Biology subjects

Kluger, H.

Publications and source records attributed to Kluger, H..

6 recordsLinked to original sources

bioRxiv2026

CAR-T-drug conjugate against solid tumor

Chimeric antigen receptor (CAR)-T cell therapy has been clinically successful in hematologic cancers but faces challenges in solid tumors, primarily due to limited tumor infiltration, immunosuppressive tumor microenvironments (TME), and antigen heterogeneity. While combining CAR-T cell therapy with chemotherapy can enhance antitumor activity, this often leads to substantial systemic toxicity. In this study, we introduce CAR-T-drug conjugate (CAR-T-D-C), a new class of dual-functional therapeutics that effectively addresses these obstacles by integrating click chemistry for precise conjugation of cytotoxic agents onto antigen-specific CAR-T cells. By transforming the hostile TME into an ally, this approach facilitates localized delivery of cytotoxic payload directly to the tumor site, enhancing the overall effectiveness of CAR-T therapy in solid tumors. CAR-T-D-Cs with different CAR-T cell binders exhibit robust antitumor activity across diverse solid tumor models, including both human tumor xenografts and syngeneic models. Spatial transcriptomic studies reveals that CAR-T-D-C achieves improved CAR-T tumor infiltration and functional activation within the TME. Compared to conventional CAR-T therapy, CAR-T-D-C markedly enhances immune cell infiltration, augments effector functions, promotes antigen spreading, amplifies systemic immune responses, and improves overall anti-tumor immunity. CAR-T-D-C represents a versatile therapeutic concept that combines the potency of small molecule drugs and the specificity of CAR-T cells as a 2-in-1 immunochemotherapy for treatment of solid tumors.

immunology↗

bioRxiv2024

Mapping intratumoral myeloid-T cell interactomes at single-cell resolution reveals targets for overcoming checkpoint inhibitor resistance

Effective cancer immunotherapies restore anti-tumor immunity by rewiring cell-cell communication. Treatment-induced changes in communication can be inferred from single-cell RNA-sequencing (scRNA-seq) data, but current methods do not effectively manage heterogeneity within cell types. Here we developed a computational approach to efficiently analyze scRNA-seq-derived, single-cell-resolved cell-cell interactomes, which we applied to determine how agonistic CD40 (CD40ag) alters immune cell crosstalk alone, across tumor models, and in combination with immune checkpoint blockade (ICB). Our analyses suggested that CD40ag improves responses to ICB by targeting both immuno-stimulatory and immunosuppressive macrophage subsets communicating with T cells, and we experimentally validated a spatial basis for these subsets with immunofluorescence and spatial transcriptomics. Moreover, treatment with CD40ag and ICB established coordinated myeloid-T cell interaction hubs that are critical for reestablishing antitumor immunity. Our work advances the biological significance of hypotheses generated from scRNA-seq-derived cell-cell interactomes and supports the clinical translation of myeloid-targeted therapies for ICB-resistant tumors.

systems biology↗

bioRxiv2024

SORBET: Automated cell-neighborhood analysis of spatial transcriptomics or proteomics for interpretable sample classification via GNN

Spatial cellular profiling technologies have revolutionized our understanding of complex biological processes, from development and disease progression to immunity and aging. Despite their promise, integrating spatial information with multiplexed molecular data to accurately predict phenotypes poses significant challenges, especially in clinical settings. Here, we present SORBET, a geometric deep learning framework that directly analyzes complete spatial profiling data, eliminating the need to compress complete cell profiles into a limited set of annotations, such as cell types. SORBET models tissues as graphs of adjacent cells and applies graph convolutional networks to infer emergent phenotypes, such as responses to immunotherapy. The model leverages a novel data augmentation technique to ensure robust predictions, complemented by tailored interpretability analyses to identify the molecular and spatial patterns underlying the models phenotype inferences. We apply our method to a CosMx spatial transcriptomics dataset of pre-treatment metastatic melanoma samples annotated with response to immunotherapy; we show that spatial information significantly improves clinical endpoint, or phenotype, prediction and identifies important biological patterns. To our knowledge, SORBET is the first example of phenotype prediction on spatial transcriptomics data. We further validated our method using two spatial proteomics datasets, Imaging Mass Cytometry (IMC) and Co-detection by indexing (CODEX), obtained from Non-Small Cell Lung Cancer and Colorectal Cancer samples, respectively. SORBET demonstrates superior accuracy in phenotype prediction over leading spatial and non-spatial methods across various datasets of different observed phenotypes and technologies. SORBET sets a new benchmark for predictive analysis in spatial omics, promising to advance personalized medicine through refined patient treatment stratification, grounded in molecular and spatial tissue profiling.

