bioRxivCancer BiologyPreprint
CSF-Seq enables transcriptome-wide profiling of cerebrospinal fluid and identifies prognostic signature of leptomeningeal disease
Maxine C. Umeh-GarciaiD, Giuseppe BarisanoiD, Pablo Nunez PereziD, Thy Trang Hoang Trinh, Rukayat TaiwoiD, Daniel B. HerrickiD, Meaghen Roy-O'Reilly, Seunghyun LeeiD, Elias SpiliotopoulosiD, Charlotte WeixeliD, Georgiana BurnsideiD, Bryana Godfrey, Yi ZhangiD, Sophia B. ChernikovaiD, Sofia M. Tosoni, Monica GranucciiD, Cécile Riviere-CazauxiD, Gwen Coffey, Eleanor Villanueva, Terry C. BurnsiD, Seema NagpaliD, Thuy T. M. NgoiD, Melanie Hayden GephartiD
Abstract
Leptomeningeal disease (LMD) is a rapidly fatal complication of systemic cancer for which sensitive diagnostic tools and informative biomarkers remain limited. Here, we introduce CSF-Seq, a method for whole-transcriptome sequencing of cell-free RNA (cfRNA) from human cerebrospinal fluid (CSF), designed to enable molecular profiling of LMD and other central nervous system (CNS) conditions. Using a prospectively collected CSF biobank, we analyzed 125 samples spanning multiple pathologies, including breast and lung LMD, glioblastoma, traumatic brain injury, and non-cancer neurological controls. Through optimized RNA extraction, library preparation, and deep sequencing, CSF-Seq generated robust and reproducible transcriptome-wide profiles despite the low abundance and fragmentation of cfRNA in CSF. CSF transcriptomes exhibited disease-specific expression, separating LMD from non-cancer controls and from non-LMD cancers, independent of CSF collection modality. Tumor-associated epithelial transcripts, including CEACAM6 and MUC1, were consistently enriched in LMD samples, whereas immune and CNS-associated transcripts were broadly detected across disease states, consistent with contributions from both tumor and non-tumor sources. Cross-site processing of matched samples demonstrated high concordance, indicating preservation of sample-specific transcriptional signatures across independent workflows. Importantly, we identified a collection method- independent LMD gene expression signature that was significantly associated with overall survival, supporting its potential prognostic relevance. Together, these findings establish CSF-Seq as a technically robust and clinically informative platform for transcriptomic biomarker discovery in CNS metastatic disease, offering a minimally invasive approach for disease characterization, risk stratification, and longitudinal monitoring in patients with LMD.
- organism: human
- assay: bulk RNA-seq
- research focus: differential expression
- research focus: protein structure prediction
- approach: deep learning
- Citations
- 0
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