Single-Cell Analysis Reveals a CD4+ T-cell Cluster That Correlates with PD-1 Blockade Efficacy

Abstract

CD4+ T-cell immunity helps clonal proliferation, migration, and cancer cell killing activity of CD8+ T cells and is essential in antitumor immune responses. To identify CD4+ T-cell clusters responsible for antitumor immunity, we simultaneously analyzed the naïve-effector state, Th polarization, and T-cell receptor clonotype based on single-cell RNA-sequencing data. Unsupervised clustering analysis uncovered the presence of a new CD4+ T-cell metacluster in the CD62Llow CD4+ T-cell subpopulation, which contained multicellular clonotypes associated with efficacy of programmed death-ligand 1 (PD-1) blockade therapy. The CD4+ T-cell metacluster consisted of CXCR3+CCR4-CCR6+ and CXCR3-CCR4-CCR6+ cells and was characterized by high expression of IL7 receptor and TCF7. The frequency of these cells in the peripheral blood significantly correlated with progression-free survival and overall survival of patients with lung cancer after PD-1 blockade therapy. In addition, the CD4+ metacluster in the peripheral blood correlated with CD4+ T-cell infiltration in the tumor microenvironment, whereas peripheral Th1 correlated with local CD8+ T-cell infiltration. Together, these findings suggest that CD62Llow CCR4-CCR6+ CD4+ T cells form a novel metacluster with predictive potential of the immune status and sensitivity to PD-1 blockade, which may pave the way for personalized antitumor immunotherapy strategies for patients.

Significance: The identification of a new CD4+ T-cell metacluster that corresponds with immune status could guide effective tumor treatment by predicting response to immunotherapy using peripheral blood samples from patients.

Figure 1.

Clustering of CD4⁺ T cells according to scRNA-seq. A, tSNE plots derived from integrated gene expression data from scRNA-seq of 6 patients with lung cancer collected prior to pembrolizumab therapy. All 33,604 CD3⁺CD8⁻CD4⁺ cells were divided into 15 clusters upon unsupervised clustering. Red dashed line, the extent of clusters mostly composed of CD62L^low T cells (cluster #3, #5, #6, #9, #12, #13, #14, and #15). Blue dashed line, cluster #1—mostly composed of CD62L^highCD45RA⁺ T cells. The table shows the expression levels of the CXCR3, CCR4, and CCR6 proteins in each cluster as determined by the value of TotalSeq-C (C). B and C, Cell-level annotation of CD62L, CD45RA (B) and CXCR3, CCR4, and CCR6 (C) from scRNA-seq of 6 patients (same tSNE reduction as in A). Annotation was performed according to the TotalSeq-C counts of each cell in the gating method. The gating strategy is detailed in Supplementary Fig. S2. D, Representative illustrations of mass cytometry viSNE analysis for gated CD4⁺CD3⁺ cells observed after the expression of 21 molecules and the expressions of six molecules are presented. Red lines, the extent of the region representing the CD62L^low subpopulation. Blue lines, CD45RA⁺CD62L^high region. Black lines, FoxP3⁺ region. Green line, remaining region. Black dashed lines, the five regions mapped on the basis of CXCR3, CCR4, and CCR6 expression. The chemokine receptor expression patterns in each region are presented in the table.

Figure 2.

Multicellular CD4⁺ T-cell clonotypes associated with PD-1 blockade therapy efficacy. A, On the basis of the results of TCR repertoire analysis using scRNA-seq, cells belonging to TCR clonotypes with two or more cells detected were mapped on the same tSNE plots as in Fig. 1A–C. B, Correlation between the percentage of TCR clonotypes with ≥2 cells detected against all clonotypes and the PFS of patients with lung cancer after initial pembrolizumab treatment. PR, SD, and PD were determined by RECIST ver1.1; *, continued response. C, Percentages of TCR clonotypes that contained ≥2 cells belonging to the CD45RA⁺CD62L^high, CD45RA⁻CD62L^high, and CD62L^low T-cell subpopulations. D, Correlation analysis between the percentage of multicellular clonotypes and PFS following pembrolizumab treatment.

Figure 3.

Th clusters are distinct from Th1 and Th17 found in responders. A, The trajectory of the pseudotime analysis of four CD62L^low CD4⁺ subpopulations of PR patient (P1 and P2) calculated from DEGs between Th1-Th17 clusters of this patient. Details of DEGs are summarized in Supplementary Table S4A. B, The heatmap shows the result of BEAM. The genes representing each cluster are highlighted in the box. Details of these branch-dependent genes are summarized in Supplementary Table S4B. C, Clustering analysis based on raw β values of differentially methylated probes obtained from four isolated CD62L^low CD4⁺ T-cell clusters derived from three patients (M1–M3). D, TCR clonotype overlap between four subpopulations aggregated for 6 patients. Numbers represent the number of clonotypes and the numbers within parentheses represent the number of cells included in that category. κ, κ coefficient.

Figure 4.

