A Classifier to Predict Viral Control After Antiretroviral Treatment Interruption in Chronic HIV-1-Infected Patients.
AIDS Vaccines
/ immunology
Adult
Anti-Retroviral Agents
/ therapeutic use
Bayes Theorem
CD4 Lymphocyte Count
CD4-Positive T-Lymphocytes
/ immunology
CD8-Positive T-Lymphocytes
/ immunology
Dendritic Cells
/ immunology
Female
HIV Infections
/ drug therapy
HIV-1
/ immunology
Humans
Male
Middle Aged
Receptors, CCR5
Receptors, CXCR4
Treatment Outcome
Vaccination
Viral Load
/ drug effects
Journal
Journal of acquired immune deficiency syndromes (1999)
ISSN: 1944-7884
Titre abrégé: J Acquir Immune Defic Syndr
Pays: United States
ID NLM: 100892005
Informations de publication
Date de publication:
15 04 2020
15 04 2020
Historique:
pubmed:
7
1
2020
medline:
30
10
2020
entrez:
7
1
2020
Statut:
ppublish
Résumé
To construct a classifier that predicts the probability of viral control after analytical treatment interruptions (ATI) in HIV research trials. Participants of a dendritic cell-based therapeutic vaccine trial (DCV2) constituted the derivation cohort. One of the primary endpoints of DCV2 was the drop of viral load (VL) set point after 12 weeks of ATI (delta VL12). We classified cases as "controllers" (delta VL12 > 1 log10 copies/mL, n = 12) or "noncontrollers" (delta VL12 < 0.5 log10 copies/mL, n = 10) and compared 190 variables (clinical data, lymphocyte subsets, inflammatory markers, viral reservoir, ELISPOT, and lymphoproliferative responses) between the 2 groups. Naive Bayes classifiers were built from combinations of significant variables. The best model was subsequently validated on an independent cohort. Controllers had significantly higher pre-antiretroviral treatment VL [110,250 (IQR 71,968-275,750) vs. 28,600 (IQR 18737-39365) copies/mL, P = 0.003] and significantly lower proportion of some T-lymphocyte subsets than noncontrollers: prevaccination CD4CD45RA+RO+ (1.72% vs. 7.47%, P = 0.036), CD8CD45RA+RO+ (7.92% vs. 15.69%, P = 0.017), CD4+CCR5+ (4.25% vs. 7.40%, P = 0.011), and CD8+CCR5+ (14.53% vs. 27.30%, P = 0.043), and postvaccination CD4+CXCR4+ (12.44% vs. 22.80%, P = 0.021). The classifier based on pre-antiretroviral treatment VL and prevaccine CD8CD45RA+RO+ T cells was the best predictive model (overall accuracy: 91%). In an independent validation cohort of 107 ATI episodes, the model correctly identified nonresponders (negative predictive value = 94%), while it failed to identify responders (positive predictive value = 20%). Our simple classifier could correctly classify those patients with low probability of control of VL after ATI. These data could be helpful for HIV research trial design.
Identifiants
pubmed: 31904703
doi: 10.1097/QAI.0000000000002281
pii: 00126334-202004150-00006
doi:
Substances chimiques
AIDS Vaccines
0
Anti-Retroviral Agents
0
CCR5 protein, human
0
CXCR4 protein, human
0
Receptors, CCR5
0
Receptors, CXCR4
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
479-485Références
Leal L, Lucero C, Gatell JM, et al. New challenges in therapeutic vaccines against HIV infection. Expert Rev Vaccin. 2017;16:587–600.
Anderson JL, Fromentin R, Corbelli GM, et al. Progress towards an HIV cure: update from the 2014 international AIDS society symposium. AIDS Res Hum Retroviruses. 2015;31:36–44.
Julg B, Dee L, Ananworanich J, et al. Recommendations for analytical antiretroviral treatment interruptions in HIV research trials—report of a consensus meeting. Lancet HIV. 2019;3018:1–10.
Dube K, Evans D, Dee L, et al. “We need to deploy them very thoughtfully and carefully”: perceptions of analytical treatment interruptions in HIV cure research in the United States—a qualitative inquiry. AIDS Res Hum Retroviruses. 2018;34:67–79.
Jeffreys R, Treatment Action Group. Community Recommendations for Clinical Research Involving Antiretroviral Treatment Interruptions in Adults. 2018. Available at: http://www.treatmentactiongroup.org/sites/default/files/community_recs_clinical_research_final.pdf. Accessed January 17, 2019.
