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Data Mining in the U.S. National Toxicology Program (NTP) Database Reveals a Potential Bias Regarding Liver Tumors in Rodents Irrespective of the Test Agent

Data Mining in the U.S. National Toxicology Program (NTP) Database Reveals a Potential Bias Regarding Liver Tumors in Rodents Irrespective of the Test Agent

  • Matthias Ring, 
  • Bjoern M. Eskofier
PLOS
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Abstract

Long-term studies in rodents are the benchmark method to assess carcinogenicity of single substances, mixtures, and multi-compounds. In such a study, mice and rats are exposed to a test agent at different dose levels for a period of two years and the incidence of neoplastic lesions is observed. However, this two-year study is also expensive, time-consuming, and burdensome to the experimental animals. Consequently, various alternatives have been proposed in the literature to assess carcinogenicity on basis of short-term studies. In this paper, we investigated if effects on the rodents’ liver weight in short-term studies can be exploited to predict the incidence of liver tumors in long-term studies. A set of 138 paired short- and long-term studies was compiled from the database of the U.S. National Toxicology Program (NTP), more precisely, from (long-term) two-year carcinogenicity studies and their preceding (short-term) dose finding studies. In this set, data mining methods revealed patterns that can predict the incidence of liver tumors with accuracies of over 80%. However, the results simultaneously indicated a potential bias regarding liver tumors in two-year NTP studies. The incidence of liver tumors does not only depend on the test agent but also on other confounding factors in the study design, e.g., species, sex, type of substance. We recommend considering this bias if the hazard or risk of a test agent is assessed on basis of a NTP carcinogenicity study.

Introduction

The U.S. National Toxicology Program (NTP) conducts carcinogenicity studies in rodents to identify substances that may be hazardous to humans [13]. In a typical carcinogenicity study, mice and rats of both sexes are exposed to a substance of interest. The substance is administered to the rodents at three dose levels for a period of two years. The three dose levels are defined on basis of preceding dose finding studies, and 50 rodents of every species and every sex are usually exposed to every dose level. The same amount of rodents is observed as controls which are not exposed to the substance.

This type of carcinogenicity study (CS) is currently the benchmark method to assess carcinogenicity [4]. It is motivated by the fact that all human carcinogens have also produced positive results in at least one animal model [5]. However, a two-year carcinogenicity study (2Y-CS) is also a high-cost and time-consuming procedure. Consequently, various alternative approaches have been discussed to identify carcinogenic substances [6, 7].

For example, quantitative structure-activity relationship (QSAR) models examine chemical properties of substances to predict the carcinogenic potential [815]. Another approach exploits findings from short-term CSs, from the beginning of 2Y-CSs, or from the preceding dose finding studies to predict the outcome of the 2Y-CS. For example, logistic regression was used to predict tumors in control animals based on their body weight at the beginning of a 2Y-CS [16, 17]. For male rats, the incidence of exacerbated chronic progressive nephropathy in dose finding studies was used to predict renal tubule tumors in 2Y-CSs [18]. For rats of both sexes, histopathological findings in 6- and 12-month CSs were used to predict carcinogenicity in 2Y-CSs [19]. For mice and rats of both sexes, hepatocellular lesions and increased liver weight in dose finding studies were combined to predict liver carcinogenicity in 2Y-CSs [20]. A later study also confirmed this approach and extended it to lung and kidney tumors [21].

In the present analysis, data mining methods were employed to predict liver tumors in 2Y-CSs using findings from the preceding dose finding studies. The focus on liver tumors was motivated from three perspectives. From an anatomical perspective, the liver is the organ where orally administered substances are transported to after absorption through the small intestine [22]. From a physiological perspective, the liver is the organ that is responsible for detoxification [23]. From a statistical perspective, the liver is the organ with the most positive carcinogenic results if all NTP studies are summarized [24].

In detail, the present analysis considered 138 NTP studies including mice and rats of both sexes. In addition to previous studies on the prediction of liver carcinogenicity [20, 21], the influence of different dose levels was also considered. In contrast to previous studies [20, 21], liver tumors were predicted on the finer data level of individual animals instead of summary statistics for entire 2Y-CSs.

