Immune subgroup analysis for non-small cell lung cancer may be a good choice for evaluating therapeutic efficacy and prognosis

Results

Constructing a new immune subgroup of NSCLC

The flow diagram is provided in Supplementary Figure 1. Based on the expression levels of 770 immune-related genes derived from the nCounter^® Pan-Cancer Immune Profiling array, a consistent clustering of 988 NSCLC samples was put into practice. Based on the change in the area under the consistent CDS and CDS curve, the number of consistent clusters was selected to be 4 (Supplementary Figure 2). Since there was only one sample in category 4, it was removed in the subsequent analysis. In the identified subgroups, most of the samples in cluster 1 (n=394) were squamous carcinomas, almost all of the samples in cluster 3 (n=331) were adenocarcinoma, and the samples in cluster 2 (n=272) were a mixture of the two cancer types (Figure 1A). To confirm that these three clusters were related to the tumor immune microenvironment (TIME), the Nanodissect algorithm was used to score total lymphocytes and myeloid cell infiltration. The three clusters were found to be significantly correlated with the scores of lymphocytes, myeloid cells and stromal cells (Wilcoxon rank-sum test, p<0.0001, Figure 1B).

Figure 1. (A) Expression of immune-related genes in TCGA non-small cell lung cancer data. Pearson correlation distance and ward.D2 were used for unsupervised clustering. The top annotations represent the subgroups, cancer types, and TCGA cancer subtypes identified based on consistent clustering. (B) Lymphocyte and myeloid cell infiltration and stromal cell distribution among subgroups. (B1) Lymphocyte infiltration in different clusters; (B2) stromal cell distribution in different clusters; (B3) myelocyte infiltration score in different clusters; (B4) lymphocyte infiltration score in different clusters. The Wilcoxon rank-sum test (*, P<0.05; **, P<0.01; ***, P<0.001; ****, P<0.0001) was used for statistical difference testing among different groups.

Based on pathological evaluation, the lymphatic invasion of cluster 2 was the highest and differed significantly from the other two groups, while no difference in lymphatic invasion was found between clusters 1 and 3. The levels of stromal cells in the three subgroups were found to be significantly different; cluster 2 was considered to have the highest level (Figure 1B1, 1B2). Based on the expression level of infiltration score, cluster 2 was also shown with the highest level of lymphatic invasion, while myeloid infiltration was found to be gradually increased in the three subgroups (Figure 1B3, 1B4).

Among the three clusters, the enrichment scores of all immune-related pathways showed significant differences. Most of the pathways showed stronger signal in cluster 2, and were significantly higher than the other two groups (Kruskal test, *, P <0.05; **, P <0.01; ***, P <0.001; ****, P <0.0001; Supplementary Figure 3). Among the three clusters, all suppressive immune checkpoint genes showed significant expression differences. The vast majority of pathways showed significantly high expression in cluster2 (Kruskal test, *, P <0.05; Supplementary Figure 4). Among them, the macrophage CD86 and the widely used ICB target-CD274 are both significantly highly expressed in cluster 2.

Building an immune subgroup prediction model

In order to accurately distinguish lung cancer subgroups without relying on unsupervised clustering, we then used binomial logistic regression and L1 regularization (lasso) to construct a subgroup classifier to distinguish cluster 2 from other subgroups (cluster 1 and cluster 3). It was confirmed by five-fold crossover that our model had good predictive power (ROC-AUC=0.975, PR-AUC=0.940, Figure 2A, 2B). Furthermore, we performed a new round of binomial logistic regression to distinguish cluster 1 and cluster 3, which also achieved extremely high performance (ROC-AUC=0.997, PR-AUC=0.998).

Figure 2. Subgroup prediction model based on binomial logistic regression and prognostic efficacy of immune subgroups. (A) Distinguishing the predictive power of cluster 2 from the other two groups. (B) Distinguishing the predictive power of cluster 1 from cluster 3. The upper part is ROC-AUC, and the lower part is PR-AUC. (C) In all NSCLC samples, immune subgroups cannot significantly distinguish the overall survival of patients. (D) In the GEO independent verification set, the immune subgroup can significantly distinguish the prognosis of patients. Among patients with lung squamous cell carcinoma (E) or those older than 60 years (F), immune subgroups can significantly distinguish the prognosis of the patients.

