Raman Spectroscopy and Machine Learning Reveals Early Tumor Microenvironmental Changes Induced by Immunotherapy

Cancer immunotherapy provides durable clinical benefit in only a small fraction of patients, and identifying these patients is difficult due to a lack of reliable biomarkers for prediction and evaluation of treatment response. Here, we demonstrate the first application of label-free Raman spectrosco...

Full description

Saved in:
Bibliographic Details
Published in:Cancer research (Chicago, Ill.) Ill.), 2021-11, Vol.81 (22), p.5745-5755
Main Authors: Paidi, Santosh Kumar, Rodriguez Troncoso, Joel, Raj, Piyush, Monterroso Diaz, Paola, Ivers, Jesse D, Lee, David E, Avaritt, Nathan L, Gies, Allen J, Quick, Charles M, Byrum, Stephanie D, Tackett, Alan J, Rajaram, Narasimhan, Barman, Ishan
Format: Article
Language:English
Subjects:
Animals
B7-H1 Antigen - antagonists & inhibitors
Colonic Neoplasms - drug therapy
Colonic Neoplasms - immunology
Colonic Neoplasms - metabolism
Colonic Neoplasms - pathology
CTLA-4 Antigen - antagonists & inhibitors
Immune Checkpoint Inhibitors - pharmacology
Immunotherapy - methods
Machine Learning
Mice
Mice, Inbred BALB C
Spectrum Analysis, Raman - methods
Tumor Cells, Cultured
Tumor Microenvironment
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Cancer immunotherapy provides durable clinical benefit in only a small fraction of patients, and identifying these patients is difficult due to a lack of reliable biomarkers for prediction and evaluation of treatment response. Here, we demonstrate the first application of label-free Raman spectroscopy for elucidating biomolecular changes induced by anti-CTLA4 and anti-PD-L1 immune checkpoint inhibitors (ICI) in the tumor microenvironment (TME) of colorectal tumor xenografts. Multivariate curve resolution-alternating least squares (MCR-ALS) decomposition of Raman spectral datasets revealed early changes in lipid, nucleic acid, and collagen content following therapy. Support vector machine classifiers and random forests analysis provided excellent prediction accuracies for response to both ICIs and delineated spectral markers specific to each therapy, consistent with their differential mechanisms of action. Corroborated by proteomics analysis, our observation of biomolecular changes in the TME should catalyze detailed investigations for translating such markers and label-free Raman spectroscopy for clinical monitoring of immunotherapy response in cancer patients. SIGNIFICANCE: This study provides first-in-class evidence that optical spectroscopy allows sensitive detection of early changes in the biomolecular composition of tumors that predict response to immunotherapy with immune checkpoint inhibitors.
ISSN:0008-5472
1538-7445
DOI:10.1158/0008-5472.CAN-21-1438