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Record W4297364309 · doi:10.1016/j.ebiom.2022.104271

Baseline host determinants of robust human HIV-1 vaccine-induced immune responses: A meta-analysis of 26 vaccine regimens

2022· review· en· W4297364309 on OpenAlexaff
Yunda Huang, Yuanyuan Zhang, Kelly E. Seaton, Stephen De Rosa, Jack Heptinstall, Lindsay N. Carpp, April K. Randhawa, Lyle R. McKinnon, Paul J. McLaren, Edna Viegas, Glenda Gray, Gavin Churchyard, Susan Buchbinder, Srilatha Edupuganti, Linda‐Gail Bekker, Michael C. Keefer, Mina C. Hosseinipour, Paul Goepfert, Kristen W. Cohen, Brian D. Williamson, M. Juliana McElrath, Georgia D. Tomaras, Juilee Thakar, James J. Kobie

Bibliographic record

VenueEBioMedicine · 2022
Typereview
Languageen
FieldImmunology and Microbiology
TopicHIV Research and Treatment
Canadian institutionsUniversity of ManitobaPublic Health Agency of Canada
FundersJanssen PharmaceuticalsNational Cancer InstituteCenter for AIDS Research, Duke UniversityNational Institute of Dental and Craniofacial ResearchCenters for Disease Control and PreventionJames B. Pendleton Charitable TrustPatient-Centered Outcomes Research InstituteNational Institute of General Medical SciencesNational Institute of Mental HealthBattellePaul G. Allen Family FoundationNational Institute of Allergy and Infectious DiseasesBill and Melinda Gates Foundation
KeywordsMedicineHIV vaccineImmunologyAntibodyImmune systemVaccinationMeta-analysisVaccine trialClinical trialVaccine efficacyViral loadInternal medicineHuman immunodeficiency virus (HIV)

Abstract

fetched live from OpenAlex

BACKGROUND: The identification of baseline host determinants that associate with robust HIV-1 vaccine-induced immune responses could aid HIV-1 vaccine development. We aimed to assess both the collective and relative performance of baseline characteristics in classifying individual participants in nine different Phase 1-2 HIV-1 vaccine clinical trials (26 vaccine regimens, conducted in Africa and in the Americas) as High HIV-1 vaccine responders. METHODS: This was a meta-analysis of individual participant data, with studies chosen based on participant-level (vs. study-level summary) data availability within the HIV-1 Vaccine Trials Network. We assessed the performance of 25 baseline characteristics (demographics, safety haematological measurements, vital signs, assay background measurements) and estimated the relative importance of each characteristic in classifying 831 participants as High (defined as within the top 25th percentile among positive responders or above the assay upper limit of quantification) versus Non-High responders. Immune response outcomes included HIV-1-specific serum IgG binding antibodies and Env-specific CD4+ T-cell responses assessed two weeks post-last dose, all measured at central HVTN laboratories. Three variable importance approaches based on SuperLearner ensemble machine learning were considered. FINDINGS: Overall, 30.1%, 50.5%, 36.2%, and 13.9% of participants were categorized as High responders for gp120 IgG, gp140 IgG, gp41 IgG, and Env-specific CD4+ T-cell vaccine-induced responses, respectively. When including all baseline characteristics, moderate performance was achieved for the classification of High responder status for the binding antibody responses, with cross-validated areas under the ROC curve (CV-AUC) of 0.72 (95% CI: 0.68, 0.76) for gp120 IgG, 0.73 (0.69, 0.76) for gp140 IgG, and 0.67 (95% CI: 0.63, 0.72) for gp41 IgG. In contrast, the collection of all baseline characteristics yielded little improvement over chance for predicting High Env-specific CD4+ T-cell responses [CV-AUC: 0.53 (0.48, 0.58)]. While estimated variable importance patterns differed across the three approaches, female sex assigned at birth, lower height, and higher total white blood cell count emerged as significant predictors of High responder status across multiple immune response outcomes using Approach 1. Of these three baseline variables, total white blood cell count ranked highly across all three approaches for predicting vaccine-induced gp41 and gp140 High responder status. INTERPRETATION: The identified features should be studied further in pursuit of intervention strategies to improve vaccine responses and may be adjusted for in analyses of immune response data to enhance statistical power. FUNDING: National Institute of Allergy and Infectious Diseases (UM1AI068635 to YH, UM1AI068614 to GDT, UM1AI068618 to MJM, and UM1 AI069511 to MCK), the Duke CFAR P30 AI064518 to GDT, and National Institute of Dental and Craniofacial Research (R01DE027245 to JJK). This work was also supported by the Bill and Melinda Gates Foundation. The content is solely the responsibility of the authors and does not necessarily represent the official views of any of the funding sources.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.618
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0110.004
Bibliometrics0.0030.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0260.000

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.

Opus teacher head0.156
GPT teacher head0.394
Teacher spread0.238 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designMeta-analysis
Domainnot available
GenreReview

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".

Quick stats

Citations13
Published2022
Admission routes1
Has abstractyes

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