Integrated Pipeline of Rapid Isolation and Analysis of Human Plasma Exosomes for Cancer Discrimination Based on Deep Learning of MALDI-TOF MS Fingerprints

Plasma exosomes have shown great
potential for liquid biopsy in
clinical cancer diagnosis. Herein, we present an integrated strategy
for isolating and analyzing exosomes from human plasma rapidly and
then discriminating different cancers excellently based on deep learning
fingerprints of plasma exosomes. Sequential size-exclusion chromatography
(SSEC) was developed efficiently for separating exosomes from human
plasma. SSEC isolated plasma exosomes, taking as less as 2 h for a
single sample with high purity such that the discard rates of high-density
lipoproteins and low/very low-density lipoproteins were 93 and 85%,
respectively. Benefitting from the rapid and high-purity isolation,
the contents encapsulated in exosomes, covered by plasma proteins,
were well profiled by matrix-assisted laser desorption/ionization
time-of-flight mass spectrometry (MS). We further analyzed 220 clinical
samples, including 79 breast cancer patients, 57 pancreatic cancer
patients, and 84 healthy controls. After MS data pre-processing and
feature selection, the extracted MS feature peaks were utilized as
inputs for constructing a multi-classifier artificial neural network
(denoted as Exo-ANN) model. The optimized model avoided overfitting
and performed well in both training cohorts and test cohorts. For
the samples in the independent test cohort, it realized a diagnosed
accuracy of 80.0% with an area under the curve of 0.91 for the whole
group. These results suggest that our integrated pipeline may become
a generic tool for liquid biopsy based on the analysis of plasma exosomes
in clinics.