EPA/630/P-03/001B

March 2005

Guidelines for Carcinogen 

Risk Assessment 

Risk Assessment Forum 

U.S. Environmental Protection Agency 

Washington, DC 
DISCLAIMER 

Mention of trade names or commercial products does not constitute endorsement or 

recommendation for use. 
CONTENTS

1. INTRODUCTION ........................................................ 1-1

1.1. PURPOSE AND SCOPE OF THE GUIDELINES ........................ 1-1

1.2. ORGANIZATION AND APPLICATION OF THE GUIDELINES ........... 1-3

1.2.1. Organization .............................................. 1-3

1.2.2. Application ............................................... 1-5

1.3. KEY FEATURES OF THE CANCER GUIDELINES ..................... 1-7

1.3.1. Critical Analysis of Available Information as the Starting Point for

Evaluation ............................................... 1-7

1.3.2. Mode of Action ........................................... 1-10

1.3.3. Weight of Evidence Narrative ............................... 1-11

1.3.4. Dose-response Assessment .................................. 1-12

1.3.5. Susceptible Populations and Lifestages ........................ 1-13

1.3.6. Evaluating Risks from Childhood Exposures .................... 1-15

1.3.7. Emphasis on Characterization ............................... 1-21

2. HAZARD ASSESSMENT .................................................. 2-1

2.1. OVERVIEW OF HAZARD ASSESSMENT AND CHARACTERIZATION . . . 2-1

2.1.1. Analyses of Data ........................................... 2-1

2.1.2. Presentation of Results ...................................... 2-1

2.2. ANALYSIS OF TUMOR DATA ..................................... 2-2

2.2.1. Human Data .............................................. 2-3

2.2.1.1. Assessment of evidence of carcinogenicity from human data 2-4

2.2.1.2. Types of studies .................................... 2-5

2.2.1.3. Exposure issues .................................... 2-6

2.2.1.4. Biological markers ................................. 2-7

2.2.1.5. Confounding actors ................................. 2-8

2.2.1.6. Statistical considerations. ............................ 2-9

2.2.1.6.1. Likelihood of observing an effect ............... 2-9

2.2.1.6.2. Sampling and other bias issues ................ 2-10

2.2.1.6.3. Combining statistical evidence across studies .... 2-11

2.2.1.7. Evidence for Causality .............................. 2-11

2.2.2. Animal Data ............................................. 2-15

2.2.2.1. Long-term Carcinogenicity Studies .................... 2-15

2.2.2.1.1. Dosing issues ............................. 2-16

2.2.2.1.2. Statistical considerations ..................... 2-19

2.2.2.1.3. Concurrent and historical controls ............. 2-20

2.2.2.1.4. Assessment of evidence of carcinogenicity from longterm animal studies ........................... 2-21

2.2.2.1.5. Site concordance ........................... 2-22

2.2.2.2. Perinatal Carcinogenicity Studies ..................... 2-22

2.2.2.3. Other Studies ..................................... 2-24

2.2.3. Structural Analogue Data ................................... 2-25

2.3. ANALYSIS OF OTHER KEY DATA ................................ 2-25
2.3.1. Physicochemical Properties ................................. 2-25

2.3.2. Structure-Activity Relationships .............................. 2-26

2.3.3. Comparative Metabolism and Toxicokinetics ................... 2-27

2.3.4. Toxicological and Clinical Findings ........................... 2-29

2.3.5. Events Relevant to Mode of Carcinogenic Action ................ 2-30

2.3.5.1. Direct DNA-Reactive Effects ........................ 2-31

2.3.5.2. Indirect DNA Effects or Other Effects on Genes/Gene Expression

................................................. 2-32

2.3.5.3. Precursor Events and Biomarker Information ............ 2-34

2.3.5.4. Judging Data ..................................... 2-36

2.4. MODE OF ACTION—GENERAL CONSIDERATIONS AND FRAMEWORK

FOR ANALYSIS ............................................... 2-36

2.4.1. General Considerations ..................................... 2-36

2.4.2. Evaluating a Hypothesized Mode of Action ..................... 2-40

2.4.2.1. Peer Review ...................................... 2-40

2.4.2.2. Use of the Framework .............................. 2-40

2.4.3. Framework for Evaluating Each Hypothesized Carcinogenic Mode of

Action .................................................. 2-41

2.4.3.1. Description of the Hypothesized Mode of Action ......... 2-43

2.4.3.2. Discussion of the Experimental Support for the Hypothesized

Mode of Action .................................... 2-44

2.4.3.3. Consideration of the Possibility of Other Modes of Action . 2-46

2.4.3.4. Conclusions About the Hypothesized Mode of Action ..... 2-47

2.4.4. Evolution with Experience .................................. 2-49

2.5. WEIGHT OF EVIDENCE NARRATIVE .............................. 2-49

2.6. HAZARD CHARACTERIZATION .................................. 2-59

3. DOSE-RESPONSE ASSESSMENT .......................................... 3-1

3.1. ANALYSIS OF DOSE ............................................. 3-3

3.1.1. Standardizing Different Experimental Dosing Regimens ........... 3-4

3.1.2. Toxicokinetic Data and Modeling ............................. 3-5

3.1.3. Cross-species Scaling Procedures .............................. 3-6

3.1.3.1. Oral Exposures ..................................... 3-6

3.1.3.2. Inhalation Exposures ................................ 3-8

3.1.4. Route Extrapolation ........................................ 3-9

3.2. ANALYSIS IN THE RANGE OF OBSERVATION ..................... 3-11

3.2.1. Epidemiologic Studies ..................................... 3-11

3.2.2. Toxicodynamic (“Biologically Based”) Modeling ................ 3-13

3.2.3. Empirical Modeling (“Curve Fitting”) ......................... 3-14

3.2.4. Point of Departure (POD) ................................... 3-16

3.2.5. Characterizing the POD: The POD Narrative .................... 3-18

3.2.6. Relative Potency Factors ................................... 3-20

3.3. EXTRAPOLATION TO LOWER DOSES ............................. 3-20

3.3.1. Choosing an Extrapolation Approach .......................... 3-21

3.3.2. Extrapolation Using a Toxicodynamic Model ................... 3-21
3.3.3. Extrapolation Using a Low-dose Linear Model .................. 3-23

3.3.4. Nonlinear Extrapolation to Lower Doses ...................... 3-23

3.3.5. Comparing and Combining Multiple Extrapolations .............. 3-24

3.4. EXTRAPOLATION TO DIFFERENT HUMAN EXPOSURE SCENARIOS

............................................................. 3-26

3.5. EXTRAPOLATION TO SUSCEPTIBLE POPULATIONS 

AND LIFESTAGES ............................................ 3-29

3.6. UNCERTAINTY ................................................. 3-29

3.7. DOSE-RESPONSE CHARACTERIZATION .......................... 3-32

4. EXPOSURE ASSESSMENT ................................................ 4-1

4.1. DEFINING THE ASSESSMENT QUESTIONS ......................... 4-1

4.2. SELECTING OR DEVELOPING THE CONCEPTUAL AND MATHEMATICAL

MODELS ...................................................... 4-3

4.3. COLLECTING DATA OR SELECTING AND EVALUATING AVAILABLE

DATA ........................................................ 4-3

4.3.1. Adjusting Unit Risks for Highly Exposed Populations and Lifestages . 4-4

4.4. EXPOSURE CHARACTERIZATION ................................. 4-5

5. RISK CHARACTERIZATION .............................................. 5-1

5.1. PURPOSE ....................................................... 5-1

5.2. APPLICATION ................................................... 5-4

5.3. PRESENTATION OF THE RISK CHARACTERIZATION SUMMARY ..... 5-5

5.4. CONTENT OF THE RISK CHARACTERIZATION SUMMARY ........... 5-6

APPENDIX: MAJOR DEFAULT OPTIONS .................................... A-1

APPENDIX B: EPA’s GUIDANCE FOR DATA QUALITY ASSESSMENT ............ B-1

REFERENCES ............................................................. R-1

LIST OF FIGURES 

Figure 1-1. Flow chart for early-life risk assessment using mode of action framework .... 1-23

Figure 3-1. Compatibility of Alternative Points of Departure with Observed and Modeled 

Figure 3-2. Crossing between 10% and 1% Dose-Response Curves for Bladder Carcinomas and

Tumor Incidences ............................................... 3-35

Liver Carcinomas Induced by 2-AAF ............................... 3-35
1. INTRODUCTION 

1.1. PURPOSE AND SCOPE OF THE GUIDELINES 

These guidelines revise and replace the U.S. Environmental Protection Agency’s (EPA’s, 

or the Agency’s) Guidelines for Carcinogen Risk Assessment, published in 51 FR 33992, 

September 24, 1986 (U.S. EPA, 1986a) and the 1999 interim final guidelines (U.S. EPA, 1999a; 

see U.S. EPA 2001b). They provide EPA staff with guidance for developing and using risk 

assessments. They also provide basic information to the public about the Agency's risk 

assessment methods. 

These cancer guidelines are used with other risk assessment guidelines, such as the 

Guidelines for Mutagenicity Risk Assessment (U.S. EPA, 1986b) and the Guidelines for 

Exposure Assessment (U.S. EPA, 1992a). Consideration of other Agency guidance documents is 

also important in assessing cancer risks where procedures for evaluating specific target organ 

effects have been developed (e.g., assessment of thyroid follicular cell tumors, U.S. EPA, 1998a). 

All of EPA’s guidelines should be consulted when conducting a risk assessment in order to 

ensure that information from studies on carcinogenesis and other health effects are considered 

together in the overall characterization of risk. This is particularly true in the case in which a 

precursor effect for a tumor is also a precursor or endpoint of other health effects or when there is 

a concern for a particular susceptible life-stage for which the Agency has developed guidance, for 

example, Guidelines for Developmental Toxicity Risk Assessment (U.S. EPA, 1991a). The 

developmental guidelines discuss hazards to children that may result from exposures during 

preconception and prenatal or postnatal development to sexual maturity. Similar guidelines exist 

for reproductive toxicant risk assessments (U.S. EPA, 1996a) and for neurotoxicity risk 

assessment (U.S. EPA, 1998b). The overall characterization of risk is conducted within the 

context of broader policies and guidance such as Executive Order 13045, “Protection of Children 

From Environmental Health Risks and Safety Risks”( Executive Order 13045, 1997) which is the 

primary directive to federal agencies and departments to identify and assess environmental health 

risks and safety risks that may disproportionately affect children. 

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The cancer guidelines encourage both consistency in the procedures that support 

scientific components of Agency decision making and flexibility to allow incorporation of 

innovations and contemporaneous scientific concepts. In balancing these goals, the Agency 

relies on established scientific peer review processes (U.S. EPA, 2000a; OMB 2004). The cancer 

guidelines incorporate basic principles and science policies based on evaluation of the currently 

available information. The Agency intends to revise these cancer guidelines when substantial 

changes are necessary. As more information about carcinogenesis develops, the need may arise 

to make appropriate changes in risk assessment guidance. In the interim, the Agency intends to 

issue special reports, after appropriate peer review, to supplement and update guidance on single 

topics (e.g., U.S. EPA, 1991b). One such guidance document, Supplemental Guidance for 

Assessing Susceptibility from Early-Life Exposure to Carcinogens (“Supplemental Guidance”), 

was developed in conjunction with these cancer guidelines (U.S. EPA., 2005). Because both the 

methodology and the data in the Supplemental Guidance (see Section 1.3.6) are expected to 

evolve more rapidly than the issues addressed in these cancer guidelines, the two were developed 

as separate documents. The Supplemental Guidance, however, as well as any other relevant 

(including subsequent) guidance documents, should be considered along with these cancer 

guidelines as risk assessments for carcinogens are generated. The use of supplemental guidance, 

such as the Supplemental Guidance for Assessing Cancer Susceptibility from Early-life Exposure 

to Carcinogens, has the advantage of allowing the Supplemental Guidance to be modified as 

more data become available. Thus, the consideration of new, peer-reviewed scientific 

understanding and data in an assessment can always be consistent with the purposes of these 

cancer guidelines. 

These cancer guidelines are intended as guidance only. They do not establish any 

substantive “rules” under the Administrative Procedure Act or any other law and have no binding 

effect on EPA or any regulated entity, but instead represent a non-binding statement of policy. 

EPA believes that the cancer guidelines represent a sound and up-to-date approach to cancer risk 

assessment, and the cancer guidelines enhance the application of the best available science in 

EPA’s risk assessments. However, EPA cancer risk assessments may be conducted differently 

than envisioned in the cancer guidelines for many reasons, including (but not limited to) new 

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information, new scientific understanding, or new science policy judgment. The science of risk 

assessment continues to develop rapidly, and specific components of the cancer guidelines may 

become outdated or may otherwise require modification in individual settings. Use of the cancer 

guidelines in future risk assessments will be based on decisions by EPA that the approaches are 

suitable and appropriate in the context of those particular risk assessments. These judgments will 

be tested through peer review, and risk assessments will be modified to use different approaches 

if appropriate. 

1.2. ORGANIZATION AND APPLICATION OF THE CANCER GUIDELINES 

1.2.1. Organization 

Publications by the Office of Science and Technology (OSTP, 1985) and the National 

Research Council (NRC) (NRC, 1983, 1994) provide information and general principles about 

risk assessment. Risk assessment uses available scientific information on the properties of an 

agent1

 and its effects in biological systems to provide an evaluation of the potential for harm as a 

consequence of environmental exposure. The 1983 and 1994 NRC documents organize risk 

assessment information into four areas: hazard identification, dose-response assessment, 

exposure assessment, and risk characterization. This structure appears in these cancer guidelines, 

with additional emphasis placed on characterization of evidence and conclusions in each area of 

the assessment. In particular, the cancer guidelines adopt the approach of the NRC's 1994 report 

in adding a dimension of characterization to the hazard identification step: an evaluation of the 

conditions under which its expression is anticipated. Risk assessment questions addressed in 

these cancer guidelines are as follows. 

C For hazard—Can the identified agent present a carcinogenic hazard to humans and, 

if so, under what circumstances? 

C For dose response—At what levels of exposure might effects occur? 

1

 The term “agent” refers generally to any chemical substance, mixture, or physical or biological entity 

being assessed, unless otherwise noted (See Section 1.2.2 for a note on radiation.). 

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C For exposure—What are the conditions of human exposure? 

C For risk—What is the character of the risk? How well do data support conclusions 

about the nature and extent of the risk from various exposures? 

The risk characterization process first summarizes findings on hazard, dose response, and 

exposure characterizations and then develops an integrative analysis of the whole risk case. It 

ends in the writing of a technical risk characterization. Other documents, such as summaries for 

the risk managers and the public, reflecting the key points of the risk characterization are usually 

written. A summary for managers is a presentation for those who may or may not be familiar 

with the scientific details of cancer assessment. It also provides information for other interested 

readers. The initial steps in the risk characterization process are to make building blocks in the 

form of characterizations of the assessments of hazard, dose response, and exposure. The 

individual assessments and characterizations are then integrated to arrive at risk estimates for 

exposure scenarios of interest. As part of the characterization process, explicit evaluations are 

made of the hazard and risk potential for susceptible lifestages, including children (U.S. EPA, 

1995, 2000b). 

The 1994 NRC document also explicitly called attention to the role of the risk assessment 

process in identifying scientific uncertainties that, if addressed, could serve to reduce their 

uncertainty in future iterations of the risk assessment. NRC recommended that when the Agency 

“reports estimates of risk to decisions-makers and the public, it should present not only point 

estimates of risk, but also the sources and magnitudes of uncertainty associated with these 

estimates” (p. 15). Thus, the identified uncertainties serve as a feedback loop to the research 

community and decisionmakers, specifying areas and types of information that would be 

particularly useful. 

There are several reasons for individually characterizing the hazard, dose response, and 

exposure assessments. One is that they are often done by different people than those who do the 

integrative analyses. The second is that there is very often a lapse of time between the conduct of 

hazard and dose-response analyses and the conduct of exposure assessment and integrative 

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analysis. Thus, it is important to capture characterizations of assessments as the assessments are 

done to avoid the need to go back and reconstruct them. Finally, frequently a single hazard 

assessment is used by several programs for several different exposure scenarios. There may be 

one or several documents involved. “Integrative analysis” is a generic term; and many 

documents that have other titles may contain integrative analyses. In the following sections, the 

elements of these characterizations are discussed. 

1.2.2. Application 

The cancer guidelines apply within the framework of policies provided by applicable 

EPA statutes and do not alter such policies. 

• The cancer guidelines cover the assessment of available data. They do not imply that one 

kind of data or another is prerequisite for regulatory action concerning any agent. It is 

important that, when evaluating and considering the use of any data, EPA analysts 

incorporate the basic standards of quality, as defined by the EPA Information Quality 

Guidelines (U.S. EPA, 2002a see Appendix B) and other Agency guidance on data 

quality such as the EPA Quality Manual for Environmental Programs (U.S. EPA, 2000e), 

as well as OMB Guidelines for Ensuring and Maximizing the Quality, Utility, and 

Integrity of Information Disseminated by Federal Agencies (OMB, 2002). It is very 

important that all analyses consider the basic standards of quality, including objectivity, 

utility, and integrity. A summary of the factors and considerations generally used by the 

Agency when evaluating and considering the use of scientific and technical information is 

contained in EPA's A Summary of General Assessment Factors for Evaluating the Quality 

of Scientific and Technical Information (U.S. EPA, 2003). 

• Risk management applies directives in statutes, which may require consideration of 

potential risk or solely hazard or exposure potential, along with social, economic, 

technical, and other factors in decision making. Risk assessments may be used to support 

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decisions, but in order to maintain their integrity as decision-making tools, they are not 

influenced by consideration of the social or economic consequences of regulatory action. 

The assessment of risk from radiation sources is informed by the continuing examination 

of human data by the National Academy of Sciences/NRC in its series of numbered reports: 

“Biological Effects of Ionizing Radiation.” Although some of the general principles of these 

cancer guidelines may also apply to radiation risk assessments, some of the details of their risk 

assessment procedures may not, as they are most focused on other kinds of agents. Therefore, 

these cancer guidelines are not intended to provide the primary source of, or guidance for, the 

Agency’s evaluation of the carcinogenic risks of radiation. 

Not every EPA assessment has the same scope or depth, a factor recognized by the 

National Academy of Sciences (NRC, 1996). For example, EPA’s Information Quality 

Guidelines (U.S. EPA, 2002a, see Appendix B) discuss influential information that “will have or 

does have a clear and substantial impact ... on important public policies or private sector 

decisions ... that should adhere to a rigorous standard of quality.” It is often difficult to know a 

priori how the results of a risk assessment are likely to be used by the Agency. Some risk 

assessments may be used by Agency economists and policy analysts, and the necessary 

information for such analyses, as discussed in detail later in this document, should be included 

when practicable (U.S. EPA, 2002a). On the other hand, Agency staff often conduct screeninglevel assessments for priority setting or separate assessments of hazard or exposure for ranking 

purposes or to decide whether to invest resources in collecting data for a full assessment. 

Moreover, a given assessment of hazard and dose response may be used with more than one 

exposure assessment that may be conducted separately and at different times as the need arises in 

studying environmental problems related to various exposure media. The cancer guidelines 

apply to these various situations in appropriate detail, given the scope and depth of the particular 

assessment. For example, a screening assessment may be based almost entirely on structureactivity relationships (SARs) and default options, when other data are not readily available. 

When more data and resources are readily available, assessments can use a critical analysis of all 

of the available data as the starting point of the risk assessment. Under these conditions, default 

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options would only be used to address uncertainties or the absence of critical data. Default 

options are inferences based on general scientific knowledge of the phenomena in question and 

are also matters of policy concerning the appropriate way to bridge uncertainties that concern 

potential risk to human health. 

These cancer guidelines do not suggest that all of the kinds of data covered here will need 

to be available or used for either assessment or decision making. The level of detail of an 

assessment is a matter of Agency management discretion regarding applicable decision-making 

needs. The Agency generally presumes that key cancer information (e.g., assessments contained 

in the Agency’s Integrated risk Information System) is “influential information” as defined by the 

EPA Information Quality Guidelines and “highly influential” as defined by OMB’s Information 

Quality Bulletin for Peer Review (OMB 2004). 

1.3. KEY FEATURES OF THE CANCER GUIDELINES 

1.3.1. Critical Analysis of Available Information as the Starting Point for Evaluation 

As an increasing understanding of carcinogenesis is becoming available, these cancer 

guidelines adopt a view of default options that is consistent with EPA's mission to protect human 

health while adhering to the tenets of sound science. Rather than viewing default options as the 

starting point from which departures may be justified by new scientific information, these cancer 

guidelines view a critical analysis of all of the available information that is relevant to assessing 

the carcinogenic risk as the starting point from which a default option may be invoked if needed 

to address uncertainty or the absence of critical information. Preference is given to using 

information that has been peer reviewed, e.g., reported in peer-reviewed scientific journals. The 

primary goal of EPA actions is protection of human health; accordingly, as an Agency policy, 

risk assessment procedures, including default options that are used in the absence of scientific 

data to the contrary, should be health protective (U.S. EPA, 1999b). 

Use of health protective risk assessment procedures as described in these cancer 

guidelines means that estimates, while uncertain, are more likely to overstate than understate 

hazard and/or risk. NRC (1994) reaffirmed the use of default options as “a reasonable way to 

cope with uncertainty about the choice of appropriate models or theory” (p. 104). NRC saw the 

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need to treat uncertainty in a predictable way that is “scientifically defensible, consistent with the 

agency's statutory mission, and responsive to the needs of decision-makers” (p. 86). The extent 

of health protection provided to the public ultimately depends upon what risk managers decide is 

the appropriate course of regulatory action. When risk assessments are performed using only one 

set of procedures, it may be difficult for risk managers to determine how much health 

protectiveness is built into a particular hazard determination or risk characterization. When there 

are alternative procedures having significant biological support, the Agency encourages 

assessments to be performed using these alternative procedures, if feasible, in order to shed light 

on the uncertainties in the assessment, recognizing that the Agency may decide to give greater 

weight to one set of procedures than another in a specific assessment or management decision. 

Encouraging risk assessors to be receptive to new scientific information, NRC discussed 

the need for departures from default options when a “sufficient showing” is made. It called on 

EPA to articulate clearly its criteria for a departure so that decisions to depart from default 

options would be “scientifically credible and receive public acceptance” (p. 91). It was 

concerned that ad hoc departures would undercut the scientific credibility of a risk assessment. 

NRC envisioned that principles for choosing and departing from default options would balance 

several objectives, including “protecting the public health, ensuring scientific validity, 

minimizing serious errors in estimating risks, maximizing incentives for research, creating an 

orderly and predictable process, and fostering openness and trustworthiness” (p. 81). 

Appendices N-1 and N-2 of NRC (1994) discussed two competing standards for choosing 

default options articulated by members of the committee. One suggested approach would 

evaluate a departure in terms of whether “it is scientifically plausible” and whether it “tends to 

protect public health in the face of scientific uncertainty” (p. 601). An alternative approach 

“emphasizes scientific plausibility with regard to the use of alternative models” (p. 631). 

Reaching no consensus on a single approach, NRC recognized that developing criteria for 

departures is an EPA policy matter. 

The basis for invoking a default option depends on the circumstances. Generally, if a gap 

in basic understanding exists or if agent-specific information is missing, a default option may be 

used. If agent-specific information is present but critical analysis reveals inadequacies, a default 

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option may also be used. If critical analysis of agent-specific information is consistent with one 

or more biologically based models as well as with the default option, the alternative models and 

the default option are both carried through the assessment and characterized for the risk manager. 

In this case, the default model not only fits the data, but also serves as a benchmark for 

comparison with other analyses. This case also highlights the importance of extensive 

experimentation to support a conclusion about mode of action, including addressing the issue of 

whether alternative modes of action are also plausible. Section 2.4 provides a framework for 

critical analysis of mode of action information to address the extent to which the available 

information supports the hypothesized mode of action, whether alternative modes of action are 

also plausible, and whether there is confidence that the same inferences can be extended to 

populations and lifestages that are not represented among the experimental data. 

Generally, cancer risk decisions strive to be “scientifically defensible, consistent with the 

agency’s statutory mission, and responsive to the needs of decision-makers” (NRC, 1994). 

Scientific defensibility would be evaluated through use of EPA's Science Advisory Board, EPA’s 

Office of Pesticide Programs’ Scientific Advisory Panel, or other independent expert peer review 

panels to determine whether a consensus among scientific experts exists. Consistency with the 

Agency's statutory mission would consider whether the risk assessment overall supports EPA's 

mission to protect human health and safeguard the natural environment. Responsiveness to the 

needs of decisionmakers would take into account pragmatic considerations such as the nature of 

the decision; the required depth of analysis; the utility, time, and cost of generating new scientific 

data; and the time, personnel, and resources allotted to the risk assessment. 

With a multitude of types of data, analyses, and risk assessments, as well as the diversity 

of needs of decisionmakers, it is neither possible nor desirable to specify step-by-step criteria for 

decisions to invoke a default option. A discussion of major default options appears in the 

Appendix. Screening-level assessments may more readily use default parameters, even worstcase assumptions, that would not be appropriate in a full-scale assessment. On the other hand, 

significant risk management decisions will often benefit from a more comprehensive assessment, 

including alternative risk models having significant biological support. To the extent practicable, 

such assessments should provide central estimates of potential risks in conjunction with lower 

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and upper bounds (e.g., confidence limits) and a clear statement of the uncertainty associated 

with these estimates. 

In the absence of sufficient data or understanding to develop of a robust, biologically 

based model, an appropriate policy choice is to have a single preferred curve-fitting model for 

each type of data set. Many different curve-fitting models have been developed, and those that 

fit the observed data reasonably well may lead to several-fold differences in estimated risk at the 

lower end of the observed range. In addition, goodness-of-fit to the experimental observations is 

not by itself an effective means of discriminating among models that adequately fit the data 

(OSTP, 1985). To provide some measure of consistency across different carcinogen 

assessments, EPA uses a standard curve-fitting procedure for tumor incidence data. Assessments 

that include a different approach should provide an adequate justification and compare their 

results with those from the standard procedure. Application of models to data should be 

conducted in an open and transparent manner. 

1.3.2. Mode of Action 

The use of mode of action2

 in the assessment of potential carcinogens is a main focus of 

these cancer guidelines. This area of emphasis arose because of the significant scientific 

advances that have developed concerning the causes of cancer induction. Elucidation of a mode 

of action for a particular cancer response in animals or humans is a data-rich determination. 

Significant information should be developed to ensure that a scientifically justifiable mode of 

action underlies the process leading to cancer at a given site. In the absence of sufficiently, 

scientifically justifiable mode of action information, EPA generally takes public healthprotective, default positions regarding the interpretation of toxicologic and epidemiologic data: 

2

 The term “mode of action” is defined as a sequence of key events and processes, starting with interaction 

of an agent with a cell, proceeding through operational and anatomical changes, and resulting in cancer formation. A 

“key event” is an empirically observable precursor step that is itself a necessary element of the mode of action or is a 

biologically based marker for such an element. Mode of action is contrasted with “mechanism of action,” which 

implies a more detailed understanding and description of events, often at the molecular level, than is meant by mode 

of action. The toxicokinetic processes that lead to formation or distribution of the active agent to the target tissue are 

considered in estimating dose but are not part of the mode of action as the term is used here. There are many 

examples of possible modes of carcinogenic action, such as mutagenicity, mitogenesis, inhibition of cell death, 

cytotoxicity with reparative cell proliferation, and immune suppression. 

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animal tumor findings are judged to be relevant to humans, and cancer risks are assumed to 

conform with low dose linearity. 

Understanding of mode of action can be a key to identifying processes that may cause 

chemical exposures to differentially affect a particular population segment or lifestage. Some 

modes of action are anticipated to be mutagenic and are assessed with a linear approach. This is 

the mode of action of radiation and several other agents that are known carcinogens. Other 

modes of action may be modeled with either linear or nonlinear3 approaches after a rigorous 

analysis of available data under the guidance provided in the framework for mode of action 

analysis (see Section 2.4.3). 

1.3.3. Weight of Evidence Narrative 

The cancer guidelines emphasize the importance of weighing all of the evidence in 

reaching conclusions about the human carcinogenic potential of agents. This is accomplished in 

a single integrative step after assessing all of the individual lines of evidence, which is in contrast 

to the step-wise approach in the 1986 cancer guidelines. Evidence considered includes tumor 

findings, or lack thereof, in humans and laboratory animals; an agent’s chemical and physical 

properties; its structure-activity relationships (SARs) as compared with other carcinogenic 

agents; and studies addressing potential carcinogenic processes and mode(s) of action, either in 

vivo or in vitro. Data from epidemiologic studies are generally preferred for characterizing 

human cancer hazard and risk. However, all of the information discussed above could provide 

valuable insights into the possible mode(s) of action and likelihood of human cancer hazard and 

risk. The cancer guidelines recognize the growing sophistication of research methods, 

3

The term “nonlinear” is used here in a narrower sense than its usual meaning in the field of mathematical 

modeling. In these cancer guidelines, the term “nonlinear” refers to threshold models (which show no response over 

a range of low doses that include zero) and some nonthreshold models (e.g., a quadractic model, which shows some 

response at all doses above zero). In these cancer guidelines, a nonlinear model is one whose slope is zero at (and 

perhaps above) a dose of zero. A low-dose-linear model is one whose slope is greater than zero at a dose of zero. A 

low-dose-linear model approximates a straight line only at very low doses; at higher doses near the observed data, a 

low-dose-linear model can display curvature. The term “low-dose-linear” is often abbreviated “linear,” although a 

low-dose-linear model is not linear at all doses. Use of nonlinear approaches does not imply a biological threshold 

dose below which the response is zero. Estimating thresholds can be problematic; for example, a response that is not 

statistically significant can be consistent with a small risk that falls below an experiment’s power of detection. 

1-11 
particularly in their ability to reveal the modes of action of carcinogenic agents at cellular and 

subcellular levels as well as toxicokinetic processes. 

Weighing of the evidence includes addressing not only the likelihood of human 

carcinogenic effects of the agent but also the conditions under which such effects may be 

expressed, to the extent that these are revealed in the toxicological and other biologically 

important features of the agent. 

The weight of evidence narrative to characterize hazard summarizes the results of the 

hazard assessment and provides a conclusion with regard to human carcinogenic potential. The 

narrative explains the kinds of evidence available and how they fit together in drawing 

conclusions, and it points out significant issues/strengths/limitations of the data and conclusions. 

Because the narrative also summarizes the mode of action information, it sets the stage for the 

discussion of the rationale underlying a recommended approach to dose-response assessment. 

