Biology subjects

Conejo-Garcia, J. R.

Publications and source records attributed to Conejo-Garcia, J. R..

6 recordsLinked to original sources

bioRxiv2026

BCL11B targeting in tumor CD8+ T cells amplifies anti-tumor response by blocking exhaustion while promoting stemness and cytotoxicity

Tumor infiltrating CD8+ T cells (TILs) progress to a state of terminal exhaustion (Ttex) which have impaired functionality and are nonrenewable. However their precursors (Tpex) are renewable and can generate efficient effector cells. We started from the observation that melanoma patients undergoing therapy with checkpoint inhibitors show increased survival when their T cells have low BCL11B mRNA. In line with this, ablation of Bcl11b in CD8+ TILs conferred a superior anti-tumor response in murine melanoma and ovarian cancer models. Bcl11b KO TILs failed to progress to the Ttex state and retained elevated stemness. Bcl11b exerted its role by repressing expression of essential transcription factors (TF) controlling stemness, and conversely by promoting expression of exhaustion-associated TFs and inhibitory receptor genes, through complex epigenetic control. In addition, Bcl11b KO CD8+ T cells showed increased Ag-specific cytolytic activity and elevated Gzmb and Prf1 proteins, but no increase in their mRNAs, however presented higher expression of genes with role in translation. Furthermore, CRISPR-CAS9-mediated deletion of BCL11B in human TILs from a patient with poor response to adoptive cell therapy with autologous TILs, improved their cytolytic activity and promoted expression of the stemness-associated TF TCF1, underlying its potential therapeutic use. HIGHLIGHTS- Adoptive transfer of Bcl11b KO CD8+ TILs surpasses WT in tumor burden reduction - Bcl11b ablation reprograms TILs and impairs the progression to Ttex state - Bcl11b KO CD8+ T cells have elevated cytotoxicity and kill only Ag-MHCI targets - BCL11B deletion in nonresponder ACT-TIL improves cytolytic activity and elevates TCF1 GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=64 SRC="FIGDIR/small/742578v1_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@c3b965org.highwire.dtl.DTLVardef@1c09d75org.highwire.dtl.DTLVardef@a19748org.highwire.dtl.DTLVardef@d2fd6b_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2024

CGPA: multi-context insights from the cancer gene prognosis atlas

Cancer transcriptomic data are used extensively to interrogate the prognostic value of targeted genes, yet basic scientists and clinicians have predominantly relied on univariable survival analysis for this purpose. This method often fails to capture the full prognostic potential and contextual relevance of the genes under study, inadvertently omitting a group of genes we term univariable missed-opportunity prognostic (UMOP) genes. Recognizing the complexity of revealing multifaceted prognostic implications, especially when extending the analysis to include various covariates and thresholds, we present the Cancer Gene Prognosis Atlas (CGPA). This platform greatly enhances gene-centric biomarker research across cancer types by offering an interactive and user-friendly interface for highly customized, in-depth prognostic analysis. CGPA notably supports data-driven exploration of gene pairs and gene-hallmark relationships, elucidating key composite biological mechanisms like synthetic lethality and immunosuppression. It further expands its capabilities to assess multi-gene panels using both public and user-provided data, facilitating a seamless mechanism-to-machine analysis. Additionally, CGPA features a designated portal for discovering prognostic gene modules using curated cancer immunotherapy data. Ultimately, CGPAs comprehensive, accessible tools allow cancer researchers, including those without statistical expertise, to precisely investigate the prognostic landscape of genes, customizing the model to fit specific research hypotheses and enhancing biomarker discovery and validation through a synergy of mechanistic and data-driven strategies. SignificanceCGPA is a streamlined, interactive platform for multi-context gene-centric prognostic analysis, simplifying biomarker discovery and validation in oncology for clinicians and basic scientists, and bridging a critical gap in translational cancer research.

bioinformatics↗

bioRxiv2024

A pan-cancer gamma delta T cell repertoire

This report presents the largest collection of gamma-delta T cell receptor ({gamma}{delta} TCR) reads in human cancer to date, analyzing about 11,000 patient tumor samples across 33 cancer types using the TRUST4 algorithm. Despite {gamma}{delta} T cells being a small fraction of the T cell population, they play a key role in both innate and adaptive immunity. Our comprehensive analysis reveals their significant presence across all cancer types, specifically highlighting the diverse spectrum and clonality patterns of their {gamma}{delta} receptors. This research highlights the complex roles of {gamma}{delta} T cells in tumor tissues and their potential as prognostic biomarkers. We also demonstrate the utility of T cell receptor gamma (TRG) and delta (TRD) gene expression values from standard RNA-seq data. Ultimately, our work establishes a fundamental resource for future tumor-infiltrating {gamma}{delta} T cell research and may facilitate the development of novel {gamma}{delta}-T-cell-based therapeutic strategies. Together, we demonstrate the strong diversity and prognostic potential of {gamma}{delta} T cells in multiple cancer types. HighlightsComprehensive analysis of {gamma}{delta} TCRs from 11,473 tumor samples Significant variability and overall consistency in {gamma}{delta} gene expression and clonotype {gamma}{delta}TCR expression and diversity as prognostic biomarkers across multiple cancers Centralized {gamma}{delta} TCR repertoire database for future therapeutic discovery

