Fig. 7

Method of therapeutic evaluation for personalized cancer treatment (Step 4 of CanSeer). The final step of CanSeer involves personalized cancer therapeutics. This includes screening personalized in silico cancer models with different drugs and their combinations. (A) First, druggable genome and clinically actionable target nodes in the network are identified, and their oncogenic roles are obtained from OncoKB. Drugs targeting these nodes are gathered from literature and databases (DrugBank, PanDrugs, The Drug Gene Interaction Database (DGIdb), etc. Moreover, cancer driver genes are acquired from OncoVar and IntOGen to prioritize druggability. (B) Subsequently, drug IC₅₀ values sourced from “Genomics of Drug Sensitivity in Cancer (GDSC2)” are employed to compute drug activity scores (DAS), which are then normalized (NDAS). (C) NDAS values together with the normalized RNA-seq based gene expression values of a patient in cancer (NRGEPC) and maximal efficacy gain induced by a drug (MEGID) per se, are utilized to calculate drug scores for patients (DS). (D) Using DS, personalized cancer models undergo re-annotation and re-analysis. (E and F) Dynamical analysis cell fate outcomes are then utilized to assess patient-specific responses to individual drugs and combinations, considering both efficacy and cytotoxic effect. (G) Next, CanSeer establishes the "therapeutic response index (TRI)" by quantifying the difference between efficacy and cytotoxic effect. (H and I) TRI is then used to prioritize treatment options followed by comparing treatment-induced cell fates with those from normal and cancer models.