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Cancer Epidemiology, Biomarkers & Prevention
Cancer Epidemiology, Biomarkers & Prevention
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Regulatory T-cell Genes Drive Altered Immune Microenvironment in Adult Solid Cancers and Allow for Immune Contextual Patient Subtyping

Jurriaan Brouwer-Visser, Wei-Yi Cheng, Anna Bauer-Mehren, Daniela Maisel, Katharina Lechner, Emilia Andersson, Joel T. Dudley and Francesca Milletti
Jurriaan Brouwer-Visser
1Roche Pharma Research and Early Development - Operations, Roche Innovation Center, New York, New York.
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  • For correspondence: jurriaan.brouwer@roche.com
Wei-Yi Cheng
1Roche Pharma Research and Early Development - Operations, Roche Innovation Center, New York, New York.
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Anna Bauer-Mehren
2Roche Pharma Research and Early Development, Roche Innovation Center Munich, Penzberg, Germany.
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Daniela Maisel
2Roche Pharma Research and Early Development, Roche Innovation Center Munich, Penzberg, Germany.
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Katharina Lechner
2Roche Pharma Research and Early Development, Roche Innovation Center Munich, Penzberg, Germany.
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Emilia Andersson
2Roche Pharma Research and Early Development, Roche Innovation Center Munich, Penzberg, Germany.
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Joel T. Dudley
3Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York.
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Francesca Milletti
1Roche Pharma Research and Early Development - Operations, Roche Innovation Center, New York, New York.
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DOI: 10.1158/1055-9965.EPI-17-0461 Published January 2018
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    Figure 1.

    Correlated immune genes reveal relations between cancer types. A, Flowchart of steps to find the 129 correlated immune genes starting from the 11 chosen seed genes. TME, tumor microenvironment; TCGA, the Cancer Genome Atlas. B, Adjacency graph of cancer types correlated by immune gene expression. Immune-correlated genes for each solid tumor type from TCGA were determined and compared with other cancer types. The graph shows how many correlated genes are common between cancer types (indicated by thickness of the connecting edges). The color of the vertices indicates the expression of the 129 common immune genes (red indicates higher absolute mean expression). The abbreviations of cancer types are shown in the table. Skin and uveal melanoma have the most correlated genes in common, whereas thymoma has the highest mean immune gene expression.

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    Figure 2.

    Immune gene expression shows pattern differences between cancer types but shows no correlation with number of mutations. A, Gene expression heatmap of the 129 common immune genes across all solid cancer types of TCGA. Red indicates higher mean absolute expression of the patients of one tumor type. Thymoma has the highest mean expression of the correlated genes but shows a distinct pattern when compared with other cancers. Kidney and lung have high mean immune gene expression, while adrenocortical carcinoma and uveal melanoma have the lowest mean expression. B, Scatterplot depicting the correlation between number of mutations and mean immune gene expression. The graph shows the number of mutations of each tumor sample versus the mean of immune gene expression. Only a low correlation (R2 = 0.015, straight line fit Pearson correlation) is observed, showing mutation number is not definitive in the expression of immune-correlated genes. Standard TCGA abbreviations for cancer types are shown in Fig. 1B, colors indicate different cancer types.

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    Figure 3.

    Normal tissue has higher immune gene expression in some cancers. A, Gene expression heatmap comparing the immune gene expression of matched normal and tumor tissue samples across solid cancers. Top heatmap shows the mean absolute gene expression of normal samples while the bottom shows the tumor samples. Below the heatmap a scaled heatmap showing for each cancer type whether the normal or the tumor tissue has higher mean immune gene expression (red means higher; blue means lower). Tumor types are grouped by having a higher mean immune gene expression (High) in normal tissue, lower (Low) or similar (Sim). Five of 16 cancer types have higher immune gene expression in normal tissue compared with tumor tissue, 3 have lower, and 8 are similar. B, Boxplot showing the difference in expression of normal and matched tumor tissue of the 11 seed genes. The median of each gene was taken for the matched samples across solid cancers. Pink, normal tissue; blue, tumor. FOXP3 showed the highest increase from normal to tumor tissue, while NCAM1 showed the highest decrease.

