Machine learning characterization of cancer patients-derived extracellular vesicles using vibrational spectroscopies: results from a pilot study.

The early detection of cancer is a challenging problem in medicine. The blood sera of cancer patients are enriched with heterogeneous secretory lipid-bound extracellular vesicles (EVs), which present a complex repertoire of information and biomarkers, representing their cell of origin, that are being currently studied in the field of liquid biopsy and cancer screening. Vibrational spectroscopies provide non- invasive approaches for the assessment of structural and biophysical properties in complex biological samples. Methods In this pilot study, multiple Raman spectroscopy measurements were performed on the EVs extracted from the blood sera of n = 9 patients consisting of four different cancer subtypes (colorectal cancer, hepatocellular carcinoma, breast cancer and pancreatic cancer) and five healthy patients (controls). FTIR (Fourier Transform Infrared) spectroscopy measurements were performed as a complementary approach to Raman analysis, on two of the four cancer subtypes. The spectra were subjected to various machine learning classifiers with hyperparameter optimization to discriminate between healthy and cancer patients-derived EVs. The AdaBoost Random Forest Classifier, Decision Trees, and Support Vector Machines (SVM) distinguished the baseline corrected Raman spectra of cancer EVs from those of healthy controls (N = 18 spectra) with a classification accuracy of >90% when reduced to a spectral frequency range of 1800 − 1940 𝑐𝑚 −1 and subjected to a 50:50 training: testing split. FTIR classification accuracy on N = 14 spectra showed an 80% classification accuracy. Our findings demonstrate that basic machine learning algorithms are powerful applied intelligence tools to distinguish the complex vibrational spectra of cancer patient EVs from those of healthy patients. These experimental methods hold promise as valid and efficient liquid biopsy for artificial intelligence-assisted early cancer screening.