A high OXPHOS CD8 T cell subset is predictive of immunotherapy resistance in melanoma patients

2021 ◽  
Vol 219 (1) ◽  
Author(s):  
Chuan Li ◽  
Yee Peng Phoon ◽  
Keaton Karlinsey ◽  
Ye F. Tian ◽  
Samjhana Thapaliya ◽  
...  
Keyword(s):  
T Cell ◽  
Peripheral Blood ◽  
Immune Checkpoint Inhibitor ◽  
Checkpoint Inhibitor ◽  
Cell Subset ◽  
Therapy Response ◽  
Cd8 T Cell ◽  
Melanoma Treatment ◽  
Melanoma Patients ◽  
Predictive Assays

Immune checkpoint inhibitor (ICI) therapy continues to revolutionize melanoma treatment, but only a subset of patients respond. Major efforts are underway to develop minimally invasive predictive assays of ICI response. Using single-cell transcriptomics, we discovered a unique CD8 T cell blood/tumor-shared subpopulation in melanoma patients with high levels of oxidative phosphorylation (OXPHOS), the ectonucleotidases CD38 and CD39, and both exhaustion and cytotoxicity markers. We called this population with high levels of OXPHOS “CD8+ TOXPHOS cells.” We validated that higher levels of OXPHOS in tumor- and peripheral blood–derived CD8+ TOXPHOS cells correlated with ICI resistance in melanoma patients. We then developed an ICI therapy response predictive model using a transcriptomic profile of CD8+ TOXPHOS cells. This model is capable of discerning responders from nonresponders using either tumor or peripheral blood CD8 T cells with high accuracy in multiple validation cohorts. In sum, CD8+ TOXPHOS cells represent a critical immune population to assess ICI response with the potential to be a new target to improve outcomes in melanoma patients.

scholarly journals Differential effects of PD-1 and CTLA-4 blockade on the melanoma-reactive CD8 T cell response

2020 ◽  
Author(s):  
Anastasia Gangaev ◽  
Elisa A Rozeman ◽  
Maartje W Rohaan ◽  
Daisy Philips ◽  
Sanne Patiwael ◽  
...  
Keyword(s):  
T Cell ◽  
Mechanism Of Action ◽  
Peripheral Blood ◽  
Cell Response ◽  
T Cell Responses ◽  
Cd8 T Cell ◽  
T Cell Response ◽  
Tumor Site ◽  
Cell Responses ◽  
Melanoma Patients

Immune checkpoint inhibitors targeting programmed cell death protein 1 (PD-1) and cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) have revolutionized the treatment of melanoma patients. Based on early studies addressing the mechanism of action, it was assumed that PD-1 blockade mostly influences T cell responses at the tumor site. However, recent work has demonstrated that PD-1 blockade can influence the T cell compartment in peripheral blood. If activation of circulating tumor-reactive T cells would form an important mechanism of action of PD-1 blockade, it may be predicted that such blockade would alter either the frequency and/or the breadth of the tumor-reactive CD8 T cell response. To address this question, we analyzed CD8 T cell responses towards 71 melanoma associated epitopes in peripheral blood of 24 melanoma patients. We show that both the frequency and the breadth of the melanoma-reactive CD8 T cell response in peripheral blood was unaltered upon PD-1 blockade. In contrast, a broadening of the melanoma-reactive CD8 T cell response was observed upon CTLA-4 blockade, in concordance with our prior data. On the basis of these results, we conclude that PD-1 and CTLA-4 blockade impact the tumor-reactive CD8 T cell response in a distinct manner. In addition, the data provide an argument in favor of the hypothesis that anti-PD-1 therapy may primarily act at the tumor site.

Peripheral blood T cell subset phenotype analysis in melanoma patients treated with combination nivolumab + ipilimumab compared to ipilimumab alone.

