Review: Challenges in Discovering Extracellular Vesicle–based Cancer Biomarkers
Key findings
- Extracellular vesicles (EVs) are lipid-covered particles that all cells secrete into the blood and other biofluids that carry RNA, DNA and proteins from that cell; they are showing great promise as cancer biomarkers
- Discovery of EV-based biomarkers has been hampered by general challenges common to biomedical research as well as specific challenges inherent to nanoparticles
- A principal problem is distinguishing tumor-specific EVs from the vast background of EVs generated by non-diseased cells
- Innovations needed are a gold-standard protocol for EV isolation and novel EV analysis platforms that maximize the sensitivity of biomarker detection
A rapidly growing focus of cancer research is the role of extracellular vesicles (EVs) in cancer diagnosis and monitoring. EVs are lipid-covered particles that all cells secrete into the blood and other biofluids, carrying RNA, DNA and proteins from that cell. After a decade of research EVs are showing great promise as biomarkers.
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Anudeep Yekula, MBBS, a neurosurgery research fellow, Leonora Balaj, PhD, investigator in Neurosurgery, and Bob S. Carter, MD, PhD, chief of the Department of Neurosurgery at Massachusetts General Hospital, recently reviewed in Methods the challenges and potential solutions in developing EV biomarkers, from discovery to clinical application. This summary focuses on hurdles in biomarker discovery.
Clinical Studies
Clinical studies of EV biomarkers must have well-defined outcomes and state what impact the test is expected to have in clinical practice (e.g., diagnosis, monitoring of treatment response, prognostication). Studies must also have appropriate positive and negative control groups and a sufficient sample size to generate statistically significant results. All samples must be linked to data on demographics, clinical status, collection method and handling history.
In vitro and in vivo Studies
A major challenge in developing EV biomarkers is distinguishing tumor-specific EVs from the vast background of EVs generated by non-diseased cells. The ideal approach is to determine in vitro whether a total pool or a subfraction of EVs is more beneficial to address the clinical scenario of interest. EV analysis in vivo can further separate target EVs from the heterogeneous background.
Biofluid Type
EVs can be isolated from multiple biofluids, including plasma, serum, urine, cerebrospinal fluid, saliva and nasal secretions. Anatomic location, accessibility and specific biophysical and chemical characteristics all potentially influence which biofluid should be sampled for the study of a particular disease.
Sample Quality
To ensure high quality, it's imperative that biofluids be collected in a standardized manner. Protocols and training should be in place for sample collection, processing and storage. Even so, there will be subtle differences in sample collections between different personnel and different institutions, so careful interpretation of results is essential when working with samples from multiple centers.
EV Isolation
According to a paper in Nature Methods, 1,000 different EV isolation protocols have been reported in the scientific literature. This variability affects every subsequent analysis. The field needs a gold-standard method of EV separation that allows high recovery and has high specificity.
EV Analysis Platforms
The major challenge for EV analysis platforms is to detect a biomarker signal from the low-input genetic and protein cargo that EVs carry. Platforms for nanoparticle analysis have even greater inherent limitations. Novel platforms are needed that maximize the sensitivity of biomarker detection and can be validated rigorously.
Interpretation
Interpretation of EV data needs to account for biofluid type, sample quality, isolation method employed and analysis platform. Complex analyses may be needed to demonstrate a statistically significant difference between the potential biomarker and background noise, so personnel must be carefully trained and supervised. Also, since most biomarker discovery studies examine large numbers of potential biomarkers in parallel, statisticians must account for multiple comparisons properly.
Reporting
The REporting recommendations for tumor MARKer prognostic studies (REMARK) are guidelines for what information to include when publishing clinical studies of prognostic cancer biomarkers. The original checklist was simultaneously published in the Journal of Clinical Oncology and other journals in 2005. A subsequent publication, in PLoS Medicine, explains each item in detail.
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