The CSF Consortium

Scientists from the ERCC have joined forces to create a CSF Consortium to pool resources and establish standard practices in the study of cerebrospinal fluid (CSF).

One of the goals of the ERCC is not only to understand the fundamental biology of extracellular RNA (exRNA), but to develop exRNA-based biomarkers of disease. When such biomarkers have been found, studied, and cleared for clinical use, liquid biopsy of blood and other biofluids can enable earlier disease detection and less invasive tracking of disease progression. For neurological disorders, drawing CSF from the spinal cord has clear benefits over a more invasive brain biopsy. Progress in our technical understanding of how to accurately assess biomarkers in CSF will increase our basic understanding and promote clinical advancements in the diagnosis and treatment of neurological disease. Unfortunately, there are many inconsistencies between the processing of CSF in current studies. Data replication is often difficult, in large part due to variability across laboratories and institutions in protocols for sample isolation, purification, and analysis. Thus, the CSF Consortium, spearheaded by Dr. Fred Hochberg (https://fredhhochbergmd.com), was designed to be a resource for researchers to help minimize these discrepancies.

The CSF consortium plan calls for CSF researchers and clinicians to work together to improve standard practices. A major focus is transparency through open sharing of their work. Researchers are encouraged to establish collaborations, share in-depth details of experimental designs and reagents (including batch/lot numbers), and release any details of in-house protocol modifications. Working with the same biosamples shared through the Virtual Biorepository (VBR) enables multiple labs to compare and synchronize their protocols with one source of variability removed. The expectation is that sharing of detailed information will enable future researchers to avoid common pitfalls and plan their own experiments appropriately. Ultimately, the goal is to have open-access information available from each stage of every project: from biofluid, RNA, and extracellular vesicle (EV) collection, isolation, and storage to downstream analyses such as RT-qPCR and RNA sequencing.

If you are a CSF researcher, please contact us so that we can work with you as well!

Highlights of recent CSF Consortium efforts
Saugstad et al. (2017) recently demonstrated the strength of the CSF consortium. In a collaboration between three institutions (UC San Diego, Oregon Health & Science University, and the Translational Genomics Research Institute), researchers worked together to characterize the EV and RNA composition of identical pools of CSF at each institute from patients with five different neurological disorders. This work in parallel allowed the groups to identify potential sources of variability in protocols including sample preparation, RNA isolation, and quantification of RNA via RNA sequencing and RT-qPCR. The study identified changes in EVs and RNA in the disease CSF samples and detected an enrichment of microRNAs and mRNAs related to disease in both EV and total RNA. The paper highlights the importance of stringent standard operating procedures, including the use of common standard sample collection and data analysis protocols across institutions.

In other work, Figueroa et al., 2017 performed a multi-institutional study of RNA extracted from CSF-derived EVs of patients with glioblastoma (GBM), a very aggressive form of brain cancer. (See this related blog on glioblastoma.) A key diagnostic biomarker in classical GBM is the functional status of the Epidermal Growth Factor Receptor (EGFR). This cell-surface receptor is the starting point of a series of signaling pathways related to cell growth. When its expression surges or it folds incorrectly, the result is cells with hyper-active signaling that never stop growing. This study involved the development of a liquid biopsy that scans RNA extracted from CSF EVs for tumor-associated amplifications and mutations in EGFR. The test has very high specificity and fair sensitivity: it almost never incorrectly flags a healthy patient as having GBM and correctly identifies almost two thirds of GBM sufferers. The clinical standard for diagnosis of GBM is magnetic resonance imaging (MRI), which correctly classifies most brain tumors, but in too many cases incorrectly suggests that healthy brain tissue might be cancerous. The complementarity of highly sensitive MRI and highly specific RNA liquid biopsy argues that updating the standard of care to include collection of CSF and brain images at the same time would better separate healthy from diseased brain tissue.

The CSF Consortium is casting its nets wider in its fight against glioblastoma, looking at molecules beyond EGFR in the attempt to develop an RNA-based diagnosis tool for GBM. Akers et al., 2017 developed a diagnostic panel of 9 miRNA biomarkers by analyzing the EV RNA from 135 CSF samples in 3 cohorts, followed by validation of the miRNA panel in 60 CSF samples from 2 cohorts. The researchers found that even with that fairly large sample size, the miRNA profiles in lumbar and cisternal CSF — fluid collected from the spine or the base of the neck, respectively — are significantly different, which is problematic, since cisternal CSF is much more difficult to collect. On the other hand, they also found that RNAs extracted from raw CSF had a similar profile and diagnostic power as RNAs extracted from vesicles after an initial EV purification step, which might simplify the translation of this biomarker research into the clinic.

References
Akers, J.C. et al. A cerebrospinal fluid microRNA signature as biomarker for glioblastoma. Oncotarget (2017) 8: 68769-68779.
Figueroa, J.M et al. Detection of wtEGFR amplification and EGFRvIII mutation in CSF-derived extracellular vesicles of glioblastoma patients. Neuro. Oncol. (2017) Advance online publication. doi: 10.1093/neuonc/nox085
Saugstad, J.A. et al. Analysis of extracellular RNA in cerebrospinal fluid. J. Extracellular Vesicles (2017) 6: 1317577.