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We address here the issue of prioritizing non-coding mutations in the tumoral genome. To this aim, we created two independent computational models. The first (germline) model estimates purifying selection based on population SNP data. The second (somatic) model estimates tumor mutation density based on whole genome tumor sequencing. We show that each model(More)
Progress in next-generation sequencing provides unprecedented opportunities to fully characterize the spectrum of somatic mutations of cancer genomes. Given the large number of somatic mutations identified by such technologies, the prioritization of cancer-driving events is a consistent bottleneck. Most bioinformatics tools concentrate on driver mutations(More)
The most commonly used method for dose finding, the 3 + 3, has poor performance. New adaptive designs are more efficient. Nevertheless, they have reached a maximum performance level, and further improvement requires either larger sample sizes or outcomes measures richer than the simplistic severe toxicity measured at cycle 1. Clin Cancer Res; 23(15); 1-3.(More)
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