Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2018 Apr 1.
Published in final edited form as: Environ Mol Mutagen. 2017 Apr;58(3):146–161. doi: 10.1002/em.22083

Interlaboratory Evaluation of a Multiplexed High Information Content In Vitro Genotoxicity Assay

Steven M Bryce 1, Derek T Bernacki 1, Jeffrey C Bemis 1, Richard A Spellman 2, Maria E Engel 2, Maik Schuler 2, Elisabeth Lorge 3, Pekka T Heikkinen 4, Ulrike Hemmann 5, Véronique Thybaud 6, Sabrina Wilde 7, Nina Queisser 7, Andreas Sutter 7, Andreas Zeller 8, Melanie Guérard 8, David Kirkland 9, Stephen D Dertinger 1
PMCID: PMC5436310  NIHMSID: NIHMS857400  PMID: 28370322

Abstract

We previously described a multiplexed in vitro genotoxicity assay based on flow cytometric analysis of detergent-liberated nuclei that are simultaneously stained with propidium iodide and labeled with fluorescent antibodies against p53, γH2AX, and phospho-histone H3. Inclusion of a known number of microspheres provides absolute nuclei counts. The work described herein was undertaken to evaluate the interlaboratory transferability of this assay, commercially known as MultiFlow™ DNA Damage Kit— p53, γH2AX, Phospho-histone H3. For these experiments seven laboratories studied reference chemicals from a group of 84 representing clastogens, aneugens, and non-genotoxicants. TK6 cells were exposed to chemicals in 96-well plates over a range of concentrations for 24 hrs. At 4 and 24 hrs cell aliquots were added to the MultiFlow reagent mix and following a brief incubation period flow cytometric analysis occurred, in most cases directly from a 96-well plate via a robotic walk-away data acquisition system. Multiplexed response data were evaluated using two analysis approaches, one based on global evaluation factors (i.e., cutoff values derived from all inter-laboratory data), and a second based on multinomial logistic regression that considers multiple biomarkers simultaneously. Both data analysis strategies were devised to categorize chemicals as predominately exhibiting a clastogenic, aneugenic, or non-genotoxic mode of action (MoA). Based on the aggregate 231 experiments that were performed, assay sensitivity, specificity, and concordance in relation to a priori MoA grouping were ≥ 92%. These results are encouraging as they suggest that two distinct data analysis strategies can rapidly and reliably predict new chemicals’ predominant genotoxic MoA based on data from an efficient and transferable multiplexed in vitro assay.

Keywords: DNA damage, mode of action, γH2AX, phospho-histone H3, p53

Introduction

Genotoxicity assessment has historically involved the application of several assays, each of which is sensitive to different type(s) of DNA damage [Dearfield et al., 1991]. This is related to the fact that no single assay can reliably detect all three key types of DNA damage—gene mutation, structural chromosomal damage, and aneuploidy [Cimino, 2006]. In regard to in vitro systems, the reverse mutation assay in Salmonella bacteria, and micronucleus and chromosomal aberration assays in mammalian cells are common examples [Ames et al., 1973; Corvi et al., 2008; Galloway et al., 1994]. Results from multiple in vitro and in vivo assays are used to form an understanding about a chemical’s intrinsic genotoxic potential, and furthermore results from several assays can be indicative of a predominant genotoxic MoA (meaning primarily arising from clastogenic or aneugenic activity for the purposes of this report). However, in practice reaching firm conclusions regarding MoA is often challenging. For one, the in vitro tests in mammalian cells have been described as being prone to giving genotoxic responses that are secondary to cellular stress such as high levels of cytotoxicity or damage to certain non-DNA targets [Kirland et al., 2007]. Second, the conventional tests within the batteries are usually performed with different indicator organisms, treatment schedules, and test chemical concentrations. Finally, resources and test chemical requirements are relatively high for the series of single endpoint assays.

In the future, especially in regard to early stages of safety assessments, it would be advantageous to utilize assay(s) that provide higher throughput, require lower amounts of test chemical, and evaluate multiple biomarkers/parameters in parallel that can provide insights into MoA. These goals explain recent efforts to develop higher information content genotoxicity assays that are amenable to rapid, automatic data acquisition platforms. Diverse approaches have been investigated, and include automation of conventional genotoxicity endpoints [Avlasevich et al., 2006; Rossnerova et al., 2011], identification of toxicogenomic signatures [Li et al., 2015], use of stably transfected cells with reporters for GADD45a, p53, or other DNA damage signaling pathways [Yang and Duerksen-Hughes, 1998; Walmsley and Tate, 2012; Hendriks et al., 2012], and use of various phenotype-based biomarkers of genotoxicity, for example induction of hypo-/hyperdiploidy in mitotic cells [Muehlbauer and Schuler, 2005; Muelhbauer et al., 2008], or γH2AX [Audebert et al., 2010; Smart et al., 2011; Garcia-Canton et al., 2013; Nikolova et al., 2014; Cheung et al., 2015].

One line of investigation that has been pursued by our laboratories is the development of a multiplexed flow cytometric assay that combines several biomarkers relevant to DNA damage response pathways [Bryce et al., 2016; Bernaki et al., 2016]. This method involves a one-step, add-and-read process that efficiently prepares samples in microtiter plate-format for high throughput analysis via flow cytometry. The biomarker responses studied include phosphorylation of histone H3 (p-H3) and histone H2AX (γH2AX) to identify mitotic cells and DNA double strand break repair foci, respectively; nuclear p53 content to measure DNA damage response; frequency of 8n cells as an indicator of polyploidization; and determination of absolute nuclei counts which conveys information about treatment-related cytotoxicity. As initially described the multiplexed assay was accompanied by logistic regression analyses that provided probability scores for grouping by MoA: clastogen, aneugen, or non-genotoxicant. We recognize that some chemicals may possess both clastogenic and aneugenic properties, but we considered a systematic investigation of such mixed activities beyond the scope of the present investigation. Thus, the work described herein more simply sought to evaluate the MutliFlow assay’s ability to make an attribution about predominant genotoxic MoA. Given the high throughput and dimensionality of the assay, our present report also explores multiple approaches for efficiently analyzing and interpreting the collection of biomarker responses that are generated.

In order to assess the assay’s performance across laboratories, the current work involved seven laboratories that treated cells with chemicals chosen from a set of 84. The type of cells (human TK6) and other key experimental design considerations were kept constant. Beyond evaluating transferability aspects of the bioassay itself, the robustness of two data analysis strategies was evaluated. The results are discussed in terms of interlaboratory performance, and recommendations are made regarding approaches for working with multiplexed data of this nature.

Materials and Methods

Chemicals

The identity of the 84 selected test chemicals are provided in Table I. Our a priori categorization of genotoxic MoA is also provided. Aneugens (13) and clastogens (33) were selected from the literature and represent a broad range of genotoxic mechanisms that have generally been evaluated in multiple laboratories and in both in vitro as well as in vivo systems. The non-genotoxicants (38) were selected for their diverse range of toxic mechanisms, and many are part of an ECVAM list of agents that are considered to be negative in in vitro genotoxicity testing [Kirkland et al., 2008, 2016]. Collaborators chose a number of aneugens, clastogens and non-genotoxicants from the list, influenced by considerations that included availability, project resources, and their interest in particular chemical spaces.

Table I.

Chemicals with a priori Classification.

