Department of Pharmaceutical Sciences, School of Pharmacy and Pharmaceutical Sciences, The State University of New York at Buffalo, 455 Kapoor Hall, Buffalo, New York, 14214, USA.
Metrum Research Group, Tariffville, Connecticut, USA.
AAPS J. 2019 Feb 8;21(2):27. doi: 10.1208/s12248-019-0302-5.
Tumor doubling time can significantly affect the outcome of anticancer therapy, but it is very challenging to determine. Here, we present a statistical approach that extracts doubling times from progression-free survival (PFS) plots, which inherently contains information regarding the growth of solid tumors. Twelve cancers were investigated and multiple PFS plots were evaluated for each type. The PFS plot showing fastest tumor growth was deemed to best represent the inherent growth kinetics of the solid tumor, and selected for further analysis. The exponential tumor growth rates were extracted from each PFS plot, along with associated variabilities, which ultimately allowed for the estimation of solid tumor doubling times. The mean simulated doubling times for pancreatic cancer, melanoma, hepatocellular carcinoma (HCC), renal cell carcinoma, triple negative breast cancer, non-small cell lung cancer, hormone receptor positive (HR+) breast cancer, human epidermal growth factor receptor-2 positive (HER-2+) breast cancer, gastric cancer, glioblastoma multiforme, colorectal cancer, and prostate cancer were 5.06, 3.78, 3.06, 2.67, 2.38, 2.40, 4.31, 4.12, and 3.84 months, respectively. For all cancers, clinically reported doubling times were within the estimated ranges. For all cancers, except HCC, the growth rates were best characterized by a log-normal distribution. For HCC, the gamma distribution best described the data. The statistical approach presented here provides a qualified method for extracting tumor growth rates and doubling times from PFS plots. It also allows estimation of the distributional characteristics for tumor growth rates and doubling times in a given patient population.
肿瘤倍增时间会显著影响抗癌治疗的结果，但确定肿瘤倍增时间非常具有挑战性。在此，我们提出了一种统计方法，该方法从无进展生存期 (PFS) 图中提取倍增时间，PFS 图中固有地包含有关实体瘤生长的信息。我们研究了 12 种癌症，并对每种类型的多个 PFS 图进行了评估。被认为最能代表实体瘤固有生长动力学的 PFS 图显示出最快的肿瘤生长速度，并选择用于进一步分析。从每个 PFS 图中提取指数肿瘤生长率，以及相关的变异性，最终可以估计实体瘤倍增时间。胰腺癌、黑色素瘤、肝细胞癌 (HCC)、肾细胞癌、三阴性乳腺癌、非小细胞肺癌、激素受体阳性 (HR+)乳腺癌、人类表皮生长因子受体 2 阳性 (HER-2+)乳腺癌、胃癌、胶质母细胞瘤多形性、结直肠癌和前列腺癌的平均模拟倍增时间分别为 5.06、3.78、3.06、2.67、2.38、2.40、4.31、4.12 和 3.84 个月。对于所有癌症，临床报告的倍增时间都在估计范围内。对于所有癌症（HCC 除外），对数正态分布最能描述其增长率。对于 HCC，伽马分布最能描述数据。这里提出的统计方法为从 PFS 图中提取肿瘤生长率和倍增时间提供了一种合格的方法。它还允许估计给定患者人群中肿瘤生长率和倍增时间的分布特征。
