Introduction

1. Poster 54 The Relationship between Susceptibility to Enfuvirtide of Baseline Viral Recombinants and Polymorphisms in the Env Region of R5-tropic HIV-1 XII International HIV Drug Resistance Workshop Cabo Del Sol, Los Cabos, Mexico June 10-14, 2003 C. Su1, G. Heilek-Snyder1, D. Fenger1, P. Ravindran1, K. Tsai1, N. Cammack1, P. Sista2, S. Chiu1 1Roche, Palo Alto, CA, USA; 2Trimeris, Inc., Durham, NC, USA 3Ins 3Ins 3Ins 3Ins T T T T A L M V A L M V 1.59 X X None None X X None None [2] [2] 0.2 [2] [2] 1.26 0.1 1 0 24 24 24 24 42 42 42 42 0.79 -0.1 0.63 -0.2 Effects 0.50 -0.3 0.40 -0.4 0.32 -0.5 N N S S D H X D H X N N S S [1] [1] D H X D H X [1] [1] 0.25 -0.6 M M I L T V X I L T V X M M I L T V X I L T V X M24I I270I I245A V301I L209L L289L L210L L264L N42S* T130L L263F T101S A306T A212A E220E V267A E125D E148D W285L S293G H331N R229Q T130T* G215G G221G Q294Q E151A* W286G N305D* 24 24 24 24 mean= mean= - - 0.027 0.027 mean= mean= - - 0.027 0.027 Non-B Cluster (n=12) n=66, n=66, n=66, n=66, 100 I L V X I L V X I L V X I L V X M M M M dev=11.0 dev=11.0 dev=11.0 dev=11.0 80 mean= 0.573 mean= 0.573 mean= 0.149 mean= 0.149 mean= 0.573 mean= 0.573 mean= 0.149 mean= 0.149 60 n=43 n=43 n=14 n=14 n=43 n=43 n=14 n=14 Percent 40 mean= 0.235 mean= 0.235 mean= mean= - - 0.036 0.036 mean= 0.235 mean= 0.235 mean= mean= - - 0.036 0.036 dev=8.4 dev=8.4 dev=2.0 dev=2.0 dev=8.4 dev=8.4 dev=2.0 dev=2.0 20 0 n=217 n=217 n=36 n=36 n=217 n=217 n=36 n=36 dev=29.4 dev=29.4 dev=4.0 dev=4.0 I270I dev=29.4 dev=29.4 dev=4.0 dev=4.0 M24I N42S I245A V301I L209L L210L T130L L263F L264L L289L T130T T101S E220E V267A E151A A212A E148D E125D R229Q G215G A306T S293G H331N W285L N305D G221G Q294Q W286G Min. -1.398 Median 0.185 Mean 0.198 Max. 1.576 SD 0.421 Geometric Mean 1.578 B Cluster (n=365) 80 100 80 60 Percent 40 60 20 0 I270I M24I N42S I245A V301I L209L L210L L264L L289L L263F T130T T130L T101S A306T E151A A212A E220E V267A E125D E148D R229Q W285L S293G N305D H331N G215G G221G Q294Q W286G Percent (%) 40 20 0 B Cluster Non-B Cluster (N=365) (N=12) -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 [1] 180 (49%) 12 (100%) TORO 2 (N, %) LFCIC50 [1] Male (N, %) 336 (91%) 5 (42%) White (N, %) 335 (92%) 8 (75%) Age (median, years) 41 40 [1] Region (N, % in Beglium) 3 (0.82%) 7 (58%) BL HIV-1 RNA (median, log copies/ ml) 5.20 5.43 10 3 94 89.5 BL CD4+ T cells (median, cell/mm ) 3.8 4 Number of ARV in OB (mean) [2] GSS at entry (mean) 1.7 1.9 [2] 1.6 1.9 PSS at entry (mean) Prior Lopinavir/r use (%) 60 50 Adherence to ENF (mean) 96% 100% 94% 96% Adherence to all regimen (mean) [1]: Differences between clusters were statistically significant (p-value<0.05) from a chi-square test. [2]: Genotypic and phenotypic sensitivity scores (GSS and PSS) represent the number of drugs in the regimen to which virus was sensitive, excluding ENF. 6 377 13 12 57 114 105 62 11 68 15 280 13 11 33 5 5 25 27 39 17 225 152 32 94 37 30 48 11 167 10 1.00 6.31 0.80 3.98 0.60 2.51 0.40 corrected by overall average 1.58 0.20 1 0.00 0.63 -0.20 B Cluster Non-B Cluster Parameter (n=365) (n=12) Diff. 95% CI P-value 0.40 -0.40 50 BL ENF susceptibility (LFCIC50) 0.25 -0.60 TORO 2 only (mean) 0.208 -0.109 -0.317 (-0.558, -0.076) 0.010 Average LFCIC 0.16 -0.80 TORO 1 & 2 (mean) 0.236 -0.109 -0.346 (-0.602, -0.089) 0.009 -0.10 -1.00 Change from BL HIV-1 RNA (log10 copies/ml) * K77G M54L * M115L * E125E A243T N305D I318V Overall * V2^ * V28A * I62V * V69L * V72L * N105S * Q147L E151A * R259H * G3A * L44M T130T I135I * G3GT I4I L7L * M24M * N42S * E49D * T130V I135L * G314W [1] TORO 2 only (LSM) -1.556 -2.315 -0.759 (-1.409, -0.108) 0.023 [2] TORO 1 & 2 (LSM) -1.557 -2.327 -0.771 (-1.402, -0.140) 0.017 HIV-1 RNA <50 Copies/ml % responder TORO 2 only 10 16.67 0.362 TORO 1 & 2 13.7 16.67 0.675 HIV-1 RNA <400 Copies/ml % responder Predictors Forms of # of Tree Mean TORO 2 only 27.78 