Journal of Mass Spectrometry and Advances in the Clinical Lab (Nov 2023)

Molecular and translational biology of the blood-based VeriStrat® proteomic test used in cancer immunotherapy treatment guidance

  • Matthew A. Koc,
  • Timothy Aaron Wiles,
  • Daniel C. Weinhold,
  • Steven Rightmyer,
  • Amanda L. Weaver,
  • Colin T. McDowell,
  • Joanna Roder,
  • Senait Asmellash,
  • Gary A. Pestano,
  • Heinrich Roder,
  • Robert W. Georgantas III

Journal volume & issue
Vol. 30
pp. 51 – 60

Abstract

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Introduction: The VeriStrat® test (VS) is a blood-based assay that predicts a patient's response to therapy by analyzing eight features in a spectrum obtained from matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) analysis of human serum and plasma. In a recent analysis of the INSIGHT clinical trial (NCT03289780), it was found that the VS labels, VS Good and VS Poor, can effectively predict the responsiveness of non-small cell lung cancer (NSCLC) patients to immune checkpoint inhibitor (ICI) therapy. However, while VS measures the intensities of spectral features using MALDI-TOF analysis, the specific proteoforms underlying these features have not been comprehensively identified. Objectives: The objective of this study was to identify the proteoforms that are measured by VS. Methods: To resolve the features obtained from the low-resolution MALDI-TOF procedure used to acquire mass spectra for VS DeepMALDI® analysis of serum was employed. This technique allowed for the identification of finer peaks within these features. Additionally, a combination of reversed-phase fractionation and liquid chromatography-tandem mass spectrometry (LC-MS/MS) was then used to identify the proteoforms associated with these peaks. Results: The analysis revealed that the primary constituents of the spectrum measured by VS are serum amyloid A1, serum amyloid A2, serum amyloid A4, C-reactive protein, and beta-2 microglobulin. Conclusion: Proteoforms involved in host immunity were identified as significant components of these features. This newly acquired information improves our understanding of how VS can accurately predict patient response to therapy. It opens up additional studies that can expand our understanding even further.

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