Extracellular Vesicle–Based Multianalyte Liquid Biopsy as a Diagnostic for Cancer

Abstract

Liquid biopsy is the analysis of materials shed by tumors into circulation, such as circulating tumor cells, nucleic acids, and extracellular vesicles (EVs), for the diagnosis and management of cancer. These assays have rapidly evolved with recent FDA approvals of single biomarkers in patients with advanced metastatic disease. However, they have lacked sensitivity or specificity as a diagnostic in early-stage cancer, primarily due to low concentrations in circulating plasma. EVs, membrane-enclosed nanoscale vesicles shed by tumor and other cells into circulation, are a promising liquid biopsy analyte owing to their protein and nucleic acid cargoes carried from their mother cells, their surface proteins specific to their cells of origin, and their higher concentrations over other noninvasive biomarkers across disease stages. Recently, the combination of EVs with non-EV biomarkers has driven improvements in sensitivity and accuracy; this has been fueled by the use of machine learning (ML) to algorithmically identify and combine multiple biomarkers into a composite biomarker for clinical prediction. This review presents an analysis of EV isolation methods, surveys approaches for and issues with using ML in multianalyte EV datasets, and describes best practices for bringing multianalyte liquid biopsy to clinical implementation. Expected final online publication date for the Annual Review of Biomedical Data Science, Volume 5 is August 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

The next generation of hybrid microfluidic/integrated circuit chips: recent and upcoming advances in high-speed, high-throughput, and multifunctional lab-on-IC systems

“…An emerging paradigm in using microfluidic chips to guide complex clinical decisions is to measure large numbers of orthogonal biomarkers and synthesizing the data using machine learning to produce a single predictor that is robust to heterogeneity between individuals or individual samples. [153][154][155] Although there has been much early success using conventional microfluidics for these approaches, several challenges remain: a) the number of concurrently quantifiable biomarkers is still limited by the throughput of benchtop experiments; b) many biomarkers are too rare to be readily measured; c) finding a sufficiently large set of orthogonal markers remains a difficult task. ICbased massively multiplexed sensors with single-molecule sensitivity and parallelized readout are uniquely suited to solve these challenges.…”

Recent advances of liquid biopsy: Interdisciplinary strategies toward clinical decision‐making

“…In addition to leveraging machine learning to enhance the performance in CTC analysis, EVs with a wealth of information from various analyses can also greatly benefit from machine learning for disease diagnosis, classification, and deep mining of EV data. 11,237 Basile et al evaluated the potential of Fourier Transform Infrared Spectroscopy (FTIR) to differentiate between EVs obtained from the sera of HCC patients and control subjects based on their mid-IR spectral response (Figure 7C). Machine learning models have proven their ability to process unstructured data and autonomously generate high-quality features, enhancing the precision of diagnoses and the optimization of treatment plans.…”

“…In addition to leveraging machine learning to enhance the performance in CTC analysis, EVs with a wealth of information from various analyses can also greatly benefit from machine learning for disease diagnosis, classification, and deep mining of EV data 11,237 . Basile et al.…”

Modeling and optimization of parallelized immunomagnetic nanopore sorting for surface marker specific isolation of extracellular vesicles from complex media

“…Extracellular vesicles (EVs) are nanoscale (< 800 nm) membranous particles containing nucleic acid cargoes and expressing surface proteins which reflect their cells of origin 1 . Because of their multiple cargoes and their ability to circumvent anatomical barriers such as the blood–brain barrier to circulate in peripheral bodily fluids such as blood (10 10 –10 12 EVs/mL) 2 and urine (10 10 EVs/mL) 3 , EVs have become a promising biomarker source for the diagnosis and characterization of multiple cancers 4 – 9 , as well as in other disease contexts including traumatic brain injury 10 and infectious disease 11 . Additionally, EVs play a mechanistic role in biological processes such as metastatic seeding 12 and tumor-immune interactions in cancer 13 , as well as pathologies including traumatic brain injury 14 , autoimmune disease 15 , and cardiac arrest 16 .…”

“…In response to this challenge, multiple microfluidic approaches have been developed using EV-sized micro/nanoscale feature sizes to perform precision size-based or surface-marker EV sorting. However, limitations such as the requirement for complex nanofabrication 4 , 21 , 22 , low maximum input volumes 23 , 24 , reliance on a single molecular biomarker target 25 , or low sample throughput 21 have hindered the applicability of microfluidic size or surface marker EV sorting.…”