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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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Clustering analysis over pathway modules. A) Heatmaps of pathway activation (blue = high relative activation, yellow = low relative activation) over the merged ER- and ER+ cohorts [2,24,40-44]. Color bars indicate the intrinsic subtype (Pink = HER2+, green = normal, dark-red = basal, skyblue = luminal A, blue = luminal B) and the cluster inferred using a variational Bayesian method [52]. B) Kaplan Meier plots for distant metastasis free survival (DMFS) for the predicted clusters in ER- and ER+ breast cancer, respectively.
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Figure 2: Clustering analysis over pathway modules. A) Heatmaps of pathway activation (blue = high relative activation, yellow = low relative activation) over the merged ER- and ER+ cohorts [2,24,40-44]. Color bars indicate the intrinsic subtype (Pink = HER2+, green = normal, dark-red = basal, skyblue = luminal A, blue = luminal B) and the cluster inferred using a variational Bayesian method [52]. B) Kaplan Meier plots for distant metastasis free survival (DMFS) for the predicted clusters in ER- and ER+ breast cancer, respectively.

Mentions: The estimation of activity levels for the selected modules across clinical tumours yielded a pathway activity level matrix. Clustering was performed using a variational Bayesian mixture model [52] over the 8 largest molecular pathway modules to see if samples segregated significantly according to intrinsic subtype [46] (Figure 2A). We observed that inferred clusters mapped to intrinsic subtypes, as well as providing evidence for further heterogeneity within subtypes, confirming similar results reported in [14]. In line with the fact that intrinsic subtypes in ER+ breast cancer show differences in distant metastasis free survival (DMFS), inferred clusters also correlated significantly with outcome (Figure 2B). Importantly, we observed a significant survival difference in ER- breast cancer with those samples having overactive TGFB exhibiting worst survival (Figure 2B).


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)

Clustering analysis over pathway modules. A) Heatmaps of pathway activation (blue = high relative activation, yellow = low relative activation) over the merged ER- and ER+ cohorts [2,24,40-44]. Color bars indicate the intrinsic subtype (Pink = HER2+, green = normal, dark-red = basal, skyblue = luminal A, blue = luminal B) and the cluster inferred using a variational Bayesian method [52]. B) Kaplan Meier plots for distant metastasis free survival (DMFS) for the predicted clusters in ER- and ER+ breast cancer, respectively.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 2: Clustering analysis over pathway modules. A) Heatmaps of pathway activation (blue = high relative activation, yellow = low relative activation) over the merged ER- and ER+ cohorts [2,24,40-44]. Color bars indicate the intrinsic subtype (Pink = HER2+, green = normal, dark-red = basal, skyblue = luminal A, blue = luminal B) and the cluster inferred using a variational Bayesian method [52]. B) Kaplan Meier plots for distant metastasis free survival (DMFS) for the predicted clusters in ER- and ER+ breast cancer, respectively.
Mentions: The estimation of activity levels for the selected modules across clinical tumours yielded a pathway activity level matrix. Clustering was performed using a variational Bayesian mixture model [52] over the 8 largest molecular pathway modules to see if samples segregated significantly according to intrinsic subtype [46] (Figure 2A). We observed that inferred clusters mapped to intrinsic subtypes, as well as providing evidence for further heterogeneity within subtypes, confirming similar results reported in [14]. In line with the fact that intrinsic subtypes in ER+ breast cancer show differences in distant metastasis free survival (DMFS), inferred clusters also correlated significantly with outcome (Figure 2B). Importantly, we observed a significant survival difference in ER- breast cancer with those samples having overactive TGFB exhibiting worst survival (Figure 2B).

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