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Event: 1636

Key Event Title

The KE title should describe a discrete biological change that can be measured. It should generally define the biological object or process being measured and whether it is increased, decreased, or otherwise definably altered relative to a control state. For example “enzyme activity, decreased”, “hormone concentration, increased”, or “growth rate, decreased”, where the specific enzyme or hormone being measured is defined. More help

Increase, Chromosomal aberrations

Short name
The KE short name should be a reasonable abbreviation of the KE title and is used in labelling this object throughout the AOP-Wiki. The short name should be less than 80 characters in length. More help
Increase, Chromosomal aberrations

Biological Context

Structured terms, selected from a drop-down menu, are used to identify the level of biological organization for each KE. Note, KEs should be defined within a particular level of biological organization. Only KERs should be used to transition from one level of organization to another. Selection of the level of biological organization defines which structured terms will be available to select when defining the Event Components (below). More help

Cell term

Further information on Event Components and Biological Context may be viewed on the attached pdf.The biological context describes the location/biological environment in which the event takes place.  For molecular/cellular events this would include the cellular context (if known), organ context, and species/life stage/sex for which the event is relevant. For tissue/organ events cellular context is not applicable.  For individual/population events, the organ context is not applicable. More help

Organ term

Further information on Event Components and Biological Context may be viewed on the attached pdf.The biological context describes the location/biological environment in which the event takes place.  For molecular/cellular events this would include the cellular context (if known), organ context, and species/life stage/sex for which the event is relevant. For tissue/organ events cellular context is not applicable.  For individual/population events, the organ context is not applicable. More help

Key Event Components

Further information on Event Components and Biological Context may be viewed on the attached pdf.Because one of the aims of the AOP-KB is to facilitate de facto construction of AOP networks through the use of shared KE and KER elements, authors are also asked to define their KEs using a set of structured ontology terms (Event Components). In the absence of structured terms, the same KE can readily be defined using a number of synonymous titles (read by a computer as character strings). In order to make these synonymous KEs more machine-readable, KEs should also be defined by one or more “event components” consisting of a biological process, object, and action with each term originating from one of 22 biological ontologies (Ives, et al., 2017; See List). Biological process describes dynamics of the underlying biological system (e.g., receptor signalling). The biological object is the subject of the perturbation (e.g., a specific biological receptor that is activated or inhibited). Action represents the direction of perturbation of this system (generally increased or decreased; e.g., ‘decreased’ in the case of a receptor that is inhibited to indicate a decrease in the signalling by that receptor).Note that when editing Event Components, clicking an existing Event Component from the Suggestions menu will autopopulate these fields, along with their source ID and description. To clear any fields before submitting the event component, use the 'Clear process,' 'Clear object,' or 'Clear action' buttons. If a desired term does not exist, a new term request may be made via Term Requests. Event components may not be edited; to edit an event component, remove the existing event component and create a new one using the terms that you wish to add. More help

Key Event Overview

AOPs Including This Key Event

All of the AOPs that are linked to this KE will automatically be listed in this subsection. This table can be particularly useful for derivation of AOP networks including the KE. Clicking on the name of the AOP will bring you to the individual page for that AOP. More help
AOP Name Role of event in AOP Point of Contact Author Status OECD Status
Oxidative DNA damage, chromosomal aberrations and mutations AdverseOutcome Carole Yauk (send email) Open for comment. Do not cite EAGMST Under Review
Ionizing energy leading to lung cancer KeyEvent Vinita Chauhan (send email) Under development: Not open for comment. Do not cite EAGMST Under Review

Stressors

This is a structured field used to identify specific agents (generally chemicals) that can trigger the KE. Stressors identified in this field will be linked to the KE in a machine-readable manner, such that, for example, a stressor search would identify this as an event the stressor can trigger. NOTE: intermediate or downstream KEs in one AOP may function as MIEs in other AOPs, meaning that stressor information may be added to the KE description, even if it is a downstream KE in the pathway currently under development.Information concerning the stressors that may trigger an MIE can be defined using a combination of structured and unstructured (free-text) fields. For example, structured fields may be used to indicate specific chemicals for which there is evidence of an interaction relevant to this MIE. By linking the KE description to a structured chemical name, it will be increasingly possible to link the MIE to other sources of chemical data and information, enhancing searchability and inter-operability among different data-sources and knowledgebases. The free-text section “Evidence for perturbation of this MIE by stressor” can be used both to identify the supporting evidence for specific stressors triggering the MIE as well as to define broad chemical categories or other properties that classify the stressors able to trigger the MIE for which specific structured terms may not exist. More help

