Animal models as a tool in hepatocellular carcinoma research: A Review

It is one of the central aims of the philosophy of science to elucidate the meanings of scientific terms and also to think critically about their application. The focus of this essay is the scientific term predict and whether there is credible evidence that animal models, especially in toxicology and pathophysiology, can be used to predict human outcomes. Whether animals can be used to predict human response to drugs and other chemicals is apparently a contentious issue. However, when one empirically analyzes animal models using scientific tools they fall far short of being able to predict human responses. This is not surprising considering what we have learned from fields such evolutionary and developmental biology, gene regulation and expression, epigenetics, complexity theory, and comparative genomics.

One hundred and twenty-seven cultured human tumor cell lines produced tumors after sc inoculation of 1-20 million cells into nude mice. They included 56 carcinoma lines, 14 sarcoma lines, and 57 lines from miscellaneous tumors and were all glucose-6-phosphate dehydrogenase type B. Twenty-nine percent of the lines produced tumors of 1 cm3 size within 1 month and 41% in the second month after inoculation. The histopathology correlated with the human tumor of origin in all cases.

Efforts to discover new cancer drugs and predict their clinical activity are limited by the fact that laboratory models to test drug efficacy do not faithfully recapitulate this complex disease. One important model system for evaluating candidate anticancer agents is human tumour-derived cell lines. Although cultured cancer cells can exhibit distinct properties compared with their naturally growing counterparts, recent technologies that facilitate the parallel analysis of large panels of such lines, together with genomic technologies that define their genetic constitution, have revitalized efforts to use cancer cell lines to assess the clinical utility of new investigational cancer drugs and to discover predictive biomarkers.