Dataset Information

Use of gene expression and pathway signatures to characterize the complexity of human melanoma.

ABSTRACT: A defining characteristic of most human cancers is heterogeneity, resulting from the somatic acquisition of a complex array of genetic and genomic alterations. Dissecting this heterogeneity is critical to developing an understanding of the underlying mechanisms of disease and to paving the way toward personalized treatments of the disease. We used gene expression data sets from the analysis of primary and metastatic melanomas to develop a molecular description of the heterogeneity that characterizes this disease. Unsupervised hierarchical clustering, gene set enrichment analyses, and pathway activity analyses were used to describe the genetic heterogeneity of melanomas. Patterns of gene expression that revealed two distinct classes of primary melanoma, two distinct classes of in-transit melanoma, and at least three subgroups of metastatic melanoma were identified. Expression signatures developed to predict the status of oncogenic signaling pathways were used to explore the biological basis underlying these differential patterns of expression. This analysis of activities revealed unique pathways that distinguished the primary and metastatic subgroups of melanoma. Distinct patterns of gene expression across primary, in-transit, and metastatic melanomas underline the genetic heterogeneity of this disease. This heterogeneity can be described in terms of deregulation of signaling pathways, thus increasing the knowledge of the biological features underlying individual melanomas and potentially directing therapeutic opportunities to individual patients with melanoma.

SUBMITTER: Freedman JA

PROVIDER: S-EPMC3124358 | biostudies-literature | 2011 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Use of gene expression and pathway signatures to characterize the complexity of human melanoma.

Freedman Jennifer A JA Tyler Douglas S DS Nevins Joseph R JR Augustine Christina K CK

The American journal of pathology 20110601 6

A defining characteristic of most human cancers is heterogeneity, resulting from the somatic acquisition of a complex array of genetic and genomic alterations. Dissecting this heterogeneity is critical to developing an understanding of the underlying mechanisms of disease and to paving the way toward personalized treatments of the disease. We used gene expression data sets from the analysis of primary and metastatic melanomas to develop a molecular description of the heterogeneity that character ...[more]

PMID: 21641377

Similar Datasets

Project description:Personalizing treatment regimes based on gene expression profiles of individual tumors will facilitate management of cancer. Although many methods have been developed to identify pathways perturbed in tumors, the results are often not generalizable across independent datasets due to the presence of platform/batch effects. There is a need to develop methods that are robust to platform/batch effects and able to identify perturbed pathways in individual samples.We present Gene-Ranking Analysis of Pathway Expression (GRAPE) as a novel method to identify abnormal pathways in individual samples that is robust to platform/batch effects in gene expression profiles generated by multiple platforms. GRAPE first defines a template consisting of an ordered set of pathway genes to characterize the normative state of a pathway based on the relative rankings of gene expression levels across a set of reference samples. This template can be used to assess whether a sample conforms to or deviates from the typical behavior of the reference samples for this pathway. We demonstrate that GRAPE performs well versus existing methods in classifying tissue types within a single dataset, and that GRAPE achieves superior robustness and generalizability across different datasets. A powerful feature of GRAPE is the ability to represent individual gene expression profiles as a vector of pathways scores. We present applications to the analyses of breast cancer subtypes and different colonic diseases. We perform survival analysis of several TCGA subtypes and find that GRAPE pathway scores perform well in comparison to other methods.GRAPE templates offer a novel approach for summarizing the behavior of gene-sets across a collection of gene expression profiles. These templates offer superior robustness across distinct experimental batches compared to existing methods. GRAPE pathway scores enable identification of abnormal gene-set behavior in individual samples using a non-competitive approach that is fundamentally distinct from popular enrichment-based methods. GRAPE may be an appropriate tool for researchers seeking to identify individual samples displaying abnormal gene-set behavior as well as to explore differences in the consensus gene-set behavior of groups of samples. GRAPE is available in R for download at https://CRAN.R-project.org/package=GRAPE .

Project description:ObjectiveCanakinumab is a human anti-interleukin-1β (anti-IL-1β) blocking agent that effectively neutralizes IL-1β-mediated signaling for treatment of systemic juvenile idiopathic arthritis (JIA). While many patients have dramatic clinical response to IL-1 blockade, approximately one-third fail to respond, but there are currently no validated clinical or immunologic predictors of response. We undertook this study to characterize distinct gene signatures for treatment response and nonresponse to canakinumab in systemic JIA patients.MethodsWe performed a secondary analysis of whole-blood gene expression microarrays using blood samples obtained from healthy controls and systemic JIA patients at baseline and on day 3 after canakinumab treatment (GEO accession no. GSE80060). Patients were considered strong clinical responders if they met the ACR90 response (exhibited ≥90% improvement in the American College of Rheumatology [ACR] JIA response criteria; nonresponders were those who met ACR30 [exhibiting ≤30% improvement in the ACR JIA response criteria]). A random-effects model with patient identity as the random variable was used for differential expression analysis.ResultsWe identified a distinct gene expression signature in patients with a strong clinical response to canakinumab treatment as compared to nonresponders, mediated by up-regulation of neutrophil- and IL-1-associated genes and characterized by increasing divergence from control transcriptomes with increasing clinical response. We also identified a signature including up-regulated CD163 expression that was associated with canakinumab nonresponse. Intriguingly, canakinumab treatment induced either up- or down-regulation of type I interferon (IFN) genes, independent of clinical response.ConclusionHere, we identify a gene signature in systemic JIA patients prior to receiving treatment that distinguishes strong responders to canakinumab from nonresponders. Further prospective studies are needed to assess the utility of these insights for treatment decisions in systemic JIA and to track the association of up-regulated type I IFN signatures with systemic JIA complications.

Dataset Information

Use of gene expression and pathway signatures to characterize the complexity of human melanoma.

Publications

Use of gene expression and pathway signatures to characterize the complexity of human melanoma.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets