Improving power and reproducibility in preclinical cancer models with self-assembled biomaterials

In this work we sought to improve the reproducibility and power of preclinical cancer models by engineering a tumor inoculation method exploiting an exceedingly simple, self-assembled, injectable hydrogel that improves inoculation efficiency to reliably generate tumors with low variance in growth.

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A reproducibility crisis in preclinical research, which is heavily dependent on murine allograft and xenograft models, has contributed to many disappointing outcomes in clinical trials. Many current methods for tumor inoculation in murine models yield inconsistent tumor formation and growth, ultimately wasting valuable resources and often yielding underpowered studies. Protocols to establish tumor models typically involve injecting cancer cells in saline or basement membrane formulations. Unfortunately, upwards of 30% of inoculated mice often fail to form tumors, and those which do form tumors typically show significant variance in the rates of tumor growth. The inconsistency of these models results in poorly powered studies, driving unnecessary overuse of research animals and hindering cancer research progress.

In recent work we engineered an exceedingly simple, self-assembled, chemically defined, and injectable hydrogel to improve inoculation efficiency to reliably generate tumors with low variance in growth to address the severe limitations of standard inoculation protocols. These shear-thinning hydrogels enable facile cell encapsulation and tumor cell inoculation through standard injection procedures, yet demonstrate improved tumor growth with consistent pathology. We validate our methods with the widely used B16F10 cancer model and demonstrate improved results over the current gold standard methods which use either vehicles of either simple saline or basement membrane matrix. Using statistical power analyses we show the dramatic reduction in tumor variance observed with our improved inoculation procedures enables smaller animal cohorts, improved effect observation and higher powered studies. For example, to observe a 30% effect size for a treatment with 80% power, models established with our engineered hydrogels would require only 6 mice compared to 23 mice for models established by traditional methods using saline or Matrigel. From another perspective, if researchers planned to use only 10 mice in each experimental group (which is often observed in the field), our engineered hydrogels enable an increase in power from <40% to 98% over traditional methods for observations of a 30% effect size. 

Overall, this work characterizes a major driver behind many poorly powered studies appearing in the literature and reports a simple, inexpensive, and effective solution to improving the reliability and consistency of cancer models. Our approach enables evaluation of more subtle treatment effects, and dramatically reduces resource usage (i.e., fewer mice and less researcher time). While extensive research has focused on developing biomaterials for controlled delivery of cells for tissue engineering applications, the use of biomaterials to generate more reproducible in vivo cancer models has not been extensively explored. Using predictive modeling we demonstrate that a biomaterials-enhanced cancer model can reduce technical burden and simplify study design, thereby solving a nagging technical challenge in preclinical cancer research with important implications for future clinical translation.

Eric Appel

Assistant Professor, Stanford University