Next Generation Sequencing (NGS) technologies transformed cancer genetics by providing unprecedented access to big genomic and transcriptomic data (1, 2). Curation of cancer genetic profiles has led to many successful applications crosslinking mutations or polymorphisms with individual tumor response to therapies. Obviously valuable, these approaches however still cannot generate clinically actionable information for most of the cancer patients. In turn, high throughput RNA (transcriptome) analysis can be considered a rising star that may complement mutational screenings (1, 3). A combination of both approaches may be synergistic for many tasks in molecular diagnostics in oncology (4–6). Cancer mutation and expression biomarkers can not only help to set a diagnosis but also to identify appropriate personalized molecular-based treatment (7–9). Furthermore, novel bioinformatic approaches enable squeezing more and more clinically meaningful data from large genetic datasets (10).