How well can carcinogenicity be predicted by high throughput “characteristics of carcinogens” mechanistic data?

• Published in: Regulatory toxicology and pharmacology Vol. 90; pp. 185 - 196
• Main Authors: Becker, Richard A., Dreier, David A., Manibusan, Mary K., Cox, Louis A. (Tony), Simon, Ted W., Bus, James S.
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier Inc 01-11-2017
• Subjects: Algorithms; Animals; Cancer hazard classification; Cancer prediction modeling; Carcinogenicity Tests; Carcinogens - toxicity; Data Interpretation, Statistical; High throughput screening (HTS); High-Throughput Screening Assays; Humans; IARC; Machine Learning; Models, Statistical; Neoplasms - chemically induced; Neoplasms - classification; Risk Assessment - methods; United States; United States Environmental Protection Agency; Cancer hazard classification; Cancer prediction modeling; IARC; High throughput screening (HTS)
• ISSN: 0273-2300; 1096-0295
• DOI: 10.1016/j.yrtph.2017.08.021

IARC has begun using ToxCast/Tox21 data in efforts to represent key characteristics of carcinogens to organize and weigh mechanistic evidence in cancer hazard determinations and this implicit inference approach also is being considered by USEPA. To determine how well ToxCast/Tox21 data can explicitly predict cancer hazard, this approach was evaluated with statistical analyses and machine learning prediction algorithms. Substances USEPA previously classified as having cancer hazard potential were designated as positives and substances not posing a carcinogenic hazard were designated as negatives. Then ToxCast/Tox21 data were analyzed both with and without adjusting for the cytotoxicity burst effect commonly observed in such assays. Using the same assignments as IARC of ToxCast/Tox21 assays to the seven key characteristics of carcinogens, the ability to predict cancer hazard for each key characteristic, alone or in combination, was found to be no better than chance. Hence, we have little scientific confidence in IARC's inference models derived from current ToxCast/Tox21 assays for key characteristics to predict cancer. This finding supports the need for a more rigorous mode-of-action pathway-based framework to organize, evaluate, and integrate mechanistic evidence with animal toxicity, epidemiological investigations, and knowledge of exposure and dosimetry to evaluate potential carcinogenic hazards and risks to humans.
•IARC's use of ToxCast/Tox21 HTS data has followed implied/unverified inference.•Hence, we explicitly tested how well such HTS data predict cancer classifications.•HTS data for EPA classified carcinogens were fit to 7 IARC key characteristics (KC).•Statistics & machine learning algorithms were used to analyze predictions of cancer.•Results: HTS 7 KC data were no better than chance alone in predicting cancer.