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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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Related in: MedlinePlus

Correlation patterns of molecular pathway modules. A) Pearson correlation heatmaps between molecular pathway modules in the ER- and ER+ breast cancer (Set1), respectively (Red = high positive correlation, White = zero or insignificant correlation, Green = high negative correlation). B) Validation of pairwise pathway module Pearson correlations in external set (Set2). Left panel (ER-), right panel (ER+). Overall Pearson correlation (PC) between training (Set1) and validation set (Set2) is given.
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Figure 5: Correlation patterns of molecular pathway modules. A) Pearson correlation heatmaps between molecular pathway modules in the ER- and ER+ breast cancer (Set1), respectively (Red = high positive correlation, White = zero or insignificant correlation, Green = high negative correlation). B) Validation of pairwise pathway module Pearson correlations in external set (Set2). Left panel (ER-), right panel (ER+). Overall Pearson correlation (PC) between training (Set1) and validation set (Set2) is given.

Mentions: Next, we investigated the correlation pattern between molecular pathway modules (Figure 5A). In both ER-and ER+ breast cancer we observed a strong correlation between the ERBB2, RAS and AKT pathways (Pearson correlation between RAS and AKT was 0.61 in ER+ and 0.59 in ER-), consistent with AKT-signalling a direct downstream target of RAS and ERBB2 [3,58,59]. Interestingly, in ER+ breast cancer these pathways were also correlated with MYC and E2F3. MYC and E2F3 pathways showed mutual strong correlations (Pearson correlations: 0.59 in ER+, 0.24 in ER-), consistent with E2F being a known transcriptional downstream target of MYC [60,61]. Another cluster was made up of immune response pathways.


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)

Correlation patterns of molecular pathway modules. A) Pearson correlation heatmaps between molecular pathway modules in the ER- and ER+ breast cancer (Set1), respectively (Red = high positive correlation, White = zero or insignificant correlation, Green = high negative correlation). B) Validation of pairwise pathway module Pearson correlations in external set (Set2). Left panel (ER-), right panel (ER+). Overall Pearson correlation (PC) between training (Set1) and validation set (Set2) is given.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 5: Correlation patterns of molecular pathway modules. A) Pearson correlation heatmaps between molecular pathway modules in the ER- and ER+ breast cancer (Set1), respectively (Red = high positive correlation, White = zero or insignificant correlation, Green = high negative correlation). B) Validation of pairwise pathway module Pearson correlations in external set (Set2). Left panel (ER-), right panel (ER+). Overall Pearson correlation (PC) between training (Set1) and validation set (Set2) is given.
Mentions: Next, we investigated the correlation pattern between molecular pathway modules (Figure 5A). In both ER-and ER+ breast cancer we observed a strong correlation between the ERBB2, RAS and AKT pathways (Pearson correlation between RAS and AKT was 0.61 in ER+ and 0.59 in ER-), consistent with AKT-signalling a direct downstream target of RAS and ERBB2 [3,58,59]. Interestingly, in ER+ breast cancer these pathways were also correlated with MYC and E2F3. MYC and E2F3 pathways showed mutual strong correlations (Pearson correlations: 0.59 in ER+, 0.24 in ER-), consistent with E2F being a known transcriptional downstream target of MYC [60,61]. Another cluster was made up of immune response pathways.

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