Using genotypic and phenotypic methods to determine the HIV co-receptor phenotype in the clinical setting
Bibliographic record
Abstract
Objective: The human immunodeficiency virus type 1 (HIV-1) currently infects over 30 million people worldwide. It uses one of two main co-receptors to infect cells. The primary objective of this thesis is to evaluate genotypic and phenotypic assays for co-receptor usage in the clinical setting and investigate approaches for improvement of these assays. Methods: The concordance of recombinant co-receptor phenotyping assays and the predictive ability of genotype-based methods including the ‘11/25’ rule, position specific scoring matrices (PSSMs), and support vector machines (SVMs) were evaluated in the clinical setting using patient-derived plasma samples. Samples and patient data were evaluated in cross-sectional analyses from a retrospective population-based cohort of HIV-infected individuals enrolled in the HIV/AIDS Drug Treatment Program in British Columbia, Canada. Results: Current implementations of HIV V3 region-based predictors for HIV co-receptor usage tested on patient derived samples are inadequate in the clinical setting, primarily due to low sensitivities as a result of difficult to detect minority species. Recombinant phenotype assays also show discordances when tested against each other on the same set of patient derived samples, raising doubts if any of these assays can truly be considered a ‘gold standard’. Significant associations between clinical progression, viral sequence-based predictors of co-receptor usage and the output of recombinant assays are observed, suggesting that sensitivity can be improved by incorporating CD4% into genotype-based predictors. This is verified with a SVM model which showed a 17% increase in sensitivity when CD4% was incorporated into training and testing. Conclusion: This work in this thesis has exposed the difficulty in determining the co-receptor phenotype in the clinical setting, primarily due to minority species. Although genotypic methods of screening for HIV co-receptor usage prior to the administration of CCR5 antagonists may reduce costs and increase turn-around time over phenotypic methods, they are currently inadequate for use in the clinical setting due to low sensitivities. Although the addition of clinical parameters such as CD4 count significantly increases the predictive ability of genotypic methods, the presence of low-levels of X4 virus continues to reduce the sensitivity of both genotypic and phenotypic methods.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".