Genitourinary Malignancies Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
AstraZeneca, Gaithersburg, MD, USA.
Lancet Oncol. 2017 Jan;18(1):143-154. doi: 10.1016/S1470-2045(16)30633-7. Epub 2016 Dec 13.
We applied mathematical models to clinical trial data available at Project Data Sphere LLC (Cary, NC, USA), a non-profit universal access data-sharing warehouse. Our aim was to assess the rates of cancer growth and regression using the comparator groups of eight randomised clinical trials that enrolled patients with metastatic castration-resistant prostate cancer.
In this retrospective analysis, we used data from eight randomised clinical trials with metastatic castration-resistant prostate cancer to estimate the growth (g) and regression (d) rates of disease burden over time. Rates were obtained by applying mathematical models to prostate-specific antigen levels as the representation of tumour quantity. Rates were compared between study interventions (prednisone, mitoxantrone, and docetaxel) and off-treatment data when on-study treatment had been discontinued to understand disease behaviour during treatment and after discontinuation. Growth (g) was examined for association with a traditional endpoint (overall survival) and for its potential use as an endpoint to reduce sample size in clinical trials.
Estimates for g, d, or both were obtained in 2353 (88%) of 2678 patients with data available for analysis; g differentiated docetaxel (a US Food and Drug Administration-approved therapy) from prednisone and mitoxantrone and was predictive of overall survival in a landmark analysis at 8 months. A simulated sample size analysis, in which g was used as the endpoint, compared docetaxel data with mitoxantrone data and showed that small sample sizes were sufficient to achieve 80% power (16, 47, and 25 patients, respectively, in the three docetaxel comparator groups). Similar results were found when the mitoxantrone data were compared with the prednisone data (41, 39, and 41 patients in the three mitoxantrone comparator groups). Finally, after discontinuation of docetaxel therapy, median tumour growth (g) increased by nearly five times.
The application of mathematical models to existing clinical data allowed estimation of rates of growth and regression that provided new insights in metastatic castration-resistant prostate cancer. The availability of clinical data through initiatives such as Project Data Sphere, when combined with innovative modelling techniques, could greatly enhance our understanding of how cancer responds to treatment, and accelerate the productivity of clinical development programmes.
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我们将数学模型应用于 Project Data Sphere LLC（美国北卡罗来纳州卡里）提供的临床试验数据，该公司是一个非营利性的通用访问数据共享仓库。我们的目的是利用纳入转移性去势抵抗性前列腺癌患者的八项随机临床试验的对照组来评估癌症的生长和消退率。
在这项回顾性分析中，我们使用八项转移性去势抵抗性前列腺癌随机临床试验的数据来估计疾病负担随时间的增长（g）和消退（d）率。通过将前列腺特异性抗原水平作为肿瘤数量的代表应用数学模型来获得这些率。我们比较了研究干预措施（泼尼松、米托蒽醌和多西他赛）与研究期间停止治疗时的停药数据之间的差异，以了解治疗期间和停药后的疾病行为。我们检查了生长（g）与传统终点（总生存）的相关性，以及它作为临床试验中减少样本量的终点的潜在用途。
在可进行分析的 2678 名患者中，有 2353 名（88%）患者的数据可获得 g、d 或两者的估计值；g 区分了多西他赛（一种美国食品和药物管理局批准的治疗方法）与泼尼松和米托蒽醌，并在 8 个月的里程碑分析中预测了总生存。一项模拟样本量分析显示，将 g 用作终点时，多西他赛数据与米托蒽醌数据相比，小样本量足以达到 80%的效能（三个多西他赛对照组中分别为 16、47 和 25 名患者）。当米托蒽醌数据与泼尼松数据进行比较时，也得到了相似的结果（三个米托蒽醌对照组中分别为 41、39 和 41 名患者）。最后，在多西他赛治疗停止后，肿瘤生长（g）中位数增加了近五倍。
将数学模型应用于现有临床数据，可估计生长和消退率，为转移性去势抵抗性前列腺癌提供新的见解。通过像 Project Data Sphere 这样的举措提供临床数据，结合创新的建模技术，可以极大地增强我们对癌症对治疗反应的理解，并加速临床开发项目的生产力。
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