scholarly journals Transcriptional programs of neoantigen-specific TIL in anti-PD-1-treated lung cancers

Nature ◽  
2021 ◽  
Author(s):  
Justina X. Caushi ◽  
Jiajia Zhang ◽  
Zhicheng Ji ◽  
Ajay Vaghasia ◽  
Boyang Zhang ◽  
...  

AbstractPD-1 blockade unleashes CD8 T cells1, including those specific for mutation-associated neoantigens (MANA), but factors in the tumour microenvironment can inhibit these T cell responses. Single-cell transcriptomics have revealed global T cell dysfunction programs in tumour-infiltrating lymphocytes (TIL). However, the majority of TIL do not recognize tumour antigens2, and little is known about transcriptional programs of MANA-specific TIL. Here, we identify MANA-specific T cell clones using the MANA functional expansion of specific T cells assay3 in neoadjuvant anti-PD-1-treated non-small cell lung cancers (NSCLC). We use their T cell receptors as a ‘barcode’ to track and analyse their transcriptional programs in the tumour microenvironment using coupled single-cell RNA sequencing and T cell receptor sequencing. We find both MANA- and virus-specific clones in TIL, regardless of response, and MANA-, influenza- and Epstein–Barr virus-specific TIL each have unique transcriptional programs. Despite exposure to cognate antigen, MANA-specific TIL express an incompletely activated cytolytic program. MANA-specific CD8 T cells have hallmark transcriptional programs of tissue-resident memory (TRM) cells, but low levels of interleukin-7 receptor (IL-7R) and are functionally less responsive to interleukin-7 (IL-7) compared with influenza-specific TRM cells. Compared with those from responding tumours, MANA-specific clones from non-responding tumours express T cell receptors with markedly lower ligand-dependent signalling, are largely confined to HOBIThigh TRM subsets, and coordinately upregulate checkpoints, killer inhibitory receptors and inhibitors of T cell activation. These findings provide important insights for overcoming resistance to PD-1 blockade.

2019 ◽  
Author(s):  
Ang A. Tu ◽  
Todd M. Gierahn ◽  
Brinda Monian ◽  
Duncan M. Morgan ◽  
Naveen K. Mehta ◽  
...  

Abstract High-throughput 3’ single-cell RNA-Sequencing (scRNA-Seq) allows for cost-effective, detailed characterization of thousands of individual immune cells from healthy and diseased tissues. Current techniques, however, are limited in their ability to elucidate essential immune cell features, including the variable sequences of T cell receptors (TCRs) that confer antigen specificity in T cells. Here, we present an enrichment strategy that enables simultaneous analysis of TCR variable sequences and corresponding full transcriptomes from 3’ barcoded scRNA-Seq samples. This approach is compatible with common 3’ scRNA-Seq methods, and adaptable to processed samples post hoc. We applied the technique to resolve clonotype-to-phenotype relationships among antigen-activated T cells from immunized mice and from patients with food allergy. We observed diverse but preferential cellular phenotypes manifest among subsets of expanded clonotypes, including functional Th2 states associated with food allergy. These results demonstrate the utility of our method when studying complex diseases in which clonotype-driven immune responses are critical to understanding the underlying biology.


2016 ◽  
Vol 113 (26) ◽  
pp. 7201-7206 ◽  
Author(s):  
Ying S. Hu ◽  
Hu Cang ◽  
Björn F. Lillemeier

T cells become activated when T-cell receptors (TCRs) recognize agonist peptides bound to major histocompatibility complex molecules on antigen-presenting cells. T-cell activation critically relies on the spatiotemporal arrangements of TCRs on the plasma membrane. However, the molecular organizations of TCRs on lymph node-resident T cells have not yet been determined, owing to the diffraction limit of light. Here we visualized nanometer- and micrometer-scale TCR distributions in lymph nodes by light sheet direct stochastic optical reconstruction microscopy (dSTORM) and structured illumination microscopy (SIM). This dSTORM and SIM approach provides the first evidence, to our knowledge, of multiscale reorganization of TCRs during in vivo immune responses. We observed nanometer-scale plasma membrane domains, known as protein islands, on naïve T cells. These protein islands were enriched within micrometer-sized surface areas that we call territories. In vivo T-cell activation caused the TCR territories to contract, leading to the coalescence of protein islands and formation of stable TCR microclusters.


