Abstract PO-091: Leveraging tissue-resident CD103+ NK cells for adoptive cell therapy in head and neck cancer
Bibliographic record
Abstract
Abstract A major obstacle for all cell therapy of solid tumors has been the ability of adoptively transferred cells to infiltrate into immunosuppressive solid tumor microenvironments. In this study, we developed a method of differentiating and expanding a novel subset of tissue-resident natural killer (NK) cells and assessed their capacity to infiltrate head and neck squamous cell carcinoma (HNSCC) and control tumor growth in vivo. In previous work, single-cell RNA sequencing was performed to profile the entire innate lymphoid cell (ILC) family of lymphocytes (including NK cells) isolated from primary human head and neck squamous cell carcinoma (HNSCC). This revealed multiple subsets of NK cell and ILC states within the tumors. A particular NK cell state, resembling intraepithelial ILC1s (ieILC1), expressed integrins characteristic of tissue residency (such as CD103) and expressed the highest levels of granzyme A, granzyme B, and perforin, suggesting potent cytolytic activity. In the current study, we investigated the potential of these cells for adoptive cell immunotherapy against solid tumors, such as HNSCC and melanoma. Using a co-culture system, we developed a method to differentiate and expand a highly purified population of human CD103+ ieILC1-like NK cells ex vivo to clinically relevant numbers. In vitro impedance-based killing assays were used to assess and demonstrate that these cells have superior cytolytic activity against a variety of epithelial target cells, compared to conventional NK cells. In vivo mouse models using xenografts of HNSCC and melanoma in NSG mice were used to assess and demonstrate superior capacity of ieILC1-like NK cells to control tumor growth compared to conventional NK cells from the same donor. Importantly, the CD103+ ieILC1-like NK cells were able to infiltrate the tumor microenvironment significantly better than conventional NK cells, which likely contributed to their potent control of tumors in vivo. Thus, these findings introduce a novel subset of immune cells that have efficient tumor-infiltrating capacity and potent cytolytic activity for adoptive cell therapy of solid tumors. Because conventional NK cells have already demonstrated clinical activity, these preclinical data indicate that CD103+ ieILC1-like NK cells warrant investigation in the clinic. Citation Format: Nina B. Horowitz, June Ho Shin, Sainiteesh Maddineni, Imran A. Mohammad, Alice Xinyuan Liu, John B. Sunwoo. Leveraging tissue-resident CD103+ NK cells for adoptive cell therapy in head and neck cancer [abstract]. In: Proceedings of the AACR-AHNS Head and Neck Cancer Conference: Innovating through Basic, Clinical, and Translational Research; 2023 Jul 7-8; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2023;29(18_Suppl):Abstract nr PO-091.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".