The lack of comprehensive diagnostics and consensus analytical models for evaluating the status of a patient's immune system has hindered a wider adoption of immunoprofiling for treatment monitoring and response prediction in cancer patients. To address this unmet need, we developed an immunoprofiling platform that uses multiparameter flow cytometry to characterize immune cell heterogeneity in the peripheral blood of healthy donors and patients with advanced cancers. Using unsupervised clustering, we identified five immunotypes with unique distributions of different cell types and gene expression profiles. An independent analysis of 17,800 open-source transcriptomes with the same approach corroborated these findings. Continuous immunotype-based signature scores were developed to correlate systemic immunity with patient responses to different cancer treatments, including immunotherapy, prognostically and predictively. Our approach and findings illustrate the potential utility of a simple blood test as a flexible tool for stratifying cancer patients into therapy response groups based on systemic immunoprofiling.
Keywords: B cells; Immunotherapy; Kassandra; Machine learning; NK cells; RNA Sequencing; T cells; blood test; cancer; cancer patients; cell-typing; classifier; flow cytometry; immune cells; immune checkpoint blockade; immune status; immune system; immunoprofiling; immunotherapy biomarker; immunotype; immunotype signature scores; immunotype signatures; monocytes; peripheral blood; systems immunology; tumor microenvironment.
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