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Improved prognostic classification of breast cancer defined by antagonistic activation patterns of immune response pathway modules.

Teschendorff AE, Gomez S, Arenas A, El-Ashry D, Schmidt M, Gehrmann M, Caldas C - BMC Cancer (2010)

Bottom Line: Using Boolean interaction Cox-regression models to identify non-linear pathway combinations associated with clinical outcome, we show that simultaneous high activation of Th1 and low activation of a TGF-beta pathway module defines a subtype of particularly good prognosis and that this classification provides a better prognostic model than those based on the individual pathways.In ER+ breast cancer, we find that simultaneous high MYC and RAS activity confers significantly worse prognosis than either high MYC or high RAS activity alone.Specifically, our results suggest that simultaneous modulation of T-helper differentiation and TGF-beta pathways may improve clinical outcome of hormone insensitive breast cancers over treatments that target only one of these pathways.

View Article: PubMed Central - HTML - PubMed

Affiliation: Breast Cancer Functional Genomics Laboratory, Department of Oncology University of Cambridge, Cancer Research UK Cambridge Research Institute, Li Ka-Shing Centre, Robinson Way, Cambridge CB2 0RE, UK. a.teschendorff@ucl.ac.uk

ABSTRACT

Background: Elucidating the activation pattern of molecular pathways across a given tumour type is a key challenge necessary for understanding the heterogeneity in clinical response and for developing novel more effective therapies. Gene expression signatures of molecular pathway activation derived from perturbation experiments in model systems as well as structural models of molecular interactions ("model signatures") constitute an important resource for estimating corresponding activation levels in tumours. However, relatively few strategies for estimating pathway activity from such model signatures exist and only few studies have used activation patterns of pathways to refine molecular classifications of cancer.

Methods: Here we propose a novel network-based method for estimating pathway activation in tumours from model signatures. We find that although the pathway networks inferred from cancer expression data are highly consistent with the prior information contained in the model signatures, that they also exhibit a highly modular structure and that estimation of pathway activity is dependent on this modular structure. We apply our methodology to a panel of 438 estrogen receptor negative (ER-) and 785 estrogen receptor positive (ER+) breast cancers to infer activation patterns of important cancer related molecular pathways.

Results: We show that in ER negative basal and HER2+ breast cancer, gene expression modules reflecting T-cell helper-1 (Th1) and T-cell helper-2 (Th2) mediated immune responses play antagonistic roles as major risk factors for distant metastasis. Using Boolean interaction Cox-regression models to identify non-linear pathway combinations associated with clinical outcome, we show that simultaneous high activation of Th1 and low activation of a TGF-beta pathway module defines a subtype of particularly good prognosis and that this classification provides a better prognostic model than those based on the individual pathways. In ER+ breast cancer, we find that simultaneous high MYC and RAS activity confers significantly worse prognosis than either high MYC or high RAS activity alone. We further validate these novel prognostic classifications in independent sets of 173 ER- and 567 ER+ breast cancers.

Conclusion: We have proposed a novel method for pathway activity estimation in tumours and have shown that pathway modules antagonize or synergize to delineate novel prognostic subtypes. Specifically, our results suggest that simultaneous modulation of T-helper differentiation and TGF-beta pathways may improve clinical outcome of hormone insensitive breast cancers over treatments that target only one of these pathways.

