Autor segons l'article: Teschendorff, Andrew E; Gomez, Sergio; Arenas, Alex; El-Ashry, Dorraya; Schmidt, Marcus; Gehrmann, Mathias; Caldas, Carlos
Departament: Enginyeria Informàtica i Matemàtiques
e-ISSN: 1471-2407
Autor/s de la URV: Arenas Moreno, Alejandro / Gómez Jiménez, Sergio
Paraules clau: Targets Survival Signature Protein Networks Molecular subtypes Metastasis Identification Gene-expression profiles Carcinomas
Resum: 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.
Àrees temàtiques: Saúde coletiva Química Planejamento urbano e regional / demografia Oncology Odontología Nutrição Medicina veterinaria Medicina iii Medicina ii Medicina i Interdisciplinar Genetics Farmacia Ensino Engenharias iii Engenharias ii Educação física Ciências biológicas iii Ciências biológicas ii Ciências biológicas i Ciência de alimentos Ciência da computação Cancer research Biotecnología Biodiversidade Administração, ciências contábeis e turismo
Accès a la llicència d'ús: https://creativecommons.org/licenses/by/3.0/es/
Adreça de correu electrònic de l'autor: sergio.gomez@urv.cat alexandre.arenas@urv.cat
Identificador de l'autor: 0000-0003-1820-0062 0000-0003-0937-0334
Data d'alta del registre: 2024-09-28
Volum de revista: 10
Versió de l'article dipositat: info:eu-repo/semantics/publishedVersion
Enllaç font original: https://bmccancer.biomedcentral.com/articles/10.1186/1471-2407-10-604
URL Document de llicència: https://repositori.urv.cat/ca/proteccio-de-dades/
Referència a l'article segons font original: Bmc Cancer. 10 604-
Referència de l'ítem segons les normes APA: Teschendorff, Andrew E; Gomez, Sergio; Arenas, Alex; El-Ashry, Dorraya; Schmidt, Marcus; Gehrmann, Mathias; Caldas, Carlos (2010). Improved prognostic classification of breast cancer defined by antagonistic activation patterns of immune response pathway modules. Bmc Cancer, 10(), 604-. DOI: 10.1186/1471-2407-10-604
DOI de l'article: 10.1186/1471-2407-10-604
Entitat: Universitat Rovira i Virgili
Any de publicació de la revista: 2010
Tipus de publicació: Journal Publications
Repositori URV