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. 2022 Jul 27:2022:3592990.
doi: 10.1155/2022/3592990. eCollection 2022.

Alteration in the Immune Microenvironment Based on APC Status in MSS/pMMR Colon Cancer

Haishan Lin  1 , Bangwei Cao  1
Affiliations

Alteration in the Immune Microenvironment Based on APC Status in MSS/pMMR Colon Cancer

Haishan Lin et al. Dis Markers. .

Abstract

Introduction: Immunotherapy is currently the most promising antitumor treatment approach. However, the colon cancer immunotherapy indication dMMR/MSI-H do not cover all colon cancer patients suitable for immunotherapy. We performed transcriptome-wide expression profile analyses of pMMR/MSS colon adenocarcinoma (COAD) specimens from TCGA database to identify a genetype signature associated with tumor immune microenvironment types (TIMTs).

Methods: TCGA database was used to identify tumor genotypes suitable for antitumor immunotherapy. We analyzed RNA-sequencing profiles of 338 COAD targeted to the pMMR/MSS group from TCGA public dataset. The ESTIMATE and the CIBERSORT were used to analyze the pMMR/MSS COAD immune microenvironment between APC wild and APC mutation. Furthermore, we further verified the relationship between APC genotype and TIMTs and the efficacy of immunotherapy in 42 colon cancer specimens.

Results: We identified that in APC-wt/MSS colon cancer, the expressions of PD-1, PD-L1, CTLA4, and CYT (GZMA and PRF1) were increased. The TMB, Immunoscore, and the proportion of CT8+ T cell infiltration also were identified increasing in these patients. And pathway enrichment analysis for differentially expressed genes (DEGs) between APC-wt and APC-mt MSS COAD was done to further explore their biological function. Similarly, the significant pathways for DEGs were mainly enriched in the immune response, extracellular matrix, and cell adhesion which involved in immune response. Specimens from 42 colon cancer patients, including 22 APC-mt/MSS and 20 APC-wt/MSS, were immunohistochemically evaluated for expression of CD8 and PD-L1. And APC-wt/MSS tumors showed significantly higher expression of CD8 and PD-L1 than APC-mt/MSS tumor. Moreover, APC-wt was compared with APC-mt MSS/pMMR colon cancer (DOR, 45% and 26.7%, respectively; P < 0.05).