The results revealed patterns that can predict the incidence of liver tumors in 2Y-CSs on basis of findings from dose finding studies, i.e., findings from short-term CSs. However, these patterns simultaneously indicated a potential bias regarding liver carcinogenicity in the 2Y-CS. The patterns illustrated that the incidence of liver tumors in 2Y-CS does not only depend on the test substance, i.e., the subject of investigation. For instance, the results indicated that male mice, which are exposed to single substances at a high dose level, will likely develop a liver tumor. In contrast, the results did not indicate the same tendency for female mice, which are exposed to single substances at a high dose level. Thus, an increase in liver tumors in male mice may not be as alerting as a comparative increase in female mice if both sexes are exposed to identical substances at identical high dose levels. This bias should be considered in the statistical evaluation of a 2Y-CS because the decision on carcinogenicity of a substance is based on this evaluation.

Methods

Data Set

The NTP provides data from CSs in two forms. The technical report (TR, [25]) for every CS includes a statistical analysis and the decision on carcinogenicity of the test substance. The NTP database (CarTox, [26]) includes detailed data for every animal used in every CS. At the time of the present analysis, TRs for 582 CSs were available. Based on these TRs and the CarTox database, a data set was built using the following three steps.

Filtering of Technical Reports. The TRs for all 582 CSs were evaluated for inclusion in the present analysis. Four inclusion criteria were specified on every CS. First, every CS was required to be labeled as a long-term CS, which usually indicates a duration of 104 weeks. Second, every CS was required to administer the substance on an oral route, which was either dosed-feed, dosed-water, gavage, or micro-encapsulation in feed. An oral route was necessary so that the substance was directly transported to the target organ, the liver, after administration. Third, every CS was required to be preceded by a dose finding study, which was usually conducted for a period of 13 weeks. Fourth, the TR for every CS was required to provide liver weight recordings from the dose finding study. The third and fourth criteria were necessary because these liver weight recordings were employed as prediction attributes (see below).

Some TRs provided liver weight recordings only for some animal groups ( e.g., TR No. 373 provided data for rats but not for mice). In such cases, the CS was included but animal groups with missing data were removed later (see below).

The inclusion criteria yielded a subset of 138 2Y-CSs (Tables 1, 2, 3). Two more 2Y-CSs (TR No. 278, TR No. 244) also fulfilled all inclusion criteria. However, they were not included because the corresponding data was not found in the CarTox database.

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Table 1. List of 2Y-CSs on single substances that were included in the analysis.

https://doi.org/10.1371/journal.pone.0116488.t001

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Table 2. List of 2Y-CSs on mixtures that were included in the analysis.

https://doi.org/10.1371/journal.pone.0116488.t002

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Table 3. List of 2Y-CSs on multi-compounds that were included in the analysis.

https://doi.org/10.1371/journal.pone.0116488.t003

Combination of Technical Reports and Animal Data. For every animal used in any of the 138 2Y-CSs, five attributes were extracted from the CarTox database. First, the species (SP) of the animal (mouse, rat). Second, the sex (SE) of the animal (female, male). Third, the indicator for control (CO) animals (true, false). Fourth, the removal reason (RR) of the animal from the 2Y-CS ( e.g., terminal sacrifice, natural death). Fifth, the incidence of at least one primary liver tumor (LT) in the histopathological examination (true, false). Table 4 specifies our distinction between primary and non-primary liver tumors.

The five database attributes were combined with two attributes from the TRs. First, the information if the administered substance (SU) was a single substance, a mixture, or a multi-compound (single, mixture, multi-compound). This information was included because carcinogenicity of multi-compounds, which were mostly herbal medicines, is currently discussed in the literature [27, 28]. Second, the information if the administered dose level (DL) indicated potential liver toxicity in the dose finding study (toxic, non-toxic, missing data). Potential liver toxicity for a dose level was declared if the TR reported a statistically significant increase in liver weight in the dosing finding study for this dose level, or the nearest lower dose level, in the group of animals that was exposed to this dose level compared to the group of control animals. The liver weight was utilized for this purpose because increases in liver weight were observed to be associated with hepatomegaly, increased enzyme induction, and increased mitogenesis [2931]. These three factors were, in turn, reported as potential early indicators of (non-genotoxic) liver tumors [32]. Thus, increased liver weight may indicate potential liver toxicity and, consequently, the development of liver tumors.

