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Rapid implementation of novel technologies for biology-guided stratification of adult cancer patients

“Precision oncology” describes the ability to predict which patients will likely respond to specific cancer therapies based on increasingly accurate, high-resolution molecular diagnostics as well as the functional understanding of individual tumors.

Such a stratification of patients can be achieved through “next-generation” sequencing of tumor DNA and RNA, which can reveal genomic alterations that have immediate clinical implications. NCT MASTER provides a complete workflow for selection and consenting of patients, sample processing, whole-exome/genome and RNA sequencing, bioinformatic analysis and genomics-guided clinical decision making by a weekly Molecular Tumor Board composed of members with expertise in clinical oncology, pathology, molecular genetics, bioinformatics, medical genetics and counseling, pharmaceutics, and bioethics. The Department of Human Genetics in Dresden systematically evaluates the impact of germline mutations affecting known cancer genes within the MASTER cohort.

Since most of the mutations identified in human cancers have unknown functional consequences and can therefore not directly be interpreted regarding their suitability as therapeutic targets, separating “driver” mutations from biologically neutral “passenger” alterations is critical for the translation of genetic information into the clinic. Furthermore, the therapeutic value of known oncogenic mutations may vary depending on tissue context. To address these challenges, we jointly investigate the functional role of genetic alterations predicted to be damaging in appropriate experimental systems in Heidelberg and Dresden, followed by analysis of the phenotypic consequences. The intermediate goal of these studies is to establish a versatile platform for rapid functional testing of mutations identified in individual cancers and to develop a continuously evolving, “learning” system to support treatment decisions at NCT.


NCT Master Workflow ©Daniela Richter