Clinical trial data analysis to design novel treatment regimens in oncology

临床试验数据分析以设计肿瘤学新治疗方案

• 批准号: 10626877
• 负责人: Deborah Plana
• 金额: $ 5.27万
• 依托单位: HARVARD MEDICAL SCHOOL
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-06-01 至 2024-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10626877
• 关键词: Area; Breast Cancer cell line; CDK4 gene; Cell Line; Cellular biology; Characteristics; Clinical; Clinical Data; Clinical Oncology; Clinical Trials; Colorectal Cancer; Combined Modality Therapy; Computer Simulation; Computing Methodologies; Confidence Intervals; Critical Pathways; Dana-Farber Cancer Institute; Data; Data Analyses; Data Science; Data Set; Development; Dose; Drug Combinations; Drug Design; Drug resistance; Enrollment; Environment; Future; Heterogeneity; Human; Immune checkpoint inhibitor; Immune system; Laboratories; Learning; Malignant Neoplasms; Malignant neoplasm of lung; Malignant neoplasm of prostate; Mathematics; Mentorship; Methodology; Methods; Modality; Modeling; Oncology; PIK3CA gene; Patients; Pharmaceutical Preparations; Pharmacology; Phase; Phase I Clinical Trials; Phase III Clinical Trials; Population; Research; Resources; Sample Size; Science; Shapes; Signal Pathway; Solid; Solid Neoplasm; Statistical Methods; Sum; System; T-Cell Lymphoma; Targeted Research; Testing; Therapeutic; Training; Treatment Efficacy; Treatment Protocols; Work; anticancer research; cancer cell; cancer therapy; cancer type; cell growth; clinical trial analysis; design; drug candidate; drug development; drug efficacy; drug testing; early phase clinical trial; early phase trial; efficacy testing; immunoregulation; improved; malignant breast neoplasm; medical schools; neoplastic cell; novel; novel drug combination; novel therapeutics; patient derived xenograft model; patient population; phase III trial; pre-clinical; precision drugs; response; standard of care; success; targeted agent; theories; triple-negative invasive breast carcinoma; tumor; tumor growth

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.

项目摘要 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 治疗较低：最终发现只有3％的药物在1阶段设置中测试的药物优于标准 在3阶段设置中的护理。更准确地了解小型患者人群的药物活性的方法， 例如第1阶段临床试验中的那些，可以更好地估计药物开发的药物效率 管道，有助于提高肿瘤学临床试验的成功率，并且是NCI的2020年“挑衅性”之一 问题。”随着新型药物单层的数量继续增长，这种方法将变得至关重要 测试所有有希望的药物组合变得越来越不切实际。我的建议旨在发展 从少量临床上对组合药物效率进行更精确估计的静态方法 数据，并开发实验方法以选择可能有效的组合疗法 试验。 AIM 1将开发方法，从传统早期阶段进行更精确的药物效率估算 （第1阶段和第2阶段）临床试验。通过对152次乳腺癌，结直肠癌的临床试验的系统分析， 肺和前列腺癌，我发现单个参数形式描述了跨癌症的生存分布 类型和疗法。我将测试该参数形式是否提高了估计的精度 早期试验的3阶段药物效率。我将公开使用此数据集和方法 在临床试验分析中催化未来的进步。 AIM 2将采用新的统计方法，包括 AIM 1中描述的是分析早期单相治疗数据并估计药物的效率 组合。我们使用这种方法来分析少量患者衍生的Xenographictics的数据。我们 估计在数学“收益之和”模型下的新型组合预期的收益，其中 单疗法对肿瘤收缩产生独立的影响，以确定有希望的药物组合 细胞淋巴瘤。我将使用类似的方法来分析早期人类临床试验单一疗法数据 晚期实体瘤的设置并建模药物组合的预期生存益处。目标3意志 开发一个实验范式，用于选择可能在人类临床上成功的组合疗法 试验，并将其应用于三阴性乳腺癌。我们小组对临床试验数据的先前分析 证明在高级固体恶性肿瘤的情况下，大多数成功的药物组合是 具有耐药性的有效单药物的有效单药物，该原理被描述为 独立行动。 AIM 3将测试基于独立作用的药物组合 18个三阴性乳腺癌细胞系的异质面板以及AIM 2中鉴定的异质面板 评估该设计范式是否可能产生有效的组合。总体而言，我的建议旨在 精确，准确地使用少量患者数据和 确定有前途的候选药物用于三阴性乳腺癌的组合疗法。