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Computational Immune Monitoring Reveals Abnormal Double-Negative T Cells Present across Human Tumor Types

A. Greenplate,Daniel D McClanahan,Brian K. Oberholtzer,D. Doxie,Caroline E. Roe,K. Diggins,Nalin Leelatian,Megan L. Rasmussen,M. Kelley,V. Gama,Peter J. Siska,J. Rathmell,P. Ferrell,Douglas B. Johnson,J. Irish

Published 2018 in Cancer immunology research

ABSTRACT

An analysis toolkit was developed for characterizing immunological changes over time in clinical samples. Use of this toolkit revealed the presence of a double-negative T-cell subset in melanoma, glioblastoma, and renal cell carcinoma but not in healthy tissues. Advances in single-cell biology have enabled measurements of >40 protein features on millions of immune cells within clinical samples. However, the data analysis steps following cell population identification are susceptible to bias, time-consuming, and challenging to compare across studies. Here, an ensemble of unsupervised tools was developed to evaluate four essential types of immune cell information, incorporate changes over time, and address diverse immune monitoring challenges. The four complementary properties characterized were (i) systemic plasticity, (ii) change in population abundance, (iii) change in signature population features, and (iv) novelty of cellular phenotype. Three systems immune monitoring studies were selected to challenge this ensemble approach. In serial biopsies of melanoma tumors undergoing targeted therapy, the ensemble approach revealed enrichment of double-negative (DN) T cells. Melanoma tumor-resident DN T cells were abnormal and phenotypically distinct from those found in nonmalignant lymphoid tissues, but similar to those found in glioblastoma and renal cell carcinoma. Overall, ensemble systems immune monitoring provided a robust, quantitative view of changes in both the system and cell subsets, allowed for transparent review by human experts, and revealed abnormal immune cells present across multiple human tumor types.

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