Transcriptomic profiling of peripheral blood cells in <scp>HPV</scp> ‐associated carcinoma patients receiving combined valproic acid and avelumab
Bibliographic record
Abstract
Human papillomavirus (HPV)-associated cancer continues to evade the immune system by promoting a suppressive tumor microenvironment. Therefore, immunotherapy appears to be a promising approach for targeting HPV-associated tumors. We hypothesized that valproic acid (VA) as an epigenetic agent combined with avelumab may enhance the antitumor immunity in HPV-associated solid tumors. We performed bulk RNA-sequencing (RNA-Seq) on total peripheral blood mononuclear cells (PBMCs) of seven nonresponders (NRs) and four responders (Rs). A total of 39 samples (e.g., pretreatment, post-VA, postavelumab, and endpoint) were analyzed. Also, we quantified plasma analytes and performed flow cytometry. We observed a differential pattern in immune response following treatment with VA and/or avelumab in NRs vs. Rs. A significant upregulation of transcripts associated with NETosis [the formation of neutrophil extracellular traps (NETs)] and neutrophil degranulation pathways was linked to the presence of a myeloid-derived suppressor cell signature in NRs. We noted the elevation of IL-8/IL-18 cytokines and a distinct transcriptome signature at the baseline and endpoint in NRs. By using the receiver operator characteristics, we identified a cutoff value for the plasma IL-8/IL-18 to discriminate NRs from Rs. We found differential therapeutic effects for VA and avelumab in NRs vs. Rs. Thus, our results imply that measuring the plasma IL-8/IL-18 and bulk RNA-Seq of PBMCs may serve as valuable biomarkers to predict immunotherapy outcomes.
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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) | 0.001 | 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".