Assessing the performance of different outcomes for tumor growth studies with animal models

Abstract

The consistency of reporting results for patient‐derived xenograft (PDX) studies is an area of concern. The PDX method commonly starts by implanting a derivative of a human tumor into a mouse, then comparing the tumor growth under different treatment conditions. Currently, a wide array of statistical methods (e.g., t‐test, regression, chi‐squared test) are used to analyze these data, which ultimately depend on the outcome chosen (e.g., tumor volume, relative growth, categorical growth). In this simulation study, we provide empirical evidence for the outcome selection process by comparing the performance of both commonly used outcomes and novel variations of common outcomes used in PDX studies. Data were simulated to mimic tumor growth under multiple scenarios, then each outcome of interest was evaluated for 10 000 iterations. Comparisons between different outcomes were made with respect to average bias, variance, type‐1 error, and power. A total of 18 continuous, categorical, and time‐to‐event outcomes were evaluated, with ultimately 2 outcomes outperforming the others: final tumor volume and change in tumor volume from baseline. Notably, the novel variations of the tumor growth inhibition index (TGII)—a commonly used outcome in PDX studies—was found to perform poorly in several scenarios with inflated type‐1 error rates and a relatively large bias. Finally, all outcomes of interest were applied to a real‐world dataset.

Focused ultrasound ablation of melanoma with boiling histotripsy yields abscopal tumor control and antigen-dependent dendritic cell activation

“…The fixed effect of BH treatment was used to determine significance. Furthermore, to summarize the logistic average tumor growth curves with a single parameter, we calculated the AUC from day 14 to 35 for the averaged curves using the trapezoid rule 39 …”

Cellular and molecular characteristics of the premetastatic niches

“…The biological interaction between TME and PMN is increasingly important in tumor progression. Providing an animal model for the PMN is crucial for research and prediction 91,92 . Using liquid biopsy technology to detect the factors in PMN, exosomes, and CTCs as markers for early diagnosis and prediction of cancer is of great significance.…”

“…Providing an animal model for the PMN is crucial for research and prediction. 91 , 92 Using liquid biopsy technology to detect the factors in PMN, exosomes, and CTCs as markers for early diagnosis and prediction of cancer is of great significance. Some studies have confirmed that the expression of Tenascin‐C in regional lymph nodes may be a good predictor of bladder cancer metastasis, providing an important target for early metastasis intervention and treatment.…”