Taxonomic Applicability

Latin or common names of a species or broader taxonomic grouping (e.g., class, order, family) can be selected from an ontology. In many cases, individual species identified in these structured fields will be those for which the strongest evidence used in constructing the AOP was available in relation to this KE. More help
Term Scientific Term Evidence Link
human Homo sapiens High NCBI
rat Rattus norvegicus High NCBI
mouse Mus musculus High NCBI

Life Stages

The structured ontology terms for life-stage are more comprehensive than those for taxa, but may still require further description/development and explanation in the free text section. More help
Life stage Evidence
All life stages High

Sex Applicability

The authors must select from one of the following: Male, female, mixed, asexual, third gender, hermaphrodite, or unspecific. More help
Term Evidence
Unspecific High

Key Event Description

A description of the biological state being observed or measured, the biological compartment in which it is measured, and its general role in the biology should be provided. For example, the biological state being measured could be the activity of an enzyme, the expression of a gene or abundance of an mRNA transcript, the concentration of a hormone or protein, neuronal activity, heart rate, etc. The biological compartment may be a particular cell type, tissue, organ, fluid (e.g., plasma, cerebrospinal fluid), etc. The role in the biology could describe the reaction that an enzyme catalyses and the role of that reaction within a given metabolic pathway; the protein that a gene or mRNA transcript codes for and the function of that protein; the function of a hormone in a given target tissue, physiological function of an organ, etc. Careful attention should be taken to avoid reference to other KEs, KERs or AOPs. Only describe this KE as a single isolated measurable event/state. This will ensure that the KE is modular and can be used by other AOPs, thereby facilitating construction of AOP networks. More help

Chromosomal aberrations describe the structural damage to chromosomes that result from breaks along the DNA and may lead to deletion, addition, or rearrangement of sections in the chromosome. Chromosomal aberrations can be divided in two major categories: chromatid-type or chromosome-type depending on whether one or both chromatids are involved, respectively. They can be further classified as rejoined or non-rejoined aberrations. Rejoined aberrations include translocations, insertions, dicentrics and rings, while unrejoined aberrations include acentric fragments and breaks (Savage, 1976). Some of these aberrations are stable (i.e., reciprocal translocations) and can persist for many years (Tucker and Preston, 1996). Others are unstable (i.e., dicentrics, acentric fragments) and decline at each cell division because of cell death (Boei et al., 1996). These events may be detectable after cell division and such damage to DNA is irreversible. Chromosomal aberrations are associated with cell death and carcinogenicity (Mitelman, 1982).

Chromosomal aberrations (CA) refer to a missing, extra or irregular portion of chromosomal DNA. These DNA changes in the chromosome structure may be produced by different double strand break (DSB) repair mechanisms (Obe et al., 2002).

There are 4 main types of CAs: deletions, duplications, translocations, and inversions. Deletions happen when a portion of the genetic material from a chromosome is lost. Terminal deletions occur when an end piece of the chromosome is cleaved. Interstitial deletions arise when a chromosome breaks in two separate locations and rejoins incorrectly, with the center piece being omitted. Duplications transpire when there is any addition or rearrangement of excess genetic material; types of duplications include transpositions, tandem duplications, reverse duplications, and displaced duplications (Griffiths et al., 2000). Translocations result from a section of one chromosome being transferred to a non-homologous chromosome (Bunting and Nussenzweig, 2013). When there is an exchange of segments on two non-homologous chromosomes, it is called a reciprocal translocation. Inversions occur in a single chromosome and involve both of the ends breaking and being ligated on the opposite ends, effectively inverting the DNA sequence.      

A fifth type of CA that can occur in the genome is the copy number variant (CNV). CNVs, which may comprise greater than 10% of the human genome (Shlien et al., 2009; Zhang et al., 2016; Hastings et al., 2009),  are deletions or duplications that can vary in size from 50 base pairs (Arlt et al., 2012; Arlt et al., 2014; Liu et al., 2013) up into the megabase pair range (Arlt et al., 2012; Wilson et al., 2015; Arlt et al., 2014; Zhang et al., 2016). CNV regions are especially enriched in large genes and large active transcription units (Wilson et al., 2015), and are of particular concern when they cause deletions in tumour suppressor genes or duplications in oncogenes (Liu et al., 2013; Curtis et al., 2012). There are two types of CNVs: recurrent and non-recurrent. Recurrent CNVs are thought to be produced through a recombination process during meiosis known as non-allelic homologous recombination (NAHR) (Arlt et al., 2012; Hastings et al., 2009). These recurrent CNVs, also called germline CNVs, could be inherited and are thus common across different individuals (Shlien et al., 2009; Liu et al., 2013). Non-recurrent CNVs are believed to be produced in mitotic cells during the process of replication. Although the mechanism is not well studied, it has been suggested that stress during replication, in particular stalling replication forks, prompt microhomology-mediated mechanisms to overcome the replication stall, which often results in duplications or deletions. Two models that have been proposed to explain this mechanism include the Fork Stalling and Template Switching (FoSTeS) model, and the Microhomology-Mediated Break-Induced Replication (MMBIR) model (Arlt et al., 2012; Wilson et al., 2015; Lee et al., 2007; Hastings et al., 2009).

