1 / 1

Poster # 388 CROI 2009 8-11 Feb Montreal, Canada

Association of HIV-1 Co-receptor Tropism with Immunologic and Virologic Parameters in HIV-1 infected, Treatment-Naïve Subjects in ACTG 384

alena
Download Presentation

Poster # 388 CROI 2009 8-11 Feb Montreal, Canada

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Association of HIV-1 Co-receptor Tropism with Immunologic and Virologic Parameters in HIV-1 infected, Treatment-Naïve Subjects in ACTG 384 G Skowron1, E Chan2, J Weidler3, G Robbins4, V Johnson5, J Spritzler2, D Asmuth6, R Gandhi4, Y Lie3, E Coakley3, R Pollard6, for the AIDS Clinical Trials Group 384 protocol team. 1 Roger Williams Medical Center/The Miriam Hospital ACTU, Providence, RI, and Boston Univ School of Medicine, Boston, MA, USA, 2 Harvard School of Public Health, Boston, MA, USA, 3 Monogram Biosciences, Inc., South San Francisco, CA, USA, 4 Massachusetts General Hospital, Boston, CA, USA, 5 Birmingham VA Medical Center and University of Alabama at Birmingham School of Medicine, Birmingham, AL, USA, 6 University of California Davis Medical School, Sacramento, CA, USA Gail Skowron, M.D. Division of Infectious Diseases Roger Williams Medical Center 825 Chalkstone Ave, Providence, RI 02908 gail_skowron@brown.edu telephone: 401-456-2437 FAX 401-456-6839 Poster # 388 CROI 2009 8-11 Feb Montreal, Canada Results Background Discussion Viral co-receptor tropism is associated with disease progression in untreated chronic HIV-1 infection. The relationships between tropism and other pre-treatment factors, such as replication capacity and lymphocyte subsets, have not been described. Higher baseline (BL) viral replication capacity (RC) has previously been associated with higher HIV-1 RNA and CD4 activation %, and lower CD4 and CD4 memory counts. Figure 1. Baseline Immunologic and Virologic Parameters by Baseline Tropism Table 3. Linear Regression Model of Baseline CD8 activation percent* 1. In this analysis of selected treatment-naive subjects participating in ACTG 384, BL DM/X4 tropism was associated with lower BL memory, naive and total CD4 count, higher BL HIV-1 RNA, higher replication capacity, and higher CD4 and CD8 percent activation (Table 2). 2. We previously demonstrated, in an analysis of a larger subset of subjects participating in ACTG 384, that higher baseline RC was significantly associated with higher HIV-1 RNA, higher CD4 cell activation, lower CD4 cell count, and lower CD4 memory cell count (Skowron, 2009). 3. In a linear regression model (Table 3), higher baseline CD8 activation percent was significantly associated with higher baseline viral load (RNA), controlling for tropism, CD4 cell count and replication capacity at baseline. 4. In a linear regression model (Table 4), higher baseline CD4 activation percent was significantly associated with non-R5 tropism, lower baseline CD4 cell count, and higher baseline replication capacity (RC), adjusted for BL RNA. We had previously noted an association between higher baseline RC and higher baseline CD4 activation percent (r = 0.23, p < 0.0001). 5. In our previous analysis, we constructed a multivariable model, demonstrating that an increase in CD4 cell count from baseline to week 48 was associated with lower baseline CD4 memory count and higher baseline CD4 naive percent (p=0.004, p=0.015, respectively), adjusting for baseline RC, baseline CD4 and CD8 count, baseline RNA, and the interaction between baseline CD4 and baseline RC. The current analysis adds baseline tropism to this model (Table 5), but only CD4 naive percent remains significant, i.e., an increase in CD4 cell count is associated with higher baseline CD4 naive percent, controlling for the above baseline covariates and tropism. 6. These data raise provocative questions regarding the interplay between RC and DM/X4 tropism, in depleting naive and memory CD4 cells and increasing CD4 activation, and the resultant effect on CD4 recovery in patients on effective ART. Methods Table 4. Linear Regression Model of Baseline CD4 activation percent* Using Monogram’s original Trofile Assay, we determined HIV-1 co-receptor tropism on pre-treatment plasma samples from 230 HIV-infected, treatment-naive subjects enrolled in ACTG 384 and selected on CD4 and viral response status (a subset of subjects with sample available from the group described in Skowron, 2009). Of these, 210 had undetectable HIV-1 RNA (50) at week 48. HIV-1 RNA, CD4 and CD8 subsets and RC were used to investigate BL and week 48 associations with tropism. Continuous outcomes were compared between R5 vs. DM/X4 groups with Wilcoxon Rank Sum tests. Linear and logistic regression models were also applied when controlling for BL covariates. Table 2. Baseline Immunologic and Virologic Parameters by Baseline Tropism Table 5. Linear Regression Model of Change in CD4 cell count from BL to wk 48* Results Table 1. Baseline Characteristics • References • Skowron G, Spritzler JG, Weidler J, et al. Replication Capacity in Relation to Immunologic and Virologic Outcomes in HIV-1 infected, Treatment-Naïve Subjects, • J Acquir Immune Defic Syndr, 2009, in press. *DM and X4 subsets combined in this analysis (DM/X4) *restricted to 210 subjects who had virologic suppression (RNA  50) at wk 48 This project was supported by SBIR Grant Number R44AI050321 from the National Institute of Allergy and Infectious Diseases (NIAID) to Monogram Biosciences and NIAID grant numbers AI38855, AI27659, AI38858, AI25879, AI27666. The ACTG 384 study was also supported in part by Agouron/Pfizer, Bristol Myers Squibb, and GlaxoSmithKline. *in a linear regression model adjusting for BL RC

More Related