In order to provide some measure of clarity and consistency in an otherwise free-form, 

narrative characterization, standard descriptors are used as part of the hazard narrative to express 

the conclusion regarding the weight of evidence for carcinogenic hazard potential. There are five 

recommended standard hazard descriptors: “Carcinogenic to Humans,” “Likely to Be 

Carcinogenic to Humans,” “Suggestive Evidence of Carcinogenic Potential,” “Inadequate 

Information to Assess Carcinogenic Potential,” and “Not Likely to Be Carcinogenic to Humans.” 

Each standard descriptor may be applicable to a wide variety of data sets and weights of evidence 

and is presented only in the context of a weight of evidence narrative. Furthermore, as described 

in Section 2.5 of these cancer guidelines, more than one conclusion may be reached for an agent. 

1.3.4. Dose-response Assessment 

Dose-response assessment evaluates potential risks to humans at particular exposure 

levels. The approach to dose-response assessment for a particular agent is based on the 

conclusion reached as to its potential mode(s) of action for each tumor type. Because an agent 

may induce multiple tumor types, the dose-response assessment includes an analysis of all tumor 

types, followed by an overall synthesis that includes a characterization of the risk estimates 

across tumor types, the strength of the mode of action information of each tumor type, and the 

1-12
anticipated relevance of each tumor type to humans, including susceptible populations and 

lifestages (e.g., childhood). 

Dose-response assessment for each tumor type is performed in two steps: assessment of 

observed data to derive a point of departure (POD),4

 followed by extrapolation to lower 

exposures to the extent that is necessary. Data from epidemiologic studies, of sufficient quality, 

are generally preferred for estimating risks. When animal studies are the basis of the analysis, 

the estimation of a human-equivalent dose should utilize toxicokinetic data to inform crossspecies dose scaling if appropriate and if adequate data are available. Otherwise, default 

procedures should be applied. For oral dose, based on current science, an appropriate default 

option is to scale daily applied doses experienced for a lifetime in proportion to body weight 

raised to the 3/4 power (U.S. EPA, 1992b). For inhalation dose, based on current science, an 

appropriate default methodology estimates respiratory deposition of particles and gases and 

estimates internal doses of gases with different absorption characteristics. When toxicokinetic 

modeling (see Section 3.1.2) is used without toxicodynamic modeling (see Section 3.2.2), the 

dose-response assessment develops and supports an approach for addressing toxicodynamic 

equivalence, perhaps by retaining some of the cross-species scaling factor (see Section 3.1.3). 

Guidance is also provided for adjustment of dose from adults to children (see Section 4.3.1). 

Response data on effects of the agent on carcinogenic processes are analyzed (nontumor 

data) in addition to data on tumor incidence. If appropriate, the analyses of data on tumor 

incidence and on precursor effects may be used in combination. To the extent the relationship 

between precursor effects and tumor incidence are known, precursor data may be used to 

estimate a dose-response function below the observable tumor data. Study of the dose-response 

function for effects believed to be part of the carcinogenic process influenced by the agent may 

also assist in evaluating the relationship of exposure and response in the range of observation and 

at exposure levels below the range of observation. 

4 A “point of departure” (POD) marks the beginning of extrapolation to lower doses. The POD is an 

estimated dose (usually expressed in human-equivalent terms) near the lower end of the observed range, without 

significant extrapolation to lower doses. 

1-13 
The first step of dose-response assessment is evaluation within the range of observation. 

Approaches to analysis of the range of observation of epidemiologic studies are determined by 

the type of study and how dose and response are measured in the study. In the absence of 

adequate human data for dose-response analysis, animal data are generally used. If there are 

sufficient quantitative data and adequate understanding of the carcinogenic process, a 

biologically based model may be developed to relate dose and response data on an agent-specific 

basis. Otherwise, as a default procedure, a standard model can be used to curve-fit the data. 

The POD for extrapolating the relationship to environmental exposure levels of interest, 

when the latter are outside the range of observed data, is generally the lower 95% confidence 

limit on the lowest dose level that can be supported for modeling by the data. SAB (1997) 

suggested that, "it may be appropriate to emphasize lower statistical bounds in screening analyses 

and in activities designed to develop an appropriate human exposure value, since such activities 

require accounting for various types of uncertainties and a lower bound on the central estimate is 

a scientifically-based approach accounting for the uncertainty in the true value of the ED10 [or 

central estimate].” However, the consensus of the SAB (1997) was that, “both point estimates 

and statistical bounds can be useful in different circumstances, and recommended that the 

Agency routinely calculate and present the point estimate of the ED10 [or central estimate] and 

the corresponding upper and lower 95% statistical bounds.” For example, it may be appropriate 

to emphasize the central estimate in activities that involve formal uncertainty analysis that are 

required by OMB Circular A-4 (OMB, 2003) as well as ranking agents as to their carcinogenic 

hazard. Thus, risk assessors should calculate, to the extent practicable, and present the central 

estimate and the corresponding upper and lower statistical bounds (such as confidence limits) to 

inform decisionmakers. 

The second step of dose-response assessment is extrapolation to lower dose levels, if 

needed. This extrapolation is based on extension of a biologically based model if supported by 

substantial data (see Section 3.3.2). Otherwise, default approaches can be applied that are 

consistent with current understanding of mode(s) of action of the agent, including approaches 

that assume linearity or nonlinearity of the dose-response relationship, or both. A default 

approach for linearity extends a straight line from the POD to zero dose/zero response (see 

1-14
Section 3.3.3). The linear approach is used when: (1) there is an absence of sufficient 

information on modes of action or (2) the mode of action information indicates that the doseresponse curve at low dose is or is expected to be linear. Where alternative approaches have 

significant biological support, and no scientific consensus favors a single approach, an 

assessment may present results using alternative approaches. A nonlinear approach can be used 

to develop a reference dose or a reference concentration (see Section 3.3.4). 

1.3.5. Susceptible Populations and Lifestages 

An important use of mode of action information is to identify susceptible populations and 

lifestages. It is rare to have epidemiologic studies or animal bioassays conducted in susceptible 

individuals. This information need can be filled by identifying the key events of the mode of 

action and then identifying risk factors, such as differences due to genetic polymorphisms, 

disease, altered organ function, lifestyle, and lifestage, that can augment these key events. To do 

this, the information about the key precursor events is reviewed to identify particular populations 

or lifestages that can be particularly susceptible to their occurrence (see Section 2.4.3.4). Any 

information suggesting quantitative differences between populations or lifestages is flagged for 

consideration in the dose-response assessment (see Section 3.5 and U.S. EPA 2002b). 

1.3.6. Evaluating Risks from Childhood Exposures 

NRC (1994) recommended that “EPA should assess risks to infants and children 

whenever it appears that their risks might be greater than those of adults.” Executive Order 

13045 (1997) requires that “each Federal Agency shall make it a high priority to identify and 

assess environmental health and safety risks that may disproportionately affect children, and shall 

ensure that their policies, programs, and standards address disproportionate risks that result from 

environmental health risks or safety risks.” In assessing risks to children, EPA considers both 

effects manifest during childhood and early-life exposures that can contribute to effects at any 

time later in life. 

These cancer guidelines view childhood as a sequence of lifestages rather than viewing 

children as a subpopulation, the distinction being that a subpopulation refers to a portion of the 

1-15
population, whereas a lifestage is inclusive of the entire population. Exposures that are of 

concern extend from conception through adolescence and also include pre-conception exposures 

of both parents. These cancer guidelines use the term “childhood” in this more inclusive sense. 

Rarely are there studies that directly evaluate risks following early-life exposure. 

Epidemiologic studies of early-life exposure to environmental agents are seldom available. 

Standard animal bioassays generally begin dosing after the animals are several weeks old, when 

many organ systems are mature. This could lead to an understatement of risk, because an 

accepted concept in the science of carcinogenesis is that young animals are usually more 

susceptible to the carcinogenic activity of a chemical than are mature animals (McConnell, 

1992). 

At this time, there is some evidence of higher cancer risks following early-life exposure. 

For radiation carcinogenesis, data indicate that risks for several forms of cancer are highest 

following childhood exposure (NRC, 1990; Miller, 1995; U.S. EPA, 1999c). These human 

results are supported by the few animal bioassays that include perinatal (prenatal or early 

postnatal) exposure. Perinatal exposure to some agents can induce higher incidences of the 

tumors seen in standard bioassays; some examples include vinyl chloride (Maltoni et al., 1981), 

diethylnitrosamine (Peto et al., 1984), benzidine, DDT, dieldrin, and safrole (Vesselinovitch et 

al., 1979). Moreover, perinatal exposure to some agents, including vinyl chloride (Maltoni et al., 

1981) and saccharin (Cohen, 1995; Whysner and Williams, 1996), can induce different tumors 

that are not seen in standard bioassays. Surveys comparing perinatal carcinogenesis bioassays 

with standard bioassays for a limited number of chemicals (McConnell, 1992; U.S. EPA, 1996b) 

have concluded that 

• the same tumor sites are usually observed following either perinatal or adult 

exposure, and 

• perinatal exposure in conjunction with adult exposure usually increases the 

incidence of tumors or reduces the latent period before tumors are observed. 

1-16
The risk attributable to early-life exposure often appears modest compared with the risk 

from lifetime exposure, but it can be about 10-fold higher than the risk from an exposure of 

similar duration occurring later in life (Ginsberg, 2003). Further research is warranted to 

investigate the extent to which these findings apply to specific agents, chemical classes, and 

modes of action or in general. 

These empirical results are consistent with current understanding of the biological 

processes involved in carcinogenesis, which leads to a reasonable expectation that children can 

be more susceptible to many carcinogenic agents (Anderson et al., 2000; Birnbaum and Fenton, 

2003; Ginsberg, 2003; Miller et al., 2002; Scheuplein et al., 2002). Some aspects potentially 

leading to childhood susceptibility are listed below. 

• Differences in the capacity to metabolize and clear chemicals can result in larger or 

smaller internal doses of the active agent(s). 

• More frequent cell division during development can result in enhanced expression 

of mutations due to the reduced time available for repair of DNA lesions (Slikker et 

al., 2004). 

• Some embryonic cells, such as brain cells, lack key DNA repair enzymes. 

• More frequent cell division during development can result in clonal expansion of 

cells with mutations from prior unrepaired DNA damage (Slikker et al., 2004). 

• Some components of the immune system are not fully functional during 

development (Holladay and Smialowicz, 2000; Holsapple et al., 2003). 

• Hormonal systems operate at different levels during different lifestages. 

1-17 
• Induction of developmental abnormalities can result in a predisposition to 

carcinogenic effects later in life (Anderson et al., 2000; Birnbaum and Fenton, 

2003; Fenton and Davis, 2002). 

To evaluate risks from early-life exposure, these cancer guidelines emphasize the role of 

toxicokinetic information to estimate levels of the active agent in children and toxicodynamic 

information to identify whether any key events of the mode of action are of increased concern 

early in life. Developmental toxicity studies can provide information on critical periods of 

exposure for particular targets of toxicity. 

An approach to assessing risks from early-life exposure is presented in Figure 1-1. In the 

hazard assessment, when there are mode of action data, the assessment considers whether these 

data have special relevance during childhood, considering the various aspects of development 

listed above. Examples of such data include toxicokinetics that predict a sufficiently large 

internal dose in children or a mode of action where a key precursor event is more likely to occur 

during childhood. There is no recommended default to settle the question of whether tumors 

arising through a mode of action are relevant during childhood; and adequate understanding the 

mode of action implies that there are sufficient data (on either the specific agent or the general 

mode of action) to form a confident conclusion about relevance during childhood (see Section 

2.4.3.4). 

In the dose-response assessment, the potential for susceptibility during childhood 

warrants explicit consideration in each assessment. These cancer guidelines encourage 

developing separate risk estimates for children according to a tiered approach that considers what 

pertinent data are available (see Section 3.5). Childhood may be a susceptible period; moreover, 

exposures during childhood generally are not equivalent to exposures at other times and may be 

treated differently from exposures occurring later in life (see Section 3.5). In addition, 

adjustment of unit risk estimates may be warranted when used to estimate risks from childhood 

exposure (see Section 4.4). 

At this time, several limitations preclude a full assessment of children's risk. There are no 

generally used testing protocols to identify potential environmental causes of cancers that are 

1-18
unique to children, including several forms of childhood cancer and cancers that develop from 

parental exposures, and cases where developmental exposure may alter susceptibility to 

carcinogen exposure in the adult (Birnbaum and Fenton, 2003). Dose-response assessment is 

limited by an inability to observe how developmental exposure can modify incidence and latency 

and an inability to estimate the ultimate tumor response resulting from induced susceptibility to 

later carcinogen exposures. 

To partially address the limitations identified above, EPA developed in conjunction with 

these cancer guidelines, Supplemental Guidance for Assessing Susceptibility from Early-Life 

Exposure to Carcinogens (“Supplemental Guidance”). The Supplemental Guidance addresses a 

number of issues pertaining to cancer risks associated with early-life exposures generally, but 

provides specific guidance on procedures for adjusting cancer potency estimates only for 

carcinogens acting through a mutagenic mode of action. This Supplemental Guidance 

recommends, for such chemicals when no chemical-specific data exist, a default approach using 

estimates from chronic studies (i.e., cancer slope factors) with appropriate modifications to 

address the potential for differential risk of early-lifestage exposure. 

The Agency considered both the advantages and disadvantages to extending the 

recommended, age dependent adjustment factors for carcinogenic potency to carcinogenic agents 

for which the mode of action remains unknown. EPA decided to recommend these factors only 

for carcinogens acting through a mutagenic mode of action based on a combination of analysis of 

available data and long-standing science policy positions which govern the Agency’s overall 

approach to carcinogen risk assessment. In general, the Agency prefers to rely on analyses of 

data, rather than general defaults. When data are available for a sensitive lifestage, they would be 

used directly to evaluate risks for that chemical and that lifestage on a case-by-case basis. In the 

case of nonmutagenic carcinogens, when the mode of action is unknown, the data were judged by 

EPA to be too limited and the modes of action too diverse to use this as a category for which a 

general default adjustment factor approach can be applied. In this situation, a linear low-dose 

extrapolation methodology (without further adjustment) is recommended. It is the Agency’s 

long-standing science policy position that use of the linear low-dose extrapolation approach 

1-19
provides adequate public health conservatism in the absence of chemical-specific data indicating 

differential early-life sensitivity or when the mode of action is not mutagenic. 

The Agency expects to produce additional supplemental guidance for other modes of 

action, as data from new research and toxicity testing indicate it is warranted. EPA intends to 

focus its research, and work collaboratively with its federal partners, to improve understanding of 

the implications of early life exposure to carcinogens. Development of guidance for estrogenic 

agents and chemicals acting through other processes resulting in endocrine disruption and 

subsequent carcinogenesis, for example, might be a reasonable priority in light of the human 

experience with diethylstilbesterol and the existing early life animal studies. It is worth noting 

that each mode of action for endocrine disruption will probably require separate analysis. 

As the Agency examines additional carcinogenic agents, the age groupings may differ 

from those recommended for assessing cancer risks from early-life exposure to chemicals with a 

mutagenic mode of action. Puberty and its associated biological changes, for example, involve 

many biological processes that could lead to changes in sensitivity to the effects of some 

carcinogens, depending on their mode of action. The Agency is interested in identifying 

lifestages that may be particularly sensitive or refractory for carcinogenesis, and believes that the 

mode of action framework described in these cancer guidelines is an appropriate mechanism for 

elucidating these lifestages. For each additional mode of action evaluated, the various age 

groupings determined to be at differential risk may differ from those proposed in the 

Supplemental Guidance. For example, the age groupings selected for the age-dependent 

adjustments for carcinogens acting through a mutagenic mode of action were initially selected 

based on the available data, i.e., for the laboratory animal age range representative of birth to < 2 

years in humans. More limited data and information on human biology were used to determine a 

science-informed policy regarding 2 to < 16 years. Data were not available to refine the latter 

age group. If more data become available regarding carcinogens with a mutagenic mode of 

action, consideration may be given to further refinement of these age groups. 

1-20
1.3.7. Emphasis on Characterization 

The cancer guidelines emphasize the importance of a clear and useful characterization 

narrative that summarizes the analyses of hazard, dose-response, and exposure assessment. 

These characterizations summarize the assessments to explain the extent and weight of evidence, 

major points of interpretation and rationale for their selection, strengths and weaknesses of the 

evidence and the analysis, and discuss alternative conclusions and uncertainties that deserve 

serious consideration (U.S. EPA, 2000b). They serve as starting materials for the overall risk 

characterization process that completes the risk assessment. 

1-21
Figure 1-1 . Flow cha rt for ea rly -life risk assessment using mode of a ction framework. 

( s) 

(s) ( s) 

(

) 

-

/

No 

(

) ? 

Yes 

j ( )

No 

Use framework in Ca ncer Guide lines to 

establish MOA

MOA sufficient ly 

supported in animals? 

MOA releva nt to 

humans? 

Flag lifestage or population that 

co uld be susceptib le b ased on 

infor mation about the specific MOA

fo r dose response ana ly sis. 

Use linear e xtrapo latio n as 

a defau lt. 

Deter mine extrapolation 

based on informat ion 

ab ou t specific MOA. 

Model using MOA or use RfD RfC 

method as default. Adjustments for 

susceptible lif estages or populations 

are part o f th e p rocess. 

L inearity due to 

mutagenic MOA 

MOA can not 

be deter mined 

Nonlin ear 

Yes 

Yes 

Line ar, b ut 

non mutag enic 

MOA 

Use the same lin ear extrap olation f or 

all lifestages, u nless hav e c hemicalsp ecific in for mation o n lifestag es o r 

populations. 

Were che mical-sp ecific data available 

in MOA analysis to evaluate d iff erences 

between adults and juveniles more, 

le ss, or t he same susceptibility

Develo p ch emical-specific 

risk estimates incorpor ating 

lifestage susceptibility. 

Early-lif e suscept ib ility assu med. App ly ag edepe ndent ad u st ment factors ADAFs as 

appr opriate to develop r isk estimates. 

Su pplem ental Gu idanc e for Ear ly-Life Exposur es 

No fur ther ana ly sis of 

tumors. 

1-22
2. HAZARD ASSESSMENT 

2.1. OVERVIEW OF HAZARD ASSESSMENT AND CHARACTERIZATION 

2.1.1. Analyses of Data 

The purpose of hazard assessment is to review and evaluate data pertinent to two 

questions: (1) whether an agent may pose a carcinogenic hazard to human beings, and (2) under 

what circumstances an identified hazard may be expressed (NRC, 1994). Hazard assessment 

involves analyses of a variety of data that may range from observations of tumor responses to 

analysis of structure-activity relationships (SARs). The purpose of the assessment is not simply 

to assemble these separate evaluations; its purpose is to construct a total analysis examining what 

the biological data reveal as a whole about carcinogenic effects and mode of action of the agent, 

and their implications for human hazard and dose-response evaluation. Conclusions are drawn 

from weight-of-evidence evaluations based on the combined strength and coherence of 

inferences appropriately drawn from all of the available information. To the extent that data 

permit, hazard assessment addresses the question of mode of action of an agent as both an initial 

step in identifying human hazard potential and as a component in considering appropriate 

approaches to dose-response assessment. 

The topics in this chapter include analysis of tumor data, both human and animal, and 

analysis of other key information about properties and effects that relate to carcinogenic 

potential. The chapter addresses how information can be used to evaluate potential modes of 

action. It also provides guidance on performing a weight of evidence evaluation. 

2.1.2. Presentation of Results 

Presentation of the results of hazard assessment should be informed by Agency guidance 

as discussed in Section 2.6. The results are presented in a technical hazard characterization that 

serves as a support to later risk characterization. It includes: 

C a summary of the evaluations of hazard data,

C the rationales for its conclusions, and

2-1
C an explanation of the significant strengths or limitations of the conclusions. 

Another presentation feature is the use of a weight of evidence narrative that includes 

both a conclusion about the weight of evidence of carcinogenic potential and a summary of the 

data on which the conclusion rests. This narrative is a brief summary that in toto replaces the 

alphanumerical classification system used in EPA’s 1986 cancer guidelines (U.S. EPA, 1986a). 

2.2. ANALYSIS OF TUMOR DATA 

Evidence of carcinogenicity comes from finding tumor increases in humans or laboratory 

animals exposed to a given agent or from finding tumors following exposure to structural 

analogues to the compound under review. The significance of observed or anticipated tumor 

effects is evaluated in reference to all the other key data on the agent. This section contains 

guidance for analyzing human and animal studies to decide whether there is an association 

between exposure to an agent or a structural analogue and occurrence of tumors. Note that the 

use of the term “tumor” in these cancer guidelines is defined as malignant neoplasms or a 

combination of malignant and corresponding benign neoplasms. 

Observation of only benign neoplasia may or may not have significance for evaluation 

under these cancer guidelines. Benign tumors that are not observed to progress to malignancy 

are assessed on a case-by-case basis. There is a range of possibilities for their overall 

significance. They may deserve attention because they are serious health problems even though 

they are not malignant; for instance, benign tumors may be a health risk because of their effect on 

the function of a target tissue such as the brain. They may be significant indicators of the need 

for further testing of an agent if they are observed in a short- term test protocol, or such an 

observation may add to the overall weight of evidence if the same agent causes malignancies in a 

long-term study. Knowledge of the mode of action associated with a benign tumor response may 

aid in the interpretation of other tumor responses associated with the same agent. In other cases, 

observation of a benign tumor response alone may have no significant health hazard implications 

when other sources of evidence show no suggestion of carcinogenicity. 

2-2
2.2.1. Human Data 

Human data may come from epidemiologic studies or case reports. (Clinical human 

studies, which involve intentional exposures to substances, may provide toxicokinetic data, but 

generally not data on carcinogenicity.) The most common sources of human data for cancer risk 

assessment are epidemiologic investigations. Epidemiology is the study of the distribution of 

disease in human populations and the factors that may influence that distribution. The goals of 

cancer epidemiology are to identify distribution of cancer risk and determine the extent to which 

the risk can be attributed causally to specific exposures to exogenous or endogenous factors (see 

Centers for Disease Control and Prevention [CDC, 2004]). Epidemiologic data are extremely 

valuable in risk assessment because they provide direct evidence on whether a substance is likely 

to produce cancer in humans, thereby avoiding issues such as: species-to-species inference, 

extrapolation to exposures relevant to people, effects of concomitant exposures due to lifestyles. 

Thus, epidemiologic studies typically evaluate agents under more relevant conditions. When 

human data of high quality and adequate statistical power are available, they are generally 

preferable over animal data and should be given greater weight in hazard characterization and 

dose-response assessment, although both can be used. 

Null results from epidemiologic studies alone generally do not prove the absence of 

carcinogenic effects because such results can arise either from an agent being truly not 

carcinogenic or from other factors such as: inadequate statistical power, inadequate study design, 

imprecise estimates, or confounding factors. Moreover, null results from a well-designed and 

well-conducted epidemiologic study that contains usable exposure data can help to define upper 

limits for the estimated dose of concern for human exposure in cases where the overall weight of 

the evidence indicates that the agent is potentially carcinogenic in humans. Furthermore, data 

from a well designed and well conducted epidemiologic study that does not show positive results, 

in conjunction with compelling mechanistic information, can lend support to a conclusion that 

animal responses may not be predictive of a human cancer hazard. 

Epidemiology can also complement experimental evidence in corroborating or clarifying 

the carcinogenic potential of the agent in question. For example, epidemiologic studies that 

show elevated cancer risk for tumor sites corresponding to those at which laboratory animals 

2-3
experience increased tumor incidence can strengthen the weight of evidence of human 

carcinogenicity. Furthermore, biochemical or molecular epidemiology may help improve 

understanding of the mechanisms of human carcinogenesis. 

2.2.1.1. Assessment of Evidence of Carcinogenicity from Human Data 

All studies that are considered to be of acceptable quality, whether yielding positive or 

null results, or even suggesting protective carcinogenic effects, should be considered in assessing 

the totality of the human evidence. Conclusions about the overall evidence for carcinogenicity 

from available studies in humans should be summarized along with a discussion of uncertainties 

and gaps in knowledge. Conclusions regarding the strength of the evidence for positive or 

negative associations observed, as well as evidence supporting judgments of causality, should be 

clearly described. In assessing the human data within the overall weight of evidence, 

determination about the strength of the epidemiologic evidence should clearly identify the degree 

to which the observed associations may be explained by other factors, including bias or 

confounding. 

Characteristics that are generally desirable in epidemiologic studies include (1) clear 

articulation of study objectives or hypothesis; (2) proper selection and characterization of 

comparison groups (exposed and unexposed groups or case and control groups); (3) adequate 

characterization of exposure; (4) sufficient length of follow-up for disease occurrence; (5) valid 

ascertainment of the causes of cancer morbidity and mortality; (6) proper consideration of bias 

and confounding factors; (7) adequate sample size to detect an effect; (8) clear, well-documented, 

and appropriate methodology for data collection and analysis; (9) adequate response rate and 

methodology for handling missing data; and (10) complete and clear documentation of results. 

No single criterion determines the overall adequacy of a study. Practical and resource constraints 

may limit the ability to address all of these characteristics in a study. The risk assessor is 

encouraged to consider how the limitations of the available studies might influence the 

conclusions. While positive biases may be due, for example, to a healthy worker effect, it is also 

important to consider negative biases, for example, workers who may leave the workforce due to 

illness caused either by high exposures to the agent or to effects of confounders such as smoking. 

2-4
The following discussions highlight the major factors included in an analysis of epidemiologic 

studies. 

2.2.1.2. Types of Studies 

The major types of cancer epidemiologic study designs used for examining environmental 

causes of cancer are analytical studies and descriptive studies. Each study type has well-known 

strengths and weaknesses that affect interpretation of results, as summarized below (Lilienfeld 

and Lilienfeld, 1979; Mausner and Kramer, 1985; Kelsey et al., 1996; Rothman and Greenland, 

1998). 

Analytical epidemiologic studies, which include case-control and cohort designs, are 

generally relied on for identifying a causal association between human exposure and adverse 

health effects. In case-control studies, groups of individuals with (cases) and without (controls) a 

particular disease are identified and compared to determine differences in exposure. In cohort 

studies, a group of “exposed” and “nonexposed” individuals are identified and studied over time 

to determine differences in disease occurrence. Cohort studies can be performed either 

prospectively or retrospectively from historical records. The type of study chosen may depend on 

the hypothesis to be evaluated. For example, case-control studies may be more appropriate for 

rare cancers while cohort studies may be more appropriate for more commonly occurring 

cancers. 

On the other hand, descriptive epidemiologic studies examine symptom or disease rates 

among populations in relation to personal characteristics such as age, gender, race, and temporal 

or environmental conditions. Descriptive studies are most frequently used to generate 

hypotheses about exposure factors, but subsequent analytical designs are necessary to infer 

causality. For example, cross-sectional designs might be used to compare the prevalence of 

cancer between areas near and far from a Superfund site. However, in studies where exposure 

and disease information applies only to the current conditions, it is not possible to infer that the 

exposure actually caused the disease. Therefore, these studies are used to identify patterns or 

trends in disease occurrence over time or in different geographical locations, but typical 

2-5
limitations in the characterization of populations in these studies make it difficult to infer the 

causal agent or degree of exposure. 

Case reports describe a particular effect in an individual or group of individuals who were 

exposed to a substance. These reports are often anecdotal or highly selective in nature and 

generally are of limited use for hazard assessment. Specifically, cancer causality can rarely be 

inferred from case reports alone. Investigative follow-up may or may not accompany such 

reports. For cancer, the most common types of case series are associated with occupational and 

childhood exposures. Case reports can be particularly valuable for identifying unique features, 

such as an association with an uncommon tumor (e.g., inhalation of vinyl chloride and hepatic 

angiosarcoma in workers or ingestion of diethylstilbestrol by mothers and clear-cell carcinoma of 

the vagina in offspring). 

2.2.1.3. Exposure Issues. 

For epidemiologic data to be useful in determining whether there is an association 

between health effects and exposure to an agent, there should be adequate characterization of 

exposure information. In general, greater weight should be given to studies with more precise 

and specific exposure estimates. 

Questions to address about exposure are: What can one reliably conclude about the 

exposure parameters including (but not limited to) the level, duration, route, and frequency of 

exposure of individuals in one population as compared with another? How sensitive are study 

results to uncertainties in these parameters? 

Actual exposure measurements are not available for many retrospective studies. 

Therefore, surrogates are often used to reconstruct exposure parameters. These may involve 

attributing exposures to job classifications in a workplace or to broader occupational or 

geographic groupings. Use of surrogates carries a potential for misclassification, i.e., individuals 

may be placed in an incorrect exposure group. Misclassification generally leads to reduced 

ability of a study to detect differences between study and referent populations. 

When either current or historical monitoring data are available, the exposure evaluation 

includes consideration of the error bounds of the monitoring and analytic methods and whether 

2-6
the data are from routine or accidental exposures. The potential for misclassification and for 

measurement errors is amenable to both qualitative and quantitative analysis. These are essential 

analyses for judging a study’s results, because exposure estimation is the most critical part of a 

retrospective study. 

2.2.1.4. Biological Markers. 

Biological markers potentially offer excellent measures of exposure (Hulka and Margolin, 

1992; Peto and Darby, 1994). In some cases, molecular or cellular effects (e.g., DNA or protein 

adducts, mutation, chromosomal aberrations, levels of thyroid-stimulating hormone) can be 

measured in blood, body fluids, cells, and tissues to serve as biomarkers of exposure in humans 

and animals (Callemen et al., 1978; Birner et al., 1990). As such, they can act as an internal 

surrogate measure of chemical dose, representing, as appropriate, either recent exposure (e.g., 

serum concentration) or accumulated exposure over some period (e.g., hemoglobin adducts). 

Validated markers of exposure such as alkylated hemoglobin from exposure to ethylene oxide 

(Van Sittert et al., 1985) or urinary arsenic (Enterline et al., 1987) can improve estimates of dose 

over the relevant time periods for the markers. Markers closely identified with effects promise to 

greatly increase the ability of studies to distinguish real effects from bias at low levels of relative 

risk between populations (Taylor et al., 1994; Biggs et al., 1993) and to resolve problems of 

confounding risk factors. However, when using molecular or cellular effects as biomarkers of 

exposure, since many of these changes are often not specific to just one type of exposure, it is 

important to be aware that changes may be due to exposures unrelated to the exposure of interest 

and attention must be paid to controlling for potential confounders. 

Biochemical or molecular epidemiologic studies may use biological markers of effect as 

indicators of disease or its precursors. The application of techniques for measuring cellular and 

molecular alterations due to exposure to specific environmental agents may allow conclusions to 

be drawn about the mechanisms of carcinogenesis (see section 2.4 for more information on this 

topic). 

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2.2.1.5. Confounding Factors. 

