An intrahepatic transcriptional signature of enhanced immune activity predicts response to peginterferon in chronic hepatitis B
Bibliographic record
Abstract
BACKGROUND & AIMS: Differences in intrahepatic gene expression patterns may be associated with therapy response in peginterferon-treated chronic hepatitis B (CHB) patients. METHODS: We employed gene expression profiling in baseline liver biopsies of 40 CHB patients (19 HBeAg-positive; 21 HBeAg-negative) treated with peginterferon and adefovir for 48 weeks, and compared expression patterns of combined responders (HBeAg loss, HBV-DNA <2000 IU/ml, alanine aminotransferase normalization after 1 year of treatment-free follow-up) with non-responders. Genes identified by transcriptome analysis in 15 biopsies were confirmed in 25 additional biopsies by RT-qPCR. RESULTS: Transcriptome analysis demonstrated significant differences in expression of 41 genes between responders and non-responders. In responders, pathway analysis showed specific upregulation of genes related to the immune response, including chemotaxis and antigen processing and presentation. Genes upregulated in responders exhibited strongest similarity with a set of genes induced in livers of chimpanzees with acute Hepatitis B infection. Differential expression was confirmed for eight selected genes. A 2-gene subset (HLA-DPB1, SERPIN-E1) was found to predict response most accurately. Incorporation of these genes in a multivariable model with HBeAg status, HBV genotype and baseline HBsAg level correctly classified 90% of all patients, in which HLA-DPB1 and SERPIN-E1 were independent predictors of response. CONCLUSION: We identified an intrahepatic transcriptional signature associated with enhanced immune activation which predicts therapy response. These novel associations could lead to better understanding of responsiveness to peginterferon in CHB patients, and may assist in selecting possible responders to interferon-based treatment.
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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".