Clinical pharmacology and therapeutics · 2026
Quantitative Lesion-Level Modeling Reveals Organ-Dependent Therapeutic Response.
СтатьяПоддержка NIH: внешняя программаНазвание и аннотация: Перевод пока не подготовлен
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Dilley Ian, Zhou Jiawei, Li Quefeng, Cao Yanguang
Аннотация
Conventional RECIST criteria compress heterogeneous metastatic lesions into a single sum-of-diameters endpoint, which can obscure organ-specific pharmacologic effects on response durability and thereby limit the interpretability of efficacy signals used for model-informed drug development and clinical decision making. We developed a lesion-level modeling framework to quantify compartment-dependent therapeutic effects using routinely collected RECIST diameters. Lesion trajectories were characterized using nonlinear mixed-effects modeling to estimate regression/kill (kkill), regrowth/progression (kge), and a resistance-like fraction (Fx). We then fit pan-cancer multivariable Cox models (adjusted for cancer lineage, treatment class, and clinical covariates) to quantify the independent contribution of metastatic site to response and progression hazards. Organ site emerged as a consistent determinant of response durability across lineages. Liver metastases frequently showed early shrinkage yet higher progression risk, whereas bone metastases, particularly in prostate cancer, tended to regress more slowly but exhibited more durable control once response occurred. Inter-lesion heterogeneity in progression dynamics was greatest in breast and non-small cell lung cancer and lowest in colorectal and pancreatic cancer. To contextualize site effects, we explored literature-derived estimates of Vascular Perfusion and Leakiness Index (VaPLI) for each organ, along with a broad immune surveillance classification. VaPLI correlated with response probability (ρ = 0.44; P < 0.01), and immune-tolerant sites showed higher progression hazards (P < 0.05). With further verification, this analysis can support lesion site-stratified efficacy evaluation by distinguishing true treatment effects from site-driven bias, enabling more balanced interpretation of treatment effects and dose-response relationships.