Abstract
Information on individual lesion dynamics and organ location are often ignored in pharmacometric modeling analyses of tumor response. Typically, the sum of their longest diameters is utilized. Herein, a tumor growth inhibition model was developed for describing the individual lesion time‐course data from 183 patients with metastatic HER2‐negative breast cancer receiving docetaxel. The interindividual variability (IIV), interlesion variability (ILV), and interorgan variability of parameters describing the lesion time‐courses were evaluated. Additionally, a model describing the probability of new lesion appearance and a time‐to‐event model for overall survival (OS), were developed. Before treatment initiation, the lesions were largest in the soft tissues and smallest in the lungs, and associated with a significant IIV and ILV. The tumor growth rate was 2.6 times higher in the breasts and liver, compared with other metastatic sites. The docetaxel drug effect in the liver, breasts, and soft tissues was greater than or equal to 1.2 times higher compared with other organs. The time‐course of the largest lesion, the presence of at least 3 liver lesions, and the time since study enrollment, increased the probability of new lesion appearance. New lesion appearance, along with the time to growth and time‐course of the largest lesion at baseline, were identified as the best predictors of OS. This tumor modeling approach, incorporating individual lesion dynamics, provided a more complete understanding of heterogeneity in tumor growth and drug effect in different organs. Thus, there may be potential to tailor treatments based on lesion location, lesion size, and early lesion response to provide better clinical outcomes.
Highlights
Pharmacometric models are increasingly applied to data collected from oncology trials toward a better understanding of the drug response in patients over time
Tumors that originate from the same organ can behave and respond to treatment differently at different metastatic sites even though they are histologically similar, indicating that the growth and drug-induced shrinkage of individual tumor lesions may be highly dependent on the microenvironment.5–9 It is noteworthy that the metastasis of vital organs is one of the main reasons for death in patients with cancer, and metastasis-associated death was reported as high as 90% in some cancer types.10–12 Breast cancer, the most common cancer in women, has a 5-year survival rate of 99% if the patient had cancer only in the breasts; the survival rate drops to 26% for patients with metastasis, and ~ 75% of deaths in breast cancer were associated with metastasis.[10]
interlesion variability (ILV) was higher than interindividual variability (IIV) (29% CV) for the baseline lesion diameter
Summary
Pharmacometric models are increasingly applied to data collected from oncology trials toward a better understanding of the drug response in patients over time. The time-c ourse of the sum of the longest diameters (SLDs), or metrics thereof, are investigated as predictors of long-term clinical end points, such as overall survival (OS).1-4 The individual tumor lesion data, which comprise the SLD, contain lesion dynamic data, and organ location that are often ignored. These data may better describe disease progression and OS more so than a composite SLD measurement. A model-based approach, incorporating the understanding of the time-course of the disease in distinct metastatic sites, and the contribution of lesions to overall response, would help in predicting the expected responses at different timepoints. In addition to survival analysis, we explored the ability of various lesion-related metrics to predict new lesions and dropout from tumor measurements
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