Identification and quantification of immune infiltration landscape on therapy and prognosis in left- and right-sided colon cancer

Abstract

Background: The left-sided and right-sided colon cancer (LCCs and RCCs, respectively) have unique molecular features and clinical heterogeneity. This study aimed to identify the characteristics of immune cell infiltration (ICI) subtypes for evaluating prognosis and therapeutic benefits.

Methods: The independent gene datasets, corresponding somatic mutation and clinical information were collected from The Cancer Genome Atlas and Gene Expression Omnibus. The ICI contents were evaluated by "ESTIMATE" and "CIBERSORT." We performed two computational algorithms to identify the ICI landscape related to prognosis and found the unique infiltration characteristics. Next, principal component analysis was conducted to construct ICI score based on three ICI patterns. We analyzed the correlation between ICI score and tumor mutation burden (TMB), and stratified patients into prognostic-related high- and low- ICI score groups (HSG and LSG, respectively). The role of ICI scores in the prediction of therapeutic benefits was investigated by "pRRophetic" and verified by Immunophenoscores (IPS) (TCIA database) and an independent immunotherapy cohort (IMvigor210). The key genes were preliminary screened by weighted gene co-expression network analysis based on ICI scores. And they were further identified at various levels, including single cell, protein and immunotherapy response. The predictive ability of ICI score for prognosis was also verified in IMvigor210 cohort.

Results: The ICI features with a better prognosis were marked by high plasma cells, dendritic cells and mast cells, low memory CD4⁺ T cells, M0 macrophages, M1 macrophages, as well as M2 macrophages. A high ICI score was characterized by an increased TMB and genomic instability related signaling pathways. The prognosis, sensitivities of targeted inhibitors and immunotherapy, IPS and expression of immune checkpoints were significantly different in HSG and LSG. The genes identified by ICI scores and various levels included CA2 and TSPAN1.

Conclusion: The identification of ICI subtypes and ICI scores will help gain insights into the heterogeneity in LCC and RCC, and identify patients probably benefiting from treatments. ICI scores and the key genes could serve as an effective biomarker to predict prognosis and the sensitivity of immunotherapy.

Keywords: Colon cancer; Immune cell infiltration (ICI); Left-sided; Prognosis; Right-sided; Therapeutic sensitivity.

Fig. 1

A The correlation heat map visualized the universal landscape of immune cell interaction in TME. The correlation coefficient decreased in size from red to blue. B Consensus matrixes of all CC samples for appropriate k value (k = 3), displaying the clustering stability using 1000 iterations of hierarchical clustering. All samples were clustered into 3 subtypes. C Kaplan–Meier curves of overall survival in different ICI clusters. Log rank test showed an overall p = 0.072. D Kaplan–Meier curves of overall survival between ICI cluster A and B. Log rank test showed an overall p = 0.045. E The heat map depicted unsupervised clustering of ICI in all CC samples. Rows represented tumor-infiltrating immune cells, and columns represented samples. F The fraction of tumor-infiltrating immune cells, immune score and stromal score in three ICI clusters. The statistical difference of three ICI clusters was compared by the Kruskal–Wallis test. G The difference of PD-L1 expression among distinct ICI clusters. (^*p < 0.05, ^**p < 0.01, ^***p < 0.001, ^nsp > 0.05)

Fig. 2

| A Consensus matrixes of TCGA-COAD cohorts for appropriate k value (k = 3), displaying the clustering stability using 1000 iterations of hierarchical clustering. TCGA samples were clustered into 3 subtypes based on the DEGs among three ICI clusters. B GO enrichment analysis of the ICI-relevant signature genes A. C GO enrichment analysis of the ICI-relevant signature genes B. The X axis indicated the number of genes within each GO term. D The heat map depicted the expression of DEGs in different ICI clusters and gene clusters. Heat map colors indicate relative DEGs expression levels. E Kaplan–Meier curves of overall survival in different gene clusters. The log rank test showed an overall p = 0.039. F The fraction of tumor-infiltrating immune cells, immune score and stromal score in three gene clusters. The statistical difference of three gene clusters was compared by the Kruskal–Wallis test (^*p < 0.05, ^**p < 0.01, ^***p < 0.001, ^nsp > 0.05)

