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

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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.