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Comparative Study
. 2020 Jun 11;15(6):e0234752.
doi: 10.1371/journal.pone.0234752. eCollection 2020.

Bayesian networks established functional differences between breast cancer subtypes

Lucía Trilla-Fuertes  1 , Angelo Gámez-Pozo  1   2 , Jorge M Arevalillo  3 , Rocío López-Vacas  2 , Elena López-Camacho  1 , Guillermo Prado-Vázquez  1   2 , Andrea Zapater-Moros  1   2 , Mariana Díaz-Almirón  4 , María Ferrer-Gómez  2 , Hilario Navarro  3 , Paolo Nanni  5 , Pilar Zamora  6 , Enrique Espinosa  6   7 , Paloma Maín  8 , Juan Ángel Fresno Vara  1   2   7
Affiliations
Comparative Study

Bayesian networks established functional differences between breast cancer subtypes

Lucía Trilla-Fuertes et al. PLoS One. .

Abstract

Breast cancer is a heterogeneous disease. In clinical practice, tumors are classified as hormonal receptor positive, Her2 positive and triple negative tumors. In previous works, our group defined a new hormonal receptor positive subgroup, the TN-like subtype, which had a prognosis and a molecular profile more similar to triple negative tumors. In this study, proteomics and Bayesian networks were used to characterize protein relationships in 96 breast tumor samples. Components obtained by these methods had a clear functional structure. The analysis of these components suggested differences in processes such as mitochondrial function or extracellular matrix between breast cancer subtypes, including our new defined subtype TN-like. In addition, one of the components, mainly related with extracellular matrix processes, had prognostic value in this cohort. Functional approaches allow to build hypotheses about regulatory mechanisms and to establish new relationships among proteins in the breast cancer context.

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Conflict of interest statement

JAFV, EE and AG-P are shareholders in Biomedica Molecular Medicine SL, and LT-F, EL-C, AZ-M, and GP-V are currently employees of the company. The other authors declare that they have no competing interests. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

Figures

Fig 1
Fig 1. Component activity measurements for ER-true, TN-like and TNBC respectively.
Fig 2
Fig 2. Component 13 activity prognostic value in the whole cohort.
Fig 3
Fig 3. Component 13.
Orange nodes: Proteins related to extracellular matrix ontology.
Fig 4
Fig 4. Network built with the proteins from Component13 using STRING.
See this image and copyright information in PMC

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