Abstract
BackgroundBuilding 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.MethodsA 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.ResultsThe 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).ConclusionsPLS-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.
Highlights
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
Inclusion criteria The conditions that patients should meet to enter this study were (a) a diagnosis of metastatic tumor with a baseline FDG-positron emission tomography (PET)/CT in order to evaluate the extent of disease and at a later point treatment response, (b) a fresh frozen tumor biopsy taken at the same metastatic location in which FDG uptake was measured for a gene expression microarray, that was performed within a maximum interval of 8 weeks of the FDG-PET/CT, (c) the patients had not received chemotherapy treatment in the 3 weeks prior to the inclusion in the study, and (d) patients in whom no active tumor could be identified by FDG-PET were excluded from the study
The different bioinformatics methods used (DAVID, Consensus Pathway and STRING databases, and single-sample GSEA (ssGSEA)) in this study show some degree of overlapping in the biological processes identified, but they are complementary as each one finds some relevant unique biological processes not identified by the others
Summary
The purpose of the present study was to build a genomic signature able to predict FDG uptake intensity in a diverse population of metastatic tumors, by using an unbiased gene expression profiling not limited to a predefined set of genes, but rather using whole human genome gene expression microarrays
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