Transcriptomic analysis identifies differences in gene expression in actinic keratoses after treatment with imiquimod and between responders and non responders
Bibliographic record
Abstract
The presence of actinic keratoses (AKs) increases a patient's risk of developing squamous cell carcinoma by greater than six-fold. We evaluated the effect of topical treatment with imiquimod on the tumor microenvironment by measuring transcriptomic differences in AKs before and after treatment with imiquimod 3.75%. Biopsies were collected prospectively from 21 patients and examined histologically. RNA was extracted and transcriptomic analyses of 788 genes were performed using the nanoString assay. Imiquimod decreased number of AKs by study endpoint at week 14 (p < 0.0001). Post-imiquimod therapy, levels of CDK1, CXCL13, IL1B, GADPH, TTK, ILF3, EWSR1, BIRC5, PLAUR, ISG20, and C1QBP were significantly lower (adjusted p < 0.05). Complete responders (CR) exhibited a distinct pattern of inflammatory gene expression pre-treatment relative to incomplete responders (IR), with alterations in 15 inflammatory pathways (p < 0.05) reflecting differential expression of 103 genes (p < 0.05). Presence of adverse effects was associated with improved treatment response. Differences in gene expression were found between pre-treatment samples in CR versus IR, suggesting that higher levels of inflammation pre-treament may play a part in regression of AKs. Further characterization of the immune micro-environment in AKs may help develop biomarkers predictive of response to topical immune modulators and may guide therapy.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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".