Control for potential confounding factors is an important consideration in the evaluation 

of the design and in the analysis of observational epidemiologic studies. A confounder is a 

variable that is related to both the health outcome of concern (cancer) and exposure. Common 

examples include age, socioeconomic status, smoking habits, and diet. For instance, if older 

people are more likely to be exposed to a given contaminant as well as more likely to have cancer 

because of their age, age is considered a confounder. Adjustment for potentially confounding 

factors (from a statistical as contrasted with an epidemiologic point of view) can occur either in 

the design of the study (e.g., individual or group matching on critical factors) or in the statistical 

analysis of the results (stratification or direct or indirect adjustment). Direct adjustment in the 

statistical analysis may not be possible owing to the presentation of the data or because needed 

information was not collected during the study. In this case, indirect comparisons may be 

possible. For example, in the absence of data on smoking status among individuals in the study 

population, an examination of the possible contribution of cigarette smoking to increased lung 

cancer risk may be based on information from other sources, such as the American Cancer 

Society’s longitudinal studies (Hammand, 1966; Garfinkel and Silverberg, 1991). The 

effectiveness of adjustments contributes to the ability to draw inferences from a study. 

Different studies involving exposure to an agent may have different confounding factors. 

If consistent increases in cancer risk are observed across a collection of studies with different 

confounding factors, the inference that the agent under investigation was the etiologic factor is 

strengthened. 

There may also be instances where the agent of interest is a risk factor in conjunction with 

another agent. For instance, interaction as well as effect-measure modification are sometimes 

construed to be confounding, but they are different than confounding. Interaction is described as 

a situation in which two or more risk factors modify the effect of each other with regard to the 

occurrence of a given effect. This phenomenon is sometimes described as effect-measure 

modification or heterogeneity of effect (Szklo and Nieto, 2000). Effect-measure modification 

refers to variation in the magnitude of measure exposure effect across levels of another variable 

(Rothman and Greenland, 1998). The variable across which the effect measure varies and is 

2-8
called an effect modifier (e.g., hepatitis virus B and aflatoxin in hepatic cancer). Interaction, on 

the other hand, means effect of the exposure on the outcome differs, depending on the presence 

of another variable (the effect modifier). When the effect of the exposure of interest is 

accentuated by another variable, it is said to be synergistic interaction. Synergistic interaction 

can be additive (e.g., hepatitis virus B and aflatoxin in hepatic cancer) or multiplicative (e.g., 

asbestos and smoking in lung cancer). If the effect of exposure is diminished or eliminated by 

another variable, it said to be antagonistic interaction (e.g., intake of vitamin E and lower 

occurrence of lung cancer). 

2.2.1.6. Statistical Considerations. 

 The analysis should apply appropriate statistical methods to ascertain whether the 

observed association between exposure and effects would be expected by chance. A description 

of the method or methods used should include the reasons for their selection. Statistical analyses 

of the bias, confounding, and interaction are part of addressing the significance of an association 

and the power of a study to detect an effect. 

The analysis augments examination of the results for the whole population with 

exploration of the results for groups with comparatively greater exposure or time since first 

exposure. This may support identifying an association or establishing a dose-response trend. 

When studies show no association, such exploration may apply to determining an upper limit on 

potential human risk for consideration alongside results of animal tumor effects studies. 

2.2.1.6.1. Likelihood of observing an effect. The power of a study – the likelihood of observing 

an effect if one exists – increases with sample size, i.e., the number of subjects studied from a 

population. (For example, a quadrupling of a background rate in the 1 per 10,000 range would 

require more subjects who have experienced greater or longer exposure or lengthier follow-up, 

than a doubling of a background rate in the 1 per 100 range.) If the size of the effect is expected 

to be very small at low doses, higher doses or longer durations of exposure may be needed to 

have an appreciable likelihood of observing an effect with a given sample size. Because of the 

often long latency period in cancer development, the likelihood of observing an effect also 

2-9
depends on whether adequate time has elapsed since exposure began for effects to occur. Since 

the design of the study and the choice of analysis, as well as the design level of certainty in the 

results and the magnitude of response in an unexposed population also affect the likelihood of 

observing an effect, it is important to carefully interpret the absence of an observed effect. A 

unique feature that can be ascribed to the effects of a particular agent (such as a tumor type that is 

seen only rarely in the absence of the agent) can increase sensitivity by permitting separation of 

bias and confounding factors from real effects. Similarly, a biomarker particular to the agent can 

permit these distinctions. Statistical re-analyses of data, particularly an examination of different 

exposure indices, can give insight into potential exposure-response relationships. These are all 

factors to explore in statistical analysis of the data. 

2.2.1.6.2. Sampling and other bias issues. When comparing cases and controls or exposed and 

non-exposed populations, it would be preferable for the two populations to differ only in 

exposure to the agent in question. Because this is seldom the case, it is important to identify 

sources of sampling and other potential biases inherent in a study design or data collection 

methods. 

Bias is a systematic error. In epidemiologic studies, bias can occur in the selection of 

cases and controls or exposed and non-exposed populations, as well as the follow up of the 

groups, or the classification of disease or exposure. The size of the risks observed can be 

affected by noncomparability between populations of factors such as general health, diet, 

lifestyle, or geographic location; differences in the way case and control individuals recall past 

events; differences in data collection that result in unequal ascertainment of health effects in the 

populations; and unequal follow-up of individuals (Rothman and Greenland, 1998). Other 

factors worth consideration can be inherent in the available cohorts, e.g., use of occupational 

studies (the healthy worker effect), absence of one sex, or limitations in sample size for one or 

more ethnicities. 

The mere presence of biases does not invalidate a study, but should be reflected in the 

judgment of its strengths or weaknesses. Acceptance of studies for assessment depends on 

identifying their sources of bias and the possible effects on study results. 

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2.2.1.6.3. Combining statistical evidence across studies. Meta-analysis is a means of 

integrating the results of multiple studies of similar health effects and risk factors. This 

technique is particularly useful when various studies yield varying degrees of risk or even 

conflicting associations (negative and positive). It is intended to introduce consistency and 

comprehensiveness into what otherwise might be a more subjective review of the literature. The 

value of such an analysis is dependent upon a systematic review of the literature that uses 

transparent criteria of inclusion and exclusion. In interpreting such analyses, it is important to 

consider the effects of differences in study quality, as well as the effect of publication bias. 

Meta-analysis may not be advantageous in some circumstances. These include when the 

relationship between exposure and disease is obvious from the individual studies; when there are 

only a few studies of the key health outcomes; when there is insufficient information from 

available studies related to disease, risk estimate, or exposure classification to insure 

comparability; or when there are substantial confounding or other biases that cannot be adjusted 

for in the analysis (Blair et al., 1995; Greenland, 1987; Peto, 1992). 

2.2.1.7. Evidence for Causality 

Determining whether an observed association (risk) is causal rather than spurious 

involves consideration of a number of factors. Sir Bradford Hill (Hill, 1965) developed a set of 

guidelines for evaluating epidemiologic associations that can be used in conjunction with the 

discussion of causality such as the 2004 Surgeon General’s report on smoking (CDC, 2004) and 

in other documents (e.g., Rothman and Greenland 1998; IPCS, 1999) . The critical assessment 

of epidemiologic evidence is conceptually based upon consideration of salient aspects of the 

evidence of associations so as to reach fundamental judgments as to the likely causal significance 

of the observed associations. In so doing, it is appropriate to draw from those aspects initially 

presented in Hill’s classic monograph (Hill, 1965) and widely used by the scientific community 

in conducting such evidence-based reviews. A number of these aspects are judged to be 

particularly salient in evaluating the body of evidence available in this review, including the 

aspects described by Hill as strength, experiment, consistency, plausibility, and coherence. Other 

aspects identified by Hill, including temporality and biological gradient, are also relevant and 

2-11
considered here (e.g., in characterizing lag structures and concentration-response relationships), 

but are more directly addressed in the design and analyses of the individual epidemiologic studies 

included in this assessment. As discussed below, these salient aspects are interrelated and 

considered throughout the evaluation of the epidemiologic evidence generally reflected in the 

integrative synthesis of the mode of action framework. 

The general evaluation of the strength of the epidemiological evidence reflects 

consideration not only of the magnitude of reported effects estimates and their statistical 

significance, but also of the precision of the effects estimates and the robustness of the effects 

associations. Consideration of the robustness of the associations takes into account a number of 

factors, including in particular the impact of alternative models and model specifications and 

potential confounding factors, as well issues related to the consequences of measurement error. 

Consideration of the consistency of the effects associations involves looking across the results of 

studies conducted by different investigators in different places and times. Particular weight may 

be given, consistent with Hill’s views, to the presence of “similar results reached in quite 

different ways, e.g., prospectively and retrospectively” (Hill, 1965). Looking beyond the 

epidemiological evidence, evaluation of the biological plausibility of the associations observed in 

epidemiologic studies reflects consideration of both exposure-related factors and toxicological 

evidence relevant to identification of potential modes of action (MOAs). Similarly, consideration 

of the coherence of health effects associations reported in the epidemiologic literature reflects 

broad consideration of information pertaining to the nature of the biological markers evaluated in 

toxicologic and epidemiologic studies. 

In identifying these aspects as being particularly salient in this assessment, it is also 

important to recognize that no one aspect is either necessary or sufficient for drawing inferences 

of causality. As Hill (1965) emphasized: 

“None of my nine viewpoints can bring indisputable evidence for or against the 

cause-and-effect hypothesis and none can be required as a sine qua non. What 

they can do, with greater or less strength, is to help us to make up our minds on 

the fundamental question — is there any other way of explaining the set of facts 

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before us, is there any other answer equally, or more, likely than cause and 

effect?” 

While these aspects frame considerations weighed in assessing the epidemiologic evidence, they 

do not lend themselves to being considered in terms of simple formulas or hard-and-fast rules of 

evidence leading to answers about causality (Hill, 1965). One, for example, cannot simply count 

up the numbers of studies reporting statistically significant results or statistically non-significant 

results for carcinogenesis and related MOAs and reach credible conclusions about the relative 

strength of the evidence and the likelihood of causality. Rather, these important considerations 

are taken into account throughout the assessment with a goal of producing an objective appraisal 

of the evidence (informed by peer and public comment and advice), which includes the weighing 

of alternative views on controversial issues. Thus, although these guidelines have become 

known as “causal criteria,” it is important to note that they cannot be used as a strictly 

quantitative checklist. Rather, these “criteria” should be used to determine the strength of the 

evidence for concluding causality. In particular, the absence of one or more of the “criteria” does 

not automatically exclude a study from consideration (e.g., see discussion in CDC, 2004). The 

list below has been adapted from Hill’s guidelines as an aid in judging causality. 

(a) Consistency of the observed association. An inference of causality is strengthened 

when a pattern of elevated risks is observed across several independent studies. The 

reproducibility of findings constitutes one of the strongest arguments for causality. If there are 

discordant results among investigations, possible reasons such as differences in exposure, 

confounding factors, and the power of the study are considered. 

(b) Strength of the observed association. The finding of large, precise risks increases 

confidence that the association is not likely due to chance, bias, or other factors. A modest risk, 

however, does not preclude a causal association and may reflect a lower level of exposure, an 

agent of lower potency, or a common disease with a high background level. 

(c) Specificity of the observed association. As originally intended, this refers to 

increased inference of causality if one cause is associated with a single effect or disease (Hill, 

1965). Based on our current understanding that many agents cause cancer at multiple sites, and 

2-13
many cancers have multiple causes, this is now considered one of the weaker guidelines for 

causality. Thus, although the presence of specificity may support causality, its absence does not 

exclude it. 

(d) Temporal relationship of the observed association. A causal interpretation is 

strengthened when exposure is known to precede development of the disease. Because a latent 

period of up to 20 years or longer is often associated with cancer development in adults, the study 

should consider whether exposures occurred sufficiently long ago to produce an effect at the time 

the cancer is assessed. This is among the strongest criteria for an inference of causality. 

(e) Biological gradient (exposure-response relationship). A clear exposure-response 

relationship (e.g., increasing effects associated with greater exposure) strongly suggests cause 

and effect, especially when such relationships are also observed for duration of exposure (e.g., 

increasing effects observed following longer exposure times). There are many possible reasons 

that an epidemiologic study may fail to detect an exposure-response relationship. For example, 

an analysis that included decreasing exposures due to improved technology that is combined with 

higher prior exposure in an initial analysis can require a segmented analysis to apportion 

exposure. Other reasons for failure to detect a relationship may include a small range of 

exposures. Thus, the absence of an exposure-response relationship does not exclude a causal 

relationship. 

(f) Biological plausibility. An inference of causality tends to be strengthened by 

consistency with data from experimental studies or other sources demonstrating plausible 

biological mechanisms. A lack of mechanistic data, however, is not a reason to reject causality. 

(g) Coherence. An inference of causality may be strengthened by other lines of evidence 

that support a cause-and-effect interpretation of the association. Information is considered from 

animal bioassays, toxicokinetic studies, and short-term studies. The absence of other lines of 

evidence, however, is not a reason to reject causality. 

(h) Experimental evidence (from human populations). Experimental evidence is 

seldom available from human populations and exists only when conditions of human exposure 

have occurred to create a “natural experiment” at different levels of exposure. Strong evidence 

2-14
for causality can be provided when a change in exposure brings about a change in disease 

frequency, for example, the decrease in the risk of lung cancer that follows cessation of smoking. 

(i) Analogy. SARs and information on the agent's structural analogues can provide 

insight into whether an association is causal. Similarly, information on mode of action for a 

chemical, as one of many structural analogues, can inform decisions regarding likely causality. 

2.2.2. Animal Data 

Various whole-animal test systems are currently used or are under development for 

evaluating potential carcinogenicity. Cancer studies involving chronic exposure for most of the 

lifespan of an animal are generally accepted for evaluation of tumor effects (Tomatis et al., 1989; 

Rall, 1991; Allen et al., 1988; but see Ames and Gold, 1990). Other studies of special design are 

useful for observing formation of preneoplastic lesions or tumors or investigating specific modes 

of action. Their applicability is determined on a case-by-case basis. 

2.2.2.1. Long-term Carcinogenicity Studies 

The objective of long-term carcinogenesis bioassays is to determine the potential 

carcinogenic hazard and dose-response relationships of the test agent. Carcinogenicity rodent 

studies are designed to examine the production of tumors as well as preneoplastic lesions and 

other indications of chronic toxicity that may provide evidence of treatment-related effects and 

insights into the way the test agent produces tumors. Current standardized carcinogenicity 

studies in rodents test at least 50 animals per sex per dose group in each of three treatment groups 

and in a concurrent control group, usually for 18 to 24 months, depending on the rodent species 

tested (OECD, 1981; U.S. EPA, 1998c). The high dose in long-term studies is generally selected 

to provide the maximum ability to detect treatment-related carcinogenic effects while not 

compromising the outcome of the study through excessive toxicity or inducing inappropriate 

toxicokinetics (e.g., overwhelming absorption or detoxification mechanisms). The purpose of 

two or more lower doses is to provide some information on the shape of the dose-response curve. 

Similar protocols have been and continue to be used by many laboratories worldwide. 

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All available studies of tumor effects in whole animals should be considered, at least 

preliminarily. The analysis should discard studies judged to be wholly inadequate in protocol, 

conduct, or results. Criteria for the technical adequacy of animal carcinogenicity studies have 

been published and should be used as guidance to judge the acceptability of individual studies 

(e.g., NTP, 1984; OSTP, 1985; Chhabra et al., 1990). As these criteria, in whole or in part, may 

be updated by the National Toxicology Program (NTP) and others, the analyst should consult the 

appropriate sources to determine both the current standards as well as those that were 

contemporaneous with the study. Care should be taken to include studies that provide some 

evidence bearing on carcinogenicity or that help interpret effects noted in other studies, even if 

these studies have some limitations of protocol or conduct. Such limited, but not wholly 

inadequate, studies can contribute as their deficiencies permit. The findings of long-term rodent 

bioassays should be interpreted in conjunction with results of prechronic studies along with 

toxicokinetic studies and other pertinent information, if available. Evaluation of tumor effects 

takes into consideration both biological and statistical significance of the findings (Haseman, 

1984, 1985, 1990, 1995). The following sections highlight the major issues in the evaluation of 

long-term carcinogenicity studies. 

2.2.2.1.1. Dosing issues. Among the many criteria for technical adequacy of animal 

carcinogenicity studies is the appropriateness of dose selection. The selection of doses for 

chronic bioassays is based on scientific judgments and sound toxicologic principles. Dose 

selection should be made on the basis of relevant toxicologic information from prechronic, 

mechanistic, and toxicokinetic and mechanistic studies. A scientific rationale for dose selection 

should be clearly articulated (e.g., NTP, 1984; ILSI, 1997). How well the dose selection is made 

is evaluated after the completion of the bioassay. 

Interpretation of carcinogenicity study results is profoundly affected by study exposure 

conditions, especially by inappropriate dose selection. This is particularly important in studies 

that do not show positive results for carcinogenicity, because failure to use a sufficiently high 

dose reduces the sensitivity of the studies. A lack of tumorigenic responses at exposure levels 

that cause significant impairment of animal survival may also not be acceptable. In addition, 

2-16
overt toxicity or qualitatively altered toxicokinetics due to excessively high doses may result in 

tumor effects that are secondary to the toxicity rather than directly attributable to the agent. 

With regard to the appropriateness of the high dose, an adequate high dose would 

generally be one that produces some toxic effects without unduly affecting mortality from effects 

other than cancer or producing significant adverse effects on the nutrition and health of the test 

animals (OECD, 1981; NRC, 1993a). If the test agent does not appear to cause any specific 

target organ toxicity or perturbation of physiological function, an adequate high dose can be 

specified in terms of a percentage reduction of body weight gain over the lifespan of the animals. 

The high dose would generally be considered inadequate if neither toxicity nor change in weight 

gain is observed. On the other hand, significant increases in mortality from effects other than 

cancer generally indicate that an adequate high dose has been exceeded. 

Other signs of treatment-related toxicity associated with an excessive high dose may 

include (a) significant reduction of body weight gain (e.g., greater than 10%), (b) significant 

increases in abnormal behavioral and clinical signs, (c) significant changes in hematology or 

clinical chemistry, (d) saturation of absorption and detoxification mechanisms, or (e) marked 

changes in organ weight, morphology, and histopathology. It should be noted that practical 

upper limits have been established to avoid the use of excessively high doses in long-term 

carcinogenicity studies of environmental chemicals (e.g., 5% of the test substance in the feed for 

dietary studies or 1 g/kg body weight for oral gavage studies [OECD, 1981]). 

For dietary studies, weight gain reductions should be evaluated as to whether there is a 

palatability problem or an issue with food efficiency; certainly, the latter is a toxic manifestation. 

In the case of inhalation studies with respirable particles, evidence of impairment of normal 

clearance of particles from the lung should be considered along with other signs of toxicity to the 

respiratory airways to determine whether the high exposure concentration has been appropriately 

selected (U.S. EPA, 2001a). For dermal studies, evidence of skin irritation may indicate that an 

adequate high dose has been reached (U.S. EPA, 1989). 

In order to obtain the most relevant information from a long-term carcinogenicity study, it 

is important to maximize exposure conditions to the test material. At the same time, caution is 

appropriate in using excessive high-dose levels that would confound the interpretation of study 

2-17
results to humans. The middle and lowest doses should be selected to characterize the shape of 

the dose-response curve as much as possible. It is important that the doses be adequately spaced 

so that the study can provide relevant dose-response data for assessing human hazard and risk. If 

the testing of potential carcinogenicity is being combined with an evaluation of noncancer 

chronic toxicity, the study should be designed to include one dose in addition to the control(s) 

that is not expected to elicit adverse effects. 

There are several possible outcomes regarding the study interpretation of the significance 

and relevance of tumorigenic effects associated with exposure or dose levels below, at, or above 

an adequate high dose. The general guidance is given here; for each case, the information at 

hand should be evaluated and a rationale should be given for the position taken. 

C Adequately high dose. If an adequately high dose has been used, tumor effects are 

judged positive or negative depending on the presence or absence of significant 

tumor incidence increases, respectively. 

C Excessively high dose. If toxicity or mortality is excessive at the high dose, 

interpretation depends on whether or not tumors are found. 

– Studies that show tumor effects only at excessive doses may be 

compromised and may or may not carry weight, depending on the 

interpretation in the context of other study results and other lines of 

evidence. Results of such studies, however, are generally not considered 

suitable for dose-response extrapolation if it is determined that the 

mode(s) of action underlying the tumorigenic responses at high doses is 

not operative at lower doses. 

– Studies that show tumors at lower doses, even though the high dose is 

excessive and may be discounted, should be evaluated on their own merits. 

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– If a study does not show an increase in tumor incidence at a toxic high 

dose and appropriately spaced lower doses are used without such toxicity 

or tumors, the study is generally judged as negative for carcinogenicity. 

C Inadequately high dose. Studies of inadequate sensitivity where an adequately high 

dose has not been reached may be used to bound the dose range where carcinogenic 

effects might be expected. 

2.2.2.1.2. Statistical considerations. The main aim of statistical evaluation is to determine 

whether exposure to the test agent is associated with an increase of tumor development. 

Statistical analysis of a long-term study should be performed for each tumor type separately. The 

incidence of benign and malignant lesions of the same cell type, usually within a single tissue or 

organ, are considered separately but may be combined when scientifically defensible (McConnell 

et al., 1986). 

Trend tests and pairwise comparison tests are the recommended tests for determining 

whether chance, rather than a treatment-related effect, is a plausible explanation for an apparent 

increase in tumor incidence. A trend test such as the Cochran-Armitage test (Snedecor and 

Cochran, 1967) asks whether the results in all dose groups together increase as dose increases. A 

pairwise comparison test such as the Fisher exact test (Fisher, 1950) asks whether an incidence in 

one dose group is increased over that of the control group. By convention, for both tests a 

statistically significant comparison is one for which p is less than 0.05 that the increased 

incidence is due to chance. Significance in either kind of test is sufficient to reject the hypothesis 

that chance accounts for the result. 

A statistically significant response may or may not be biologically significant and vice 

versa. The selection of a significance level is a policy choice based on a trade-off between the 

risks of false positives and false negatives. A result with a significance level of greater or less 

than 5% (the most common significance level) is examined to see if the result confirms other 

scientific information. When the assessment departs from a simple 5% level, this should be 

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highlighted in the risk characterization. A two-tailed test or a one-tailed test can be used. In 

either case a rationale is provided. 

Statistical power can affect the likelihood that a statistically significant result could 

reasonably be expected. This is especially important in studies or dose groups with small sample 

sizes or low dose rates. Reporting the statistical power can be useful for comparing and 

reconciling positive and negative results from different studies. 

Considerations of multiple comparisons should also be taken into account. Haseman 

(1983) analyzed typical animal bioassays that tested both sexes of two species and concluded 

that, because of multiple comparisons, a single tumor increase for a species-sex-site combination 

that is statistically significant at the 1% level for common tumors or 5% for rare tumors 

corresponds to a 7–8% significance level for the study as a whole. Therefore, animal bioassays 

presenting only one significant result that falls short of the 1% level for a common tumor should 

be treated with caution. 

2.2.2.1.3. Concurrent and historical controls. The standard for determining statistical 

significance of tumor incidence comes from a comparison of tumors in dosed animals with those 

in concurrent control animals. Additional insights about both statistical and biological 

significance can come from an examination of historical control data (Tarone, 1982; Haseman, 

1995). Historical control data can add to the analysis, particularly by enabling identification of 

uncommon tumor types or high spontaneous incidence of a tumor in a given animal strain. 

Identification of common or uncommon situations prompts further thought about the meaning of 

the response in the current study in context with other observations in animal studies and with 

other evidence about the carcinogenic potential of the agent. These other sources of information 

may reinforce or weaken the significance given to the response in the hazard assessment. 

Caution should be exercised in simply looking at the ranges of historical responses, because the 

range ignores differences in survival of animals among studies and is related to the number of 

studies in the database. 

In analyzing results for uncommon tumors in a treated group that are not statistically 

significant in comparison with concurrent controls, the analyst may be informed by the 

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experience of historical controls to conclude that the result is in fact unlikely to be due to chance. 

However, caution should be used in interpreting results. In analyzing results for common 

tumors, a different set of considerations comes into play. Generally speaking, statistically 

significant increases in tumors should not be discounted simply because incidence rates in the 

treated groups are within the range of historical controls or because incidence rates in the 

concurrent controls are somewhat lower than average. Random assignment of animals to groups 

and proper statistical procedures provide assurance that statistically significant results are 

unlikely to be due to chance alone. However, caution should be used in interpreting results that 

are barely statistically significant or in which incidence rates in concurrent controls are unusually 

low in comparison with historical controls. 

In cases where there may be reason to discount the biological relevance to humans of 

increases in common animal tumors, such considerations should be weighed on their own merits 

and clearly distinguished from statistical concerns. 

When historical control data are used, the discussion should address several issues that 

affect comparability of historical and concurrent control data, such as genetic drift in the 

laboratory strains, differences in pathology examination at different times and in different 

laboratories (e.g., in criteria for evaluating lesions; variations in the techniques for the 

preparation or reading of tissue samples among laboratories), and comparability of animals from 

different suppliers. The most relevant historical data come from the same laboratory and the 

same supplier and are gathered within 2 or 3 years one way or the other of the study under 

review; other data should be used only with extreme caution. 

2.2.2.1.4. Assessment of evidence of carcinogenicity from long-term animal studies. In 

general, observation of tumors under different circumstances lends support to the significance of 

the findings for animal carcinogenicity. Significance is generally increased by the observation of 

more of the factors listed below. For a factor such as malignancy, the severity of the observed 

pathology can also affect the significance. The following observations add significance to the 

tumor findings: 

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C uncommon tumor types; 

C tumors at multiple sites; 

C tumors by more than one route of administration; 

C tumors in multiple species, strains, or both sexes; 

C progression of lesions from preneoplastic to benign to malignant; 

C reduced latency of neoplastic lesions; 

C metastases; 

C unusual magnitude of tumor response; 

C proportion of malignant tumors; and 

C dose-related increases. 

In these cancer guidelines, tumors observed in animals are generally assumed to indicate 

that an agent may produce tumors in humans. Mode of action may help inform this assumption 

on a chemical-specific basis. Moreover, the absence of tumors in well-conducted, long-term 

animal studies in at least two species provides reasonable assurance that an agent may not be a 

carcinogenic concern for humans. 

2.2.2.1.5. Site concordance. Site concordance of tumor effects between animals and humans 

should be considered in each case. Thus far, there is evidence that growth control mechanisms at 

the level of the cell are homologous among mammals, but there is no evidence that these 

mechanisms are site concordant. Moreover, agents observed to produce tumors in both humans 

and animals have produced tumors either at the same site (e.g., vinyl chloride) or different sites 

(e.g., benzene) (NRC, 1994). Hence, site concordance is not always assumed between animals 

and humans. On the other hand, certain modes of action with consequences for particular tissue 

sites (e.g., disruption of thyroid function) may lead to an anticipation of site concordance. 

2.2.2.2. Perinatal Carcinogenicity Studies 

The objective of perinatal carcinogenesis studies is to determine the carcinogenic 

potential and dose-response relationships of the test agent in the developing organism. Some 

2-22
investigators have hypothesized that the age of initial exposure to a chemical carcinogen may 

influence the carcinogenic response (Vesselinovitch et al., 1979; Rice, 1979; McConnell, 1992). 

Current standardized long-term carcinogenesis bioassays generally begin dosing animals at 6–8 

weeks of age and continue dosing for the lifespan of the animal (18–24 months). This protocol 

has been modified in some cases to investigate the potential of the test agent to induce 

transplacental carcinogenesis or to investigate the potential differences following perinatal and 

adult exposures, but currently there is not a standardized protocol for testing agents for 

carcinogenic effects following prenatal or early postnatal exposure. 

Several cancer bioassay studies have compared adult and perinatal exposures (see 

McConnell, 1992; U.S. EPA, 1996b). A review of these studies reveals that perinatal exposure 

rarely identifies carcinogens that are not found in standard animal bioassays. Exposure that is 

perinatal can increase the incidence of a given type of tumor. The increase may reflect an 

increased length of exposure and a higher dose for the developing organism relative to the adult 

or an increase in susceptibility in some cases. Additionally, exposure that is perinatal through 

adulthood sometimes reduces the latency period for tumors to develop in the growing organism 

(U.S. EPA, 1996b). EPA evaluates the usefulness of perinatal studies on an agent-by-agent basis 

(e.g., U.S. EPA, 1997a, b). 

Perinatal study data analysis generally follows the principles discussed above for 

evaluating other long-term carcinogenicity studies. When differences in responses between 

perinatal animals and adult animals suggest an increased susceptibility of perinatal or postnatal 

animals, such as the ones below, a separate evaluation of the response should be prepared: 

C a difference in dose-response relationship, 

C the presence of different tumor types, 

C an earlier onset of tumors, or 

C an increase in the incidence of tumors. 

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2.2.2.3. Other Studies 

Intermediate-term and acute dosing studies often use protocols that screen for 

carcinogenic or preneoplastic effects, sometimes in a single tissue. Some protocols involve the 

development of various proliferative lesions, such as foci of alteration in the liver (Goldsworthy 

et al., 1986). Others use tumor endpoints, such as the induction of lung adenomas in the 

sensitive strain A mouse (Maronpot et al., 1986) or tumor induction in initiation-promotion 

studies using various organs such as the bladder, intestine, liver, lung, mammary gland, and 

thyroid (Ito et al., 1992). In these tests, the selected tissue rather than the whole animal is, in a 

sense, the test system. Important information concerning the steps in the carcinogenic process 

and mode of action can be obtained from “start/stop” experiments. In these protocols, an agent is 

given for a period of time to induce particular lesions or effects and then stopped in order to 

evaluate the progression or reversibility of processes (Todd, 1986; Marsman and Popp, 1994). 

Assays in genetically engineered rodents may provide insight into the chemical and gene 

interactions involved in carcinogenesis (Tennant et al., 1995). These mechanistically based 

approaches involve activated oncogenes that are introduced (transgenic) or tumor suppressor 

genes that are deleted (knocked out). If appropriate genes are selected, not only may these 

systems provide information on mechanisms, but the rodents typically show tumor development 

earlier than in the standard bioassay. Transgenic mutagenesis assays also represent a mechanistic 

approach for assessing the mutagenic properties of agents as well as developing quantitative 

linkages between exposure, internal dose, and mutation related to tumor induction (Morrison and 

Ashby, 1994; Sisk et al., 1994; Hayward et al., 1995). 

The support that these studies give to a determination of carcinogenicity rests on their 

contribution to the consistency of other evidence about an agent. For instance, benzoyl peroxide 

has promoter activity on the skin, but the overall evidence may be less supportive (Kraus et al., 

1995). These studies also may contribute information about mode of action. It is important to 

recognize the limitations of these experimental protocols, such as short duration, limited 

histology, lack of complete development of tumors, or experimental manipulation of the 

carcinogenic process, that may limit their contribution to the overall assessment. Generally, their 

results are appropriate as aids in the interpretation of other toxicological evidence (e.g., rodent 

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chronic bioassays), especially regarding potential modes of action. On the basis of currently 

available information, it is unlikely that any of these assays, which are conducted for 6 months 

with 15 animals per group, will replace all chronic bioassays for hazard identification (Spalding 

et al., 2000; Gulezian et al., 2000; ILSI, 2001). 