This representation for the dose level using only two categories (toxic and non-toxic) provided a consistent descriptor across all 2Y-CSs, in contrast to the specification of the dose level in form of a concrete number. This is because of two reasons. First, carcinogenic activity of two different substances may not be identical, even if the substances are administered at identical dose levels. Second, the dose level was reported in a variety of different notations and units ( e.g., about 600 different specifications for the dose level were found in the CarTox database for the considered 2Y-CSs).

This procedure yielded a data set of 116673 (test substance exposed and control) animals, and every animal was described by seven attributes (SP, SE, CO, RR, LT, SU, DL). There are more attributes available in the CarTox database to describe animals. However, these attributes were either not suitable for the present analysis, e.g., the Chemical Abstracts Service Registry Number (CASRN), or their attribute values were correlated with certain TRs, e.g., the strain, because some strains were employed only in specific TRs.

Filtering of Animal Data. All 116673 animals were evaluated for inclusion in the analysis. Three inclusion criteria were specified on every animal. First, every animal was required to be exposed to the test substance, i.e., the attribute CO was required to be false. Second, the dose level for every animal was required to be available in the above mentioned representation, i.e., animals with missing data in the attribute DL were excluded. Third, every animal was required to be removed from the 2Y-CS because of an ordinary reason (attribute RR). For example, animals that were removed from a 2Y-CS because they drowned were excluded. Table 5 lists all removal reasons and specifies our distinction between ordinary and non-ordinary reasons.

The inclusion criteria yielded a subset of 68778 (test substance exposed) animals, and every animal was described by five attributes (SP, SE, LT, SU, DL). The attributes CO and RR were removed because they were not used again after the filtering procedure.

Data Mining

Data mining methods were employed to predict for every animal if at least one primary liver tumor (LT) will be diagnosed at the end of the 2Y-CS. The predictions were performed using information about the animal (SE, SP), specifications on the 2Y-CS (SU), and findings from the dose finding study (DL). No findings from the 2Y-CS itself were employed to perform predictions.

If this approach results in a positive outcome, it may not only indicate that the incidence of liver tumors can be predicted on basis of short-term CSs. It might also indicate a potential bias regarding liver carcinogenicity in the 2Y-CS. This is because the predictions are independent of the test article, i.e., the actual subject of investigation. The predictions are only dependent on factors (SE, SP, SU, DL) that are specified and controlled by the conductors of the 2Y-CSs.

Algorithms. The C4.5 algorithm [33] was employed as the primary data mining algorithm. It creates a decision tree to predict the incidence of liver tumors and was selected because decision trees provide a simple and reasonable basis for further mechanistic interpretations [34]. There are also other algorithms that create decision trees, however, the C4.5 algorithm is one of the most popular algorithms [35, Ch. 8.4.2].

In brief, a decision tree predicts the incidence of liver tumors for every animal by querying its input attributes (DL, SE, SP, SU) in a tree-formed manner (for an illustration, see Fig. 1 in the results section). Every inner node (oval-shaped nodes in Fig. 1) represents a query on an attribute. Every leaf (rectangular-shaped nodes in Fig. 1) represents a prediction. Thus, the prediction for an animal (LT, no LT) is given by the leaf that is reached at the end of the animal’s path through the tree.

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Figure 1. Decision tree to predict liver tumors.

The tree was learned using the C4.5 algorithm. It predicts liver tumors (LT) with information about the animal (SP = species, SE = sex), specifications on the 2Y-CS (SU = substance), and an indicator for liver toxicity extracted from the dose finding study (DL = dose level).

https://doi.org/10.1371/journal.pone.0116488.g001

Roughly speaking, the C4.5 algorithm uses the following method to create a decision tree for a given set of animals. Every query on an input attribute will split the set of animals into two, or more, subsets. For every attribute and the corresponding subsets, the algorithm computes a measure of impurity (gain ratio, see, e.g., [36, Ch. 4.3]). This measures becomes minimal if the animals in every subset have the same target value, and maximal if the amount of animals with liver tumors and the amount of animals without liver tumors is identical. Beginning with the entire set of animals, the algorithm declares the attribute as the upmost tree node that minimizes the impurity. Then, the algorithm recursively processes every branch of the upmost node in the same fashion.

