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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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Novel prognostic subtypes in ER- and ER+ breast cancer. A) & B) Kaplan Meier DMFS curves for dichotomised pathway activity levels, for IL12 and TGFB, in ER- breast cancer (Set1). C) Corresponding Kaplan Meier curve for the four subtypes stratified according to up/down activity of the two pathways. Hazard ratio refers to the IL12up-TGFBdn subtype relative to the rest. D) Independent validation in the test cohort (Set2). E) & F) Kaplan Meier DMFS curves for dichotomised pathway activity levels, for MYC and RAS, in ER+ breast cancer (Set1). G) Corresponding Kaplan Meier curve for the four subtypes stratified according to up/down activity of the two pathways. Hazard ratio refers to the MYCup-RASup subtype relative to the rest. H) Independent validation in the test cohort (Set2).
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Figure 7: Novel prognostic subtypes in ER- and ER+ breast cancer. A) & B) Kaplan Meier DMFS curves for dichotomised pathway activity levels, for IL12 and TGFB, in ER- breast cancer (Set1). C) Corresponding Kaplan Meier curve for the four subtypes stratified according to up/down activity of the two pathways. Hazard ratio refers to the IL12up-TGFBdn subtype relative to the rest. D) Independent validation in the test cohort (Set2). E) & F) Kaplan Meier DMFS curves for dichotomised pathway activity levels, for MYC and RAS, in ER+ breast cancer (Set1). G) Corresponding Kaplan Meier curve for the four subtypes stratified according to up/down activity of the two pathways. Hazard ratio refers to the MYCup-RASup subtype relative to the rest. H) Independent validation in the test cohort (Set2).

Mentions: In ER- breast cancer, we observed that IL12 (or IFNG) synergized with TGFB to provide a better prognostic model than either pathway considered separately (Figure 6A). Specifically, simultaneous high IL12 (or IFNG) and low TGFB activity defined a good prognosis subtype relative to all other samples (HR = 0.41 (0.26-0.64) P < 10-4) (Figure 7A). Moreover, this result held true in both basal and HER2+ subtypes (Additional file 10). Using likelihood ratio tests we verified that the non-linear interaction between IL12 (IFNG) and TGFB added prognostic value over models based on only TGFB or IL12 (Figure 6B). Conversely, the single pathway models did not improve the prognostic model provided by the non-linear interaction term (Figure 6C). We also observed that stratifying ER- samples according to high EGFR low IL12/IFNG activity provided a better prognostic model than stratifications based on the individuals pathways (Figure 6A), and Specifically that this non-linear interaction added prognostic value over the model using IL12/IFNG alone (Figure 6B). Consistent with this, simultaneous high EGFR low IL12/IFNG activity defined a subtype of poor prognosis (HR = 2.43 (1.71-3.44) P < 10-6, Additional file 11).


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)

Novel prognostic subtypes in ER- and ER+ breast cancer. A) & B) Kaplan Meier DMFS curves for dichotomised pathway activity levels, for IL12 and TGFB, in ER- breast cancer (Set1). C) Corresponding Kaplan Meier curve for the four subtypes stratified according to up/down activity of the two pathways. Hazard ratio refers to the IL12up-TGFBdn subtype relative to the rest. D) Independent validation in the test cohort (Set2). E) & F) Kaplan Meier DMFS curves for dichotomised pathway activity levels, for MYC and RAS, in ER+ breast cancer (Set1). G) Corresponding Kaplan Meier curve for the four subtypes stratified according to up/down activity of the two pathways. Hazard ratio refers to the MYCup-RASup subtype relative to the rest. H) Independent validation in the test cohort (Set2).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 7: Novel prognostic subtypes in ER- and ER+ breast cancer. A) & B) Kaplan Meier DMFS curves for dichotomised pathway activity levels, for IL12 and TGFB, in ER- breast cancer (Set1). C) Corresponding Kaplan Meier curve for the four subtypes stratified according to up/down activity of the two pathways. Hazard ratio refers to the IL12up-TGFBdn subtype relative to the rest. D) Independent validation in the test cohort (Set2). E) & F) Kaplan Meier DMFS curves for dichotomised pathway activity levels, for MYC and RAS, in ER+ breast cancer (Set1). G) Corresponding Kaplan Meier curve for the four subtypes stratified according to up/down activity of the two pathways. Hazard ratio refers to the MYCup-RASup subtype relative to the rest. H) Independent validation in the test cohort (Set2).
Mentions: In ER- breast cancer, we observed that IL12 (or IFNG) synergized with TGFB to provide a better prognostic model than either pathway considered separately (Figure 6A). Specifically, simultaneous high IL12 (or IFNG) and low TGFB activity defined a good prognosis subtype relative to all other samples (HR = 0.41 (0.26-0.64) P < 10-4) (Figure 7A). Moreover, this result held true in both basal and HER2+ subtypes (Additional file 10). Using likelihood ratio tests we verified that the non-linear interaction between IL12 (IFNG) and TGFB added prognostic value over models based on only TGFB or IL12 (Figure 6B). Conversely, the single pathway models did not improve the prognostic model provided by the non-linear interaction term (Figure 6C). We also observed that stratifying ER- samples according to high EGFR low IL12/IFNG activity provided a better prognostic model than stratifications based on the individuals pathways (Figure 6A), and Specifically that this non-linear interaction added prognostic value over the model using IL12/IFNG alone (Figure 6B). Consistent with this, simultaneous high EGFR low IL12/IFNG activity defined a subtype of poor prognosis (HR = 2.43 (1.71-3.44) P < 10-6, Additional file 11).

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