CAs can be classified according to whether the chromosome or chromatid is affected by the aberration. Chromosome-type aberrations (CSAs) include chromosome-type breaks, ring chromosomes, marker chromosomes, and dicentric chromosomes; chromatid-type aberrations (CTAs) refer to chromatid breaks and chromatid exchanges (Bonassi et al., 2008; Hagmar et al., 2004). When cells are blocked at the cytokinesis step, CAs are evident in binucleated cells as micronuclei (MN; small nucleus-like structures that contain a chromosome or a piece of a chromosome that was lost during mitosis) and nucleoplasmic bridges (NPBs; physical connections that exist between the two nuclei) (El-Zein et al., 2014). Other CAs can be assessed by examining the DNA sequence, as is the case when detecting copy number variants (CNVs) (Liu et al., 2013).

OECD defines clastogens as ‘any substance that causes structural chromosomal aberrations in populations of cells or organisms’.

How It Is Measured or Detected

One of the primary considerations in evaluating AOPs is the relevance and reliability of the methods with which the KEs can be measured. The aim of this section of the KE description is not to provide detailed protocols, but rather to capture, in a sentence or two, per method, the type(s) of measurements that can be employed to evaluate the KE and the relative level of scientific confidence in those measurements. Methods that can be used to detect or measure the biological state represented in the KE should be briefly described and/or cited. These can range from citation of specific validated test guidelines, citation of specific methods published in the peer reviewed literature, or outlines of a general protocol or approach (e.g., a protein may be measured by ELISA).Key considerations regarding scientific confidence in the measurement approach include whether the assay is fit for purpose, whether it provides a direct or indirect measure of the biological state in question, whether it is repeatable and reproducible, and the extent to which it is accepted in the scientific and/or regulatory community. Information can be obtained from the OECD Test Guidelines website and the EURL ECVAM Database Service on Alternative Methods to Animal Experimentation (DB-ALM). ?

Chromosome aberrations are typically measured after cell division.

  • Micronucleus detection:
    • Micronuclei are DNA fragments that are not incorporated in the nucleus during cell division. Micronucleus induction indicates chromosomal breakage and irreversible damage.
  • Traditional (microscopy-based) micronucleus assay; OECD guidelines for both in vivo (#474) and in vitro (#487) testing are available (OECD, 2014; OECD, 2016b)
  • In vivo and in vitro flow cytometry-based, automated micronuclei measurements (Dertinger et al., 2004; Bryce et al., 2014)
  • High content imaging (Shahane et al., 2016)
    • DNA can be stained using fluorescent dyes and micronuclei can be scored in microscope images.
  • Chromosomal aberration test
    • OECD guidelines exist for both in vitro (#473) and in vivo (#475 and #483) testing (OECD, 2015; OECD, 2016a; OECD, 2016c)
    • In vitro, the cell cycle is arrested at metaphase after 1.5 cell cycle following 3-6 hour exposure
    • In vivo, the test chemically is administered as a single treatment and bone marrow is collected 18-24 hrs later (#475) while testis is collected 24-48 hrs later (#483). The cell cycle is arrested with a metaphase-arresting chemical (e.g., colchicine) 2-5 hours before cell collection.
    • Once cells are fixed and stained on microscope slides, chromosomal aberrations are scored
  • Indirect measurement of clastogenicity via protein expression:
    • Flow cytometry-based quatification of γH2AX foci and p53 protein expression (Bryce et al., 2016).
    • Prediscreen Assay– In-Cell Western -based quantification of γH2AX (Khoury et al., 2013, Khoury et al., 2016)
    • Green fluorescent protein reporter assay to detect the activation of stress signaling pathways, including DNA damage signaling including a reporter porter that is associated with DNA double strand breaks (Hendriks et al., 2012; Hendriks et al., 2016; Wink et al., 2014).
Assay Name References Description OECD Approved Assay
Fluorescent In Situ Hybridization (FISH)

