Bayesian Methods for Complex Precision Biotherapy Trials in Oncology

肿瘤学中复杂精密生物治疗试验的贝叶斯方法

• 批准号: 10271754
• 负责人: Ruitao Lin
• 金额: $ 37.06万
• 依托单位: UNIVERSITY OF TX MD ANDERSON CAN CTR
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-15 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10271754
• 关键词: Accounting; Acute Graft Versus Host Disease; Address; Allogenic; Area; B-Lymphocytes; Bayesian Method; Bayesian Modeling; Biological; Biological Markers; Biological Response Modifier Therapy; Biometry; Biotechnology; CD19 gene; CD22 gene; Cations; Cell Therapy; Cell surface; Cells; Characteristics; Chemotherapy and/or radiation; Clinic; Clinical; Clinical Trials; Clinical Trials Design; Complex; Computer software; Cytometry; Cytotoxic Chemotherapy; Decision Making; Development; Diagnosis; Diagnostic; Disease; Disease Progression; Dose; Drops; Equilibrium; Evaluation; Futility; Future; Genes; Genomics; Goals; Growth Factor; Guidelines; Hematologic Neoplasms; Hematological Disease; Heterogeneity; Hybrids; Immune response; Immunotherapeutic agent; Immunotherapy; Malignant Neoplasms; Mass Spectrum Analysis; Mean Survival Times; Medical; Mesenchymal Stem Cells; Methods; Modeling; Modernization; Molecular Target; Monitor; Natural Killer Cells; Non-Small-Cell Lung Carcinoma; Oncology; Operative Surgical Procedures; Outcome; Partial Remission; Patients; Phase; Phase I/II Trial; Physicians; Precision therapeutics; Probability; Proteomics; Protocols documentation; Randomized; Refractory; Research; Resources; Risk; Running; Safety; Salvage Therapy; Sample Size; Savings; Schedule; Software Design; Stable Disease; Statistical Models; Stem cell transplant; Steroid therapy; Structure; Subgroup; Testing; Therapeutic; Therapeutic Uses; Time; Toxic effect; Transplant Recipients; adverse outcome; arm; base; cancer immunotherapy; chemotherapy; chimeric antigen receptor; chimeric antigen receptor T cells; clinical practice; computer program; conventional therapy; cytokine release syndrome; design; disorder subtype; engineered NK cell; graphical user interface; immunotherapy trials; improved; individual patient; model design; novel; patient safety; patient subsets; precision medicine; primary outcome; prognostic; programmed cell death ligand 1; programs; research clinical testing; response; screening; simulation; social; software development; targeted agent; tool; trial comparing; trial design; tumor; user friendly software; user-friendly; vector; web site

Project Summary/Abstract Most clinical trial designs use \one-size- ts-all" rules for treatment assignment and evaluation based on models that ignore patient heterogeneity. This is disconnected from medical practice, where physicians use each patient's diagnosis and prognostic variables to make personalized, precision medicine treatment decisions. Modern precision medicine exploits biotechnologies such as proteomics, genomics, gene sequencing, mass spectrometry, or cytometry methods that evaluate multiple cell surface markers. These generate vectors of biomarkers that may be used to re ne existing disease subgroup de nitions, construct new disease classi cations, and formulate clinical trial designs and statistical rules for personalized/precision treatment assignment. In oncology and other disease areas, there is rapidly increasing development of new biotherapies, including cell therapies, immunotherapies, and targeted molecular agents. A biotherapy may be administered once or in multiple cycles; used in combination with conventional treatments such as cytotoxic chemotherapy, radiation, or surgery; and often generates complex outcomes, such as repeatedly evaluated tumor status, multiple biological variables, and occurrence times of both early and late onset toxicities. This complicates the de nitions of \response" and \toxicity," and produces multidimensional treatment e ects that may di er between subgroups. An example is a phase I-II trial to optimize subgroup-speci c doses of donor derived natural killer (NK) cells for treating B-cell hematologic malignancies, where donated NK cells are engineered using chimeric antigen receptors to enhance their cancer killing e ects, then expanded using growth factors to obtain cell doses large enough for therapeutic use. Subgroups may be de ned using disease subtypes and prognostic variables. Co-primary outcomes may include ordinal disease status, including complete or partial remission, stable disease, or disease progression, evaluated either once or at monthly intervals; time to severe NK cell-related toxicity, such as cytokine release syndrome; and a binary indicator of 100-day survival. Considering (biotherapy, dose, administration schedule) a treatment regime, a clinical trial of one or more new biotherapies may include a subgroup-speci c risk-bene t tradeo based dose or schedule optimization for each biotherapy, randomization among regimes restricted to achieve balance within subgroups, and subgroup-speci c group sequential rules to select superior regimes or drop unsafe or ine ective regimes. The proposed research will construct robust Bayesian regression models for regime-outcome e ects that account for patient heterogeneity, including possible regime-subgroup interactions. These will be the basis for sequential decision making and regime assignment, and they may include latent variables to adaptively combine subgroups with similar regime-outcome e ects. Each clinical trial design will be tailored to address a combination of these goals in speci c biotherapy settings. For each design, user-friendly computer software will be provided, including programs for trial simulation to establish design operating characteristics, trial conduct, and use by practicing physicians to choose optimal regimes for their patients. The overarching goal of the proposed research is to develop and identify optimal personalized biotherapy regimes, spanning a variety of di erent diseases and clinical settings, for greater anti-disease e ects, increased safety, and improved survival.