2016 ◽  
Vol 34 (15_suppl) ◽  
pp. 3073-3073 ◽  
Author(s):  
Michael Andrew Postow ◽  
Deborah Kuk ◽  
Matthew Adamow ◽  
Cristina Carrera ◽  
Phillip Wong ◽  
...  
Keyword(s):  
T Cell ◽  
Peripheral Blood ◽  
Cell Subset ◽  
T Cell Subset ◽  
Phenotype Analysis ◽  
Melanoma Patients

scholarly journals Tumor-Infiltrating PD-1hiCD8+-T-Cell Signature as an Effective Biomarker for Immune Checkpoint Inhibitor Therapy Response Across Multiple Cancers

2021 ◽  
Vol 11 ◽  
Author(s):  
Zhenyu Yang ◽  
Yulan Deng ◽  
Jiahan Cheng ◽  
Shiyou Wei ◽  
Hao Luo ◽  
...  
Keyword(s):  
Lung Cancer ◽  
T Cells ◽  
T Cell ◽  
Immune Cells ◽  
Immune Checkpoint ◽  
Immune Checkpoint Inhibitor ◽  
Checkpoint Inhibitor ◽  
Therapy Response ◽  
Rna Seq ◽  
Cancer Types

BackgroundStratification of patients who could benefit from immune checkpoint inhibitor (ICI) therapy is of much importance. PD-1hiCD8+ T cells represent a newly identified and effective biomarker for ICI therapy response biomarker in lung cancer. Accurately quantifying these T cells using commonly available RNA sequencing (RNA-seq) data may extend their applications to more cancer types.MethodWe built a transcriptome signature of PD-1hiCD8+ T cells from bulk RNA-seq and single-cell RNA-seq (scRNA-seq) data of tumor-infiltrating immune cells. The signature was validated by flow cytometry and in independent datasets. The clinical applications of the signature were explored in non-small-cell lung cancer, melanoma, gastric cancer, urothelial cancer, and a mouse model of breast cancer samples treated with ICI, and systematically evaluated across 21 cancer types in The Cancer Genome Atlas (TCGA). Its associations with other biomarkers were also determined.ResultsSignature scores could be used to identify the PD-1hiCD8+ T subset and were correlated with the fraction of PD-1hiCD8+ T cells in tumor tissue (Pearson correlation, R=0.76, p=0.0004). Furthermore, in the scRNA-seq dataset, we confirmed the capability of PD-1hiCD8+ T cells to secrete CXCL13, as well as their interactions with other immune cells. In 581 clinical samples and 204 mouse models treated with ICIs, high signature scores were associated with increased survival, and the signature achieved area under the receiver operating characteristic curve scores of 0.755 (ranging from 0.61 to 0.91) in predicting therapy response. In TCGA pan-cancer datasets, our signature scores were consistently correlated with therapy response (R=0.78, p<0.0001) and partially explained the diverse response rates among different cancer types. Finally, our signature generally outperformed other mRNA-based predictors and showed improved predictive performance when used in combination with tumor mutational burden (TMB). The signature score is available in the R package “PD1highCD8Tscore” (https://github.com/Liulab/PD1highCD8Tscore).ConclusionThrough estimating the fraction of the PD-1hiCD8+ T cell, our signature could predict response to ICI therapy across multiple cancers and could serve as a complementary biomarker to TMB.

scholarly journals Exploring Markers of Exhausted CD8 T Cells to Predict Response to Immune Checkpoint Inhibitor Therapy for Hepatocellular Carcinoma

Liver Cancer ◽  
2021 ◽  
pp. 1-14
Author(s):  
Chia-Lang Hsu ◽  
Da-Liang Ou ◽  
Li-Yuan Bai ◽  
Chia-Wei Chen ◽  
Li Lin ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
T Cell ◽  
Tumor Microenvironment ◽  
Immune Checkpoint ◽  
Immune Checkpoint Inhibitor ◽  
Checkpoint Inhibitor ◽  
Cd8 T Cell ◽  
Gene Signature ◽  
T Cell Exhaustion ◽  
Cell Exhaustion