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Measuring pathway module activation. Flowchart figure showing overall strategy used for inferring pathway module activity in clinical tumor samples from a model (perturbation) signature. A) A gene mRNA signature that represents a perturbed cancer cell phenotype (i.e oncogene overexpression) is combined with mRNA expression data of a large panel of clinical tumor specimens to derive an "expression relevance network" where nodes represent genes from the signature and an edge between two nodes indicates a statistically significant Pearson correlation between the two corresponding genes as measured over the clinical tumor panel. Having constructed the relevance network, the network is first pruned so that network edges that are inconsistent with prior information are removed. Signs on edges between labelled genes indicate the sign of the significant correlation between the two genes, which must be consistent with their directionality as given by the model signature. Modules defined as subnetworks with higher than average edge density are then inferred using a spectral decomposition algorithm (see Methods). B) For a given relatively large module, the module of pathway activation (MPA), pathway activity is then computed using a metric defined over the topology of the module. In the formula, PAs stands for the estimated pathway module activity in sample s, M is the number of genes in the module, σi is a binary weight (1,-1) indicating the directionality of gene expression of gene i (1 = upregulated, -1 = downregulated), zis is the z-score normalised gene expression value in sample s and Aij is the adjacency matrix of the module. Effectively, this metric gives more weight to gene interactions that are supported by the data. Color and sign of nodes reflect the directionality of expression in the in-vitro signature (Red = upregulated &σ = 1, Green = downregulated &σ = -1). Pathway activity levels can then be shown as heatmaps (blue = high activity, yellow = low activity).
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Figure 1: Measuring pathway module activation. Flowchart figure showing overall strategy used for inferring pathway module activity in clinical tumor samples from a model (perturbation) signature. A) A gene mRNA signature that represents a perturbed cancer cell phenotype (i.e oncogene overexpression) is combined with mRNA expression data of a large panel of clinical tumor specimens to derive an "expression relevance network" where nodes represent genes from the signature and an edge between two nodes indicates a statistically significant Pearson correlation between the two corresponding genes as measured over the clinical tumor panel. Having constructed the relevance network, the network is first pruned so that network edges that are inconsistent with prior information are removed. Signs on edges between labelled genes indicate the sign of the significant correlation between the two genes, which must be consistent with their directionality as given by the model signature. Modules defined as subnetworks with higher than average edge density are then inferred using a spectral decomposition algorithm (see Methods). B) For a given relatively large module, the module of pathway activation (MPA), pathway activity is then computed using a metric defined over the topology of the module. In the formula, PAs stands for the estimated pathway module activity in sample s, M is the number of genes in the module, σi is a binary weight (1,-1) indicating the directionality of gene expression of gene i (1 = upregulated, -1 = downregulated), zis is the z-score normalised gene expression value in sample s and Aij is the adjacency matrix of the module. Effectively, this metric gives more weight to gene interactions that are supported by the data. Color and sign of nodes reflect the directionality of expression in the in-vitro signature (Red = upregulated &σ = 1, Green = downregulated &σ = -1). Pathway activity levels can then be shown as heatmaps (blue = high activity, yellow = low activity).

Mentions: Our strategy to estimate pathway activation for a given model signature is llustrated in Figure 1 (see also Methods) and is carried out separately for ER+ and ER- disease. Briefly, the algorithm constructs a pruned relevance correlation network of the genes in the model signature across the expression tumour panel. Only genes and correlations between genes that are consistent with the prior information are allowed in the network. This strategy therefore filters out genes and gene-pairs with irrelevant or inconsistent expression patterns, while also identifying modules of high-edge density, that is, subnetworks of genes that show consistent and significantly correlated (or anticorrelated) patterns across the panel of tumours.


Improved prognostic classification of breast cancer defined by antagonistic activation patterns of immune response pathway modules.

Teschendorff AE, Gomez S, Arenas A, El-Ashry D, Schmidt M, Gehrmann M, Caldas C - BMC Cancer (2010)

Measuring pathway module activation. Flowchart figure showing overall strategy used for inferring pathway module activity in clinical tumor samples from a model (perturbation) signature. A) A gene mRNA signature that represents a perturbed cancer cell phenotype (i.e oncogene overexpression) is combined with mRNA expression data of a large panel of clinical tumor specimens to derive an "expression relevance network" where nodes represent genes from the signature and an edge between two nodes indicates a statistically significant Pearson correlation between the two corresponding genes as measured over the clinical tumor panel. Having constructed the relevance network, the network is first pruned so that network edges that are inconsistent with prior information are removed. Signs on edges between labelled genes indicate the sign of the significant correlation between the two genes, which must be consistent with their directionality as given by the model signature. Modules defined as subnetworks with higher than average edge density are then inferred using a spectral decomposition algorithm (see Methods). B) For a given relatively large module, the module of pathway activation (MPA), pathway activity is then computed using a metric defined over the topology of the module. In the formula, PAs stands for the estimated pathway module activity in sample s, M is the number of genes in the module, σi is a binary weight (1,-1) indicating the directionality of gene expression of gene i (1 = upregulated, -1 = downregulated), zis is the z-score normalised gene expression value in sample s and Aij is the adjacency matrix of the module. Effectively, this metric gives more weight to gene interactions that are supported by the data. Color and sign of nodes reflect the directionality of expression in the in-vitro signature (Red = upregulated &σ = 1, Green = downregulated &σ = -1). Pathway activity levels can then be shown as heatmaps (blue = high activity, yellow = low activity).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2991308&req=5

