A signature for immune response correlates with HCV treatment outcome in Caucasian subjects
Bibliographic record
Abstract
Broad proteomic profiling was performed on serum samples of phase 2 studies (PROVE1, PROVE2, and PROVE3) of the direct-acting antiviral drug telaprevir in combination with peg-interferon and ribavirin in subjects with HCV. Using only profiling data from subjects treated with peg-interferon and ribavirin, a signature composed of pretreatment levels of 13 components was identified that correlated well (R(2)=0.68) with subjects' underlying immune response as measured by week 4 viral decline and was highly predictive of sustained virologic response in non-African American subjects (AUC=0.99). The signature was validated by predicting in an independent cohort of non-African American subjects treated with telaprevir, peg-interferon and ribavirin (AUC=0.854). Samples from extreme responders were over-represented in these analyses. Proteins identified as differentially-expressed between responders and non-responders to HCV treatment were quantified using multiple reaction monitoring in samples from all Caucasian subjects in the peg-interferon and ribavirin arms of PROVE1 and PROVE2, revealing 15 proteins that were significantly differentially expressed between treatment responders and non-responders. Seven of the proteins are part of focal adhesions or other macromolecular assemblies that form structural links between integrins and the actin cytoskeleton and are involved in antiviral response. BIOLOGICAL SIGNIFICANCE: HCV is a significant health problem. We describe a novel approach for identifying markers that predicts HCV treatment response different treatment regimens and use this approach to identify a novel HCV treatment response signature. The signature has potential to guide optimization of HCV treatment regimens.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".