Pan-cancer T cell atlas links a cellular stress response state to immunotherapy resistance

Chu, Yanshuo; Dai, Enyu; Li, Yating; Han, Guangchun; Pei, Guangsheng; Ingram, Davis R.; Thakkar, Krupa; Qin, Jiang‐Jiang; Dang, Minghao; Le, Xiuning; Hu, Can; Deng, Qing; Sinjab, Ansam; Gupta, Pravesh; Hao, Dapeng; Peng, Fuduan; Yan, Xinmiao; Liu, Yunhe; Song, Shumei; Zhang, Shaojun; Heymach, John V.; Reuben, Alexandre; Elamin, Yasir Y.; Pizzi, Melissa Pool; Lü, Yang; Lazcano, Rossana; Hu, Jian; Li, Mingyao; Futreal, Andrew; Maitra, Anirban; Jazaeri, Amir A.; Ajani, Jaffer A.; Swanton, Charles; Cheng, Xin‐Bing; Abbas, Hussein A.; Gillison, Maura L.; Bhat, Krishna; Lazar, Alexander J.; Green, Michael R.; Litchfield, Kevin; Kadara, Humam; Yee, Cassian; Wang, Linghua

doi:10.1038/s41591-023-02371-y

Cited by 127 publications

(73 citation statements)

References 84 publications

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“…In this study, we systematically charted pan-cancer B cells in multifaceted ways by deciphering their transcriptomics, epigenomics, and BCR repertoires. Strikingly, the validated presence of stressed B cells, which shared similarities with the recently discovered pan-cancer stressed T cells (32) and were originally considered to be an artifact (100), highlighted the importance of systemic analysis at a pan-cancer scale. Our study uncovered diverse ASC differentiation pathways and the cancer-type preference of these pathways.…”

Section: Discussionmentioning

confidence: 91%

A blueprint for tumor-infiltrating B cells across human cancers

Ma,

Wu,

et al. 2024

Science

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B lymphocytes are essential mediators of humoral immunity and play multiple roles in human cancer. To decode the functions of tumor-infiltrating B cells, we generated a B cell blueprint encompassing single-cell transcriptome, B cell–receptor repertoire, and chromatin accessibility data across 20 different cancer types (477 samples, 269 patients). B cells harbored extraordinary heterogeneity and comprised 15 subsets, which could be grouped into two independent developmental paths (extrafollicular versus germinal center). Tumor types grouped into the extrafollicular pathway were linked with worse clinical outcomes and resistance to immunotherapy. The dysfunctional extrafollicular program was associated with glutamine-derived metabolites through epigenetic-metabolic cross-talk, which promoted a T cell–driven immunosuppressive program. These data suggest an intratumor B cell balance between extrafollicular and germinal-center responses and suggest that humoral immunity could possibly be harnessed for B cell–targeting immunotherapy.

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Section: Discussionmentioning

confidence: 91%

A blueprint for tumor-infiltrating B cells across human cancers

Ma,

Wu,

et al. 2024

Science

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“…Within the identified T cell regions, we further discern the different states of T cells. By adding specific cell lineage markers such as CD4, CD8A , and CD8B [36], we can further distinguish CD4 T cells, CD8 T cells, and their various states including CD4 + Tregs (e.g., FOXP3, IL2RA ) and CD8 + Tex cells by incorporating known immune checkpoint genes (e.g., PD-1, TIM-3 , and LAG-3, CTLA-4, TIGIT ) and Tex related transcription factors (e.g., TOX) [36]. Furthermore, this module provides function of overlaying two or more different T cell states within defined cancer cell regions directly on the same tissue section, allowing us to visualize their spatial relationships.…”

Section: Resultsmentioning

confidence: 99%

“…METI’s module 4 is capable of detecting immune cell types other than T cells that are critical components in the TME including neutrophils, macrophages, B cells, and plasma cells. METI utilizes validated gene signatures to identify specific immune cell types/states [36, 39-41]. We have applied this module to two bladder cancer samples B1 and B2 for neutrophil detection.…”