Chemical CAS No. Lab(s) a priori Clasification; notes References
17β-Estradiol 50-28-2 LIT Aneugen; steroid hormone Hernández et al., 2013
AMG 900 945595-80-2 LIT, BAY, PFI, SER Aneugen; pan-Aurora kinase inhibitor Payton et al., 2010
Carbendazim 10605-21-7 LIT, ORI Aneugen; mitotic spindle poison Van Hummelen et al., 1995
Colchicine 64-86-8 LIT, ORI, PFI, SER Aneugen; mitotic spindle poison Kirkland et al., 2016
Diethylstilbestrol 56-53-1 LIT, BAY, ORI, PFI, SAN Aneugen; synthetic estrogen Elhajouji et al., 1997
Flubendazole 31430-15-6 LIT Aneugen; inhibits tubulin polymerization Tweats et al., 2016
Griseofulvin 126-07-8 LIT, ORI Aneugen; mitotic spindle poison Oliver et al., 2006
Mebendazole 31431-39-7 LIT, BAY Aneugen; mitotic spindle poison Van Hummelen et al., 1995
Nocodazole 31430-18-9 LIT, BAY, PFI, ROC, SER Aneugen Attia, 2013
Noscapine 128-62-1 LIT, BAY, PFI, SER Aneugen; mitotic spindle poison Schuler et al., 1999
Paclitaxel 33069-62-4 LIT, PFI, SAN Aneugen; mitotic spindle poison Kirkland et al., 2016
Vinblastine sulfate 143-67-9 LIT, ORI, ROC Aneugen; mitotic spindle poison Kirkland et al., 2016
Vincristine sulfate 2068-78-2 LIT Aneugen; mitotic spindle poison Kondo et al., 1992
1,3-Propane sultone 1120-71-4 LIT, ORI Clastogen; alkylator Dertinger et al., 2011
4-Nitroquinoline 1-oxide 56-57-5 LIT, BAY, ORI, PFI, ROC, SER Clastogen; likely several modes of action that may include ROS Kirkland et al., 2016
5-Fluorouracil 51-21-8 LIT, BAY, ORI, PFI, ROC, SER Clastogen; anti-metabolite Kirkland et al., 2016
Aphidicolin 38966-21-1 LIT, ORI, SAN, SER Clastogen; DNA polymerase inhibitor Glover et al., 1984
Azathioprine 446-86-6 LIT Clastogen; prodrug of mercaptopurine, purine analog Henderson et al., 1993
Azidothymidine 30516-87-1 LIT, ROC Clastogen; nucleoside analog Kirkland et al., 2016
Bleomycin sulfate 9041-93-4 LIT Clastogen; radiomimetic
Camptothecin 7689-03-4 LIT, ORI, SAN, SER Clastogen; topoisomerase I inhibitor Attia et al., 2009
Chlorambucil 305-03-3 LIT Clastogen; nitrogen mustard-type alkylator Dertinger et al., 2012
Cisplatin 15663-27-1 LIT, BAY, ORI, PFI Clastogen; atypical alkylator; prepared immediately before use Kirkland et al., 2016
Cytosine arabinoside 147-94-4 LIT, ORI, PFI, ROC, SER Clastogen; anti-metabolite Kirkland et al., 2016
Doxorubicin 23214-92-8 LIT Clastogen; anthracycline Gewirtz DA, 1999
Emodin 518-82-1 LIT Clastogen; anthraquinone; topoisomerase II inhibitor Li et al., 2010
Ethyl methanesulfonate 62-50-0 LIT, BAY, ORI, PFI, ROC Clastogen; alkylator Gocke et al., 2009
Etoposide 33419-42-0 LIT, ORI, ROC, SAN Clastogen; topoisomerase II inhibitor Kirkland et al., 2016
Glycidamide 5694-00-8 LIT Clastogen; major in vivo metabolite of acrylamide Paulsson et al., 2003
Hydralazine HCl 304-20-1 LIT, PFI Clastogen; prepared in RPMI medium Martelli et al., 1995
Hydrogen peroxide 7722-84-1 LIT, BAY, SER Clastogen; ROS; prepared in RPMI medium immediately before use Kimura et al., 2013
Hydroxyurea 127-07-1 LIT, ORI, PFI, SAN, SER Clastogen; anti-metabolite, ribonucleotide reductase inhibitor Dertinger et al., 2012
Melphalan 142-82-3 LIT Clastogen; nitrogen mustard-type alkylator Dertinger et al., 2012
Menadione 58-27-5 LIT, ORI, PFI, SER Clastogen; ROS implicated Cojocel et al., 2006
Methotrexate 59-05-2 LIT, ORI, PFI, SAN, SER Clastogen; anti-metabolite Keshava et al., 1998
Methyl methanesulfonate 66-27-3 LIT, ORI Clastogen; alkylator Kirkland et al., 2016
N-Methyl-N′-nitro-N-nitrosoguanidine (MNNG) 70-25-7 LIT Clastogen; alkylator Nikolova et al., 2014
Mitomycin C 50-07-7 LIT, ORI, PFI, ROC, SAN, SER Clastogen; DNA cross-linker Kirkland et al., 2016
N-Ethyl-N-nitrosourea 759-73-9 LIT, PFI, ROC Clastogen; alkylator Kirkland et al., 2016
Olaparib 763113-22-0 LIT Clastogen; PARP inhibitor FDA approved label (Lynparza™)
Propyl gallate 121-79-9 LIT Clastogen; ROS likely Tayama and Nakagawa, 2001
Resorcinol diglycidyl ether 101-90-6 LIT Clastogen Gulati et al., 1989
Stavudine 3056-17-5 LIT Clastogen; nucleoside analog FDA approved label (Zerit®)
Temozolomide 85622-93-1 LIT, PFI, ROC Clastogen; alkylator Chinnasamy et al., 1997
Thiotepa 52-24-4 LIT Clastogen; alkylator Dertinger et al., 2012
Topotecan 123948-87-8 LIT, SAN Clastogen; topoisomerase I inhibitor Aydemir and Bilaloğlu, 2003
Alosetron HCl 122852-42-0 LIT Non-genotoxicant; 5-HT3 antagonist Kirkland et al., 2016
Amitrole 61-82-5 LIT, ORI Non-genotoxicant Kirkland et al., 2016
Anthranilic acid 118-92-3 LIT, ORI Non-genotoxicant Kirkland et al., 2016
Brefeldin A 20350-15-6 LIT, ORI Non-genotoxicant; ER-golgi transporter inhibitor, ER stress-induced apoptosis Moon et al., 2012
Caffeine 58-08-2 LIT, BAY, PFI, ORI, SER Non-genotoxicant; mitochondria-dependent apoptosis, ROS involvement likely Lu et al., 2008
Carbonyl cyanide m-chlorophenyl hydrazone (CCCP) 555-60-2 LIT, ORI, PFI, ROC Non-genotoxicant; uncoupler of oxidative phosphorylation de Graaf et al., 2004
Clofibrate 637-07-0 LIT, PFI, SAN, SER Non-genotoxicant; antilipidemic agent IARC monograph
Cyclohexanone 108-94-1 LIT, ORI Non-genotoxicant; industrial chemical Kirkland et al., 2008
Cycloheximide 66-81-9 LIT, PFI, SAN Non-genotoxicant; protein synthesis inhibitor; Youngblom et al., 1989
D-Limonene 5989-27-5 LIT Non-genotoxicant; male rat kidney tumors due to α2μ-globulin nephropathy Kirkland et al., 2016
D-Mannitol 69-65-8 LIT, BAY, ORI, PFI, SAN, SER Non-genotoxicant; polyol Kirkland et al., 2016
Dexamethasone 50-02-2 LIT, BAY, ORI, PFI, ROC, SAN, SER Non-genotoxicant; glucocorticoid receptor agonist Krishna et al., 1995
Dextrose 50-99-7 LIT, ORI, PFI Non-genotoxicant; sugar Lotz et al., 2009
Di-(2-ethylhexyl)phthalate (DEHP) 117-81-7 LIT Non-genotoxicant; organic plasticizer Kirkland et al., 2016
Diethanolamine 111-42-2 LIT Non-genotoxicant; secondary amine Kirkland et al., 2016
Erythromycin 114-07-8 LIT, ORI, SER Non-genotoxicant; antibiotic Kirkland et al., 2016
Famotidine 76824-35-6 LIT, SER Non-genotoxicant; histamine H2 receptor antagonist FDA approved label (Pepcid®)
Imatinib mesylate 152459-95-5 LIT, BAY, ORI, SER Non-genotoxicant; protein-tyrosine kinase inhibitor FDA approved label (Gleevec®)
Hexachloroethane 67-72-1 LIT, ORI Non-genotoxicant; industrial chemical Kirkland et al., 2016
Lidocaine 137-58-6 LIT, ORI, SER Non-genotoxicant; amide local anesthetic FDA approved label (Lidoderm®)
Lovastatin 75330-75-5 LIT, BAY, PFI, SAN Non-genotoxicant; HMG-CoA reductase inhibitor FDA approved label (Mevacor®)
Melamine 108-78-1 LIT, PFI, SAN Non-genotoxicant: industrial organic base Kirkland et al., 2016
Methyl carbamate 598-55-0 LIT Non-genotoxicant; industrial intermediate Kirkland et al., 2016
N-Butyl chloride 109-69-3 LIT Non-genotoxicant; fumigant Kirkland et al., 2016
Ofloxacin 82419-36-1 LIT, BAY, ORI, PFI, SAN Non-genotoxicant; fluoroquinoline antibiotic FDA approved label (Floxin®)
Paroxetine 61869-08-7 LIT, SER Non-genotoxicant; SSRI antidepressant FDA approved label (Paxil®)
Phenanthrene 85-01-8 LIT, ORI Non-genotoxicant; polycyclic aromatic hydrocarbon Kirkland et al., 2008
Phenformin HCl 834-28-6 LIT, ORI, SER Non-genotoxicant; biguanide antidiabetic Kirkland et al., 2016
Progesterone 57-83-0 LIT, ORI Non-genotoxicant; steroid hormone Kirkland et al., 2008
Pyridine 110-86-1 LIT, SER Non-genotoxicant; heterocyclic organic compound Kirkland et al., 2016
Sodium chloride 7647-14-5 LIT, BAY, ORI, PFI, SER Non-genotoxicant; prepared in RPMI medium Matsushima et al., 1999
Sodium dodecyl sulfate 151-21-3 LIT, ORI, SER Non-genotoxicant; ionic detergent NTP website
Sucrose 57-50-1 LIT, ORI, PFI Non-genotoxicant Diaz et al., 2007
Tert-butyl alcohol 75-65-0 LIT, ORI Non-genotoxicant Kirkland et al., 2016
Thapsigargin 67526-95-8 LIT, PFI, SAN Non-genotoxicant; ER stress-induced apoptosis Futami et al., 2005
Tolterodine L-tartrate 124937-52-6 LIT Non-genotoxicant; muscarinic receptor antagonist Kirkland et al., 2016
Tunicamycin 11089-65-9 LIT, PFI, SAN Non-genotoxicant; glycosylation inhibitor, ER stress-mediated apoptosis; top conc. 500 μM due to solubility Han et al., 2008
Zonisamide 68291-97-4 LIT Non-genotoxicant; sulfonamide anticonvulsant Kirkland et al., 2016
Open in a new tab

