Current methods in translational cancer research

Abstract

Recent developments in pre-clinical screening tools, that more reliably predict the clinical effects and adverse events of candidate therapeutic agents, has ushered in a new era of drug development and screening. However, given the rapid pace with which these models have emerged, the individual merits of these translational research tools warrant careful evaluation in order to furnish clinical researchers with appropriate information to conduct pre-clinical screening in an accelerated and rational manner. This review assesses the predictive utility of both well-established and emerging pre-clinical methods in terms of their suitability as a screening platform for treatment response, ability to represent pharmacodynamic and pharmacokinetic drug properties, and lastly debates the translational limitations and benefits of these models. To this end, we will describe the current literature on cell culture, organoids, in vivo mouse models, and in silico computational approaches. Particular focus will be devoted to discussing gaps and unmet needs in the literature as well as current advancements and innovations achieved in the field, such as co-clinical trials and future avenues for refinement.

Keywords: Cancer; GEMMs; PDX; Translational research; Xenograft; tumor immunology.

Fig. 1

Comparison of strengths and weaknesses of current models for translational cancer research. For each model, the number of clocks and dollar signs correspond to the preparation time and relative cost of establishing and maintaining the model. Likewise, color coding indicates the degree to which the model is suited for a particular type of translational research (dark red denoting poorly suited to dark green denoting well suited)