CAR-T(CAR-T)细胞疗法在非肿瘤性疾病中的应用
Chimeric antigen receptor T (CAR-T) cell therapy in non-oncological diseases.
CAR-T(CAR-T)细胞在血液系统恶性肿瘤中的应用推动了这种免疫治疗形式的显著进展。
CELL INTELLIGENCE · 肿瘤细胞治疗研究
肿瘤细胞治疗研究
英文原题:BASECAMP-1 screening study: a model for efficient enrolment in precision oncology clinical trials.
BASECAMP-1 screening study: a model for efficient enrolment in precision oncology clinical trials.
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BASECAMP-1 研究证明,临床-基因组筛查方法可以更高效地为精准肿瘤学试验识别患者。此外,通过协作数据共享可以增强精准肿瘤学。
识别适合精准肿瘤学临床试验的患者具有挑战性,尤其是对于罕见的分子亚群。为应对这一挑战,A2 Biotherapeutics 开发了 BASECAMP-1(NCT04981119),这是一项非干预性主筛选研究,旨在识别符合逻辑门控 Tmod CAR-T 细胞疗法干预性研究条件的患者。这些干预性试验的合格患者患有晚期实体恶性肿瘤,为种系人类白细胞抗原(HLA)-A*02 杂合子,且存在肿瘤相关 HLA-A 杂合性缺失(LOH)。HLA-A LOH 发生在大约 16% 的晚期实体恶性肿瘤中;因此,需要一种高效的筛选策略。本报告描述了 BASECAMP-1;比较了两种筛选方法的效率;并讨论了 BASECAMP-1 在高效入组之外的更广泛优势。方法与分析:通过两种方法为 BASECAMP-1 识别患者。在临床试验中常见的传统方法中,研究者在事先不了解患者 HLA-A 类型或 LOH 状态的情况下,对所有可能适合细胞疗法试验的患者进行知情同意和筛选。为了进一步优化我们的方法,我们与 Tempus AI(Tempus)共同开发了生物信息学程序 Aware,该程序可在包含常规诊疗期间收集的关联基因组和转录组测序及临床数据的临床基因组数据库中,识别具有肿瘤相关 HLA-A*02 LOH 的潜在合格患者。
在采用传统方法识别合格患者的超过42个月里,13个研究中心的1918名患者同意并接受了BASECAMP-1的筛选;其中,30名具有肿瘤相关HLA-A*02 LOH的患者被纳入(约每月0.7名参与者)。在同一时期的最后30个月里,实施了Tempus Aware筛选,55名具有肿瘤相关HLA-A*02 LOH的患者被纳入(约每月1.8名参与者)。该生物信息学方法识别出的患者多于传统方法,并且使用了作为标准临床肿瘤测序工作流程一部分产生的测序结果,减少了资源使用和研究人员的负担。使用筛选研究(如BASECAMP-1)的其他优势包括生产效率和收集大量分子和临床参数数据集,可用于补充试验分析。
Identifying eligible patients for precision oncology clinical trials is challenging, particularly for rare molecular subpopulations. To address this challenge, A2 Biotherapeutics developed BASECAMP-1 (NCT04981119), a non-interventional master screening study to identify patients eligible for interventional studies of logic-gated Tmod chimeric antigen receptor T-cell therapies. Eligible patients for these interventional trials have an advanced solid malignancy and are germline human leucocyte antigen (HLA)-A*02 heterozygous, with tumour-associated HLA-A loss of heterozygosity (LOH). HLA-A LOH occurs in ~16% of advanced solid malignancies; therefore, an efficient screening strategy is required. This report describes BASECAMP-1; compares the efficiency of two screening methods; and discusses the broader advantages of BASECAMP-1 beyond efficient enrolment. METHODS AND ANALYSIS: Patients are identified for BASECAMP-1 using two approaches. In the traditional approach, common for clinical trials, investigators consent and screen all patients who might be good candidates for cell therapy trials, with no prior knowledge of patient HLA-A type or LOH status. To further optimise our approach, we co-developed with Tempus AI (Tempus) the bioinformatic programme Aware, which identifies potentially eligible patients with tumour-associated HLA-A*02 LOH within a clinico-genomic database that includes linked genomic and transcriptomic sequencing and clinical data collected during routine care.
Over 42 months of using a traditional approach to identify eligible patients, 1918 patients at 13 study sites were consented and screened for BASECAMP-1; of these, 30 patients with tumour-associated HLA-A*02 LOH were enrolled (~0.7 participants per month). Over the last 30 months of that same period, Tempus Aware screening was implemented and 55 patients with tumour-associated HLA-A*02 LOH were enrolled (~1.8 participants per month). The bioinformatic approach identified more patients than the traditional approach and used sequencing results produced as part of the standard clinical tumour sequencing workflow, reducing resource use and study staff burden. Additional advantages of using a screening study, such as BASECAMP-1, include manufacturing efficiencies and collection of a large dataset of molecular and clinical parameters that can be used to supplement trial analyses.
The BASECAMP-1 study demonstrates a clinico-genomic screening approach can more efficiently identify patients for precision oncology trials. Furthermore, precision oncology can be enhanced through collaborative data-sharing. TRIAL REGISTRATION NUMBER: NCT04981119.
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