hDirect-MAP: projection-free single-cell modeling of response to checkpoint immunotherapy

Yong Lu, Gang Xue, Ningbo Zheng, Kun Han, Wenzhong Yang, Rui-Sheng Wang, Lingyun Wu, Lance D Miller, Timothy Pardee, Pierre L Triozzi, Hui-Wen Lo, Kounosuke Watabe, Stephen T C Wong, Boris C Pasche, Wei Zhang, Guangxu Jin

Research output: Contribution to journalArticlepeer-review

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 languageEnglish (US)
JournalBriefings in bioinformatics
Volume23
Issue number2
DOIs
StatePublished - Mar 10 2022

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