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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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Heterogeneous distribution of genes in immune databases and an incomplete catalogue of immune knowledge. (A) Bar chart depicting the shared gene distribution of the immune resources. 82 of the total integrated set of 4833 genes are common to all 6 manually curate resources (orange colored bar). Few genes were unique to an individual database, ranging from a minimum of two for "Immunome" and 122 for the "Innate". (B) An approximation using a Venn Euler diagram illustrates the heterogeneous overlap among the different databases. The Innate database being the largest resource has the largest intersections. The septic shock resource has smaller overlaps with the others (with the exception of Innate) highlighting its focus on collating genes related to the response to bacterial toxins during septic shock.
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Figure 1: Heterogeneous distribution of genes in immune databases and an incomplete catalogue of immune knowledge. (A) Bar chart depicting the shared gene distribution of the immune resources. 82 of the total integrated set of 4833 genes are common to all 6 manually curate resources (orange colored bar). Few genes were unique to an individual database, ranging from a minimum of two for "Immunome" and 122 for the "Innate". (B) An approximation using a Venn Euler diagram illustrates the heterogeneous overlap among the different databases. The Innate database being the largest resource has the largest intersections. The septic shock resource has smaller overlaps with the others (with the exception of Innate) highlighting its focus on collating genes related to the response to bacterial toxins during septic shock.

Mentions: In order to benchmark this immunological relevance for genes, we compared the score against a set of validated immune resources. We utilized gene sets from six manually curated immune efforts (see Methods: "Collating manually curated immune relevant gene sets") that contain independently annotated genes relevant for various aspects of immunity. There were a total of 4833 genes in this integrated set, which had a heterogeneous distribution across the six resources, in that only 82 core immune genes were common to all databases. Many genes in each resource were shared with merely one of the other resources, and few genes were unique to an individual resource (Figure 1). The benchmarking of the immunological relevance score against this set of manually curated immune resources is presented in Figure 2A. The average immunological relevance score over all genes in each database was determined, compared against each other and the genes not manually curated by these resources. The Immunome [20] ranked the highest among the six manually curated resources in terms of immune information content, reflecting its focus on collating genes enacting functions specific to immune cells. When measuring the immunological relevance of all genes assigned a name by the Human Genome Organization (HUGO) and not catalogued in any of the immune resources, the average approaches zero. The frequency distribution of immunological relevance for all human genes assigned a name in HUGO shows a sharp decline from high to low immunological relevance (Figure 2B), revealing distinct categories of immune and non-immune genes. Moreover, the top ranked genes in the non-curated list represent novel candidates for entry in immune resources (Additional file 3). To assess further the benefit of assigning an automatic immunological relevance score to genes, the integrated set of manually curated genes was compared against two large scale studies that have characterized the human inflammatory response: (1) the endotoxin response network from gene expression profiling in human leukocytes [21], and (2) the inflammation assembly, which consists of genes detected in genetic variants in inflammatory pathways [22]. The endotoxin response network and inflammation assembly had 66% and 13% non-overlapping genes with respect to the manually curated resources. The non-correspondence of these six expert resources with large-scale experimental efforts partly indicates the specialized nature of some of these resources and partly may indicate potential in further management of immune knowledge from expert curators. It may also illustrate that there could still be more genes to be implicated in human immunity that are as yet uncharted.


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)

Heterogeneous distribution of genes in immune databases and an incomplete catalogue of immune knowledge. (A) Bar chart depicting the shared gene distribution of the immune resources. 82 of the total integrated set of 4833 genes are common to all 6 manually curate resources (orange colored bar). Few genes were unique to an individual database, ranging from a minimum of two for "Immunome" and 122 for the "Innate". (B) An approximation using a Venn Euler diagram illustrates the heterogeneous overlap among the different databases. The Innate database being the largest resource has the largest intersections. The septic shock resource has smaller overlaps with the others (with the exception of Innate) highlighting its focus on collating genes related to the response to bacterial toxins during septic shock.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3094196&req=5

Figure 1: Heterogeneous distribution of genes in immune databases and an incomplete catalogue of immune knowledge. (A) Bar chart depicting the shared gene distribution of the immune resources. 82 of the total integrated set of 4833 genes are common to all 6 manually curate resources (orange colored bar). Few genes were unique to an individual database, ranging from a minimum of two for "Immunome" and 122 for the "Innate". (B) An approximation using a Venn Euler diagram illustrates the heterogeneous overlap among the different databases. The Innate database being the largest resource has the largest intersections. The septic shock resource has smaller overlaps with the others (with the exception of Innate) highlighting its focus on collating genes related to the response to bacterial toxins during septic shock.
Mentions: In order to benchmark this immunological relevance for genes, we compared the score against a set of validated immune resources. We utilized gene sets from six manually curated immune efforts (see Methods: "Collating manually curated immune relevant gene sets") that contain independently annotated genes relevant for various aspects of immunity. There were a total of 4833 genes in this integrated set, which had a heterogeneous distribution across the six resources, in that only 82 core immune genes were common to all databases. Many genes in each resource were shared with merely one of the other resources, and few genes were unique to an individual resource (Figure 1). The benchmarking of the immunological relevance score against this set of manually curated immune resources is presented in Figure 2A. The average immunological relevance score over all genes in each database was determined, compared against each other and the genes not manually curated by these resources. The Immunome [20] ranked the highest among the six manually curated resources in terms of immune information content, reflecting its focus on collating genes enacting functions specific to immune cells. When measuring the immunological relevance of all genes assigned a name by the Human Genome Organization (HUGO) and not catalogued in any of the immune resources, the average approaches zero. The frequency distribution of immunological relevance for all human genes assigned a name in HUGO shows a sharp decline from high to low immunological relevance (Figure 2B), revealing distinct categories of immune and non-immune genes. Moreover, the top ranked genes in the non-curated list represent novel candidates for entry in immune resources (Additional file 3). To assess further the benefit of assigning an automatic immunological relevance score to genes, the integrated set of manually curated genes was compared against two large scale studies that have characterized the human inflammatory response: (1) the endotoxin response network from gene expression profiling in human leukocytes [21], and (2) the inflammation assembly, which consists of genes detected in genetic variants in inflammatory pathways [22]. The endotoxin response network and inflammation assembly had 66% and 13% non-overlapping genes with respect to the manually curated resources. The non-correspondence of these six expert resources with large-scale experimental efforts partly indicates the specialized nature of some of these resources and partly may indicate potential in further management of immune knowledge from expert curators. It may also illustrate that there could still be more genes to be implicated in human immunity that are as yet uncharted.

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