Beaton et al., 2013; Pathak et al., 2017

Fluorescent assay of condensed chromosomes that can detect CAs through chromosome painting and microscopic analysis N/A
Cytokinesis Block Micronucleus (CBMN)  Assay with Microscopy Fenech, 2000 Cells are cultured with cytokinesis blocked, fixed to slides, and undergo MN quantification using microscopy Yes (No.487) 
CBMN with Imaging Flow Cytometry Rodrigues et al., 2015 Cells are cultured with cytokinesis blocked, fixed in solution, and imaged with flow cytometry to quantify MN  N/A
Dicentric Chromosome Assay (DCA) Abe et al., 2018 Cells are fixed on microscope slides, chromosomes are stained, and the number of dicentric chromosomes are quantified N/A
Array Comparative Genomic Hybridization (aCGH) or SNP Microarray

Adewoye et al., 2015; Wilson et al., 2015; Arlt et al., 2014; Redon et al., 2006; Keren, 2014; Mukherjee, 2017

CNVs are detected in single-stranded and fluorescently-tagged DNA using a microarray plate with fixed, known DNA (or SNP) probes; This method, however, is unable to detect balanced CAs, such as inversions N/A
Next Generation Sequencing (NGS): Whole Genome Sequencing (WGS) or Whole Exome Sequencing (WES)

Liu, 2013; Shen, 2016; Mukherjee, 2017

CNVs are detected by fragmenting the genome and  using NGS to sequence either the entire genome (WGS), or only the exome (WES); Challenges with this methodology include only being able to detect CNVs in exon-rich areas  if using WES, the computational investment required for the storage and analysis of these large datasets, and the lack of computational algorithms available for effectively detecting somatic CNVs N/A

Domain of Applicability

This free text section should be used to elaborate on the scientific basis for the indicated domains of applicability and the WoE calls (if provided). While structured terms may be selected to define the taxonomic, life stage and sex applicability (see structured applicability terms, above) of the KE, the structured terms may not adequately reflect or capture the overall biological applicability domain (particularly with regard to taxa). Likewise, the structured terms do not provide an explanation or rationale for the selection. The free-text section on evidence for taxonomic, life stage, and sex applicability can be used to elaborate on why the specific structured terms were selected, and provide supporting references and background information.  More help

Chromosomal aberrations indicating clastogenicity can occur in any eukaryotic or prokaryotic cell. However, dose-response curves can differ depending on the cell cycle stage when the DSB agent was introduced (Obe et al., 2002).

Evidence for Perturbation by Stressor

Regulatory Significance of the Adverse Outcome

An AO is a specialised KE that represents the end (an adverse outcome of regulatory significance) of an AOP. For KEs that are designated as an AO, one additional field of information (regulatory significance of the AO) should be completed, to the extent feasible. If the KE is being described is not an AO, simply indicate “not an AO” in this section.A key criterion for defining an AO is its relevance for regulatory decision-making (i.e., it corresponds to an accepted protection goal or common apical endpoint in an established regulatory guideline study). For example, in humans this may constitute increased risk of disease-related pathology in a particular organ or organ system in an individual or in either the entire or a specified subset of the population. In wildlife, this will most often be an outcome of demographic significance that has meaning in terms of estimates of population sustainability. Given this consideration, in addition to describing the biological state associated with the AO, how it can be measured, and its taxonomic, life stage, and sex applicability, it is useful to describe regulatory examples using this AO. More help

References

List of the literature that was cited for this KE description. Ideally, the list of references, should conform, to the extent possible, with the OECD Style Guide (https://www.oecd.org/about/publishing/OECD-Style-Guide-Third-Edition.pdf) (OECD, 2015). More help

Abe, Y et al. (2018), “Dose-response curves for analyzing of dicentric chromosomes and chromosome translocations following doses of 1000 mGy or less, based on irradiated peripheral blood samples from five healthy individuals”,  J Radiat Res. 59(1), 35-42. doi:10.1093/jrr/rrx052

Adewoye, A.B.et al. (2015), “The genome-wide effects of ionizing radiation on mutation induction in the mammalian germline”, Nat. Commun. 6:66-84. doi: 10.1038/ncomms7684.