Gene expression analysis of CD4⁺ T-cell clusters. A, Gene expression profiles of four CD62L^low CD4⁺ T-cell clusters. The heatmap shows DEGs among the Th1, Th1/17, CCR6 SP, and Th17 clusters using the integrated data from 6 patients; further details are presented in Supplementary Table S6. B, Gene expression of representative genes. C, Gene expression of TBX-21 and RORC was observed pre (−) and post (+) stimulation with anti-CD3/CD28 antibody-coated beads. Differences in methylation status are also shown by boxplot, which represents scaled β values of all DMPs of these genes. Details of these DMPs are summarized in Supplementary Table S7. D, DEGs of prestimulation (blue) and poststimulation (red) with anti-CD3/CD28 antibody-coated beads of each Th cluster cells that belonged to multicellular clonotypes.

Figure 5.

Correlation of CD4⁺ T-cell clusters with clinical outcomes after PD-1 blockade therapy. A, CONSORT diagram of patients with NSCLC treated with first-line pembrolizumab. B, Linear regression analysis of PFS and the percentage of pretreatment Th7R metacluster, which consisted of CD62L^low CXCR3⁺CCR4⁻CCR6⁺ and CD62L^low CXCR3⁻CCR4⁻CCR6⁺ CD4⁺ T-cell clusters, in the peripheral blood in the discovery cohort and the validation cohort1 treated with the first-line pembrolizumab therapy and the validation cohort 2 treated with the second-line pembrolizumab or nivolumab therapy. C, Results of ROC curve analysis of the percentage of CD62L^low CCR4CCR6⁺ CD4⁺ T-cell metacluster for the discovery cohort patients treated with first-line pembrolizumab with the indicated parameters. D and E, The Kaplan–Meier analysis of PFS after PD-1 blockade therapy in the previously untreated (validation cohort 1, n = 45; D) and treated (validation cohort 2, n = 31; E) NSCLC cohort. F and G, The Kaplan–Meier analysis of OS after PD-1 blockade therapy in the previously untreated NSCLC cohort (F) and treated NSCLC cohort (G). C.I., 95% confidence interval; HR, hazard ratio; mOS, median overall survival; mPFS, median progression-free survival.

Figure 6.

Correlation of T cells in the TME with clinical outcome after PD-1 blockade therapy and network analysis among T cells in the peripheral blood and TME. A, Representative multiplex IHC analysis results. Formalin-fixed and paraffin-embedded sections of biopsy or resected samples from NSCLC specimens were stained for CD4, CD8, Foxp3, PD-1, PD-L1, and cytokeratin with OPAL. Stained sections were imaged using the Vectra Automated Imaging System. The distribution of tumor cells (blue; cytokeratin) and PD-L1 (membranous; yellow-green) or infiltrating CD4⁺ T cells (yellow), CD8⁺ T cells (red), Foxp3⁺ T cells (pink), and PD-1⁺ T cells (orange) are shown. B and C, The Kaplan–Meier analysis of PFS (B) and OS (C) after pembrolizumab therapy in previously untreated NSCLC cohort for whom IHC analysis of tumor tissues was available (n = 46). Red line, group that had one or more FoxP3⁻CD4⁺ cells in the tumor stroma; blue line, group that had less than one. C.I., 95% confidence interval; HR, hazard ratio; mOS, median overall survival; mPFS, median progression-free survival. C, Results of the linear regression analysis of peritumoral stromal CD4⁺ cell counts and PFS of patients with lung cancer treated with pembrolizumab as first-line therapy. D, Correlation coefficients between T-cell subpopulations with FDR < 0.01 were drawn in network form. The positive and negative correlations between the subpopulations are depicted by the red and blue lines, respectively. E, The network of 18 cell clusters was inferred with a modified path consistency algorithm based on the cell fraction data in the peripheral blood and the microenvironment. The inference was performed 1,000 times, and the connection established more than 500 times was set to be significant in this analysis. The cell clusters with positive and negative correlations are connected by red and blue lines, and the fractions of established correlations by 1,000 inferences are indicated on each line. F, Correlation between peripheral blood Th7R metacluster percentage and tumor-stromal CD4⁺ T-cell count. G, Correlation between peripheral blood CD62L^low Th1 cell percentage and tumor-stromal CD8⁺ T-cell count.

Figure 7.

PD-1 blockade therapy effects on CD4⁺ Th clusters. A, The percentages of CD62L^low Th clusters before and after pembrolizumab treatment in the lung cancer cohort treated with first-line pembrolizumab therapy divided into PFS < 300 days and PFS > 300 days groups are shown. Statistical analyses were performed using the paired Student t test. B, Comparison of the percentages of Th clusters in the PFS < 300 days and PFS > 300 days groups before and after treatment, respectively, is shown. Statistical analyses were performed using the unpaired Student t test. C and D, The changes in the percentage of PD-1–positive cells and the percentage of T-bet–positive cells in each Th cluster before and after pembrolizumab treatment are shown. Statistical analyses were performed using the paired Student t test.