50 0.112 TORO 1 & 2 30.96 50 0.206 (ENV regions) Predictors Splits Residual ³ HIV-1 RNA log decrease % Deviance * 10 responder gp41 only a.a. position 4 0.144 TORO 2 only 40.56 66.67 0.128 gp41 only indicator 4 0.147 TORO 1 & 2 45.45 66.67 0.237 gp120 only a.a. position 4 0.143 [1] Least square means were calculated from ANCOVA model with covariates: Number of ARV, BL CD4, Prior Lopinavir/r use, GSS, PSS, and BL viral load. gp120 only indicator 6 0.154 [2] Least square means were calculated from ANCOVA model with covariates: BL CD4, Prior Lopinavir/r use, GSS, PSS, Adherence to all treatments, and BL viral load. gp120+gp41 a.a. position 4 0.139 gp120+gp41 indicator 5 0.141 *: the mean deviance at the root note is 0.176 377 15 13 7 37 5 5 7 6 92 22 5 5 13 10 6 5 5 17 8 6.31 0.80 3.98 0.60 2.51 0.40 corrected by overall 1.58 0.20 1 0.00 0.63 -0.20 50 0.40 -0.40 0.25 -0.60 Average LFCIC 0.16 -0.80 D61 * T50I * M25I * I330V * L124I * L486I D322N * M426I * G22W * P297L * L475X * S361T * N470X * K492X Overall * W471X * N287T * K340T * L444M * Q256H Cheng Su Roche Palo Alto 3431 Hillview Avenue Palo Alto, CA 94304 Tel: 650-855-6510 Fax: 650-855-5627 E-mail: cheng.su@roche.com Figure 4: Pruned tree with 4 splits for predicting BL ENF susceptibility based on BL gp41 a.a. sequences. • Among the gp41 and gp120 amino acids positions listed in Figure 2a and 2b, the best descriptors for baseline variability of ENF susceptibility were positions 42 (R2=9.3%) and 3Ins (insertion at position gp41 a.a. 3) (R2=9.2%). • The gp160 amino acid positions included in the final multi-factor ANOVA model were 3, 3Ins, 24, 42, 54, 69, 77, 130, 135, 243 for gp41 and 297, 340, 361, 471 for gp120. This model had a R2 of 57.6% with 85 degrees of freedom (df). If only the gp41 positions were used, the ANOVA model had a R2 of 48.0% (83% of 57.6%) with 59 df, demonstrating a dominating contribution from gp41. • Cluster Analysis Results • Cluster analysis identified two clusters of 365 (Cluster 1) and 12 (Cluster 2) recombinants. Based on the novel methods of Fenger el at.5, recombinants in Cluster 1 and Cluster 2 were identified as B subtype and non-B subtype, respectively. They can be also referred to as B Cluster and non-B Cluster. • Table 1: Demographics, baseline characteristics and adherence The S-Plus RPART routines of Therneau et al.4 were used to build trees. The final or optimal trees were determined by pruning. The within-node sum of square, or deviance, was used as the measure of variability in trees. Figure 3:Polymorphism frequency in clusters and their effects on BL ENF susceptibility. Introduction Here we present the results of a study of the relationship between susceptibility to enfuvirtide (ENF) of baseline (BL) viral recombinants and polymorphisms in the env region of R5-tropic HIV-1. We explored such relationships by mining the TORO 1 and TORO 2 clinical trial databases using both univariate and multivariate statistical methods. Analysis of variance (ANOVA) was used to explore this relationship at each individual gp160 position. Cluster analysis and tree regression were used to explore the joint relationship between the entire gp160 sequence and ENF susceptibility1. Results Summary of BL ENF susceptibility Figure 1: Distribution of BL ENF susceptibility and summary statistics • The a.a. positions in the green ovals were used for tree splitting. The blue and red color indicate an amino acid as wild type or polymorphism respectively. Rectangles indicate terminal nodes. Numbers in each terminal node are the LFCIC50 average, the number of cases in the node and the deviance of the node. [1]: 62 cases of ‘S’ at postition 42, [2]: 297 cases of no insertion at position 3. • This tree had an ~20% reduction in deviance. G3GT was associated with decreased BL ENF susceptibility, N42S and M24M was associated with increased BL ENF susceptibility, which