Cancer Explant Models

Abstract

Overcoming the challenges of understanding and treating cancer requires reliable patient-derived models of cancer (PDMCs). For decades, cancer research and therapeutic development relied primarily on cancer cell lines because of their prevalence, reproducibility, and simplicity to maintain. However, findings from research conducted in cell lines are rarely recapitulated in vivo and seldom directly translatable to patients. The tumor microenvironment (TME), tumor-stromal interactions, and associations with host immune cells produce profound changes in tumor phenotype and complexity not captured in traditional monolayer cell culture. In this chapter, we present various cancer explant models and discuss their applicability based on specific research aims. We discuss the appropriateness of these models for basic science questions, drug screening/development, and for personalized, precision medicine. We also consider logistical factors such as resource cost, technical difficulty, and accessibility. We finish this chapter with a practical guide intended to help the reader select the cancer explant model system(s) that best address their research aims.

Fig. 1

Introduction to the hallmark phenotypic traits of cancer and the complexity of in situ conditions that should be considered in selecting a cancer explant model. In addition to the hallmark phenotypic traits listed, higher order systems considerations including intratumoral heterogeneity, tumor microenvironment (hypoxia, pH, and osmotic pressure indicated), tumor–stromal interactions, tumor–immune interactions, and therapy response should also be considered

Fig. 2

Cancer explant model selection guide. The x-axis represents increasing model complexity which combines technical complexity and biological complexity. The y-axis represents increasing total variance including biological and technical variances. The solid, black trendline indicates that total variance increases with model complexity. Increasing variance will decrease the ability to distinguish effect size, thus decreasing statistical power. The green, dashed trendline represents the requirement for increased sample size to achieve sufficient power. The models are separated in nine bins under the black, solid trendline ordered by increasing model complexity. Each bin is colored to represent a spectrum of clinical/translational applicability. More heat (red) indicates suitability for precision medicine applications. Less heat (blue) indicates suitability for high-throughput screening applications. The solid, black line below the x-axis indicates that the above cancer explant models increase in similarity to the in situ tumor conditions from left to right