Arlt MF, Wilson TE, Glover TW. (2012), “Replication stress and mechanisms of CNV formation”, Curr Opin Genet Dev. 22(3):204-10. doi: 10.1016/j.gde.2012.01.009.

Arlt, MF. Et al. (2014), “Copy number variants are produced in response to low-dose ionizing radiation in cultured cells”, Environ Mol Mutagen. 55(2):103-13. doi: 10.1002/em.21840.

Beaton, L. A. et al. (2013), “Investigating chromosome damage using fluorescent in situ hybridization to identify biomarkers of radiosensitivity in prostate cancer patients”, Int J Radiat Biol. 89(12): 1087-1093. doi:10.3109/09553002.2013.825060

Boei, J.J., Vermeulen, S., Natarajan, A.T. (1996), “Detection of chromosomal aberrations by fluorescence in situ hybridization in the first three postirradiation divisions of human lymphocytes”, Mutat Res, 349:127-135. Doi: 10.1016/0027-5107(95)00171-9.

Bonassi, S.  (2008),”Chromosomal aberration frequency in lymphocytes predicts the risk of cancer: results from a pooled cohort study of 22 358 subjects in 11 countries”, Carcinogenesis. 29(6):1178-83. doi: 10.1093/carcin/bgn075.

Bryce, S. et al. (2014), “Interpreting In VitroMicronucleus Positive Results: Simple Biomarker Matrix Discriminates Clastogens, Aneugens, and Misleading Positive Agents”, Environ Mol Mutagen, 55:542-555. Doi:10.1002/em.21868.

Bryce, S. et al.(2016), “Genotoxic mode of action predictions from a multiplexed flow cytometric assay and a machine learning approach”, Environ Mol Mutagen, 57:171-189. Doi: 10.1002/em.21996.

Bunting, S. F., & Nussenzweig, A. (2013), “End-joining, translocations and cancer”, Nature Reviews Cancer.13 (7): 443-454. doi:10.1038/nrc3537

Curtis, C. et al. (2012), “The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups”, Nature. 486(7403):346-52. doi: 10.1038/nature10983.

Dertinger, S.D. et al.(2004), “Three-color labeling method for flow cytometric measurement of cytogenetic damage in rodent and human blood”, Environ Mol Mutagen, 44:427-435. Doi: 10.1002/em.20075.

El-Zein, RA. Et al. (2014), “The cytokinesis-blocked micronucleus assay as a strong predictor of lung cancer: extension of a lung cancer risk prediction model”,  Cancer Epidemiol Biomarkers Prev. 23(11):2462-70. doi: 10.1158/1055-9965.EPI-14-0462.

Fenech, M. (2000), “The in vitro micronucleus technique”, Mutation Research. 455(1-2), 81-95. Doi: 10.1016/s0027-5107(00)00065-8

Griffiths, A. J. F., Miller, J. H., & Suzuki, D. T. (2000), “An Introduction to Genetic Analysis”, 7th edition. New York: W. H. Freeman. Available from: https://www.ncbi.nlm.nih.gov/books/NBK21766/

Hagmar, L. et al. (2004), “Impact of types of lymphocyte chromosomal aberrations on human cancer risk: results from Nordic and Italian cohorts”, Cancer Res. 64(6):2258-63.

Hastings PJ, Ira G & Lupski JR. (2009), “A microhomology-mediated break-induced replication model for the origin of human copy number variation”. PLoS Genet. 2009 Jan;5(1): e1000327. doi: 10.1371/journal.pgen.1000327.

Hendriks, G. et al. (2012), “The ToxTracker assay: novel GFP reporter systems that provide mechanistic insight into the genotoxic properties of chemicals”, Toxicol Sci, 125:285-298. Doi: 10.1093/toxsci/kfr281.

Hendriks, G. et al. (2016), “The Extended ToxTracker Assay Discriminates Between Induction of DNA Damage, Oxidative Stress, and Protein Misfolding”, Toxicol Sci, 150:190-203. Doi: 10.1093/toxsci/kfv323.

Keren, B. (2014),”The advantages of SNP arrays over CGH arrays”, Molecular Cytogenetics.7( 1):I31. Doi: 10.1186/1755-8166-7-S1-I31.

Khoury, L., Zalko, D., Audebert, M. (2016), “Evaluation of four human cell lines with distinct biotransformation properties for genotoxic screening”, Mutagenesis. 31:83-96. Doi: 10.1093/mutage/gev058.

Khoury, L., Zalko, D., Audebert, M. (2013), “Validation of high-throughput genotoxicity assay screening using cH2AX in-cell Western assay on HepG2 cells”, Environ Mol Mutagen, 54:737-746. Doi: 10.1002/em.21817.

