Biology subjects

Hoffer-Hawlik, K.

Publications and source records attributed to Hoffer-Hawlik, K..

4 recordsLinked to original sources

bioRxiv2026

Immune Checkpoint Therapy Drives Maturation of a Cellular Neighborhood Nucleated by T Cell-APC Triads Enabling Spatially Compartmentalized Tumor Immunity

Spatially organized immune hubs of T cells and antigen-presenting cells (APCs) have been linked to immune checkpoint therapy (ICT) efficacy, yet the mechanisms underlying their function remain unclear. Using CODEX multiplex imaging, we longitudinally characterized the dynamic evolution of intratumoral cellular neighborhoods (CN) defined by "triad" interactions of CD4 and CD8 T cells with two distinct myeloid APC populations: cDC1s and IFN{gamma}-activated macrophages. We termed this CN the immunity-promoting CN (IP-CN) and tracked its progressive development during tumor rejection induced by -CTLA-4/-PD-1 therapy. A coordinated IFN{gamma} and TNF signaling signature accompanied the IP-CN assembly. Over time, the IP-CN underwent functional maturation, forming specialized sub-neighborhoods that compartmentalized proliferating T cells at the tumor periphery versus cytotoxic T effector cells interacting with tumor cell targets. Our findings reveal a spatiotemporal mechanism by which the IP-CN sustains and amplifies cytotoxic T cell responses, demonstrating how T cell-APC neighborhoods orchestrate tumor immunity.

immunology↗

bioRxiv2025

Conserved Landscape of Chemokine Receptor Co-expression Defines the Functional States of CD8+ T Cells in Melanoma

Cancer immunotherapies, from checkpoint blockade to adoptive cell therapies like tumor-infiltrating lymphocytes (TILs), have revolutionized cancer treatment but are limited by variable efficacy and significant toxicities. A central challenge is identifying ideal T-cell populations that effectively eliminate tumors without causing off-target damage, a distinction not captured by existing biomarkers. We show that co-expression patterns of chemokine receptors (CRs) CXCR3, CCR5, and CXCR6 on CD8+ T cells provide a functional "code" defining subsets with divergent roles in on-target immunity versus off-target inflammation. In mouse and human melanoma, a triple-positive (CXCR3+CCR5+CXCR6+) T-cell subset is essential for tumor control, and its genetic signature correlates with positive clinical response, while a distinct CCR5+CXCR6+ subset drives liver immune-related adverse events (IRAEs). Crucially, this CR code reveals that immunotherapy actively reshapes T-cell trafficking patterns, uncovering profound heterogeneity within conventional populations and distinguishing potent anti-tumor progenitors from cells predisposed to exhaustion or off-target migration. This work establishes CR co-expression as a practical tool, providing a surface marker-based strategy to identify and enrich optimized T cells for adoptive therapies, thereby offering a framework to uncouple efficacy from toxicity. One Sentence SummaryCo-expression of CCR5, CXCR6, and CXCR3 provides a functional code that separates T-cell-mediated anti-tumor efficacy from off-target toxicity, enabling the selection of superior cells for safer and more effective cancer immunotherapies. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=120 SRC="FIGDIR/small/693486v2_ufig1.gif" ALT="Figure 1"> View larger version (29K): org.highwire.dtl.DTLVardef@19b0f44org.highwire.dtl.DTLVardef@1076129org.highwire.dtl.DTLVardef@17c0be6org.highwire.dtl.DTLVardef@f14893_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LI{middle dot} CXCR6, CXCR3 and CCR5 co-expression signature stratifies patient survival in human melanoma C_LIO_LI{middle dot} CD8+ T cells co-expressing CXCR6, CXCR3 and CCR5 are critical effectors with high proliferative, cytotoxic, and activation profile in human and mice melanoma C_LIO_LI{middle dot} CD8+ T cells co-expressing CXCR6, CXCR3 and CCR5 drive anti-tumoral responses during checkpoint blockade in both human and mice C_LI

immunology↗

bioRxiv2025

ARCADIA Reveals Spatially Dependent Transcriptional Programs through Integration of scRNA-seq and Spatial Proteomics

MotivationCellular states are strongly influenced by spatial context, but single-cell RNA sequencing (scRNA-seq) loses information about local tissue organization, while spatial proteomic assays capture limited marker panels that constrain transcriptomic inference. Integrating these modalities can elucidate how spatial niches shape transcriptional programs, yet existing approaches depend on either feature-level correspondence such as gene-protein linkage or cell-level barcode pairing, which is often unavailable. ResultsWe present ARCADIA (ARchetype-based Clustering and Alignment with Dual Integrative Autoencoders), a generative framework for cross-modal integration that operates without cell barcode pairing and does not assume direct feature-to-feature correspondence. ARCADIA identifies modality-specific archetypes, i.e., convex combinations of cells representing extreme phenotypic states, and aligns these anchors across modalities by minimizing the discrepancy between their cell-type composition profiles. The aligned archetypes define a shared coordinate system that anchors dual variational autoencoders (VAEs) trained with cross-modal geometric regularization, preserving archetype structure and spatial neighborhood information while enabling bidirectional translation between modalities. On semi-synthetic CITE-seq data, ARCADIA outperforms existing weak-linkage methods. Applied to independent human tonsil scRNA-seq and CODEX data, ARCADIA reconstructs known tissue architecture and reveals spatially dependent transcriptional programs linking B-cell maturation and T-cell activation or exhaustion to microenvironmental niches. Availability and ImplementationSource code is accessible at https://github.com/azizilab/ARCADIA_public. Reproducibility scripts and data are available at https://github.com/azizilab/arcadia_reproducibility.

bioinformatics↗

bioRxiv2024

Echidna: A Bayesian framework for quantifying gene dosage effect impacting phenotypic plasticity

Phenotypic plasticity, the ability of cells to adapt their behavior in response to genetic or environmental changes, is a fundamental biological process that drives cellular diversity in both normal and pathological contexts, including in tumor evolution. While chromosomal instability and somatic copy number alterations (CNAs) are known to influence cellular states, it remains difficult to separate genetic from cell non-autonomous mechanisms that govern transcriptional variability. Here, we present Echidna, a Bayesian hierarchical model that integrates single-cell RNA sequencing (scRNA-seq) and bulk whole-genome sequencing (WGS) data to quantify the impact of CNAs on gene expression dynamics. By jointly inferring clone-specific CNA profiles and uncovering clonal dependencies, Echidna bridges genomic and transcriptomic landscapes within and across multiple time points, enabling the decoupling of gene dosage effects from cell-extrinsic factors on phenotypic plasticity. Applying Echidna to patient tumor specimens, we demonstrate its superior performance in clonal reconstruction and derive insights into resistance mechanisms.

bioinformatics↗