Li, Ying

Abstract Chimeric antigen receptor (CAR) T-cell therapy achieves high response rates in relapsed/refractory aggressive B-cell non-Hodgkin lymphoma, yet only 40% of patients achieve durable remission. Identifying biological programs linked to long-term response remains critical. As metabolic fitness underlies T-cell persistence and effector function, we profiled metabolic pathways associated with clinical outcomes following commercial CD19 CAR T-cell therapy. Single-cell RNA sequencing of peripheral blood mononuclear cells collected at baseline (BL), peak CAR-T expansion (PK), and one-month post-infusion (M1) was analyzed using CellRanger v7.0.1, immunopipe. Seurat v4.3.0 was applied for unsupervised clustering to delineate cell subsets based on top differentially expressed genes. Patients were categorized as durable complete remission (CR ≥6 months; n = 16), primary refractory (PD1; n = 4), or relapse after initial response (PD2; n = 12). Gene-set enrichment analysis defined metabolic pathway activity across T-cell, monocyte, dendritic cell (DC), and natural killer (NK) subsets. Oxidative phosphorylation (OXPHOS) emerged as the dominant metabolic program distinguishing clinical outcomes. At BL and PK, OXPHOS was consistently enriched in PD1 relative to CR across T-cell, monocyte, and DC subsets, suggesting early oxidative activation in non-responders. By M1, this pattern inverted, with higher OXPHOS activity in CR. PD2 largely paralleled PD1, but several subsets including CD8 T central memory, classical monocytes TGFβ, intermediate monocytes CD38, monocytic myeloid-derived suppressor cell (mMDSC) HIF1A, and mMDSC SIRPA, showed OXPHOS enrichment in CR at BL and/or PK that persisted through M1. In contrast, DC and NK subsets exhibited the opposite pattern: OXPHOS was enriched in CR at PK (conventional DC 2 (cDC2), plasmacytoid DC (pDC), NK, proliferating NK), but shifted toward enrichment in PD2 at M1 (cDC2, NK, NK CD56bright). Glycolysis (GLY) followed a similar trajectory in monocytes and NK cells, with enrichment in PD1/PD2 at BL and PK, followed by enrichment in CR at M1. In T-cells, PD1 maintained GLY enrichment from BL through PK, with no significant differences observed at M1. The inositol-phosphate metabolism pathway showed a more static pattern and was consistently enriched in multiple PD2 effector and memory T-cell subsets without reversal at M1. Taken together, these data reveal distinct, lineage-specific metabolic signatures that differentiate durable remission from early and late progression. Dynamic OXPHOS and GLY programming, characterized by lower activity early and enhanced activity at M1 in CR, may reflect adaptive metabolic programming that supports sustained antitumor immunity. Immune-metabolic profiling may therefore serve as a biomarker of response and highlight actionable metabolic pathways to enhance CAR T-cell durability. Citation Format: Melinda S.Y. Tan, Panwen Wang, Patrizia Mondello, Jacqueline Turner, Andre de Menezes Silva Corraes, Chen Wu, Zuoyi Shao, Kevin Regan, Ma Audrey, Arushi Khurana, Nora N. Benanni, Yucai Wang, Paul Hampel, Jonas Paludo, Saad J. Kenderian, Urshila Durani, Patrick B. Johnston, Jose Caetano Villasboas, Stephen M. Ansell, Ying Li, Haidong Dong, Hu Zeng, Yi Lin. Metabolic pathway signatures defining response to CD19 CAR T-Cell therapy in aggressive B-cell non-hodgkin lymphoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 3278.

Abstract Single-cell sequencing technologies provide us with information at the level of individual cells. Combining single-cell RNA-seq and single-cell TCR-seq profiling enables the exploration of cell heterogeneity and T-cell receptor repertoires simultaneously. Integrating both types of data can play a crucial role in enhancing our understanding of T-cell-mediated immunity and, in turn, facilitate the advancement of immunotherapy. Here, we present immunopipe, a comprehensive and flexible pipeline to perform integrated analysis of scRNA-seq and scTCR-seq data. In addition to the command line tool, we provide a user-friendly web interface for pipeline configuration and execution monitoring, benefiting researchers without extensive programming experience. With its comprehensive functionality and ease of use, immunopipe empowers researchers to uncover valuable insights from scRNA-seq and scTCR-seq data, ultimately advancing the understanding of immune responses and immunotherapy development.

Abstract Introduction: Single-cell RNA-sequencing (scRNA-seq) pairing with single-cell T-Cell-Receptor-sequencing (scTCR-seq) enables profiling of gene expression and TCR repertoires in individual T cells, elevating our understanding of T-cell-mediated immunity. Here, we present immunopipe, a comprehensive and flexible pipeline (https://github.com/pwwang/immunopipe) for analyzing the paired data. Besides the command-line tool, it offers a user-friendly web interface that allows users with varying programming expertise to configure, initiate, and monitor the pipeline. By combining extensive functionality with ease of use, immunopipe empowers users to unlock valuable insights from the data, which notably advances our understanding of immune response and fosters the development of innovative immunotherapies. Results: Immunopipe is a comprehensive analytical pipeline for scRNA-seq and scTCR-seq data, offering various modules leveraging existing tools and novel methods to ensure data quality and enable downstream analytics. It performs quality control for scRNA-seq data, clusters cells based on gene expression to facilitate the identification of cell populations and provides insights into T-cell heterogeneity and functional diversity through T-cell selection and clustering. Enrichment analysis identifies markers and pathways for T-cell characterization. Our automated T-cell annotation tool annotates cell types accurately and hierarchically. For scTCR-seq data, immunopipe analyzes TCR clones, including clonality, diversity, gene usage, and repertoire overlap. Clone residency analysis examines the presence of clones across samples. Integrative analyses combine scRNA-seq and scTCR-seq data, providing a comprehensive understanding of the cellular landscape. Immunopipe incorporates diverse cell group information to analyze T-cell populations, which enables granular perspectives of the data for comprehensive insights. Immunopipe offers a flexible platform, allowing users to customize the analyses as needed. It supports both core and optional analyses through configuration, where users have control over the adjustable parameters. The pipeline relies on cell grouping information that can be referenced from metadata or modified to generate new variables for analysis and data filtering. Immunopipe can be executed locally or on scheduler platforms, and a docker image is provided for compatibility and simplified installation. It also offers a web interface that facilitates configuration, execution monitoring, and access to results. Conclusion: Immunopipe is a flexible and user-friendly pipeline for analyzing scRNA-seq and scTCR-seq data. It empowers researchers to gain valuable insights that could enhance the understanding of immune responses and facilitate the development of innovative immunotherapies. Citation Format: Panwen Wang, Haidong Dong, Yue Yu, Shuwen Zhang, Zhifu Sun, Jean-Pierre A. Kocher, Junwen Wang, Lin Yi, Ying Li. Immunopipe: A comprehensive and flexible scRNA-seq and scTCR-seq data analysis pipeline [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4959.