Greenplate, Allison R. and McClanahan, Daniel D. and Oberholtzer, Brian K. and Doxie, Deon B. and Roe, Caroline E. and Diggins, Kirsten E. and Leelatian, Nalin and Rasmussen, Megan L. and Kelley, Mark C. and Gama, Vivian and Siska, Peter J. and Rathmell, Jeffrey C. and Ferrell, Brent and Johnson, Douglas B. and Irish, Jonathan M. (2019) Computational Immune Monitoring Reveals Abnormal Double-Negative T Cells Present across Human Tumor Types. CANCER IMMUNOLOGY RESEARCH, 7 (1). pp. 86-99. ISSN 2326-6066, 2326-6074
Full text not available from this repository. (Request a copy)Abstract
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.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | MASS; IMMUNOLOGY; SYSTEM; FLOW; AGE; DIFFERENTIATION; VISUALIZATION; COMBINATION; ANTI-CTLA-4; THERAPY; |
| Subjects: | 600 Technology > 610 Medical sciences Medicine |
| Divisions: | Medicine > Lehrstuhl für Innere Medizin III (Hämatologie und Internistische Onkologie) |
| Depositing User: | Dr. Gernot Deinzer |
| Date Deposited: | 22 Apr 2020 07:47 |
| Last Modified: | 22 Apr 2020 07:47 |
| URI: | https://pred.uni-regensburg.de/id/eprint/27921 |
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