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Radiogenomics analysis identifies correlations of digital mammography with clinical molecular signatures in breast cancer

In breast cancer, well-known gene expression subtypes have been related to a specific clinical outcome. However, their impact on the breast tissue phenotype has been poorly studied. Here, we investigate the association of imaging data of tumors to gene expression signatures from 71 patients with bre...

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Autores principales: Tamez-Peña, Jose-Gerardo, Rodriguez-Rojas, Juan-Andrés, Gomez-Rueda, Hugo, Celaya-Padilla, Jose-Maria, Rivera-Prieto, Roxana-Alicia, Palacios-Corona, Rebeca, Garza-Montemayor, Margarita, Cardona-Huerta, Servando, Treviño, Victor
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5875760/
https://www.ncbi.nlm.nih.gov/pubmed/29596496
http://dx.doi.org/10.1371/journal.pone.0193871
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author Tamez-Peña, Jose-Gerardo
Rodriguez-Rojas, Juan-Andrés
Gomez-Rueda, Hugo
Celaya-Padilla, Jose-Maria
Rivera-Prieto, Roxana-Alicia
Palacios-Corona, Rebeca
Garza-Montemayor, Margarita
Cardona-Huerta, Servando
Treviño, Victor
author_facet Tamez-Peña, Jose-Gerardo
Rodriguez-Rojas, Juan-Andrés
Gomez-Rueda, Hugo
Celaya-Padilla, Jose-Maria
Rivera-Prieto, Roxana-Alicia
Palacios-Corona, Rebeca
Garza-Montemayor, Margarita
Cardona-Huerta, Servando
Treviño, Victor
author_sort Tamez-Peña, Jose-Gerardo
collection PubMed
description In breast cancer, well-known gene expression subtypes have been related to a specific clinical outcome. However, their impact on the breast tissue phenotype has been poorly studied. Here, we investigate the association of imaging data of tumors to gene expression signatures from 71 patients with breast cancer that underwent pre-treatment digital mammograms and tumor biopsies. From digital mammograms, a semi-automated radiogenomics analysis generated 1,078 features describing the shape, signal distribution, and texture of tumors along their contralateral image used as control. From tumor biopsy, we estimated the OncotypeDX and PAM50 recurrence scores using gene expression microarrays. Then, we used multivariate analysis under stringent cross-validation to train models predicting recurrence scores. Few univariate features reached Spearman correlation coefficients above 0.4. Nevertheless, multivariate analysis yielded significantly correlated models for both signatures (correlation of OncotypeDX = 0.49 ± 0.07 and PAM50 = 0.32 ± 0.10 in stringent cross-validation and OncotypeDX = 0.83 and PAM50 = 0.78 for a unique model). Equivalent models trained from the unaffected contralateral breast were not correlated suggesting that the image signatures were tumor-specific and that overfitting was not a considerable issue. We also noted that models were improved by combining clinical information (triple negative status and progesterone receptor). The models used mostly wavelets and fractal features suggesting their importance to capture tumor information. Our results suggest that molecular-based recurrence risk and breast cancer subtypes have observable radiographic phenotypes. To our knowledge, this is the first study associating mammographic information to gene expression recurrence signatures.
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spelling pubmed-58757602018-04-13 Radiogenomics analysis identifies correlations of digital mammography with clinical molecular signatures in breast cancer Tamez-Peña, Jose-Gerardo Rodriguez-Rojas, Juan-Andrés Gomez-Rueda, Hugo Celaya-Padilla, Jose-Maria Rivera-Prieto, Roxana-Alicia Palacios-Corona, Rebeca Garza-Montemayor, Margarita Cardona-Huerta, Servando Treviño, Victor PLoS One Research Article In breast cancer, well-known gene expression subtypes have been related to a specific clinical outcome. However, their impact on the breast tissue phenotype has been poorly studied. Here, we investigate the association of imaging data of tumors to gene expression signatures from 71 patients with breast cancer that underwent pre-treatment digital mammograms and tumor biopsies. From digital mammograms, a semi-automated radiogenomics analysis generated 1,078 features describing the shape, signal distribution, and texture of tumors along their contralateral image used as control. From tumor biopsy, we estimated the OncotypeDX and PAM50 recurrence scores using gene expression microarrays. Then, we used multivariate analysis under stringent cross-validation to train models predicting recurrence scores. Few univariate features reached Spearman correlation coefficients above 0.4. Nevertheless, multivariate analysis yielded significantly correlated models for both signatures (correlation of OncotypeDX = 0.49 ± 0.07 and PAM50 = 0.32 ± 0.10 in stringent cross-validation and OncotypeDX = 0.83 and PAM50 = 0.78 for a unique model). Equivalent models trained from the unaffected contralateral breast were not correlated suggesting that the image signatures were tumor-specific and that overfitting was not a considerable issue. We also noted that models were improved by combining clinical information (triple negative status and progesterone receptor). The models used mostly wavelets and fractal features suggesting their importance to capture tumor information. Our results suggest that molecular-based recurrence risk and breast cancer subtypes have observable radiographic phenotypes. To our knowledge, this is the first study associating mammographic information to gene expression recurrence signatures. Public Library of Science 2018-03-29 /pmc/articles/PMC5875760/ /pubmed/29596496 http://dx.doi.org/10.1371/journal.pone.0193871 Text en © 2018 Tamez-Peña et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Tamez-Peña, Jose-Gerardo
Rodriguez-Rojas, Juan-Andrés
Gomez-Rueda, Hugo
Celaya-Padilla, Jose-Maria
Rivera-Prieto, Roxana-Alicia
Palacios-Corona, Rebeca
Garza-Montemayor, Margarita
Cardona-Huerta, Servando
Treviño, Victor
Radiogenomics analysis identifies correlations of digital mammography with clinical molecular signatures in breast cancer
title Radiogenomics analysis identifies correlations of digital mammography with clinical molecular signatures in breast cancer
title_full Radiogenomics analysis identifies correlations of digital mammography with clinical molecular signatures in breast cancer
title_fullStr Radiogenomics analysis identifies correlations of digital mammography with clinical molecular signatures in breast cancer
title_full_unstemmed Radiogenomics analysis identifies correlations of digital mammography with clinical molecular signatures in breast cancer
title_short Radiogenomics analysis identifies correlations of digital mammography with clinical molecular signatures in breast cancer
title_sort radiogenomics analysis identifies correlations of digital mammography with clinical molecular signatures in breast cancer
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5875760/
https://www.ncbi.nlm.nih.gov/pubmed/29596496
http://dx.doi.org/10.1371/journal.pone.0193871
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