CROI 2015 Program and Abstracts

Abstract Listing

Poster Abstracts

611 Integrase S119P Mutation Correlates With Disease Progression in HIV-1 Naïve Patients Daniele Armenia 1 ; Maria Mercedes Santoro 1 ; Caterina Gori 2 ; Emanuele Nicastri 2 ; Antonio Cristaudo 3 ; Massimo Andreoni 4 ; Andrea Antinori 2 ; Zeger Debyser 5 ; Carlo-Federico Perno 2 ; Francesca Ceccherini- Silberstein 1 1 UniversityofRomeTorVergata,Rome, Italy; 2 L.SpallanzaniHospital,Rome, Italy; 3 SanGallicanoDermatological Institute,Rome, Italy; 4 UniversityHospitalTorVergata,Rome, Italy; 5 KatholiekeUniversiteitLeuven,Leuven,Belgium Background: Recent finding showed that specific HIV integrase (IN) polymorphisms at positions S119, T122 and R231 affect integration site targeting in the host genome. Moreover, mutations at position S119 have been associated with HLA selection. We thus characterized the prevalence of IN polymorphisms at these positions and their association with CD4 cell count and HIV-RNA in HIV-1 infected HAART-naïve patients. Methods: The association between IN polymorphisms at position S119, T122 and R231 with CD4 count and HIV-RNA has been evaluated in a cohort of HAART-naïve patients with IN genotypic resistance test (GRT). In patients with at least 2 HIV-RNA/CD4 measurements before HAART start, survival analyses were used to evaluate the event of starting HAART or to reach a CD4 cell count <350 cells/ m l. Results: 625 patients, mainly HIV-1 subtype B infected (B: 71.2%; CRF_02AG: 7.2%, F: 6.6%, C: 3.7%, other subtypes: 11.3%) have been enrolled. The following IN polymorphisms were found: at position 119, P (19.2%), G (9.0%) and A/R/T (8.7%); at position 122, I (20.8%) and S/V (2.1%); at position 231, K (1.8%). One hundred sixty-nine patients, with available follow-up before treatment start (median [IQR] time: 13.1 [5.5-24.1] months) were longitudinally analysed. Baseline CD4 count and HIV-RNA were 458 (IQR: 376-600) cells/ m l and 4.6 (IQR: 4.1-5.0) log 10 cps/mL, respectively. At baseline, no significant associations between polymorphisms at positions 119/122/231 with CD4 count and HIV-RNA were found. The median [95% CI] time of starting HAART or to reach a CD4 count <350 cell/ m l was 15.8 [12.8-18.6] months. Interestingly, patients with 119P mutation showed a shorter time to reach the end-point compared to those with G or S wt residues (9.3 [5.6-13.0] vs. 14.7 [9.4-20.0] vs. 16.8 [14.0-19.7] months, p=0.020). Cox multivariable analyses (adjusting for age, gender, subtype, baseline CD4 and HIV-RNA) confirmed that, among all polymorphisms found at positions 119/122/231, only S119P had a higher hazard to achieve the event compared to those with 119S wt (RH [95 CI%]: 2.0 [1.2-3.2], p=0.006). Conclusions: IN position S119, beyond the observed correlations with integration site targeting and host immune response, might define patients with accelerated disease progression. Further investigations on polymorphisms at position S119 are necessary to understand our observation.

TUESDAY, FEBRUARY 24, 2015 Session P-M1 Poster Session

Poster Hall

2:30 pm– 4:00 pm Nucleic-Acid–Based Detection of HIV 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.

Poster Abstracts

Comparing predictive performances of different algorithms.

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CROI 2015