Biology subjects

Pardoll, D.

Publications and source records attributed to Pardoll, D..

7 recordsLinked to original sources

bioRxiv2026

Immune checkpoint inhibitors amplify type 2 immune mediated repair bypro-regenerative scaffolds

Extracellular matrix (ECM) scaffolds induce type 2 immunity to promote repair. Here, we show that immune cells recruited to ECM-treated murine muscle injuries and clinical soft tissue defects express immune checkpoints. Specifically, TH2 cells and regulatory T cells (Tregs) increase LAG3 expression, while macrophages express PDL2. TCR analysis and a triple-reporter strain for interleukin (IL)-13 and Treg fate-mapping suggest that Tregs in ECM-treated wounds transition into TH2-like exTregs that express LAG3. Immune checkpoint inhibition (ICI) significantly stimulated type 2 immunity in ECM-treated wounds, including increased TH2 cells, Treg transition to TH2-like exTregs, and pro-regenerative macrophages. Moreover, ICI enhanced muscle repair and reduced fibrosis in ECM-treated wounds. Collectively, these findings show Treg/TH2 plasticity in wound healing and introduce a novel ICI application to enhance immune-mediated regeneration.

bioengineering↗

bioRxiv2025

Dual checkpoint blockade of glioblastoma with Anti-PD-1 and Anti-LAG-3 promotes expansion of tumor-reactive T cell clones along a unique pathway of differentiation

IDH-wildtype grade IV glioblastoma is the most aggressive adult primary brain tumor and remains refractory to anti-PD-1 monotherapy despite evidence of limited tumor-specific T cell induction. To determine the impact of immune checkpoint inhibitors (ICIs) on glioblastoma T cell transcriptional landscape and repertoire, we conducted paired single-cell RNA sequencing (scRNA-seq) and T cell receptor sequencing (TCR-seq) of tumor-infiltrating lymphocytes (TILs) from patients with untreated, newly diagnosed glioblastoma and from recurrent glioblastoma treated with dual checkpoint blockade targeting PD-1 and LAG-3. Using a validated transcriptional signature, we found that predicted tumor-reactive T cells (TRC) in untreated glioblastomas reside almost exclusively in a clonally expanded GZMKhi population with developmental plasticity, affording them the potential to differentiate into both tissue-resident and terminal effector T cells. Dual ICI therapy induced substantial clonal remodeling, characterized by the recruitment of new TRC from the periphery into the tumor microenvironment (TME) and differentiation into transitional effectors and ultimately terminal effectors along a gradient characterized by simultaneous acquisition of cytotoxic and exhaustion genes, regulated by specific transcriptional, metabolic, and epigenetic programs. Longitudinal clonal tracking in peripheral blood confirmed that with ICI treatment, most TRC expand transiently in circulation prior to tumor infiltration, with peripherally derived clones becoming the major contributor to the GZMKhi TRC that further expand in the tumor. Our study provides the first comprehensive map of T cell clonal dynamics and differentiation in glioblastoma following dual ICIs and highlights a potential mechanism of immune activation and peripheral recruitment of TRC in glioblastoma not previously described. Our results suggest that therapeutic strategies to sustain these GZMKhi early effector and transitional effector T cells may further enhance ICI therapeutic efficacy in glioblastoma.

immunology↗

bioRxiv2025

Deciphering Cell Fate and Clonal Dynamics via Integrative Single-Cell Lineage Modeling

Through natural or synthetic lineage barcodes, single-cell technologies now enable the joint measurement of molecular states and clonal identities, providing an unprecedented opportunity to study cell fate and dynamics. Yet, most computational methods for inferring cell development and differentiation rely exclusively on transcriptional similarity, overlooking the lineage information encoded by lineage barcodes. This limitation is exemplified by T cells, where subtle transcriptional differences mark divergent fates with distinct biological activity. Single-cell RNA and matched TCR sequencing is now ubiquitous in the analysis of clinical samples, where the TCR sequence provides an endogenous clonal barcode and could reveal clonal T cell responses. We present Clonotrace, a computational framework that jointly models gene expression and clonotype information to infer cell state transitions and fate biases with higher fidelity. While motivated by challenges in analyzing T cell populations, especially in the tumor microenvironment and immunotherapy settings, Clonotrace is broadly applicable to any lineage-barcoded single-cell dataset. Across diverse systems including T cells, hematopoietic differentiation, and cancer therapy resistance models, Clonotrace reveals differentiation hierarchies, distinguishes unipotent from multipotent states, and identifies candidate fate-determining genes driving lineage commitment.

bioinformatics↗

bioRxiv2023

Distinct Myeloid Derived Suppressor Cell Populations Promote Tumor Aggression in Glioblastoma