genomics↗

bioRxiv2024

Myeloid cell networks determine reinstatement of original immune environments in recurrent ovarian cancer

Immunotherapy has produced disappointing results in recurrent ovarian cancer (OC). However, the prognostic value of tumour-infiltrating lymphocytes (TILs) is largely based on the analysis of treatment-naive tumours. To understand the immunobiology of recurrent cancers, and their evolution, we profiled 170 patient-matched primary-recurrent OC samples from 69 patients of two independent cohorts. By capturing heterogeneous TIL distributions, we identified four immune phenotypes associated with differential prognosis, TILs states and TILs:myeloid networks, which dictate malignant evolution after chemotherapy and recurrence. Notably, recurrent tumours recapitulate the immunogenic patterns of original cancers. Mirroring inflamed human OC, preclinical recurrent Brca1mut tumours maintained activated TILs:dendritic cells (DCs) niches and immunostimulatory tumour-associated macrophages (TAMs). Conversely, recurrent Brca1wt tumours displayed loss of TILs:DCs niches and accumulated immunosuppressive myeloid networks featuring Trem2/ApoEhigh TAMs and Nduf4l2high/Galectin3high malignant states. Our study highlights that persistent immunogenicity in recurrent OC is governed by the crosstalk between dissimilar myeloid cells and TILs, which is BRCA-dependent.

immunology↗

bioRxiv2023

A Novel Humanized Immune Stroma PDX Cancer Model for Therapeutic Studies

Standard preclinical human tumor models lack a human tumor stroma. However, as stroma contributes to therapeutic resistance, the lack of human stroma may make current models less stringent for testing new therapies. To address this, using patient-derived tumor cells, patient derived cancer-associated mesenchymal stem/progenitor cells, and human endothelial cells, we created a Human Stroma-Patient Derived Xenograft (HS-PDX) tumor model. HS-PDX, compared to the standard PDX model, demonstrate greater resistance to targeted therapy and chemotherapy, and better reflect patient response to therapy. Furthermore, HS-PDX can be grown in mice with humanized bone marrow to create humanized immune stroma patient-derived xenograft (HIS-PDX) models. The HIS-PDX model contains human connective tissues, vascular and immune cell infiltrates. RNA sequencing analysis demonstrated a 94-96% correlation with primary human tumor. Using this model, we demonstrate the impact of human tumor stroma on response to CAR-T cell therapy and immune checkpoint inhibitor therapy. We show an immunosuppressive role for human tumor stroma and that this model can be used to identify immunotherapeutic combinations to overcome stromally mediated immunosuppression. Combined, our data confirm a critical role for human stoma in therapeutic response and indicate that HIS-PDX can be an important tool for preclinical drug testing. Statement of SignificanceWe developed a tumor model with human stromal, vascular, and immune cells. This model mirrors patient response to chemotherapy, targeted therapy, and immunotherapy, and can be used to study therapy resistance.

cancer biology↗

bioRxiv2021

VALIS: Virtual Alignment of pathoLogy Image Series

Spatial analyses can reveal important interactions between and among cells and their microenvironment. However, most existing staining methods are limited to a handful of markers per slice, thereby limiting the number of interactions that can be studied. This limitation is frequently overcome by registering multiple images to create a single composite image containing many markers. While there are several existing image registration methods for whole slide images (WSI), most have specific use cases. Here, we present the Virtual Alignment of pathoLogy Image Series (VALIS), a fully automated pipeline that opens, registers (rigid and/or non-rigid), and saves aligned slides in the ome.tiff format. VALIS has been tested with 273 immunohistochemistry (IHC) samples and 340 immunofluorescence (IF) samples, each of which contained between 2-69 images per sample. The registered WSI tend to have low error and are completed within a matter of minutes. In addition to registering slides, VALIS can also using the registration parameters to warp point data, such as cell centroids previously determined via cell segmentation and phenotyping. VALIS is written in Python and requires only few lines of code for execution. VALIS therefore provides a free, opensource, flexible, and simple pipeline for rigid and non-rigid registration of IF and/or IHC that can facilitate spatial analyses of WSI from novel and existing datasets.

cancer biology↗