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Page 12 of 18 Happel et al. J Cancer Metastasis Treat 2020;6:32 I http://dx.doi.org/10.20517/2394-4722.2020.71

Pancreatic cancer was the focus of 11 studies over the past 10 years and accounted for 8% of the total
number of awards. Brain, liver, and prostate cancer were each investigated in 6 studies, and the remaining
cancer types were each addressed 5 or less times. This data, along with the clinical trial data, suggest that
exRNA and exosomes have great potential as biomarkers in a variety of cancer types and across many
types of biofluids. The broad applicability, universal presence in human biofluids, general stability, and
accessibility of exRNAs demonstrate their potential in disease detection, monitoring, and prognosis.

FDA-approved exosome-based clinical diagnostics
Exosome Diagnostics (a Bio-Techne brand) recognized an opportunity to utilize exRNA as a predictive
marker for prostate cancer and developed a urine exosome gene expression assay that can identify higher-
grade prostate cancer among patients with elevated prostate-specific antigen (PSA) levels. This simple, non-
invasive, urine-based test provides an EXO106 score derived from exosome ERG and PCA RNA levels
normalized to SPEDEF mRNA copy number [50,58] . The U.S. FDA granted Bio-Techne Breakthrough Device
Designation to this test [ExoDx Prostate IntelliScore (EPI)], making it the first exosome-based liquid biopsy
test to receive this designation, and Medicare coverage in 2019. Further, a recent publication demonstrated
that the EPI test influenced the overall decision to defer or proceed with a biopsy and improved patient
[59]
stratification in a prospective, randomized, blinded, two-armed clinical utility study .
CHALLENGES IN EXRNA RESEARCH
Even though the field of exRNA is very promising, there are challenges to this emerging area. A key barrier
toward a comprehensive understanding of exRNA biology and function has been the heterogeneity of
exRNA carriers, improved EV separation technologies, and EV targeting and cargo release.

EV biogenesis and cargo loading
ExRNA carriers include different particle subtypes such as EVs, RNPs, and LPPs, however, EVs have gained
the most interest amongst these carriers. EVs are highly heterogeneous and can be further divided into
[60]
different subpopulations that differ in size, density, morphology, and composition . EV subpopulations
[60]
broadly include MVs and exosomes . An ongoing challenge in the field is to clearly discriminate between
EVs, exosomes, and MVs.

Different EV biogenesis pathways also result in exRNA content that is extremely diverse and heterogeneous;
and the intracellular sorting mechanisms that direct exRNAs to specific export pathways are not well
understood [61,62] . Furthermore, the nature and abundance of EV cargoes are cell-type-specific and often
influenced by the physiological or pathological state of the donor cell and the stimuli that modulate their
[63]
production . EV heterogeneity and the complexity of its exRNA cargo are likely sources of variability in
exRNA profiling. Understanding the molecular mechanisms modulating EV biogenesis, the heterogeneity
in EV subtypes, and the physiological relevance of their exRNA cargo will be crucial in harnessing their
utility as cancer biomarkers.

Single vesicle EV isolation
A major challenge to the field of exRNA includes improved EV separation technologies. The heterogeneity
of EVs, their nanoscale size, and the ambiguity of EV subpopulations that often have overlapping
characteristics, are significant barriers to understanding the contribution of each specific EV subtype in
[60]
different pathological systems . Due to a substantial overlap in the physio-chemical properties of exRNA
carriers, many commonly used isolation protocols do not unambiguously separate EVs subtypes, or even
[64]
EVs from non-EV exRNA carriers (such as RNPs or LPPs) . The lack of biophysical and biochemical
markers for many different exRNA carriers makes the analysis and interpretation of exRNA data uniquely
challenging. To address the variability in exRNA profiling studies, Murillo and colleagues applied
computational deconvolution to exRNA-seq and exRNA qPCR profiles found in the Extracellular RNA