Prognostic efficacy of immune subgroups

To complete our observations, we mapped patient survival based on the identified immune subgroups. Unfortunately, immune subgroup failed to distinguish the overall survival of patients effectively in TCGA-NSCLC (log-rank test, p>0.05, Figure 2C). In the independent validation set, patient survival could be distinguished effectively by the identified immune subgroups (log-rank test, p<0.05, Figure 2D). After considering the effects of age, tumor stage, gender, and cancer type, it was found that the current immune subgroup could significantly distinguish the overall survival from others when patients were over 60 years or had squamous cell carcinoma (log-rank test, p<0.05, Figure 2E, 2F). In addition, progression-free survival (PFS) of Stage II NSCLC patients could also be significantly distinguished by immune subgroup (log-rank test, p<0.05, Supplementary Figure 5).

Molecular indicators of immune subgroups

Subsequently, we systematically analyzed the molecular and clinical indicators of immune subgroups, including genomic alterations, pathological typing, and immune infiltration. By comparing the three immune subgroups, we found that EGFR and KRAS were significantly different in the subgroups (chi square test, p<0.05, Figure 3A), and mainly appeared in cluster 3. EGFR and KRAS were mutually exclusive in cluster 2 and cluster 3 (chi square test, p<0.05). Analyzing the correlation between the three subgroups and clinical information, we found that smoking was significantly related to immune subgroup, mainly apparent in cluster 3 (chi square test, p<0.05). However, the immune subgroup had nothing to do with American Joint Committee on Cancer (AJCC) Stage or chemotherapy (chi square test, p>0.05). In addition, the silent mutation and non-silent mutation loads of cluster 3 were found to be at the lowest level among the clusters, significantly lower than those of cluster 1 and cluster 2 (Wilcoxon rank-sum test, Figure 3B); there was no significant difference in tumor mutation burden (TMB) between cluster 2 and cluster 1. Turning our attention to PD-1 and PD-L1, we found that the expression levels of these two molecules (z-score of log2-transformed FPKM) in the cluster 2 subgroup were significantly higher than those of the other two subgroups (Wilcoxon rank-sum test, Figure 3C).

Figure 3. Differences in molecular indicators among immune subgroups and subtype-related immune cell infiltration. (A) Mutation landscape in all clusters. KRAS and EGFR mutations occurred significantly in cluster 3 (chi square test, p<0.05). (B) Tumor mutation burden (Silent and Non-silent) in different clusters. Cluster 3 had the lowest tumor mutation burden. (C) Expression levels of PD-1/PD-L1 in different clusters. Cluster 2 had the highest levels of PD-1 and PD-L1 expression. The numerical statistical test was based on the Wilcoxon rank sum test (*, P<0.05; **, P<0.01; ***, P<0.001; ****, P<0.0001). (D) Differences in immune cell infiltration subtypes. Statistical significance was calculated using the Kruskal-Wallis test (*, P<0.05; **, P<0.01; ***, P<0.001; ****, P<0.0001). (E) Immune cell infiltration significantly associated with cluster 2 (based on logistics regression identification, p<0.05).

Immune cell infiltration in different subgroups

Based on the CIBERSORT method, we calculated the immune cell infiltration of each sample.

We found that 22 immune cell types and their status subdivisions showed significant differences in the three subgroups. Among them, M1 Macrophages were found to be have the greatest infiltration in cluster 2, while resting Mast cells had the greatest infiltration in cluster 3 (Figure 3D and Supplementary Figure 6). Using the generalized linear model, we identified the immune cell types that could explain the difference between cluster 2 and cluster 1_cluster 3 (Figure 3E).

We then observed the correlation between immune cell infiltration and the prognosis of NSCLC, and found that Mast cells and their status subdivisions were significantly associated with the prognosis of patients (Figure 4A). To further subdivide cancer types, we found that the Mast cell type and its status subdivisions could still distinguish the prognosis of patients in LUAD from the others, while Monocytes in LUSC patients was found to be related to the overall survival of patients (Figure 4B). Prior research suggested that the Mast cell type and its phenotype might be related to the prognosis of NSCLC [29]. Furthermore, it was reported that Mast cells might have a prognostic effect on lung cancer [30]. Our results showed that activated and resting Mast cells were significantly associated with better and worse prognoses, respectively, which further supports the prognostic role of the Mast cell type and its status subdivisions in NSCLC. Unlike previous reports, in LUSC, we found that Monocytes were associated with a better prognosis [31–34].

