High-dimensional immunomonitoring models of HIV-1–specific CD8 T-cell responses accurately identify subjects achieving spontaneous viral control
Bibliographic record
Abstract
UNLABELLED: The development of immunomonitoring models to determine HIV-1 vaccine efficacy is a major challenge. Studies suggest that HIV-1–specific CD8 T cells play a critical role in subjects achieving spontaneous viral control (HIV-1 controllers) and that they will be important in immune interventions. However, no single CD8 T-cell function is uniquely associated with controller status and the heterogeneity of responses targeting different epitopes further complicates the discovery of determinants of protective immunity. In the present study, we describe immunomonitoring models integrating multiple functions of epitope-specific CD8 T cells that distinguish controllers from subjects with treated or untreated progressive infection. Models integrating higher numbers of variables and trained with the least absolute shrinkage and selection operator (LASSO) variant of logistic regression and 10-fold cross-validation produce “diagnostic tests” that display an excellent capacity to delineate subject categories. The test accuracy reaches 75% area under the receiving operating characteristic curve in cohorts matched for prevalence of protective alleles. Linear mixed-effects model analyses show that the proliferative capacity, cytokine production, and kinetics of cytokine secretion are associated with HIV-1 control. Although proliferative capacity is the strongest single discriminant, integrated modeling of different dimensions of data leverages individual associations. This strategy may have important applications in predictive model development and immune monitoring of HIV-1 vaccine trials. KEY POINTS: Immune monitoring models integrating multiple functions of HIV-1-specific CD8 T cells distinguish controllers from subjects with progressive HIV-1 infection. This strategy may have important applications in predictive model development and immune monitoring of HIV-1 vaccine trials.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".