title: Genotoxic mode of action predictions from a multiplexed flow cytometric assay and a machine learning approach.
abstract: Several endpoints associated with cellular responses to DNA damage as well as overt cytotoxicity were multiplexed into a miniaturized, "add and read" type flow cytometric assay. Reagents included a detergent to liberate nuclei, RNase and propidium iodide to serve as a pan-DNA dye, fluorescent antibodies against γH2AX, phospho-histone H3, and p53, and fluorescent microspheres for absolute nuclei counts. The assay was applied to TK6 cells and 67 diverse reference chemicals that served as a training set. Exposure was for 24 hrs in 96-well plates, and unless precipitation or foreknowledge about cytotoxicity suggested otherwise, the highest concentration was 1 mM. At 4- and 24-hrs aliquots were removed and added to microtiter plates containing the reagent mix. Following a brief incubation period robotic sampling facilitated walk-away data acquisition. Univariate analyses identified biomarkers and time points that were valuable for classifying agents into one of three groups: clastogenic, aneugenic, or non-genotoxic. These mode of action predictions were optimized with a forward-stepping process that considered Wald test p-values, receiver operator characteristic curves, and pseudo R(2) values, among others. A particularly high performing multinomial logistic regression model was comprised of four factors: 4 hr γH2AX and phospho-histone H3 values, and 24 hr p53 and polyploidy values. For the training set chemicals, the four-factor model resulted in 94% concordance with our a priori classifications. Cross validation occurred via a leave-one-out approach, and in this case 91% concordance was observed. A test set of 17 chemicals that were not used to construct the model were evaluated, some of which utilized a short-term treatment in the presence of a metabolic activation system, and in 16 cases mode of action was correctly predicted. These initial results are encouraging as they suggest a machine learning strategy can be used to rapidly and reliably predict new chemicals' genotoxic mode of action based on data from an efficient and highly scalable multiplexed assay.
authors.affiliation: Litron Laboratories, 3500 Winton Place, Rochester, New York.
authors.forename: Steven M
authors.initials: SM
authors.lastname: Bryce
authors.affiliation: Litron Laboratories, 3500 Winton Place, Rochester, New York.
authors.forename: Derek T
authors.initials: DT
authors.lastname: Bernacki
authors.affiliation: Litron Laboratories, 3500 Winton Place, Rochester, New York.
authors.forename: Jeffrey C
authors.initials: JC
authors.lastname: Bemis
authors.affiliation: Litron Laboratories, 3500 Winton Place, Rochester, New York.
authors.forename: Stephen D
authors.initials: SD
authors.lastname: Dertinger
mesh_terms: D000465:Algorithms; D024422:Aneugens / Q000633:toxicity; D057205:Automation, Laboratory / Q000379:methods; D015415:Biomarkers / Q000032:analysis; D002460:Cell Line / Q000187:drug effects; D004249:DNA Damage / Q000187:drug effects; D005434:Flow Cytometry / Q000379:methods*; D006657:Histones / Q000378:metabolism; D006801:Humans; D000069550:Machine Learning; D009152:Mutagenicity Tests / Q000379:methods*; D009153:Mutagens / Q000633:toxicity; D015203:Reproducibility of Results; D016159:Tumor Suppressor Protein p53 / Q000235:genetics
chemical_list: D024422:Aneugens; D015415:Biomarkers; C468783:H2AX protein, human; D006657:Histones; D009153:Mutagens; C495901:TP53 protein, human; D016159:Tumor Suppressor Protein p53
keywords: H2AX; flow cytometry; mode of action; p53; phospho-histone H3