The diversity of genetic programs and cellular plasticity of glioma-associated myeloid cells, and thus their contribution to tumor growth and immune evasion, is poorly understood. We performed single cell RNA-sequencing of immune and tumor cells from 33 glioma patients of varying tumor grades. We identified two populations characteristic of myeloid derived suppressor cells (MDSC), unique to glioblastoma (GBM) and absent in grades II and III tumors: i) an early progenitor population (E-MDSC) characterized by strong upregulation of multiple catabolic, anabolic, oxidative stress, and hypoxia pathways typically observed within tumor cells themselves, and ii) a monocytic MDSC (M-MDSC) population. The E-MDSCs geographically co-localize with a subset of highly metabolic glioma stem-like tumor cells with a mesenchymal program in the pseudopalisading region, a pathognomonic feature of GBMs associated with poor prognosis. Ligand-receptor interaction analysis revealed symbiotic cross-talk between the stemlike tumor cells and E-MDSCs in GBM, whereby glioma stem cells produce chemokines attracting E-MDSCs, which in turn produce growth and survival factors for the tumor cells. Our large-scale single-cell analysis elucidated unique MDSC populations as key facilitators of GBM progression and mediators of tumor immunosuppression, suggesting that targeting these specific myeloid compartments, including their metabolic programs, may be a promising therapeutic intervention in this deadly cancer. One-Sentence SummaryAggressive glioblastoma harbors two unique myeloid populations capable of promoting stem-like properties of tumor cells and suppressing T cell function in the tumor microenvironment.

immunology↗

bioRxiv2020

Supraphysiological testosterone induces ferroptosis and activates NF-kappaB mediated immune pathways in prostate cancer through nucleophagy

The discovery that androgens play an important role in the progression of prostate cancer (PCa) has led to the development of androgen deprivation therapy as a first line of treatment against PCa. However, paradoxical growth inhibition has been observed, both experimentally and clinically, in a subset of PCa upon administration of supraphysiological levels of testosterone (SupraT). Here we report that SupraT activates cytoplasmic nucleic acid sensors and induces growth inhibition of SupraT-sensitive PCa cells. This is initiated by induction of two parallel autophagy-mediated processes, namely, ferritinophagy and nucleophagy. Consequently, autophagosomal DNA activates nucleic acid sensors that converge on NF-kappaB to drive immune signaling pathways. Chemokines and cytokines secreted by the tumor cells in response to SupraT results in increased migration of cytotoxic immune cells to tumor beds of animal xenografts and patient tumors. Collectively, our findings indicate that SupraT may inhibit a subset of PCa by activating nucleic acid sensors and downstream immune signaling.

cancer biology↗

bioRxiv2019

Interleukin 17 and senescence regulate the foreign body response

Synthetic biomaterials and medical devices suffer to varying levels from fibrosis via the foreign body response (FBR). To explore mechanistic connections between the immune response and fibrosis from the FBR, we first analyzed fibrotic capsule surrounding human breast implants and found increased numbers of interleukin (IL)17-producing {gamma}{delta}+ T cells and CD4+ TH17 cells as well as senescent cells. Further analysis in a murine model demonstrated an early innate IL17 response to synthetic implants, mediated by innate lymphoid cells and {gamma}{delta}+ T cells, was followed by a chronic adaptive antigen dependent CD4+ TH17 cell response. Mice deficient in IL17 signaling established that IL17 was required for the fibrotic response to materials and the development of p16INK4a senescent cells. Treatment with a senolytic agent reduced IL17 expression and fibrosis. Discovery of a feed-forward loop between the TH17 and senescence response to synthetic materials introduces new targets for therapeutic intervention in the foreign body response.

immunology↗

bioRxiv2018

AI-MHC: an allele-integrated deep learning framework for improving Class I & Class II HLA-binding predictions

MotivationThe immune system has potential to present a wide variety of peptides to itself as a means of surveillance for pathogenic invaders. This means of surveillances allows the immune system to detect peptides derives from bacterial, viral, and even oncologic sources. However, given the breadth of the epitope repertoire, in order to study immune responses to these epitopes, investigators have relied on in-silico prediction algorithms to help narrow down the list of candidate epitopes, and current methods still have much in the way of improvement.\n\nResultsWe present Allele-Integrated MHC (AI-MHC), a deep learning architecture with improved performance over the current state-of-the-art algorithms in human Class I and Class II MHC binding prediction. Our architecture utilizes a convolutional neural network that improves prediction accuracy by 1) allowing one neural network to be trained on all peptides for all alleles of a given class of MHC molecules by making the allele an input to the net and 2) introducing a global max pooling operation with an optimized kernel size that allows the architecture to achieve translational invariance in MHC-peptide binding analysis, making it suitable for sequence analytics where a frame of interest needs to be learned in a longer, variable length sequence. We assess AI-MHC against internal independent test sets and compare against all algorithms in the IEDB automated server benchmarks, demonstrating our algorithm achieves state-of-the-art for both Class I and Class II prediction.\n\nAvailability and ImplementationAI-MHC can be used via web interface at baras.pathology.jhu.edu/AI-MHC\n\nContactjsidhom1@jhmi.edu

bioinformatics↗