The Landscape of Immuno-cell Infiltration in the TME of LUAD
Firstly, the CIBERSORT and ESTIMATE algorithms were employed to identify the immuno-cell infiltration in LUAD tumor tissues. Based on 1646 tumor samples with matched immune cell infiltration (ICI) profiles from the meta-cohort (Array express database: GSE31210, GSE30219, GSE68465 and GSE72094; The Cancer Genome Atlas TCGA-LUAD), unsupervised clustering of LUAD patients into different subtypes was performed as descripted in the MATERIAL and METHOD section.
To our knowledge, the immune cell infiltration condition is highly associated with the patients’ prognosis. To identify the role of specific immune cells in tumor progression, classification of LUAD based on the immune cell infiltration was employed. Here, there are three ICI clusters were identified with various infiltrated immune cells (Figure 1A). Meantime, we also found that the ICI cluster 2 has a favorable prognosis(Figure 1B). Considering the immune landscape of LUAD (Figure 1A), the infiltration percentage of Dendritic cells resting, Dendritic cells activated, Macrophages M2, Mast cells resting, NK cells activated and T cells CD4 memory resting in the ICI cluster 2 were significantly higher than ICI cluster 1 and 3.
Meanwhile, the landscape of immune cell interaction in TME are pictured in the Figure 1C. We can observe that the correlation between T CD8+ cell and T CD4+ cell memory resting and activated more than other infiltration immune cells. Moreover, we also tested the PD-1 and CLAT4 expression level in each ICI clusters by using the Kruskal-Wallis test, and the results showed that the cluster 3 has higher expression level with significant difference (Figures 1D and 1F). While, we cannot observe any difference between cluster 1 and 2, in which the expression level of PD-1 and CTLA4 is similar.
Identified Immune Gene Subtype
For confirming the underlying biological functions in different immune-phenotypes, we employed the Limma R package to identify the differential expression genes. Owing that the clinical information of TCGA-LUAD cohort is not entire, we mainly use LUAD patients with entire clinical information to further perform the analysis in the following study.
Firstly, we analyzed the aforementioned differentially expressed genes (DEGs) by using the ConsensusClusterPlus package with the unsupervised clustering analysis functions, and these genes can be classified into three genomic clusters: gene clusters
A, B and C. To reduce the noises of cluster genes, we used the Boruta package to reduce the dimension in both signature A and B. Here, we identified 144 genes, which were positively correlated with the gene cluster A. And, the residual 64 genes were identified as the signature B. All the gene expression in LUAD patients were pictured in Figure 2A mainly based on the genecluster types. To analyze by the expression level, the genecluster A is significantly different with other two types, in which ICI signature gene A is lower and ICI signature B is higher. These results are consistent with the prognosis of LUAD patients based on the genecluster classification (Figure 2B).
Moreover, we also performed the Gene Ontology (GO) analysis to identify the role of differentially expressed genes. As shown in Figure 2C-D, some signal pathways associated with T regulation processes and cytokines secretion were enriched, for example positive regulation of T cell activation and T cell activation. These results indicated that the T cell regulation related pathways played a critical role in promoting the tumorigenesis.
The previous investigations have proved that the immune system may display the different roles with both favorable and adverse outcomes, which is determined by the role of immune cells as the pro-tumor or the anti-tumor as we observed in our previous analysis. As shown in Figure 2E, the gene cluster A exhibited the high B cells memory, Dendritic cells activated, Dendritic cells resting, Macrophages M2, Mast cells resting, Monocytes and T cells CD4 memory resting. These immune cells displayed the higher level of infiltration, and may involve into the improving the therapy. PD-1 is the critical biomarker for immunotherapeutic intervention and is always expressed on activated T, natural killer ad B lymphocytes, macrophages, dendritic cells and monocytes28. In addition, the expression level of PD-1 was significantly different among the three genomic clusters (Kruskal-Wallis, P<0.01; Figure 2F). ICI gene clusters of A and B were associated with lower PD1/PD-1 expression levels, while ICI gene clusters C were associated with higher PD1/PD-L1 expression levels. Our investigation showed that the consistency of immune-profile of different gene groups indicated the different prognostic profile.
