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. 2022 Mar-Apr;19(2):130-144.
doi: 10.21873/cgp.20309.

Mapping Proteome Changes in Microsatellite Stable, Recurrent Colon Cancer Reveals a Significant Immune System Signature

Magnus Berle  1   2   3 , Kjersti E Hestetun  4   5 , Heidrun Vethe  6 , Simona Chera  4   7 , Joao A Paulo  8 , Olav Dahl  4   5 , Mette Pernille Myklebust  5
Affiliations

Mapping Proteome Changes in Microsatellite Stable, Recurrent Colon Cancer Reveals a Significant Immune System Signature

Magnus Berle et al. Cancer Genomics Proteomics. 2022 Mar-Apr.

Abstract

Background/aim: Better stratification of the risk of relapse will help select the right patients for adjuvant treatment and improve targeted therapies for patients with colon cancer.

Materials and methods: To understand why a subset of tumors relapse, we compared the proteome of two groups of patients with colon cancer with similar stage, stratified based on the presence or absence of recurrence.

Results: Using tumor biopsies from the primary operation, we identified dissimilarity between recurrent and nonrecurrent mismatch satellite stable colon cancer and found that signaling related to immune activation and inflammation was associated with relapse.

Conclusion: Immune modulation may have an effect on mismatch satellite stable colon cancer. At present, immune therapy is offered primarily to microsatellite instable colon cancer. Hopefully, immune therapy in mismatch satellite stable colon cancer beyond PD-1 and PD-L1 inhibitors can be implemented.

Keywords: Colon cancer; immune system; microsatellite stable; proteomics; recurrence.

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Conflict of interest statement

K.H has been on an advisory board for Daiichi Sankyo. The other Authors declare no conflict of interest in relation to this study. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

