New interpretable machine-learning method for single-cell data reveals correlates of clinical response to cancer immunotherapy

Evan Greene, Greg Finak, Leonard A. D'Amico, Nina Bhardwaj, Candice D. Church, Chihiro Morishima, Nirasha Ramchurren, Janis M. Taube, Paul T. Nghiem, Martin A. Cheever, Steven P. Fling, Raphael Gottardo

Research output: Contribution to journalArticlepeer-review

12 Scopus citations

Abstract

We introduce a new method for single-cell cytometry studies, FAUST, which performs unbiased cell population discovery and annotation. FAUST processes experimental data on a per-sample basis and returns biologically interpretable cell phenotypes, making it well suited for the analysis of complex datasets. We provide simulation studies that compare FAUST with existing methodology, exemplifying its strength. We apply FAUST to data from a Merkel cell carcinoma anti-PD-1 trial and discover pre-treatment effector memory T cell correlates of outcome co-expressing PD-1, HLA-DR, and CD28. Using FAUST, we then validate these correlates in cryopreserved peripheral blood mononuclear cell samples from the same study, as well as an independent CyTOF dataset from a published metastatic melanoma trial. Finally, we show how FAUST's phenotypes can be used to perform cross-study data integration in the presence of diverse staining panels. Together, these results establish FAUST as a powerful new approach for unbiased discovery in single-cell cytometry.

Original languageEnglish
Article number100372
JournalPatterns
Volume2
Issue number12
DOIs
StatePublished - 10 Dec 2021

Keywords

  • DSML 3: Development/Pre-production: Data science output has been rolled out/validated across multiple domains/problems
  • algorithms
  • bioinformatics
  • cancer
  • immunology
  • single-cell
  • statistics & probability

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