Label-free Raman spectroscopy and machine learning enables sensitive evaluation of differential response to immunotherapy

Abstract

Cancer immunotherapy provides durable clinical benefit in only a small fraction of patients, particularly due to a lack of reliable biomarkers for accurate prediction of treatment outcomes and evaluation of response. Here, we demonstrate the first application of label-free Raman spectroscopy for elucidating biochemical changes induced by immunotherapy in the tumor microenvironment. We used CT26 murine colorectal cancer cells to grow tumor xenografts and subjected them to treatment with anti-CTLA-4 and anti-PD-L1 antibodies. Multivariate curve resolution - alternating least squares (MCR-ALS) decomposition of Raman spectral dataset obtained from the treated and control tumors revealed subtle differences in lipid, nucleic acid, and collagen content due to therapy. Our supervised classification analysis using support vector machines and random forests provided excellent prediction accuracies for both immune checkpoint inhibitors and delineated important spectral markers specific to each therapy, consistent with their differential mechanisms of action. Our findings pave the way for in vivo studies of response to immunotherapy in clinical patients using label-free Raman spectroscopy and machine learning.