Systematic identification of cancer-type specific drugs based on essential genes and validations in lung adenocarcinoma

Background Depicting a global landscape of essential gene-targeting drugs would provide more opportunities for cancer therapy. Essential genes have been determined in many cancer cell lines. However, systematic investigation on drugs targeting essential genes still has not been reported. Methods We suppose that drugs targeting cancer-type specific essential genes would generally have less toxicity than those targeting pan-caner essential genes. A scoring function-based strategy was developed to identify cancer-type specific targets and drugs. The EssentialitySpecificityScore ranked the essential genes in 19 cancer types, and 1151 top genes were identified as cancer-type specific targets. Combining target-drug interaction database records with drug research/marketing status, 370 cancer-type specific drugs were identified, bound to 100 out of all identified targets. Cell-inhibiting experiments evaluated the seven chosen drugs’ toxicity on normal and cancer cells. Student’s t-test was used to compare the difference in inhibiting rates. Pearson’s correlation analysis measures the association of targets’ pan-cancer essentialities and drugs’ inhibiting rates on normal cells. Results Profiles of applied cancer types of identified targets and drugs illustrate the effectiveness of the scoring strategy and most of them apply to cancer types less than 10. Seven drugs with no previous anti-cancer evidence were validated in 11 lung adenocarcinoma cell lines, and lower inhibition rates (from 9.4% to 44.0%) were observed in 10 normal cell lines. This difference is statistically significant (Student’s t-test, p = 0.0001). We also found that the aggregative essentiality values of potentially bound targets are correlated (Pearson's correlation analysis, p = 0.062) with the inhibiting rate of seven tested drugs on normal cells, further confirming the rationality of our supposition. Conclusions Our work, for the first time, systematically identified drugs targeting cancer-type-specific essential genes and validated the safety of such drugs to normal cells compared to lung adenocarcinoma cells. The EGKG forms a computational basis to uncover essential gene targets and drugs for specific cancer types. Also, our cell experimental result suggests that combining drugs with orthogonal essentiality may be an alternative way to improve anti-cancer effects while maintaining biocompatibility.