p53 is functionally inhibited in clear cell renal cell carcinoma (ccRCC): a mechanistic and correlative investigation into genetic and molecular characteristics.

PURPOSE: Although p53 is rarely mutated in ccRCC, its overexpression has been linked to poor prognosis. The current study sought to elucidate the unique role of p53 in ccRCC with genomic, proteomic, and functional analyses. MATERIALS AND METHODS: Data from the Cancer Genome Atlas (TCGA) were evaluated for genomic and proteomic characteristics of p53; a tissue micro array (TMA) study was carried out to evaluate the association of p53 and phosphorylated p53 (pp53) with clinical outcome. Mechanistic in vitro experiments were performed to confirm a pro-apoptotic loss of p53 in ccRCC and p53 isoforms as well as posttranslational modifications of p53 where assessed to provide possible reasons for a functional inhibition of p53 in ccRCC. RESULTS: A low somatic mutation rate of p53 could be confirmed. Although mRNA levels were correlated with poor prognosis and clinicopathological features, there was no monotonous association of mRNA levels with survival outcome. Higher p53 protein levels could be confirmed as poor prognostic features. In vitro, irradiation of ccRCC cell lines markedly induced levels of p53 and of activated (phosphorylated) p53. However, irradiated ccRCC cells demonstrated similar proliferation, migration, and p53 transcriptional activity like non-irradiated controls indicating a functional inhibition of p53. p53 isoforms and could not be correlated with clinical outcome of ccRCC patients. CONCLUSIONS: p53 is rarely mutated but the wildtype p53 is functionally inhibited in ccRCC. To investigate mechanisms that underlie functional inhibition of p53 may provide attractive therapeutic targets in ccRCC.

Combined unsupervised-supervised classification of multiparametric PET/MRI data: application to prostate cancer.

Multiparametric medical imaging data can be large and are often complex. Machine learning algorithms can assist in image interpretation when reliable training data exist. In most cases, however, knowledge about ground truth (e.g. histology) and thus training data is limited, which makes application of machine learning algorithms difficult. The purpose of this study was to design and implement a learning algorithm for classification of multidimensional medical imaging data that is robust and accurate even with limited prior knowledge and that allows for generalization and application to unseen data. Local prostate cancer was chosen as a model for application and validation. 16 patients underwent combined simultaneous [(11) C]-choline positron emission tomography (PET)/MRI. The following imaging parameters were acquired: T2 signal intensities, apparent diffusion coefficients, parameters Ktrans and Kep from dynamic contrast-enhanced MRI, and PET standardized uptake values (SUVs). A spatially constrained fuzzy c-means algorithm (sFCM) was applied to the single datasets and the resulting labeled data were used for training of a support vector machine (SVM) classifier. Accuracy and false positive and false negative rates of the proposed algorithm were determined in comparison with manual tumor delineation. For five of the 16 patients rates were also determined in comparison with the histopathological standard of reference. The combined sFCM/SVM algorithm proposed in this study revealed reliable classification results consistent with the histopathological reference standard and comparable to those of manual tumor delineation. sFCM/SVM generally performed better than unsupervised sFCM alone. We observed an improvement in accuracy with increasing number of imaging parameters used for clustering and SVM training. In particular, including PET SUVs as an additional parameter markedly improved classification results. A variety of applications are conceivable, especially for imaging of tissues without easily available histopathological correlation.