was consistent with the results in Figure 2a. Methods HIV-1 Envelope Genotypic and Phenotypic Data Using the GeneSeqTM and PhenoSenseTM HIV Entry assays, BL HIV-1 envelope (complete gp160) amino acid sequences and ENF susceptibility were generated for 377 R5 tropic recombinant Env pools from patients prior to their receiving ENF plus an optimized background (OB) regimen. Needleman-Wunsch algorithm2 was used to align the gp160 sequences to JRCSF reference. Insertions occurred at 84 positions. Ambiguous amino acids were annotated as “X”. ENF susceptibility was expressed as log10-scaled fold change IC50 (LFCIC50) relative to JRCSF reference. ANOVA One-way ANOVA was used as a filter for detecting potentially “interesting” polymorphisms rather than as a tool for testing a hypothesis. At each gp160 position, including insertions and deletions, ANOVA was performed to compare the polymorphisms to JRCSF reference in terms of the LFCIC50 averages. Dunnett’s adjustment3 was applied for multiple comparisons within each gp160 position. For those polymorphisms with Dunnett’s p-values < 0.05, individual effects on ENF susceptibility were expressed as LFCIC50 averages corrected by the overall LFCIC50 average and the joint effects were studied through multi-factor ANOVA with backward model selection using a significance level to stay of 0.05. Cluster Analysis The agglomerative hierarchical clustering algorithm was applied using the number of differing amino acids between two sequences as the distance metric. The optimal number of clusters was selected to achieve the maximum Silhouette value (Rousseeuw 1987). The frequencies of polymorphisms were compared among the clusters; the genotypic difference among the clusters were represented by those polymorphisms which were distributed most differently across the clusters. BL ENF susceptibility was compared among the clusters using a Student’s t-Test. Clinical efficacy outcomes were compared among the clusters using analysis of covariance (ANCOVA) with baseline characteristics as covariates. Regression Tree Analysis Trees predicting ENF susceptibility were built using gp41, gp120 or gp41 + gp120 amino acid sequences as predictors. Initially, amino acid positions were used in the tree model as predictors. Such an approach may favor the selection of more variable amino acid positions. To study this possibility, trees were also built using indicator variables created for all amino acids at each position. • Polymorphisms displayed (with the exception of T130T) are those whose frequency in clusters differed by >50% or >25 fold. The ‘*’ indicates a significant effect on BL ENF susceptibility (Figure 2a). • Non-B Cluster had higher frequencies of polymorphisms N42S (75% vs. 16%), E151A (92% vs. 23%), N305D (75% vs. 11%) and T130T (42% vs. 9%), which were all associated with increased ENF susceptibility (Figure 2a). • Preliminary cluster analysis on gp120 sequences did not produce clusters that associated with ENF susceptibility. Further elucidation of the clusters is ongoing. • Cluster analysis on B subtype gp41 sequences did not produce clusters that associated with ENF susceptibility. • Analyses were also conducted using only the data from B subtype samples versus the complete data. Though there were some differences in results, the polymorphisms identified in multi-factor ANOVA and tree regression analysis were similar. Regression Tree Analysis Results Conclusions Poster 54 - XII International HIV Drug Resistance Workshop ANOVA Results Figure 2a: Effects of significant gp41 polymorphisms (Dunnett's p-value <0.05). • Data mining of genotypic and phenotypic data have revealed an association between polymorphic sites in HIV Env and BL ENF susceptibility in HIV-1 R5 tropic recombinants. This association was predominantly caused by gp41 genotype and explained a fraction of the baseline variability