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    Figure 4.

    Treg gene signature expression is higher in most cancers compared with normal tissue, and FOXP3 shows more hypomethylation in tumor tissue. A, Gene expression heatmap of the Treg and CD8 signatures across cancer types, comparing matched normal tissue to tumor tissue. Below the individual absolute gene expression heatmap is the summary heatmap showing whether normal or tumor tissue had higher expression of both signatures (red means higher expression). Tumor types are ordered in the same groups as Fig. 3A. The CD8 signature was higher in the normal tissue of more than half of the cancer types (12 of 16). However, the Treg signature was higher in all tumor tissue compared with normal tissue with the exception of liver cancer. B, Boxplots showing that more tumor samples have FOXP3 hypomethylation compared with normal tissue. Many samples of the tumor tissue (blue) fall below the third quarter of samples and have a lower median of FOXP3 methylation compared with normal tissue (pink). FOXP3 methylation data for all matched samples of solid cancers in TCGA were included. All seven methylation sites assigned to the FOXP3 gene were included in the analysis. C, Boxplots showing the correlation between the Treg gene signature and the methylation of the 205 site of FOXP3. The methylation of samples of cg10858077_X_49121205 were binned according to their SD and the expression of the Treg signature for each bin was graphed. No correlation between methylation and Treg signature expression was found.

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    Figure 5.

    Colorectal cancer patients can be clustered into two groups based on Treg signature that show distinct clinical characteristics. A, Gene expression heatmap of the genes forming part of the Treg signature. Using the clustering method described in the methods, two clusters were found and named the high and low Treg cluster. Each gene is scaled individually from red (high gene expression) to blue. B, Bar charts showing proportions of samples within each CMS (left) and anatomical site (right) by Treg cluster. The majority of samples in the high Treg cluster are in CMS1 or CMS4, whereas the low Treg cluster has mainly CMS2 samples. Similarly, samples from the High Treg cluster are mainly right-sided tumors while samples from the low Treg cluster are mainly left-sided. NAs in CMS are samples that are not significantly assigned to a CMS. The P values shown indicate that distribution between Treg cluster and CMS or anatomic sites were independent as calculated by the χ2 test of independence.

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    Figure 6.

    Low correlation between IHC and gene signatures are observed but the high Treg cluster has significantly more CD8+ and FoxP3+ cells. Scatterplots showing the correlations between positive cell densities by IHC and mean gene signature expression in the testing dataset. Samples were divided by Treg cluster (rows). The high Treg cluster has significantly more CD8+ (left) and FoxP3+ (right) cells (see box plot inserts) in this dataset. x-axes are log2 of the number of positive objects per square millimeter and y-axes are mean gene expression values of the respective gene signatures.

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Cancer Epidemiology Biomarkers & Prevention: 27 (1)
January 2018
Volume 27, Issue 1
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Regulatory T-cell Genes Drive Altered Immune Microenvironment in Adult Solid Cancers and Allow for Immune Contextual Patient Subtyping
Jurriaan Brouwer-Visser, Wei-Yi Cheng, Anna Bauer-Mehren, Daniela Maisel, Katharina Lechner, Emilia Andersson, Joel T. Dudley and Francesca Milletti
Cancer Epidemiol Biomarkers Prev January 1 2018 (27) (1) 103-112; DOI: 10.1158/1055-9965.EPI-17-0461

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Regulatory T-cell Genes Drive Altered Immune Microenvironment in Adult Solid Cancers and Allow for Immune Contextual Patient Subtyping
Jurriaan Brouwer-Visser, Wei-Yi Cheng, Anna Bauer-Mehren, Daniela Maisel, Katharina Lechner, Emilia Andersson, Joel T. Dudley and Francesca Milletti
Cancer Epidemiol Biomarkers Prev January 1 2018 (27) (1) 103-112; DOI: 10.1158/1055-9965.EPI-17-0461
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