The C4.5 decision tree will form the basis for mechanistic interpretations in the following sections. In addition, further algorithms were employed to examine stability of predictions on the present data set. These algorithms were AdaBoost [37], PART [38], and Random Forests [39]. In brief, AdaBoost combines several “weak” algorithms to build a “strong” algorithm. In the present analysis, the weak algorithms were decision stumps, i.e., decision trees with exactly one node, and the number of weak algorithms was set to ten. In the following, the abbreviation AdaBoost-DS refers to the AdaBoost algorithm utilizing decision stumps. PART generates several “partial” decision trees and extracts decision rules from these partial trees. Random Forests create several “random” decision trees and output the majority decision of all individual trees. In the present analysis, the number of random trees was set to ten, and the number of available attributes for every split was set to three (according to the formula logc M + 1 = 3, where M = 4 is the number of attributes, see [39]).

The WEKA software (version 3.6.8, [40]) was used to perform all algorithms.

Settings. Three settings were examined. First, all algorithms were applied to the set of all 68778 animals (SET1). This setting included 15029 animals with liver tumors and 53749 animals without liver tumors. Second, all algorithms were applied to the set of animals that was exposed to a multi-compound (SET2). This setting was examined because carcinogenicity of multi-compounds, which were mostly herbal medicines, is currently discussed in the literature [27, 28]. There were a total of 4532 animals exposed to multi-compounds, and these animals originated from 8 different studies (Table 3). This setting included 1129 animals with liver tumors and 3403 animals without liver tumors. Third, all algorithms were applied to the set of mice that was exposed to a multi-compound (SET3). This setting was examined because the decision tree for SET2 predicted liver tumors only for mice (see results section). There were a total of 2135 mice exposed to multi-compounds. This setting included 1046 animals with liver tumors and 1089 animals without liver tumors.

Both SET1 and SET2 include more animals without liver tumors than animals with liver tumors. In such situations, a subset of the larger class is often randomly sampled to have balanced distributions. However, in the present analysis, the imbalanced classes are explicitly employed to provide the information that liver tumors are less frequent to the algorithms.

Prediction Performance. Three performance measurements were computed for every setting and every algorithm. First, prediction accuracy was computed as the sum of true positives and true negatives divided by the sum of all positives and all negatives. Second, sensitivity was computed as true positives divided by all positives. Third, specificity was computed as true negatives divided by all negatives. Both sensitivity and specificity have the advantage that they are, per definitionem, not affected by imbalanced class distributions, which are present in SET1 and SET2. Furthermore, the absolute amount of true positives, true negatives, false positives, and false negatives were recorded in the form of confusion matrices.

The performance measurements were computed using a stratified 10-fold cross-validation [41, Ch. 7.10]. In brief, the cross-validation procedure simulates the application scenario for every algorithm. For this purpose, the data set is randomly partitioned into a training and a testing set. Every algorithm learns its prediction schema on the training set and tests it on the testing set. Then, the performance measurements are computed for the results on the testing set.

The 10-fold cross-validation repeats this procedure ten times to get averaged estimations for the performance measurements. For this purpose, the data set is partitioned into ten equally-sized subsets. Every subset is used once for testing while the remaining nine subsets are used for training. Then, the means of the ten individual performance measurements give the averaged estimations for the performance measurements. To ensure identical conditions for all algorithms, ten identical (but randomly defined) subsets were employed for all algorithms.

The stratified 10-fold cross-validation creates the ten subsets such that the ratio between animals with liver tumors and animals without liver tumors is preserved with respect to the entire data set.

The WEKA software (version 3.6.8, [40]) was used to perform the stratified 10-fold cross-validation.

Statistical Significance of Predictions. The performance measures in the previous section describe the algorithms’ ability to predict the incidence of liver tumors. If the performance measures are adequately high, these algorithms could be employed in practical applications. If the performance measures are not adequately high, the results may still provide insights into the data set. As long as the predictions are significantly better than trivial heuristics, the algorithms’ prediction schemas represent insights between the input attributes (DL, SE, SP, SU) and the target attribute (LT) that were extracted from the data set.

Therefore, predictions with the C4.5 decision tree were deemed significant if they were significantly better than trivial heuristics, i.e., random guessing and majority voting. Random guessing means that a liver tumor is predicted with probability s1 = 0.5, regardless of the attribute values of an animal. Majority voting means that the same target value is constantly predicted, regardless of the attribute values of an animal. This target value is the one that most frequently occurred in the training set.