<b><i>Background:</i></b> Reversal of CD8 T-cell exhaustion was considered a major antitumor mechanism of anti-programmed cell death-1 (PD-1)/ anti-programmed death ligand-1 (PD-L1)-based immune checkpoint inhibitor (ICI) therapy. <b><i>Objectives:</i></b> The aim of this study was to identify markers of T-cell exhaustion that is best associated with ICI treatment efficacy for advanced hepatocellular carcinoma (HCC). <b><i>Methods:</i></b> Immune cell composition of archival tumor samples was analyzed by transcriptomic analysis and multiplex immunofluorescence staining. <b><i>Results:</i></b> HCC patients with objective response after anti-PD-1/anti-PD-L1-based ICI therapy (<i>n</i> = 42) had higher expression of genes related to T-cell exhaustion. A 9-gene signature (LAG3, CD244, CCL5, CXCL9, CXCL13, MSR1, CSF3R, CYBB, and KLRK1) was defined, whose expression was higher in patients with response to ICI therapy, correlated with density of CD8<sup>+</sup>LAG3<sup>+</sup> cells in tumor microenvironment, and independently predicted better progression-free and overall survival. This 9-gene signature had similar predictive values for patients who received single-agent or combination ICI therapy and was not associated with prognosis in HCC patients who received surgery, suggesting that it may outperform other T-cell signatures for predicting efficacy of ICI therapy for HCC. For HCC patients who underwent surgery for both the primary liver and metastatic lung tumors (<i>n</i> = 31), lung metastatic HCC was associated with a higher exhausted CD8 T-cell signature, consistent with prior observation that patients with lung metastatic HCC may have higher probability of response to ICI therapy. <b><i>Conclusions:</i></b> CD8 T-cell exhaustion in tumor microenvironment may predict better efficacy of ICI therapy for HCC.

scholarly journals On the Role of Melanoma-Specific CD8+ T-Cell Immunity in Disease Progression of Advanced-Stage Melanoma Patients

2004 ◽  
Vol 10 (14) ◽  
pp. 4754-4760 ◽  
Author(s):  
Monique van Oijen ◽  
Adriaan Bins ◽  
Sjoerd Elias ◽  
Johan Sein ◽  
Pauline Weder ◽  
...  
Keyword(s):  
T Cell ◽  
Disease Progression ◽  
Cd8 T Cell ◽  
T Cell Immunity ◽  
Advanced Stage ◽  
Cell Immunity ◽  
Melanoma Patients

scholarly journals Tumor response evaluation in patients with malignant melanoma undergoing immune checkpoint inhibitor therapy and prognosis prediction using 18F-FDG PET/CT: multicenter study for comparison of EORTC, PERCIST, and imPERCIST

2021 ◽  
Author(s):  
Kazuhiro Kitajima ◽  
Tadashi Watabe ◽  
Masatoyo Nakajo ◽  
Mana Ishibashi ◽  
Hiromitsu Daisaki ◽  
...  
Keyword(s):  
Metabolic Disease ◽  
Malignant Melanoma ◽  
Fdg Pet ◽  
Immune Checkpoint Inhibitor ◽  
Metabolic Response ◽  
Checkpoint Inhibitor ◽  
Response Evaluation ◽  
Pet Ct ◽  
Melanoma Patients ◽  
Fdg Pet Ct

Abstract Objective In malignant melanoma patients treated with immune checkpoint inhibitor (ICI) therapy, three different FDG-PET criteria, European Organization for Research and Treatment of Cancer (EORTC), PET Response Criteria in Solid Tumors (PERCIST), immunotherapy-modified PERCIST (imPERCIST), were compared regarding response evaluation and prognosis prediction using standardized uptake value (SUV) harmonization of results obtained with various PET/CT scanners installed at different centers. Materials and methods Malignant melanoma patients (n = 27) underwent FDG-PET/CT examinations before and again 3 to 9 months after therapy initiation (nivolumab, n = 21; pembrolizumab, n = 6) with different PET scanners at five hospitals. EORTC, PERCIST, and imPERCIST criteria were used to evaluate therapeutic response, then concordance of the results was assessed using Cohen’s κ coefficient. Log-rank and Cox methods were employed to determine progression-free (PFS) and overall (OS) survival. Results Complete metabolic response (CMR)/partial metabolic response (PMR)/stable metabolic disease (SMD)/progressive metabolic disease (PMD) with harmonized EORTC, PERCIST, and imPERCIST was seen in 3/5/4/15, 4/5/3/15, and 4/5/5/13 patients, respectively. Nearly perfect concordance between each pair of criteria was noted (κ = 0.939–0.972). Twenty patients showed progression and 14 died from malignant melanoma after a median 19.2 months. Responders (CMR/PMR) showed significantly longer PFS and OS than non-responders (SMD/PMD) (harmonized EORTC: p < 0.0001 and p = 0.011; harmonized PERCIST: p < 0.0001 and p = 0.0012; harmonized imPERCIST: p < 0.0001 and p = 0.0012, respectively). Conclusions All harmonized FDG-PET criteria (EORTC, PERCIST, imPERCIST) showed accuracy for response evaluation of ICI therapy and prediction of malignant melanoma patient prognosis. Additional studies to determine their value in larger study populations will be necessary.