bioinformatics↗

bioRxiv2023

SIMVI reveals intrinsic and spatial-induced states in spatial omics data

Spatial omics technologies enable the analysis of gene expression and interaction dynamics in relation to tissue structure and function. However, existing computational methods may not properly distinguish cellular intrinsic variability and intercellular interactions, and may thus fail to capture spatial regulations for further biological discoveries. Here, we present Spatial Interaction Modeling using Variational Inference (SIMVI), an annotation-free framework that disentangles cell intrinsic and spatial-induced latent variables for modeling gene expression in spatial omics data. We derive theoretical support for SIMVI in disentangling intrinsic and spatial-induced variations. By this disentanglement, SIMVI enables estimation of spatial effects (SE) at a single-cell resolution, and opens up various opportunities for novel downstream analyses. To demonstrate the potential of SIMVI, we applied SIMVI to spatial omics data from diverse platforms and tissues (MERFISH human cortex, Slide-seqv2 mouse hippocampus, Slide-tags human tonsil, spatial multiome human melanoma, cohort-level CosMx melanoma). In all tested datasets, SIMVI effectively disentangles variations and infers accurate spatial effects compared with alternative methods. Moreover, on these datasets, SIMVI uniquely uncovers complex spatial regulations and dynamics of biological significance. In the human tonsil data, SIMVI illuminates the cyclical spatial dynamics of germinal center B cells during maturation. Applying SIMVI to both RNA and ATAC modalities of the multiome melanoma data reveals potential tumor epigenetic reprogramming states. Application of SIMVI on our newly-collected cohort-level CosMx melanoma dataset uncovers space-and-outcome-dependent macrophage states and the underlying cellular communication machinery in the tumor microenvironments.

bioinformatics↗

bioRxiv2020

Circulating Clonally Expanded T Cells Reflect Functions of Tumor Infiltrating T Cells

Understanding the relationship between tumor and peripheral immune environments could allow longitudinal immune monitoring in cancer. Here, we examined whether T cells that share the same TCR{beta} and are found in both tumor and blood can be interrogated to gain insight into the ongoing tumor T cell response. Paired transcriptome and TCR{beta} repertoire of circulating and tumor-infiltrating T cells were analyzed from matched tumor and blood from patients with metastatic melanoma at the single cell level. We found that in circulating T cells matching clonally expanded tumor-infiltrating T cells (circulating TILs), gene signatures of effector functions, but not terminal exhaustion, reflect those observed in the tumor. In contrast, features of exhaustion are displayed predominantly by T cells present only in tumor. Finally, genes associated with a high degree of blood-tumor TCR sharing were overexpressed in tumor tissue after immunotherapy. These data demonstrate that circulating TILs, identified by TCRs shared with T cells in tumors, have unique transcriptional expression patterns that may have utility for the interrogation of T cell function in cancer immunotherapy. SummaryCombining transcriptomic and TCR{beta} repertoire analysis of circulating and tumor-infiltrating CD8 T cells from patients with metastatic melanoma, we identify a blood-based population with effector properties that reflect those of clonally related tumor-resident T cells.

immunology↗

bioRxiv2016

Methods for detecting co-mutated pathways in cancer samples to inform treatment selection

Tumor genomes evolve through a selection of mutations. These mutations may complement each other to promote tumorigenesis. To better understand the functional interactions of different processes in cancer, we studied mutation data of a set of tumors and identified significantly co-mutated pathways. Fishers exact test is a standard approach that can be used to assess the significance of the joint dysregulation of pathways pairs across a patient population. We developed a robust test to identify co-occurrence using DNA mutations, which overcomes deficiencies of the Fishers exact test by taking into account the large variability in overall mutation load and sequencing depth. Applying our method to a study of six common cancer types, we identify enrichment of co-mutated signal transduction pathways such as IP3 synthesis and PI3K and pairs of co-mutated pathways involving other processes such as immunity and development. We observed enrichment of clonal co-mutation of the proteasome and apoptosis pathways in colorectal cancer, which suggests potential mechanisms for immune evasion.

bioinformatics↗