Conclusion: Based on the results, we found that more colon cancers of APC-wt/MSS are classified by TMIT I. And APC-wt/MSS colon cancer patients are more likely to benefit from antitumor immunotherapy.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Figure 1
Figure 1
Genomic landscape and clinicopathological findings in COAD samples, based on APC status in the cohort retrieved from TCGA (The Cancer Genome Atlas). (a) Frequency and type of mutations in the top 30 COAD-associated genes. Genes were sorted according to the frequency of mutations. (b) Interactions among mutations in the top 25 genes in COAD. (c) Summary of frequency and classification of mutations in the top 10 COAD-associated genes. (d) Summary of frequency and classification of mutations in the top 10 in APC-mt/MSS COAD-associated genes. (e) Summary of frequency and classification of mutations in the top 10 genes in APC-wt/MSS COAD-associated genes.
Figure 2
Figure 2
Immune score, stromal score, and TMB are associated with APC mutation status. (a, b) PRF1 and GZMA were significantly higher expression in APC-wt/MSS than APC-mt/MSS colon cancer. (c–e) Expression of CTLA4, PD-1, and PD-L1 in MSS/pMMR colon cancer with different APC gene subtypes. (f–h) Distribution of ESTIMATE score, immune score, and stromal score for APC-wt and APC-mt MSS/pMMR colon cancer. (i) Distribution of TMB for APC-wt and APC-mt MSS/pMMR colon cancer. ∗P < 0.05, ∗∗P < 0.01, ∗∗∗P < 0.001.
Figure 3
Figure 3
Composition of infiltrated immune cells in association with different genetic subtypes in the cohort retrieved from TCGA. The CIBERSORT tool deemed all samples eligible at P < 0.05. Twenty different immune cells were filtered and analyzed in the cohort retrieved from TCGA. (a) Fractions of immune cells in the 338 MSS/pMMR colon cancer samples from TCGA. (b) Comparisons of immune cells between APC-mt/MSS and APC-wt/MSS colon cancer tissues from TCGA. (c) Interaction among the 20 different immune cells in MSS/pMMR colon cancer. (d) Forest plots showing an association between different immune cell subsets in the cohort retrieved from TCGA. (e) Logistics regression analysis the PD-L1 expression the affecting factors.
Figure 4
Figure 4
Genomic landscape and gene set enrichment analysis of the COAD samples, based on APC status in the cohort retrieved from TCGA (The Cancer Genome Atlas). (a) The immunity and cancer pathways that are significantly enriched in APC-wt/MSS COAD patients, compared with those in APC-mt/MSS COAD patients. (b, c) Gene Ontology (GO) analysis of the immune-related DEGs. Circular plot of GO pathways enrich in APC-wt/MSS samples. GO pathways cluster distribution. (d, e) GO analysis of the immune-related DEGs. Immune-related DEGs in the significantly enriched immunologic and cancer biological processes. (f, g) KEGG analysis of immune-related DEGs.
Figure 5
Figure 5
Immune-related DEGs and construction of the Immunoscore model. (a, b) Lasso coefficient profiles of 8 genes were related to prognosis. The optimal values of the penalty parameter λ were determined by tenfold crossvalidation. (c–e) Patients were stratified based on low or high Immunoscore (low or high score). Kaplan-Meier curves, heatmap, and time-dependent ROC curve in the cohort retrieved from TCGA. (g) Consensus matrix for k = 2. (h) The overall survival curves of cluster 1 and cluster 2 estimated by the Kaplan-Meier plotter. (f) The heatmap shows the expression of the powerful prognostic markers in cluster 1 and cluster 2 (∗P < 0.05, ∗∗P < 0.01, ∗∗∗P < 0.001, ∗∗∗∗P < 0.0001).
Figure 6
Figure 6
Histology (H&E: hematoxylin and eosin) and immunohistochemistry showing four different expression patterns of CD8 and PD-L1 in the representative case. We identified patients with 22 APC-mt/MSS (left) and 20 APC-wt/MSS (right) colon cancer. APC-wt/MSS is significantly higher than APC-mt/MSS in the positive rate and the degrees of positive expression for CD8 and PD-L1 with immunostaining.
Figure 7
Figure 7
Work flow of the current work.
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References

    1. Hanahan D., Weinberg R. A. Hallmarks of cancer: the next generation. Cell . 2011;144(5):646–674. doi: 10.1016/j.cell.2011.02.013. - DOI - PubMed
    1. Choucair K., Morand S., Stanbery L., Edelman G., Dworkin L., Nemunaitis J. TMB: a promising immune-response biomarker, and potential spearhead in advancing targeted therapy trials. Cancer Gene Therapy . 2020;27(12):841–853. doi: 10.1038/s41417-020-0174-y. - DOI - PubMed
    1. Dai J., Fang P., Saredy J., et al. Metabolism-associated danger signal-induced immune response and reverse immune checkpoint-activated CD40+ monocyte differentiation. Journal of Hematology & Oncology . 2017;10(1):1–8. doi: 10.1186/s13045-017-0504-1. - DOI - PMC - PubMed
    1. Geremia S., Editor A. Molecular interactions of antibody drugs targeting PD-1, PD-L1, and CTLA-4 in immuno-oncology. Molecules . 2019;24(6):p. 1190. doi: 10.3390/molecules24061190. - DOI - PMC - PubMed
    1. Wu Y., Chen W., Xu Z. P., Wenyi G. PD-L1 distribution and perspective for cancer immunotherapy—blockade, knockdown, or inhibition. Frontiers in Immunology . 2019;10:p. 2022. doi: 10.3389/fimmu.2019.02022. - DOI - PMC - PubMed