Figure 1: Measuring pathway module activation. Flowchart figure showing overall strategy used for inferring pathway module activity in clinical tumor samples from a model (perturbation) signature. A) A gene mRNA signature that represents a perturbed cancer cell phenotype (i.e oncogene overexpression) is combined with mRNA expression data of a large panel of clinical tumor specimens to derive an "expression relevance network" where nodes represent genes from the signature and an edge between two nodes indicates a statistically significant Pearson correlation between the two corresponding genes as measured over the clinical tumor panel. Having constructed the relevance network, the network is first pruned so that network edges that are inconsistent with prior information are removed. Signs on edges between labelled genes indicate the sign of the significant correlation between the two genes, which must be consistent with their directionality as given by the model signature. Modules defined as subnetworks with higher than average edge density are then inferred using a spectral decomposition algorithm (see Methods). B) For a given relatively large module, the module of pathway activation (MPA), pathway activity is then computed using a metric defined over the topology of the module. In the formula, PAs stands for the estimated pathway module activity in sample s, M is the number of genes in the module, σi is a binary weight (1,-1) indicating the directionality of gene expression of gene i (1 = upregulated, -1 = downregulated), zis is the z-score normalised gene expression value in sample s and Aij is the adjacency matrix of the module. Effectively, this metric gives more weight to gene interactions that are supported by the data. Color and sign of nodes reflect the directionality of expression in the in-vitro signature (Red = upregulated &σ = 1, Green = downregulated &σ = -1). Pathway activity levels can then be shown as heatmaps (blue = high activity, yellow = low activity).
Mentions: Our strategy to estimate pathway activation for a given model signature is llustrated in Figure 1 (see also Methods) and is carried out separately for ER+ and ER- disease. Briefly, the algorithm constructs a pruned relevance correlation network of the genes in the model signature across the expression tumour panel. Only genes and correlations between genes that are consistent with the prior information are allowed in the network. This strategy therefore filters out genes and gene-pairs with irrelevant or inconsistent expression patterns, while also identifying modules of high-edge density, that is, subnetworks of genes that show consistent and significantly correlated (or anticorrelated) patterns across the panel of tumours.

Bottom Line: Using Boolean interaction Cox-regression models to identify non-linear pathway combinations associated with clinical outcome, we show that simultaneous high activation of Th1 and low activation of a TGF-beta pathway module defines a subtype of particularly good prognosis and that this classification provides a better prognostic model than those based on the individual pathways.In ER+ breast cancer, we find that simultaneous high MYC and RAS activity confers significantly worse prognosis than either high MYC or high RAS activity alone.Specifically, our results suggest that simultaneous modulation of T-helper differentiation and TGF-beta pathways may improve clinical outcome of hormone insensitive breast cancers over treatments that target only one of these pathways.

View Article: PubMed Central - HTML - PubMed

Affiliation: Breast Cancer Functional Genomics Laboratory, Department of Oncology University of Cambridge, Cancer Research UK Cambridge Research Institute, Li Ka-Shing Centre, Robinson Way, Cambridge CB2 0RE, UK. a.teschendorff@ucl.ac.uk

ABSTRACT

Background: Elucidating the activation pattern of molecular pathways across a given tumour type is a key challenge necessary for understanding the heterogeneity in clinical response and for developing novel more effective therapies. Gene expression signatures of molecular pathway activation derived from perturbation experiments in model systems as well as structural models of molecular interactions ("model signatures") constitute an important resource for estimating corresponding activation levels in tumours. However, relatively few strategies for estimating pathway activity from such model signatures exist and only few studies have used activation patterns of pathways to refine molecular classifications of cancer.

Methods: Here we propose a novel network-based method for estimating pathway activation in tumours from model signatures. We find that although the pathway networks inferred from cancer expression data are highly consistent with the prior information contained in the model signatures, that they also exhibit a highly modular structure and that estimation of pathway activity is dependent on this modular structure. We apply our methodology to a panel of 438 estrogen receptor negative (ER-) and 785 estrogen receptor positive (ER+) breast cancers to infer activation patterns of important cancer related molecular pathways.

Results: We show that in ER negative basal and HER2+ breast cancer, gene expression modules reflecting T-cell helper-1 (Th1) and T-cell helper-2 (Th2) mediated immune responses play antagonistic roles as major risk factors for distant metastasis. Using Boolean interaction Cox-regression models to identify non-linear pathway combinations associated with clinical outcome, we show that simultaneous high activation of Th1 and low activation of a TGF-beta pathway module defines a subtype of particularly good prognosis and that this classification provides a better prognostic model than those based on the individual pathways. In ER+ breast cancer, we find that simultaneous high MYC and RAS activity confers significantly worse prognosis than either high MYC or high RAS activity alone. We further validate these novel prognostic classifications in independent sets of 173 ER- and 567 ER+ breast cancers.

Conclusion: We have proposed a novel method for pathway activity estimation in tumours and have shown that pathway modules antagonize or synergize to delineate novel prognostic subtypes. Specifically, our results suggest that simultaneous modulation of T-helper differentiation and TGF-beta pathways may improve clinical outcome of hormone insensitive breast cancers over treatments that target only one of these pathways.

Show MeSH
Related in: MedlinePlus