ELISPOT as a Functional for Biomarker Study in Cancer Immunotherapy: Applications and Future Directions

Laura R Fernández Castro1,2,3, Matias Regiart4, Francisco Gabriel Ortega-Sánchez1,2,3

  • 1GENYO, Centre for Genomics and Oncological Research, Pfizer/University of Granada/Andalusian Regional Government PTS, Avenida de la Ilustración, 114, 18016 Granada, Spain.

Insights

The Enzyme-Linked ImmunoSpot (ELISPOT) assay is valuable for monitoring T cell responses in cancer immunotherapy. Standardization is needed for its use as a clinical biomarker.

Area of Science:

  • Immunology
  • Oncology
  • Biotechnology

Background:

  • The Enzyme-Linked ImmunoSpot (ELISPOT) assay quantifies antigen-specific T cell responses, particularly interferon gamma (IFN-γ) secretion, at a single-cell level.
  • It is widely applied in cancer research for evaluating immune responses to various immunotherapies, including vaccines, oncolytic viruses, cellular therapies, and immune checkpoint inhibitors.

Purpose of the Study:

  • To systematically review and synthesize evidence on the application of ELISPOT in cancer immunotherapy research.
  • To assess the utility of ELISPOT in validating antigens, monitoring T cell activity, and characterizing T cell function in preclinical and clinical settings.

Main Methods:

  • A systematic qualitative synthesis of 78 studies was conducted, adhering to PRISMA guidelines.
  • Studies were selected from PubMed, Scopus, and Embase, focusing on research with translational relevance in cancer immunotherapy.

Main Results:

  • ELISPOT is extensively used to validate tumor antigens, monitor T cell responses during immunotherapy, and characterize T cell function.
  • A limited number of studies demonstrate direct links between ELISPOT results and clinical outcomes.
  • Significant variability in assay protocols and reporting hinders cross-study comparability and reproducibility.

Conclusions:

  • ELISPOT is a valuable tool for immune monitoring in cancer immunotherapy research.
  • Further standardization, prospective validation, and integration with other analytical methods are necessary for its clinical biomarker implementation.

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