To compare predictions by the decision tree to these trivial heuristics, a binomial test was applied, because predictions with the decision tree can be modeled by a Bernoulli process [36, Ch. 5.2]. In terms of a Bernoulli process, true positives and true negatives represent successes, while false positives and false negatives represent failures. The probability of a drawing a success from a Bernoulli process is called success probability π. In this setting, the null hypothesis of a binomial test states that the empirically observed success probability, i.e., the prediction accuracy of the decision tree, is identical to the true success probability π, i.e., the success probability of random guessing or majority voting.

Therefore, the null hypothesis that predictions with the decision tree are identical to random guessing was tested by setting the success probability π to s1 = 0.5. The null hypothesis that predictions with the decision tree are identical to majority voting was tested by setting the success probability π to s2. This probability s2 was estimated by using an algorithm that actually performed majority voting. The accuracy of this algorithm was estimated with a stratified 10-fold cross-validation, and the success probability s2 was set to the prediction accuracy. The ten subsets in this cross-validation were identical to the subsets of the other algorithms (see previous section).

For both binomial tests, the empirical number of successes was set to the sum of true positives and true negatives from the cross-validation procedure for the C4.5 algorithm. The significance level was set to p<0.01.

The R software (version 3.0.2, [42]) was used to perform the statistical tests. The WEKA software (version 3.6.8, [40]) was used to perform the majority voting algorithm.

Results

For the C4.5 algorithm applied to SET1, the cross-validation estimated an accuracy of 80.6%, sensitivity of 27.8%, and specificity of 95.4% (Table 6; confusion matrix in Table 7). The decision trees in all ten folds of the cross-validation procedure were identical (Fig. 1). These trees may be interpreted as follows: For rats, the decision tree predicted no liver tumors at all. For mice, the tree first differentiated between females and males. For female mice, the tree predicted liver tumors if a mixture or a multi-compound was administered at a dose level which indicated liver toxicity in the dose finding study. For male mice, the tree predicted liver tumors if a mixture or a multi-compound was administered, or if the dose level indicated liver toxicity in the dose finding study.

For the C4.5 algorithm applied to SET2, the cross-validation estimated an accuracy of 82.7%, sensitivity of 79.0%, specificity of 84.0% (Table 6; confusion matrix in Table 7). The decision trees in all ten folds of the cross-validation procedure were identical (Fig. 2). These trees predicted liver tumors in the same situations as the decision trees for SET1 in case of animals exposed to multi-compounds.

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Figure 2. Decision tree to predict liver tumors in case of multi-compounds.

The tree was learned using the C4.5 algorithm. It predicts liver tumors (LT) with information about the animal (SP = species, SE = sex), and an indicator for liver toxicity extracted from the dose finding study (DL = dose level).

https://doi.org/10.1371/journal.pone.0116488.g002

For the C4.5 algorithm applied to SET3, the cross-validation estimated an accuracy of 67.3%, sensitivity of 85.3%, and specificity of 50.0% (Table 6; confusion matrix in Table 7). The decision trees in all ten folds of the cross-validation procedure were identical and also identical to the decision tree for SET2 in case of mice (Fig. 2).

For all three settings, predictions with the C4.5 algorithm were significantly better than trivial heuristics (Table 6).

For the PART and Random Forest algorithms, the performance measures were similar to the C4.5 algorithm, in case of all three settings (Table 8). For the AdaBoost-DS algorithm applied to SET1 and SET2, the sensitivity slightly decreased while the specificity slightly increased (Table 8). For the AdaBoost-DS algorithm applied to SET3, the performance measures were similar to all other algorithms (Table 8).

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Table 8. Performance measures for the AdaBoost-DS, PART, and Random Forest algorithms.

https://doi.org/10.1371/journal.pone.0116488.t008

Discussion

In the present analysis, data mining was employed to predict the incidence of liver tumors in 2Y-CSs. Several algorithms performed predictions with information about the animals, specifications on the 2Y-CS, and findings from the preceding dose finding study, but without findings from the 2Y-CS itself. Three settings were examined with this approach, and prediction accuracies of about 80%, 83%, and 67% were achieved in the three settings, respectively.

Prediction Performance

For the C4.5 algorithm in SET1, the high specificity of 95% shows that most animals without liver tumors were recognized as such. A high specificity also indicates that the number of false positives was small. In other words, the C4.5 algorithm is very likely to be correct whenever it predicts a liver tumor, because only few animals without liver tumors were classified as animals with liver tumors. Hence, the following pattern may be extracted from the tree (Fig. 1): Female mice, which are exposed to mixtures or multi-compounds at a dose level that indicated liver toxicity, as well as male mice, which are exposed to mixtures or multi-compounds, or to single substances at a dose level that indicated liver toxicity, will very likely develop a liver tumor.

