Summary
In this Cell Patterns publication, Ozette co-founders and collaborators at the Cancer Immunotherapy Trials Network (CITN) outline a novel method of analysis of single-cell data, FAUST, and validate its multiple potential applications. The paper tackles two important problems: analysis of complex single-cell datasets, and prediction of response to immunotherapy in cancer.
Manual analysis of large volumes of highly complex cytometry and other single-cell data — such as that generated in clinical trials — is not only slow but biased, in that investigators can generally only identify cell populations they are specifically looking for. By performing simulation studies comparing FAUST with existing data analysis across 7 cytometry datasets (CyTOF and flow) from 5 independent studies, the paper shows that FAUST processes experimental data on a per-sample basis and returns biologically interpretable cell phenotypes, making it well suited to complex datasets.
The authors also tackle the clinical challenge of predicting response and patient outcomes after immunotherapy. Immunotherapies are now important treatments across multiple cancers, yet biomarkers of response are lacking in the clinic. FAUST was applied to data from a pivotal Merkel cell carcinoma anti-PD-1 trial and discovered hundreds of phenotypes, visualized with a novel embedding. Four were significantly associated with clinical outcome in pre-treatment samples — effector-memory T cells co-expressing PD-1, HLA-DR, and CD28. Notably, manual gating did not identify any statistically significant T-cell correlates of outcome in the same study.
Findings were validated in cryopreserved PBMC samples from the same study, as well as an independent CyTOF dataset from a published metastatic melanoma trial. Together, these results establish FAUST as a powerful approach for unbiased discovery in single-cell cytometry, and indicate that a population of effector-memory CD4+ and CD8+ T cells co-expressing CD28, HLA-DR, and PD-1 are candidate biomarkers for response to pembrolizumab therapy.