Abbreviations: LIT = Litron, BAY = Bayer, ORI = Orion, PFI = Pfizer, ROC = Roche, SAN = Sanofi, SER = Servier

Cell culture and treatments

Litron, Bayer, Sanofi, and Pfizer purchased TK6 cells from ATCC® (cat. no. CRL-8015). Servier, Roche, and Orion obtained TK6 cells from ECACC (cat. no. 13051501). Cells were grown in a humidified atmosphere at 37°C with 5% CO2. For routine culturing, the cells were maintained at or below 1 × 106 cells/mL. The culture medium consisted of RPMI 1640 with 200 μg/mL sodium pyruvate, 200 μM L-glutamine, 50 units/mL penicillin, 50 μg/mL streptomycin, and 10% v/v heat-inactivated horse serum. Note that minor variations to culture medium were utilized by several laboratories as follows: Pfizer did not supplement with additional L-glutamine; Sanofi used Glutamax instead of L-glutamine; Bayer used 10% v/v heat-inactivated fetal bovine serum, and 1% penicillin-streptomycin solution (Sigma-Aldrich, cat. no. P4333); Sanofi did not include antibiotics; Roche included kanamycin in their growth medium.

On the day of treatment, logarithmically growing cells were exposed to 20 concentrations of test chemical. Each concentration was evaluated in a single well except for solvent controls that were evaluated in 4 replicate wells. The solvent was DMSO except in several instances noted in Table I. In all cases the final solvent concentration was 1% v/v in culture. Soluble non-toxic chemicals were tested up to 1000 μM. In some cases solubility influenced top concentration, and in these instances it was the highest non-precipitating concentration that was studied. In other cases Litron’s prior experience with these chemicals suggested lower top concentrations would be appropriate, for example in the case of highly cytotoxic compounds. Regardless of top starting concentration, each successively lower concentration differed by a factor of square root 2, for example 1000, 707, 500, 354, 250 μM, etc. Note that Orion concentration spacing was slightly different—1000, 750, 500, 375, 250 μM, etc.

Treatment of cells took place after they were adjusted to 2 × 105 cells/mL and 198 μL of this cell suspension was added to each well of round-bottom 96-well plates. Addition of test chemical (2 μL/well) was immediately followed by re-incubation of the cells in a humidified atmosphere at 37°C with 5% CO2.

DNA Damage Assay

At each sampling time nuclei were prepared for cytometric analysis using reagents and instructions included in prototype MultiFlow™ DNA Damage Kit—p53, γH2AX, Phospho-Histone H3 (Litron Laboratories, Rochester, NY). The proprietary working solution was used to simultaneously digest cytoplasmic membranes, stain chromatin with a fluorescent nucleic acid dye, and label several nuclear epitopes with fluorescent antibodies. Specifically, anti-γH2AX-Alexa Fluor® 647 was used to detect DNA double strand breaks, anti-phospho-histone H3-PE served as a mitotic cell marker, and anti-p53-FITC labeled an N-terminal domain of p53 and represented a DNA damage response biomarker. Also included in the working solution were RNase and propidium iodide to provide cell cycle and polyploidization information, and counting beads to calculate absolute nuclei counts (Sphero™ Multi-Fluorophore Particles, cat. no. FP-3057-2; Spherotech, Inc., Lake Forest, IL).

Two biomarkers, γH2AX and p53, were assessed based on median fluorescence intensity, and for all graphical representation and statistical analyses they were expressed as fold-change relative to a plate-specific solvent control arithmetic mean value. Gating logic required these events to exhibit propidium iodide-associated fluorescence corresponding to 2n - 4n DNA content. In order to limit the influence that mitotic and apoptotic cells might have on these measurements, phospho-histone H3 (p-H3) positive cells and highly fluorescent γH2AX-positive events were excluded from analysis [McManus and Hendzel, 2005; Huang et al., 2006; Rogakou et al., 2000]. Region positions and gating logic are described in greater detail in Supplemental file 1.

p-H3 and polyploidy biomarkers were assessed based on their frequency among other cells. The p-H3 measurements were the proportion of p-H3-positive events that exhibited propidium iodide-associated fluorescence of 4n and greater DNA content relative to the number of total events with 2n and greater DNA content. Polyploidy was quantified as the proportion of 8n-positive events relative to the number of total events with 2n and greater DNA content. For all graphical representations and statistical analyses presented herein the p-H3 and polyploidy data were converted to fold-change relative to a plate-specific solvent control arithmetic mean value.

Fluorescent latex microspheres were included in the working dye/antibody solution at a known concentration and this allowed these particles to serve as counting beads. Nuclei to counting bead ratios were calculated for each sample, and this was used to determine absolute nuclei counts (those with 2n and greater DNA-associated propidium iodide fluorescence). These values were used to calculate 24 hr cytotoxicity values relative to plate-specific mean solvent control wells, and were expressed as %cytotoxicity, that is 100 - relative nuclei count at 24 hrs.

Flow cytometric analysis

At 4 and 24 hr time points cells were resuspended with pipetting and then 25 μL were removed from each well and added to a new 96-well plate containing 50 μL/well of pre-aliquoted working MultiFlow Kit reagent. Mixing occurred by pipetting the contents of each well several times. After at least a 30 min room temperature incubation period, flow cytometric analysis occurred using either a FACSCanto™ II flow cytometer equipped with a BD™ High Throughput Sampler (Litron, Pfizer), an LSR II flow cytometer with a BD™ High Throughput Sampler (Roche), a Miltenyi Biotec MACSQuant® Analyzer 10 flow cytometer with integrated 96-well MiniSampler device (Litron, Bayer and Servier), a BD Accuri C6 (Bayer), an Intellicyte iQue Screener (Orion), or a BD FACSVerse™ flow cytometer equipped with a BD FACS universal loader with mixing function (Sanofi). At each site stock photomultiplier tube detectors and associated optical filter sets were used to detect fluorescence emissions associated with the fluorochromes: FITC (i.e., FL1 in BD instrument parlance), PE (FL2), propidium iodide (FL3), and Alexa Fluor® 647 (FL4). Recommended mixing and fluidics parameters for the autosampler devices were described in Bryce et al., 2016. Generally speaking, across the sites, 4 hr samples provided 1000 or more 2n and greater nuclei for analysis, and 24 hr samples provided 4000 or more 2n and greater nuclei.

Data analysis: Global evaluation factors

The Mouse Lymphoma Assay utilizes a global evaluation factor (GEF) to signify when a biologically significant increase in mutant phenotype cell frequency occurs [Moore et al., 2006]. This value was based on the distribution of the negative control mutation frequency data from several proficient laboratories. In analogy to this, we evaluated whether chemical MoA determinations could be accomplished by comparing MultiFlow assay responses to biomarker- and time point-specific GEFs. To generate GEFs, aggregate fold-increase response data for each chemical studied across seven laboratories were pooled and applied to a partition platform (JMP v12.0.1, SAS Institute, Cary, NC). The chemicals were coded according to the a priori MoA information indicated in Table I, and for each biomarker and time point under consideration, the partition platform’s decision tree calculated the split value that best parses the chemicals into their respective genotoxic MoA groups. Additional details about cutoff value calculations are provided as Supplemental file 2. The resulting GEFs, shown below, are expressed as fold-increase over concurrent solvent controls.

  • GEFs for the three clastogen-responsive biormarkers 4 hr γH2AX, 4 hr p53, and 24 hr γH2AX, were 1.51-, 1.40-, and 2.11-fold, respectively;

  • GEFs for the three aneugen-responsive biomarkers 4 hr p-H3, 24 hr p-H3, and 24 hr polyploidy, were 1.71-, 1.52-, and 5.86-fold, respectively; and

  • the GEF for the pan-genotoxicant (clastogen- and aneugen-responsive) biomarker, 24 hr p53, was 1.45-fold.

Meeting or exceeding these interlaboratory-derived values identified a significant biomarker response at a particular time point. Note that as previously described [Bryce et al., 2016], assay data were restricted to those concentrations that exhibited ≥ 20% relative nuclei counts at 24 hrs. In general, the greatest differentiation of genotoxic MoA was evident with increasing chemical exposure. To normalize the responses across the diverse set of chemicals studied, and to give results stemming from higher exposures greater significance in these analyses, the partition platform’s weight function was utilized, specifically the variable %cytotoxicity at 24 hrs.