Section: Resultsmentioning

confidence: 99%

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METI: Deep profiling of tumor ecosystems by integrating cell morphology and spatial transcriptomics

Jiang,

Liu,

Qin

et al. 2023

Preprint

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The recent advance of spatial transcriptomics (ST) technique provides valuable insights into the organization and interactions of cells within the tumor microenvironment (TME). While various analytical tools have been developed for tasks such as spatial clustering, spatially variable gene identification, and cell type deconvolution, most of them are general methods lacking consideration of histological features in spatial data analysis. This limitation results in reduced performance and interpretability of their results when studying the TME. Here, we present a computational framework named,Morphology-Enhanced SpatialTranscriptome Analysis Integrator (METI) to address this gap. METI is an end-to-end framework capable of spatial mapping of both cancer cells and various TME cell components, robust stratification of cell type and transcriptional states, and cell co-localization analysis. By integrating both spatial transcriptomics, cell morphology and curated gene signatures, METI enhances our understanding of the molecular landscape and cellular interactions within the tissue, facilitating detailed investigations of the TME and its functional implications. The performance of METI has been evaluated on ST data generated from various tumor tissues, including gastric, lung, and bladder cancers, as well as premalignant tissues. Across all these tissues and conditions, METI has demonstrated robust performance with consistency.

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“…Top 30 most significant DEGs were carefully reviewed. Besides, the expressions of canonical markers for T cell types and states, as reported previously, 51 were manually checked, and bubble plots were generated accordingly. T cell types and states were then inferred by integrating the above information, and annotations were added to each cluster with the AddMetaData function in Seurat.…”

Section: Sub-clustering Analysis Of T and Nk Cellsmentioning

confidence: 99%

Molecular pathways and cellular subsets associated with adverse clinical outcomes in overlapping immune-related myocarditis and myositis

Siddiqui,

Palaskas,

Basu

et al. 2023

Preprint

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Immune checkpoint therapies (ICTs) can induce life-threatening immune-related adverse events, including myocarditis and myositis, which are rare but often concurrent. The molecular pathways and immune subsets underlying these toxicities remain poorly understood. To address this need, we obtained heart and skeletal muscle biopsies for single-cell RNA sequencing in living patients with cancers treated with ICTs admitted to the hospital with myocarditis and/or myositis (overlapping myocarditis plus myositis, n=10; myocarditis-only, n=1) compared to ICT-exposed patients ruled out for toxicity utilized as controls (n=9) within 96 hours of clinical presentation. Analyses of 58,523 cells revealed clonally expanded CD8+ T cells with a cytotoxic phenotype expressing activation/exhaustion markers in both myocarditis and myositis. Furthermore, the analyses identified a population of tissue-resident myeloid cells expressed FcγRIIIa, which is known to bind IgG and regulate complement activation. Immunohistochemistry of affected cardiac and skeletal muscle tissues revealed protein expression of pan-IgG and complement product C4d that were associated with the presence of high-titer serum autoantibodies against muscle antigens in a subset of patients. We further identified a population of inflammatory IL-1B+TNF+ myeloid cells specifically enriched in myocarditis and associated with greater toxicity severity and poorer clinical outcomes. These results are the first to recognize these myeloid subsets in human immune-related myocarditis and myositis tissues and nominate new targets for investigation into rational treatments to overcome these high-mortality toxicities.

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Pan-cancer T cell atlas links a cellular stress response state to immunotherapy resistance

Cited by 127 publications

References 84 publications

A blueprint for tumor-infiltrating B cells across human cancers

A blueprint for tumor-infiltrating B cells across human cancers

METI: Deep profiling of tumor ecosystems by integrating cell morphology and spatial transcriptomics

Molecular pathways and cellular subsets associated with adverse clinical outcomes in overlapping immune-related myocarditis and myositis

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