  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.
