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Single-Molecule Arrays for Ultrasensitive Detection of Blood-Based Biomarkers for Immunotherapy

  • Limor Cohen
  • Alissa KeeganEmail author
  • David R. Walt
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 2055)

Abstract

Single-molecule array (Simoa) technology enables ultrasensitive protein detection that is suited to the development of peripheral blood-based assays for assessing immuno-oncology responses. We adapted a panel of Simoa assays to measure systemic cytokine levels from plasma and characterized physiologic variation in healthy individuals and preanalytic variation arising from processing and handling of patient samples. Insights from these preclinical studies led us to a well-defined set of Simoa assay conditions, a specimen processing protocol, and a data processing approach that we describe here. Simoa enables accurate quantitation of soluble immune signaling molecules in an unprecedented femtomolar range, opening up the potential for liquid biopsy-type approaches in immuno-oncology. We are using the method described here to distinguish PD-1 inhibitor nonresponders as early as after one dose after therapy and envision applications in characterizing PD-1 inhibitor resistance and detection of immune-related adverse effects.

Key words

Single-molecule array Cytokine Ultrasensitive Liquid biopsy 

Notes

Acknowledgments

L.C. and D.R.W. were funded by DARPA (HR0011-12-2-0001; Pass-through-entity: Univ. of North Carolina Chapel-Hill, subaward 5055065).

Conflict of Interest: The authors declare the following competing financial interest: David R. Walt is the scientific founder and a board member of Quanterix Corporation. All other authors declare no competing financial interest.

