Abstract
There is a lack of robust generalizable predictive biomarkers of response to immune checkpoint blockade in multiple types of cancer. We develop hDirect-MAP, an algorithm that maps T cells into a shared high-dimensional (HD) expression space of diverse T cell functional signatures in which cells group by the common T cell phenotypes rather than dimensional reduced features or a distorted view of these features. Using projection-free single-cell modeling, hDirect-MAP first removed a large group of cells that did not contribute to response and then clearly distinguished T cells into response-specific subpopulations that were defined by critical T cell functional markers of strong differential expression patterns. We found that these grouped cells cannot be distinguished by dimensional-reduction algorithms but are blended by diluted expression patterns. Moreover, these identified response-specific T cell subpopulations enabled a generalizable prediction by their HD metrics. Tested using five single-cell RNA-seq or mass cytometry datasets from basal cell carcinoma, squamous cell carcinoma and melanoma, hDirect-MAP demonstrated common response-specific T cell phenotypes that defined a generalizable and accurate predictive biomarker.
Original language | English (US) |
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Article number | bbab575 |
Journal | Briefings in bioinformatics |
Volume | 23 |
Issue number | 2 |
DOIs | |
State | Published - Mar 1 2022 |
Keywords
- Pareto optimization
- Single-cell RNA sequencing (scRNA-seq)
- projection-free single-cell modeling
- response to immune checkpoint blockade
- single-cell mass cytometry (CyTOF)
ASJC Scopus subject areas
- Information Systems
- Molecular Biology