https://orcid.org/0000-0001-9522-3220
Figures & data
Table 1. Characteristics of patients with non-small cell lung cancer according to CD200 and CD200R1 expression
Figure 1. Mutual correlations between CD200 and CD200R1 expression and their associations with tumor-infiltrating lymphocytes (TILs)
(a) Representative images of tumors with CD200 expression and CD200R1 expression. Staining intensity was categorized as 0 (absent), 1 (weak), 2 (moderate), or 3 (strong). CD200R1 expression in the stromal area. Stromal expression levels were semi-quantitatively categorized into four grades: 0 (no staining), 1 (a few and weakly), 2 (moderate), and 3 (many and strong). (b) Correlations between H-scores of CD200 and CD200R1 expression in tumor nest. r = −0.045, P =.265 (Pearson correlation test). (c) Association between H-scores of tumoral CD200R1 expression and stromal CD200R1 expression grades. P =.002 (Kruskal-Wallis test) and P =.002 for trend (Jonckheere–Terpstra test). The variables represent the mean ± SD. (d) Correlation between CD200 and CD200R1 mRNA expression z-scores (RNA Seq V2 RSEM) in the online cohort (NSCLC, TCGA, Provisional). r = 0.130, P <.001 (Pearson correlation test). (e) Association between numbers of tumoral TILs and CD200 or CD200R1 expression in each subset of TILs including CD8+, Foxp3+, CD45RO+, and PD-1+ TILs. *P <.05 and **P <.001 (Mann-Whitney U-test). The variables represent the mean ± SD.
Table 2. Multivariate Cox hazards models of survivals in all patients with non-small cell lung cancer
Figure 2. Survival analysis according to CD200 or CD200R1 expression in patients with non-small cell lung cancer (NSCLC)
(a–c) Kaplan–Meier curves for overall survival (OS), recurrence-free survival (RFS), and cancer-specific survival (CSS) based on tumoral CD200 (a), tumoral CD200R1 (b), or stromal CD200R1 expression. Patients were stratified based on a cutoff determined by the minimum P-value method for OS based on tumoral CD200 and CD200R1. Stromal CD200R1 was divided based on the median expression, such as grade 0–1 and grade 2–3.
Figure 3. CD200 and CD200R1 expression profiles in lung cancer cell lines and effect of CD200 knockdown
(a) CD200 and CD200R1 protein levels in cell lines were analyzed by western blotting. (b) Subcellular localization of CD200 and CD200R1 as visualized by immunofluorescence (×100). Membranous localization of CD200 and CD200R1 (red) was observed. A control was performed without each specific antibody. (c) Immunoblot analysis showing effective siRNA-mediated CD200 knockdown in H1299 cells. (d) Effect of CD200 knockdown on cell proliferation in H1299 cells as analyzed by CCK-8 assays. The negative control (NC) was scramble RNA-transfected cells. The data represent the mean ± SD, N = 5. (e) Effect of CD200 knockdown on endogenous mRNA expression levels of immune markers in H1299 cells as analyzed by RT-qPCR. Gene expression was normalized to the expression of GAPDH and is shown relative to negative control expression. The data represent the mean ± SD, N = 3. *P <.05 and **P <.001 vs. NC (Student’s t-test).
Figure 4. Evaluation of CD200R1 functions with CD200R1 knockdown and CD200Fc administration
(a) Western blots in the left part showing the representing protein levels after CD200R1 knockdown with siRNA in PC9 and H358 cells, respectively. Bar graphs on the right part show western blotting quantification of pAKT/AKT and pERK/ERK in the siRNA1 and siRNA2 groups relative to those in negative controls (NCs). The data represent the mean ± SD, N = 4. *P <.05 and **P <.001 vs. NC (one-way ANOVA). (b–c) Effect of CD200R1 knockdown with siRNA on cell proliferation in PC9 and H358 cells as analyzed by CCK-8 assays. The negative control (NC) was scramble RNA-transfected cells. The data represent the mean ± SD, N = 5. *P <.05 and **P <.001 vs. NC (one-way ANOVA). (d) Effect of CD200Fc treatment on cell proliferation in PC9 cells as analyzed by CCK-8 assays. (e) Effect of CD200Fc treatment on endogenous mRNA expression levels of immune markers in PC9 cells as analyzed by RT-qPCR. Gene expression was normalized to the expression of GAPDH and is shown relative to vehicle control expression. The data represent the mean ± SD, N = 3. *P <.05 and **P <.001 vs. vehicle (Student’s t-test).
Figure 5. Enriched gene profiles in tumors with high CD200R1 expression and differentially-expressed genes in response to CD200Fc administration as assessed by cDNA microarray
(a) Volcano plots showing the significantly overexpressed genes among tumors with high CD200R1 expression using online RNA sequencing data (NSCLC, TCGA, Provisional) including 230 adenocarcinomas (ADCs), and 501 squamous cell carcinomas (SCCs). The overexpressed genes in high CD200R1-expressing tumors are surrounded by dashed lines in the volcano plots, and these were additionally analyzed based on GSEA Investigation gene set analysis using the hallmark gene set. (b) Log2 fold expression changes of the 35 most strongly up- and downregulated genes in PC9 cells treated with CD200Fc versus expression in cells treated with vehicle (N = 2). (c) GSEA analysis comparing up- and downregulated cancer hallmark gene sets and oncogenic signature gene sets in PC9 cells treated with CD200Fc versus expression in cells treated with vehicle. (d–e) Expression of certain genes differentially-expressed upon CD200Fc administration in PC9 cells based on validation by RT-qPCR. Gene expression was normalized to the expression of GAPDH and is shown relative to the vehicle-treated control expression. The data represent the mean ± SD, N = 3. *P <.05 and **P <.001 vs. vehicle (Student’s t-test).
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