2.2.3. Structural Analogue Data 

For some chemical classes, there is significant available information, largely from rodent 

bioassays, on the carcinogenicity of analogues. Analogue effects are instructive in investigating 

carcinogenic potential of an agent as well as in identifying potential target organs, exposures 

associated with effects, and potential functional class effects or modes of action. All appropriate 

studies should be included and analyzed, whether indicative of a positive effect or not. 

Evaluation includes tests in various animal species, strains, and sexes; with different routes of 

administration; and at various doses, as data are available. Confidence in conclusions is a 

function of how similar the analogues are to the agent under review in structure, metabolism, and 

biological activity. It is important to consider this confidence to ensure a balanced position. 

2.3. ANALYSIS OF OTHER KEY DATA 

The physical, chemical, and structural properties of an agent, as well as data on endpoints 

that are thought to be critical elements of the carcinogenic process, provide valuable insights into 

the likelihood of human cancer risk. The following sections provide guidance for analyses of 

these data. 

2.3.1. Physicochemical Properties 

Physicochemical properties affect an agent’s absorption, tissue distribution 

(bioavailability), biotransformation, and degradation in the body and are important determinants 

of hazard potential (and dose-response analysis). Properties that should be analyzed include, but 

are not limited to, molecular weight, size, and shape; valence state; physical state (gas, liquid, 

solid); water or lipid solubility, which can influence retention and tissue distribution; and 

potential for chemical degradation or stabilization in the body. 

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An agent’s potential for chemical reaction with cellular components, particularly with 

DNA and proteins, is also important. The agent’s molecular size and shape, electrophilicity, and 

charge distribution are considered in order to decide whether they would facilitate such reactions. 

2.3.2. Structure-Activity Relationships (SARs) 

SAR analyses and models can be used to predict molecular properties, surrogate 

biological endpoints, and carcinogenicity (see, e.g., Richard, 1998a, b; Richard and Williams, 

2002; Contrera et al., 2003). Overall, these analyses provide valuable initial information on 

agents, they may strengthen or weaken concern, and they are part of the weight of evidence. 

Currently, SAR analysis is most useful for chemicals and metabolites that are believed to 

initiate carcinogenesis through covalent interaction with DNA (i.e., DNA-reactive, mutagenic, 

electrophilic, or proelectrophilic chemicals) (Ashby and Tennant, 1991). For organic chemicals, 

the predictive capability of SAR analysis combined with other toxicity information has been 

demonstrated (Ashby and Tennant, 1994). The following parameters are useful in comparing an 

agent to its structural analogues and congeners that produce tumors and affect related biological 

processes such as receptor binding and activation, mutagenicity, and general toxicity (Woo and 

Arcos, 1989): 

C nature and reactivity of the electrophilic moiety or moieties present; 

C potential to form electrophilic reactive intermediate(s) through chemical, 

photochemical, or metabolic activation; 

C contribution of the carrier molecule to which the electrophilic moiety(ies) is 

attached; 

C physicochemical properties (e.g., physical state, solubility, octanol/water partition 

coefficient, half-life in aqueous solution); 

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C structural and substructural features (e.g., electronic, stearic, molecular geometric); 

C metabolic pattern (e.g., metabolic pathways and activation and detoxification ratio); 

and 

C possible exposure route(s) of the agent. 

Suitable SAR analysis of non-DNA-reactive chemicals and of DNA-reactive chemicals 

that do not appear to bind covalently to DNA should be based on knowledge or postulation of the 

probable mode(s) of action of closely related carcinogenic structural analogues (e.g., receptor 

mediated, cytotoxicity related). Examination of the physicochemical and biochemical properties 

of the agent may then provide the rest of the information needed in order to make an assessment 

of the likelihood of the agent’s activity by that mode of action. 

2.3.3. Comparative Metabolism and Toxicokinetics 

Studies of the absorption, distribution, biotransformation, and excretion of agents permit 

comparisons among species to assist in determining the implications of animal responses for 

human hazard assessment, supporting identification of active metabolites, identifying changes in 

distribution and metabolic pathway or pathways over a dose range, and making comparisons 

among different routes of exposure. 

If extensive data are available (e.g., blood/tissue partition coefficients and pertinent 

physiological parameters of the species of interest), physiologically based toxicokinetic models 

can be constructed to assist in a determination of tissue dosimetry, species-to-species 

extrapolation of dose, and route-to-route extrapolation (Conolly and Andersen, 1991; see Section 

3.1.2). If sufficient data are not available, it may be assumed as a default that toxicokinetic and 

metabolic processes are qualitatively comparable among species. Discussion of appropriate 

procedures for quantitative, interspecies comparisons appears in Chapter 3. 

The qualitative question of whether an agent is absorbed by a particular route of exposure 

is important for weight of evidence classification, discussed in Section 2.5. Decisions about 

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whether route of exposure is a limiting factor on expression of any hazard, e.g., absorption does 

not occur by a specified route, are generally based on studies in which effects of the agent or its 

structural analogues have been observed by different routes, on physical-chemical properties, or 

on toxicokinetics studies. 

Adequate metabolism and toxicokinetic data can be applied toward the following, as data 

permit. Confidence in conclusions is enhanced when in vivo data are available. 

C Identifying metabolites and reactive intermediates of metabolism and determining 

whether one or more of these intermediates is likely to be responsible for the 

observed effects. Information on the reactive intermediates focuses on SAR 

analysis, analysis of potential modes of action, and estimation of internal dose in 

dose-response assessment (D’Souza et al., 1987; Krewski et al., 1987). 

C Identifying and comparing the relative activities of metabolic pathways in animals 

and in humans, and at different ages. This analysis can provide insights for 

extrapolating results of animal studies to humans. 

C Describing anticipated distribution within the body and possibly identifying target 

organs. Use of water solubility, molecular weight, and structure analysis can 

support qualitative inferences about anticipated distribution and excretion. In 

addition, describing whether the agent or metabolite of concern will be excreted 

rapidly or slowly or whether it will be stored in a particular tissue or tissues to be 

mobilized later can identify issues in comparing species and formulating doseresponse assessment approaches. 

C Identifying changes in toxicokinetics and metabolic pathways with increases in 

dose. These changes may result in important differences between high and low 

dose levels in disposition of the agent or generation of its active forms. These 

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studies play an important role in providing a rationale for dose selection in 

carcinogenicity studies. 

C Identifying and comparing metabolic process differences by age, sex, or other 

characteristic so that susceptible subpopulations can be recognized. For example, 

metabolic capacity with respect to P450 enzymes in newborn children is extremely 

limited compared to that in adults, so that a carcinogenic metabolite formed through 

P450 activity will have limited effect in the young, whereas a carcinogenic agent 

deactivated through P450 activity will result in increased susceptibility of this 

lifestage (Cresteil, 1998). A variety of changes in toxicokinetics and physiology 

occur from the fetal stage to post-weaning to young child. Any of these changes 

may make a difference for risk (Renwick, 1998). 

C Determining bioavailability via different routes of exposure by analyzing uptake 

processes under various exposure conditions. This analysis supports identification 

of hazards for untested routes. In addition, use of physicochemical data (e.g., 

octanol-water partition coefficient information) can support an inference about the 

likelihood of dermal absorption (Flynn, 1990). 

Attempts should be made in all of these areas to clarify and describe as much as possible 

the variability to be expected because of differences in species, sex, age, and route of exposure. 

The analysis takes into account the presence of subpopulations of individuals who are 

particularly vulnerable to the effects of an agent because of toxicokinetic or metabolic differences 

(genetically or environmentally determined) (Bois et al., 1995) and is a special emphasis for 

assessment of risks to children. 

2.3.4. Toxicological and Clinical Findings 

Toxicological findings in experimental animals and clinical observations in humans are 

important resources for the cancer hazard assessment. Such findings provide information on 

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physiological effects and effects on enzymes, hormones, and other important macromolecules as 

well as on target organs for toxicity. For example, given that the cancer process represents 

defects in processes such as terminal differentiation, growth control, and cell death, 

developmental studies of agents may provide an understanding of the activity of an agent that 

carries over to cancer assessment. Toxicity studies in animals by different routes of 

administration support comparison of absorption and metabolism by those routes. Data on 

human variability in standard clinical tests may also provide insight into the range of human 

susceptibility and the common mechanisms of agents that affect the tested parameters. 

2.3.5. Events Relevant to Mode of Carcinogenic Action 

Knowledge of the biochemical and biological changes that precede tumor development 

(which include, but are not limited to, mutagenesis, increased cell proliferation, inhibition of 

programmed cell death, and receptor activation) may provide important insight for determining 

whether a cancer hazard exists and may help inform appropriate consideration of the doseresponse relationship below the range of observable tumor response. Because cancer can result 

from a series of genetic alterations in the genes that control cell growth, division, and 

differentiation (Vogelstein et al., 1988; Hanahan and Weinberg, 2000; Kinzler and Vogelstein, 

2002), the ability of an agent to affect genotype (and hence gene products) or gene expression is 

of obvious importance in evaluating its influence on the carcinogenic process. Initial and key 

questions to examine are: Does the agent (or its metabolite) interact directly with DNA, leading 

to mutations that bring about changes in gene products or gene expression? Does the agent bring 

about effects on gene expression via other nondirect DNA interaction processes? 

Furthermore, carcinogenesis involves a complex series and interplay of events that alter 

the signals a cell receives from its extracellular environment, thereby promoting uncontrolled 

growth. Many, but not all, mutagens are carcinogens, and some, but not all, agents that induce 

cell proliferation lead to tumor development. Thus, understanding the range of key steps in the 

carcinogenic process upon which an agent might act is essential for evaluating its mode of action. 

Determination of carcinogens that are operating by a mutagenic mode of action, for example, 

entails evaluation of in vivo or in vitro short-term testing results for genetic endpoints, metabolic 

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profiles, physicochemical properties, and structure-activity relationship (SAR) analyses in a 

weight-of-evidence approach (Dearfield et al., 1991; U.S. EPA, 1986b; Waters et al., 1999). Key 

data for a mutagenic mode of action may be evidence that the carcinogen or a metabolite is 

DNA-reactive and/or has the ability to bind to DNA. Also, mutagenic carcinogens usually 

produce positive effects in multiple test systems for different genetic endpoints, particularly gene 

mutations and structural chromosome aberrations, and in tests performed in vivo which generally 

are supported by positive tests in vitro. Additionally, carcinogens may be identified as operating 

via a mutagenic mode of action if they have similar properties and SAR to mutagenic 

carcinogens. Endpoints that provide insight into an agent’s ability to alter gene products and 

gene expression, together with other features of an agent’s potential mode of carcinogenic action, 

are discussed below. 

2.3.5.1. Direct DNA-Reactive Effects 

It is well known that many carcinogens are electrophiles that interact with DNA, resulting 

in DNA adducts and breakage (referred to in these cancer guidelines as direct DNA effects). 

Usually during the process of DNA replication, these DNA lesions can be converted into and 

fixed as mutations and chromosomal alterations that then may initiate and otherwise contribute to 

the carcinogenic process (Shelby and Zeiger, 1990; Tinwell and Ashby, 1991; IARC, 1999). 

Thus, studies of mutations and other genetic lesions continue to inform the assessment of 

potential human cancer hazard and in the understanding of an agent’s mode of carcinogenic 

action. 

EPA has published testing guidelines for detecting the ability of an agent to damage DNA 

and produce mutations and chromosomal alterations (as discussed in Dearfield et al., 1991). 

Briefly, standard tests for gene mutations in bacteria and mammalian cells in vitro and in vivo 

and for structural chromosomal aberrations in vitro and in vivo are important examples of 

relevant methods. New molecular approaches, such as mouse mutations and cancer transgenic 

models, are providing a means to examine mutation at tissue sites where the tumor response is 

observed (Heddle and Swiger, 1996; Tennant et al., 1999). Additionally, continued 

improvements in fluorescent-based chromosome staining methods (fluorescent in situ 

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hybridization [FISH] ) will allow the detection of specific chromosomal abnormalities in relevant 

target tissues (Tucker and Preston, 1998). 

Endpoints indicative of DNA damage but not measures of mutation per se, such as DNA 

adducts or strand breakage, may be detected in relevant target tissues and thus contribute to 

evaluating an agent’s mutagenic potential. Evidence of chemical-specific DNA adducts (e.g., 

reactions at oxygen sites in DNA bases or with ring nitrogens of guanine and adenine) provides 

information on a mutagen’s ability to directly interact with DNA (La and Swenberg, 1996). 

Some planar molecules (e.g., 9-aminoacridine) intercalate between base pairs of DNA, which 

results in a physical distortion in DNA that may lead to mutations when DNA replicates. As 

discussed below, some carcinogens do not interact directly with DNA, but they can produce 

increases in endogenous levels of DNA adducts (e.g., 8-hydroxyguanine) by indirect 

mechanisms. 

2.3.5.2. Indirect DNA Effects or Other Effects on Genes/Gene Expression 

Although some carcinogens may result in an elevation of mutations or cytogenetic 

anomalies, as detected in standard assays, they may do so by indirect mechanisms. These effects 

may be brought about by chemical-cell interactions rather than by the chemical (or its metabolite) 

directly interacting with DNA. An increase in mutations might be due to cytotoxic exposures 

causing regenerative proliferation or to mitogenic influences (Cohen and Ellwein, 1990). 

Increased cell division may elevate mutation by clonal expansion of initiated cells or by 

increasing the number of genetic errors by rapid cell division and reduced time for DNA repair. 

Some agents might result in an elevation of mutations by interfering with the enzymes involved 

in DNA repair and recombination (Barrett and Lee, 1992). Damage to certain critical DNA 

repair genes or other genes (e.g., the p53 gene) may result in genomic instability, which 

predisposes cells to further genetic alterations and increases the probability of neoplastic 

progression (Harris and Hollstein, 1993; Levine et al., 1994; Rouse and Jackson, 2002). 

Likewise, DNA repair processes may be saturated at certain doses of a chemical, leading to an 

elevation of genetic alterations. 

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The initiation of programmed cell death (apoptosis) can potentially be blocked by an 

agent, thereby permitting replication of cells carrying genetic errors that would normally be 

removed from the proliferative pool. At certain doses an agent may also generate reactive 

oxygen species that produce oxidative damage to DNA and other macromolecules (Chang et al. 

1988; Kehrer, 1993; Clayson et al., 1994). The role of cellular alterations that are attributable to 

oxidative damage in tumorigenesis (e.g., 8-hydroxyguanine) is currently unclear. 

Several carcinogens have been shown to induce aneuploidy (the loss or gain of 

chromosomes) (Barrett, 1992; Gibson et al., 1995). Aneuploidy can result in the loss of 

heterozygosity or genomic instability (Cavenee et al., 1986; Fearon and Vogelstein, 1990). 

Agents that cause aneuploidy typically interfere with the normal process of chromosome 

segregation by interacting with non-DNA targets such as the proteins needed for chromosome 

segregation and chromosome movement. Whether this chromosome imbalance is the cause or 

the effect of tumorigenesis is not clear. Thus, it is important to understand if the agent induces 

aneuploidy as a key early event in the carcinogenic process. 

It is possible for an agent to alter gene expression by transcriptional, translational, or posttranslational modifications. For example, perturbation of DNA methylation patterns may cause 

effects that contribute to carcinogenesis (Jones, 1986; Holliday, 1987; Goodman and Counts, 

1993; Chuang et al., 1996; Baylin and Bestor, 2002). Overexpression of genes by DNA 

amplification has been observed in certain tumors (Vainio et al., 1992). Mechanisms of altering 

gene expression may involve cellular reprogramming through hormonal or receptor-mediated 

mechanisms (Barrett, 1992; Ashby et al., 1994). 

Both cell proliferation and programmed cell death can be part of the maintenance of 

homeostasis in many normal tissues, and alterations in the level or rate of either can be important 

elements of the carcinogenic process. The balance between the two can directly affect the 

survival and growth of initiated cells as well as preneoplastic and tumor cell populations (i.e., 

increase in cell proliferation or decrease in cell death) (Moolgavkar, 1986; Cohen and Ellwein, 

1990, 1991; Cohen et al., 1991; Bellamy et al., 1995). Thus, measurements of these events can 

contribute to the weight of the evidence for cancer hazard prediction and to mode of action 

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understanding. In studies of proliferative effects, distinctions should be made between 

mitogenesis and regenerative proliferation (Cohen and Ellwein, 1990, 1991; Cohen et al., 1991). 

In applying information from studies on cell proliferation and apoptosis to risk 

assessment, it is important to identify the tissues and target cells involved, to measure effects in 

both normal and neoplastic tissue, to distinguish between apoptosis and necrosis, and to 

determine the dose that affects these processes. Gap-junctional intercellular communication is 

believed to play a role in tissue and organ development and in the maintenance of a normal 

cellular phenotype within tissues. A growing body of evidence suggests that chemical 

interference with gap-junctional intercellular communication is a contributing factor in tumor 

development (Swierenga and Yamasaki, 1992; Yamasaki, 1995). 

2.3.5.3. Precursor Events and Biomarker Information 

Most testing schemes for mutagenicity and other short-term assays were designed for 

hazard identification purposes; thus, these assays are generally conducted using acute exposures. 

For data on “precursor steps” to be useful in informing the dose-response curve for tumor 

induction below the level of observation, it is often useful for data to come from in vivo studies 

and from studies where exposure is repeated or given over an extended period of time. Although 

consistency of results across different assays and animal models provides a stronger basis for 

drawing conclusions, it is desirable to have data on the precursor event in the same target organ, 

sex, animal strain, and species as the tumor data. In evaluating an agent’s mode of action, it is 

usually not sufficient to determine that some event commences upon dosing. It is important to 

understand whether it is a necessary event that plays a key role in the process that leads to tumor 

development versus an effect of the cancer process itself or simply an associated event. 

Various endpoints can serve as biological markers of effects in biological systems or 

samples. These may help identify doses at which elements of the carcinogenic process are 

operating; aid in interspecies extrapolations when data are available from both experimental 

animal and human cells; and under certain circumstances, provide insights into the possible 

shape of the dose-response curve below levels where tumor incidences are observed (e.g., Choy, 

1993). 

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Genetic and other findings (such as changes in proto-oncogenes and tumor suppressor 

genes in preneoplastic and neoplastic tissue or, possibly, measures of endocrine disruption) can 

indicate the potential for disease and, as such, serve as biomarkers of effect. They, too, can be 

used in different ways. 

C The spectrum of genetic changes in proliferative lesions and tumors following 

chemical administration to experimental animals can be determined and compared 

with that in spontaneous tumors in control animals, in animals exposed to other 

agents of varying structural and functional activities, and in persons exposed to the 

agent under study. 

C Biomarkers of effect and/or precursors may help to identify subpopulations of 

individuals who may be at an elevated risk for a certain cancer or exposure to a 

certain agent, e.g., cytochrome P450 2D6/debrisoquine sensitivity for lung cancer 

(Caporaso et al., 1989) or inherited colon cancer syndromes (Kinzler et al., 1991; 

Peltomäki et al., 1993). 

C As with biomarkers of exposure, it may be justified in some cases to use biomarkers 

of effect and/or precursors for dose-response assessment or to provide insight into 

the potential shape of the dose-response curve at doses below those at which tumors 

are induced experimentally. 

In applying biomarker data to cancer assessment an assessment should consider: 

C analytical methodology, 

• routes of exposure,

C exposure to mixtures,

C time after exposure,

C sensitivity and specificity of biomarkers, and

C dose-response relationships.

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2.3.5.4. Judging Data 

Criteria that are generally applicable for judging the adequacy of mechanistically based 

data include: 

C mechanistic relevance of the data to carcinogenicity, 

C number of studies of each endpoint, 

C consistency of results in different test systems and different species, 

C similar dose-response relationships for tumor and mode of action-related effects, 

C conduct of the tests in accordance with generally accepted protocols, and 

C degree of consensus and general acceptance among scientists regarding 

interpretation of the significance and specificity of the tests. 

Although important information can be gained from in vitro test systems, a higher level of 

confidence is generally given to data that are derived from in vivo systems, particularly those 

results that show a site concordance with the tumor data. 

It is important to remember that when judging and considering the use of any data, the 

basic standard of quality, as defined by the EPA Information Quality Guidelines, should be 

satisfied. 

2.4. MODE OF ACTION—GENERAL CONSIDERATIONS AND FRAMEWORK 

FOR ANALYSIS 

2.4.1. General Considerations 

The interaction between the biology of the organism and the chemical properties of the 

agent determine whether there is an adverse effect. Thus, mode of action analysis is based on 

physical, chemical, and biological information that helps to explain key events in an agent’s 

influence on development of tumors. The entire range of information developed in the 

assessment is reviewed to arrive at a reasoned judgment. An agent may work by more than one 

mode of action, both at different sites and at the same tumor site. Thus the mode of action and 

human relevance cannot necessarily be generalized to other toxic endpoints or tissues or cell 

types without additional analyses (IPCS, 1999; Meek et al., 2003). At least some information 

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bearing on mode of action (e.g., SAR, screening tests for mutagenicity) is present for most agents 

undergoing assessment of carcinogenicity, even though certainty about exact molecular 

mechanisms may be rare. 

Information for mode of action analysis generally includes tumor data in humans and 

animals and among structural analogues, as well as the other key data. The more complete the 

data package and the generic knowledge about a given mode of action, the more confidence one 

has and the more one can rely on assessment of available data rather than reverting to default 

options to address the absence of information on mode of action. Reasoned judgments are 

generally based on a data-rich source of chemical, chemical class, and tumor type-specific 

information. Many times there will be conflicting data and gaps in the information base; it is 

important to carefully evaluate these uncertainties before reaching any conclusion. 

In making decisions about potential modes of action and the relevance of animal tumor 

findings to humans (Ashby et al., 1990; Ashby and Tennant, 1991; Tennant, 1993; IPCS 1999; 

Sonich-Mullin et al., 2001; Meek et al., 2003), very often the results of chronic animal studies 

may give important clues. Some of the important factors to review include: 

C tumor types, for example, those responsive to endocrine influence or those produced 

by DNA-reactive carcinogens; 

C number of studies and of tumor sites, sexes, and species affected or unaffected in 

those studies and if the data present a coherent story; 

• similarity of metabolic activation and detoxification for a specific chemical between 

humans and tested species; 

C influence of route of exposure on the spectrum of tumors and whether they occur at 

point of exposure or systemic sites; 

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C effect of high dose exposures on the target organ or systemic toxicity that may not 

reflect typical physiological conditions, for example, urinary chemical changes 

associated with stone formation, effects on immune surveillance; 

C presence of proliferative lesions, for example, hepatic foci, or hyperplasia; 

C effect of dose and time on the progression of lesions from preneoplastic to benign 

tumors, then to malignant; 

C ratio of malignant to benign tumors as a function of dose and time; 

C time of appearance of tumors after commencing exposure; 

C development of tumors that invade locally or systemically, or lead to death; 

C tumors at organ sites with high or low background historical incidence in laboratory 

animals; 

C biomarkers in tumor cells, both induced and spontaneous, for example, DNA or 

protein adducts, mutation spectra, chromosome changes, oncogene activation; 

and/or 

C shape of the dose-response curve in the range of tumor observation, for example, 

linear versus nonlinear. 

Some of the myriad ways in which information from chronic animal studies influences 

mode of action judgments include, but are not limited to, the following: 

• multisite and multispecies tumor effects that are often associated with mutagenic agents; 

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• tumors restricted to one sex or species suggesting an influence restricted to gender, strain, 

or species; 

• late onset of tumors that are primarily benign, are at sites with a high historical 

background incidence, or show reversal of lesions on cessation of exposure suggesting a 

growth-promoting mode of action; 

• the possibility that an agent acting differently in different tissues; or 

• the possibility that has more than one mode of action in a single tissue. 

Simple knowledge of sites of tumor increase in rodent studies can give preliminary clues 

as to mode of action. Experience at the National Toxicology Program (NTP) indicates that 

substances that are DNA reactive and that produce gene mutations may be unique in producing 

tumors in certain anatomical sites, whereas tumors at other sites may arise from both mutagenic 

or nonmutagenic influences (Ashby and Tennant, 1991; Huff et al., 1991). 

The types of data and their influence on judgments regarding mode of action are expected 

to evolve, both as science advances and as the risk assessment community gains more experience 

with these analyses. This section contains a framework for evaluating hypothesized mode(s) of 

action. This framework has similarities to and differences with the concepts presented in other 

MOA frameworks (e.g., IPCS, 1999; Sonich-Mullin et al., 2001; Meek et al., 2003). Differences 

are often due to the context of the use for the framework. For example, the Meek et al. (2003) 

presents a stand-alone document for addressing mode of action issues; thus, it recommends that 

conclusions concerning MOA be rendered separately. In these cancer guidelines, however, they 

are incorporated into the context of all of the data regarding weight of the evidence for 

carcinogenicity. 

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2.4.2. Evaluating an Hypothesized Mode of Action 

2.4.2.1. Peer Review 

In reaching conclusions, the question of “general acceptance” of a mode of action should 

be tested as part of the independent peer review that EPA obtains for its assessment and 

conclusions. In some cases the mode of action may already have been established by 

development of a large body of research information and characterization of the phenomenon 

over time. In some cases there will have been development of an Agency policy (e.g., mode of 

action involving alpha-2u-globulin in the male rat [U.S. EPA, 1991b]) or a series of previous 

assessments in which both the mode of action and its applicability to particular cases has been 

explored. If so, the assessment and its peer review can focus on the evidence that a particular 

agent acts in this mode. The peer review should also evaluate the strengths and weaknesses of 

competing modes of action. 

In other cases, the mode of action may not have previously been the subject of an Agency 

document. If so, the data to support both the mode of action and the associated activity of the 

agent should undergo EPA assessment and subsequent peer review. 

2.4.2.2. Use of the Framework 

The framework supports a full analysis of mode of action information, but it can also be 

used as a screen to decide whether sufficient information is available to evaluate or whether the 

data gaps are too substantial to justify further analysis. Mode of action conclusions are used to 

address the question of human relevance of animal tumor responses, to address differences in 

anticipated response among humans, such as between children and adults or men and women; 

and as the basis of decisions about the anticipated shape of the dose-response relationship. 

Guidance on the latter appears in Section 3. 

This framework is intended to provide an analytical approach for evaluating the mode of 

action. It is neither a checklist nor a list of required criteria. As the type and amount of 

information will depend on the mode of action postulated, scientific judgment is important to 

determine if the weight of evidence is sufficient. 

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2.4.3. Framework for Evaluating Each Hypothesized Carcinogenic Mode of Action 

This framework is intended to be an analytic tool for judging whether available data 

support a mode of carcinogenic action hypothesized for an agent. It is based upon considerations 

for causality in epidemiologic investigations originally articulated by Hill (1965) but later 

modified by others and extended to experimental studies. The original Hill criteria were applied 

to epidemiologic data, whereas this framework is applied to a much wider assortment of 

experimental data, so it retains the basic principles of Hill but is much modified in content. 

The modified Hill criteria can be useful for organizing thinking about aspects of 

causation, and they are consistent with the scientific method of developing hypotheses and 

testing those hypotheses experimentally. During analysis by EPA, and as guidance for peer 

review, a key question is whether the data to support a mode of action meet the standards 

generally applied in experimental biology regarding inference of causation. 

All pertinent studies are reviewed in analyzing a mode of action, and an overall weighing 

of evidence is performed, laying out the strengths, weaknesses, and uncertainties of the case as 

well as potential alternative positions and rationales. Identifying data gaps and research needs is 

also part of the assessment. 

To evaluate whether an hypothesized mode of action is operative, an analysis starts with 

an outline of the scientific findings regarding the hypothesized key events leading to cancer, and 

then weighing information to determine whether there is a causal relationship between these 

events and cancer formation, i.e., that the effects are critical for induction of tumors. It is not 

generally expected that the complete sequence will be known at the molecular level. Instead, 

empirical observations made at different levels of biological organization—biochemical, cellular, 

physiological, tissue, organ, and system—are analyzed. 

Several important points should be considered when working with the framework: 

C The topics listed for analysis should not be regarded as a checklist of necessary 

“proofs.” The judgment of whether an hypothesized mode of action is supported by 

available data takes account of the analysis as a whole. 

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C The framework provides a structure for organizing the facts upon which conclusions 

as to mode of action rest. The purpose of using the framework is to make analysis 

transparent and to allow the reader to understand the facts and reasoning behind a 

conclusion. 

C The framework does not dictate an answer. The weight of evidence that is 

sufficient to support a decision about a mode of action may be less or more, 

depending on the purpose of the analysis, for example, screening, research needs 

identification, or full risk assessment. To make the reasoning transparent, the 

purpose of the analysis should be made apparent to the reader. 

C Toxicokinetic studies may contribute to mode of action analysis by contributing to 

identifying the active form(s) of an agent that is central to the mode of action. Apart 

from contributing in this way, toxicokinetics studies may reveal effects of saturation 

of metabolic processes. These may not be considered key events in a mode of 

action, but they are given separate consideration in assessing dose metrics and 

potential nonlinearity of the dose-response relationship. 

C Generally, “sufficient” support is a matter of scientific judgment in the context of 

the requirements of the decisionmaker or in the context of science policy guidance 

regarding a certain mode of action. 

C Even when an hypothesized mode of action is supported for a described response in 

a specific tissue, it may not explain other tumor responses observed, which should 

get separate consideration in hazard and dose-response assessment. 

For each tumor site being evaluated, the mode of action analysis should begin with a 

description of the relevant data and key events that may be associated with an hypothesized mode 

of action and its sequence of key events (see Section 2.4.3.1). This can be followed by a 

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discussion of various aspects of the experimental support for hypothesized mode(s) of action in 

animals and humans (see Section 2.4.3.2). The possibility of other modes of action also should 

be considered and discussed (see Section 2.4.3.3); if there is evidence for more than one mode of 

action, each should receive a separate analysis. Conclusions about each hypothesized mode of 

action should address whether the mode of action is supported in animals and is relevant to 

humans and which populations or lifestages can be particularly susceptible (see Section 2.4.3.4). 

In a risk assessment document, the analysis of an hypothesized mode of action can be presented 

before or with the characterization of an agent’s potential hazard to humans. 

2.4.3.1. Description of the Hypothesized Mode of Action 

Summary description of the hypothesized mode of action. For each tumor site, the mode 

of action analysis begins with a description of the hypothesized mode of action and its sequence 

of key events. If there is evidence for more than one mode of action, each receives a separate 

analysis. 

