Identification and validation of novel microenvironment-based immune molecular subgroups of head and neck squamous cell carcinoma: implications for immunotherapy
Bibliographic record
Abstract
Background: Targeting the immune checkpoint pathway has demonstrated antitumor cytotoxicity in treatment-refractory head and neck squamous cell carcinoma (HNSC). To understand the molecular mechanisms underpinning its antitumor response, we characterized the immune landscape of HNSC by their tumor and stromal compartments to identify novel immune molecular subgroups. Patients and methods: A training cohort of 522 HNSC samples from the Cancer Genome Atlas profiled by RNA sequencing was analyzed. We separated gene expression patterns from tumor, stromal, and immune cell gene using a non-negative matrix factorization algorithm. We correlated the expression patterns with a set of immune-related gene signatures, potential immune biomarkers, and clinicopathological features. Six independent datasets containing 838 HNSC samples were used for validation. Results: Approximately 40% of HNSCs in the cohort (211/522) were identified to show enriched inflammatory response, enhanced cytolytic activity, and active interferon-γ signaling (all, P < 0.001). We named this new molecular class of tumors the Immune Class. Then we found it contained two distinct microenvironment-based subtypes, characterized by markers of active or exhausted immune response. The Exhausted Immune Class was characterized by enrichment of activated stroma and anti-inflammatory M2 macrophage signatures, WNT/transforming growth factor-β signaling pathway activation and poor survival (all, P < 0.05). An enriched proinflammatory M1 macrophage signature, enhanced cytolytic activity, abundant tumor-infiltrating lymphocytes, high human papillomavirus (HPV) infection, and favorable prognosis were associated with Active Immune Class (all, P < 0.05). The robustness of these immune molecular subgroups was verified in the validation cohorts, and Active Immune Class showed potential response to programmed cell death-1 blockade (P = 0.01). Conclusions: This study revealed a novel Immune Class in HNSC; two subclasses characterized by active or exhausted immune responses were also identified. These findings provide new insights into tailoring immunotherapeutic strategies for different HNSC subgroups.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".