项目概要/摘要 大多数临床试验设计使用“一刀切”的规则来进行治疗分配和基于以下模型的评估： 忽略患者的异质性。这与医疗实践脱节，医生使用每个患者的诊断 和预后变量，以做出个性化、精准的医学治疗决策。现代精准医学 利用蛋白质组学、基因组学、基因测序、质谱或细胞计数方法等生物技术 评估多个细胞表面标记。这些生成的生物标志物载体可用于更新现有的 疾病亚组定义，构建新的疾病分类，并制定临床试验设计和统计 个性化/精准治疗分配的规则。在肿瘤学和其他疾病领域，这种情况正在迅速增加 开发新的生物疗法，包括细胞疗法、免疫疗法和靶向分子制剂。生物疗法 可以施用一次或多个周期；与细胞毒性等常规治疗联合使用 化疗、放疗或手术；并且经常产生复杂的结果，例如反复评估肿瘤状态， 多个生物学变量以及早发型和晚发型毒性的发生时间。这使定义变得复杂 的“反应”和“毒性”，并产生在亚组之间可能有所不同的多维治疗效果。一个 例如，一项 I-II 期试验旨在优化供体来源的自然杀伤 (NK) 细胞的亚组特异性剂量，用于治疗 B 细胞血液恶性肿瘤，其中捐赠的 NK 细胞使用嵌合抗原受体进行改造，以增强 他们的癌症杀伤作用，然后使用生长因子进行扩展以获得足够大的细胞剂量用于治疗用途。 可以使用疾病亚型和预后变量来定义亚组。共同主要结果可能包括序数 疾病状态，包括完全或部分缓解、疾病稳定或疾病进展，评估一次或一次 每月间隔；出现严重 NK 细胞相关毒性的时间，例如细胞因子释放综合征；和一个二进制指标 100 天生存。考虑（生物疗法、剂量、给药方案）治疗方案、一种或多种的临床试验 更多新的生物疗法可能包括基于亚组特定风险收益权衡的剂量或时间表优化 生物治疗，限制方案之间的随机化以实现亚组内的平衡，以及亚组特定组 顺序规则来选择优越的制度或放弃不安全或无效的制度。拟议的研究将构建 用于治疗方案结果效应的稳健贝叶斯回归模型，考虑了患者的异质性，包括可能的异质性 政权-子群体相互作用。这些将成为顺序决策和政权分配的基础，并且 它们可能包括潜在变量，以自适应地组合具有类似制度结果效应的子组。每个临床 试验设计将根据特定生物治疗环境中的这些目标进行定制。对于每一个设计， 将提供用户友好的计算机软件，包括用于建立设计操作的试验模拟程序 特点、试验进行以及执业医师为患者选择最佳治疗方案的使用。这 拟议研究的总体目标是开发和确定最佳的个性化生物治疗方案，涵盖 各种不同的疾病和临床环境，以提高抗病效果、提高安全性并提高生存率。