CROI 2015 Program and Abstracts

Abstract Listing

Oral Abstracts

612 A Generalized Entropy Measure of Viral Diversity for Identifying Recent HIV-1 Infections JuliaW. Wu ; Oscar Patterson-Lomba; Marcello Pagano Harvard School of Public Health, Boston, MA, US

Background: There is a need for incidence assays that accurately estimate HIV incidence based on cross-sectional specimens. Viral diversity-based assays have shown promises but are not particularly accurate. We hypothesize that certain viral genetic segments are more predictive of recent infection than others and aim to improve assay accuracy by employing classification algorithms that focus on the highly informative regions (HIR). Methods: We analyzed HIV gag sequences from a cohort in Botswana. Forty-two subjects newly infected by HIV-1 Subtype C were followed longitudinally through 500 days post- seroconversion. Using sliding window analysis, we screened for genetic segments within gag that best differentiate acute versus chronic infection. We used both non-parametric and parametric approaches to evaluate the discriminatory abilities of sequence segments. Segmented Shannon Entropy measures on HIRs were aggregated to develop generalized entropy measures to improve prediction of recency, defined as infection within past 6 months. With logistic regression as the basis for our classification algorithm, we evaluated the predictive power of these novel biomarkers and compared themwith recently reported viral diversity measures using Area under the Curve (AUC) analysis. To further improve prediction, we also explored other diversity-related biomarkers. Results: Change of diversity over time varied across different sequence segments within gag . The top 50%most informative segments were identified through non-parametric and parametric approaches. In both cases HIRs were in non-flanking regions and less likely in the p24 coding region. These new indices outperformed previously reported viral- diversity-based biomarkers. Including skewness in the assay further improved the AUC (see Figure 1), whereas other existing methods did not add much additional predictive power. Sensitivity analysis suggests that antiretroviral use had little impact on our assay performances. We also demonstrate that sensitivity and specificity depend on the datasets used and the underlying distributions of time-since-infection. This explains why we obtained different AUC values compared to previous studies.

Oral Abstracts

Comparing predictive performances of different algorithms. Conclusions: Our generalized entropy measure of viral diversity demonstrates the potential for improving accuracy when identifying recent HIV-1 infections. We also show that to properly compare and evaluate assay performances, the distribution of time-since-infection in the validation dataset needs to be accounted for. 626 Viral Load is Critical in Limiting False-Recent Results FromHIV Incidence Assays Reshma Kassanjee 1 ; Shelley Facente 2 ; Sheila Keating 3 ; Elaine McKinney 4 ; Kara Marson 2 ; Christopher D. Pilcher 2 ; Michael Busch 3 ; Gary Murphy 4 ; Alex Welte 1 The Consortium for the Evaluation and Performance of HIV Incidence Assays (CEPHIA) 1 South African DST/NRF Centre of Excellence in Epidemiological Modelling and Analysis (SACEMA), University of Stellenbosch, Stellenbosch, South Africa; 2 University of California San Francisco, San Francisco, CA, US; 3 Blood Systems Research Institute, San Francisco, CA, US; 4 Public Health England, London, United Kingdom Background: The cross-sectional use of (biomarker) tests for recent HIV infection in principle offers affordable, low-bias options for incidence estimation. For currently available assays, viral suppression (due to elite control or antiretroviral treatment) is predictive of long-term infections being (‘falsely’) classified as ‘recent’. Surveillance requires a not- too-transient ‘mean duration of recent infection’ (MDRI) – preferably at least 6 months. Assay readings below a chosen threshold are interpreted as indicating ‘recent’ infection, and any assay threshold sufficiently high to achieve a large MDRI inevitably incurs a substantial ‘false-recent rate’ (FRR), which should preferably be no higher than 1%. The performances of seven assays (BED, Limiting Antigen (LAg), Less-Sensitive (LS) Vitros, Vitros Avidity, BioRad Avidity, Architect Avidity, Geenius) were compared, in stand-alone form and in conjunction with a rule that low viral load is indicative of non-recent infection, allowing for varying assay and viral load thresholds. Methods: Specimens were used from a growing repository, previously described, of over 6000 specimens representing over 2000 subjects from studies in Africa, Brazil and the United States. Assay thresholds were adapted to produce the same MDRI, estimated by binomial regression. Within a model scenario inspired by the contemporary South African context, the net model population-level FRRs were estimated by combining FRR estimates for key subgroups (stratifying by time since infection and treatment status). Results: Table 1 shows the model population-level FRR for each assay, using an assay threshold that provides an MDRI of 200 days in each case, used either alone or with a viral load rule (using viral load thresholds of 75 and 1000 copies/ml).

181

CROI 2015