A Classifier to Predict Viral Control After Antiretroviral Treatment Interruption in Chronic HIV-1-Infected Patients.

OBJECTIVES

To construct a classifier that predicts the probability of viral control after analytical treatment interruptions (ATI) in HIV research trials.

METHODS

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.

RESULTS

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%).

CONCLUSIONS

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.