Exploring the performance of a novel machine learning classifier for minimal-invasive CNS lymphoma diagnosis through ultrasensitive profiling of circulating tumor DNA from cerebrospinal fluid and blood plasma – a prospective oligo-center trial (DETECT_CNS

通过对脑脊液和血浆中的循环肿瘤 DNA 进行超灵敏分析，探索新型机器学习分类器在微创 CNS 淋巴瘤诊断中的性能——一项前瞻性寡中心试验 (DETECT_CNS)

• 批准号: 525584696
• 负责人: Dr. Florian Paul Scherer
• 金额: --
• 依托单位: Klinik für Innere Medizin I - Hämatologie, Onkologie und Stammzelltransplantation
• 依托单位国家: 德国
• 项目类别: Clinical Trials
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/525584696?language=en
• 关键词: Exploring; performance; novel; machine; learning

The diagnosis of CNS lymphoma (CNSL) requires invasive neurosurgical procedures that often cannot be safely performed in certain high-risk situations (e.g., in elderly/frail patients or when lesions are located in eloquent brain structures) or are delayed due to concurrent corticosteroid or anti-platelet therapies. Conventional analysis of cerebrospinal fluid (CSF) by cytopathology or flow cytometry and diagnostic MRI have demonstrated suboptimal sensitivity and discriminative capacity to allow surgery-free CNSL diagnosis. Therefore, improved methods that overcome these limitations and allow reliable minimal-invasive identification of CNSL would be transformative for the clinical care of these patients. We have established a novel machine learning approach that allows robust CNSL identification from mutational landscapes profiled by ultrasensitive next-generation sequencing (NGS) of circulating tumor DNA (ctDNA) from CSF or blood plasma. In a training-validation approach, we have demonstrated that our classification model correctly identified CNSL in 60% of cases from CSF-ctDNA, showing a specificity and positive predictive value (PPV) of 100%. However, this retrospective study harbors several limitations that remain to be overcome before such an approach can be used in clinical routine. First, the machine learning classifier requires further prospective testing on larger patient cohorts and under real-world conditions, standardizing sample collection, processing, and sample volumes. In addition, PPV and specificity of the classifier was assessed based on only 16 Non-CNSL patients; thus, higher numbers of non-lymphoma patients comprising a wider range of malignant and non-malignant entities are needed to confirm its performance for robust CNSL identification. Therefore, we here propose a prospective, diagnostic, non-randomized, oligo-center clinical trial that aims to explore and validate the performance of our novel minimal-invasive classifier for correct and robust CNSL identification (DETECT_CNSL). We will enroll 120 patients (36 CNSL patients, 84 Non-CNSL patients) with a novel brain lesion and indication for stereotactic biopsy, with CNSL being a differential diagnosis. These patients will undergo lumbar puncture and blood draw before neurosurgical biopsy to genetically profile ctDNA from CSF and blood plasma by targeted NGS. Using our novel machine learning algorithm, we will then classify samples as CNSL vs. Non-CNSL and compare our results to the gold standard histopathology, with the ultimate goal to correctly classify 80% of CNSL from CSF-ctDNA, assuming a specificity and PPV of 100%. If successful, DETECT_CNSL could chave practice-changing impact on the clinical management of patients with suspected CNSL in high-risk situations or when the diagnosis is delayed, and could inform future interventional trials testing this appraoch in the proposed scenarios.

CNS淋巴瘤（CNSL）的诊断需要侵入性神经外科手术，在某些高风险情况下（例如，在老年/虚弱患者中或当病变位于功能脑结构中时）或由于同时使用皮质类固醇或抗血小板治疗而延迟。通过细胞病理学或流式细胞术和诊断性MRI对脑脊液（CSF）进行的常规分析已证明灵敏度和鉴别能力欠佳，无法进行无手术CNSL诊断。因此，克服这些限制并允许可靠的CNSL微创识别的改进方法将对这些患者的临床护理产生变革性影响。我们已经建立了一种新的机器学习方法，该方法允许从CSF或血浆中循环肿瘤DNA（ctDNA）的超灵敏下一代测序（NGS）分析的突变景观中进行稳健的CNSL鉴定。在训练验证方法中，我们已经证明，我们的分类模型在60%的CSF-ctDNA病例中正确识别了CNSL，特异性和阳性预测值（PPV）为100%。然而，这项回顾性研究存在一些局限性，在这种方法用于临床常规之前仍需克服。首先，机器学习分类器需要在更大的患者队列和真实条件下进行进一步的前瞻性测试，标准化样本收集，处理和样本量。此外，仅基于16例非CNSL患者评估了分类器的PPV和特异性;因此，需要更多数量的非淋巴瘤患者（包括更广泛的恶性和非恶性实体）来确认其稳健CNSL识别的性能。因此，我们在这里提出了一个前瞻性，诊断，非随机，寡中心临床试验，旨在探索和验证我们的新型微创分类器的性能，正确和强大的CNSL识别（检测_CNSL）。我们将入组120例具有新的脑病变和立体定向活检指征的患者（36例CNSL患者，84例非CNSL患者），CNSL是鉴别诊断。这些患者将在神经外科活检前接受腰椎穿刺和抽血，以通过靶向NGS对来自CSF和血浆的ctDNA进行遗传分析。使用我们的新型机器学习算法，我们将样本分类为CNSL与非CNSL，并将我们的结果与金标准组织病理学进行比较，最终目标是正确地将80%的CNSL从CSF-ctDNA中分类出来，假设特异性和PPV为100%。如果成功，DETECT_CNSL可能会对高危情况下或诊断延迟时疑似CNSL患者的临床管理产生实践改变的影响，并可能为未来在拟议场景中测试该方法的干预性试验提供信息。