TCGA PanCanAtlas Data Analysis Suggests Multiple Possibilities for Personalized Cancer Therapy

Abstract

Personalized cancer medicine holds promise for the future of cancer treatment. One of the keys to success is the knowledge of exact molecular alterations that drive tumorigenesis in a given patient, so that a suitable targeted therapy can be selected. However, the extent of such alterations, i.e., number of various kinds of driver mutations per patient, is still not known. We have utilized the largest database of human cancer mutations—TCGA PanCanAtlas, multiple popular algorithms for cancer driver prediction and several custom scripts to estimate the number of various kinds of driver mutations in primary tumors. We have found that there are on average 12 driver mutations per patient’s tumor, of which 0.6 are hyperactivating point mutations in oncogenes, 1.5 are amplifications of oncogenes, 0.1 have both in the same oncogene, 1.2 are inactivating point mutations in tumor suppressors, 2.1 are deletions in tumor suppressors, 0.3 have both in the same tumor suppressor, 1.5 are driver chromosome losses, 1 is driver chromosome gain, 2 are driver chromosome arm losses, and 1.5 are driver chromosome arm gains. The number of driver mutations per tumor gradually increased with age, from 6.7 for < 25 y.o. to 14.9 for > 85 y.o., and cancer stage, from 10.0 to 15.2. There was no significant difference between genders (12.0 in males vs. 11.9 in females). The number of driver mutations per tumor varied strongly between cancer types, from 1.2 in thyroid carcinoma to 23.8 in bladder carcinoma. Overall, our results provide valuable insights into the extent of driver alterations in tumors and suggest that multiple possibilities to choose a suitable targeted therapy exist in each patient.