FROM THE TUMOR GENOME TO THE TARGETED THERAPY 

Background:

 Molecular and clinical interpretation of molecular genetic tumor profiles will be more and more important in the clinical routine work of medical oncologists. We aimed to develop a standardized three-step algorithm for the decision support process to predict sensitivity and resistance to targeted therapies based on the highest available evidences, and to develop the best personalized treatment strategy.

Patients and Methods:

We have analyzed the molecular profile of solid cancer tumors (n = 70) sequenced by next-generation sequencing (NGS) for 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 HER2, ALK, ROS1, 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:

We found evidence for positive association between the molecular profile and targeted compound in 80.6%, negative association in 43.5% of cases. As a clinical consequence 20% of cases participated in targeted clinical trials and 10% received molecular profile based targeted therapies.

Conclusions:

Multi-gene molecular profile analysis and medical information technologies empowers the clinical oncologists in the planning of the best treatment strategy, reduces the unnecessary treatment delays due to multiple unsuccessful screening of low frequency biomarkers one by one in central labs. Better prediction of therapy response can also accelerate the reimbursement of novel targeted therapies.