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• W4308382015 abstract "<h3>Background</h3> Natural killer (NK) cell can serve as an effective anti-cancer treatment,^1,2 and enhancing NK function has been shown to enhance patient outcomes.^1,2 Therefore, insight into NK cell states and subtypes may lead to new treatment options. Initial characterization of NK cells relied on flow cytometry with surface markers dividing NK cells into less mature CD56^bright NKs and more mature CD56^dim NKs.⁴ More recently, single-cell RNAseq profiling has enabled deeper characterization and revealed three novel subtypes: cytokine-induced memory-like (CIML), adaptive, low ribosomal, and type I IFN responding NKs, which enhanced the previously known CD56^bright, CD56^dim, and CD56^dim CD57⁺ subtypes.⁵ Here, we extend this single cell subtyping by building a cancer-focused NK cell Atlas that integrates 25 public datasets from multiple types of cancers and delineates both the subtypes and states of the NK cells. <h3>Methods</h3> Single cell sequencing datasets were downloaded from the studies listed in [table 1]. NK cells were identified using the HaiTam cell type prediction algorithm v1.⁶ Harmony v0.1.0⁷ was used for batch correction and ACTIONet v3.0.0⁸ was used for data integration and cell state identification. Cell state abundance comparisons were performed using 1-way ANOVA with Dunnett’s post hoc test. <h3>Results</h3> We generated an Atlas of 89,704 NK cells from 281 donors in 21 cancer focused studies, 3 studies using healthy donors, and 1 study of ulcerative colitis [table 1]. We identified 12 unique NK cellular states in our Atlas [figure 1]. Some states recapitulated known NK subtypes such as CIMLs and type 1 IFN responders. By contrast, CD56^dim NKs were represented by 2 cellular states and CD56^bright NKs were represented by 3 states. We found that one of these CD56^bright states is more abundant in blood from renal cell cancer patients than in blood from healthy donors (2.1-fold change; p < 0.02). Compared to other CD56^bright states, this overrepresented state expresses higher levels of the cytotoxic gene <i>GZMK</i> and of migratory markers <i>CD44</i>, <i>CXCR3</i> and <i>SELL</i>. <h3>Conclusions</h3> Our Atlas describes 12 NK cell states reflecting maturation, activation, and exhaustion in cancer. These states provide a framework for assessing co-expression of targets for NK modulators and for understanding the effects of treatments on NK cells. <h3>References</h3> Chu J, Gao F, Yan M, Zhao S, Yan Z, Shi B, Liu Y. Natural killer cells: a promising immunotherapy for cancer. <i>eBioMedicine</i> 2020;<b>59</b>:102975. Suen W, Lee W, Leung K, Pan X, Li G. Natural Killer Cell-Based Cancer Immunotherapy: A Review on 10 Years Completed <i>Clinical Trials</i>. 2018;<b>36</b>:431–457. Abou-El-Enein M, Bauer G, Medcalf N, Volk H, Reinke P. Putting a price tag on novel autologous cellular therapies. <i>Cytotherapy</i> 2016;<b>18</b>:1056–1061. Moretta L. Dissecting CD56dim human NK cells. <i>Blood</i> 2010; <b>116</b>:3689–3691. Smith L, Kennedy P, Stacey K, Worboys J, Yarwood A, Seo A, Solloa W, Mistretta B, Chatterjee S, Gunaratne P, Allette K, Wang Y, Smith M, Sebra R, Mace E, Horowitz A, Thomson W, Martin P, Eyre S, Davis D. Diversity of peripheral blood human NK cells identified by single-cell RNA sequencing. <i>Blood Adv</i> 2020;<b>4</b>:1388–1406. Talk2data [https://talk2data.bioturing.com/predict] Korsunsky I, Millard N, Fan J, Slowikowski K, Zhang F, Wei K, Baglaenko Y, Brenner M, Loh P, Raychaudhuri S. Fast, sensitive and accurate integration of single-cell data with Harmony. 2019;<b>16</b>:1289–1296. Mohammadi S, Velderrain J, Kellis M. A multiresolution framework to characterize single-cell state landscapes. <i>Nature Communications</i> 2020;<b>11</b>:5399." @default.
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• W4308382015 date "2022-11-01" @default.
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• W4308382015 title "1010 A single-cell transcriptomic atlas of human NK cells to guide cancer immunotherapy" @default.
• W4308382015 doi "https://doi.org/10.1136/jitc-2022-sitc2022.1010" @default.
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