Lee JA, Carvalho CM, Lupski JR. (2007). “Replication mechanism for generating nonrecurrent rearrangements associated with genomic disorders”, Cell. 131(7):1235-47. Doi: 10.1016/j.cell.2007.11.037.

Liu B. et al. (2013). “Computational methods for detecting copy number variations in cancer genome using next generation sequencing: principles and challenges”, Oncotarget. 4(11):1868-81. Doi: 10.18632/oncotarget.1537.

Mitelman, F. (1982), “Application of cytogenetic methods to analysis of etiologic factors in carcinogenesis”, IARC Sci Publ, 39:481-496.

Mukherjee. S. et al. (2017), “Addition of chromosomal microarray and next generation sequencing to FISH and classical cytogenetics enhances genomic profiling of myeloid malignancies, Cancer Genet. 216-217:128-141. doi: 10.1016/j.cancergen.2017.07.010.

Obe, G. et al. (2002), “Chromosomal Aberrations: formation, Identification, and Distribution”, Mutation Research/Fundamental and Molecular Mechanisms of Mutagenesis. 504(1-2), 17-36. Doi: 10.1016/s0027-5107(02)00076-3.

Savage, J.R. (1976), “Classification and relationships of induced chromosomal structual changes”, J Med Genet, 13:103-122. Doi: 10.1136/jmg.13.2.103.

OECD. (2016a), “In Vitro Mammalian Chromosomal Aberration Test 473.”

OECD. (2016b), “Test No. 474: Mammalian Erythrocyte Micronucleus Test. OECD Guideline for the Testing of Chemicals, Section 4.”Paris: OECD Publishing.

OECD. (2016c). Test No. 475: Mammalian Bone Marrow Chromosomal Aberration Test. OECD Guideline for the Testing of Chemicals, Section 4. Paris: OECD Publishing.

OECD. (2015). Test No. 483: Mammalian Spermatogonial Chromosomal Aberration Test. Paris: OECD Publishing.

OECD. (2014). Test No. 487: In Vitro Mammalian Cell Micronucleus Test. Paris: OECD Publishing.

Pathak, R., Koturbash, I., & Hauer-Jensen, M. (2017), “Detection of Inter-chromosomal Stable Aberrations by Multiple Fluorescence In Situ Hybridization (mFISH) and Spectral Karyotyping (SKY) in Irradiated Mice”, J Vis Exp(119). doi:10.3791/55162.

Redon, R. et al. (2006), “Global variation in copy number in the human genome”, Nature. 444(7118):444-54. 10.1038/nature05329.

Rodrigues, M. A., Beaton-Green, L. A., & Wilkins, R. C. (2016), “Validation of the Cytokinesis-block Micronucleus Assay Using Imaging Flow Cytometry for High Throughput Radiation Biodosimetry”, Health Phys. 110(1): 29-36. doi:10.1097/HP.0000000000000371

Shahane S, Nishihara K, Xia M. (2016), “High-Throughput and High-Content Micronucleus Assay in CHO-K1 Cells”, In: Zhu H, Xia M, editors. High-Throughput Screening Assays in Toxicology. New York, NY: Humana Press. p 77-85.

Shen.TW,  (2016),”Concurrent detection of targeted copy number variants and mutations using a myeloid malignancy next generation sequencing panel allows comprehensive genetic analysis using a single testing strategy”, Br J Haematol. 173(1):49-58. doi: 10.1111/bjh.13921.

Shlien A, Malkin D. (2009), “Copy number variations and cancer”, Genome Med. 1(6):62. doi: 10.1186/gm62.

Tucker, J.D., Preston, R.J. (1996), “Chromosome aberrations, micronuclei, aneuploidy, sister chromatid exchanges, and cancer risk assessment”, Mutat Res, 365:147-159.

Wilson, TE. et al.  (2015), “Large transcription units unify copy number variants and common fragile sites arising under replication stress”, Genome Res. 25(2):189-200. doi: 10.1101/gr.177121.114.

Wink, S. et al. (2014), “Quantitative high content imaging of cellular adaptive stress response pathways in toxicity for chemical safety assessment”, Chem Res Toxicol, 27:338-355.

Zhang N, Wang M, Zhang P, Huang T. 2016. Classification of cancers based on copy number variation landscapes. Biochim Biophys Acta.  1860(11 Pt B):2750-5. doi: 10.1016/j.bbagen.2016.06.003.