The ICFs of major immune cell types in the TME can be predicted from the blood. A, Correlation matrix of ICFs between the TME and the blood. For immune cell types in the TME (columns), the cell types in the blood (rows) with the highest correlations are marked in black boxes. Pearson correlation values shown are mean ± SD over 1,000-replicate bootstrapping. B, Distribution of correlations between the model-predicted ICFs and the true TME ICFs are shown on the test sets across 1,000 replicates. C, Correlation between the model-predicted TME ICF and the measured TME ICF for each cell type on a small, independent validation dataset (n = 5). Cycling T cells are not studied as they are not present in all tumor tissue samples; monocytes and DC are excluded as their pertaining predictive clinical information is missing. r denotes the Pearson correlation coefficient, and ρ denotes the Spearman correlation coefficient. Statistical notation: ***, FDR < 0.001; **, FDR < 0.01; *, FDR < 0.05. FDRs are calculated using Benjamini–Hochberg correction. Alc., alcohol; info., information; Tob., tobacco.
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.
