Our results indicate that the performance of the UTE method as implemented in VB20P is close to the theoretical maximum of such an MRAC method in the brain, while it does not perform satisfactorily in the neck or face/nasal area. Further improvement of the UTE MRAC or other available methods for more accurate segmentation of bone should be incorporated.

Bevacizumab is included in an increasing number of clinical trials. To find biomarkers to predict and monitor treatment response, cancer and angiogenesis relevant mutations in tumour and circulating tumour DNA (ctDNA) were investigated in 26 metastatic melanoma patients treated with bevacizumab. Patients with >1% BRAF/NRAS ctDNA at treatment start had significantly decreased progression free survival (PFS) and overall survival (OS) (PFS: p = 0.019, median 54 vs 774 days, OS: p = 0.026, median 209 vs 1064 days). Patients with >1% BRAF/NRAS ctDNA during treatment showed similar results (PFS: p = 0.002, OS: p = 0.003). ≤1% BRAF/NRAS ctDNA and normal lactate dehydrogenase (LDH) levels both significantly predicted increased response to treatment, but BRAF/NRAS ctDNA was better at predicting response compared to LDH at treatment start (OR 16.94, p = 0.032 vs OR 4.57, p = 0.190), and at predicting PFS (HR 6.76, p = 0.002) and OS (HR 6.78, p = 0.002) during therapy. ctDNA BRAF p.V600D/E/K and NRAS p.G12V/p.Q61K/L/R were better biomarkers for response prediction than TERT promoter mutations (OR 1.50, p = 0.657). Next generation sequencing showed that all patients with ≥2 mutations in angiogenesis-relevant genes had progressive disease, but did not reveal other biomarkers identifying responders. To conclude, ctDNA and LDH are useful biomarkers for both monitoring and predicting response to bevacizumab.

SummaryIndividual tumor genomes pose a major challenge for clinical interpretation due to their unique sets of acquired mutations. There is a general scarcity of tools that can (i) systematically interrogate cancer genomes in the context of diagnostic, prognostic, and therapeutic biomarkers, (ii) prioritize and highlight the most important findings and (iii) present the results in a format accessible to clinical experts. We have developed a stand-alone, open-source software package for somatic variant annotation that integrates a comprehensive set of knowledge resources related to tumor biology and therapeutic biomarkers, both at the gene and variant level. Our application generates a tiered report that will aid the interpretation of individual cancer genomes in a clinical setting.Availability and implementationThe software is implemented in Python/R, and is freely available through Docker technology. Documentation, example reports, and installation instructions are accessible via the project GitHub page: https://github.com/sigven/pcgr.Supplementary information Supplementary data are available at Bioinformatics online.