A hitchhiker's guide to cancer models

Abstract

Cancer is a complex and uniquely personal disease. More than 1.7 million people in the United States are diagnosed with cancer every year. As the burden of cancer grows, so does the need for new, more effective therapeutics and for predictive tools to identify optimal, personalized treatment options for every patient. Cancer models that recapitulate various aspects of the disease are fundamental to making advances along the continuum of cancer treatment from benchside discoveries to bedside delivery. In this review, we use a thought experiment as a vehicle to arrive at four broad categories of cancer models and explore the strengths, weaknesses, opportunities, and threats for each category in advancing our understanding of the disease and improving treatment strategies.

Keywords: SWOT analysis; clinical translation; disease modeling; ex vivo; in silico; in vitro; in vivo; personalized medicine; precision medicine; therapeutic discovery.

Figure 1.. A brief history of cancer models.

A timeline of saliant developments in cancer models over the course of the last hundred years.

Figure 2.. Patient derived in vitro cultures.

From 2D cell cultures to self-assembling multicellular 3D organoids.

Figure 3.. Evolution of in vivo models.

From cell line derived xenografts (CDX) to genetically engineered mouse models (GEMM).

Figure 4.. From in vitro platforms to ex vivo models.

Building on strengths of in vitro platforms, ex vivo models gain momentum toward personalized medicine applications. Image adapted with permission from [76].

Figure 5.. SWOT Summary.

A visual SWOT summary of each category of cancer models to highlight relative strengths and opportunities for potential innovation and synergy. Violin plots represent relative strengths of current models. For instance, broadly speaking, in vivo and ex vivo models better recapitulate tumor microenvironment (TME) whereas in vitro and in silico are more favorable from a lower cost perspective.

Figure 6.. Cancer supermodels.

Next generation cancer supermodels assemble models in best-in-class configurations to harness strengths while muting weaknesses of individual contributing model categories.