of ENF susceptibility. • Gp41 polymorphisms G3GT, N42S and M24M were identified by both ANOVA and regression tree analysis as associated with ENF susceptibility. In addition, regression tree models provided insights into how they interacted together to affect BL ENF susceptibility. • Cluster analysis on R5 tropic gp41 viral sequences resulted in divisions of two clusters, non-B subtype (n=12) and B subtype (n=365) recombinants. Multiple virological response metrics were compared between clusters. The only significant differences were a higher BL ENF susceptibility and larger drop of viral load from baseline for the non-B Cluster. (See * under Table 2). However, the subtype B group clearly also demonstrated clinically meaningful reductions in plasma HIV-1 RNA. The differences in ENF susceptibility were associated with the higher frequencies of a combination of polymorphisms in the non-B Cluster. • Clusters derived from a preliminary analysis on gp120 sequences, while not associated with BL ENF susceptibility, will be investigated in future studies. • Our studies demonstrated the utility of combining multiple statistical methods to study a large number of potential factors in exploring the relationship between Env phenotype and genotype. Table 2: Summary of BL ENF susceptibility and the main efficacy endpoints * The ‘*’ indicates the effect is greater than 0.176, which is equivalent to greater than 1.5 fold difference from the overall geometric mean (GM) of fold change IC50. V2^ indicates deletion at a.a. position 2. G3GT indicates insertion T at a.a. position 3. The error bars indicate the standard error of the mean. The numbers on the top indicates the number of recombinants for each polymorphism. The numbers on the right y-axis indicate the fold change from the overall GM. Table 3:Summary of Regression Tree Analysis Figure 2b: Effects of significant gp120 polymorphisms (Dunnett's p-value <0.05). *: This analysis differs from the previous results by Greenberg et al.6 in using only the R5 tropic data and a novel method for subtype assignments. The following results would be best confirmed by analysis with a larger number of non-B samples: • Non-B Cluster (n=12) has higher BL ENF susceptibility (p-value =0.01) than B Cluster (n=365). • Patients with non-B subtype viruses experienced a larger drop in viral load (p-value=0.023) than patients with B subtype viruses. • Trees built from different forms of gp41 predictors were similar. Trees built from different forms of gp120 predictors were different, indicating that the tree algorithm favored the more variable gp120 a.a. positions. • The inclusion of gp120 predictors in tree building did not significantly improve the tree performance in terms of reducing mean residual deviance. References 1. A. Sevin, V. Degruttola, et al. The journal of infectious disease, 2000 2. SB Needleman, CD Wunsch, J Mol Biol 1970; 48(3):443-53. 3. CW Dunnett, J American Statistical Association, 1995, 50: 1096 -1121. 4. TM Therneau, EJ, Atkinson, Rochester, MN: Mayo Clinic, 1997. 5. D. Fenger, C. Su, et al. HIV-1 Subtype Analysis Using gp41 Sequences from Patients in the Enfuvirtide Phase III Clinical Trials, in preparation. 6. ML Greenberg, T Melby, et al. Baseline and On-Treatment Susceptibility to Enfuvirtide Seen in TORO 1 and TORO 2 Through 24 Weeks, presented at 10th CROI, Boston, Feburary 2003.

2. XII International HIV Drug Resistance Workshop Cabo Del Sol, Los Cabos, Mexico June 10-14, 2003 The Relationship between Susceptibility to Enfuvirtide of Baseline Viral Recombinants and Polymorphisms in the Env Region of R5-tropic HIV-1 C. Su1, G. Heilek-Snyder1, D. Fenger1, P. Ravindran1, K. Tsai1, N. Cammack1, P. Sista2, S. Chiu1 1Roche, Palo Alto, CA, USA 2Trimeris, Inc., Durham, NC, USA Poster 54 - XII International HIV Drug Resistance Workshop, Cabo Del Sol, Los Cabos, Mexico, June 10-14, 2003