealed costimulatory domain-dependent differences in CD19 loss. While CD19-4-1BB-CAR-T-cells induced combination epitope/total CD19 protein loss, CD19-CD28-CAR-T-cells did not drive antigen-escape. Consistent with observations in patients relapsing after CD19-4-1BB-CAR-T-cells, we identified CD19 frameshift/missense mutations affecting residues critical for FMC63 epitope recognition. Mathematical simulations revealed that differences between CD19-4-1BB- and CD19-CD28-CAR-T-cells activity against low-antigen-expressing tumor contribute to heterogeneous therapeutic responses. By integrating in vitro and in silico data, we propose a biological scenario where CD19-4-1BB-CAR-T-cells fail to eliminate low-antigen tumor cells, fostering CAR-resistance. These findings offer mechanistic insight into the observed clinical differences between axi-cel (CD28-based) and tisa-cel (4-1BB-based)-treated B-cell lymphoma patients and advance our understanding on CAR-T resistance. Furthermore, we underscore the need for specific FMC63 epitope detection to deliver information on antigen levels accessible for CD19-CAR-T-cells. Visual abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=117 SRC="FIGDIR/small/640707v1_ufig1.gif" ALT="Figure 1"> View larger version (40K): org.highwire.dtl.DTLVardef@1728c4org.highwire.dtl.DTLVardef@759660org.highwire.dtl.DTLVardef@1e3a0deorg.highwire.dtl.DTLVardef@15653e4_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2022

RFcaller: a machine learning approach combined with read-level features to detect somatic mutations

MotivationThe cost reduction in sequencing and the extensive genomic characterization of a wide variety of cancers is expanding the use of tumor sequencing approaches to a wide number of research groups and to the clinical practice. Although specific pipelines have been generated for the identification of somatic mutations, their results usually differ considerably, and a common approach in many projects is to use several callers to achieve a more reliable set of mutations. This procedure is computationally very expensive and time-consuming, and it suffers from the same limitations in sensitivity and specificity as other approaches. Expert revision of mutant calls is therefore required to verify calls that might be used for clinical diagnosis. Machine learning techniques provide a useful approach to incorporate expert-reviewed information for the identification of somatic mutations. ResultsWe have developed RFcaller, a pipeline based on machine learning algorithms, for the detection of somatic mutations in tumor-normal paired samples. RFcaller shows high accuracy for the detection of substitutions and indels from whole genome or exome data. It allows the detection of mutations in driver genes missed by other approaches, and has been validated by comparison to deep sequencing and Sanger sequencing. The pipeline is able to analyze a whole genome in a small period of time, and with a small computational footprint. Availability and implementationRFcaller is available at GitHub repository (https://github.com/xa-lab/RFcaller) and DockerHub (https://hub.docker.com/repository/docker/labxa/rfcaller). Contactxspuente@uniovi.es Supplementary informationSupplementary data is available online.

cancer biology↗

bioRxiv2020

The proliferative history shapes the DNA methylome of B-cell tumors and predicts clinical outcome

We report a systematic analysis of the biological and clinical implications of DNA methylation variability in five categories of B-cell tumors derived from B cells spanning the entire maturation spectrum. We used 2056 primary samples including training and validation series and show that 88% of the human DNA methylome is dynamically modulated under normal and neoplastic conditions. B-cell tumors display both epigenetic imprints of their cellular origin and de novo, disease-specific epigenetic alterations that in part are related to differential transcription factor binding. These differential methylation patterns were used by a machine-learning approach to create a diagnostic algorithm that accurately classifies 14 B-cell tumor entities and subtypes with different clinical management. Beyond this, we identified extensive patient-specific epigenetic variability targeting constitutively silenced chromatin regions, a phenomenon we could relate to the proliferative history of normal and neoplastic B cells. We observed that, depending on the maturation stage of the tumor cell of origin, mitotic activity leaves different imprints into the DNA methylome. Subsequently, we constructed a novel DNA methylation-based mitotic clock called epiCMIT (epigenetically-determined Cumulative MIToses), whose lapse magnitude represents a strong independent prognostic variable within specific B-cell tumor subtypes and is associated with particular driver genetic alterations. Our findings reveal DNA methylation as a holistic tracker of B-cell tumor developmental history, with implications in the differential diagnosis and prediction of the outcome of the patients.

cancer biology↗