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

Journal: Cancer research
PMID: 34645610

Abstract

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.

Authors

  • Santosh Kumar Paidi
    Department of Mechanical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, United States.
  • Joel Rodriguez Troncoso
    Department of Biomedical Engineering, University of Arkansas, Fayetteville, Arkansas.
  • Piyush Raj
    Department of Mechanical Engineering, Johns Hopkins University, Baltimore, Maryland.
  • Paola Monterroso Diaz
    Department of Biomedical Engineering, University of Arkansas, Fayetteville, Arkansas.
  • Jesse D Ivers
    Department of Biomedical Engineering, University of Arkansas, Fayetteville, Arkansas.
  • David E Lee
    Department of Health, Human Performance, and Recreation, University of Arkansas, Fayetteville, Arkansas.
  • Nathan L Avaritt
    Department of Biochemistry and Molecular Biology, University of Arkansas for Medical Sciences, Little Rock, Arkansas.
  • Allen J Gies
    Department of Biochemistry and Molecular Biology, University of Arkansas for Medical Sciences, Little Rock, Arkansas.
  • Charles M Quick
    Division of Pathology, University of Arkansas for Medical Sciences, Little Rock, Arkansas.
  • Stephanie D Byrum
    Department of Biochemistry and Molecular Biology, University of Arkansas for Medical Sciences, Little Rock, Arkansas.
  • Alan J Tackett
    Department of Biochemistry and Molecular Biology, University of Arkansas for Medical Sciences, Little Rock, Arkansas.
  • Narasimhan Rajaram
    Department of Biomedical Engineering, University of Arkansas, Fayetteville, Arkansas. ibarman@jhu.edu nrajaram@uark.edu.
  • Ishan Barman
    Department of Mechanical Engineering , Johns Hopkins University , Baltimore , Maryland 21218 , United States.

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