2014 ◽  
Vol 30 (S1) ◽  
pp. A18-A18
Author(s):  
Hongbing Yang ◽  
Sandrine Buisson ◽  
Giovanna Bossi ◽  
Gemma Hancock ◽  
Rebecca Ashfield ◽  
...  

1995 ◽  
Vol 756 (1 T-Cell Recept) ◽  
pp. 370-381 ◽  
Author(s):  
JENNIE C. C. CHANG ◽  
LAWRENCE R. SMITH ◽  
KAREN J. FRONING ◽  
BEVERLY J. SCHWABE ◽  
JULIE A. LAXER ◽  
...  

2019 ◽  
Vol 10 ◽  
Author(s):  
Cordula Hansel ◽  
Stephanie Erschfeld ◽  
Maike Baues ◽  
Twan Lammers ◽  
Ralf Weiskirchen ◽  
...  

2020 ◽  
Vol 8 (Suppl 1) ◽  
pp. A5.1-A5
Author(s):  
Chuan Li ◽  
Yee Peng Phoon ◽  
Keaton Karlinsey ◽  
Ye Tian ◽  
Samjhana Thapaliya ◽  
...  

BackgroundImmune checkpoint blockade (ICB) has greatly advanced the treatment of melanoma. A key component of ICB is the stimulation of CD8+ T cells in the tumor. However, ICB therapy only benefits a subset of patients and a reliable prediction method that does not require invasive biopsies is still a major challenge in the field.MethodsWe conducted a set of comprehensive single-cell transcriptomic analyses of CD8+ T cells in the peripheral blood (mPBL) and tumors (mTIL) from 8 patients with metastatic melanoma.ResultsCompared to circulating CD8+ T cells from healthy donors (hPBL), mPBLs contained subsets resembling certain features of mTIL. More importantly, three clusters (2, 6 and 15) were represented in both mPBL and mTIL. Cluster 2 was the major subset of the majority of hPBL, which phenocopied hallmark parameters of resting T cells. Cluster 6 and 15 were uniquely presented in melanoma patients. Cluster 15 had the highest PD-1 levels, with elevated markers of both activation and dysfunction/exhaustion; while Cluster 6 was enriched for ‘dormant’ cells with overall toned-down transcriptional activity except PPAR signaling, a known suppressor for T cell activation. Interestingly, unlike other mTIL clusters that would classically be defined as exhausted, Cluster 15 exhibited the highest metabolic activity (oxidative-phosphorylation and glycolysis). We further analyzed total sc-transcriptomics using cell trajectory algorithms and identified that these three clusters were the most distinct subtypes of CD8 T cells from each other, representing: resting (cluster 2), metabolically active-dysfunctional (cluster 15), and dormant phenotypes (cluster 6). Further, three unique intracellular programs in melanoma drive the transition of resting CD8+ T cells (cluster 2) to both metabolic/dysfunctional (cluster 15) and dormant states (cluster 6) that are unique to tumor bearing conditions. Based on these high-resolution analyses, we developed original algorithms to build a novel ICB response predictive model using immune-blockade co-expression gene patterns. The model was trained and tested using previously published GEO datasets containing CD8 T cells from anti-PD-1 treated patients and presented an AUC of 0.82, with 92% and 89% accuracy of ICB response in the two datasets.ConclusionsWe identified and analyzed unique populations of CD8+ T cells in circulation and tumor using high-resolution single-cell transcriptomics to define the landscape of CD8+ T cell states, revealing critical subsets with shared features in PBLs and TILs. Most importantly, we established an innovative model for ICB response prediction by using peripheral blood lymphocytes.Ethics ApprovalThis study was performed under an IRB approved protocol.


2015 ◽  
Vol 16 (9) ◽  
pp. 1323-1331 ◽  
Author(s):  
Sandra Höfflin ◽  
Sabrina Prommersberger ◽  
Ugur Uslu ◽  
Gerold Schuler ◽  
Christopher W Schmidt ◽  
...  

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