Comparisons of individual chemical and laboratory response data to GEFs were facilitated by a conditionally-formatted Excel file (Microsoft Corp., Redmond, WA), available as Supplemental file 3. To synthesize the results of these multiple comparisons and to make judgments about genotoxic MoA, the following rules were applied:

  • a clastogen call required two successive concentrations to meet or exceed the GEF for at least two out of four clastogen-sensitive biomarkers (4 hr γH2AX, 4 hr p53, 24 hr γH2AX, and 24 hr p53);

  • an aneugen call required two successive concentrations to meet or exceed the GEF for at least two out of four aneugen-sensitive biomarkers (4 hr p-H3, 24 hr p-H3, 24 hr polyploidy, and 24 hr p53);

  • in cases where both clastogen- and aneugen-sensitive biomarkers exceeded GEF(s), the category with the greater number of significant biomarkers was selected as the predominant MoA; and

  • when less than two clastogen- and two aneugen-sensitive biomarkers met or exceed the GEFs, the call was non-genotoxic under the test conditions utilized.

Data analysis: Multinomial logistic regression

In addition to GEFs, we also evaluated a second strategy for assessing whether a genotoxic response occurred. A 4-factor multinomial logistic regression model that predicts predominant genotoxic MoA based on MultiFlow response data has been previously described [Bryce et al., 2016]. The approach used herein is a variant, an ensemble of two 4-factor multinomial logistic regression models that we believe has certain advantages for detecting genotoxicants that exhibit atypical response profiles. As with the previous logistic regression model, all chemicals were assigned an a priori genotoxic MoA classification, and fold-change values at non-precipitating concentrations with ≥ 20% relative nuclei counts at 24 hrs were evaluated in JMP software’s logistic regression modeling platform (v12.0.1). Using a forward-stepping approach, a clastogen-detection model was developed based on the fold-change data obtained for the following clastogen-sensitive biomarkers: 4 hr γH2AX, 4 hr p53, 24 hr γH2AX, and 24 hr p53. The resulting algorithm provides probability scores for a clastogenic MoA at every concentration evaluated. The same strategy was used to construct an aneugen-detection model, and this was based on the fold-change data obtained for the four following aneugen-sensitive biomarkers: 4 hr p-H3, 24 hr p-H3, 24 hr polyploidy, and 24 hr p53. The resulting algorithm provides probability scores for an aneugenic MoA at every concentration evaluated. Note that these models were only constructed for the two laboratories that studied a sufficient number of chemicals to support logistic regression model building, Litron and Orion, and were created on a laboratory-specific basis. As described above for the partition platform, a weight function was utilized using the variable %cytotoxicity at 24 hrs. The full algorithms are available upon request.

The logistic regression output was synthesized into a final call regarding predominant MoA by considering both the clastogen- and aneugen-detection models’ probability scores as follows:

  • a clastogen call required two successive concentrations to exhibit clastogen probability scores ≥ 80%, or one concentration to exhibit one clastogen probability score ≥ 90%;

  • an aneugen call required two successive concentrations to exhibit aneugen probability scores ≥ 80%, or one concentration to exhibit one aneugen probability score ≥ 90%; and

  • a non-genotoxic call was defined as the absence of two successive concentrations exhibiting clastogen or aneugen probability scores ≥ 80%, and no one concentration exhibiting a clastogen or aneugen probability score ≥ 90%.

Definitions

Sensitivity and specificity values were used to assess the degree to which assay-derived MoA calls agreed with a priori designations. Therefore sensitivity is the percentage of clastogens and aneugens whose MoA call agreed with the a priori designation, and specificity is the percentage of non-genotoxicants identified as such. Overall concordance, relative to a priori MoA designations, is the percentage of clastogens, aneugens, and non-genotoxicants predicted as such. A second concordance value was determined, one based on the subset of chemicals that were studied at more than one laboratory (n = 60 chemicals; referred to herein as concordance60).

Results and Discussion

Pilot studies

Collaborators were introduced to kit reagents and flow cytometric analyses with pilot experiments that initially considered untreated cells and culminated with exposures to a prototypical clastogen and aneugen over a limited number of concentrations. These experiments served to ensure that adequate technical proficiency and instrumentation considerations were in place before larger scale experimentation was initiated. This pilot work was conducted successfully without major issues. One noteworthy early finding was that not every flow cytometer model was capable of sufficiently resuspending nuclei and counting beads. This was readily evident from lower acquired nuclei and bead counts as sampling progressed through the plate. In general, syringe-type mixing was very effective, whereas orbital shaker-type mixers were not. In the latter case, operators were instructed to manually mix wells with multichannel pipettors immediately prior to each row being analyzed (i.e., every 12 samples). This was considerably less than ideal relative to the walk-away data collection capability that instruments with effective mixing were able to provide.

Collaborators readily achieved adequate antibody labeling, and after a longer incubation time was instituted (at least 30 min instead of 10), better stability of propidium iodide-associated fluorescence was achieved. The six groups’ earliest experiments with several prototypical clastogens and aneugens demonstrated expected responses to each of the biomarkers. At this point collaborators were invited to proceed to definitive experiments whereby chemicals listed in Table I were studied over a large range of concentrations (i.e., 20).

Interlaboratory transferability

Table I indicates the specific chemicals that were studied at each laboratory. Initial, qualitative impressions of the data suggested that each of the four key biomarkers were responding as expected. One way this was assessed was by graphing the collaborators’ aggregate data against concentration (log10, μM). Figure 1 shows several biomarker responses for all chemicals and all concentrations, biomarkers that were observed to be sensitive to clastogens (4 hr γH2AX), aneugens (p-H3 and polyploidy), or both (24 hr p53). This view demonstrates that across laboratories the magnitude of effects increased with increasing exposure and there were obvious differences in the way the biomarkers were responding to clastogens versus aneugens. As reported by Bryce et al. [2016], such signatures should be useful for predicting predominant MoA.

Figure 1.

Figure 1

Open in a new tab

Fold-increase responses for four biomarkers are graphed against chemical concentration (log10). The graphs are aggregate data for 231 experiments conducted by seven laboratories. The data are coded according to genotoxic MoA: clastogens = red circles, aneugens = blue squares, and non-genotoxicants = green triangles. These graphs support the fact that these biomarkers as transferable across sites, and suggest that different response profiles among chemical classes should be valuable for elucidating genotoxic mode of action.

Individual laboratory data are graphed for one clastogen and one aneugen that were studied across several sites. Figure 2 (upper panel) shows responses to ethyl methanesulfonate, a relatively weak clastogen that none-the-less produced consistent results across laboratories. The ethyl methanesulfonate data are instructive, as they show prototypical clastogen signatures: concentration-related increases in 4 hr γH2AX, some reduction to mitotic cells (p-H3 positive) at 4 hrs, increases in p53 at 24 hrs, and little to no polyploidy induction. Figure 2 (lower panel) shows responses to noscapine, an aneugen that also showed relatively consistent response profiles across sites. Noscapine is representative of a prototypical aneugenic signature: nil to slight increases in 4 hr γH2AX, marked elevation of p-H3 at 4 hrs, increases in p53 at 24 hrs, and elevated polyploidy. This example is also useful because it highlights an important aspect of the aneugenic signature. As shown in Figure 2, two laboratories observed marked increases in polyploidy (≥ 18-fold), whereas two laboratories did not. We do not currently have an explanation for this finding, but it may be related to the fact that for some chemicals polyploidization occurs over a relatively narrow range of concentrations. In any event, as a practical matter, when considering signatures of aneugenicity it would appear to be useful to think of polyploidy as supportive of aneugenic MoA, and unwise to consider it a required feature.

Figure 2.

Figure 2

Open in a new tab

Upper panel shows biomarker response profiles for ethyl methanesulfonate-treated TK6 cells for each of 5 test sites. Lower panel shows biomarker response profiles for noscapine-treated TK6 cells for each of 4 test sites. The differences in response magnitude form the basis of the prediction algorithms described herein.

Global evaluation factors

As described above, it was clear that collaborating laboratories observed response profiles that were generally similar to those previously reported by the reference laboratory. It was therefore of interest to evaluate whether the aggregate interlaboratory data could be used to derive GEFs for each endpoint and facilitate predominant genotoxic MoA predictions. The summary data each of 231 experiments are presented in Figure 3, and the complete data set is available as a conditionally formatted spreadsheet (Supplemental file 3).

Figure 3.

Figure 3

Open in a new tab

Color-coded results summarizing the Global Evaluation Factors approach for determining chemicals’ predominant genotoxic mode of action. The data are organized according to chemical (A = aneugen, C = clastogen, and NG = non-genotoxicant) as well as laboratory (LIT = Litron, BAY = Bayer, ORI = Orion, PFI = Pfizer, ROC = Roche, SAN = Sanofi, SER = Servier). The red color is indicative of a significant clastogen-sensitive biomarker response, blue is indicative of a significant aneugen-sensitive biomarker response, violet is indicative of a pan-genotoxicant biomarker response (i.e., p53 at 24 hrs), and white indicates no significant response. The right-most columns indicate the predominant MoA call (red for clastogen, blue for aneugen, white for non-genotoxicant. As described in the Materials and Methods section, a clastogen or aneugen call requires two or more supportive biomarker responses.