Figure 4. Prognostic efficacy of immune cell infiltration in TCGA-NSCLC and the functional differences involved in cluster 2. (A) Prognostic efficacy of immune cell infiltration in TCGA-NSCLC: Forest plot of the prognostic efficacy of immune cells. Single factor cox regression was used to evaluate the significance of the correlation between cell infiltration ratio and Overall Survival (OS). (B) Prognostic efficacy of immune cell infiltration in TCGA-NSCLC: In the corresponding metabolic subtypes, the Kaplan-Meier curve of the two groups of patients with high and low cell infiltration (Low). The log-rank test was used for calculating the difference in OS between the high and low groups. The division threshold was based on the maximum selection method. (C) Functional differences involved in cluster 2: Identifying the functional differences based on the GSEA method. The MSigDB cancer hallmark gene set was used. (D) Functional differences involved in cluster 2: Visualization of important cancer-related pathways. (E) Functional differences involved in cluster 2: Visualization of important immune-related pathways.

Functional differences in different subgroups

Based on the MSigDB cancer hallmark gene set, we used the Gene Set Enrichment Analysis (GSEA) method to identify the cluster 2-related functional differences. The results showed that compared with cluster_1_and_3, cluster 2's dysregulated genes were enriched in a variety of pathways related to malignant cancer progression, including stem cell biology and EMT-related pathways corresponding to cancer-related functions (Figure 4C). Further visualizing the important cancer-related pathways, we found that cluster 2 had more EMT, KRAS and TNF signaling pathways, which was consistent with the worse prognosis associated with this cluster (Figure 4D, 4E). At the same time, we found that cluster 2 also had an enriched immune-related factor and active inflammatory response.

In order to further explain the relationship between immune subgroup and cancer cell phenotype, we calculated the signature score of the aforementioned gene set based on the Gene Set Variation Analysis (GSVA) method for each sample. This score reflected the activity of each pathway in the immune subgroup. Immune subgroups could be distinguished clearly by unsupervised clustering of gene set scores (Figure 5A). Through the unsupervised clustering of GSVA enrichment scores, we found that those signatures appeared with high scores in cluster 2, but displayed erratic patterns in other subgroups. In order to formally determine which gene set score could explain cluster 2, we used the logistics model to test the contribution of each gene set to cluster 2 and cluster_1_and_3. The results indicate that pathways such as EMT and IL2-STAT5-SIGNALING have a positive contribution to cluster 2, while cluster_1_and_3 is related to inflammatory response (Figure 5B).

Figure 5. The functional differences involved in cluster 2 and the correlation between immune infiltration and cluster 2. (A) GSVA score spectrum of functional gene set in cluster 2. (B) Application of logistic regression to identify pathways that were significantly related to cluster 2. (C) Correlation between cluster function and immune infiltration. (D) Gene mutation characteristic blueprint of TCGA-NSCLC immune subgroup. 39 oncogenes mutated in >6% samples in the three immune subgroups of TCGA-NSCLC (missense mutation, dark blue; insert deletion, light blue). The bar graph on the right represents the mutation frequency of each gene in the sample population, and the colored bar graph on the left represents whether the occurrence of the gene mutation was related to the immune subgroup (chi-square test). Each column represents a patient, and the colored bar chart at the top indicates the immune subgroup to which the patient belonged: the subtype of NSCLC, the patient's gender, age group, and tumor grade. (E) Copy number variation blueprint of TCGA-NSCLC immune subgroups; the status of 26 oncogenes with copy number changes in >10% samples in the three immune subgroups of TCGA-NSCLC (copy number amplification, red; copy number deletion, blue). The bar graph on the right represents the copy number variation frequency of each gene in the sample population. The colored bar chart on the left represents whether the occurrence of copy number variation of the gene was related to the immune subgroup (chi-square test). Each column represents a patient, and the colored bar chart at the top indicates the immune subgroup to which the patient belonged: the subtype of NSCLC, the patient's gender, age group, and tumor grade.