However, this pattern has to be interpreted in combination with the sensitivity. The low sensitivity of 27% shows that many animals with liver tumors were not recognized as such. This indicates that the above pattern will also miss many animals with liver tumors. In other words, there are more situations in which animals will develop liver tumors. An explanation for this low sensitivity might be that truly carcinogenic substances cause liver tumors regardless of the type of animal or dose level, i.e., regardless of the attributes available to the C4.5 algorithm. In fact, truly carcinogenic substances may cause liver tumors without necessarily increasing liver weight [43]. Thus, the C4.5 algorithm cannot detect such tumors on basis of the available attributes.

As the performance measures for the other algorithms (AdaBoost-DS, PART, Random Forests) were similar, it may be concluded that the present approach would miss many substances that cause liver tumors. Therefore, it cannot be recommended as an alternative to assess liver carcinogenicity in SET1. The general approach of SET1 was probably too optimistic for the high variety among 138 different TRs.

For the C4.5, PART, and Random Forest algorithms in SET2, both sensitivity and specificity were as promising as the prediction accuracy. Most animals with liver tumors were identified as such (80% sensitivity), and most animals without liver tumors were also identified as such (84% specificity).

Therefore, the relationship between liver tumors and multi-compounds was examined in more detail in SET3.

Because the C4.5 decision tree only predicted liver tumors in case of mice, only this species was further examined in SET3. The high sensitivity of 85% shows that most animals with liver tumors were recognized as such. A high sensitivity also indicates that the number of false negatives was small. In other words, the C4.5 algorithm is very likely to be correct whenever it predicts the absence of a liver tumor, because only few animals with liver tumors were classified as animals without liver tumors. Hence, the following pattern may be extracted from the decision tree (Fig. 2): Female mice, which are exposed to multi-compounds at a dose level that did not indicate liver toxicity, will very unlikely develop a liver tumor.

As in SET1, this pattern has to be interpreted in combination with the specificity. The rather low specificity of 50% shows that many animals without liver tumors were classified as animals with liver tumors. In other words, there are more situations in which animals will not develop liver tumors. Thus, female mice, which are exposed to multi-compounds at a dose level that indicated liver toxicity, and male mice will not develop a liver tumor in general.

In summary, the following concluding statement might be formulated: If multi-compounds cause liver tumors at all, then in female mice, which are exposed to a dose level that indicated liver toxicity in the dose finding study, and in male mice, which seem to be more sensitive in general (as also indicated in SET1). This observation may also support the intuitive argument that mice, which are exposed to any multi-compound, have a high chance to develop liver tumors as long as the dose level is high enough, i.e., as long as the liver weight significantly increases because the organ is overused.

Comparison to Previous Work

To the best of our knowledge, there are only two previous approaches on the prediction of liver carcinogenicity. Allen et al. [20] considered results for mice from 83 NTP TRs and results for rats from 87 NTP TRs. Boobis et al. [21] considered results for mice and rats from 16 NTP TRs. However, both approaches employed prediction attributes based on summary statistics for entire 2Y-CSs, in contrast to the finer data level of individual animals in the present analysis.

In the work of Allen et al. [20], a significant increase in three histopathological findings (hepatocellular hypertrophy, hepatocellular cytomegaly, hepatocellular necrosis) in the dose finding study was employed to predict the decision on carcinogenicity after the 2Y-CS. This prediction approach achieved an accuracy of 81%, sensitivity of 63%, and specificity of 86%; the additional inclusion of a significant increase in liver weight achieved an accuracy of 69%, sensitivity of 95%, and specificity of 62%. (These numbers are not explicitly reported in the work of Allen et al. because studies on mice and rats were evaluated separately. For compatibility with the present analysis, they were computed by combining the results given in Table 3 (results for mice) and Table 4 (results for rats) in the work of Allen et al.) These results are similar to the present results for SET1. The best possible prediction accuracy was about 80%. Furthermore, there was also a considerable difference between sensitivity and specificity. Either many false positive decisions or many false negative decisions have to be accepted.