As shown in Figure 3, the MultiFlow assay in conjunction with GEFs was effective at detecting the reference aneugens, with 37/37 experiments yielding the correct call. Of the 194 experiments with clastogens and non-genotoxicants, only the presumed non-genotoxicant phenformin HCl was characterized as being aneugenic in 1 of 3 experiments (it might be described as an indeterminate MoA call given the equal number of aneugen and clastogen responses).

Of 90 experiments with clastogens, 80 were correctly predicted as clastogenic MoA. As shown in Figure 3, half of the false negative results were from experiments with two chemicals, menadione and azidothymidine. In the latter case, it is conceivable that use of an alternate treatment/harvest schedule may have provided more robust responses [Kirsch-Volders et al., 2011]. Of 127 experiments with aneugens and non-genotoxicants, only 3 agents were misclassified as clastogenic, and all were presumed non-genotoxicants: imatinib mesylate (1 of 4 experiments), lovastatin (1 of 4 experiments), and phenformin HCl (indeterminate MoA in 1 of 3 experiments).

Using the GEFs approach, aggregate interlaboratory assay sensitivity and specificity values were 117/127 or 92.1% and 100/104 or 96.2%, respectively. Overall concordance with a priori MoA designations was 217/231 or 93.9%. Restricting the analysis to the subset of 60 chemicals that were studied at more than one laboratory produced a similarly high value (concordance60 = 196/207 or 94.7%). These performance metrics suggest GEFs generated across several laboratories represent one effective strategy for identifying when clastogen- as well as aneugen-sensitive biomarker responses are significantly induced.

While these performance metrics indicate the GEFs approach provides a good balance between sensitivity and specificity, there may be certain placements and roles for the assay that place a premium on maximizing sensitivity. One strategy for improving sensitivity may be to use laboratory-specific cutoff values as opposed to GEFs generated from data generated at multiple laboratories. This approach was studied by calculating cutoff values based solely on data from one laboratory (Litron). As shown by Figure 3, the GEFs approach resulted in 28/33 successful clastogen experiments conducted at Litron (azathioprine, azidothymidine, emodin, menadione, and stavudine were misidentified as non-genotoxic). When Litron-specific cutoff values are applied to Litron data, 2 of the 5 false negatives noted above became correctly identified as clastogenic (emodin and stavudine), with no change to assay specificity. This analysis suggests modest gains to sensitivity may be realized with this strategy.

Multinomial logistic regression

In addition to global- and lab-specific evaluation factors, we also evaluated multinomial logistic regression’s ability to detect clastogenic, aneugenic, and non-genotoxicant response profiles. This strategy was investigated for the two laboratories that studied enough chemicals to support construction of such models. Figure 4a shows aneugen predictions for each of 84 experiments performed at Litron. All 13 reference aneugens were correctly predicted as exhibiting a predominant aneugenic MoA. Of 71 experiments with clastogens and non-genotoxicants, none were misclassified as aneugenic.

Figure 4.

Figure 4

Figure 4

Open in a new tab

Figure 4a. Multinomial logistic regression probabilities for aneugen classification are graphed for each of 84 chemicals studied at Litron. Chemicals are coded according to genotoxic MoA: clastogens = red circles, aneugens = blue squares, and non-genotoxicants = green triangles. A series of probabilities are plotted for each chemical, with each point representing a different concentration. These data show that each of the aneugens were correctly classified by the 4-factor aneugen detection model (two successive concentrations with probabilities in excess of 0.8, or one concentration in excess of 0.9). None of the clastogens or non-genotoxicants were misclassified as aneugens.

Figure 4b. Multinomial logistic regression probabilities for clastogen classification are graphed for each of 84 chemicals studied at Litron. Chemicals are coded according to genotoxic MoA: clastogens = red circles, aneugens = blue squares, and non-genotoxicants = green triangles. A series of probabilities are plotted for each chemical, with each point representing a different concentration. These data show that 32 of 33 clastogens were correctly classified by the clastogen-detection model. None of the aneugens were misclassified as clastogens, and only 1 of 38 non-genotoxicants was misclassified as clastogenic (imatinib mesylate).

Figure 4b shows clastogen predictions for each of 84 experiments performed at Litron. Of 33 experiments with clastogens, 32 were correctly predicted as clastogenic MoA. The false negative result was for azathioprine. Of 51 experiments with aneugens and non-genotoxicants, only imatinib mesylate was mischaracterized as being clastogenic.

Figure 5a shows aneugen predictions for each of 40 experiments performed at Orion. All 5 aneugens were correctly predicted as exhibiting a predominant aneugenic MoA. Of 35 experiments with clastogens and non-genotoxicants, none were misclassified as aneugens.

Figure 5.

Figure 5

Figure 5

Open in a new tab

Figure 5a. Multinomial logistic regression probabilities for aneugen classification are graphed for each of 40 chemicals studied at Orion. Chemicals are coded according to genotoxic MoA: clastogens = red circles, aneugens = blue squares, and non-genotoxicants = green triangles. A series of probabilities are plotted for each chemical, as each represents a different concentration. These data show that each of the aneugens were correctly classified by the 4-factor aneugen detection model (two successive concentrations with probabilities in excess of 0.8, or one concentration in excess of 0.9). None of the clastogens or non-genotoxicants were misclassified as aneugens.

Figure 5b. Multinomial logistic regression probabilities for clastogen classification are graphed for each of 40 chemicals studied at Orion. Chemicals are coded according to genotoxic MoA: clastogens = red circles, aneugens = blue squares, and non-genotoxicants = green triangles. A series of probabilities are plotted for each chemical, with each point representing a different concentration. These data show that all 14 clastogens were correctly classified by the clastogen-detection model. One aneugen at one concentration showed a high clastogen probability score (griseofulvin), and only 1 of 21 non-genotoxicants was misclassified as clastogenic (dexamethasone).

Figure 5b shows clastogen predictions based on the data generated at Orion. All 14 experiments with clastogens resulted in the correct MoA call. Of 26 experiments with aneugens and non-genotoxicants, dexamethasone was mischaracterized as being clastogenic, and griseofulvin showed one elevated clastogenic probability score in addition to the several high aneugen probability scores that were observed in the aneugen-detection model.

Pooling Litron and Orion results, we find that assay sensitivity and specificity values were 64/65 or 98.5% and 57/59 or 96.6%, respectively. Even if we consider the mixed MoA result for griseofulvin as an incorrect call, overall concordance with a priori MoA designations was 120/124 or 96.6%. These performance metrics suggest multinomial logistic regression represents another effective strategy for elucidating genotoxicants’ predominant MoA.

Advice to novice laboratories

Considering the transferability of the MultiFlow methodology and companion data analysis strategies described herein, new groups may be interested in evaluating this technology. Reflecting on our collective experiences, several pieces of advice are provided. Initially, one valuable exercise is to collect negative control data over several weeks of experimentation and consider the variability occurring for each biomarker and time point. To facilitate comparisons to a reference laboratory, Litron’s aggregate solvent control data are available upon request. In terms of positive controls, we recommend beginning with reference chemicals from Table I, agents with relatively well known MoA that have been studied at multiple laboratories. Biomarker response profiles described here and elsewhere [Bryce et al., 2016] should be observed before initiating more extensive testing or beginning work with uncharacterized compounds, especially if they represent new chemical space.

Another piece of advice regards the manner by which the multiplexed data are analyzed and interpreted. Based on the current studies it would seem reasonable to begin by using the GEFs reported herein. Over time, as more chemicals and new chemical spaces are studied, these GEFs may need to be reconsidered. Whether current or even updated GEF values will be applicable to all chemical space(s) or whether they need to be tailored for some use cases is something that will require more research. Finally, whereas laboratory-specific cutoff values appeared to modestly increase assay sensitivity relative to GEFs, we speculate that there are likely more powerful ways to accomplish this, for example using chemical training sets and machine learning approach(es).

In support of this view, an analysis strategy based on multinomial logistic regression was shown to work effectively with MultiFlow data. In this case, laboratory-specific models were built that provided clastogen and aneugen probability scores that are readily applicable to making MoA predictions. Whereas GEFs or even laboratory-specific cutoff values are static, the logistic regression methodology considers the data more holistically. For instance with this approach modest fold-changes across several clastogen-sensitive responses would tend to synergize and can provide a high clastogen probability score even for weak clastogens that are not recognized by evaluating individual biomarker responses relative to GEFs. This is exemplified by Litron’s experience with menadione. Whereas GEFs or even lab-specific cutoff values failed to detect menadione genotoxicity, the logistic regression algorithms interpreted several moderate biomarker responses as a clear indication of clastogenicity (> 90% probability) (see Figure 6). Although machine learning approaches such as this hold much promise, it may be difficult for some groups to institute them, depending on biostatistical support and other considerations. It is also worth pointing out that there are alternatives to logistic regression that would likely be useful for predicting predominant genotoxic MoA. It is beyond the scope of this report to describe the multitude of machine learning approaches that we expect would be effective. In support of the suggestion that alternate prediction tools would likely have utility, one of the collaborating laboratories used a Random Forest technique to generate a high performing MoA prediction model.