Figures

Figure 1
Figure 1. Cluster analysis of the cancer samples. (A) Cartoon depicting tumour development and the stages selected for this study according to the TNM system. (B) Experimental design depicting the cohorts employed and workflow. (C) Hierarchical analysis of the proteome landscape in the 10 different samples (n=5 /sample/TNM stage). (D) Graphs depicting the centroid of Cluster #1 and Cluster #15 (light green – Cancer samples, black – control samples) and the total number of proteins belonging to each cluster. (E) Scheme depicting the proteome analysis workflow. (F) Top 10 Canonical Pathways characterising the analysed proteome landscape, as determined by Ingenuity Pathway Analysis (IPA) software. (G) Top Predicted Upstream Regulators for the analysed proteome landscape, as inferred by IPA. (H) Top Canonical Pathways and Top Predicted Upstream Regulators characterising the group of proteins with low abundance in the tumour samples (Cluster #1), as determined by pathway analysis (IPA). (I) Top Canonical Pathways and Predicted Upstream Regulators characterising the group of proteins with high abundance in the tumour samples (Cluster #15), as determined by pathway analysis (IPA).
Figure 1
Figure 1. Cluster analysis of the cancer samples. (A) Cartoon depicting tumour development and the stages selected for this study according to the TNM system. (B) Experimental design depicting the cohorts employed and workflow. (C) Hierarchical analysis of the proteome landscape in the 10 different samples (n=5 /sample/TNM stage). (D) Graphs depicting the centroid of Cluster #1 and Cluster #15 (light green – Cancer samples, black – control samples) and the total number of proteins belonging to each cluster. (E) Scheme depicting the proteome analysis workflow. (F) Top 10 Canonical Pathways characterising the analysed proteome landscape, as determined by Ingenuity Pathway Analysis (IPA) software. (G) Top Predicted Upstream Regulators for the analysed proteome landscape, as inferred by IPA. (H) Top Canonical Pathways and Top Predicted Upstream Regulators characterising the group of proteins with low abundance in the tumour samples (Cluster #1), as determined by pathway analysis (IPA). (I) Top Canonical Pathways and Predicted Upstream Regulators characterising the group of proteins with high abundance in the tumour samples (Cluster #15), as determined by pathway analysis (IPA).
Figure 2
Figure 2. Pathway analysis of the proteins displaying a gradual variation in abundance according to TNM stage. (A) Graphs depicting the centroid of Cluster #5 and Cluster #11 (light green – Cancer samples, black – control samples, red rectangle highlights the increase (upper panel) of protein abundance according to TNM stage, blue rectangle highlights the decrease (lower panel) of protein abundance according to TNM stage) and the total number of proteins belonging to each cluster. (B) Scheme depicting the proteome analysis workflow. (C) Top Canonical Pathways and Top Predicted Upstream Regulators characterising the analysed proteome landscape, as determined by Ingenuity Pathway Analysis (IPA) software. (D) Top Canonical Pathways and Predicted Upstream Regulators characterising the group of proteins with gradually increased abundance towards TNM3 (Cluster #5), as determined by pathway analysis (IPA). (E) Top Canonical Pathways and Top Predicted Upstream Regulators characterising the group of proteins with gradually decreased abundance towards TNM3 (Cluster #11), as determined by pathway analysis (IPA).
Figure 3
Figure 3. Pathway analysis of the differentially expressed proteins (DEPs) between overall TNMR+ and TNMR-. (A) Scheme depicting the proteome analysis workflow. (B) Top Canonical Pathways with predicted regulation characterizing the TNMR+ vs. TNMR- DEPs landscape, as determined by Ingenuity Pathway Analysis (IPA) software (z-score >0.1). (C) Top 5 Predicted Activated Upstream Regulators and IFNG targeted proteins observed regulated in the DEP set analyzed. (D) Top 5 Predicted Inhibited Upstream Regulators and MAPK1 targeted proteins observed regulated in the DEP set analyzed. (E) Graph of relevant DEPs observed dysregulated in TNMR+.
Figure 4
Figure 4. Pathway analysis of the differentially expressed proteins (DEPs) between TNM2R+ and TNM2R-. (A) Scheme depicting the proteome analysis workflow. (B) Top Canonical Pathways with predicted regulation characterizing the TNM2R+ vs. TNM2R- DEPs landscape, as determined by Ingenuity Pathway Analysis (IPA) software (z-score >0.1). (C) Top 5 Predicted Activated Upstream Regulators and KRAS targeted proteins observed regulated in the DEP set analysed (coloured borders define DEPs discussed in text). (D) Top 5 Predicted Inhibited Upstream Regulators and AGT targeted proteins observed regulated in the DEP set analysed. (E) Graph depicting AGT observed down-regulated. (F) Graph depicting relevant DEPs observed down-regulated in TNM2R+. (G) Graph depicting relevant DEPs observed up-regulated in TNM2R+.
Figure 4
Figure 4. Pathway analysis of the differentially expressed proteins (DEPs) between TNM2R+ and TNM2R-. (A) Scheme depicting the proteome analysis workflow. (B) Top Canonical Pathways with predicted regulation characterizing the TNM2R+ vs. TNM2R- DEPs landscape, as determined by Ingenuity Pathway Analysis (IPA) software (z-score >0.1). (C) Top 5 Predicted Activated Upstream Regulators and KRAS targeted proteins observed regulated in the DEP set analysed (coloured borders define DEPs discussed in text). (D) Top 5 Predicted Inhibited Upstream Regulators and AGT targeted proteins observed regulated in the DEP set analysed. (E) Graph depicting AGT observed down-regulated. (F) Graph depicting relevant DEPs observed down-regulated in TNM2R+. (G) Graph depicting relevant DEPs observed up-regulated in TNM2R+.
Figure 5
Figure 5. Pathway analysis of the differentially expressed proteins (DEPs) between TNM3R+ and TNM3R-. (A) Scheme depicting the proteome analysis workflow. (B) Top Canonical Pathways with predicted regulation characterizing the TNM3R+ vs. TNM3R- DEPs landscape, as determined by Ingenuity Pathway Analysis (IPA) software (z-score >0.1). (C) Top 5 Predicted Activated Upstream Regulators and IL1A targeted proteins observed regulated in the DEP set analyzed (colored borders define DEPs discussed in text). (D) Top 5 Predicted Inhibited Upstream Regulators and IL10RA targeted proteins observed regulated in the DEP set analysed. (E) IPA generated graphical summary of the analyzed proteome landscape. (F) Graph depicting relevant DEPs observed up-regulated in TNM3R+. (G) Graph MECP2 observed down-regulation in TNM3R+.
Figure 5
Figure 5. Pathway analysis of the differentially expressed proteins (DEPs) between TNM3R+ and TNM3R-. (A) Scheme depicting the proteome analysis workflow. (B) Top Canonical Pathways with predicted regulation characterizing the TNM3R+ vs. TNM3R- DEPs landscape, as determined by Ingenuity Pathway Analysis (IPA) software (z-score >0.1). (C) Top 5 Predicted Activated Upstream Regulators and IL1A targeted proteins observed regulated in the DEP set analyzed (colored borders define DEPs discussed in text). (D) Top 5 Predicted Inhibited Upstream Regulators and IL10RA targeted proteins observed regulated in the DEP set analysed. (E) IPA generated graphical summary of the analyzed proteome landscape. (F) Graph depicting relevant DEPs observed up-regulated in TNM3R+. (G) Graph MECP2 observed down-regulation in TNM3R+.
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