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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".