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Probabilistic graphical models relate immune status with response to neoadjuvant chemotherapy in breast cancer

Authors :
Mariana Díaz-Almirón
Jaime Feliu
Guillermo Prado-Vázquez
Pilar Zamora
Hilario Navarro
Angelo Gámez-Pozo
Andrea Zapater-Moros
Enrique Espinosa
Lucía Trilla-Fuertes
Jorge M. Arevalillo
Paloma Main
Juan Ángel Fresno Vara
Source :
Repositorio Institucional de la Consejería de Sanidad de la Comunidad de Madrid, Consejería de Sanidad de la Comunidad de Madrid, Oncotarget
Publication Year :
2018
Publisher :
Impact Journals, LLC, 2018.

Abstract

Breast cancer is the most frequent tumor in women and its incidence is increasing. Neoadjuvant chemotherapy has become standard of care as a complement to surgery in locally advanced or poor-prognosis early stage disease. The achievement of a complete response to neoadjuvant chemotherapy correlates with prognosis but it is not possible to predict who will obtain an excellent response. The molecular analysis of the tumor offers a unique opportunity to unveil predictive factors. In this work, gene expression profiling in 279 tumor samples from patients receiving neoadjuvant chemotherapy was performed and probabilistic graphical models were used. This approach enables addressing biological and clinical questions from a Systems Biology perspective, allowing to deal with large gene expression data and their interactions. Tumors presenting complete response to neoadjuvant chemotherapy had a higher activity of immune related functions compared to resistant tumors. Similarly, samples from complete responders presented higher expression of lymphocyte cell lineage markers, immune-activating and immune-suppressive markers, which may correlate with tumor infiltration by lymphocytes (TILs). These results suggest that the patient’s immune system plays a key role in tumor response to neoadjuvant treatment. However, future studies with larger cohorts are necessary to validate these hypotheses.

Details

ISSN :
19492553
Volume :
9
Database :
OpenAIRE
Journal :
Oncotarget
Accession number :
edsair.doi.dedup.....3439b957d3aa85f60d59a51b394e0218
Full Text :
https://doi.org/10.18632/oncotarget.25496