Current methods in translational cancer research
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NON-THEMATIC REVIEW
Current methods in translational cancer research Michael W. Lee 1,2,3 & Mihailo Miljanic 2,3 & Todd Triplett 2,3 & Craig Ramirez 2,3 & Kyaw L. Aung 2,3 & S. Gail Eckhardt 2,3 & Anna Capasso 2,3 Received: 15 July 2020 / Accepted: 4 September 2020 # The Author(s) 2020
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 Translational research . Cancer . GEMMs . PDX . Xenograft . tumor immunology
1 Introduction Extensive efforts directed towards mapping the cancer genome have yielded remarkable insight into the genomic changes that occur during tumorigenesis. Analysis of 2658 whole-cancer genomes from 38 tumor types by the PanCancer Analysis of Whole Genomes (PCAWG), Consortium of the International Cancer Genome Consortium (ICGC), and The Cancer Genome Atlas (TCGA) demonstrated that on average, cancer genomes contain 4–5 driver mutations from coding and non-coding genome elements [1]. They also found that approximately 5% of tumors had no identifiable driver, suggesting that additional unidentified driver genes exist [1].
* Anna Capasso [email protected] 1
Department of Medical Education, Dell Medical School, University of Texas at Austin, Austin, TX, USA
2
Department of Oncology, Dell Medical School, University of Texas at Austin, Austin, TX, USA
3
Livestrong Cancer Institutes, Dell Medical School, University of Texas at Austin, Austin, TX, USA
In one of several companion articles published by the ICGC/TCGA/PCAWG, an evolutionary history of the cancers based on the aforementioned sequence data was identified [2]. Based on these data, it is not far-fetched to conceive that rational deployment of therapeutics or interventions could shift the evolutionary trajectory of the malignant phenotype. When taken together, the voluminous amount of cancer genome sequencing data that has been generated provides a high-fidelity roadmap of t
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