Identification of key events. In order to judge how well data support involvement of a key 

event in carcinogenic processes, the experimental definition of the event or events should be 

clear and reproducible. To support an association, experiments should define and measure an 

event consistently. 

C Can a list of events be identified that are key to the carcinogenic process? 

C Are the events well defined? 

Pertinent observations may include, but are not limited to, receptor-ligand changes, cytotoxicity, 

cell cycle effects, increased cell growth, organ weight differences, histological changes, hormone 

or other protein perturbations, or DNA and chromosome effects. 

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2.4.3.2. Discussion of the Experimental Support for the Hypothesized Mode of Action 

The experimental support for the hypothesized mode of action should be discussed from 

several viewpoints patterned after the Hill criteria (see Section 2.2.1.7). For illustration, the 

explanation of each topic includes typical questions to be addressed to the available empirical 

data and experimental observations anticipated to be pertinent. The latter will vary from case to 

case. For a particular mode of action, certain observations may be established as essential in 

practice or policy, for example, measures of thyroid hormone levels in supporting thyroid 

hormone elevation as a key event in carcinogenesis. 

Strength, consistency, specificity of association. A statistically significant association 

between events and a tumor response observed in well-conducted studies is generally supportive 

of causation. Consistent observations in a number of such studies with differing experimental 

designs increase that support, because different designs may reduce unknown biases. Studies 

showing “recovery,” i.e, absence or reduction of carcinogenicity when the event is blocked or 

diminished, are particularly useful tests of the association. Specificity of the association, without 

evidence of other modes of action, strengthens a causal conclusion. A lack of strength, 

consistency, and specificity of association weakens the causal conclusions for a particular mode 

of action. 

C What is the level of statistical and biological significance for each event and for 

cancer? 

C Do independent studies and different experimental hypothesis-testing approaches 

produce the same associations? 

C Does the agent produce effects other than those hypothesized? 

C Is the key event associated with precursor lesions? 

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Pertinent observations include tumor response associated with events (site of action logically 

relates to event[s]), precursor lesions associated with events, initiation-promotion studies, and 

stop/recovery studies. 

Dose-response concordance. If a key event and tumor endpoints increase with dose such 

that the key events forecast the appearance of tumors at a later time or higher dose, a causal 

association can be strengthened. Dose-response associations of the key event with other 

precursor events can add further strength. Difficulty arises when an event is not causal but 

accompanies the process generally. For example, if tumors and the hypothesized precursor both 

increase with dose, the two responses will be correlated regardless of whether a causal 

relationship exists. This is similar to the issue of confounding in epidemiologic studies. Doseresponse studies coupled with mechanistic studies can assist in clarifying these relationships. 

C What are the correlations among doses producing events and cancer? 

Pertinent observations include, but are not limited to, 2-year bioassay observation of lesions 

correlated with observations of hormone changes and the same lesions in shorter term studies or 

in interim sacrifice. 

Temporal relationship. If an event is shown to be causally linked to tumorigenesis, it will 

precede tumor appearance. An event may also be observed contemporaneously or after tumor 

appearance; these observations may add to the strength of association but not to the temporal 

association. 

C What is the ordering of events that underlie the carcinogenic process? 

C Is this ordering consistent among independent studies? 

Pertinent observations include studies of varying duration observing the temporal sequence of 

events and development of tumors. 

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Biological plausibility and coherence. It is important that the hypothesized mode of 

action and the events that are part of it be based on contemporaneous understanding of the 

biology of cancer to be accepted. If the body of information under scrutiny is consistent with 

other examples (including structurally related agents) for which the hypothesized mode of action 

is accepted, the case is strengthened. Because some modes of action can be anticipated to evoke 

effects other than cancer, the available toxicity database on noncancer effects, for example, 

reproductive effects of certain hormonal disturbances, can contribute to this evaluation. 

C Is the mode of action consistent with what is known about carcinogenesis in general 

and for the case specifically? 

C Are carcinogenic effects and events consistent across structural analogues? 

C Is the database on the agent internally consistent in supporting the purported mode 

of action, including relevant noncancer toxicities? 

Pertinent observations include the scientific basis for considering an hypothesized mode of action 

generally, given the contemporaneous state of knowledge of carcinogenic processes; previous 

examples of data sets showing the mode of action; data sets on analogues; and coherence of data 

in this case from cancer and noncancer toxicity studies. 

2.4.3.3. Consideration of the Possibility of Other Modes of Action 

The possible involvement of more than one mode of action at the tumor site should be 

considered. Pertinent observations that are not consistent with the hypothesized mode of action 

can suggest the possibility of other modes of action. Some pertinent observations can be 

consistent with more than one mode of action. Furthermore, different modes of action can 

operate in different dose ranges; for example, an agent can act predominantly through 

cytotoxicity at high doses and through mutagenicity at lower doses where cytotoxicity may not 

occur. 

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If there is evidence for more than one mode of action, each should receive a separate 

analysis. There may be an uneven level of experimental support for the different modes of 

action. Sometimes this can reflect disproportionate resources spent on investigating one 

particular mode of action and not the validity or relative importance of the other possible modes 

of action. Ultimately, however, the information on all of the modes of action should be 

integrated to better understand how and when each mode acts, and which mode(s) may be of 

interest for exposure levels relevant to human exposures of interest. 

2.4.3.4. Conclusions About the Hypothesized Mode of Action 

Conclusions about the hypothesized mode of action should address the issues listed 

below. For those agents for which the mode of action is considered useful for the risk 

assessment, the weight of the evidence concerning mode of action in animals as well as its 

relevance for humans would be incorporated into the weight of evidence narrative (Section 2.5). 

(a) Is the hypothesized mode of action sufficiently supported in the test animals?

Associations observed between key events and tumors may or may not support an inference of 

causation. The conclusion that the agent causes one or more key events that results in tumors is 

strengthened as more aspects of causation are satisfied and weakened as fewer are satisfied. 

Consistent results in different experiments that test the hypothesized mode of action build 

support for that mode of action. Replicating results in a similar experiment does not generally 

meaningfully strengthen the original evidence, and discordant results generally weaken that 

support. Experimental challenge to the hypothesized mode of action, where interrupting the 

sequence of key events suppresses the tumor response or enhancement of key events increases 

the tumor response, creates very strong support for the mode of action. 

(b) Is the hypothesized mode of action relevant to humans? If an hypothesized mode of 

action is sufficiently supported in the test animals, the sequence of key precursor events should 

be reviewed to identify critical similarities and differences between the test animals and humans. 

The question of concordance can be complicated by cross-species differences in toxicokinetics or 

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toxicodynamics. For example, the active agent can be formed through different metabolic 

pathways in animals and humans. Any information suggesting quantitative differences between 

animals and humans is flagged for consideration in the dose-response assessment. This includes 

the potential for different internal doses of the active agent or for differential occurrence of a key 

precursor event. 

“Relevance” of a potential mode of action is considered in the context of characterization 

of hazard, not level of risk. Anticipated levels of human exposure are not used to determine 

whether the hypothesized mode of action is relevant to humans. Exposure information is 

integrated into the overall risk characterization. 

The question of relevance considers all populations and lifestages. It is possible that the 

conditions under which a mode of action operates exist primarily in a particular population or 

lifestage, for example, in those with a pre-existing hormonal imbalance. Other populations or 

lifestages may not be analogous to the test animals, in which case the question of relevance 

would be decided by inference. 

Special attention should be paid to whether tumors can arise from childhood exposure, 

considering various aspects of development during these lifestages. Because the studies that 

support a mode of action are typically conducted in mature animals, conclusions about relevance 

during childhood generally rely on inference. There is currently no standard Agency position 

regarding the issue of whether tumors arising through the hypothesized mode of action are 

relevant during childhood; understanding the mode of action implies that there are sufficient data 

(on either the specific agent or the general mode of action) to form a confident conclusion about 

relevance during childhood. 

(c) Which populations or lifestages can be particularly susceptible to the hypothesized

mode of action? If an hypothesized mode of action is judged relevant to humans, information 

about the key precursor event(s) is reviewed to identify populations or lifestages that might 

reasonably expected to be particularly susceptible to their occurrence. Although agent-specific 

data would provide the strongest indication of susceptibility, this review may also rely on general 

knowledge about the precursor events and characteristics of individuals susceptible to these 

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events. Any information suggesting quantitative differences between populations or lifestages 

should be flagged for consideration in the dose-response assessment (see Section 3.5). This 

includes the potential for a higher internal dose of the active agent or for an increased occurrence 

of a key precursor event. Quantitative differences may result in separate risk estimates for 

susceptible populations or lifestages. 

The possibility that childhood is a susceptible period for exposure should be explicitly 

addressed. Generic understanding of the mode of action can be used to gauge childhood 

susceptibility, and this determination can be refined through analysis of agent-specific data. 

2.4.4 Evolution with Experience 

Several groups have proposed or incorporated mode of action into their risk assessments 

(see, e.g., U.S. EPA , 1991b; Sonich-Mullin et al., 2001; Meek et al., 2003). As the frameworks 

and mandates under which these evaluations were produced differ, the specific procedures 

described in and conclusions drawn may also differ. Nevertheless, the number of case studies 

from all venues remains limited. More experience with differing modes of action are expected to 

highlight and illustrate the strengths and limitations of the general framework proposed in these 

cancer guidelines. Moreover, additional toxicological techniques may expand or change 

scientific judgments regarding which information is useful for mode of action determinations. 

As warranted, additional guidance may be proposed as experience is gained and/or as 

toxicological knowledge advances. 

2.5. WEIGHT OF EVIDENCE NARRATIVE 

The weight of evidence narrative is a short summary (one to two pages) that explains an 

agent's human carcinogenic potential and the conditions that characterize its expression. It 

should be sufficiently complete to be able to stand alone, highlighting the key issues and 

decisions that were the basis for the evaluation of the agent’s potential hazard. It should be 

sufficiently clear and transparent to be useful to risk managers and non-expert readers. It may be 

useful to summarize all of the significant components and conclusions in the first paragraph of 

the narrative and to explain complex issues in more depth in the rest of the narrative. 

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The weight of the evidence should be presented as a narrative laying out the complexity 

of information that is essential to understanding the hazard and its dependence on the quality, 

quantity, and type(s) of data available, as well as the circumstances of exposure or the traits of an 

exposed population that may be required for expression of cancer. For example, the narrative 

can clearly state to what extent the determination was based on data from human exposure, from 

animal experiments, from some combination of the two, or from other data. Similarly, 

information on mode of action can specify to what extent the data are from in vivo or in vitro 

exposures or based on similarities to other chemicals. The extent to which an agent’s mode of 

action occurs only on reaching a minimum dose or a minimum duration should also be presented. 

A hazard might also be expressed disproportionately in individuals possessing a specific gene; 

such characterizations may follow from a better understanding of the human genome. 

Furthermore, route of exposure should be used to qualify a hazard if, for example, an agent is not 

absorbed by some routes. Similarly, a hazard can be attributable to exposures during a 

susceptible lifestage on the basis of our understanding of human development. 

The weight of evidence-of-evidence narrative should highlight: 

• the quality and quantity of the data; 

• all key decisions and the basis for these major decisions; and 

• any data, analyses, or assumptions that are unusual for or new to EPA. 

To capture this complexity, a weight of evidence narrative generally includes 

C conclusions about human carcinogenic potential (choice of descriptor(s), described 

below), 

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C a summary of the key evidence supporting these conclusions (for each descriptor 

used), including information on the type(s) of data (human and/or animal, in vivo 

and/or in vitro) used to support the conclusion(s), 

C available information on the epidemiologic or experimental conditions that 

characterize expression of carcinogenicity (e.g., if carcinogenicity is possible only 

by one exposure route or only above a certain human exposure level), 

C a summary of potential modes of action and how they reinforce the conclusions, 

C indications of any susceptible populations or lifestages, when available, and 

C a summary of the key default options invoked when the available information is 

inconclusive. 

To provide some measure of clarity and consistency in an otherwise free-form narrative, 

the weight of evidence descriptors are included in the first sentence of the narrative. Choosing a 

descriptor is a matter of judgment and cannot be reduced to a formula. Each descriptor may be 

applicable to a wide variety of potential data sets and weights of evidence. These descriptors and 

narratives are intended to permit sufficient flexibility to accommodate new scientific 

understanding and new testing methods as they are developed and accepted by the scientific 

community and the public. Descriptors represent points along a continuum of evidence; 

consequently, there are gradations and borderline cases that are clarified by the full narrative. 

Descriptors, as well as an introductory paragraph, are a short summary of the complete narrative 

that preserves the complexity that is an essential part of the hazard characterization. Users of 

these cancer guidelines and of the risk assessments that result from the use of these cancer 

guidelines should consider the entire range of information included in the narrative rather 

than focusing simply on the descriptor. 

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In borderline cases, the narrative explains the case for choosing one descriptor and 

discusses the arguments for considering but not choosing another. For example, between 

“suggestive” and “likely” or between “suggestive” and “inadequate,” the explanation clearly 

communicates the information needed to consider appropriately the agent's carcinogenic potential 

in subsequent decisions. 

Multiple descriptors can be used for a single agent, for example, when carcinogenesis is 

dose- or route-dependent. For example, if an agent causes point-of-contact tumors by one 

exposure route but adequate testing is negative by another route, then the agent could be 

described as likely to be carcinogenic by the first route but not likely to be carcinogenic by the 

second. Another example is when the mode of action is sufficiently understood to conclude that 

a key event in tumor development would not occur below a certain dose range. In this case, the 

agent could be described as likely to be carcinogenic above a certain dose range but not likely to 

be carcinogenic below that range. 

Descriptors can be selected for an agent that has not been tested in a cancer bioassay if 

sufficient other information, e.g., toxicokinetic and mode of action information, is available to 

make a strong, convincing, and logical case through scientific inference. For example, if an 

agent is one of a well-defined class of agents that are understood to operate through a common 

mode of action and if that agent has the same mode of action, then in the narrative the untested 

agent would have the same descriptor as the class. Another example is when an untested agent's 

effects are understood to be caused by a human metabolite, in which case in the narrative the 

untested agent could have the same descriptor as the metabolite. As new testing methods are 

developed and used, assessments may increasingly be based on inferences from toxicokinetic and 

mode of action information in the absence of tumor studies in animals or humans. 

When a well-studied agent produces tumors only at a point of initial contact, the 

descriptor generally applies only to the exposure route producing tumors unless the mode of 

action is relevant to other routes. The rationale for this conclusion would be explained in the 

narrative. 

When tumors occur at a site other than the point of initial contact, the descriptor generally 

applies to all exposure routes that have not been adequately tested at sufficient doses. An 

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exception occurs when there is convincing information, e.g., toxicokinetic data that absorption 

does not occur by another route. 

When the response differs qualitatively as well as quantitatively with dose, this 

information should be part of the characterization of the hazard. In some cases reaching a certain 

dose range can be a precondition for effects to occur, as when cancer is secondary to another 

toxic effect that appears only above a certain dose. In other cases exposure duration can be a 

precondition for hazard if effects occur only after exposure is sustained for a certain duration. 

These considerations differ from the issues of relative absorption or potency at different dose 

levels because they may represent a discontinuity in a dose-response function. 

When multiple bioassays are inconclusive, mode of action data are likely to hold the key 

to resolution of the more appropriate descriptor. When bioassays are few, further bioassays to 

replicate a study's results or to investigate the potential for effects in another sex, strain, or 

species may be useful. 

When there are few pertinent data, the descriptor makes a statement about the database, 

for example, “Inadequate Information to Assess Carcinogenic Potential,” or a database that 

provides “Suggestive Evidence of Carcinogenic Potential.” With more information, the 

descriptor expresses a conclusion about the agent’s carcinogenic potential to humans. If the 

conclusion is positive, the agent could be described as “Likely to Be Carcinogenic to Humans” 

or, with strong evidence, “Carcinogenic to Humans.” If the conclusion is negative, the agent 

could be described as “Not Likely to Be Carcinogenic to Humans.” 

 Although the term “likely” can have a probabilistic connotation in other contexts, its use 

as a weight of evidence descriptor does not correspond to a quantifiable probability of whether 

the chemical is carcinogenic. This is because the data that support cancer assessments generally 

are not suitable for numerical calculations of the probability that an agent is a carcinogen. Other 

health agencies have expressed a comparable weight of evidence using terms such as 

“Reasonably Anticipated to Be a Human Carcinogen” (NTP) or “Probably Carcinogenic to 

Humans” (International Agency for Research on Cancer). 

The following descriptors can be used as an introduction to the weight of evidence 

narrative. The examples presented in the discussion of the descriptors are illustrative. The 

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examples are neither a checklist nor a limitation for the descriptor. The complete weight of 

evidence narrative, rather than the descriptor alone, provides the conclusions and the basis for 

them. 

“Carcinogenic to Humans” 

This descriptor indicates strong evidence of human carcinogenicity. It covers different 

combinations of evidence. 

C This descriptor is appropriate when there is convincing epidemiologic evidence of a 

causal association between human exposure and cancer. 

C Exceptionally, this descriptor may be equally appropriate with a lesser weight of 

epidemiologic evidence that is strengthened by other lines of evidence. It can be 

used when all of the following conditions are met: (a) there is strong evidence of an 

association between human exposure and either cancer or the key precursor events 

of the agent's mode of action but not enough for a causal association, and (b) there 

is extensive evidence of carcinogenicity in animals, and (c) the mode(s) of 

carcinogenic action and associated key precursor events have been identified in 

animals, and (d) there is strong evidence that the key precursor events that precede 

the cancer response in animals are anticipated to occur in humans and progress to 

tumors, based on available biological information. In this case, the narrative 

includes a summary of both the experimental and epidemiologic information on 

mode of action and also an indication of the relative weight that each source of 

information carries, e.g., based on human information, based on limited human and 

extensive animal experiments. 

“Likely to Be Carcinogenic to Humans” 

This descriptor is appropriate when the weight of the evidence is adequate to demonstrate 

carcinogenic potential to humans but does not reach the weight of evidence for the descriptor 

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“Carcinogenic to Humans.” Adequate evidence consistent with this descriptor covers a broad 

spectrum. As stated previously, the use of the term “likely” as a weight of evidence descriptor 

does not correspond to a quantifiable probability. The examples below are meant to represent the 

broad range of data combinations that are covered by this descriptor; they are illustrative and 

provide neither a checklist nor a limitation for the data that might support use of this descriptor. 

Moreover, additional information, e.g., on mode of action, might change the choice of descriptor 

for the illustrated examples. Supporting data for this descriptor may include: 

• an agent demonstrating a plausible (but not definitively causal) association between 

human exposure and cancer, in most cases with some supporting biological, 

experimental evidence, though not necessarily carcinogenicity data from animal 

experiments; 

• an agent that has tested positive in animal experiments in more than one species, 

sex, strain, site, or exposure route, with or without evidence of carcinogenicity in 

humans; 

• a positive tumor study that raises additional biological concerns beyond that of a 

statistically significant result, for example, a high degree of malignancy, or an early 

age at onset; 

• a rare animal tumor response in a single experiment that is assumed to be relevant to 

humans; or 

• a positive tumor study that is strengthened by other lines of evidence, for example, 

either plausible (but not definitively causal) association between human exposure 

and cancer or evidence that the agent or an important metabolite causes events 

generally known to be associated with tumor formation (such as DNA reactivity or 

effects on cell growth control) likely to be related to the tumor response in this case. 

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“Suggestive Evidence of Carcinogenic Potential” 

This descriptor of the database is appropriate when the weight of evidence is suggestive 

of carcinogenicity; a concern for potential carcinogenic effects in humans is raised, but the data 

are judged not sufficient for a stronger conclusion. This descriptor covers a spectrum of evidence 

associated with varying levels of concern for carcinogenicity, ranging from a positive cancer 

result in the only study on an agent to a single positive cancer result in an extensive database that 

includes negative studies in other species. Depending on the extent of the database, additional 

studies may or may not provide further insights. Some examples include: 

• a small, and possibly not statistically significant, increase in tumor incidence 

observed in a single animal or human study that does not reach the weight of 

evidence for the descriptor "Likely to Be Carcinogenic to Humans." The study 

generally would not be contradicted by other studies of equal quality in the same 

population group or experimental system (see discussions of conflicting evidence 

and differing results, below); 

• a small increase in a tumor with a high background rate in that sex and strain, when 

there is some but insufficient evidence that the observed tumors may be due to 

intrinsic factors that cause background tumors and not due to the agent being 

assessed. (When there is a high background rate of a specific tumor in animals of a 

particular sex and strain, then there may be biological factors operating 

independently of the agent being assessed that could be responsible for the 

development of the observed tumors.) In this case, the reasons for determining that 

the tumors are not due to the agent are explained; 

• evidence of a positive response in a study whose power, design, or conduct limits 

the ability to draw a confident conclusion (but does not make the study fatally 

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flawed), but where the carcinogenic potential is strengthened by other lines of 

evidence (such as structure-activity relationships); or 

• a statistically significant increase at one dose only, but no significant response at the 

other doses and no overall trend. 

“Inadequate Information to Assess Carcinogenic Potential” 

This descriptor of the database is appropriate when available data are judged inadequate 

for applying one of the other descriptors. Additional studies generally would be expected to 

provide further insights. Some examples include: 

• little or no pertinent information; 

• conflicting evidence, that is, some studies provide evidence of carcinogenicity but 

other studies of equal quality in the same sex and strain are negative. Differing 

results, that is, positive results in some studies and negative results in one or more 

different experimental systems, do not constitute conflicting evidence, as the term is 

used here. Depending on the overall weight of evidence, differing results can be 

considered either suggestive evidence or likely evidence; or 

• negative results that are not sufficiently robust for the descriptor, “Not Likely to Be 

Carcinogenic to Humans.” 

“Not Likely to Be Carcinogenic to Humans” 

This descriptor is appropriate when the available data are considered robust for deciding 

that there is no basis for human hazard concern. In some instances, there can be positive results 

in experimental animals when there is strong, consistent evidence that each mode of action in 

experimental animals does not operate in humans. In other cases, there can be convincing 

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evidence in both humans and animals that the agent is not carcinogenic. The judgment may be 

based on data such as: 

• animal evidence that demonstrates lack of carcinogenic effect in both sexes in welldesigned and well-conducted studies in at least two appropriate animal species (in 

the absence of other animal or human data suggesting a potential for cancer effects), 

• convincing and extensive experimental evidence showing that the only carcinogenic 

effects observed in animals are not relevant to humans, 

• convincing evidence that carcinogenic effects are not likely by a particular exposure 

route (see Section 2.3), or 

• convincing evidence that carcinogenic effects are not likely below a defined dose 

range. 

A descriptor of “not likely” applies only to the circumstances supported by the data. For 

example, an agent may be “Not Likely to Be Carcinogenic” by one route but not necessarily by 

another. In those cases that have positive animal experiment(s) but the results are judged to be 

not relevant to humans, the narrative discusses why the results are not relevant. 

Multiple Descriptors 

More than one descriptor can be used when an agent's effects differ by dose or exposure 

route. For example, an agent may be “Carcinogenic to Humans” by one exposure route but “Not 

Likely to Be Carcinogenic” by a route by which it is not absorbed. Also, an agent could be 

“Likely to Be Carcinogenic” above a specified dose but “Not Likely to Be Carcinogenic” below 

that dose because a key event in tumor formation does not occur below that dose. 

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2.6. HAZARD CHARACTERIZATION 

The hazard characterization contains the hazard information needed for a full risk 

characterization (U.S. EPA, 2000b). It presents the results of the hazard assessment and explains 

how the weight of evidence conclusion was reached. The hazard characterization summarizes, in 

plain language, conclusions about the agent’s potential effects, whether they can be expected to 

depend qualitatively on the circumstances of exposure, and if anyone can be expected to be 

especially susceptible. It discusses the extent to which these conclusions are supported by data or 

are the result of default options invoked because the data are inconclusive. It explains how 

complex cases with differing results in different studies were resolved. The hazard 

characterization highlights the major issues addressed in the hazard assessment and discusses 

alternative interpretations of the data and the degree to which they are supportable scientifically 

and are consistent with EPA guidelines. 

When the conclusion is supported by mode of action information, the hazard 

characterization also provides a clear summary of the mode of action conclusions (see Section 

2.4.3.4), including the completeness of the data, the strengths and limitations of the inferences 

made, the potential for other modes of action, and the implications of the mode of action for 

selecting viable approaches to the dose-response assessment. The hazard characterization also 

discusses the extent to which mode of action information is available to address the potential for 

disproportionate risks in specific populations or lifestages or the potential for enhanced risks on 

the basis of interactions with other agents or stressors, if anticipated. 

Topics that can be addressed in a hazard characterization include: 

• summary of the results of the hazard assessment; 

• identification of any likely susceptible populations and lifestages, especially 

attending to children, infants, and fetuses; 

• conclusions about the agent's mode of action, and implications for selecting 

approaches to the dose-response assessment; 

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• identification of the available lines of evidence (e.g., animal bioassays, 

epidemiologic studies, toxicokinetic information, mode of action studies, and 

information about structural analogues or metabolites), highlighting data quality and 

coherence of results from different lines of evidence; and 

• strengths and limitations of the hazard assessment, highlighting significant issues in 

interpreting the data, alternative interpretations that are considered equally 

plausible, critical data gaps, and default options invoked when the available 

information is inconclusive. 

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3. DOSE-RESPONSE ASSESSMENT 

Dose-response assessment estimates potential risks to humans at exposure levels of 

interest. Dose-response assessments are useful in many applications: estimating risk at different 

exposure levels, estimating the risk reduction for different decision options, estimating the risk 

remaining after an action is taken, providing the risk information needed for benefit-cost analyses 

of different decision options, comparing risks across different agents or health effects, and setting 

research priorities. The purpose of the assessment should consider the quality of the data 

available, which will vary from case to case. 

A dose-response analysis is generally developed from each study that reports quantitative 

data on dose and response. Alternative measures of dose are available for analyzing human and 

animal studies (see Section 3.1). A two-step approach distinguishes analysis of the doseresponse data from inferences made about lower doses. The first step is an analysis of dose and 

response in the range of observation of the experimental or epidemiologic studies (see Section 

3.2). Modeling is encouraged to incorporate a wide range of experimental data into the doseresponse assessment (see Sections 3.1.2, 3.2.1, 3.2.2, 3.2.3). The modeling yields a point of 

departure (POD) near the lower end of the observed range, without significant extrapolation to 

lower doses (see Sections 3.2.4, 3.2.5). The second step is extrapolation to lower doses (see 

Section 3.3). The extrapolation approach considers what is known about the agent's mode of 

action (see Section 3.3.1). Both linear and nonlinear approaches are available (see Sections 

3.3.3, 3.3.4). When multiple estimates can be developed, the strengths and weaknesses of each 

are presented. In some cases, they may be combined in a way that best represents human cancer 

risk (see Section 3.3.5). Special consideration is given to describing dose-response differences 

attributable to different human exposure scenarios (see Section 3.4) and to susceptible 

populations and lifestages (see Section 3.5). It is important to discuss significant uncertainties 

encountered in the analysis (see Section 3.6) and to characterize other important aspects of the 

dose-response assessment (see Section 3.7). 

The scope, depth, and use of a dose-response assessment vary in different circumstances. 

Although the quality of dose-response data is not necessarily related to the weight of evidence 

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descriptor, dose-response assessments are generally completed for agents considered 

“Carcinogenic to Humans” and “Likely to Be Carcinogenic to Humans.” When there is 

suggestive evidence, the Agency generally would not attempt a dose-response assessment, as the 

nature of the data generally would not support one; however, when the evidence includes a wellconducted study, quantitative analyses may be useful for some purposes, for example, providing 

a sense of the magnitude and uncertainty of potential risks, ranking potential hazards, or setting 

research priorities. In each case, the rationale for the quantitative analysis is explained, 

considering the uncertainty in the data and the suggestive nature of the weight of evidence. 

These analyses generally would not be considered Agency consensus estimates. Dose-response 

assessments are generally not done when there is inadequate evidence, although calculating a 

bounding estimate from an epidemiologic or experimental study that does not show positive 

results can indicate the study's level of sensitivity and capacity to detect risk levels of concern. 

Cancer is a collection of several diseases that develop through cell and tissue changes 

over time. Dose-response assessment procedures based on tumor incidence have seldom taken 

into account the effects of key precursor events within the whole biological process due to lack of 

empirical data and understanding about these events. In this discussion, response data include 

measures of key precursor events considered integral to the carcinogenic process in addition to 

tumor incidence. These responses may include changes in DNA, chromosomes, or other key 

macromolecules; effects on growth signal transduction, including induction of hormonal 

changes; or physiological or toxic effects that include proliferative events diagnosed as 

precancerous but not pathology that is judged to be cancer. Analysis of such responses may be 

done along with that of tumor incidence to enhance the tumor dose-response analysis. If doseresponse analysis of nontumor key events is more informative about the carcinogenic process for 

an agent, it can be used in lieu of, or in conjunction with, tumor incidence analysis for the overall 

dose-response assessment. 

As understanding of mode of action improves and new types of data become available, 

dose-response assessment will continue to evolve. These cancer guidelines encourage the 

development and application of new methods that improve dose-response assessment by 

reflecting new scientific understanding and new sources of information. 

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3.1. ANALYSIS OF DOSE 

For each effect observed, dose-response assessment should begin by determining an 

appropriate dose metric. Several dose metrics have been used, e.g., delivered dose, body burden, 

and area under the curve, and others may be appropriate depending on the data and mode of 

action. 

Selection of an appropriate dose metric considers what data are available and what is 

known about the agent's mode of action at the target site, and uncertainties involved in estimation 

and application of alternative metrics. The dose metric specifies: 

• the agent measured, preferably the active agent (administered agent or a metabolite); 

• proximity to the target site (exposure concentration, potential dose, internal dose, or 

delivered dose,5

 reflecting increasing proximity); and 

• the time component of the effective dose (cumulative dose, average dose, peak 

dose, or body burden). 

Analyses can be based on estimates of animal dose metrics or human dose metrics. The 

assessment should describe the approach used to select a dose metric and the reasons for this 

approach. The final analysis, however, should determine a human equivalent dose metric. This 

facilitates comparing results from different datasets and effects by using human equivalent 

dose/concentrations as common metrics. When appropriate, it may be necessary to convert dose 

metrics across exposure routes. When route-to-route extrapolations are made, the underlying 

data, algorithms, and assumptions are clearly described. 

5 Exposure is contact of an agent with the outer boundary of an organism. Exposure concentration is the 

concentration of a chemical in its transport or carrier medium at the point of contact. Dose is the amount of a 

substance available for interaction with metabolic processes or biologically significant receptors after crossing the 

outer boundary of an organism. Potential dose is the amount ingested, inhaled, or applied to the skin. Applied dose 

is the amount of a substance presented to an absorption barrier and available for absorption (although not necessarily 

having yet crossed the outer boundary of the organism). Absorbed dose is the amount crossing a specific absorption 

barrier (e.g., the exchange boundaries of skin, lung, and digestive tract) through uptake processes. Internal dose is a 

more general term, used without respect to specific absorption barriers or exchange boundaries. Delivered dose is 

the amount of the chemical available for interaction by any particular organ or cell (U.S. EPA, 1992a). 

