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A novel genomic signature predicting FDG uptake in diverse metastatic tumors
BACKGROUND: Building a universal genomic signature predicting the intensity of FDG uptake in diverse metastatic tumors may allow us to understand better the biological processes underlying this phenomenon and their requirements of glucose uptake. METHODS: A balanced training set (n = 71) of metastat...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5773462/ https://www.ncbi.nlm.nih.gov/pubmed/29349517 http://dx.doi.org/10.1186/s13550-017-0355-3 |
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author | Crespo-Jara, Aurora Redal-Peña, Maria Carmen Martinez-Navarro, Elena Maria Sureda, Manuel Fernandez-Morejon, Francisco Jose Garcia-Cases, Francisco J. Manzano, Ramon Gonzalez Brugarolas, Antonio |
author_facet | Crespo-Jara, Aurora Redal-Peña, Maria Carmen Martinez-Navarro, Elena Maria Sureda, Manuel Fernandez-Morejon, Francisco Jose Garcia-Cases, Francisco J. Manzano, Ramon Gonzalez Brugarolas, Antonio |
author_sort | Crespo-Jara, Aurora |
collection | PubMed |
description | BACKGROUND: Building a universal genomic signature predicting the intensity of FDG uptake in diverse metastatic tumors may allow us to understand better the biological processes underlying this phenomenon and their requirements of glucose uptake. METHODS: A balanced training set (n = 71) of metastatic tumors including some of the most frequent histologies, with matched PET/CT quantification measurements and whole human genome gene expression microarrays, was used to build the signature. Selection of microarray features was carried out exclusively on the basis of their strong association with FDG uptake (as measured by SUVmean35) by means of univariate linear regression. A thorough bioinformatics study of these genes was performed, and multivariable models were built by fitting several state of the art regression techniques to the training set for comparison. RESULTS: The 909 probes with the strongest association with the SUVmean35 (comprising 742 identifiable genes and 62 probes not matched to a symbol) were used to build the signature. Partial least squares using three components (PLS-3) was the best performing model in the training dataset cross-validation (root mean square error, RMSE = 0.443) and was validated further in an independent validation dataset (n = 13) obtaining a performance within the 95% CI of that obtained in the training dataset (RMSE = 0.645). Significantly overrepresented biological processes correlating with the SUVmean35 were identified beyond glycolysis, such as ribosome biogenesis and DNA replication (correlating with a higher SUVmean35) and cytoskeleton reorganization and autophagy (correlating with a lower SUVmean35). CONCLUSIONS: PLS-3 is a signature predicting accurately the intensity of FDG uptake in diverse metastatic tumors. FDG-PET might help in the design of specific targeted therapies directed to counteract the identified malignant biological processes more likely activated in a tumor as inferred from the SUVmean35 and also from its variations in response to antineoplastic treatments. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13550-017-0355-3) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-5773462 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-57734622018-01-30 A novel genomic signature predicting FDG uptake in diverse metastatic tumors Crespo-Jara, Aurora Redal-Peña, Maria Carmen Martinez-Navarro, Elena Maria Sureda, Manuel Fernandez-Morejon, Francisco Jose Garcia-Cases, Francisco J. Manzano, Ramon Gonzalez Brugarolas, Antonio EJNMMI Res Original Research BACKGROUND: Building a universal genomic signature predicting the intensity of FDG uptake in diverse metastatic tumors may allow us to understand better the biological processes underlying this phenomenon and their requirements of glucose uptake. METHODS: A balanced training set (n = 71) of metastatic tumors including some of the most frequent histologies, with matched PET/CT quantification measurements and whole human genome gene expression microarrays, was used to build the signature. Selection of microarray features was carried out exclusively on the basis of their strong association with FDG uptake (as measured by SUVmean35) by means of univariate linear regression. A thorough bioinformatics study of these genes was performed, and multivariable models were built by fitting several state of the art regression techniques to the training set for comparison. RESULTS: The 909 probes with the strongest association with the SUVmean35 (comprising 742 identifiable genes and 62 probes not matched to a symbol) were used to build the signature. Partial least squares using three components (PLS-3) was the best performing model in the training dataset cross-validation (root mean square error, RMSE = 0.443) and was validated further in an independent validation dataset (n = 13) obtaining a performance within the 95% CI of that obtained in the training dataset (RMSE = 0.645). Significantly overrepresented biological processes correlating with the SUVmean35 were identified beyond glycolysis, such as ribosome biogenesis and DNA replication (correlating with a higher SUVmean35) and cytoskeleton reorganization and autophagy (correlating with a lower SUVmean35). CONCLUSIONS: PLS-3 is a signature predicting accurately the intensity of FDG uptake in diverse metastatic tumors. FDG-PET might help in the design of specific targeted therapies directed to counteract the identified malignant biological processes more likely activated in a tumor as inferred from the SUVmean35 and also from its variations in response to antineoplastic treatments. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13550-017-0355-3) contains supplementary material, which is available to authorized users. Springer Berlin Heidelberg 2018-01-18 /pmc/articles/PMC5773462/ /pubmed/29349517 http://dx.doi.org/10.1186/s13550-017-0355-3 Text en © The Author(s). 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. |
spellingShingle | Original Research Crespo-Jara, Aurora Redal-Peña, Maria Carmen Martinez-Navarro, Elena Maria Sureda, Manuel Fernandez-Morejon, Francisco Jose Garcia-Cases, Francisco J. Manzano, Ramon Gonzalez Brugarolas, Antonio A novel genomic signature predicting FDG uptake in diverse metastatic tumors |
title | A novel genomic signature predicting FDG uptake in diverse metastatic tumors |
title_full | A novel genomic signature predicting FDG uptake in diverse metastatic tumors |
title_fullStr | A novel genomic signature predicting FDG uptake in diverse metastatic tumors |
title_full_unstemmed | A novel genomic signature predicting FDG uptake in diverse metastatic tumors |
title_short | A novel genomic signature predicting FDG uptake in diverse metastatic tumors |
title_sort | novel genomic signature predicting fdg uptake in diverse metastatic tumors |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5773462/ https://www.ncbi.nlm.nih.gov/pubmed/29349517 http://dx.doi.org/10.1186/s13550-017-0355-3 |
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