scholarly journals 673 Precision microbiome mapping identifies a microbiome signature predictive of Immune checkpoint inhibitor response across multiple research study cohorts

2020 ◽  
Vol 8 (Suppl 3) ◽  
pp. A711-A711
Author(s):  
Matthew Robinson ◽  
Kevin Vervier ◽  
Simon Harris ◽  
David Adams ◽  
Doreen Milne ◽  
...  
Keyword(s):  
Gut Microbiome ◽  
Immune Checkpoint ◽  
Immune Checkpoint Inhibitor ◽  
Checkpoint Inhibitor ◽  
Advanced Melanoma ◽  
List Type ◽  
Metagenomic Sequencing ◽  
Genome Database ◽  
Sequencing Platform ◽  
Melanoma Patients

BackgroundThe gut microbiome of cancer patients appears to be associated with response to Immune Checkpoint Inhibitor (ICIs) treatment.1–4 However, the bacteria linked to response differ between published studies.MethodsLongitudinal stool samples were collected from 69 patients with advanced melanoma receiving approved ICIs in the Cambridge (UK) MELRESIST study. Pretreatment samples were analysed by Microbiotica, using shotgun metagenomic sequencing. Microbiotica’s sequencing platform comprises the world’s leading Reference Genome Database and advanced Microbiome Bioinformatics to give the most comprehensive and precise mapping of the gut microbiome. This has enabled us to identify gut bacteria associated with ICI response missed using public reference genomes. Published microbiome studies in advanced melanoma,1–3renal cell carcinoma (RCC) and non-small cell lung cancer (NSCLC)4 were reanalysed with the same platform.ResultsAnalysis of the MELRESIST samples showed an overall change in the microbiome composition between advanced melanoma patients and a panel of healthy donor samples, but not between patients who subsequently responded or did not respond to ICIs. However, we did identify a discrete microbiome signature which correlated with response. This signature predicted response with an accuracy of 93% in the MELRESIST cohort, but was less predictive in the published melanoma cohorts.1–3 Therefore, we developed a bioinformatic analytical model, incorporating an interactive random forest model and the MELRESIST dataset, to identify a microbiome signature which was consistent across all published melanoma studies. This model was validated three times by accurately predicting the outcome of an independent cohort. A final microbiome signature was defined using the validated model on MELRESIST and the three published melanoma cohorts. This was very accurate at predicting response in all four studies combined (91%), or individually (82–100%). This signature was also predictive of response in a NSCLC study and to a lesser extent in RCC. The core of this signature is nine bacteria significantly increased in abundance in responders.ConclusionsAnalysis of the MELRESIST study samples, precision microbiome profiling by the Microbiotica Platform and a validated bioinformatic analysis, have enabled us to identify a unique microbiome signature predictive of response to ICI therapy in four independent melanoma studies. This removes the challenge to the field of different bacteria apparently being associated with response in different studies, and could represent a new microbiome biomarker with clinical application. Nine core bacteria may be driving response and hold potential for co-therapy with ICIs.Ethics ApprovalThe study was approved by Newcastle & North Tyneside 2 Research Ethics Committee, approval number 11/NE/0312.ReferencesMatson V, Fessler J, Bao R, et al. The commensal microbiome is associated with anti-PD-1 efficacy in metastatic melanoma patients. Science 2018;359(6371):104–108.Gopalakrishnan V, Spencer CN, Nezi L, et al. Gut microbiome modulates response to anti-PD-1 immunotherapy in melanoma patients. Science 2018;359(6371):97–103.Frankel AE, Coughlin LA, Kim J, et al. Metagenomic shotgun sequencing and unbiased metabolomic profiling identify specific human gut microbiota and metabolites associated with immune checkpoint therapy efficacy in melanoma patients. Neoplasia 2017;19(10):848–855.Routy B, Le Chatelier E, Derosa L, et al. Gut microbiome influences efficacy of PD-1-based immunotherapy against epithelial tumors. Science 2018;359(6371):91–97.

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