Increased immune infiltration and chemokine receptor expression in head and neck epithelial tumors after neoadjuvant immunotherapy with the IRX-2 regimen
Bibliographic record
Abstract
IRX-2 is an injectable cancer immunotherapy composed of cytokines purified from stimulated normal-donor peripheral blood mononuclear cells. In a phase 2a trial (n = 27), neoadjuvant IRX-2 significantly increased lymphocyte infiltration (LI) into resected head and neck tumors and was associated with changes in fibrosis and necrosis. Event-free survival was 65% at 2 years, and overall survival 65% at 5 years. Overall survival was longer for patients with LI greater versus lower than the median. This substudy of the mechanisms responsible for the increase in LI with neoadjuvant IRX-2 employed multiplex immunohistochemistry (IHC) and transcriptome analysis to interrogate matched pre- and post-treatment tumor specimens from 7 available phase 2a trial patients. Multiplex IHC showed substantial increases in CD68-expressing cells (5 patients), T-cell density (4 patients), and PDL1 mean fluorescent intensity (4 patients). Consistent with IRX-2 activation of multiple immune cells, transcriptome analysis showed mean increases in expression of genes associated with NK cells, B cells, CD4+ T cells, CD8+ T cells, and dendritic cells, but not of genes associated with neutrophils. There were increases in mean expression of genes for most immune subsets, most markedly (2- to 3-fold) for B cells and dendritic cells. Mean increases in gene expression for chemokines suggest that tumor LI may be driven in part by IRX-2-induced production of chemo-attractants. Upregulation of checkpoint genes including PDL1 and CTLA4 along with increased T-cell infiltration suggests a functional antitumor immune response such that the efficacy of IRX-2 may be enhanced by combination with immune checkpoint inhibitors.
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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) | 0.001 | 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".