Use of Cellular HIV DNA to Predict Virologic Response to Maraviroc: Performance of Population-Based and Deep Sequencing
Bibliographic record
Abstract
BACKGROUND: A tropism test is required before administration of the antiretroviral drug maraviroc. However, plasma RNA testing is not possible in patients with undetectable plasma viral loads. Here we assess genotypic testing of cellular human immunodeficiency virus (HIV) DNA from peripheral blood mononuclear cells (PBMCs) to predict virologic responses in treatment-experienced patients beginning maraviroc-containing regimens. METHODS: PBMC samples from 181 maraviroc recipients at study entry in MOTIVATE or A4001029 (51% R5 by original Trofile). The V3 loop was amplified in triplicate from cellular HIV DNA, and matching plasma RNA (n = 156). Sequencing was performed using standard population-based methods and next-generation deep sequencing, with tropism assessment as previously defined. RESULTS: Genotypic DNA-based tropism testing from the cellular compartment had 78%-81% sensitivity relative to RNA-based Trofile at the same time point. Cell-based genotypic tropism methods and plasma-based phenotypic and genotypic methods were predictive of virologic response. However, when classifications were discordant, the outcomes favored the plasma predictions over the DNA ones. CONCLUSIONS: Genotypic determination of HIV tropism can be performed using cell-derived viral DNA, and is a predictor of virologic success on maraviroc in therapy-experienced patients. However, the PBMC compartment appears to be a suboptimal predictor compared to plasma.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".