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Timing of exposure can also be important. When there is a susceptible lifestage, doses 

during the susceptible period are not equivalent to doses at other times, and they would be 

analyzed separately. 

3.1.1. Standardizing Different Experimental Exposure Regimens 

Complex exposure or dosing regimens are often present in experimental and 

epidemiologic studies. The resulting internal dose depends on many variables, including 

concentration, duration, frequency of administration, and duration of recovery periods between 

administrations. Internal dose also depends on variables that are intrinsic to the exposed 

individual, such as lifestage and rates of metabolism and clearance. To facilitate comparing 

results from different study designs and to make inferences about human exposures, a summary 

estimate of the dose metric, whether the administered dose or inhalation exposure concentration 

or an internal metric, may be derived for a complex exposure regimen. 

Toxicokinetic modeling is the preferred approach for estimating dose metrics from 

exposure. Toxicokinetic models generally describe the relationship between exposure and 

measures of internal dose over time. More complex models can reflect sources of intrinsic 

variation, such as polymorphisms in metabolism and clearance rates. When a robust model is not 

available, or when the purpose of the assessment does not warrant developing a model, simpler 

approaches may be used. 

For chronic exposure studies, the cumulative exposure or dose administered often is 

expressed as an average over the duration of the study, as one consistent dose metric. This 

approach implies that a higher dose administered over a short duration is equivalent to a 

commensurately lower dose administered over a longer duration. Uncertainty usually increases 

as the duration becomes shorter relative to the averaging duration or the intermittent doses 

become more intense than the averaged dose. Moreover, doses during any specific susceptible or 

refractory period would not be equivalent to doses at other times. For these reasons, cumulative 

exposure or potential dose may be replaced by a more appropriate dose metric when indicated by 

the data. 

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For mode of action studies, the dose metric should be calculated over a duration that 

reflects the time to occurrence of the key precursor effects. Mode of action studies are often of 

limited duration, as the precursors can be observed after less-than-chronic exposures. When the 

experimental exposure regimen is specified on a weekly basis (for example, 4 hours a day, 5 days 

a week), the daily exposure may be averaged over the week, where appropriate. 

Doses in studies at the cellular or molecular level can be difficult to relate to organ- or 

organism-level dose metrics. Toxicokinetic modeling can sometimes be used to relate doses at 

the cellular or molecular level to doses or exposures at higher levels of organization. 

3.1.2. Toxicokinetic Data and Modeling 

In the absence of chemical-specific data, physiologically based toxicokinetic modeling is 

potentially the most comprehensive way to account for biological processes that determine 

internal dose. Physiologically based models commonly describe blood flow between 

physiological compartments and simulate the relationship between applied dose and internal 

dose. Toxicokinetic models generally need data on absorption, distribution, metabolism, and 

elimination of the administered agent and its metabolites. 

Additionally, in the case of inhalation exposures, models can explicitly characterize the 

geometry of the respiratory tract and the airflow through it, as well as the interaction of this 

airflow with the entrained particles or fibers and gases (Kimbell et al., 2001; Subramaniam et al., 

2003). Because of large interspecies differences in airway morphometry such models can be 

particularly useful in interspecies extrapolations. When employed, however, the potential for 

large inter-individual differences in airway morphometry, are considered to ensure that the 

models provide information representative of human populations. 

Toxicokinetic models can improve dose-response assessment by revealing and describing 

nonlinear relationships between applied and internal dose. Nonlinearity observed in a doseresponse curve often can be attributed to toxicokinetics (Hoel et al., 1983; Gaylor et al., 1994), 

involving, for example, saturation or induction of enzymatic processes at high doses. In some 

cases, toxicokinetic processes tend to become linear at sufficiently low doses (Hattis, 1990). 

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A discussion of confidence should accompany the presentation of model results and 

include consideration of model validation and sensitivity analysis, stressing the predictive 

performance of the model and whether the model is sufficient to support decision-making. 

Quantitative uncertainty analysis is important for evaluating the performance of a model, whether 

the model is based primarily on default assumptions or chemical-specific data. The uncertainty 

analysis covers questions of model uncertainty (e.g., Is the model based on the appropriate 

biology and how does that affect estimates of dose metrics?) and parameter uncertainty (e.g., Do 

the data support unbiased and stable estimates of the model parameters?). When a delivered dose 

measure is used in animal-to-human extrapolation, the assessment discusses the confidence of 

the target tissue and its toxicodynamics being the same in both species (see Section 3.6). 

Toxicokinetic modeling results may be presented alone as the preferred method of estimating 

human equivalent exposures or doses, or these results may be presented in parallel with default 

procedures (see Section 3.1.3), depending on the confidence in the modeling. 

3.1.3. Cross-species Scaling Procedures 

Standard cross-species scaling procedures are available when the data are not sufficient to 

support a toxicokinetic model or when the purpose of the assessment does not warrant 

developing one. The aim is to define exposure levels for humans and animals that are expected 

to produce the same degree of effect (U.S. EPA, 1992b), taking into account differences in scale 

between test animals and humans, such as size and lifespan. 

3.1.3.1. Oral Exposures 

For oral exposures, administered doses should be scaled from animals to humans on the 

basis of equivalence of mg/kg3/4-d (milligrams of the agent normalized by the 3/4 power of body 

weight per day) (U.S. EPA, 1992b). The 3/4 power is consistent with current science, including 

empirical data that allow comparison of potencies in humans and animals, and it is also 

supported by analysis of the allometric variation of key physiological parameters across 

mammalian species. It is generally more appropriate at low doses, where sources of nonlinearity 

such as saturation of enzyme activity are less likely to occur. This scaling is intended as an 

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unbiased estimate rather than a conservative one. Equating exposure concentrations in food or 

water is an alternative version of the same approach, because daily intakes of food or water are 

approximately proportional to the 3/4 power of body weight. 

The aim of these cross-species scaling procedures is to estimate administered doses in 

animals and humans that result in equal lifetime risks. It is useful to recognize two components 

of this equivalence: toxicokinetic equivalence, which determines administered doses in animals 

and humans that yield equal tissue doses, and toxicodynamic equivalence, which determines 

tissue doses in animals and humans that yield equal lifetime risks (U.S. EPA, 1992b). 

Toxicokinetic modeling (see Section 3.1.2) addresses factors associated with toxicokinetic 

equivalence, and toxicodynamic modeling (see Section 3.2.2) addresses factors associated with 

toxicodynamic equivalence. When toxicokinetic modeling is used without toxicodynamic 

modeling, the dose-response assessment develops and supports an approach for addressing 

toxicodynamic equivalence, perhaps by retaining some of the cross-species scaling factor (e.g., 

using the square root of the cross-species scaling factor or using a factor of 3 to cover 

toxicodynamic differences between animals and humans, as is currently done in deriving 

inhalation reference concentrations [U.S. EPA, 1994]). 

When assessing risks from childhood exposure, the mg/kg3/4-d scaling factor does not use 

the child's body weight (U.S. EPA, 1992b). This reflects several uncertainties in extrapolating 

risks to children: 

• The data supporting the mg/kg3/4-d scaling factor were derived for differences across 

species and may not apply as well to differently sized individuals of the same 

species or to different lifestages. 

• In addition to metabolic differences, there are also important toxicodynamic 

differences; for example, children have faster rates of cell division than do adults, so 

scaling across different lifestages and species simultaneously may be particularly 

uncertain. 

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3.1.3.2. Inhalation Exposures 

For inhalation exposures experimental exposure concentrations are replaced with human 

equivalent concentrations calculated using EPA’s methods for deriving inhalation reference 

concentrations (U.S. EPA, 1994), which give preference to the use of toxicokinetic modeling. 

When toxicokinetic models are unavailable, default dosimetry models are employed to 

extrapolate from experimental exposure concentrations to human equivalent concentrations. 

When toxicokinetic modeling or dosimetry modeling is used without toxicodynamic modeling, 

the dose-response assessment develops and supports an approach for addressing toxicodynamic 

equivalence. 

The default dosimetry models typically involve the use of species-specific physiologic 

and anatomic factors relevant to the form of the agent (e.g., particle or gas) and categorized with 

regard to whether the response occurs either locally (i.e., within the respiratory tract) or remotely. 

For example, current default models (U.S. EPA, 1994) use parameters such as: 

• inhalation rate and surface area of the affected part of the respiratory tract for gases 

eliciting the response locally, 

• blood:gas partition coefficients for remote acting gases, 

• fractional deposition with inhalation rate and surface area of the affected part of the 

respiratory tract for particles eliciting the response locally, and 

• fractional deposition with inhalation rate and body weight for particles eliciting the 

response remotely. 

The current default values for some parameters used in the default models (e.g., breathing rate 

and respiratory tract surface area) are based on data from adults (U.S. EPA, 1994). The human 

respiratory system passes through several distinct stages of maturation and growth during the first 

several years of life and into adolescence (Pinkerton and Joad, 2000), during which 

3-8
characteristics important to disposition of inhaled toxicants may vary. Children and adults 

breathing the same concentration of an agent may receive different doses to the body or lungs 

(U.S. EPA, 2002b). Consequently, it may be appropriate to evaluate the default models by 

considering physiologic and anatomic factors representative of early lifestages, for example 

through the substitution of child-specific parameters (U.S. EPA, 2002b). Such evaluation uses 

the default model and dosimetric adjustment in use at the time of the assessment coupled with 

the best understanding of child-specific parameters at that time (e.g., drawn from the scientific 

literature). This analysis is undertaken with caution: (1) because of the correlations between 

activity level, breathing rate, respiratory tract dimensions, and body weight and (2) to avoid the 

possibility of mismatching the type of agent (gas or particle) and its site of response (within the 

respiratory tract or remote from the respiratory tract) with the relevant dosimetry factors in use at 

the time of the assessment. Analyses of children’s inhalation dosimetry are also considered when 

using model structures beyond the default models (e.g., physiologically based toxicokinetic 

models). 

When using dosimetry modeling, the comparison of human-equivalent concentrations for 

different lifestages (e.g., for an adult and a child) can indicate whether it is important to carry 

both concentrations forward in the dose-response assessment or whether a verbal characterization 

of any findings will suffice. 

3.1.4. Route Extrapolation 

In certain situations, an assessment based on studies of one exposure route may be 

applied to another exposure route. Route-to-route extrapolation has both qualitative and 

quantitative aspects. For the qualitative aspect, the assessor should weigh the degree to which 

positive results by one exposure route support a judgment that similar results would be expected 

by another route. In general, confidence in making such a judgment is strengthened when tumors 

are observed at a site distant from the portal of entry and when absorption is similar through both 

routes. In the absence of contrary data, a qualitative default option can be used: if the agent is 

absorbed through an exposure route to give an internal dose, it may be carcinogenic by that route. 

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When a qualitative extrapolation can be supported, quantitative extrapolation may still be 

problematic due to the absence of adequate data. The differences in biological processes among 

routes of exposure (oral, inhalation, dermal) can be great because of, for example, first-pass 

effects and different results from different exposure patterns. There is no generally applicable 

method for accounting for these differences in uptake processes in a quantitative route-to-route 

extrapolation of dose-response data in the absence of good data on the agent of interest. 

Therefore, route-to-route extrapolation of dose data relies on a case-by-case analysis of available 

data. When good data on the agent itself are limited, an extrapolation analysis can be based on 

expectations from physical and chemical properties of the agent, properties and route-specific 

data on structurally analogous compounds, or in vitro or in vivo uptake data on the agent. 

Route-to-route uptake models may be applied if model parameters are suitable for the 

compound of interest. Such models are currently considered interim methods; further model 

development and validation is awaiting the development of more extensive data. For screening 

or hazard ranking, route-to-route extrapolation may be based on assumed quantitative 

comparability as a default, as long as it is reasonable to assume absorption by compared routes. 

When route-to-route extrapolation is used, the assessor’s degree of confidence in both the 

qualitative and quantitative extrapolation is discussed in the assessment and highlighted in the 

dose-response characterization. 

Toxicokinetic modeling can be used to compare results of studies by different exposure 

routes. Results can also be compared on the basis of internal dose for effects distant from the 

point of contact. 

Route extrapolation can be used to understand how internal dose and subsequent effects 

depend on exposure route. If testing by different exposure routes is available, the observation of 

similar or dissimilar internal doses can be important in determining whether and what 

conclusions can be made concerning the dose-response function(s) for different routes of 

exposure. 

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3.2. ANALYSIS IN THE RANGE OF OBSERVATION 

The principle underlying these cancer guidelines is to use approaches that include as 

much information as possible. Quantitative information about key precursor events can be used 

to develop a toxicodynamic model. Alternatively, such information can be fitted by empirical 

models to extend the dose-response analysis of tumor incidence to lower doses and response 

levels. The analysis in the range of observation is used to establish a POD near the lower end of 

the observed range (see Section 3.3). 

3.2.1. Epidemiologic Studies 

Ideally, epidemiologic data would be used to select the dose-response function for human 

exposures. Because epidemiologic data are usually limited and many models may fit the data 

(Samet et al.,1998), other factors may influence model choice. For epidemiologic studies, 

including those with grouped data, analysis by linear models in the range of observation is 

generally appropriate unless the fit is poor. The relatively small exposure range observed in 

many epidemiologic studies, for example, makes it difficult to discern the shape of the exposureor dose-response curve. Exposure misclassification and errors in exposure estimation also 

obscure the shape of the dose-response curve. When these errors are unsystematic or random, 

the result is frequently to bias the risk estimates toward zero. When a linear model fits poorly, 

more flexible models that allow for low-dose linearity, for example, a linear-quadratic model or a 

Hill model (Murrell et al., 1998), are often considered next. 

Analysis of epidemiologic studies depends on the type of study and quality of the data, 

particularly the availability of quantitative measures of exposure. The objective is to develop a 

dose-response curve that estimates the incidence of cancer attributable to the dose (as estimated 

from the exposure) to the agent. In some cases, e.g., tobacco smoke or occupational exposures, 

the data are in the range of the exposures of interest. In other cases, as with data from animal 

experiments, information from the observable range is extrapolated to exposures of interest. 

Analysis of effects raises additional issues: 

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• Many studies collect information from death certificates, which leads to estimates of 

mortality rather than incidence. Because survival rates vary for different cancers, the 

analysis may be improved by adjusting mortality figures to reflect the relationship 

between incidence and mortality. 

• Epidemiologic studies, by their nature, are limited in the extent to which they can control 

for effects due to exposures from other agents. In some cases, the agent can have 

discernible interactive effects with another agent, making it possible to estimate the 

contribution of each agent as a risk factor for the effects of the other. For example, 

competing risks in a study population can limit the observed occurrence of cancer, while 

additive effects may lead to an increase occurrence of cancer. In the case of rates not 

already so adjusted, the analysis can be improved by correcting for competing or additive 

risks that are not similar in exposed and comparison groups. 

• Comparison groups that are not free from exposure to the agent can bias the risk 

estimates toward zero. The analysis can be improved by considering background 

exposures in the exposed and comparison groups. 

• The latent period for most cancers implies that exposures immediately preceding the 

detection of a tumor would be less likely to have contributed to its development and, 

therefore, may count less in the analysis. Study subjects who were first exposed near the 

end of the study may not have had adequate time since exposure for cancer to develop; 

therefore, analysis of their data may be similar to analysis of data for those who were not 

exposed. However, for carcinogens that act on multiple stages of the carcinogenic 

process, especially the later stages, all periods of exposure. including recent exposures, 

may be important. 

Some study designs can yield only a partial characterization of the overall hazard and 

therefore risk as, for example, in studies that: (1) investigate only one effect (typical of many 

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case-control studies), (2) include only one population segment (e.g., male workers or workers of 

one socioeconomic class), or (3) include only one lifestage (e.g., childhood leukemia following 

maternal exposure to contaminated drinking water). To obtain a more complete characterization 

that includes risks of other cancers, estimates from these studies can be supplemented with 

estimates from other studies that investigated other cancers, population segments, or lifestages 

(see Section 3.5). 

When several studies are available for dose-response analysis, meta-analysis can provide 

a systematic approach to weighing positive studies and those studies that do not show positive 

results, and calculating an overall risk estimate with greater precision. Issues considered include 

the comparability of studies, heterogeneity across studies, and the potential for a single large 

study to dominate the analysis. Confidence in a meta-analysis is increased when it considers 

study quality, including definition of the study population and comparison group, measurement 

of exposure, potential for exposure misclassification, adequacy of follow-up period, and analysis 

of confounders (see Section 2.2.1.3). 

3.2.2. Toxicodynamic (“Biologically Based”) Modeling 

Toxicodynamic modeling can be used when there are sufficient data to ascertain the mode 

of action (see Section 2.4) and quantitatively support model parameters that represent rates and 

other quantities associated with the key precursor events of the mode of action. Toxicodynamic 

modeling is potentially the most comprehensive way to account for the biological processes 

involved in a response. Such models seek to reflect the sequence of key precursor events that 

lead to cancer. Toxicodynamic models can contribute to dose-response assessment by revealing 

and describing nonlinear relationships between internal dose and cancer response. Such models 

may provide a useful approach for analysis in the range of observation, provided the purpose of 

the assessment justifies the effort involved. 

If a new model is developed for a specific agent, extensive data on the agent are important 

for identifying the form of the model, estimating its parameters, and building confidence in its 

results. Conformance to the observed tumor incidence data alone does not establish a model's 

validity, as a model can be designed with a sufficiently large number of parameters so as to fit 

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any given dataset. Peer review, including both an examination of the scientific basis supporting 

the model and an independent evaluation of the model's performance, is an essential part of 

evaluating the new model. 

If a standard model already exists for the agent's mode of action, the model can be 

adapted for the agent by using agent-specific data to estimate the model's parameters. An 

example is the two-stage clonal expansion model developed by Moolgavkar and Knudson (1981) 

and Chen and Farland (1991). These models continue to be improved as more information 

becomes available. 

It is possible for different models to provide equivalent fits to the observed data but to 

diverge substantially in their projections at lower doses. When model parameters are estimated 

from tumor incidence data, it is often the case that different combinations of parameter estimates 

can yield similar results in the observed range. For this reason, critical parameters (e.g., mutation 

rates and cell birth and death rates) are estimated from laboratory studies and not by curve-fitting 

to tumor incidence data (Portier, 1987). This approach reduces model uncertainty (see Section 

3.6) and ensures that the model does not give answers that are biologically unrealistic. This 

approach also provides a robustness of results, where the results are not likely to change 

substantially if fitted to slightly different data. 

Toxicodynamic modeling can provide insight into the relationship between tumors and 

key precursor events. For example, a model that includes cell proliferation can be used to 

explore the extent to which small increases in the cell proliferation rate can lead to large lifetime 

tumor incidences (Gaylor and Zheng, 1996). In this way, toxicodynamic modeling can be used to 

select and characterize an appropriate precursor response level (see Section 3.2.2, 3.2.5). 

3.2.3. Empirical Modeling (“Curve Fitting”) 

When a toxicodynamic model is not available or when the purpose of the assessment does 

not warrant developing such a model, empirical modeling (sometimes called “curve fitting”) 

should be used in the range of observation. A model can be fitted to data on either tumor 

incidence or a key precursor event. Goodness-of-fit to the experimental observations is not by 

itself an effective means of discriminating among models that adequately fit the data (OSTP, 

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1985). Many different curve-fitting models have been developed, and those that fit the observed 

data reasonably well may lead to several-fold differences in estimated risk at the lower end of the 

observed range. Another problem occurs when a multitude of alternatives are presented without 

sufficient context to make a reasoned judgment about the alternatives. This form of model 

uncertainty reflects primarily the availability of different computer models and not biological 

information about the agent being assessed or about carcinogenesis in general. In cases where 

curve-fitting models are used because the data are not adequate to support a toxicodynamic 

model, there generally would be no biological basis to choose among alternative curve-fitting 

models. However, in situations where there are alternative models with significant biological 

support, the decisionmaker can be informed by the presentation of these alternatives along with 

their strengths and uncertainties. 

Quantitative data on precursors can be used in conjunction with, or in lieu of, data on 

tumor incidence to extend the dose-response curve to lower doses. Caution is used with rates of 

molecular events such as mutation or cell proliferation or signal transduction. Such rates can be 

difficult to relate to cell or tissue changes overall. The timing of observations of these 

phenomena, as well as the cell type involved, is linked to other precursor events to ensure that the 

measurement is truly a key event (Section 2.4). 

For incidence data on either tumors or a precursor, an established empirical procedure is 

used to provide objectivity and consistency among assessments. The procedure models 

incidence, corrected for background, as an increasing function of dose. The models are 

sufficiently flexible in the observed range to fit linear and nonlinear datasets. Additional 

judgments and perhaps alternative analyses are used when the procedure fails to yield reliable 

results. For example, when a model’s fit is poor, the highest dose is often omitted in cases where 

it is judged that the highest dose reflects competing toxicity that is more relevant at high doses 

than at lower doses. Another example is when there are large differences in survival across dose 

groups; here, models that includes time-to-tumor or time-to-event information may be useful. 

For continuous data on key precursor effects, empirical models can be chosen on the 

basis of the structure of the data. The rationale for the choice of model, the alternatives 

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considered and rejected, and a discussion of model uncertainty are included in the dose-response 

characterization. 

3.2.4. Point of Departure (POD) 

For each tumor response, a POD from the observed data should be estimated to mark the 

beginning of extrapolation to lower doses. The POD is an estimated dose (expressed in humanequivalent terms) near the lower end of the observed range without significant extrapolation to 

lower doses. 

The POD is used as the starting point for subsequent extrapolations and analyses. For 

linear extrapolation, the POD is used to calculate a slope factor (see Section 3.3.3), and for 

nonlinear extrapolation the POD is used in the calculation of a reference dose or reference 

concentration (see Section 3.3.4). In a risk characterization, the POD is part of the determination 

of a margin of exposure (see Section 5.4). With appropriate adjustments, it can also be used as 

the basis for hazard rankings that compare different agents or health effects. 

The lowest POD is used that is adequately supported by the data. If the POD is above 

some data points, it can fail to reflect the shape of the dose-response curve at the lowest doses 

and can introduce bias into subsequent extrapolations (see Figure 3-1). On the other hand, if the 

POD is far below all observed data points, it can introduce model uncertainty and parameter 

uncertainty (see Section 3.6) that increase with the distance between the data and the POD. Use 

of a POD at the lowest level supported by the data seeks to balance these considerations. It uses 

information from the model(s) a small distance below the observed range rather than discarding 

this information and using extrapolation procedures in a range where the model(s) can provide 

some useful information. Statistical tests involving the ratio of the central estimate and its lower 

bound (i.e., EDxx/LEDxx) can be useful for evaluating how well the data support a model’s 

estimates at a particular response level. (Note that the ability to model at a particular response 

level is not the same as the study's ability to identify an increase at that response level as 

statistically significant.) 

The POD for extrapolating the relationship to environmental exposure levels of interest, 

when the latter are outside the range of observed data, is generally the lower 95% confidence 

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limit on the lowest dose level that can be supported for modeling by the data. SAB (1997) 

suggested that, "it may be appropriate to emphasize lower statistical bounds in screening analyses 

and in activities designed to develop an appropriate human exposure value, since such activities 

require accounting for various types of uncertainties and a lower bound on the central estimate is 

a scientifically-based approach accounting for the uncertainty in the true value of the ED10 [or 

central estimate].” However, the consensus of the SAB (1997) was that, “both point estimates 

and statistical bounds can be useful in different circumstances, and recommended that the 

Agency routinely calculate and present the point estimate of the ED10 [or central estimate] and 

the corresponding upper and lower 95% statistical bounds.” For example, it may be appropriate 

to emphasize the central estimate in activities that involve formal uncertainty analysis that are 

required by OMB Circular A-4 (OMB, 2003) as well as ranking agents as to their carcinogenic 

hazard. Thus, risk assessors should calculate, to the extent practicable, and present the central 

estimate and the corresponding upper and lower statistical bounds (such as confidence limits) to 

inform decisionmakers. 

When tumor data are used, a POD is obtained from the modeled tumor incidences. 

Conventional cancer bioassays, with approximately 50 animals per group, generally can support 

modeling down to an increased incidence of 1–10%; epidemiologic studies, with larger sample 

sizes, below 1%. Various models commonly used for carcinogens yield similar estimates of the 

POD at response levels as low as 1% (Krewski and Van Ryzin, 1981; Gaylor et al., 1994). 

Consequently, response levels at or below 10% can often be used as the POD. As a modeling 

convention, the lower bound on the doses associated with standard response levels of 1, 5, and 

10% can be analyzed, presented, and considered. For making comparisons at doses within the 

observed range, the ED10 and LED10 are also reported and can be used, with appropriate 

adjustments, in hazard rankings that compare different agents or health effects (U.S. EPA, 

2002c). A no-observed-adverse-effect level (NOAEL) generally is not used for assessing the 

potential for carcinogenic response when one or more models can be fitted to the data. 

When good quality precursor data are available and are clearly tied to the mode of 

action of the compound of interest, models that include both tumors and their precursors may be 

advantageous for deriving a POD. Such models can provide insight into quantitative 

3-17
relationships between tumors and precursors (see Section 3.2.2), possibly suggesting the 

precursor response level that is associated with a particular tumor response level. The goal is to 

use precursor data to extend the observed range below what can be observed in tumor studies. 

EPA is continuing to examine this issue and anticipates that findings and conclusions may result 

in supplemental guidance to these cancer guidelines. If the precursor data are drawn from small 

samples or if the quantitative relationship between tumors and precursors is not well defined, 

then the tumor data will provide a more reliable POD. Precursor effects may or may not be 

biologically adverse in themselves; the intent is to consider not only tumors but also damage that 

can lead to subsequent tumor development by the agent. Analysis of continuous data may differ 

from discrete data; Murrell et al. (1998) discuss alternative approaches to deriving a POD from 

continuous data. 

3.2.5. Characterizing the POD: The POD Narrative 

As a single-point summary of a single dose-response curve, the POD alone does not 

convey all the critical information present in the data from which it is derived. To convey a 

measure of uncertainty, the POD should be presented as a central estimate with upper and lower 

bounds. A POD narrative summarizes other important features of the database and the POD that 

are important to account for in low-dose extrapolations or other analyses. 

(a) Nature of the response. Is the POD based on tumors or a precursor? If on tumors, 

does the POD measure incidence or mortality? Is it a lifetime measure or was the study 

terminated early? The relationships between precursors and tumors, incidence and mortality, and 

lifetime and early-termination results vary from case to case. Modeling can provide quantitative 

insight into these relationships, for example, linking a change in a precursor response to a tumor 

incidence (see Section 3.2.2). This can aid in evaluating the significance of the response at the 

POD and adjusting different PODs to make them comparable. 

(b) Level of the response. What level of response is associated with the POD, for 

example, 1% cancer risk, 10% cancer risk, or 10% change in a precursor measure? 

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(c) Nature of the study population. Is the POD based on humans or animals? How large 

is the effective sample size? Is the study group representative of the general population, of 

healthy adult workers, or of a susceptible group? Are both sexes represented? Did exposure 

occur during a susceptible lifestage? 

(d) Slope of the dose-response curve at the POD. How does response change as dose is 

reduced below the POD? A steep slope indicates that risk decreases rapidly as dose decreases. 

On the other hand, a steep slope also indicates that errors in an exposure assessment can lead to 

large errors in estimating risk. Both aspects of the slope are important. The slope also indicates 

whether dose-response curves for different effects are likely to cross below the POD. For 

example, in the ED01 study where 2-acetylaminofluorene caused bladder carcinomas and liver 

carcinomas in mice (Littlefield et al., 1980), the dose-response curves for these tumors cross 

between 10% and 1% response (see Figure 3-2). This crossing, which can be inferred from the 

slopes of the curves at a 10% response, shows how considering the slope can lead to better 

inferences about the predominant effects expected at lower doses. Mode of action data can also 

be useful; quantitative information about key precursor events can be used to describe how risk 

decreases as dose decreases below the POD. 

(e) Relationship of the POD with other cancers. How does the POD for this cancer 

relate to PODs for other cancers observed in the database? For example, a POD based on male 

workers would not reflect the implications of mammary tumors in female rats or mice. 

(f) Extent of the overall cancer database. Have potential cancer responses been 

adequately studied (e.g., were all tissues examined), or is the database limited to particular 

effects, population segments, or lifestages? Do the mode of action data suggest a potential for 

cancers not observed in the database (e.g., disruption of particular endocrine pathways leading to 

related cancers)? 

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3.2.6. Relative Potency Factors 

Relative potency factors (of which toxicity equivalence factors are a special case) can be 

used for a well-defined class of agents that operate through a common mode of action for the 

same toxic endpoint. A complete dose-response assessment is conducted for one well-studied 

member of the class that serves as the index chemical for the class. The other members of the 

class are tied to the index chemical by relative potency factors that are based on characteristics 

such as relative toxicological outcomes, relative metabolic rates, relative absorption rates, 

quantitative SARs, or receptor binding characteristics (U.S. EPA, 2000c). Examples of this 

approach are the toxicity equivalence factors for dioxin-like compounds and the relative potency 

factors for some carcinogenic polycyclic aromatic hydrocarbons. Whenever practicable, toxicity 

equivalence factors should be validated and accompanied by quantitative uncertainty analysis. 

3.3. EXTRAPOLATION TO LOWER DOSES 

The purpose of low-dose extrapolation is to provide as much information as possible 

about risk in the range of doses below the observed data. The most versatile forms of low-dose 

extrapolation are dose-response models that characterize risk as a probability over a range of 

environmental exposure levels. These risk probabilities allow estimates of the risk reduction 

under different decision options and estimates of the risk remaining after an action is taken and 

provide the risk information needed for benefit-cost analyses of different decision options. 

When a dose-response model is not developed for lower doses, another form of low-dose 

extrapolation is a safety assessment that characterizes the safety of one lower dose, with no 

explicit characterization of risks above or below that dose. Although this type of extrapolation 

may be adequate for evaluation of some decision options, it may not be adequate for other 

purposes (e.g., benefit-cost analyses) that require a quantitative characterization of risks across a 

range of doses. At this time, safety assessment is the default approach for tumors that arise 

through a nonlinear mode of action; however, EPA continues to explore methods for quantifying 

dose-response relationships over a range of environmental exposure levels for tumors that arise 

through a nonlinear mode of action (U.S. EPA, 2002c). EPA program offices that need this more 

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explicit dose-response information may develop and apply methods that are informed by the 

methods described in these cancer guidelines. 