Figure 6.

Figure 6

Open in a new tab

γH2AX (4 hr) and p53 (4 hr and 24 hr) responses are graphed for menadione-treated TK6 cells. Whereas the GEFs and lab-specific cutoff value approaches failed to recognize these modest responses as clastogenic, the multinomial logistic regression model interpreted the three clastogen-responsive biomarker results as clear evidence of clastogenic activity (probability > 90%).

Conclusions

An efficient, add-and-read, multiplexed genotoxicity assay based on γH2AX, p-H3, p53, and polyploidization biomarkers was observed to be transferable across laboratories. Cutoff values, generated from individual or multiple laboratories’ data, and multinomial logistic regression, were all found to be effective at synthesizing the results into predictions of predominant genotoxic MoA.

Future work will be needed to extend these promising results to the evaluation of additional chemical spaces and molecular mechanisms of action. In parallel with the benchtop laboratory work, it will be important to revisit the performance of the data analysis strategies presented herein. Logistic regression and other analysis strategies should also be studied for their ability to detect chemicals with mixed modes of genotoxic action, as well as their ability to include other data streams, when available, that contribute to MoA predictions. Finally, it should be acknowledged that while the current work focused on assay transferability and MoA determinations, it would be highly desirable to evaluate approaches for using these same biomarker response data to more thoroughly evaluate dose-response relationships and genotoxic potency.

Supplementary Material

Supp fileS1
Supp fileS2
Supp fileS3

Acknowledgments

The authors would like to thank the following for their technical support: Birgit Meyerhöfer, Michael Ruppert, Claudia Hempt, Miriam Darmbowsky, Merja Valovirta, Valérie Naëssens, Edith Philipp, Olivier Kuster, Sandrine De Bernouïs and Hélène Julien. The following colleagues provided intellectual support that included test chemical recommendations and/or suggestions regarding experimental design or data analysis strategies: Kristine Witt, Stephanie Smith-Roe, Raymond Tice, Marian Raschke, and David Potter.

This work was funded by a grant from the National Institute of Health/National Institute of Environmental Health Sciences (NIEHS; grant no. R44ES024039). The contents are solely the responsibility of the authors, and do not necessarily represent the official views of the NIEHS.

Footnotes

AUTHOR CONTRIBUTIONS

All authors contributed to experimental design. SDD provided a first draft of the manuscript, and all authors contributed to the substantial revisions that followed.

CONFLICT OF INTEREST STATEMENT

SMB, DB, JCB, and SDD are employed by Litron Laboratories. Litron has a patent covering the flow cytometry-based assay described in this manuscript and sells a commercial kit based on these procedures: MultiFlow™ DNA Damage Kit—p53, γH2AX, Phospho-Histone H3. DK serves as a consultant to Litron Laboratories.

