Abstract PR12: High-dimensional spatial analysis of the tumor-microenvironment to determine metastatic disease progression and response and resistance to therapy in head and neck cancer
Bibliographic record
Abstract
Abstract Cutaneous and Oral Squamous Cell Carcinoma (SCC) is the second most common skin and most common mucosal cancers, respectively. A subset of head and neck SCC are at high-risk of local recurrence or developing metastases. The prognosis of patients with metastatic and recurrent HNSCC is poor, with a median survival rate of less than 12-months in many instances. The immune system plays a critical role in the control of many malignancies including SCC. Recent clinical trials with immune checkpoint inhibitors (ICI) demonstrate response in 47% of patients with advanced cutaneous SCC (cSCC) but only 19% in patients with advanced oral SCC (oSCC). In this study we used high-dimensional imaging mass cytometry to examine up to 40 parameters to compare the tumor microenvironment of cSCC and oSCC. In cSCC we discovered that patient tumors contained immune signatures and individual cell interactions that link progression to immune status in patients absent of clinical immune suppression (ACIS). There were significant differences in the CD4+, CD8+ T cell and leukocyte immune composition of primary tumors that never progressed compared to primary tumors from DP patients ACIS. We also found significant spatial differences in primary tumors from these patients including cell-interactions and specific regions present in the TME landscape. Cellular regional analysis of the TME showed squamous cell-enriched tumor regions associated with DP tumors. In oSCC, patients who did not respond to ICI showed significantly higher infiltrates of proliferating CD4+ Regulatory T cells (Tregs). NK cells and Central Memory Cytotoxic T cells were found to be significantly higher in patients who responded to therapy. Furthermore, we identified key spatial interactions including a subset of Tregs significantly interacting with specific PMN-myeloid derived suppressor cells (MDSC) in patients who failed to respond to PD-1 ICI. Interestingly, these Tregs and PMN-MDSCs were found to significantly avoid each other in the TME of patients who responded to PD-1 ICI. Mapping the immune landscape of human tumors could provide insight into the role of immune cells in disease progression and response and resistance to therapy. The findings of this research offers a clear pathway to determine clinical outcomes based on a patients TME signature. Citation Format: Angela L. Ferguson, Thomas Beddow, Ashleigh Sharman, Ruth O. Allen, Thomas Ye, Jenny Lee, Tsu-Hui (Hubert) Low, Sydney Ch'ng, Carsten Palme, Bruce Ashford, Marie Ranson, Michael Elliot, Jonathan R. Clark, Ellis Patrick, Umaimainthan Palendira, Ruta Gupta. High-dimensional spatial analysis of the tumor-microenvironment to determine metastatic disease progression and response and resistance to 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 PR12.
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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) | 0.002 | 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".