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Immunological network signatures of cancer progression and survival.

Clancy T, Pedicini M, Castiglione F, Santoni D, Nygaard V, Lavelle TJ, Benson M, Hovig E - BMC Med Genomics (2011)

Bottom Line: This immunological relevance score was benchmarked against existing manually curated immune resources as well as high-throughput studies.Furthermore, the genome-wide immunological relevance score classified melanoma patient groups, whose immunological grade correlated with clinical features, such as immune phenotypes and survival.The application of this approach to tumor immunity represents an automated systems strategy that quantifies the immunological component in complex disease.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Tumor Biology, Institute for Cancer Research, The Norwegian Radium Hospital, Oslo University Hospital, Oslo, Norway. trevor.clancy@rr-research.no

ABSTRACT

Background: The immune contribution to cancer progression is complex and difficult to characterize. For example in tumors, immune gene expression is detected from the combination of normal, tumor and immune cells in the tumor microenvironment. Profiling the immune component of tumors may facilitate the characterization of the poorly understood roles immunity plays in cancer progression. However, the current approaches to analyze the immune component of a tumor rely on incomplete identification of immune factors.

Methods: To facilitate a more comprehensive approach, we created a ranked immunological relevance score for all human genes, developed using a novel strategy that combines text mining and information theory. We used this score to assign an immunological grade to gene expression profiles, and thereby quantify the immunological component of tumors. This immunological relevance score was benchmarked against existing manually curated immune resources as well as high-throughput studies. To further characterize immunological relevance for genes, the relevance score was charted against both the human interactome and cancer information, forming an expanded interactome landscape of tumor immunity. We applied this approach to expression profiles in melanomas, thus identifying and grading their immunological components, followed by identification of their associated protein interactions.

Results: The power of this strategy was demonstrated by the observation of early activation of the adaptive immune response and the diversity of the immune component during melanoma progression. Furthermore, the genome-wide immunological relevance score classified melanoma patient groups, whose immunological grade correlated with clinical features, such as immune phenotypes and survival.

Conclusions: The assignment of a ranked immunological relevance score to all human genes extends the content of existing immune gene resources and enriches our understanding of immune involvement in complex biological networks. The application of this approach to tumor immunity represents an automated systems strategy that quantifies the immunological component in complex disease. In so doing, it stratifies patients according to their immune profiles, which may lead to effective computational prognostic and clinical guides.

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Comparison of the immunological component of skin cancer and states of melanoma progression. A heatmap of the average bits of immune information of the differentially expressed genes (> 2 times fold-change) among the pairwise comparisons of normal skin and skin cancer states. The labels from the left to right columns refer to normal skin tissues: ("Normal"), normal melanocyte ("Melanocyte") and then various states of skin cancer: primary melanoma ("Primary"), squamous cell carcinoma ("Squamous"), basal cell carcinoma ("Basal"), in-situ melanoma ("In Situ") and metastatic melanoma ("Metastatic"). Distinct differences in the immunological component of the various skin cancer and normal states are detected. We have focused here as an example, on the comparison between metastatic melanoma and normal human melanocytes. A subnetwork module from the interactome landscape of those genes with high immunological relevance is displayed. Upregulated genes are color-coded red and downregulated genes are color-coded green in this network. The size of a gene is proportional to the immunological relevance of the gene. There is clearly increased T-cell activity such as the presence of increased expression of CD8, CD4 and CD3 T-cell markers. This coincides with upregulation of key chemokine and cytokine interactions.
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Figure 6: Comparison of the immunological component of skin cancer and states of melanoma progression. A heatmap of the average bits of immune information of the differentially expressed genes (> 2 times fold-change) among the pairwise comparisons of normal skin and skin cancer states. The labels from the left to right columns refer to normal skin tissues: ("Normal"), normal melanocyte ("Melanocyte") and then various states of skin cancer: primary melanoma ("Primary"), squamous cell carcinoma ("Squamous"), basal cell carcinoma ("Basal"), in-situ melanoma ("In Situ") and metastatic melanoma ("Metastatic"). Distinct differences in the immunological component of the various skin cancer and normal states are detected. We have focused here as an example, on the comparison between metastatic melanoma and normal human melanocytes. A subnetwork module from the interactome landscape of those genes with high immunological relevance is displayed. Upregulated genes are color-coded red and downregulated genes are color-coded green in this network. The size of a gene is proportional to the immunological relevance of the gene. There is clearly increased T-cell activity such as the presence of increased expression of CD8, CD4 and CD3 T-cell markers. This coincides with upregulation of key chemokine and cytokine interactions.