Construction of the ICI Score
We used the principal component analysis to count the total score of both the ICI score A and B for getting the appropriate indicators, and this score were regard as the summed indexes of each patient. We utilized the X-tile software to divide the TCGA cohort into the high and low ICI scores, which was showed in the Figure 3A. Here,
we chosen six biomarkers (CD274, CTLA4, HAVCR2, IDO1, LAG3, and PDCD1) as immune-checkpoint-relevant signatures, while other eight biomarkers (CD8A, CXCL10, CXCL9, GZMA, GZMB, IFNG, PRF1, TBX2, and TNF) is treated as immune-activity-related signatures. As showed in Figure 3B, these biomarkers are overexpressed in the high ICI group by using the Wilcoxon test. Then, we used the gene set enrichment analysis (GSEA) to explore the underlying biology functions. The GSEA results indicated that the enriched pathways in high ICI score group was associated with the various cell proliferation signaling (Figure 3C) and the low ICI score group was attributed to cell cycle pathways (Figure 3D). Meanwhile, the Kaplan-Meier plotter showed that the high ICI score group had much better survival rate than the low group (Figure 3E).
The Correlation between the ICI Scores and Somatic Variants
More evidences have demonstrated that high mutation load (non-synonymous variation) in tumors were associated with increasing infiltration of CD8+ T cells, which showed that the tumor load mutation (TMB) may play an important role in the immunotherapy response. Based on the important role of TMB in clinical diagnosis, we aimed to study the correlation between TMB and ICI score to clarify the genetic imprinting of each ICI subgroup.Thus,weinvestigatedtheTMBbetweenthehigh andlow ICIscores,whichshowedthatthehighICIscoregroupsufferedthe significantlyhigherTMBthanthelowICIscoregroup(Wilcoxontestp<0.001, Figure4A).Meanwhile,thecorrelationresults(Figure4B)showedthattheICIscore waspositivelycorrelatedwiththeTMB(R = 0.359, P < 0.001).Further study about TMB in survival was employed, and the result showed that the survival predication of high TMB was better than the low TMB group,to some extent(Figure 4C). In order to consider the association between the TMB and ICI scores, we analyzed the synergistic effect of these scores in prognostic stratification and the result showed that the overall survival of high ICI score (including ICIhigh -TMBhigh and ICIhigh-TMBlow) was much better than the low ICI score(including ICIlow -TMBhigh and ICIlow-TMBlow), and the overall survival of ICIhigh -TMBhigh was comparable with that of ICIhigh-TMBlow (Figure 4D),which indicated that high ICI score played a leading role in predicted survival. Meanwhile, the various ICI score groups in different TMB group had significantly survival differences. In conclusion, our study identified that the ICI score may be an important diagnosis index in the predicted survival and evaluates the immunotherapy response.
Moreover, we assessed the driver genes distribution in both high ICI score and low ICI score using the MAFtools. The top 25 genes with highest frequency were pictured in the both high ICI score group and low ICI score group (Figure 4E and F), which those genes were significantly different in both groups. These results provide one new
analysis approach for studying the mechanism of tumor ICI components and gene mutations in immune therapy.
The Role of ICI Scores in the Prediction of Immunotherapeutic Benefits
The immune therapy was applied to improve the tumor therapy outcomes, especially by using PD-1 or PD-L1 specific monoclonal antibody (mAb). However, the remission rate remains lower than expected, rarely exceeding 40 percent. In order to analyze the application of ICI score in the assessment of immune therapy, we use the IMvigor210 cohort, in which these patients received the anti-PD-L1 immunotherapy, to be divided into the high ICI score group and low ICI score group. Then, we assessed the survival rates in the different ICI score group, and the results showed that the high score had much better survival than the low score group (Figure 5B). The objective response rate of anti-PD-L1 therapy was higher in the high ICI score group than in the low ICI group (Figure 5A). We also found that higher ICI scores are correlated with objective response to anti-PD-L1 therapy (Figure 5C).
We also preformed a validation set, and we used TIDE website37 to predict the cancer immunotherapy response for the 535 patients from TCGA database, and our ICI score is consistent with TIDE index from the aspect of predictive results( (Figure 5D), which can be an effective immunotherapy index for identifying the suitable candidates to receive the immunotherapy.