3.3.1. Choosing an Extrapolation Approach 

The approach for extrapolation below the observed data considers the understanding of 

the agent's mode of action at each tumor site (see Section 2.4). Mode of action information can 

suggest the likely shape of the dose-response curve at lower doses. The extent of inter-individual 

variation is also considered, with greater variation spreading the response over a wider range of 

doses. 

Linear extrapolation should be used when there are MOA data to indicate that the doseresponse curve is expected to have a linear component below the POD. Agents that are generally 

considered to be linear in this region include: 

• agents that are DNA-reactive and have direct mutagenic activity, or 

• agents for which human exposures or body burdens are high and near doses 

associated with key precursor events in the carcinogenic process, so that 

background exposures to this and other agents operating through a common mode 

of action are in the increasing, approximately linear, portion of the dose-response 

curve. 

When the weight of evidence evaluation of all available data are insufficient to establish 

the mode of action for a tumor site and when scientifically plausible based on the available data, 

linear extrapolation is used as a default approach, because linear extrapolation generally is 

considered to be a health-protective approach. Nonlinear approaches generally should not be 

used in cases where the mode of action has not been ascertained. Where alternative approaches 

with significant biological support are available for the same tumor response and no scientific 

consensus favors a single approach, an assessment may present results based on more than one 

approach. 

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A nonlinear approach should be selected when there are sufficient data to ascertain the 

mode of action and conclude that it is not linear at low doses and the agent does not demonstrate 

mutagenic or other activity consistent with linearity at low doses. Special attention is important 

when the data support a nonlinear mode of action but there is also a suggestion of mutagenicity. 

Depending on the strength of the suggestion of mutagenicity, the assessment may justify a 

conclusion that mutagenicity is not operative at low doses and focus on a nonlinear approach, or 

alternatively, the assessment may use both linear and nonlinear approaches. 

Both linear and nonlinear approaches may be used when there are multiple modes of 

action. If there are multiple tumor sites, one with a linear and another with a nonlinear mode of 

action, then the corresponding approach is used at each site. If there are multiple modes of action 

at a single tumor site, one linear and another nonlinear, then both approaches are used to 

decouple and consider the respective contributions of each mode of action in different dose 

ranges. For example, an agent can act predominantly through cytotoxicity at high doses and 

through mutagenicity at lower doses where cytotoxicity does not occur. Modeling to a low 

response level can be useful for estimating the response at doses where the high-dose mode of 

action would be less important. 

3.3.2. Extrapolation Using a Toxicodynamic Model 

The preferred approach is to develop a toxicodynamic model of the agent’s mode of 

action and use that model for extrapolation to lower doses (see Section 3.2.2). The extent of 

extrapolation is governed by an analysis of model uncertainty, where alternative models that fit 

similarly in the observed range can diverge below that range (see Section 3.6). Substantial 

divergence is likely when model parameters are estimated from tumor incidence data, so that 

different combinations of parameter estimates yield similar fits in the observed range but have 

different implications at lower doses. An analysis of model uncertainty can be used to determine 

the range where extrapolation using the toxicodynamic model is supported and where further 

extrapolation would be based on either a linear or a nonlinear default, as appropriate (see 

Sections 3.3.3, 3.3.4). 

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3.3.3. Extrapolation Using a Low-dose, Linear Model 

Linear extrapolation should be used in two distinct circumstances: (1) when there are data 

to indicate that the dose-response curve has a linear component below the POD, or (2) as a 

default for a tumor site where the mode of action is not established (see Section 3.3.1). For 

linear extrapolation, a line should be drawn from the POD to the origin, corrected for 

background. This implies a proportional (linear) relationship between risk and dose at low doses. 

(Note that the dose-response curve generally is not linear at higher doses.) 

The slope of this line, known as the slope factor, is an upper-bound estimate of risk per 

increment of dose that can be used to estimate risk probabilities for different exposure levels. 

The slope factor is equal to 0.01/LED01 if the LED01 is used as the POD. 

Unit risk estimates express the slope in terms of :g/L drinking water or :g/m3 or ppm air. 

In general, the drinking water unit risk is derived by converting a slope factor from units of 

mg/kg-d to units of :g/L, whereas an inhalation unit risk is developed directly from a doseresponse analysis using equivalent human concentrations already expressed in units of :g/m3

. 

Unit risk estimates often assume a standard intake rate (L/day drinking water or m3

/day air) and 

body weight (kg), which may need to be reconciled with the exposure factors for the population 

of interest in an exposure assessment (see Section 4.4). Alternatively, when the slope factor for 

inhalation is in units of ppm, it may sometimes be termed the inhalation unit risk. Although unit 

risks have not been calculated in the past for dermal exposures, both exposures that are absorbed 

into the systemic circulation and those that remain in contact with the skin are also important. 

Risk-specific doses are derived from the slope factor or unit risk to estimate the dose 

associated with a specific risk level, for example, a one-in-a-million increased lifetime risk. 

3.3.4. Nonlinear Extrapolation to Lower Doses 

A nonlinear extrapolation method can be used for cases with sufficient data to ascertain 

the mode of action and to conclude that it is not linear at low doses but with not enough data to 

support a toxicodynamic model that may be either nonlinear or linear at low doses. Nonlinear 

extrapolation having a significant biological support may be presented in addition to a linear 

approach when the available data and a weight of evidence evaluation support a nonlinear 

3-23
approach, but the data are not strong enough to ascertain the mode of action applying the 

Agency’s mode of action framework. If the mode of action and other information can support 

chemical-specific modeling at low doses, it is preferable to default procedures. 

For cases where the tumors arise through a nonlinear mode of action, an oral reference 

dose or an inhalation reference concentration, or both, should be developed in accordance with 

EPA’s established practice for developing such values, taking into consideration the factors 

summarized in the characterization of the POD (see Section 3.2.5). This approach expands the 

past focus of such reference values (previously reserved for effects other than cancer) to include 

carcinogenic effects determined to have a nonlinear mode of action. As with other health effects 

of concern, it is important to put cancer in perspective with the overall health impact of an 

exposure by comparing reference value calculations for cancer with those for other health effects. 

For effects other than cancer, reference values have been described as being based on the 

assumption of biological thresholds. The Agency's more current guidelines for these effects 

(U.S. EPA, 1996a, 1998b), however, do not use this assumption, citing the difficulty of 

empirically distinguishing a true threshold from a dose-response curve that is nonlinear at low 

doses. 

Economic and policy analysts need to know how the probability of cancer varies at 

exposures above the reference value and whether, and to what extent, there are health benefits 

from reducing exposures below the reference value. The risk assessment community is working 

to develop better methods to provide more useful information to economic and policy analysts. 

3.3.5. Comparing and Combining Multiple Extrapolations 

When multiple estimates can be developed, all datasets should be considered and a 

judgment made about how best to represent the human cancer risk. Some options for presenting 

results include: 

• adding risk estimates derived from different tumor sites (NRC, 1994), 

3-24 
• combining data from different datasets in a joint analysis (Putzrath and Ginevan, 

1991; Stiteler et al., 1993; Vater et al., 1993), 

• combining responses that operate through a common mode of action, 

• representing the overall response in each experiment by counting animals with any 

tumor showing a statistically significant increase, 

• presenting a range of results from multiple datasets (in this case, the dose-response 

assessment includes guidance on how to choose an appropriate value from the 

range), 

• choosing a single dataset if it can be justified as most representative of the overall 

response in humans, or 

• a combination of these options. 

Cross-comparison of estimates from human and animal studies can provide a valuable 

risk perspective. 

• Calculating an animal-derived slope factor and using it to estimate the risk expected 

in a human study can provide information with which to evaluate the human study 

design, for example, adequacy of exposure level and sample size. 

• Calculating an upper-bound slope factor from a human study that does not show 

positive results but that has good exposure information, and comparing it to an 

animal-derived slope factor can indicate whether the animal and humans studies are 

consistent. 

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3.4. EXTRAPOLATION TO DIFFERENT HUMAN EXPOSURE SCENARIOS 

As described in the previous cancer guidelines, special problems arise when the human 

exposure situation of concern suggests exposure regimens, e.g., route and dosing schedule, that 

are substantially different from those used in the relevant animal studies. Unless there is 

evidence to the contrary in a particular case, the cumulative dose received over a lifetime, 

expressed as average daily exposure prorated over a lifetime, is recommended as an appropriate 

measure of exposure to a carcinogen. That is, the assumption is made that a high dose of a 

carcinogen received over a short period of time is equivalent to a corresponding low dose spread 

over a lifetime. This approach becomes more problematical as the exposures in question become 

more intense but less frequent, especially when there is evidence that the agent has shown doserate effects (U.S. EPA 1986a). 

Accordingly, for lifetime human exposure scenarios that involve intermittent or varying 

levels of exposure, the prevailing practice has been to assess exposure by calculating a lifetime 

average daily exposure or dose (U.S. EPA, 1992a). 

For less-than-lifetime human exposure scenarios, too, the lifetime average daily exposure 

or dose has often been used. The use of these lifetime average exposure metrics was adopted 

with low-dose linear cancer assessments in mind. The lifetime averaging implies that less-thanlifetime exposure is associated with a linearly proportional reduction of the lifetime risk, 

regardless of when exposures occur. Such averaging may be problematic in some situations. 

This can be illustrated using both the multistage model and the two-stage clonal expansion model 

that predict that short-duration risks are not necessarily proportional to exposure duration and can 

depend on the nature of the carcinogen and the timing of exposure (Goddard et al., 1995; 

Murdoch et al., 1992). These examples indicate some circumstances in which use of a lifetime 

average daily dose (LADD) would underestimate cancer risk by two- to fivefold, and others in 

which it might overestimate risk (Murdoch et al., 1992). Thus, averaging over the duration of a 

lifestage or a critical window of exposure may be appropriate. As methodological research 

focuses on new approaches for estimating risks from less-than-lifetime exposures, methods and 

defaults can be expected to change. 

3-26
This highlights the importance for each dose-response assessment to critically evaluate all 

information pertaining to less-than-lifetime exposure. For example, detailed stop-exposure 

studies can provide information about the relationship between exposure duration, precursor 

effects, potential for reversibility, and tumor development. Toxicokinetic modeling can 

investigate differences in internal dose between short-term and long-term exposure or between 

intermittent and constant exposure. Persistence in the body can be useful in explaining long-term 

effects resulting from shorter-term exposures. 

For nonlinear cancer analyses, it may be appropriate to assess exposure by calculating a 

daily dose that is averaged over the exposure duration for the study (see Section 3.1.1). For 

example, when the analysis is based on precursor effects that result from less than a lifetime 

exposure, that exposure period may be used. This reflects an expectation that the precursor 

effects on which the analysis is based can result from less-than-lifetime exposure, bringing 

consistency to the methods used for dose-response assessment and exposure assessment in such 

cases. The dose-response assessment can provide a recommendation to exposure assessors about 

the averaging time that is appropriate to the mode of action and to the exposure duration of the 

scenario. 

3.5. EXTRAPOLATION TO SUSCEPTIBLE POPULATIONS AND LIFESTAGES 

The dose-response assessment strives to derive separate estimates for susceptible 

populations and lifestages so that these risks can be explicitly characterized. For a susceptible 

population, higher risks can be expected from exposures anytime during life, but this applies to 

only a portion of the general population (e.g., those bearing a particular genetic susceptibility). 

In contrast, for a susceptible lifestage, higher risks can be expected from exposures during only a 

portion of a lifetime, but everyone in the population may pass through those lifestages. Effects of 

exposures during a susceptible period are not equivalent to effects of exposures at other times; 

consequently, it is useful to estimate the risk attributable to exposures during each period. 

Depending on the data available, a tiered approach should be used to address susceptible 

populations and lifestages. 

3-27
• When there is an epidemiologic study or an animal bioassay that reports quantitative 

results for susceptible individuals, the data should be analyzed to provide a separate 

risk estimate for those who are susceptible. If susceptibility pertains to a lifestage, it 

is useful to characterize the portion of the lifetime risk that can be attributed to the 

susceptible lifestage. 

• When there are data on some risk-related parameters that allow comparison of the 

general population and susceptible individuals, the data should be analyzed with an 

eye toward adjusting the general population estimate for susceptible individuals. 

This analysis can range from toxicokinetic modeling that uses parameter values 

representative of susceptible individuals to more simply adjusting a general 

population estimate to reflect differences in important rate-governing parameters. 

Care is taken to not make parameter adjustments in isolation, as the appropriate 

adjustment can depend on the interactions of several parameters; for example, the 

ratio of metabolic activation and clearance rates can be more appropriate than the 

activation rate alone (U.S. EPA, 1992b). 

• In the absence of such agent-specific data, there is some general information to 

indicate that childhood can be a susceptible lifestage for exposure to some 

carcinogens (U.S. EPA, 2005); this warrants explicit consideration in each 

assessment. The potential for susceptibility from early-life exposure is expected to 

vary among specific agents and chemical classes. In addition, the concern that the 

dose-averaging generally used for assessing less-than-lifetime exposure is more 

likely to understate than overstate risk (see Section 3.4) contributes to the 

suggestion that alternative approaches be considered for assessing risks from lessthan-lifetime exposure that occurs during childhood. Accompanying these cancer 

guidelines is the Supplemental Guidance that the Agency will use to assess risks 

from early-life exposure to potential carcinogens (U.S. EPA, 2005). The 

Supplemental Guidance may be updated to reflect new data and new understanding 

that may become available in the future. 

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3.6. UNCERTAINTY 

The NRC (1983, 1994, 1996, 2002) has repeatedly advised that proper characterization of 

uncertainty is essential in risk assessment. An assessment that omits or underestimates 

uncertainty can leave decisionmakers with a false sense of confidence in estimates of risk. On 

the other hand, a high level of uncertainty does not imply that a risk assessment or a risk 

management action should be delayed (NRC, 2002). Uncertainty in dose-response assessment 

can be classified as either model uncertainty or parameter uncertainty. A related concept, human 

variation, is discussed below. Assessments should discuss the significant uncertainties 

encountered in the analysis, distinguishing, if possible, between model uncertainty, parameter 

uncertainty, and human variation. Origins of these uncertainties can span a range, from a single 

causal thread supported by sparse data, to abundant information that presents multiple possible 

conclusions or that does not coalesce. As described in Section 2.6 and in Section 5.1, all 

contributing features should be noted. 

Model uncertainty refers to a lack of knowledge needed to determine which is the correct 

scientific theory on which to base a model. In risk assessment, model uncertainty is reflected in 

alternative choices for model structure, dose metrics, and extrapolation approaches. Other 

sources of model uncertainty concern whether surrogate data are appropriate, for example, using 

data on adults to make inferences about children. The full extent of model uncertainty usually 

cannot be quantified; a partial characterization can be obtained by comparing the results of 

alternative models. Model uncertainty is expressed through comparison of separate analyses 

from each model, coupled with a subjective probability statement, where feasible and 

appropriate, of the likelihood that each model might be correct (NRC, 1994). 

Some aspects of model uncertainty that should be addressed in an assessment include the 

use of animal models as a surrogate for humans, the influence of cross-species differences in 

metabolism and physiology, the use of effects observed at high doses as an indicator of the 

potential for effects at lower doses, the effect of using linear or nonlinear extrapolation to 

estimate risks, the use of using small samples and subgroups to make inferences about entire 

human populations or subpopulations with differential susceptibilities, and the use of 

3-29
experimental exposure regimens to make inferences about different human exposure scenarios 

(NRC, 2002). 

Toxicokinetic and toxicodynamic models are generally premised on site concordance 

across species, modeling, for example, the relationship between administered dose and liver 

tissue concentrations to predict increased incidences of liver cancer. This relationship, which can 

be observed in animals, is typically only inferred for humans. There are, however, numerous 

examples of an agent causing different cancers in different species. The assessment should 

discuss the relevant data that bear on this form of model uncertainty. 

Parameter uncertainty refers to a lack of knowledge about the values of a model's 

parameters. This leads to a distribution of values for each parameter. Common sources of 

parameter uncertainty include random measurement errors, systematic measurement errors, use 

of surrogate data instead of direct measurements, misclassification of exposure status, random 

sampling errors, and use of an unrepresentative sample. Most types of parameter uncertainty can 

be quantified by statistical analysis. 

Human variation refers to person-to-person differences in biological susceptibility or in 

exposure. Although both human variation and uncertainty can be characterized as ranges or 

distributions, they are fundamentally different concepts. Uncertainty can be reduced by further 

research that supports a model or improves a parameter estimate, but human variation is a reality 

that can be better characterized, but not reduced, by further research. Fields other than risk 

assessment use “variation” or “variability” to mean dispersion about a central value, including 

measurement errors and other random errors that risk assessors address as uncertainty. 

Probabilistic risk assessment, informed by expert judgment, has been used in exposure 

assessment to estimate human variation and uncertainty in lifetime average daily exposure 

concentration or dose. Probabilistic methods can be used in this exposure assessment application 

because the pertinent variables (for example, concentration, intake rate, exposure duration, and 

body weight) have been identified, their distributions can be observed, and the formula for 

combining the variables to estimate the lifetime average daily dose is well defined (see U.S. 

EPA, 1992a). Similarly, probabilistic methods can be applied in dose-response assessment when 

there is an understanding of the important parameters and their relationships, such as 

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identification of the key determinants of human variation (for example, metabolic 

polymorphisms, hormone levels, and cell replication rates), observation of the distributions of 

these variables, and valid models for combining these variables. With appropriate data and 

expert judgment, formal approaches to probabilistic risk assessment can be applied to provide 

insight into the overall extent and dominant sources of human variation and uncertainty. In 

doing this, it is important to note that analyses that omit or underestimate some principal sources 

of variation or uncertainty could provide a misleadingly narrow description of the true extent of 

variation and uncertainty and give decisionmakers a false sense of confidence in estimates of 

risk. Specification of joint probability distributions is appropriate when variables are not 

independent of each other. In each case, the assessment should carefully consider the questions 

of uncertainty and human variation and discuss the extent to which there are data to address 

them. 

Probabilistic risk assessment has also been used in dose-response assessment to 

determine and distinguish the degree of uncertainty and variability in toxicokinetic and 

toxicodynamic modeling. Although this field is less advanced that probabilistic exposure 

assessment, progress is being made and these cancer guidelines are flexible enough to 

accommodate continuing advances in these approaches. 

Advances in uncertainty analysis are expected as the field develops. The cancer 

guidelines are intended to be flexible enough to incorporate additional approaches for 

characterizing uncertainty that have less commonly been used by regulatory agencies. In all 

scientific and engineering fields, data and research limitations often limit the application of 

established methods. A dearth of data is a particular problem when quantifying the probability 

distribution of model outputs. In many of these scientific and engineering disciplines, researchers 

have used rigorous expert elicitation methods to overcome the lack of peer-reviewed methods 

and data. Although expert elicitation has not been widely used in environmental risk assessment, 

several studies have applied this methodology as a tool for understanding quantitative risk. For 

example, expert elicitation has been used in chemical risk assessment and its associated 

uncertainty (e.g., Richmond, 1981; Renn, 1999; Florig et al., 2001; Morgan et al., 2001; Willis et 

al., 2004), components of risk assessment such as hazard assessment and dose-response 

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evaluation (e.g., Hawkins and Graham 1988; Jelovsek et al., 1990; Evans et al., 1994; IEc, 2004; 

U.S. EPA 2004) and exposure assessment (e.g., Whitfield and Wallsten, 1989; Hawkins and 

Evans, 1989; Winkler et al., 1995; Stiber et al., 1999; Walker et al., 2001, 2003; Van Der FelsKlerx et al., 2002), and for evaluating other types of risks (e.g., North and Merkhofer, 1976; Fos 

and McLin, 1990). These cancer guidelines are flexible enough to accommodate the use of expert 

elicitation to characterize cancer risks, as a complement to the methods presented in the cancer 

guidelines. According to NRC (NRC, 2002), the rigorous use of expert elicitation for the 

analyses of risks is considered to be quality science. 

3.7. DOSE-RESPONSE CHARACTERIZATION 

A dose-response characterization extracts the dose-response information needed in a full 

risk characterization (U.S. EPA, 2000b), including: 

• presentation of the recommended estimates (slope factors, reference doses, 

reference concentrations) and alternatives with significant biological support, 

• a summary of the data supporting these estimates, 

• a summary and explanation of the modeling approaches used, 

• a description of any special features such as the development and consolidation of 

multiple estimates as detailed in Section 3.3.5, 

• the POD narrative (see Section 3.2.5), 

• a summary of the key defaults invoked, 

• identification of susceptible populations or lifestages and quantification of their 

differential susceptibility, and 

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• a discussion of the strengths and limitations of the dose-response assessment, 

highlighting significant issues in developing risk estimates, alternative approaches 

considered equally plausible, and how these issues were resolved. 

All estimates should be accompanied by the weight of evidence descriptor and its 

narrative (see Section 2.5) to convey a sense of the qualitative uncertainty about whether the 

agent may or may not be carcinogenic. 

Slope factors generally represent an upper bound on the average risk in a population or 

the risk for a randomly selected individual but not the risk for a highly susceptible individual or 

group. Some individuals face a higher risk and some face a lower risk. The use of upper bounds 

generally is considered to be a health-protective approach for covering the risk to susceptible 

individuals, although the calculation of upper bounds is not based on susceptibility data. 

Similarly, exposure during some lifestages can contribute more or less to the total lifetime risk 

than do similar exposures at other times. The dose-response assessment characterizes, to the 

extent possible, the extent of these variations. 

Depending on the supporting data and modeling approach, a slope factor can have a mix 

of traits that tend to either estimate, overestimate, or underestimate risk. 

Some examples of traits that tend to overestimate risk include the following. 

• The slope factor is derived from data on a highly susceptible animal strain. 

• Linear extrapolation is used as a default and extends over several orders of 

magnitude. 

• The largest of several slope factors is chosen. 

Some examples of traits that tend to underestimate risk include the following. 

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• Several tumor types were observed, but the slope factor is based on a subset of 

them. 

• The study design does not include exposure during a susceptible lifestage, for 

example, perinatal exposure. 

• The study population is of less-than-average susceptibility, for example, healthy 

adult workers. 

• There is random exposure misclassification or random exposure measurement error 

in the study from which the slope factor is derived. 

Some examples of traits that inherently neither overestimate nor underestimate risk 

include the following. 

• The slope factor is derived from data in humans or in an animal strain that responds 

like humans. 

• Linear extrapolation is appropriate for the agent’s mode of action. 

• Environmental exposures are close to the observed data. 

• Several slope factors for the same tumor are averaged or a slope factor is derived 

from pooled data from several studies. 

• The slope factor is derived from the only suitable study. 

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0 100 150 

0% 

5% 

10% 

Observed bladder tumors 

Modeled bladder tumors 

Extrapolations from LED10 and LED01 for 

bladder tumors 

Observed liver tumors 

Modeled liver tumors 

Extrapolation from LED10 and LED01 for 

liver tumors 

Figure 3-2. Crossing--between 10% and 1%--of dose-response curves 

for bladder carcinomas and liver carcinomas induced by 2-AAF 

0 5 10 15 

0% 

5% 

10% 

15% 

Observed tumor incidence 

Modeled tumor incidence 

Extrapolations from LED10 and LED01 

Figure 3-1. Compatibility of alternative points of departure with 

observed and modeled tumor incidences 

50 

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4. EXPOSURE ASSESSMENT 

Exposure assessment is the determination (qualitative and quantitative) of the magnitude, 

frequency, and duration of exposure and internal dose (U.S. EPA, 1992a). This section provides 

a brief overview of exposure assessment principles, with an emphasis on issues related to 

carcinogenic risk assessment. The information presented here should be used in conjunction 

with other guidance documents, including Guidelines for Exposure Assessment (U.S. EPA, 

1992a), Science Policy Council Handbook: Risk Characterization (U.S. EPA, 2000b), Exposure 

Factors Handbook (U.S. EPA, 1997c), the 1997 Policy for Use of Probabilistic Analysis in Risk 

Assessments (U.S. EPA, 1997d), and the 1997 Guiding Principles for Monte Carlo Analysis 

(U.S. EPA, 1997e). In addition, program-specific guidelines for exposure assessment should be 

consulted. 

Exposure assessment generally consists of four major steps: defining the assessment 

questions, selecting or developing the conceptual and mathematical models, collecting data or 

selecting and evaluating available data, and exposure characterization. Each of these steps is 

briefly described below. 

4.1. DEFINING THE ASSESSMENT QUESTIONS 

In providing a clear and unambiguous statement of the purpose and scope of the exposure 

assessment (U.S. EPA, 1997e), consider the following. 

• The management objectives of the assessment will determine whether deterministic 

screening level analyses are adequate or whether full probabilistic exposure 

characterization is needed. 

• Identify and include all important sources (e.g., pesticide applications), pathways 

(e.g., food or water), and routes (e.g., ingestion, inhalation, and dermal) of exposure 

in the assessment. If a particular source, pathway, or route is omitted, a clear and 

transparent explanation should be provided. 

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• Separate analyses should be conducted for each definable subgroup within the 

population of interest. In particular, subpopulations or lifestages that are believed to 

be highly exposed or susceptible to a particular health effect should be studied. 

These include people with certain diseases or genetic susceptibilities and others 

whose behavior or physiology may lead to higher exposure or susceptibility. 

Consider the following examples: 

— Physiological differences between men and women (e.g., body weight and 

inhalation rate) may lead to important differences in exposures. See, for 

example, the discussion in Exposure Factors Handbook (U.S. EPA, 

1997c, Appendix 1A). 

— Pregnant and lactating women may have exposures that differ from the 

general population (e.g., slightly higher water consumption) (U.S. EPA, 

1997c). Further, exposure to pregnant women may result in exposure to 

the developing fetus (NRC, 1993b). 

— Children consume more food per body weight than do adults while 

consuming fewer types of foods, i.e., have a more limited diet (ILSI, 1992; 

NRC, 1993b; U.S. EPA, 1997c). In addition, children engage in crawling 

and mouthing (i.e., putting hands and objects in the mouth) behaviors, 

which can increase their exposures. 

— The elderly and disabled may have important differences in their 

exposures due to a more sedentary lifestyle (U.S. EPA, 1997c). In 

addition, the health status of this group may affect their susceptibility to 

the detrimental effects of exposure.

For further guidance, see Guidelines for Exposure Assessment (U.S. EPA, 1992a, § 3).

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4.2. SELECTING OR DEVELOPING THE CONCEPTUAL AND MATHEMATICAL 

MODELS 

Carcinogen risk assessment models have generally been based on the premise that risk is 

proportional to cumulative lifetime dose. For lifetime human exposure scenarios, therefore, the 

exposure metric used for carcinogenic risk assessment has been the lifetime average daily dose 

(LADD) or, in the case of inhalation exposure, the lifetime average exposure concentration. 

These metrics are typically used in conjunction with the corresponding slope factor to calculate 

individual excess cancer risk. The LADD is typically an estimate of the daily intake of a 

carcinogenic agent throughout the entire life of an individual, while the lifetime average exposure 

concentration is the corresponding estimate of average exposure concentration for the 

carcinogenic agent over the entire life of an individual. Depending on the objectives of the 

assessment, the LADD or lifetime average exposure concentration may be calculated 

deterministically (using point estimates for each factor to derive a point estimate of the exposure) 

or stochastically (using probability distributions to represent each factor and such techniques as 

Monte Carlo analysis to derive a distribution of the LADD) (U.S. EPA, 1997e). Stochastic 

analyses may help to identify certain population segments or lifestages that are highly exposed 

and may need to be assessed as a special subgroup. For further guidance, see Guidelines for 

Exposure Assessment (U.S. EPA, 1992a, § 5.3.5.2 ). As methodological research focuses on new 

approaches for estimating risks from less-than-lifetime exposures, methods and defaults can be 

expected to change. 

There may be cases where the mode of action indicates that dose rates are important in 

the carcinogenic process. In these cases, short-term, less-than-lifetime exposure estimates may 

be more appropriate than the LADD for risk assessment. This may be the case when a nonlinear 

dose-response approach is used (see Section 3.3.4). 

4.3. COLLECTING DATA OR SELECTING AND EVALUATING AVAILABLE DATA 

After the assessment questions have been defined and the conceptual and mathematical 

models have been developed, it is important to compile and evaluate existing data or, if 

necessary, to collect new data. Depending on the exposure scenario under consideration, data on 

4-3
a wide variety of exposure factors may be needed. EPA’s Exposure Factors Handbook (U.S. 

EPA, 1997c) contains a large compilation of exposure data, with some analysis and 

recommendations. Some of these data are organized by age groups to assist with assessing such 

subgroups as children. See, for example, Exposure Factors Handbook (U.S. EPA, 1997c, 

Volume 1, Chapter 3). When using these existing data, it is important to evaluate the quality of 

the data and the extent to which the data are representative of the population under consideration. 

EPA’s (U.S. EPA, 2000d) and OMB’s (OMB 2002) guidance on information quality, as well as 

program-specific guidances can provide further assistance for evaluating existing data. 

When existing data fail to provide an adequate surrogate for the needs of a particular 

assessment, it is important to collect new data. Such data collection efforts should be guided by 

the references listed above (e.g., Guidance for Data Quality Assessment and program-specific 

guidance). Once again, subpopulations or lifestages of concern are an important consideration in 

any data collection effort. 

4.3.1. Adjusting Unit Risks for Highly Exposed Populations and Lifestages 

Unit risk estimates that have been developed in the dose-response assessment often 

assumed standard adult intake rates. When an exposure assessment focuses on a population or 

lifestage with differential exposure, good exposure assessment practice would replace the 

standard intake rates with values representative of the exposed population. Small changes in 

exposure assessments can be approximated by using linearly proportional adjustments of 

exposure parameters, but a more accurate integrative analysis may require an analysis stratified 

by exposure duration (see Section 5.1) . 

For example, to adjust the drinking water unit risk for an active 

population that drinks 4 L/day (instead of 2 L/day), multiply the unit 

risk by 2. 

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Because children drink more water relative to their body weight than do adults (U.S. 

EPA, 2002d), adjustments to unit risk estimates are warranted whenever they are applied in an 

assessment of childhood exposure. 

For example, to adjust the drinking water unit risk for a 9-kg infant 

who drinks 1 L/day (instead of a 70-kg adult who drinks 2 L/day), 

multiply the unit risk by [(1 L/day) / (9 kg)] / [(2 L/day) / (70 kg)] = 

3.9. 

Inhalation dosimetry is employed to derive the human equivalent exposure concentrations 

on which inhalation unit risks, and reference concentrations, are based (U.S. EPA, 1994). As 

described previously (see Sections 3.1.2, 3.1.3), different dosimetry methods may be employed 

depending on the availability of relevant data and chemical-specific characteristics of the 

pollutant. Consideration of lifestage-particular physiological characteristics in the dosimetry 

analysis may result in a refinement to the human equivalent concentration (HEC) to insure 

relevance in risk assessment across lifestages, or might conceivably conclude with multiple 

HECs, and corresponding inhalation unit risk values (e.g., separate for childhood and adulthood). 