For comparisons, it should also be noted that the evaluation procedure was different. Allen et al. [20] selected the prediction attributes because the incidence of the three lesions (combined with increased liver weight) correlated with carcinogenicity in the considered TRs. Then, they predicted carcinogenicity using these attributes in the same TRs. In the field of statistical learning, it is known that this procedure may overestimate the true prediction performance [41, 44]. This is because the prediction attributes were selected with the knowledge that they correlate with carcinogenicity in the TRs in which the predictions will be performed. However, this does not simulate the application scenario in which carcinogenicity is unknown and should be assessed (and hence, the correlation is unknown). The present results, which were computed using a cross-validation, provide more realistic estimations of the predictive potential in real application scenarios.

In the work of Boobies et al. [21], a significant increase in two histopathological findings (hepatocellular hypertrophy and/or hepatocyte necrosis) combined with increased liver weight in the dose finding study was employed to predict the incidence of liver tumors. This approach correctly identified 12 of 13 substances (92% sensitivity) that caused liver tumors in at least one sex of one species. The authors noted that these results are similar to the work of Allen et al. [20]. They also concluded that the current endpoints in dose finding studies are not sufficient to identify all substances that have carcinogenic potential. However, in comparison to the present analysis, this work has also a more correlation-based character, since no separate training and testing sets were employed.

Regarding the structure of the decision trees, the present results are in accordance with previous results. For example, differences regarding the incidence of tumors in mice and rats are known, e.g., [45, 46]. This fact is reflected in the decision trees, which identified the species as the most informative attribute and selected it as the root node. Furthermore, differences regarding the incidence of tumors in females and males are also known, e.g., [4648]. This fact is reflected in the decision trees, which identified the sex as the second most informative attribute (in the case of mice).

Alternative Perspectives

The discussion so far showed that there is potential to predict liver tumors in 2Y-CSs. However, the predictive potential may also be interpreted from alternative perspectives.

First, the perspective that all prediction attributes are controlled by the conductor of the 2Y-CS. For example, consider SET2 in which sensitivity, specificity, and accuracy enable reasonable argumentation. The C4.5 decision tree suggested that the outcome for female mice in future 2Y-CSs on multi-compounds will depend on the dose level. However, the dose level is a variable factor in the design of a 2Y-CS. For example, the 2Y-CS on the Ginkgo multi-compound (TR No. 578) administered dose levels that indicated liver toxicity in all animal groups. In contrast, the 2Y-CS on the Ginseng multi-compound (TR No. 567) administered dose levels that did not indicate liver toxicity in any animal group. Thus, the dose level might be an unknowing factor that influences the outcome of the 2Y-CS.

Second, the perspective that there seems to be a bias regarding the incidence of liver tumors. This bias is expressed by the patterns that were extracted from the C4.5 decision trees. For example, male mice, which are exposed to any substance at a dose level that indicated liver toxicity, will likely develop a liver tumor. This bias should be considered in the statistical evaluation of a 2Y-CS. For example, a weighting factor might be introduced to account for this general tendency of male mice, or a higher significance level might be defined for male mice. Another bias, for example, is that rats, which are exposed to multi-compounds, will unlikely develop a liver tumor. This bias should also be considered in the statistical evaluation. For example, multi-compounds might be liver carcinogens even if there is only a small, non-significant increase in liver tumors in rats.

However, to the best of our knowledge, this bias is currently not considered in the decision process on carcinogenicity.

Conclusion

The present study applied data mining methods to biobank data. It was shown that the incidence of liver tumors in 2Y-CSs can be predicted using findings from the preceding dose finding studies. This was particularly successful for 2Y-CSs on multi-compounds. Therefore, it may be speculated that the proposed approach can also be applied to similar settings, e.g., the examination of tumors in other organs.

However, the extracted patterns simultaneously indicated that there are situations in which liver tumors are likely to occur‚ and situations in which liver tumors are unlikely to occur. These situations are independent of the actual subject of the 2Y-CS, namely the test substance for which carcinogenicity should be assessed. Hence, the incidence of liver tumors does not depend only on the test substance. Therefore, we recommend considering this bias if the hazard or risk of a substance is assessed on basis of a 2Y-CS.

Acknowledgments

MR is supported by a research fellowship from the Deutsche Telekom Stiftung.

Author Contributions

Conceived and designed the experiments: MR BME. Performed the experiments: MR. Analyzed the data: MR. Contributed reagents/materials/analysis tools: MR BME. Wrote the paper: MR BME.

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