References

  1. Ames BN, Durston WE, Yamasaki E, Lee FD. Carcinogens are mutagens: A simple test system combining liver homogenates for activation and bacteria for detection. PNAS. 1973;70:2281–2285. doi: 10.1073/pnas.70.8.2281. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Attia SM, Aleisa AM, Bakheet SA, Al-Yahya AA, Al-Rejaie SS, Ashour AE, Al-Shabanah OA. Molecular cytogenetic evaluation of the mechanism of micronucleus formation induced by camptothecin, topotecan, and irinotecan. Environ Mol Mutagen. 2009;50:145–151. doi: 10.1002/em.20460. [DOI] [PubMed] [Google Scholar]
  3. Attia SM. Molecular cytogenetic evaluation of the mechanism of genotoxic potential of amsacrine and nocodazole in mouse bone marrow cells. J Appl Toxicol. 2013;33:426–433. doi: 10.1002/jat.1753. [DOI] [PubMed] [Google Scholar]
  4. Audebert M, Riu A, Jacques C, Hillenweck A, Jamin EL, Zalko D, Cravedi JP. Use of the γH2AX assay for assessing the genotoxicity of polycyclic aromatic hydrocarbons in human cell lines. Toxicol Lett. 2010;199:182–192. doi: 10.1016/j.toxlet.2010.08.022. [DOI] [PubMed] [Google Scholar]
  5. Avlasevich SL, Bryce SM, Cairns SE, Dertinger SD. In vitro micronucleus scoring by flow cytometry: differential staining of micronuclei versus apoptotic and necrotic chromatin enhances assay reliability. Environ Mol Mutagen. 2006;47:56–66. doi: 10.1002/em.20170. [DOI] [PubMed] [Google Scholar]
  6. Aydemir N, Bilaloğlu R. Genotoxicity of two anticancer drugs, gemcitabine and topotecan, in mouse bone marrow in vivo. Mutat Res. 2003;537:43–51. doi: 10.1016/s1383-5718(03)00049-4. [DOI] [PubMed] [Google Scholar]
  7. Bernacki DT, Bryce SM, Bemis JC, Kirkland D, Dertinger SD. γH2AX and p53 responses in TK6 cells discriminate promutagens and nongenotoxicant in the presence of rat liver S9. Environ Mol Mutagen. 2016;57:546–558. doi: 10.1002/em.22028. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Bryce SM, Bernacki DT, Bemis JC, Dertinger SD. Genotoxic mode of action predictions from a multiplexed flow cytometric assay and a machine learning approach. Environ Mol Mutagen. 2016;57:171–189. doi: 10.1002/em.21996. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Cheung JR, Dickinson DA, Moss J, Schuler MJ, Spellman RA, Heard PL. Histone markers identify the mode of action for compounds positive in the TK6 micronucleus assay. Mutat Res. 777:7–16. doi: 10.1016/j.mrgentox.2014.11.002. [DOI] [PubMed] [Google Scholar]
  10. Chinnasamy N, Rafferty JA, Hickson I, Ashby J, Tinwell H, Margison GP, Dexter TM, Fairbairn LJ. O6-benzylguanine potentiates the in vivo toxicity and clastogenicity of temozolomide and BCNU in mouse bone marrow. Blood. 1997;89:1566–73. [PubMed] [Google Scholar]
  11. Cimino MC. Comparative overview of current international strategies and guidelines for genetic toxicology testing for regulatory purposes. Environ Mol Mutagen. 2006;47:362–390. doi: 10.1002/em.20216. [DOI] [PubMed] [Google Scholar]
  12. Cojocel C, Novotny L, Vachalkova A. Mutagenic and carcinogenic potential of menadione. Neoplasma. 2006;53:316–323. [PubMed] [Google Scholar]
  13. Corvi R, Albertini S, Hartung T, Hoffmann S, Maurici D, Pfuhler S, van Benthem J, Vanparys P. ECVAM retrospective validation of the in vitro micronucleus test (MNT) Mutagenesis. 2008;23:271–283. doi: 10.1093/mutage/gen010. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Dearfield KL, Auletta AE, Cimino MC, Moore MM. Considerations in the U.S. Environmental Protection Agency’s testing approach for mutagenicity. Mutat Res. 1991;258:259–283. doi: 10.1016/0165-1110(91)90012-k. [DOI] [PubMed] [Google Scholar]
  15. de Graaf AO, van den Heuvel LP, Dijkman HB, de Abreu RA, Birkenkamp KU, de White T, van der Reijden BA, Smeitink JA, Jansen JH. Bcl-2 prevents loss of mitochondria in CCCP-induced apoptosis. Exp Cell Res. 2004;299:533–540. doi: 10.1016/j.yexcr.2004.06.024. [DOI] [PubMed] [Google Scholar]
  16. Dertinger SD, Phonethepswath S, Weller P, Avlasevich S, Torous DK, Mereness JA, Bryce SM, Bemis JC, Bell S, Portugal S, et al. Interlaboratory Pig-a gene mutation assay trial: Studies of 1,3-propane sultone with immunomagnetic enrichment of mutant erythrocytes. Environ Mol Mutagen. 2011;52:748–755. doi: 10.1002/em.20671. [DOI] [PubMed] [Google Scholar]
  17. Dertinger SD, Phonethepswath S, Avlasevich SL, Torous DK, Mereness J, Bryce SM, Bemis JC, Bell S, Weller P, MacGregor JT. Efficient monitoring of in vivo Pig-a gene mutation and chromosomal damage: Summary of 7 published studies and results from 11 new reference compounds. Toxicol Sci. 2012;130:328–348. doi: 10.1093/toxsci/kfs258. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Diaz D, Scott A, Carmichael P, Shi W, Costales C. Evaluation of an automated in vitro micronucleus assay in CHO-K1 cells. Mutat Res. 2007;630:1–13. doi: 10.1016/j.mrgentox.2007.02.006. [DOI] [PubMed] [Google Scholar]
  19. Floxin [package insert] Raritan, NJ: Ortho-McNeil; 2008. http://www.accessdata.fda.gov/drugsatfda_docs/label/2008/019735s059lbl.pdf. [Google Scholar]
  20. Futami T, Miyagishi M, Taira K. Identification of a network involved in thapsigargin-induced apoptosis using a library of small interfering RNA expression vectors. J Biol Chem. 2005;280:826–831. doi: 10.1074/jbc.M409948200. [DOI] [PubMed] [Google Scholar]
  21. Galloway SM, Aardema MJ, Ishidate M, Jr, Ivett JL, Kirkland DJ, Morita T, Mosesso P, Sofuni T. Report from working group on in vitro tests for chromosomal aberrations. Mutat Res. 1994;312:241–261. doi: 10.1016/0165-1161(94)00012-3. [DOI] [PubMed] [Google Scholar]
  22. Garcia-Canton C, Anadon A, Meredith C. Assessment of the in vitro γH2AX assay by high content screening as a novel genotoxicity test. Mutat Res. 2013;757:158–166. doi: 10.1016/j.mrgentox.2013.08.002. [DOI] [PubMed] [Google Scholar]
  23. Gewirtz DA. A critical evaluation of the mechanisms of action proposed for the antitumor effects of the anthracycline antibiotics adriamycin and daunorubicin. Biochem Pharmacol. 1999;57:727–741. doi: 10.1016/s0006-2952(98)00307-4. [DOI] [PubMed] [Google Scholar]
  24. Gleevec [package insert] East Hanover, NJ: Novartis; 2001. http://www.accessdata.fda.gov/drugsatfda_docs/label/2008/021588s024lbl.pdf. [Google Scholar]
  25. Gocke E, Bürgin H, Müller L, Pfister T. Literature review on the genotoxicity, reproductive toxicity, and carcinogenicity of ethyl methanesulfonate. Toxicol Lett. 2009;190:254–265. doi: 10.1016/j.toxlet.2009.03.016. [DOI] [PubMed] [Google Scholar]
  26. Gulati DK, Witt K, Anderson B, Zeiger E, Shelby MD. Chromosome aberration and sister chromatid exchange tests in Chinese Hamster Ovary cells in vitro III: Results with 27 chemicals. Environ Mol Mutagen. 1989;13:133–193. doi: 10.1002/em.2850130208. [DOI] [PubMed] [Google Scholar]
  27. Han C, Nam MK, Park HJ, Seong YM, Kang S, Rhim H. Tunicamycin-induced ER stress upregulates the expression of mitochondrial HtrA2 and promotes apoptosis through the cytosolic release of HtrA2. J Microbiol Biotechnol. 2008;18:1197–1202. [PubMed] [Google Scholar]
  28. Henderson L, Fedyk J, Windebank S, Smith M. Induction of micronuclei in rat bone marrow and peripheral blood following acute and subchronic administrate of azathioprine. Mutat Res. 1993;291:79–85. doi: 10.1016/0165-1161(93)90019-v. [DOI] [PubMed] [Google Scholar]
  29. Hendriks G, Atallah M, Morolli B, Calléja F, Ras-Verloop N, Huijskens I, Raamsman M, van de Water B, Vrieling H. The ToxTracker assay: novel GFP reporter systems that provide mechanistic insight into the genotoxic properties of chemicals. Toxicol Sci. 2012;125:285–298. doi: 10.1093/toxsci/kfr281. [DOI] [PubMed] [Google Scholar]
  30. Huang X, Halicka HD, Traganos F, Tanaka T, Kurose A, Darzynkiewicz Cytometric assessment of DNA damage in relation to cell cycle phase and apoptosis. Cell Prolif. 2006;38:223–243. doi: 10.1111/j.1365-2184.2005.00344.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. IARC monograph, Clofibrate. http://monographs.iarc.fr/ENG/Monographs/vol66/mono66-17.pdf.
  32. Keshava C, Keshava N, Whong WZ, Nath J, Ong TM. Inhibition of methotrexate-induced chromosomal damage by folinic acid in V79 cells. Mutat Res. 1998;397:221–228. doi: 10.1016/s0027-5107(97)00216-9. [DOI] [PubMed] [Google Scholar]
  33. Kimura A, Miyata A, Honma M. A combination of in vitro comet assay and micronucleus test using human lymphoblastoid TK6 cells. Mutagenesis. 2013;28:583–90. doi: 10.1093/mutage/get036. [DOI] [PubMed] [Google Scholar]
  34. Kirkland D, Pfuhler S, Tweats D, Aardema M, Corvi R, Darroudi F, Elhajouji A, Glatt H, Hastwell P, Hayashi M, Kasper P, Kirchner S, Lynch A, Marzin D, Maurici D, Meunier J-R, Müller L, Nohynek G, Parry J, Parry E, Thybaud V, Tice R, van Benthem J, Vanparys P, White P. How to reduce false positive results when undertaking in vitro genotoxicity testing and thus avoid unnecessary follow-up animal tests: Report of an ECVAM workshop. Mutat Res. 2007;628:31–55. doi: 10.1016/j.mrgentox.2006.11.008. [DOI] [PubMed] [Google Scholar]
  35. Kirkland D, Kasper P, Müller L, Corvi R, Speit G. Recommended lists of genotoxic and non-genotoxic chemicals for assessment of the performance of new or improved genotoxicity tests: a follow-up to an ECVAM workshop. Mutat Res. 2008;653:99–108. doi: 10.1016/j.mrgentox.2008.03.008. [DOI] [PubMed] [Google Scholar]
  36. Kirkland D, Kasper P, Martus H-J, Müller L, van Benthem J, Madia F, Corvi R. Updated recommended lists of genotoxic and non-genotoxic chemicals for assessment of the performance of new or improved genotoxicity tests. Mutat Res. 2016;795:7–30. doi: 10.1016/j.mrgentox.2015.10.006. [DOI] [PubMed] [Google Scholar]