Yerli S, Günthard HF, Fagard C, et al. Proviral HIV-DNA predicts viral rebound and viral setpoint after structured treatment interruptions. Age Aging Res Lett. 2004;18:1951–1964.
Park YJ, Etemad B, Ahmed H, et al. Impact of HLA class I alleles on timing of HIV rebound after antiretroviral treatment interruption. Pathog Immun. 2017;2:431–445.
Huang Y, Pantaleo G, Tapia G, et al. Cell-mediated immune predictors of vaccine effect on viral load and CD4 count in a phase 2 therapeutic HIV-1 vaccine clinical trial. EBioMedicine. 2017;24:195–204.
García F, Climent N, Guardo AC, et al. A dendritic cell-based vaccine elicits T cell responses associated with control of HIV-1 replication. Sci Transl Med. 2013;5:166ra2.
León A, Martinez E, Milinkovic A, et al. Influence of repeated cycles of structured therapy interruption on the rate of recovery of CD4+ T cells after highly active antiretroviral therapy resumption. J Antimicrob Chemother. 2009;63:184–188.
García F, Lejeune M, Climent NN, et al. Therapeutic immunization with dendritic cells loaded with heat-inactivated autologous HIV-1 in patients with chronic HIV-1 infection. J Infect Dis. 2005;191:1680–1685.
Castro P, Plana M, González R, et al. Influence of a vaccination schedule on viral load rebound and immune responses in successfully treated HIV-infected patients. AIDS Res Hum Retroviruses. 2009;25:1249–1259.
García F, Plana M, Ortiz GM, et al. The virological and immunological consequences of structured treatment interruptions in chronic HIV-1 infection. AIDS. 2001;15:F29–F40.
García F, Plana M, Arnedo M, et al. Effect of mycophenolate mofetil on immune response and plasma and lymphatic tissue viral load during and after interruption of highly active antiretroviral therapy for patients with chronic HIV infection: a randomized pilot study. J Acquir Immune Defic Syndr. 2004;36:823–830.
Fagard C, Oxenius A, Günthard H, et al. A prospective trial of structured treatment interruptions in human immunodeficiency virus infection. Arch Intern Med. 2003;163:1220–1226.
Fiscus S, Denny T, Habiyambere V, et al. Systematic review of the performance of HIV viral load technologies on plasma samples. PLoS One. 2014;9:e85869.
Andrés C, Plana M, Guardo AC, et al. HIV-1 reservoir dynamics after vaccination and antiretroviral therapy interruption are associated with dendritic cell-vaccine induced T-cell responses. J Virol. 2015;89:9189–9199.
Treasure GC, Aga E, Bosch RJ, et al. Relationship among viral load outcomes in HIV treatment interruption trials. J Acquir Immune Defic Syndr. 2016;72:1.
Plana M, Garcia F, Oxenius A, et al. Relevance of HIV-1-specific CD4+helper T-cell responses during structured treatment interruptions in patients with CD4+T-cell nadir above 400/mm3. J Acquir Immune Defic Syndr. 2004;36:791–799.
Roul H, Mary-Krause M, Ghosn J, et al. CD4 cell count recovery after combined antiretroviral therapy in the modern cART era. AIDS. 2018;32:2605–2614.
Co MDT, Kilpatrick ED, Rothman AL. Dynamics of the CD8 T-cell response following yellow fever virus 17D immunization. Immunology. 2009;128:e718–e727.
Appay V, Dunbar PR, Callan M, et al. Memory CD8+ T cells vary in differentiation phenotype in different persistent virus infections. Nat Med. 2002;8:379–385.
Hamann D, Baars PA, Hooibrink B, et al. Heterogeneity of the human CD4+ T-cell population: two distinct CD4+ T-cell subsets characterized by coexpression of CD45RA and CD45RO isoforms. Blood. 1996;88:3513–3521.
Trushin SA, Bren GD, Badley AD. CD4 T cells treated with gp120 acquire a CD45R0+/CD45RA+ phenotype. Open Virol J. 2009;3:21–25.
Nájera O, González C, Toledo G, et al. CD45RA and CD45RO isoforms in infected malnourished and infected well-nourished children. Clin Exp Immunol. 2001;126:461–465.
Northfield JW, Loo CP, Barbour JD, et al. Human immunodeficiency virus type 1 (HIV-1)-Specific CD8+ TEMRA cells in early infection are linked to control of HIV-1 viremia and predict the subsequent viral load set point. J Virol. 2007;81:5759–5765.
Champagne P, Ogg GS, King AS, et al. Skewed maturation of memory HIV-specific CD8 T lymphocytes. Nature. 2001;410:106–111.