Fig. 3

| A Kaplan–Meier curves of overall survival in HSG and LSG. The log rank test showed an overall p = 0.022. B GO-related GSEA showed DNA damage response detection, synthesis involved in DNA repair, postreplication repair, etc. were significantly enriched in the HSG. C KEGG-related GSEA showed base excision repair, cell cycle, mismatch repair, etc. were significantly enriched in the HSG. D The Sankey diagram showed the distribution of patients with primary tumor sites, gene clusters and ICI scores

Fig. 4

| A–F The sensitivity difference of multiple targeted inhibitors in HSG and LSG. In Lapatinib (A) and AKT inhibitor VIII (B), the median IC50 of LSG was significantly lower than that of HSG (all p < 0.05). In Sunitinib (C), Cyclopamine (D), Mitomycin.C (A) and JNK Inhibitor VIII (F), HSG had the significantly lower median IC50 than LSG (all p < 0.05). (G–I) The difference of the expression of immunosuppressive checkpoints in HSG and LSG. The expressions of PDCD1 (G), PDCD1LG2 (H), HAVCR2 (I) and LAG3 (J) were higher in HSG than those in LSG (all p < 0.05). The statistical difference of HSG and LSG was compared by the Wilcoxon test

Fig. 5

| (A–D) The relationship between IPS and ICI score groups in LCCs and RCCs patients. The IPS (A), IPS-PD1/PD-L1/PD-L2 (B) and IPS-CTLA4 (C) were significantly different within ICI score groups (all p < 0.05). E Kaplan–Meier curves of overall survival in the IMvigor210 cohort. The log rank test showed an overall p = 0.031. F The difference of ICI score between treatment outcome groups (p < 0.001). G Proportion of patients with different treatment outcomes in HSG and LSG. The proportion of CR/PR patients in HSG was significantly higher than that in LSG (p < 0.05). The statistical difference above was compared by the Wilcoxon test

Fig. 6

A The TMB of HSG was significantly higher than that of LSG. Wilcoxon test, Wilcoxon test, p = 0.027. B The scatterplots depicted the positive correlation between ICI scores and TMB. The Spearman correlation between ICI scores and TMB was 0.16 (p = 0.0089). C Kaplan–Meier curves of overall survival in different TMB subgroups. Log rank test, p = 0.021. D Kaplan–Meier curves of overall survival stratified by both TMB and ICI scores. Log rank test, p = 0.014. E–F The waterfall diagram showed the top 20 driver genes exhibiting the highest mutation frequency in HSG (E) and LSG (F)

Fig. 7

A In SCEA database, after setting the suitable parameters (t-SNE perplexity score = 25, k value = 94), all colon cancer single cells were clustered into 94 subpopulations according to their expression patterns. B–D The key genes with specific expression patterns in single-cell level, including CA2 (B), PLAC8 (c) and TSPAN1 (D). They could act as the marker genes in cluster 9

Fig. 8

(A–H) The expression differences of the 8 preliminary screened genes in different treatment outcome groups. The expressions of CA2 (A), TSPAN1 (B) and IER3 (C) were significantly different in treatment outcome groups (all p < 0.05). (i and N) The expression of CA2 (I) and TSPAN1 (N) were significantly different between cancer tissues and normal tissues at protein level (all p < 0.05). The statistical difference above was compared by the Wilcoxon test. (J–M) Immunohistochemical staining for the key genes CA2 in normal tissues (j and K) and COAD cancer tissues (L and M). O–R Immunohistochemical staining for the key genes TSPAN1 in normal tissues (O and P) and COAD cancer tissues (Q and R). (Image credit: Human Protein Atlas, images available from v20.1.proteinatlas.org.)