Figure 5 from Inferring Characteristics of the Tumor Immune Microenvironment of Patients with HNSCC from Single-Cell Transcriptomics of Peripheral Blood

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posted on 2024-09-05, 06:40 authored by Yingying Cao, Tiangen Chang, Fiorella Schischlik, Kun Wang, Sanju Sinha, Sridhar Hannenhalli, Peng Jiang, Eytan Ruppin

A new blood-predicted tumor immune signature, ICFR*, predicts both response and survival of patients with HNSCC after ICB treatment. A, The predictive power for ICB response of three categories of immune information in the blood and in the TME are thoroughly evaluated, including the expression levels of key immune checkpoint genes in their corresponding cell types, ICFs, and ICFRs. B–F, Quantifying the prediction accuracy of the ICFR* signature (B_memory − Treg)/(B_memory + Treg). B, Correlation between the predicted ICFR* values and the measured ICFR* values in the TME in the matched blood/tumor training dataset (n = 25) and validation dataset (n = 5). C, ROC curve for predicting ICB RECIST response (n = 16) and pathologic response (n = 26) by the TME ICFR* signature inferred from the blood scRNA-seq. D, OS and PFS analyses of ICB-treated patients in ICFR*-high (score > quantile 50%) vs. ICFR*-low (score ≤ quantile 50%) tumor groups for the PBMC scRNA-seq dataset (GSE200996). E, ROC curve for predicting ICB response using the ICFR* computed from the deconvoluted bulk tumor RNA-seq (GSE159067; n = 102). F, OS and PFS analysis of ICB-treated patients in ICFR*-high vs. ICFR*-low tumor groups for the bulk tumor RNA-seq dataset (GSE159067). r, Pearson correlation coefficient; ρ, Spearman correlation coefficient. FPR, false-positive rate; TPR, true-positive rate.

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ARTICLE ABSTRACT

In this study, we explore the possibility of inferring characteristics of the tumor immune microenvironment from the blood. Specifically, we investigate two datasets of patients with head and neck squamous cell carcinoma with matched single-cell RNA sequencing (scRNA-seq) from peripheral blood mononuclear cells (PBMCs) and tumor tissues. Our analysis shows that the immune cell fractions and gene expression profiles of various immune cells within the tumor microenvironment can be inferred from the matched PBMC scRNA-seq data. We find that the established exhausted T-cell signature can be predicted from the blood and serve as a valuable prognostic blood biomarker of immunotherapy response. Additionally, our study reveals that the inferred ratio between tumor memory B- and regulatory T-cell fractions is predictive of immunotherapy response and is superior to the well-established cytolytic and exhausted T-cell signatures. These results highlight the promising potential of PBMC scRNA-seq in cancer immunotherapy and warrant, and will hopefully facilitate, further investigations on a larger scale. The code for predicting tumor immune microenvironment from PBMC scRNA-seq, TIMEP, is provided, offering other researchers the opportunity to investigate its prospective applications in various other indications. Our work offers a new and promising paradigm in liquid biopsies to unlock the power of blood single-cell transcriptomics in cancer immunotherapy.