Viral genome wide association study identifies novel hepatitis C virus polymorphisms associated with sofosbuvir treatment failure
Bibliographic record
Abstract
Persistent hepatitis C virus (HCV) infection is a major cause of chronic liver disease, worldwide. With the development of direct-acting antivirals, treatment of chronically infected patients has become highly effective, although a subset of patients responds less well to therapy. Sofosbuvir is a common component of current de novo or salvage combination therapies, that targets the HCV NS5B polymerase. We use pre-treatment whole-genome sequences of HCV from 507 patients infected with HCV subtype 3a and treated with sofosbuvir containing regimens to detect viral polymorphisms associated with response to treatment. We find three common polymorphisms in non-targeted HCV NS2 and NS3 proteins are associated with reduced treatment response. These polymorphisms are enriched in post-treatment HCV sequences of patients unresponsive to treatment. They are also associated with lower reductions in viral load in the first week of therapy. Using in vitro short-term dose-response assays, these polymorphisms do not cause any reduction in sofosbuvir potency, suggesting an indirect mechanism of action in decreasing sofosbuvir efficacy. The identification of polymorphisms in NS2 and NS3 proteins associated with poor treatment outcomes emphasises the value of systematic genome-wide analyses of viruses in uncovering clinically relevant polymorphisms that impact 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".