Mentions: We next extended the principle of tissue expression profiling of immunological signatures in normal tissue to that of expression of normal skin, primary skin tumors and metastatic melanoma [17]. From the pairwise comparison of genes with a greater than two-fold change in expression across these different tissue states, we averaged the immunological score for those genes differentially expressed (> 2 times fold change) and examined how this score differed across the various expression profiles (see Figure 6). Using this average immunological score, we detected clear differences in the stages of progression and related these comparisons to their immune subnetworks from the interactome (see Figure 6). There was a particularly high immunological difference between normal melanocytes and both metastatic and primary melanoma and between normal skin and both metastatic and primary melanoma. The magnitude of the immune component difference between metastatic and normal melanocytes is depicted in Figure 6, along with a related immunological subnetwork of interactions. This network signature shows strong T-cell activation as well as diverse tumor associated chemokine and cytokine activity. There was, however, a much smaller immunological difference between metastatic melanomas and primary melanoma compared to that of normal melanocytes (Figure 6). This suggested that the framework could detect putative signatures of adaptive immunity in mediating transitions at early stages of progression in these patients. The observation that the highest ranked immune genes in these comparisons, CD4 and CD8, were upregulated in primary melanoma and metastasis compared to normal melanocytes signified early and enduring T-cell infiltration. In this comparison, immunological scoring also prioritized markers of innate immune cells such as PECAM and CD14 among others, accompanied by cytokines of inflammatory responses (IL15, IL7, IL18, IL1A, IL8). Interestingly, there was also high ranking of an early Th2 tumor-promoting environment demonstrated by presence of the IL13RA2 gene and the Th1 inhibiting cytokine IL10. The smaller amount of immunological information captured in the comparison of primary to metastatic melanoma (Figure 6) was attributable not to high scoring leukocyte or inflammation markers, but by upregulation of immunogenic melanoma antigens (MAGEA2/3) and downregulation of apoptosis inducing S100A8/9 cytokines. Summarized gene lists of the top ranked immunological transitions of normal skin, primary and metastatic melanomas are presented in Table 1. In-situ melanoma (MIS) compared to squamous cell carcinoma (SCC) held the highest immunological difference among all the state comparisons (Figure 6). Some of the top ranked immune genes in that comparison included upregulation in SCC relative to MIS of the chemokine CXCL13 and downregulation of the innate immune gene LTF


Immunological network signatures of cancer progression and survival.

Clancy T, Pedicini M, Castiglione F, Santoni D, Nygaard V, Lavelle TJ, Benson M, Hovig E - BMC Med Genomics (2011)

Comparison of the immunological component of skin cancer and states of melanoma progression. A heatmap of the average bits of immune information of the differentially expressed genes (> 2 times fold-change) among the pairwise comparisons of normal skin and skin cancer states. The labels from the left to right columns refer to normal skin tissues: ("Normal"), normal melanocyte ("Melanocyte") and then various states of skin cancer: primary melanoma ("Primary"), squamous cell carcinoma ("Squamous"), basal cell carcinoma ("Basal"), in-situ melanoma ("In Situ") and metastatic melanoma ("Metastatic"). Distinct differences in the immunological component of the various skin cancer and normal states are detected. We have focused here as an example, on the comparison between metastatic melanoma and normal human melanocytes. A subnetwork module from the interactome landscape of those genes with high immunological relevance is displayed. Upregulated genes are color-coded red and downregulated genes are color-coded green in this network. The size of a gene is proportional to the immunological relevance of the gene. There is clearly increased T-cell activity such as the presence of increased expression of CD8, CD4 and CD3 T-cell markers. This coincides with upregulation of key chemokine and cytokine interactions.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
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getmorefigures.php?uid=PMC3094196&req=5

