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Seq-ing the SINEs of Central Nervous System Tumors in Cerebrospinal Fluid DNA

Lesions within the brain cavity pose critical challenges for diagnostics, particularly distinction between cancerous and non-cancerous lesions. We here introduce an analytic technique called Real-CSF to detect cancers of the central nervous system from evaluation of DNA in the cerebrospinal fluid (CSF). Short interspersed nuclear elements (SINEs) from throughout the genome are PCR-amplified with a single primer pair and the PCR products are evaluated by next generation sequencing. Real-CSF uses machine learning to assess three features from the sequencing data - gains or losses of 39 chromosome arms, focal amplifications, and somatic nucleotide variants. Real-CSF was applied to 282 CSF samples and correctly classified 71 % of 187 cancers and misclassified only 4.2% of 95 non-neoplastic lesions in the brain.