Purpose Transcriptomic profiling has enabled the neater genomic characterization of several cancers, among them colorectal cancer (CRC), through the derivation of genes with enhanced causal role and informative gene sets. However, the identification of small-sized gene signatures, which can serve as potential biomarkers in CRC, remains challenging, mainly due to the great genetic heterogeneity of the disease. Methods We developed and exploited an analytical framework for the integrative analysis of CRC datasets, encompassing transcriptomic data and positron emission tomography (PET) measurements. Profiling data comprised two microarray datasets, pertaining biopsy specimen from 30 untreated patients with primary CRC, coupled by their F-18-Fluorodeoxyglucose (FDG) PET values, using tracer kinetic analysis measurements. The computational framework incorporates algorithms for semantic processing, multivariate analysis, data mining and dimensionality reduction. Results Transcriptomic and PET data feature sets, were evaluated for their discrimination performance between primary colorectal adenocarcinomas and adjacent normal mucosa. A composite signature was derived, pertaining 12 features: 7 genes and 5 PET variables. This compact signature manifests superior performance in classification accuracy, through the integration of gene expression and PET data. Conclusions This work represents an effort for the integrative, multilayered, signature-oriented analysis of CRC, in the context of radio-genomics, inferring a composite signature with promising results for patient stratification.

18 F-FDG kinetics are primarily dependent on the expression of genes associated with glucose transporters and hexokinases but may be modulated by other genes. The dependency of 18 F-FDG kinetics on angiogenesis-related gene expression was evaluated in this study. Methods: Patients with primary colorectal tumors (n 5 25) were examined with PET and 18 F-FDG within 2 days before surgery. Tissue specimens were obtained from the tumor and the normal colon during surgery, and gene expression was assessed using gene arrays. Results: Overall, 23 angiogenesis-related genes were identified with a tumor-to-normal ratio exceeding 1.50. Analysis revealed a significant correlation between k1 and vascular endothelial growth factor (VEGF-A, r 5 0.51) and between fractal dimension and angiopoietin-2 (r 5 0.48). k3 was negatively correlated with VEGF-B (r 5 20.46), and a positive correlation was noted for angiopoietin-like 4 gene (r 5 0.42). A multiple linear regression analysis was used for the PET parameters to predict the gene expression, and a correlation coefficient of r 5 0.75 was obtained for VEGF-A and of r 5 0.76 for the angiopoietin-2 expression. Thus, on the basis of these multiple correlation coefficients, angiogenesis-related gene expression contributes to about 50% of the variance of the 18 F-FDG kinetic data. The global 18 F-FDG uptake, as measured by the standardized uptake value and influx, was not significantly correlated with angiogenesisassociated genes. Conclusion: 18 F-FDG kinetics are modulated by angiogenesis-related genes. The transport rate for 18 F-FDG (k1) is higher in tumors with a higher expression of VEGF-A and angiopoietin-2. The regression functions for the PET parameters provide the possibility to predict the gene expression of VEGF-A and angiopoietin-2.

Quantitative FDG PET studies provide very accurate data for the differentiation of primary colorectal tumours from normal tissue. The use of quantitative data has the advantage that the detection of a colorectal tumour is not primarily dependent on the individual assessment and experience of the physician evaluating the FDG PET data only visually. The results suggest that the presence of metastatic lesions may be predicted by analysis of the dynamic PET data of the corresponding primary tumour. Further studies are needed to assess this aspect in detail.

The combined assessment of data obtained by positron emission tomography (PET) and gene array techniques provide new capabilities for the interpretation of kinetic tracer studies. The correlative analysis of the data helps to detect dependencies of the kinetics of radiotracer on gene expression. Furthermore, gene expression may be predicted using regression functions if a significant correlation exists, which raises new aspects regarding the interpretation of dynamic PET examinations. The development of new radiopharmaceuticals requires the knowledge of the enhanced expression of genes, especially genes controlling receptors and cell surface proteins. The GenePET program facilitates an interactive approach together with the use of key words to identify possible targets for new radiopharmaceuticals.

The results suggest that the FDG kinetics is modulated by proliferation associated genes. Especially K1, the parameter for the FDG transport into the cells, is modulated by cdk2. Tumors with a SUV exceeding 12 have usually a higher expression of cyclin D2. The parameters of the FDG kinetics can be used to predict the expression of proliferation associated genes individually.