Pharsight Consulting Services, Pharsight, a Certara™ Company, Marseille, France.
Genentech/Roche, Marseille, France.
Cancer Chemother Pharmacol. 2018 Jul;82(1):49-54. doi: 10.1007/s00280-018-3587-7. Epub 2018 Apr 26.
To compare lesion-level and volumetric measures of tumor burden with sum of the longest dimensions (SLD) of target lesions on overall survival (OS) predictions using time-to-growth (TTG) as predictor.
Tumor burden and OS data from a phase 3 randomized study of second-line FOLFIRI ± aflibercept in metastatic colorectal cancer were available for 918 patients out of 1216 treated (75%). A TGI model that estimates TTG was fit to the longitudinal tumor size data (nonlinear mixed effect modeling) to estimate TTG with: SLD, sum of the measured lesion volumes (SV), individual lesion diameters (ILD), or individual lesion volumes (ILV). A parametric OS model was built with TTG estimates and assessed for prediction of the hazard ratio (HR) for survival.
Individual lesions had consistent dynamics within individuals. Between-lesion variability in rate constants was lower (typically < 27% CV) than inter-patient variability (typically > 50% CV). Estimates of TTG were consistent (around 12 weeks) across tumor size assessments. TTG was highly significant in a log-logistic parametric model of OS (median over 12 months). When individual lesions were considered, TTG of the fastest progressing lesions best predicted OS. TTG obtained from the lesion-level analyses were slightly better predictors of OS than estimates from the sums, with ILV marginally better than ILD. All models predicted VELOUR HR equally well and all predicted study success.
This analysis revealed consistent TGI profiles across all tumor size assessments considered. TTG predicted VELOUR HR when based on any of the tumor size measures.
比较使用生长时间(TTG)作为预测因子时,肿瘤负担的病变水平和体积测量值与目标病变最长径(SLD)总和在总生存(OS)预测中的表现。
在转移性结直肠癌二线 FOLFIRI±aflibercept 的 3 期随机研究中,有 1216 例患者(75%)中有 918 例可获得肿瘤负担和 OS 数据。采用非线性混合效应模型拟合纵向肿瘤大小数据,建立 TGI 模型,以估计 TTG:SLD、测量病变体积总和(SV)、单个病变直径(ILD)或单个病变体积(ILV)。建立了一个包含 TTG 估计值的参数 OS 模型,并评估其对生存风险比(HR)的预测能力。
个体病变在个体内具有一致的动力学。与个体间变异性(通常>50% CV)相比,速率常数的病变间变异性较低(通常<27% CV)。在各种肿瘤大小评估中,TTG 估计值是一致的(约 12 周)。在 OS 的对数逻辑参数模型中,TTG 具有高度显著性(中位数超过 12 个月)。当考虑个体病变时,进展最快的病变的 TTG 最好地预测了 OS。从病变水平分析中获得的 TTG 是 OS 的略优预测指标,而总和的估计值则略逊一筹,其中 ILV 比 ILD 略好。所有模型对 VELOUR HR 的预测能力均相当,且均预测了研究成功。
本分析揭示了在所有考虑的肿瘤大小评估中,TGI 具有一致的特征。基于任何肿瘤大小测量值,TTG 均能预测 VELOUR HR。