Figure 6: Comparison of the immunological component of skin cancer and states of melanoma progression. A heatmap of the average bits of immune information of the differentially expressed genes (> 2 times fold-change) among the pairwise comparisons of normal skin and skin cancer states. The labels from the left to right columns refer to normal skin tissues: ("Normal"), normal melanocyte ("Melanocyte") and then various states of skin cancer: primary melanoma ("Primary"), squamous cell carcinoma ("Squamous"), basal cell carcinoma ("Basal"), in-situ melanoma ("In Situ") and metastatic melanoma ("Metastatic"). Distinct differences in the immunological component of the various skin cancer and normal states are detected. We have focused here as an example, on the comparison between metastatic melanoma and normal human melanocytes. A subnetwork module from the interactome landscape of those genes with high immunological relevance is displayed. Upregulated genes are color-coded red and downregulated genes are color-coded green in this network. The size of a gene is proportional to the immunological relevance of the gene. There is clearly increased T-cell activity such as the presence of increased expression of CD8, CD4 and CD3 T-cell markers. This coincides with upregulation of key chemokine and cytokine interactions.
Mentions: We next extended the principle of tissue expression profiling of immunological signatures in normal tissue to that of expression of normal skin, primary skin tumors and metastatic melanoma [17]. From the pairwise comparison of genes with a greater than two-fold change in expression across these different tissue states, we averaged the immunological score for those genes differentially expressed (> 2 times fold change) and examined how this score differed across the various expression profiles (see Figure 6). Using this average immunological score, we detected clear differences in the stages of progression and related these comparisons to their immune subnetworks from the interactome (see Figure 6). There was a particularly high immunological difference between normal melanocytes and both metastatic and primary melanoma and between normal skin and both metastatic and primary melanoma. The magnitude of the immune component difference between metastatic and normal melanocytes is depicted in Figure 6, along with a related immunological subnetwork of interactions. This network signature shows strong T-cell activation as well as diverse tumor associated chemokine and cytokine activity. There was, however, a much smaller immunological difference between metastatic melanomas and primary melanoma compared to that of normal melanocytes (Figure 6). This suggested that the framework could detect putative signatures of adaptive immunity in mediating transitions at early stages of progression in these patients. The observation that the highest ranked immune genes in these comparisons, CD4 and CD8, were upregulated in primary melanoma and metastasis compared to normal melanocytes signified early and enduring T-cell infiltration. In this comparison, immunological scoring also prioritized markers of innate immune cells such as PECAM and CD14 among others, accompanied by cytokines of inflammatory responses (IL15, IL7, IL18, IL1A, IL8). Interestingly, there was also high ranking of an early Th2 tumor-promoting environment demonstrated by presence of the IL13RA2 gene and the Th1 inhibiting cytokine IL10. The smaller amount of immunological information captured in the comparison of primary to metastatic melanoma (Figure 6) was attributable not to high scoring leukocyte or inflammation markers, but by upregulation of immunogenic melanoma antigens (MAGEA2/3) and downregulation of apoptosis inducing S100A8/9 cytokines. Summarized gene lists of the top ranked immunological transitions of normal skin, primary and metastatic melanomas are presented in Table 1. In-situ melanoma (MIS) compared to squamous cell carcinoma (SCC) held the highest immunological difference among all the state comparisons (Figure 6). Some of the top ranked immune genes in that comparison included upregulation in SCC relative to MIS of the chemokine CXCL13 and downregulation of the innate immune gene LTF

Bottom Line: This immunological relevance score was benchmarked against existing manually curated immune resources as well as high-throughput studies.Furthermore, the genome-wide immunological relevance score classified melanoma patient groups, whose immunological grade correlated with clinical features, such as immune phenotypes and survival.The application of this approach to tumor immunity represents an automated systems strategy that quantifies the immunological component in complex disease.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Tumor Biology, Institute for Cancer Research, The Norwegian Radium Hospital, Oslo University Hospital, Oslo, Norway. trevor.clancy@rr-research.no

ABSTRACT

Background: The immune contribution to cancer progression is complex and difficult to characterize. For example in tumors, immune gene expression is detected from the combination of normal, tumor and immune cells in the tumor microenvironment. Profiling the immune component of tumors may facilitate the characterization of the poorly understood roles immunity plays in cancer progression. However, the current approaches to analyze the immune component of a tumor rely on incomplete identification of immune factors.

Methods: To facilitate a more comprehensive approach, we created a ranked immunological relevance score for all human genes, developed using a novel strategy that combines text mining and information theory. We used this score to assign an immunological grade to gene expression profiles, and thereby quantify the immunological component of tumors. This immunological relevance score was benchmarked against existing manually curated immune resources as well as high-throughput studies. To further characterize immunological relevance for genes, the relevance score was charted against both the human interactome and cancer information, forming an expanded interactome landscape of tumor immunity. We applied this approach to expression profiles in melanomas, thus identifying and grading their immunological components, followed by identification of their associated protein interactions.

Results: The power of this strategy was demonstrated by the observation of early activation of the adaptive immune response and the diversity of the immune component during melanoma progression. Furthermore, the genome-wide immunological relevance score classified melanoma patient groups, whose immunological grade correlated with clinical features, such as immune phenotypes and survival.

Conclusions: The assignment of a ranked immunological relevance score to all human genes extends the content of existing immune gene resources and enriches our understanding of immune involvement in complex biological networks. The application of this approach to tumor immunity represents an automated systems strategy that quantifies the immunological component in complex disease. In so doing, it stratifies patients according to their immune profiles, which may lead to effective computational prognostic and clinical guides.

Show MeSH
Related in: MedlinePlus