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Specific genomic aberrations predict survival, but low mutation rate in cancer hot spots, in clear cell renal cell carcinoma.

ABSTRACT: Detailed genetic profiling of clear cell renal cell carcinoma (ccRCC) has revealed genomic regions commonly affected by structural changes and a general genetic heterogeneity. VHL and PBRM1, both located at chromosome 3p, are 2 major genes mutated at high frequency but apart from these aberrations, the mutational landscape in ccRCC is largely undefined. Potential prognostic information given by the genomic changes appears to depend on the particular cohort studied. We analyzed a Swedish ccRCC cohort of 74 patients and found common changes (loss or gain occurring in >20% of the tumors) in 12 chromosomal regions (1p, 3p, 3q, 5q, 6q, 7p, 7q 8p, 9p, 9q, 10q, and 14q). A poor outcome was associated with gain of 7q and losses on 9p, 9q, and 14q. These aberrations were more frequent in metastasized tumors, suggesting alterations of genes important for tumor progression. Sequencing of 48 genes implicated in cancer revealed that only VHL, TP53, and PTEN were mutated at a noticeable frequency (51%, 9%, and 9%, respectively). Shorter relative telomere length (RTL) has been associated with loss of specific chromosomal regions in ccRCC tumors, but we could not verify this finding. However, a significantly lower tumor/nontumor (T/N) RTL ratio was detected for tumors with losses in 4q or 9p. In conclusion, poor outcome in ccRCC was associated with gain of 7q and loss on 9p, 9q, and 14q, whereas the mutation rate overall was low in a screen of cancer-associated genes.

SUBMITTER: Kohn L

PROVIDER: S-EPMC4431677 | biostudies-literature | 2015 May-Jun

REPOSITORIES: biostudies-literature

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Specific genomic aberrations predict survival, but low mutation rate in cancer hot spots, in clear cell renal cell carcinoma.

Köhn Linda L Svenson Ulrika U Ljungberg Börje B Roos Göran G

Applied immunohistochemistry & molecular morphology : AIMM 20150501 5

Detailed genetic profiling of clear cell renal cell carcinoma (ccRCC) has revealed genomic regions commonly affected by structural changes and a general genetic heterogeneity. VHL and PBRM1, both located at chromosome 3p, are 2 major genes mutated at high frequency but apart from these aberrations, the mutational landscape in ccRCC is largely undefined. Potential prognostic information given by the genomic changes appears to depend on the particular cohort studied. We analyzed a Swedish ccRCC co ...[more]

PMID: 24992170

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Project description:PurposeUpfront knowledge of tumor growth rates of clear cell renal cell carcinoma in von Hippel-Lindau syndrome (VHL) patients can allow for a more personalized approach to either surveillance imaging frequency or surgical planning. In this study, we implement a machine learning algorithm utilizing radiomic features of renal tumors identified on baseline magnetic resonance imaging (MRI) in VHL patients to predict the volumetric growth rate category of these tumors.Materials and methodsA total of 73 VHL patients with 173 pathologically confirmed Clear Cell Renal Cell Carcinoma (ccRCCs) underwent MRI at least at two different time points between 2015 and 2021. Each tumor was manually segmented in excretory phase contrast T1 weighed MRI and co-registered on pre-contrast, corticomedullary and nephrographic phases. Radiomic features and volumetric data from each tumor were extracted using the PyRadiomics library in Python (4544 total features). Tumor doubling time (DT) was calculated and patients were divided into two groups: DT < = 1 year and DT > 1 year. Random forest classifier (RFC) was used to predict the DT category. To measure prediction performance, the cohort was randomly divided into 100 training and test sets (80% and 20%). Model performance was evaluated using area under curve of receiver operating characteristic curve (AUC-ROC), as well as accuracy, F1, precision and recall, reported as percentages with 95% confidence intervals (CIs).ResultsThe average age of patients was 47.2 ± 10.3 years. Mean interval between MRIs for each patient was 1.3 years. Tumors included in this study were categorized into 155 Grade 2; 16 Grade 3; and 2 Grade 4. Mean accuracy of RFC model was 79.0% [67.4-90.6] and mean AUC-ROC of 0.795 [0.608-0.988]. The accuracy for predicting DT classes was not different among the MRI sequences (P-value = 0.56).ConclusionHere we demonstrate the utility of machine learning in accurately predicting the renal tumor growth rate category of VHL patients based on radiomic features extracted from different T1-weighted pre- and post-contrast MRI sequences.

Dataset Information

Specific genomic aberrations predict survival, but low mutation rate in cancer hot spots, in clear cell renal cell carcinoma.

Publications

Specific genomic aberrations predict survival, but low mutation rate in cancer hot spots, in clear cell renal cell carcinoma.

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