Since the immune subgroups were related to a variety of immune-related pathways, we then checked the relationship between immune cell infiltration and the aforementioned cancer-related pathways. We found that the activity scores of these pathways were significantly correlated with infiltration of a variety of immune cells (Pearson’s correlation test, p value<0.05, Figure 5C). It was worth noting that EMT was significantly associated with all immune cells and their status subdivisions. The high EMT score was highly positively correlated with infiltration of a variety of inhibitory immune cells (e.g., M1 Macrophages, resting Dendritic cells, and regulatory T Cells), and negatively correlated with the infiltration of killer immune cells (e.g., CD8 T Cells and Activated NK Cells).

Subgroup-associated clinical features (EMT and proliferation) mutations, CNV, etc.

We observed the mutation signature blueprints of TCGA-NSCLC samples in three different immune subtypes, and compared the mutations of some key oncogenes (from Oncology Knowledge Base; OncoKB) among different immune subgroups (Figure 5D). Most of the oncogenes with mutation frequency greater than 10% appeared with missense mutations. Among different immune subgroups, the mutations of TP53, KRAS, KMT2D, NFE2L2, PIK3CA, EGFR, LRRK2, SETBP1, CDKN2A, PIK3CG were found to be distributed in a significantly biased manner (Figure 5D) [35, 36]. In TCGA-NSCLC patients, we found that CDKN2A, CDKN2B and MTAP mainly had copy number deletions (Figure 5E), whereas PIK3CA, SOX2, BCL6 had a large number of copy number amplifications and were significantly concentrated in cluster 1.

Immunotherapy in different subgroups

We further evaluated the relationship between immune subgroup and immunotherapy (Figure 6). Based on the previous model, we divided immunotherapy patients into cluster 2 and cluster_1_and_3. We found that 31.4% (11/35) of PD patients, 22.9% (8/35) of PR patients, and 11.4% (4/35) of SD patients belonged to the cluster 2 subgroup, whereas 8.6% (3/35) of PD patients, 2.9% (1/35) of PR patients and 22.9% (8/35) of SD patients belong to the cluster_1_and_3 subgroup. When comparing (CR & PR) vs. PD, there was no significant association between efficacy and immune subgroup (chi square test, p>0.05, Figure 6A). The PFS in the cluster 2 subgroup was shorter than that in the cluster_1_and_3 subgroup, although no significant difference was found (log-rank test, p>0.05, Figure 6B). Observing the expression levels of PD-1, PD-L1, and PD-L2, we found that the expression levels of these genes in the cluster_1_and_3 subgroup were significantly higher than those in the cluster 2 subgroup (Figure 6C).

Figure 6. Correlation between immune subgroups and the efficacy of immunotherapy. (A) Efficacy of immunotherapy in different immune subgroups. (B) Progression-free survival in different immune subgroups. (C) Expression differences of PD-1 (C1), PD-L1 (C2) and PD-L2 (C3) in different subgroups.

Abbreviations

PD-1: Programmed Cell Death-1; PD-L1: Programmed Cell Death Ligand 1; NSCLC: Non-Small Cell Lung Cancer; LUAD: Lung adenocarcinoma; LUSC: Lung squamous cell carcinoma; EMT: Epithelial mesenchymal transition; PFS: progression-free survival; TCGA: The Cancer Genome Atlas; GEO: Gene Expression Omnibus; CNV: Copy Number Variation; GDC: Genomic Data Commons; CDF: Cumulative Distribution Function; TIME: Tumor Immune Microenvironment; FPKM: Fragments Per Kilobase of Exon Model Per Million Mapped Reads; CDS: Connected Dominating Set; LM22: Leukocyte signature Matrix 22; AJCC: American Joint Committee on Cancer; TMB: Tumor Mutation Burden; GSVA: Gene Set Variation Analysis; GSEA: Gene Set Enrichment Analysis; OncoKB: Oncology Knowledge Base; PD: progressive disease; SD: stable disease; CR: complete response; PR: partial response.

Author Contributions

The corresponding authors (Yuping Sun and Guohai Su) had all the data and were responsible for the decision to submit it for publication. Yuan Tian and Jingnan Wang were responsible for data collection and analysis. Yuan Tian was appointed to write the draft of the manuscript. All the figures and tables were designed and typeset by Jingnan Wang. Qing Wen was responsible for all data verification in this article.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Funding

This study was funded by Academic Promotion Program of Shandong First Medical University (2019QL025; Yuping Sun), Natural Science Foundation of Shandong Province (ZR2019MH042; Yuping Sun), and National Science and Technology Major Project of the Ministry of Science and Technology of China (2020ZX09201025; Guohai Su).

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