References

  1. 1.
    Ribas A, Wolchok JD (2018) Cancer immunotherapy using checkpoint blockade. Science 359:1350–1355CrossRefGoogle Scholar
  2. 2.
    Topalian SL, Taube JM, Anders RA, Pardoll DM (2016) Mechanism-driven biomarkers to guide immune checkpoint blockade in cancer therapy. Nat Rev Cancer 16:275–287CrossRefGoogle Scholar
  3. 3.
    Nishino M, Ramaiya NH, Hatabu H, Hodi FS (2017) Monitoring immune-checkpoint blockade: response evaluation and biomarker development. Nat Rev Clin Oncol 14:655–668CrossRefGoogle Scholar
  4. 4.
    Chen P-L, Roh W, Reuben A, Cooper ZA, Spencer CN, Prieto PA et al (2016) Analysis of immune signatures in longitudinal tumor samples yields insight into biomarkers of response and mechanisms of resistance to immune checkpoint blockade. Cancer Discov 6:827–837CrossRefGoogle Scholar
  5. 5.
    Vilain RE, Menzies AM, Wilmott JS, Kakavand H, Madore J, Guminski A et al (2017) Dynamic changes in PD-L1 expression and immune infiltrates early during treatment predict response to PD-1 blockade in melanoma. Clin Cancer Res 23:5024CrossRefGoogle Scholar
  6. 6.
    Kamphorst AO, Pillai RN, Yang S, Nasti TH, Akondy RS, Wieland A et al (2017) Proliferation of PD-1+ CD8 T cells in peripheral blood after PD-1–targeted therapy in lung cancer patients. Proc Natl Acad Sci U S A 114:4993–4998CrossRefGoogle Scholar
  7. 7.
    Krieg C, Nowicka M, Guglietta S, Schindler S, Hartmann FJ, Weber LM et al (2018) High-dimensional single-cell analysis predicts response to anti-PD-1 immunotherapy. Nat Med 24:144–153CrossRefGoogle Scholar
  8. 8.
    Merker JD, Oxnard GR, Compton C, Diehn M, Hurley P, Lazar AJ et al (2018) Circulating tumor DNA analysis in patients with Cancer: American Society of Clinical Oncology and College of American Pathologists Joint Review. J Clin Oncol Off J Am Soc Clin Oncol 36:1631–1641CrossRefGoogle Scholar
  9. 9.
    Aziz N (2015) Measurement of circulating cytokines and immune-activation markers by multiplex technology in the clinical setting: what are we really measuring? Forum Immunopathol Dis Ther 6:19–22CrossRefGoogle Scholar
  10. 10.
    Yeung D, Ciotti S, Purushothama S, Gharakhani E, Kuesters G, Schlain B et al (2016) Evaluation of highly sensitive immunoassay technologies for quantitative measurements of sub-pg/mL levels of cytokines in human serum. J Immunol Methods 437:53–63CrossRefGoogle Scholar
  11. 11.
    Cohen L, Keegan A, Melanson SEF, Walt DR (2019) Impact of clinical sample handling and processing on ultra-low level measurements of plasma cytokines. Clin Biochem 65:38–44CrossRefGoogle Scholar
  12. 12.
    Rissin DM, Fournier DR, Piech T, Kan CW, Campbell TG, Song L et al (2011) Simultaneous detection of single molecules and singulated ensembles of molecules enables immunoassays with broad dynamic range. Anal Chem 83:2279–2285CrossRefGoogle Scholar
  13. 13.
    Wilson DH, Rissin DM, Kan CW, Fournier DR, Piech T, Campbell TG et al (2016) The Simoa HD-1 analyzer: a novel fully automated digital immunoassay analyzer with single-molecule sensitivity and multiplexing. J Lab Autom 21:533–547CrossRefGoogle Scholar
  14. 14.
    Wu D, Milutinovic MD, Walt DR (2015) Single molecule array (Simoa) assay with optimal antibody pairs for cytokine detection in human serum samples. Analyst 140:6277–6282CrossRefGoogle Scholar
  15. 15.
    Vgontzas AN, Papanicolaou DA, Bixler EO, Lotsikas A, Zachman K, Kales A et al (1999) Circadian interleukin-6 secretion and quantity and depth of sleep. J Clin Endocrinol Metab 84:2603–2607CrossRefGoogle Scholar
  16. 16.
    Ostrowski K, Rohde T, Zacho M, Asp S, Pedersen BK (1998) Evidence that interleukin-6 is produced in human skeletal muscle during prolonged running. J Physiol 508 (. Pt 3:949–953CrossRefGoogle Scholar
  17. 17.
    Wedell-Neergaard A-S, Lang Lehrskov L, Christensen RH, Legaard GE, Dorph E, Larsen MK et al (2019) Exercise-induced changes in visceral adipose tissue mass are regulated by IL-6 signaling: a randomized controlled trial. Cell Metab 29(4):844–855.e3CrossRefGoogle Scholar
  18. 18.
    Wu D, Dinh TL, Bausk BP, Walt DR (2017) Long-term measurements of human inflammatory cytokines reveal complex baseline variations between individuals. Am J Pathol 187:2620–2626CrossRefGoogle Scholar
  19. 19.
    Bidwell J, Keen L, Gallagher G, Kimberly R, Huizinga T, McDermott MF et al (1999) Cytokine gene polymorphism in human disease: on-line databases. Genes Immun 1:3–19CrossRefGoogle Scholar
  20. 20.
    In U.S. Department of Health and Human Services. FDA. Guidance for industry: bioanalytical method validation; 2013. p. 1–27Google Scholar
  21. 21.
    Cohen L, Walt DR (2018) Evaluation of antibody Biotinylation approaches for enhanced sensitivity of single molecule Array (Simoa) immunoassays. Bioconjug Chem 29:3452–3458CrossRefGoogle Scholar
  22. 22.
    Wild D (2013) The immunoassay handbook: theory and applications of ligand binding, ELISA and related techniques, 4th edn. Elsevier, AmsterdamGoogle Scholar
  23. 23.
    FDA. Guidance for Industry: Bioanalytical method validation. US Dep Health Hum Serv; 2013. p. 1–27Google Scholar
  24. 24.
    Van Breukelen GJP (2006) ANCOVA versus change from baseline: more power in randomized studies, more bias in nonrandomized studies [corrected]. J Clin Epidemiol 59:920–925CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2020

Authors and Affiliations

  1. 1.Department of Pathology, Brigham and Women’s HospitalHarvard Medical SchoolBostonUSA
  2. 2.Wyss Institute for Biologically Inspired Engineering at Harvard UniversityBostonUSA
  3. 3.Department of Chemical BiologyHarvard UniversityBostonUSA

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