Raman Spectroscopy and Machine Learning Reveals Early Tumor Microenvironmental Changes Induced by Immunotherapy.
Animals
B7-H1 Antigen
/ antagonists & inhibitors
CTLA-4 Antigen
/ antagonists & inhibitors
Colonic Neoplasms
/ drug therapy
Immune Checkpoint Inhibitors
/ pharmacology
Immunotherapy
/ methods
Machine Learning
Mice
Mice, Inbred BALB C
Spectrum Analysis, Raman
/ methods
Tumor Cells, Cultured
Tumor Microenvironment
Journal
Cancer research
ISSN: 1538-7445
Titre abrégé: Cancer Res
Pays: United States
ID NLM: 2984705R
Informations de publication
Date de publication:
15 11 2021
15 11 2021
Historique:
received:
06
05
2021
revised:
04
08
2021
accepted:
23
09
2021
pubmed:
15
10
2021
medline:
11
1
2022
entrez:
14
10
2021
Statut:
ppublish
Résumé
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.
Identifiants
pubmed: 34645610
pii: 0008-5472.CAN-21-1438
doi: 10.1158/0008-5472.CAN-21-1438
pmc: PMC8841097
mid: NIHMS1775431
doi:
Substances chimiques
B7-H1 Antigen
0
CTLA-4 Antigen
0
Cd274 protein, mouse
0
Ctla4 protein, mouse
0
Immune Checkpoint Inhibitors
0
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
5745-5755Subventions
Organisme : NCI NIH HHS
ID : R15 CA238861
Pays : United States
Organisme : NIGMS NIH HHS
ID : P20 GM103429
Pays : United States
Organisme : NIGMS NIH HHS
ID : P20 GM121293
Pays : United States
Organisme : NCI NIH HHS
ID : R01 CA238025
Pays : United States
Organisme : NCI NIH HHS
ID : R01 CA236209
Pays : United States
Organisme : NIGMS NIH HHS
ID : DP2 GM128198
Pays : United States
Organisme : NIGMS NIH HHS
ID : R24 GM137786
Pays : United States
Organisme : NIBIB NIH HHS
ID : P41 EB015871
Pays : United States
Informations de copyright
©2021 American Association for Cancer Research.
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