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

中枢神经系统淋巴瘤（CNSL）的诊断需要侵入性神经外科手术，但在某些高风险情况下（例如，老年人/体弱患者或病变位于重要脑结构时），或因同时使用皮质类固醇或抗血小板治疗而延迟，通常不能安全进行。通过细胞病理学或流式细胞术和诊断性MRI对脑脊液（CSF）进行常规分析，显示出不理想的敏感性和鉴别能力，从而允许无需手术的CNSL诊断。因此，克服这些限制并允许可靠的微创CNSL识别的改进方法将对这些患者的临床护理产生革命性的影响。我们已经建立了一种新的机器学习方法，可以通过来自脑脊液或血浆的循环肿瘤DNA （ctDNA）的超灵敏下一代测序（NGS），从突变景观中进行稳健的CNSL鉴定。在训练验证方法中，我们证明了我们的分类模型在60%的CSF-ctDNA病例中正确识别了CNSL，显示出100%的特异性和阳性预测值（PPV）。然而，这项回顾性研究有一些局限性，在这种方法用于临床常规之前仍有待克服。首先，机器学习分类器需要在更大的患者群体和现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，需要更多的非淋巴瘤患者，包括更广泛的恶性和非恶性实体，以证实其对CNSL识别的鲁棒性。因此，我们在此提出了一项前瞻性、诊断性、非随机、低中心的临床试验，旨在探索和验证我们的新型微创分类器在正确和鲁棒的CNSL识别方面的性能（DETECT_CNSL）。我们将招募120例新的脑病变患者（36例CNSL患者，84例非CNSL患者），并进行立体定向活检，以CNSL作为鉴别诊断。这些患者将在神经外科活检前进行腰椎穿刺和抽血，通过靶向NGS从脑脊液和血浆中提取ctDNA进行基因分析。使用我们新颖的机器学习算法，然后我们将样本分类为CNSL和非CNSL，并将我们的结果与金标准组织病理学进行比较，最终目标是从CSF-ctDNA中正确分类80%的CNSL，假设特异性和PPV为100%。如果成功，DETECT_CNSL可能会对高风险或诊断延迟的疑似CNSL患者的临床管理产生改变实践的影响，并可能为未来在上述情况下测试该方法的介入试验提供信息。

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.