The dose-response assessment discusses the key sources of uncertainty in estimating 

dosimetry, including any related to lifestage. Review of this discussion and of the dosimetric 

analysis performed in deriving the HEC and resultant unit risk will assist in the appropriate 

application of inhalation unit risk values to exposure across lifestages. 

4.4. EXPOSURE CHARACTERIZATION 

The exposure characterization is a technical characterization that presents the assessment 

results and supports the risk characterization. It provides a statement of the purpose, scope, and 

approach used in the assessment, identifying the exposure scenarios and population subgroups 

covered. It provides estimates of the magnitude, frequency, duration, and distribution of 

exposures among members of the exposed population as the data permit. It identifies and 

compares the contribution of different sources, pathways, and routes of exposure. In particular, a 

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qualitative discussion of the strengths and limitations (uncertainties) of the data and models are 

presented. 

The discussion of uncertainties is a critical component of the exposure characterization. 

Uncertainties can arise out of problems with the conceptual and mathematical models. 

Uncertainties can also arise from poor data quality and data that are not quite representative of 

the population or scenario of interest. Consider the following examples of uncertainties. 

• National data (i.e., data collected to represent the entire U.S. population) may not be 

representative of exposures occurring within a regional or local population. 

• Use of short-term data to infer chronic, lifetime exposures should be done with 

caution. Use of short-term data to estimate long-term exposures has the tendency to 

underestimate the number of people exposed while overestimating the exposure 

levels experienced by those in the upper end (i.e., above the 90th percentile) of the 

exposure distribution. For further guidance, refer to Guidelines for Exposure 

Assessment (U.S. EPA, 1992a, § 5.3.1). 

• Children’s behavior, including their more limited diet, may lead to relatively high 

but intermittent exposures. This pattern of exposure, “one that gradually declines 

over the developmental period and which remains relatively constant thereafter” is 

not accounted for in the LADD model (ILSI, 1992). Further, the physiological 

characteristics of children may lead to important differences in exposure. Some of 

these differences can be accounted for in the LADD model. For further guidance, 

see Guidelines for Exposure Assessment (U.S. EPA, 1992a, § 5.3.5.2). 

Overall, the exposure characterization should provide a full description of the sources, 

pathways, and routes of exposure. The characterization also should include a full description of 

the populations assessed. In particular, highly exposed or susceptible subpopulation or lifestage 

should be discussed. For further guidance on the exposure characterization, consult Guidelines 

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for Exposure Assessment (U.S. EPA, 1992a), the Policy and Guidance for Risk Characterization 

(U.S. EPA, 2000b,1995) and EPA’s Rule Writer's Guide to Executive Order 13045 (especially 

Attachment C: Technical Support for Risk Assessors—Suggestions for Characterizing Risks to 

Children [U.S. EPA, 1998d]). 

4-7
5. RISK CHARACTERIZATION 

5.1. PURPOSE 

EPA has developed general guidance on risk characterization for use in its risk 

assessment activities. The core of EPA's risk characterization policy (U.S. EPA, 2000b, 1995) 

includes the following. 

Each risk assessment prepared in support of decision making at EPA should 

include a risk characterization that follows the principles and reflects the 

values outlined in this policy. A risk characterization should be prepared in 

a manner that is clear, transparent, reasonable, and consistent with other risk 

characterizations of similar scope prepared across programs in the Agency. 

Further, discussion of risk in all EPA reports, presentations, decision 

packages, and other documents should be substantively consistent with the 

risk characterization. The nature of the risk characterization will depend 

upon the information available, the regulatory application of the risk 

information, and the resources (including time) available. In all cases, 

however, the assessment should identify and discuss all the major issues 

associated with determining the nature and extent of the risk and provide 

commentary on any constraints limiting fuller exposition. 

Risk characterization should be carried out in accordance with the EPA (U.S. EPA, 

2002a) and OMB (2002) information quality guidelines. EPA's risk characterization handbook 

(U.S. EPA, 2000b) provides detailed guidance to Agency staff. The discussion below does not 

attempt to duplicate this material, but it summarizes its applicability to carcinogen risk 

assessment. 

The risk characterization includes a summary for the risk manager in a nontechnical 

discussion that minimizes the use of technical terms. It is an appraisal of the science that informs 

the risk manager in public health decisions, as do other decision-making analyses of economic, 

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social, or technology issues. It also serves the needs of other interested readers. The summary is 

an information resource for preparing risk communication information, but being somewhat 

more technical than desired for communication with the general public, is not itself the usual 

vehicle for communication with every audience. 

The risk characterization also brings together the assessments of hazard, dose response, 

and exposure to make risk estimates for the exposure scenarios of interest. This analysis that 

follows the summary is generally much more extensive. It typically will identify exposure 

scenarios of interest in decision making and present risk analyses associated with them. Some of 

the analyses may concern scenarios in several media; others may examine, for example, only 

drinking water risks. As these cancer guidelines allow different hazard characterizations and 

different potencies for specified conditions, e.g., exposure level, route of exposure, or lifestage, 

some of the integrative analyses may need to be stratified to accommodate the appropriate 

combinations of parameters across relevant exposure durations. 

In constructing high end estimates of risk, the assessor should bear in mind that the highend risk is a plausible estimate of the risk for those persons at the upper end of the risk 

distribution (U.S. EPA, 1992a). The intent of this approach is to convey an estimate of risk in the 

upper range of the distribution, but to avoid estimates that are beyond the true distribution. 

Overly conservative assumptions, when combined, can lead to unrealistic estimates of risk. This 

means that when constructing estimates from a series of factors (e.g., emissions, exposure, and 

unit risk estimates) not all factors should be set to values that maximize exposure, dose, or effect, 

since this will almost always lead to an estimate that is above the 99th-percentile confidence 

level and may be of limited use to decisionmakers. This is particularly problematic when using 

unbounded lognormal factor distributions. 

While it is an appropriate aim to assure protection of health and the environment in the 

face of scientific uncertainty, common sense, reasonable applications of assumptions and policy, 

and transparency are essential to avoid unrealistically high estimates. It is also important to 

inform risk managers of the final distribution of risk estimates (U.S. EPA, 2000b; 1995). 

Otherwise, risk management decisions may be made on varying levels of conservatism, leading 

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to misplaced risk priorities and potentially higher overall risks. (Nichols and Zeckhauser,1986; 

Zeckhauser and Viscusi,1990). 

The risk characterization presents an integrated and balanced picture of the analysis of the 

hazard, dose-response, and exposure. The risk analyst should provide summaries of the evidence 

and results and describe the quality of available data and the degree of confidence to be placed in 

the risk estimates. Important features include the constraints of available data and the state of 

knowledge, significant scientific issues, and significant science and science policy choices that 

were made when alternative interpretations of data exist (U.S. EPA, 1995, 2000b ). Choices 

made about using data or default options in the assessment are explicitly discussed in the course 

of analysis, and if a choice is a significant issue, it is highlighted in the summary. In situations 

where there are alternative approaches for a risk assessment that have significant biological 

support, the decisionmaker can be informed by the presentation of these alternatives along with 

their strengths and uncertainties. 

5.2. APPLICATION 

Risk characterization is a necessary part of generating any Agency report on risk, whether 

the report is preliminary — to support allocation of resources toward further study — or 

comprehensive — to support regulatory decisions. In the former case, the detail and 

sophistication of the characterization are appropriately small in scale; in the latter case, 

appropriately extensive. Even if a document covers only parts of a risk assessment (hazard and 

dose-response analyses, for instance), the results of these are characterized. 

Risk assessment is an iterative process that grows in depth and scope in stages from 

screening for priority making to preliminary estimation to fuller examination in support of 

complex regulatory decision making. Default options may be used at any stage, but they are 

predominant at screening stages and are used less as more data are gathered and incorporated at 

later stages. Various provisions in EPA-administered statutes require decisions based on differing 

findings for which differing degrees of analysis are appropriate. There are close to 30 provisions 

within the major statutes that require decisions based on risk, hazard, or exposure assessment. 

For example, Agency review of pre-manufacture notices under Section 5 of the Toxic Substances 

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Control Act relies on screening analyses, whereas requirements for industry testing under Section 

4 of that Act rely on preliminary analyses of risk or simply of exposure. In comparison, air 

quality criteria under the Clean Air Act rest on a rich data collection and are required by statute 

to undergo periodic reassessment. There are provisions that require ranking of hazards of 

numerous pollutants — which may be addressed through a screening level of analysis — and 

other provisions for which a full assessment of risk is more appropriate. 

Given this range in the scope and depth of analyses, not all risk characterizations can or 

should be equal in coverage or depth. The risk assessor should carefully decide which issues in a 

particular assessment are important to present, choosing those that are noteworthy in their impact 

on results. For example, health effect assessments typically rely on animal data because human 

data are rarely available. The objective of characterization of the use of animal data is not to 

recount generic issues about interpreting and using animal data; Agency guidance documents 

cover these issues. Rather, the objective is to highlight any significant issues that arose within 

the particular assessment being characterized and inform the reader about significant 

uncertainties that affect conclusions. 

5.3. PRESENTATION OF THE RISK CHARACTERIZATION SUMMARY 

The presentation is a nontechnical discussion of important conclusions, issues, and 

uncertainties that uses the hazard, dose response, exposure, and integrative analyses for technical 

support. The primary technical supports within the risk assessment are the hazard 

characterization, dose-response characterization, and exposure characterization described in these 

cancer guidelines. The risk characterization is derived from these. The presentation should 

fulfill the aims outlined in the purpose section above. 

5.4. CONTENT OF THE RISK CHARACTERIZATION SUMMARY 

Specific guidance on hazard, dose-response, and exposure characterization appears in 

previous sections. Overall, the risk characterization routinely includes the following, capturing 

the important items covered in hazard, dose response, and exposure characterization: 

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• primary conclusions about hazard, dose response, and exposure, including 

alternatives with significant biological support; 

• nature of key supporting information and analytic methods; 

• risk estimates and their attendant uncertainties, including key uses of default options 

when data are missing or uncertain. 

— With linear extrapolations, risk below the POD is typically approximated 

by multiplying the slope factor by an estimate of exposure, i.e., Risk = 

Slope Factor x Exposure. For exposure levels above the POD, the doseresponse model is used instead of this approximation. 

— With nonlinear extrapolations, the method of risk assessment depends on 

the procedure used. If a nonlinear dose-response function has been 

determined, it can be used with the expected exposure to estimate a risk. 

If an RfD or RfC was calculated, the hazard can be expressed as a hazard 

quotient (HQ), defined as the ratio of an exposure estimate over the 

reference dose (RfD) or reference concentration (RfC), i.e., HQ = 

Exposure / (RfD or RfC). From the hazard quotient, it can generally be 

inferred whether the nonlinear mode of action is relevant at the 

environmental exposure level in question; 

• statement of the extent of extrapolation of risk estimates from observed data to 

exposure levels of interest and its implications for certainty or uncertainty in 

quantifying risk. The extent of extrapolation can be expressed as a margin of 

exposure (MOE), defined as the ratio of the POD over an exposure estimate (MOE 

= POD / Exposure); 

. 

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• significant strengths and limitations of the data and analyses, including any major 

peer review issues; 

• appropriate comparison with similar EPA risk analyses or common risks with which 

people may be familiar; and 

• comparison with all appropriate assessments of the same problem by others. 

It is often difficult to know a priori when or how different results of a cancer risk 

assessment are likely to be used by Agency economists, policy analysts, and decisionmakers, so it 

is important that the resulting characterizations include the necessary information for these 

analyses to the extent practicable. OMB and EPA guidelines for benefit-cost analysis require 

expected or central estimates of risk and information on the uncertainty of the estimate when it is 

possible or practicable. The extent of the uncertainty information needed for analysis depends, in 

part, on the scale of the policy being considered, with formal quantitative analysis of uncertainty 

being required in some cases.6

 OMB Circular A-4 (OMB, 2003) emphasizes that agencies 

“should try to provide some estimate of the probability distribution of regulatory benefits and 

costs.” These OMB guidelines note, “Whenever it is possible to characterize quantitatively the 

probability distribution, some estimates of expected value ... must be provided in addition to 

ranges, variances, specified low-end and high-end percentile estimates, and other characteristics 

of the distribution.” The risk characterization should therefore include, where practicable, 

expected or central estimates of risk, as well as upper and lower bounds, e.g., confidence limits, 

based on the POD, if not a full characterization of uncertainty of the risk. As discussed in EPA’s 

Guidelines for Ensuring and Maximizing the Quality, Objectivity, Utility, and Integrity of 

Information Disseminated by the Environmental Protection Agency (Appendix B), statutory 

mandates, such as the Safe Drinking Water Act, the Food Quality Protection Act, and the Clean 

Specifically, OMB guidelines state: “For rules that exceed the $1 billion annual [economic effects] 

threshold, a formal quantitative analysis of uncertainty is required. For rules with annual benefits and/or costs in the 

range from 100 million to $1 billion, you should seek to use more rigorous approaches with higher consequence 

rules” (OMB, 2003, page 158) 

5-6

6 
Air Act, call for the Agency to generate specific kinds of risk information, and thus these updated 

cancer assessment guidelines should be read in conjunction with the Agency’s statutory mandates 

regarding risk assessment. 

5-7
APPENDIX A: MAJOR DEFAULT OPTIONS 

This discussion covers the major default options commonly employed when data are 

missing or sufficiently uncertain in a cancer risk assessment, as adopted in these cancer 

guidelines. These options are predominantly inferences that help use the data observed under 

empirical conditions in order to estimate events and outcomes under environmental conditions. 

Several inferential issues arise when effects seen in a subpopulation of humans or animals are 

used to infer potential effects in the population of environmentally exposed humans. Several 

more inferential issues arise in extrapolating the exposure-effect relationship observed 

empirically to lower-exposure environmental conditions. The following issues cover the major 

default areas. 

• Is the presence or absence of effects observed in a human population predictive of 

effects in another exposed human population? 

• Is the presence or absence of effects observed in an animal population predictive of 

effects in exposed humans? 

• How do metabolic pathways relate across species and among different age groups and 

between sexes in humans? 

• How do toxicokinetic processes relate across species and among different age groups 

and between sexes in humans? 

• What is the relationship between the observed dose-response relationship to the 

relationship at lower doses? 

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Is the Presence or Absence of Effects Observed in a Human Population Predictive of Effects 

in Another Exposed Human Population? 

When cancer effects in exposed humans are attributed to exposure to an agent, the 

default option is that the resulting data are predictive of cancer in any other exposed human 

population. Most studies investigating cancer outcomes in humans from exposure to agents are 

often studies of occupationally exposed humans. By sex, age, and general health, workers may 

not be representative of the general population exposed environmentally to the same agents. In 

such studies there is no opportunity to observe subpopulations who are likely to be under 

represented, such as fetuses, infants and children, women, or people in poor health, who may 

respond differently from healthy workers. Therefore, it is understood that this option could still 

underestimate the response of certain human subpopulations (NRC, 1993b, 1994). 

When cancer effects are not found in an exposed human population, this information by 

itself is not generally sufficient to conclude that the agent poses no carcinogenic hazard to this 

or other populations of potentially exposed humans, including susceptible subpopulations or 

lifestages. This is because epidemiologic studies often have low power to detect and attribute 

responses and typically evaluate cancer potential in a restricted population (e.g., by age, healthy 

workers). The topic of susceptibility and variation is addressed further in the discussion below of 

quantitative default options about dose-response relationships. Well-conducted studies that fail 

to detect a statistically significant positive association, however, may have value and should be 

judged on their merits, including population size, duration of the study, the quality of the 

exposure characterization and measures of outcome, and the magnitude and duration of the 

exposure. 

There is not yet enough knowledge to form a basis for any generally applicable qualitative 

or quantitative inference to compensate for the gap in knowledge concerning other populations. 

In these cancer guidelines, this problem is left to analysis in individual cases, to be attended to 

with further general guidance as future research and information allow. When information on a 

susceptible subpopulation or lifestage exists, it will be used. For example, an agent such as 

diethylstilbestrol (DES) causes a rare form of vaginal cancer (clear-cell adenocarcinoma) (Herbst 

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et al., 1971) in about 1 per 1000 of adult women whose mothers were exposed during pregnancy 

(Hatch et al., 1998). 

Is the Presence or Absence of Effects Observed in an Animal Population Predictive of Effects 

in Exposed Humans? The default option is that positive effects in animal cancer studies 

indicate that the agent under study can have carcinogenic potential in humans. Thus, if no 

adequate human or mode of action data are present, positive effects in animal cancer studies are a 

basis for assessing the carcinogenic hazard to humans. This option is a public health-protective 

policy, and it is both appropriate and necessary, given that we do not test for carcinogenicity in 

humans. The option is supported by the fact that nearly all of the agents known to cause cancer 

in humans are carcinogenic in animals in tests that have adequate protocols (IARC, 1994; 

Tomatis et al., 1989; Huff, 1994). Moreover, almost one-third of human carcinogens were 

identified subsequent to animal testing (Huff, 1993). Further support is provided by research on 

the molecular biology of cancer processes, which has shown that the mechanisms of control of 

cell growth and differentiation are remarkably homologous among species and highly conserved 

in evolution. Nevertheless, the same research tools that have enabled recognition of the nature 

and commonality of cancer processes at the molecular level also have the power to reveal 

differences and instances in which animal responses are not relevant to humans (Lijinsky, 1993; 

U.S. EPA, 1991b). Under these cancer guidelines, available mode of action information is 

studied for its implications in both hazard and dose-response assessment and its ability to obviate 

default options.

 There may be instances in which the use of an animal model would identify a hazard in 

animals that is not truly a hazard in humans (e.g., the alpha-2u-globulin association with renal 

neoplasia in male rats [U.S. EPA, 1991b]). The extent to which animal studies may yield false 

positive indications for humans is a matter of scientific debate. To demonstrate that a response 

in animals is not relevant to any human situation, adequate data to assess the relevancy issue are 

important. 

In general, while effects seen at the highest dose tested are assumed to be appropriate 

for assessment, it is necessary that the experimental conditions be scrutinized. Animal studies 

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are conducted at high doses in order to provide statistical power, the highest dose being one that 

is minimally toxic (maximum tolerated dose or MTD). Consequently, the question often arises 

of whether a carcinogenic effect at the highest dose may be a consequence of cell killing with 

compensatory cell replication or of general physiological disruption rather than inherent 

carcinogenicity of the tested agent. There is little doubt that this may happen in some cases, but 

skepticism exists among some scientists that it is a pervasive problem (Ames and Gold, 1990; 

Melnick et al., 1993; Barrett, 1993). If adequate data demonstrate that the effects are solely the 

result of excessive toxicity rather than carcinogenicity of the tested agent per se, then the effects 

may be regarded as not appropriate to include in assessment of the potential for human 

carcinogenicity of the agent. This is a matter of expert judgment, with consideration given to all 

of the data available about the agent, including effects in other toxicity studies, structure-activity 

relationships, and effects on growth control and differentiation. 

When cancer effects are not found in well-conducted animal cancer studies in two or 

more appropriate species and other information does not support the carcinogenic potential of 

the agent, these data provide a basis for concluding that the agent is not likely to possess human 

carcinogenic potential, in the absence of human data to the contrary. This default option about 

lack of cancer effects has limitations. It is recognized that animal studies (and epidemiologic 

studies as well) have very low power to detect cancer effects. Detection of a 10% tumor 

incidence is generally the limit of power with standard protocols for animal studies (with the 

exception of rare tumors that are virtually markers for a particular agent, e.g., angiosarcoma 

caused by vinyl chloride). In some situations, the tested animal species may not be predictive of 

effects in humans; for example, arsenic shows only minimal or no effect in animals, whereas it is 

clearly positive in humans. Therefore, it is important to consider other information as well; 

absence of mutagenic activity or absence of carcinogenic activity among structural analogues can 

increase the confidence that negative results in animal studies indicate a lack of human hazard. 

Another limitation is that standard animal study protocols are not yet available for 

effectively studying perinatal effects. The potential for effects on the very young generally should 

be considered separately. Under existing Agency policy (U.S. EPA, 1997a, b), perinatal studies 

A-4
accomplished by modification of existing adult bioassay protocols are important in special 

circumstances. 

Target organ concordance is not a prerequisite for evaluating the implications of animal 

study results for humans. Target organs of carcinogenesis for agents that cause cancer in both 

animals and humans are most often concordant at one or more sites (Tomatis et al., 1989; Huff, 

1994). However, concordance by site is not uniform. The mechanisms of control of cell growth 

and differentiation are concordant among species, but there are marked differences among 

species in the way control is managed in various tissues. For example, in humans, mutations of 

the tumor suppressor genes p53 and retinoblastoma are frequently observed genetic changes in 

tumors. These tumor-suppressor genes are also observed to be operating in some rodent tissues, 

but other growth control mechanisms predominate in other rodent tissues. Thus, an animal 

response may be due to changes in a control that are relevant to humans but appear in animals in 

a different way. 

However, it is appropriate under these cancer guidelines to consider the influences of 

route of exposure, metabolism, and, particularly, some modes of action that may either support or 

not support target organ concordance between animals and humans. When data allow, these 

influences are considered in deciding whether agent-, species-, or organ-specific situations are 

appropriate to use in preference to this default assumption (NRC, 1994). In contrast, use of 

toxicokinetic modeling inherently assumes site concordance, as these models are used to estimate 

delivered dose to a particular tissue or organ in humans on the basis of the same tissue or organ 

from animal data.

 The default is to include benign tumors observed in animal studies in the assessment of 

animal tumor incidence, if such tumors have the capacity to progress to the malignancies with 

which they are associated. This default is consistent with the approach of the National 

Toxicology Program and the International Agency for Research on Cancer and is more protective 

of public health than not including benign tumors in the assessment; benign and malignant 

tumors are treated as representative of related responses to the test agent (McConnell et al., 

1986), which is scientifically appropriate. Nonetheless, in assessing findings from animal 

studies, a greater proportion of malignancy is weighed more heavily than is a response with a 

A-5
greater proportion of benign tumors. Greater frequency of malignancy of a particular tumor type 

in comparison with other tumor responses observed in an animal study is also a factor to be 

considered in selecting the response to be used in dose-response assessment. 

Benign tumors that are not observed to progress to malignancy are assessed on a caseby-case basis. There is a range of possibilities for the overall significance of benign tumors. 

They may deserve attention because they are serious health problems even though they are not 

malignant; for instance, benign tumors may be a health risk because of their effect on the 

function of a target tissue, such as the brain. They may be significant indicators of the need for 

further testing of an agent if they are observed in a short-term test protocol, or such an 

observation may add to the overall weight of evidence if the same agent causes malignancies in a 

long-term study. Knowledge of the mode of action associated with a benign tumor response may 

aid in the interpretation of other tumor responses associated with the same agent. 

How Do Metabolic Pathways Relate Across Species and Among Different Age Groups and 

Between Sexes in Humans? 

The default option is that there is a similarity of the basic pathways of metabolism and 

the occurrence of metabolites in tissues in regard to the species-to-species extrapolation of 

cancer hazard and risk. If comparative metabolism studies were to show no similarity between 

the tested species and humans and a metabolite(s) was the active form, there would be less 

support for an inference that the animal response(s) relates to humans. In other cases, parameters 

of metabolism may vary quantitatively between species; this becomes a factor in deciding on an 

appropriate human-equivalent dose based on animal studies, optimally in the context of a 

toxicokinetic model. Although the basic pathways are assumed to be the same among humans, 

the presence of polymorphisms in the general population and factors such as the maturation of 

the pathways in infants should be considered. The active form of an agent may be present to 

differing degrees, or it may be completely absent, which may result in greater or lesser risk for 

subpopulations. 

A-6
How Do Toxicokinetic Processes Relate Across Species and Among Different Age Groups and 

Between Sexes in Humans? 

A major issue is how to estimate human-equivalent doses in extrapolating from animal 

studies. As a default for oral exposure, a human equivalent dose for adults is estimated from 

data on another species by an adjustment of animal applied oral dose by a scaling factor based 

on body weight to the 3/4 power. The same factor is used for children because it is slightly more 

protective than using children’s body weight (see Section 3.1.3). This adjustment factor is used 

because it represents scaling of metabolic rate across animals of different size. Because the 

factor adjusts for a parameter that can be improved on and brought into more sophisticated 

toxicokinetic modeling when such data become available, they are usually preferable to the 

default option. 

For inhalation exposure, a human equivalent dose for adults is estimated by default 

methodologies that provide estimates of lung deposition and internal dose (U.S. EPA, 1994). 

The methodologies can be refined to more sophisticated forms with data on toxicokinetic and 

metabolic parameters of the specific agent. This default option, like the one for oral exposure, is 

selected in part because it lays a foundation for incorporating better data. The use of information 

to improve dose estimation from applied to internal to delivered dose is encouraged, including 

use of toxicokinetic modeling instead of any default, where data are available. 

There are important differences between infants, adults, and older adults in the processes 

of absorption, distribution, and elimination; for example, infants tend to absorb metals through 

the gut more rapidly and more efficiently than do older children or adults (Calabrese, 1986). 

Renal elimination is also not as efficient in infants. Although these processes reach adult 

competency at about the time of weaning, they may have important implications, particularly 

when the dose-response relationship for an agent is considered to be nonlinear and there is an 

exposure scenario disproportionately affecting infants, because in these cases the magnitude of 

dose is more pertinent than the usual approach in linear extrapolation of averaging dose across a 

lifetime. Efficiency of intestinal absorption in older adults tends to be generally less overall for 

most chemicals. Another notable difference is that, post-weaning (about 1 year), children have a 

A-7
higher metabolic rate than do adults (Renwick, 1998), and they may toxify or detoxify agents at a 

correspondingly higher rate. 

For a route-to-route exposure extrapolation, the default option is that an agent that causes 

internal tumors by one route of exposure will be carcinogenic by another route if it is absorbed 

by the second route to give an internal dose. This is a qualitative option and is considered to be 

public-health protective. The rationale is that for internal tumors an internal dose is significant 

no matter what the route of exposure. Additionally, the metabolism of the agent will be 

qualitatively the same for an internal dose. The issue of quantitative extrapolation of the doseresponse relationship from one route to another is addressed case by case. Quantitative 

extrapolation is complicated by considerations such as first-pass metabolism. 

What Is the Correlation of the Observed Dose-Response Relationship to the Relationship at 

Lower Doses? 

If sufficient data are available, a biologically based model for both the observed range and 

extrapolation below that range may be used. Although no standard biologically based models are 

in existence, an agent-specific model may be developed if extensive data exist in a particular case 

and the purpose of the assessment justifies the investment of the resources needed. The default 

procedure for the observed range of data when a biologically based model is not used is to use a 

curve-fitting model for incidence data. 

In the absence of data supporting a biologically based model for extrapolation outside of 

the observed range, the choice of approach is based on the view of mode of action of the agent 

arrived at in the hazard assessment. If more than one approach (e.g., both a nonlinear and linear 

approach) are supported by the data, they should be used and presented to the decisionmaker. 

A linear extrapolation approach is used when the mode of action information is 

supportive of linearity or mode of action is not understood. The linear approach is used when a 

view of the mode of action indicates a linear response, for example, when a conclusion is made 

that an agent directly causes alterations in DNA, a kind of interaction that not only theoretically 

requires one reaction but also is likely to be additive to ongoing, spontaneous gene mutation. 

Other kinds of activity may have linear implications, for example, linear rate-limiting steps 

A-8
would also support a linear procedure. The linear approach is to draw a straight line between a 

point of departure from observed data, generally as a default, an LED chosen to be representative 

of the lower end of the observed range, and the origin (zero incremental dose, zero incremental 

response). This approach is generally considered to be public-health protective. 

The linear default is thought to generally provide an upper-bound calculation of potential 

risk at low doses, for example, a 1/100,000 to 1/1,000,000 risk. This upper bound is thought to 

be public-health protective at low doses for the range of human variation, considering the typical 

Agency target range for risk management of 1/1,000,000 to 1/10,000, although it may not 

completely be so (Bois et al., 1995) if pre-existing disease or genetic constitution place a 

percentage of the population at greater risk from exposure to carcinogens. The question of what 

may be the actual variation in human susceptibility is one that was discussed in general in the 

NRC (1994) report, as well as the NRC report on pesticides in children and infants (NRC, 

1993b). NRC has recommended research on the question, and EPA and other agencies are 

conducting such research. Given the current state of knowledge, EPA will assume that the linear 

default procedure adequately accounts for human variation unless there is case-specific 

information for a given agent or mode of action that indicates a particularly susceptible 

subpopulation or lifestage, in which case the special information will be used. 

When adequate data on mode of action provide sufficient evidence to support a nonlinear 

mode of action for the general population and/or any subpopulations of concern, a different 

approach — a reference dose/reference concentration that assumes that nonlinearity – is used. 

The POD is again generally an BMDL when incidence data are modeled. A sufficient basis to 

support this nonlinear procedure is likely to include data on responses that are key events integral 

to the carcinogenic process. This means that the POD may be based on these precursor response 

data, for example, hormone levels or mitogenic effects rather than tumor incidence data. 

When the mode of action information indicates that the dose-response function may be 

adequately described by both a linear and a nonlinear approach, then the results of both the 

linear and the nonlinear analyses are presented. An assessment may use both linear and 

nonlinear approaches if different responses are thought to result from different modes of action 

or a response appears to be very different at high and low doses due to influence of separate 

A-9
modes of action. The results may be needed for assessment of combined risk from agents that 

have common modes of action. 

Absent data to the contrary, the default assumption is that the cumulative dose received 

over a lifetime, expressed as a lifetime average daily dose or lifetime average daily exposure, is 

an appropriate measure of dose or exposure. This assumes that a high dose of such an agent 

received over a shorter period of time is equivalent to a low dose spread over a lifetime. This is 

thought to be a relatively public-health-protective option and has some empirical support 

(Monro, 1992). A counter example, i.e., effects of short-term, high exposure levels that result in 

subsequent cancer development, is treatment of cancer patients with certain chemotherapeutic 

agents. When sufficient information is available to support a different approach, it can be used. 

For example, short-term exposure estimates (several days to several months) may be more 

appropriate than the lifetime average daily dose. In these cases, both agent concentration and 

duration are likely to be important, because such effects may be reversible at cessation of very 

short-term exposures. 

A-10
APPENDIX B: EPA’s GUIDANCE FOR DATA QUALITY ASSESSMENT 

U.S. EPA (U.S. Environmental Protection Agency). (2000d) Guidance for data 

quality assessment: practical methods for data analysis. Office of Environmental 

Information, Washington, DC. EPA/600/R-96/084. Available from: 

http://www.epa.gov/quality/qs-docs/g9-final.pdf. 

B-1
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