Abstract IA09: Characterization of hypoxic tumor microenvironment in HPV-negative HNSCC
Bibliographic record
Abstract
Abstract Human papillomavirus (HPV)-negative head and neck squamous cell carcinoma (HNSCC) which are often associated with heavy tobacco use has one of the most hypoxic tumor microenvironments (TME) among solid tumors. A hypoxic TME is known to be immunosuppressive due to inhibition of T-cell proliferation and effector cytokine production, unfavorable metabolic competition, upregulation of coinhibitory receptors in T-cells, and recruitment of immunosuppressive cells such as myeloid-derived suppressor cells. However, there is no proven therapeutic strategy to remodel the TME to be less hypoxic and proinflammatory. We determined hypoxic HNSCC based on a Hypoxia-Immune gene expression signature, characterized the immune cells in the TME using multiple immunohistochemical staining, and analyzed the signaling pathways to identify potential therapeutic targets. We found that the hypoxic HNSCC had significantly higher numbers of immunosuppressive cells, and patients with hypoxic HNSCC had worse outcomes after treatment with anti-Programmed Cell Death-1 (PD-1) inhibitors such as pembrolizumab and nivolumab. The gene expression analysis showed hypoxic HNSCC predominantly increased the expression of the epidermal growth factor receptor (EGFR) and transforming growth factor-β (TGF-β) pathway genes. Inhibition of EGFR pathway using cetuximab decreased the expression of hypoxia signature genes, suggesting that it may alleviate the effects of hypoxia and remodel the TME to become more proinflammatory. In addition, recent clinical trials evaluating combination of cetuximab and nivolumab as well as combination of BCA101 (an EGFR and TGF-β dual inhibitor) and pembrolizumab showed a significant response in HPV-negative HNSCC. Our study provides a rationale for treatment strategies combining EGFR- and TGF-β-targeted agents and immunotherapy in the management of HPV-negative, hypoxic HNSCC. Citation Format: Christine H. Chung. Characterization of hypoxic tumor microenvironment in HPV-negative 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 IA09.
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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 | 0.001 |
| 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 | 0.001 |
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".