Abstract PO-035: Immune profiles of long-term survivors of patients with head and neck squamous carcinoma on immune checkpoint inhibitor therapy
Bibliographic record
Abstract
Abstract Background: Only 15 – 20% of patients with head and neck squamous cell carcinoma (HNSCC) have response to anti-programmed cell death (PD)-1 immunotherapy and there remain gaps in predicting which patients will respond. Method: Here, we utilized DNA methylation cytometry to sequentially delineate detailed immune profiles of six patients with HNSCC (n=38 samples), who had durable response to immunotherapy over a one-year period without disease progression. Results: During the initial 3.5 months of anti-PD-1 immunotherapy treatment, increases in levels of CD8 T memory cells and natural killer cells were observed. Compared to healthy populations in similar age ranges, CD4 T and B cell levels were lower, while monocyte levels were higher in patients with HNSCC at baseline and throughout the year of treatment with immunotherapy. Conclusions: Our results suggest monitoring changes in immune cell type levels as potential biomarkers for patients with HNSCC who respond to anti-PD-1 immunotherapy. Citation Format: Brock Christensen, Min Kyung Lee, Ze Zhang, Rondi Butler, Geat Ramush, Kartik Sehgal, Keisuke Shirai, Devin Koestler, Lucas Salas, John Wiencke, Robert Haddad, Karl Kelsey. Immune profiles of long-term survivors of patients with head and neck squamous carcinoma on immune checkpoint inhibitor therapy [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-035.
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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 | 0.000 |
| 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".