Overall, targets and biomarkers tend to have a fair degree of overlap, and this is perhaps unsurprising since many biomarkers in cancer end up becoming targets for therapy. This could also be a result of the program picking up phrases outside of a given co ntext (i.e., biomarker vs. target), though manual curation of data by subject matter experts have ensured where possible that concepts picked out are pertinent. Among biomarkers, nectin-4, SET domain containing 2 (SETD2), 55 ENO1, 56 TROP2, tumor mutation burden (TMB), 58 PCSK9, and LILRB1 appear to be growing at a fast pace while p62, COX-2 and TDO show unremarkable/negligible growth (Fig. )