Increasingly Successful Highly Active Antiretroviral Therapy Delays the Emergence of New HLA Class I–Associated Escape Mutations in HIV-1
Bibliographic record
Abstract
BACKGROUND: HLA class I-restricted cytotoxic T lymphocytes and highly active antiretroviral therapy (HAART) exert strong selective pressures on human immunodeficiency virus type 1 (HIV-1), leading to escape mutations compromising virologic control. Immune responses continue to shape HIV-1 evolution after HAART initiation, but the extent and rate at which this occurs remain incompletely quantified. Here, we characterize the incidence and clinical correlates of HLA-associated evolution in HIV-1 Pol after HAART initiation in a large, population-based observational cohort. METHODS: British Columbia HAART Observational, Medical Evaluation and Research cohort participants with available HLA class I types and longitudinal posttherapy protease/reverse transcriptase sequences were studied (n = 619; median, 5 samples per patient and 5.2 years of follow-up). HLA-associated polymorphisms were defined according to published reference lists. Rates and correlates of immune-mediated HIV-1 evolution were investigated using multivariate Cox proportional hazard models incorporating baseline and time-dependent plasma viral load and CD4 response data. RESULTS: New HLA-associated escape events were observed in 269 (43%) patients during HAART and occurred at 49 of 63 (78%) investigated immune-associated sites in Pol. In time-dependent analyses adjusting for baseline factors, poorer virologic, but not immunologic, response to HAART was associated with increased risk of immune escape of 1.9-fold per log(10) viral load increment (P < .0001). Reversion of escape mutations following HAART initiation was extremely rare. CONCLUSIONS: HLA-associated HIV-1 evolution continues during HAART to an extent that is inversely related to the virologic success of therapy. Minimizing the degree of immune escape could represent a secondary benefit of effective HAART.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".