  37. Kirsch-Volders M, Decordier I, Elhajouji A, Plas G, Aardema M, Fenech M. In vitro genotoxicity testing using the micronucleus assay in cell lines, human lymphocytes and 3D human skin models. Mutagenesis. 2011;26:177–184. doi: 10.1093/mutage/geq068. [DOI] [PubMed] [Google Scholar]
  38. Krishna G, Urda G, Tefera W, Lalwani ND, Theiss J. Simultaneous evaluation of dexamethasone-induced apoptosis and micronuclei in rat primary spleen cell cultures. Mutat Res. 1995;332:1–8. doi: 10.1016/0027-5107(95)00075-3. [DOI] [PubMed] [Google Scholar]
  39. Kondo Y, Honda S, Nakajima M, Miyahana K, Hayashi M, Shinagawa Y, Sato S, Inoue K, Nito S, Ariyuki F. Micronucleus test with vincristine sulfate and colchicine in peripheral blood reticulocytes of mice using acridine orange supravital staining. Mutat Res. 1992;278:187–191. [PubMed] [Google Scholar]
  40. Li HH, Hyduke DR, Chen R, Heard P, Yauk CL, Aubrecht J, Fornace AJ., Jr Development of a toxicogenomics signature for genotoxicity using a dose-optimization and informatics strategy in human cells. Environ Mol Mutagen. 2015;56:505–519. doi: 10.1002/em.21941. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Li Y, Luan Y, Qi X, Li M, Gong L, Xue X, Wu X, Wu Y, Chen M, Xing G, Yao J, Ren J. Emodin triggers DNA double-strand breaks by stabilizing topoisomerase II-DNA cleavage complexes and by inhibiting ATP hydrolysis of topoisomerase II. Toxicol Sci. 2010;118:435–443. doi: 10.1093/toxsci/kfq282. [DOI] [PubMed] [Google Scholar]
  42. Lidoderm [package insert] Chad’s Ford, PA: Endo Pharmaceuticals; 2004. http://www.accessdata.fda.gov/drugsatfda_docs/label/2005/020612s007lbl.pdf. [Google Scholar]
  43. Lotz AS, Havla JB, Richter E, Frölich K, Staudenmaier R, Hagen R, Kleinsasser NH. Cytotoxic and genotoxic effects of matrices for cartilage tissue engineering. Toxicol Lett. 2009;190:128–33. doi: 10.1016/j.toxlet.2009.06.880. [DOI] [PubMed] [Google Scholar]
  44. Lu P-Z, Lai C-Y, Chan W-H. Caffeine induces cell death via activation of apoptotic signal and inactivation of survival signal in human osteoblasts. Int J Mol Sci. 2008;9:698–718. doi: 10.3390/ijms9050698. [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Lynparza [package insert] Wilmington, DE: AstraZeneca Pharmaceuticals LP; 2014. http://www.accessdata.fda.gov/drugsatfda_docs/label/2014/206162lbl.pdf. [Google Scholar]
  46. Muehlbauer PA, Schuler MJ. Detection of numerical chromosome aberrations by flow cytometry: A novel process for identifying aneugenic agents. Mutat Res. 2005;585:156–169. doi: 10.1016/j.mrgentox.2005.05.002. [DOI] [PubMed] [Google Scholar]
  47. Muehlbauer PA, Spellman RA, Gunther WC, Sanok KE, Wiersch CJ, O’Lone SD, Dobo KL, Schuler MJ. Improving dose selection and identification of aneugens in the in vitro chromosome aberration test by integration of flow cytometry-based methods. Environ Mol Mutagen. 2008;49:318–327. doi: 10.1002/em.20387. [DOI] [PubMed] [Google Scholar]
  48. Martelli A, Allavena A, Campart GB, Canonero R, Ghia M, Mattioli F, Mereto E, Robbiano L, Brambilla G. In vitro and in vivo testing of hydralazine genotoxicity. J Pharmacol Exp Ther. 1995;273:113–120. [PubMed] [Google Scholar]
  49. Matsushima T, Hayashi M, Matsuoka A, Ishidate M, Jr, Miura KF, Shimizu H, Suzuki Y, Morimoto K, Ogura H, Mure K, Koshi K, Sofuni T. Validation study of the in vitro micronucleus test in a Chinese hamster lung cell line (CHL/IU) Mutagenesis. 1999;14:569–80. doi: 10.1093/mutage/14.6.569. [DOI] [PubMed] [Google Scholar]
  50. McManus KJ, Hendzel MJ. ATM-dependent DNA damage-independent mitotic phosphorylation of H2AX in normally growing mammalian cells. Molecular Biology of the Cell. 2005;16:5013–5025. doi: 10.1091/mbc.E05-01-0065. [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. Mevacor [package insert] Whitehouse Station, NJ: Merck & Co., Inc; 2012. http://www.accessdata.fda.gov/drugsatfda_docs/label/2012/019643s085lbl.pdf. [Google Scholar]
  52. Moon JL, Kim SY, Shin SW, Park J-W. Regulation of brefeldin A-induced ER stress and apoptosis by mitochondrial NADP+-dependent isocitrate dehydrogenase. Biochem Biophys Res Commun. 2012;417:760–764. doi: 10.1016/j.bbrc.2011.12.030. [DOI] [PubMed] [Google Scholar]
  53. Moore MM, Honma M, Clements J, Bolcsfoldi G, Burlinson B, Cifone M, Clarke J, Delongchamp R, Durward R, Fellows M, Gollapudi B, Hou S, Jenkinson P, Lloyd M, Majeska J, Myhr B, O’Donovan M, Omori T, Riach C, San R, Stankowski LF, Jr, Thakur A, Van Goethem F, Wakuri S, Yoshimura I. Mouse lymphoma thymidine kinase gene mutation assay: Follow-up meeting of the International Workshop on Genotoxicity Tests—Aberdeen, Scotland, 2003—Assay acceptance criteria, positive controls, and data evaluation. Environ Mol Mutagen. 2006;47:1–5. doi: 10.1002/em.20159. [DOI] [PubMed] [Google Scholar]
  54. National Toxicology Program Report, Sodium Dodecyl Sulfate. http://ntp.niehs.nih.gov/testing/status/agents/ts-10604-g.html.
  55. Nikolova T, Dvorak M, Jung F, Adam I, Krämer E, Gerhold-Ay A, Kaina B. γH2AX assay for genotoxic and nongenotoxic agents: Comparison of H2AX phosphorylation with cell death response. Toxicol Sci. 2014;140:103–117. doi: 10.1093/toxsci/kfu066. [DOI] [PubMed] [Google Scholar]
  56. Oliver J, Meunier JR, Awogi T, Elhajouji A, Ouldelhkim MC, Bichet N, Thybaud V, Lorenzon G, Marzin D, Lorge E. SFTG international collaborative study on in vitro micronucleus test V. Using L5178Y cells. Mutat Res. 2006;607:125–152. doi: 10.1016/j.mrgentox.2006.04.004. [DOI] [PubMed] [Google Scholar]
  57. Paulsson B, Kotova N, Grawé J, Henderson A, Granath F, Golding B, Törnqvist M. Induction of micronuclei in mouse and rat by glycidamide, genotoxic metabolite of acrylamide. Mutat Res. 2003;535:15–24. doi: 10.1016/s1383-5718(02)00281-4. [DOI] [PubMed] [Google Scholar]
  58. Paxil [package insert] Research Triangle Park, NC: GlaxoSmithKline; 2011. http://www.accessdata.fda.gov/drugsatfda_docs/label/2011/020031s058s066,020710s022s030lbl.pdf. [Google Scholar]
  59. Payton M, Bush TL, Chung G, Ziegler B, Eden P, McElroy P, Ross S, Cee VJ, Deak HL, Hodous BL, Nguyen HN, et al. Preclinical evaluation of AMG 900, a novel potent and highly selective pan-aurora kinase inhibitor with activity in taxane-resistant tumor cell lines. Cancer Res. 2010;70:9846–9854. doi: 10.1158/0008-5472.CAN-10-3001. [DOI] [PubMed] [Google Scholar]
  60. Pepcid [package insert] Whitehouse Station, NJ: Merck & Co, Inc; 2011. http://www.accessdata.fda.gov/drugsatfda_docs/label/2011/019462s037lbl.pdf. [Google Scholar]
  61. Rogakou EP, Nieves-Neira W, Boon C, Pommier Y, Bonner WM. Initiation of DNA fragmentation during apoptosis induces phosphorylation of H2AX histone at serine 139. J Biol Chem. 2000;275:9390–9395. doi: 10.1074/jbc.275.13.9390. [DOI] [PubMed] [Google Scholar]
  62. Rossnerova A, Spatova M, Schunck C, Sram RJ. Automated scoring of lymphocyte micronuclei by the MetaSystems Metafer image cytometry system and its application in studies of human mutagen sensitivity and biodosimetry of genotoxin exposure. Mutagenesis. 2011;26:169–175. doi: 10.1093/mutage/geq057. [DOI] [PubMed] [Google Scholar]
  63. Schuler M, Muehlbauer P, Guzzie P, Eastmond DA. Noscapine hydrochloride disrupts the mitotic spindle in mammalian cells and induces aneuploidy as well as polyploidy in cultured human lymphocytes. Mutagenesis. 1999;14:51–56. doi: 10.1093/mutage/14.1.51. [DOI] [PubMed] [Google Scholar]
  64. Smart DJ, Ahmedi KP, Harvey JS, Lynch AM. Genotoxicity screening via the γH2AX by flow assay. Mutat Res. 2011;715:25–31. doi: 10.1016/j.mrfmmm.2011.07.001. [DOI] [PubMed] [Google Scholar]
  65. Tayama S, Nakagawa Y. Cytogenetic effects of propyl gallate in CHO-K1 cells. Mutat Res. 2001;498:117–127. doi: 10.1016/s1383-5718(01)00272-8. [DOI] [PubMed] [Google Scholar]
  66. Tweats DJ, Johnson GE, Scandale I, Whitwell J, Evans DB. Genotoxicity of flubendazole and its metabolites in vitro and the impact of a new formulation on in vivo aneugenicity. Mutagenesis. 2016;31:309–321. doi: 10.1093/mutage/gev070. [DOI] [PMC free article] [PubMed] [Google Scholar]
  67. Van Hummelen P, Elhajouji A, Kirsch-Volders M. Clastogenic and aneugenic effects of three benzimidazole derivates in the in vitro micronucleus test using human lymphocytes. Mutagenesis. 1995;10:23–29. doi: 10.1093/mutage/10.1.23. [DOI] [PubMed] [Google Scholar]
  68. Walmsley RM, Tate M. The GADD45a-GFP GreenScreen HC assay. Methods Mol Biol. 2012;817:231–250. doi: 10.1007/978-1-61779-421-6_12. [DOI] [PubMed] [Google Scholar]
  69. Yang J, Duerksen-Hughes P. A new approach to identifying genotoxic carcinogens: p53 induction as an indicator of genotoxic damage. Carcinogenesis. 1998;19:1117–1125. doi: 10.1093/carcin/19.6.1117. [DOI] [PubMed] [Google Scholar]
  70. Youngblom JH, Wiencke JK, Wolff S. Inhibition of the adaptive response of human lymphocytes to very low doses of ionizing radiation by the protein synthesis inhibitor cycloheximide. Mutat Res. 1989;227:257–261. doi: 10.1016/0165-7992(89)90107-3. [DOI] [PubMed] [Google Scholar]
  71. Zerit [packet insert] Bristol-Myers Squibb Virology; Princeton, NJ: 2002. http://www.accessdata.fda.gov/drugsatfda_docs/label/2002/20412S017.pdf. [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supp fileS1
NIHMS857400-supplement-Supp_fileS1.docx (629.1KB, docx)
Supp fileS2
NIHMS857400-supplement-Supp_fileS2.docx (746.2KB, docx)
Supp fileS3
NIHMS857400-supplement-Supp_fileS3.xlsx (911.7KB, xlsx)

RESOURCES