Abstract IA05: Nodal and systemic Immune effects of radiation in HNSCC
Bibliographic record
Abstract
Abstract When combined with immunotherapy, HPV-unrelated HNSCC has a high incidence of local and regional recurrence. Since nodal metastasis remains a common pattern of spread for HNSCC, elective nodal irradiation (ENI) is commonly employed to eradicate potential microscopic disease in non-involved lymph nodes. Given our developing understanding that (1) immune cells are highly radio-sensitive, and (2) that anti-tumor T cell priming, and expansion, occur in the draining lymph nodes (DLNs), we tested whether RT directed at gross tumor only could increase the effectiveness of immunotherapies. Using preclinical models and samples from canine and human trials, we tested the effects of RT with or without ENI. Immune cell differences in the tumor tissue, DLNs, and blood were characterized with flow cytometry. We found that tumor only irradiation decreased local and distant tumor growth and increased survival but increased regional recurrence. Sentinel lymph node resection or irradiation was effective at reducing regional metastasis. We show that the effectiveness of tumor only RT is likely mediated via induction of a systemic immune response dependent on antigen recognition by T cells in the DLNs. These data support the hypothesis that effective anti-tumor immunotherapy treatment requires a systemic, antigen-dependent immune response which is primed in the DLNs. Citation Format: Laurel B. Darragh, Jacob Gadwa, Keara Boss, Sana D. Karam. Nodal and systemic Immune effects of radiation in HNSCC [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 IA05.
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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 | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".