Driver-Target-Drug Algorithm In The Interpretation Of Molecular Cancer Profiles.

e22069 Background: The standardized integration of preclinical and clinical evidences into the interpretation of multi-gene assays is more and more important in the clinical practice of medical oncologists. We aimed to develop a simple three-step algorithm for the decision support process of the selection of the best available targeted therapies linked to the highest available evidences. Methods: We analyzed the molecular profile of lung cancer tumors (N = 82) sequenced by next-generation sequencing (NGS) of a panel of 58 cancer-related genes (ABL1, AKT1, ALK, APC, ATM, BRAF, CDH1, CDKN2A, CSF1R, CTNNB1, EGFR, ERBB2, ERBB4, EZH2, FBXW7, FGFR1, FGFR2, FGFR3, FLT3, GNA11, GNAS, GNAQ, HNF1A, HRAS, IDH1, IDH2, JAK2, JAK3, KDR, KIT, KRAS, MET, MLH1, MPL, NOTCH1, NPM1, NRAS, PDGFRA, PIK3CA, PTEN, PTPN11, RB1, RET, SMAD4, SMARCB1, SMO, SRC, STK11, TP53, VHL, DDR2, CHEK2, PIK3R1, MAP2K1, JAK1, TGFBR2, PDGFRB, IGFR1) and FISH analysis of HER-2, ALK, RS1, c-MET, FGFR, PIK3CA, EGFR. Driver mutations were defined based on their frequency in the COSMIC database, functional data clustered into published preclinical evidence types (e.g. Evidence for exclusivity with other driver genes in the same signal transduction pathway etc.), and clinical evidence types (e.g. Evidence for association with worse prognosis etc.). Driver-Target associations were evaluated base on specific evidence types (decreased or increased sensitivity to specific inhibitors in case of certain drivers). Target-Drug associations were established based on preclinical and clinical evidences related to 260 compounds in clinical use or clinical development. Results: In 16% of patients we identified only non-functional polymorphic variants or were wild types for all genes. 48% contained 1 driver mutation, 23% 2 drivers, 8% 3 drivers, 5% 4 drivers. 91,4% of cases we found positive association and 40,9% of cases negative association between the molecular profile and at least one of the targeted compounds. Conclusions: The standardized Driver-Target-Drug interpretation algorithm is highly informative and can be easily integrated into a standardized evidence–based decision process of precision medicine.