Clinical trial data analysis to design novel treatment regimens in oncology

项目总结 尽管最近在肿瘤学方面发展了新的治疗方式，但癌症的总体批准率 治疗药物很低：在第一阶段测试中，只有3%的药物最终被发现优于标准 在第三阶段的环境中进行护理。方法为了更准确地了解小患者群体中的药物活性， 例如处于第一阶段临床试验的那些，可以在药物开发的早期更好地估计药物疗效 流水线，有助于提高肿瘤学临床试验的成功率，是美国国家癌症研究所2020年的“挑衅”之一 问题。“随着新的药物单一疗法的数量持续增长，这种方法将变得至关重要 而且，测试所有有希望的药物组合变得越来越不现实。我的建议旨在发展 从少量临床病例中更准确地估计联合用药疗效的统计方法 数据，并开发实验方法来选择可能对人类有效的联合疗法 审判。目标1将开发方法，从传统的早期阶段对药物疗效进行更准确的估计 (1期和2期)临床试验。通过对152项乳腺、结直肠、 肺癌和前列腺癌，我发现一个单一参数形式描述了癌症之间的生存分布 类型和治疗方法。我将测试此参数形式的应用是否会提高估计的精度 来自早期试验的第三阶段药物疗效。我将把这个数据集和方法公开给 促进临床试验分析的未来进展。目标2将采用新的统计方法，包括 目的1：分析早期单次治疗数据并评估药物的疗效 组合。我们使用这种方法分析了来自少量患者来源的异种移植物的数据。我们 在数学“收益总和”模型下估计一种新组合的预期收益，在该模型中 单一疗法对肿瘤缩小具有独立作用，以寻找治疗T-T细胞的有前景的药物组合。 细胞性淋巴瘤。我将使用类似的方法来分析早期人类临床试验单一疗法的数据 晚期实体瘤的设定和药物组合的预期生存益处的模型。目标3将 为选择可能在人类临床上成功的联合疗法开发一种实验范例 试验，并将其应用于三阴性乳腺癌。本课题组对前期临床试验数据的分析 证明在晚期实体恶性肿瘤的背景下，最成功的药物组合是 具有非重叠耐药机制的有效单一药物，这一原理被描述为 独立行动。AIM 3将测试基于独立作用选择的药物组合 由18个三阴性乳腺癌细胞系以及AIM 2中鉴定的细胞组成的不同小组，至 评估这种设计范例是否可能产生有效的组合。总体而言，我的建议旨在 使用少量的患者数据和数据，准确和准确地评估肿瘤治疗的疗效 确定用于三阴性乳腺癌联合治疗的有前景的候选药物。

Project summary Despite the recent development of new treatment modalities in oncology, the overall approval rate for cancer therapeutics is low: only 3% of drugs tested in a phase 1 setting are ultimately found superior to the standard of care in a phase 3 setting. Methods to more accurately understand drug activity in small patient populations, such as those in phase 1 clinical trials, could better estimate drug efficacy early in the drug development pipeline, help improve the success rate of clinical trials in oncology, and is one of the NCI’s 2020 “provocative questions.” Such methods will become critical as the number of novel drug monotherapies continues to grow and it becomes increasingly impractical to test all promising drug combinations. My proposal aims to develop statistical methods to make more precise estimates of combination drug efficacy from small amounts of clinical data, and to develop experimental methods to select combination therapies likely to be effective in human trials. Aim 1 will develop methods to make more precise estimates of drug efficacy from traditional early-phase (phase 1 and phase 2) clinical trials. Through the systematic analysis of 152 clinical trials for breast, colorectal, lung, and prostate cancer, I found that a single parametric form describes survival distributions across cancer types and therapies. I will test if application of this parametric form increases the precision of estimates for phase 3 drug efficacy from early-phase trials. I will make this dataset and methods publicly available to catalyze future progress in the analysis of clinical trials. Aim 2 will apply new statistical methodology, including that described in Aim 1, to analyze early-phase monotherapy data and to estimate the efficacy of drug combinations. We used this approach to analyze data from small numbers of patient-derived xenografts. We estimated the benefit expected for a novel combination under a mathematical "sum of benefits" model, in which monotherapies exert independent effects on tumor shrinkage, to identify a promising drug combination for T- cell lymphomas. I will use a similar approach to analyze early-phase human clinical trial monotherapy data in the setting of advanced solid tumors and model the expected survival benefit of drug combinations. Aim 3 will develop an experimental paradigm for selecting combination therapies likely to be successful in human clinical trials, and apply it to triple-negative breast cancer. Previous analysis of clinical trial data by our group demonstrates that in the setting of advanced solid malignancies, most successful drug combinations are made of effective single agents with nonoverlapping mechanisms of drug resistance, a principle described as independent action. Aim 3 will test combinations of drugs selected based on independent action across a heterogeneous panel of 18 triple-negative breast cancer cell lines, as well as those identified in Aim 2, to assess whether this design paradigm is likely to produce effective combinations. Overall, my proposal aims to precisely and accurately estimate the efficacy of therapies in oncology using small amounts of patient data and to identify promising drug candidates for use in combination therapies for triple-negative breast cancer.