Quantifying association between liver tumor incidence and early-stage liver weight increase - An NTP data analysis

Abstract

Two-year toxicology and carcinogenesis rodent studies conducted at the National Toxicology Program (NTP) are used to identify potential adverse health effects in humans due to chemical exposure, including cancer. Liver tumor, the most frequently diagnosed tumor type of chemically induced neoplastic effects documented in NTP's carcinogenicity studies, is usually difficult to be detected at early stage due to the inconspicuous symptoms. However, the abnormal growth of liver cells can lead to liver weight increase, so it is hypothesized that liver tumor incidence is associated with early stage liver weight increase. In this study, the association between liver weight increase and liver tumor incidence are quantified by (1) calculating the correlation coefficient of and (2) building quantitative linear relationship between benchmark dose estimates derived from these two types of data collected from NTP studies. Together with 151 chemical/species/sex combinations of liver tumor data showing positive evidence collected from 76 NTP long-term studies, short-term liver weight data reported in the same NTP report were extracted to be paired with the liver tumor data for the analyses. Results show that the estimated correlation coefficients (as high as 0.78) along with the adequately fitted linear models suggest that the association between relative liver weight increase and aggregated liver tumor incidence are relatively strong. Additional analyses focused on some more specific situations (e.g., specific tumor type or specific strain/sex combination) further confirmed the strong association. Given the design of this study, the interpretation of the findings is not that liver weight increase can be used to predict liver tumor incidence, instead, evident increase in liver weight might be used as a reason to prioritize the test article for a two-year toxicology and carcinogenesis study.

Keywords: Benchmark dose; Carcinogenicity; Liver tumor; Liver weight; NTP; Risk assessment.

Graphical abstract

Fig. 1

The flow chart of the process of data collection.

Fig. 2

Fitted linear model to the BMDs estimated using model averaging method given BMR = 10%. X-axis represents the log-transformed BMD estimated from the continuous liver weight data, and y-axis represents the log-transformed BMD estimated from the dichotomous liver tumor data. The red line in the graph represents the maximum likelihood estimated linear model with the 95^th confidence interval represented by the two blue dashed lines. The equation of the fitted linear model and estimated R² are shown on the lower right corner.

Fig. 3

Fitted linear model to the BMDs estimated using the Linear and Quantal-linear model for continuous data and dichotomous data respectively, given BMR = 10%. X-axis represents the log-transformed BMD estimated from the continuous liver weight data, and y-axis represents the log-transformed BMD estimated from the dichotomous liver tumor data. The red line in the graph represents the maximum likelihood estimated linear model with the 95^th confidence interval represented by the two blue dashed lines. The equation of the fitted linear model and estimated R² are shown on the lower right corner.

Fig. 4

Fitted linear model to the BMDs estimated using model averaging method given BMR = 1%. X-axis represents the log-transformed BMD estimated from the continuous liver weight data, and y-axis represents the log-transformed BMD estimated from the dichotomous liver tumor data. The red line in the graph represents the maximum likelihood estimated linear model with the 95^th confidence interval represented by the two blue dashed lines. The equation of the fitted linear model and estimated R² are shown on the lower right corner.

Fig. 5

Fitted linear model to the BMDs estimated using the Linear and Quantal-linear model for continuous data and dichotomous data respectively, given BMR = 10%. X-axis represents the log-transformed BMD estimated from the continuous liver weight data, and y-axis represents the log-transformed BMD estimated from the dichotomous liver tumor data. The red line in the graph represents the maximum likelihood estimated linear model with the 95^th confidence interval represented by the two blue dashed lines. The equation of the fitted linear model and estimated R² are shown on the lower right corner.