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. 2021 Mar:5:39-57.
doi: 10.1146/annurev-cancerbio-043020-125955. Epub 2020 Dec 8.

Ex Vivo Analysis of Primary Tumor Specimens for Evaluation of Cancer Therapeutics

Cristina E Tognon  1   2 , Rosalie C Sears  2   3   4 , Gordon B Mills  2   4   5 , Joe W Gray  2   4   6   7 , Jeffrey W Tyner  1   2   5
Affiliations

Ex Vivo Analysis of Primary Tumor Specimens for Evaluation of Cancer Therapeutics

Cristina E Tognon et al. Annu Rev Cancer Biol. 2021 Mar.

Abstract

The use of ex vivo drug sensitivity testing to predict drug activity in individual patients has been actively explored for almost 50 years without delivering a generally useful predictive capability. However, extended failure should not be an indicator of futility. This is especially true in cancer research where ultimate success is often preceded by less successful attempts. For example, both immune- and genetic-based targeted therapies for cancer underwent numerous failed attempts before biological understanding, improved targets, and optimized drug development matured to facilitate an arsenal of transformational drugs. Similarly, the concept of directly assessing drug sensitivity of primary tumor biopsies-and the use of this information to help direct therapeutic approaches-has a long history with a definitive learning curve. In this review, we will survey the history of ex vivo testing as well as the current state of the art for this field. We will present an update on methodologies and approaches, describe the use of these technologies to test cutting-edge drug classes, and describe an increasingly nuanced understanding of tumor types and models for which this strategy is most likely to succeed. We will consider the relative strengths and weaknesses of predicting drug activity across the broad biological context of cancer patients and tumor types. This will include an analysis of the potential for ex vivo drug sensitivity testing to accurately predict drug activity within each of the biological hallmarks of cancer pathogenesis.

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Figures

Figure 1.
Figure 1.
The 10 hallmarks of cancer are organized into 4 categories based on feasibility of various ex vivo platforms to accurately assess sensitivity to drugs targeting those hallmarks. In Category 1, most platforms are highly capable of directly assessing cell proliferation and apoptosis or, at least, assessing biomarkers of these phenotypic states. The Category 2 hallmarks of metabolism, replicative immortality, and genomic instability are better modeled with approaches that enable robust cell proliferation and/or that can readout phenotypes beyond short term cultures. Hallmarks in Category 3 can be best modeled with assays that include heterogeneous cell mixtures that are derived from or mimic the native tumor state. In this way, some of the basic models may actually perform better than models with greater perturbations on the tumor biopsy, however, these heterogeneous cell mixtures may be added back with some of the sophisticated engineered models. Finally, Category 4 hallmarks may be the most challenging, but can be modeled using bioengineered 3-dimensional approaches or patient-derived xenograft models. In general, the 3-dimensional and in vivo approaches are able to accurately model a larger number of hallmarks, though it is worth noting that these strategies currently do not have the throughput of the more basic approaches.
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