Predicting and Understanding HIV-1 Resistance to Broadly Neutralizing Antibodies. Anna Feldmann Max Planck Institute for Informatics

Transcription

1 Predicting and Understanding HIV-1 Resistance to Broadly Neutralizing Antibodies Max Planck Institute for Informatics

2 Motivation HIV-1 drug target space is limited Drug resistance emergence under HAART Consistent change of treatment for chronic patients Need for new drug targets De Clercq et al., Nature Reviews Drug Discovery

3 Motivation De Clercq et al., Nature Reviews Drug Discovery

4 Motivation Burton et al., Nature Reviews Immunology 2002 (new) target: envelope spike treatment option: broadly neutralizing antibodies (bnabs) De Clercq et al., Nature Reviews Drug Discovery

5 Broadly Neutralizing Antibodies PG9 (V1/V2-loop) (Walker et al., 2009) PGT128 (V3-loop) (Walker et al., 2011; Mouquet et al., 2012) Glycans 45-46G54W (CD4bs) (Diskin et al., 2011) 4E10 (MPER) (Cardoso et al., 2005) Burton et al., Science

6 Broadly Neutralizing Antibodies PG9 (V1/V2-loop) (Walker et al., 2009) Developed in 10-30% of infected patients PGT128 (V3-loop) BUT: too little too late (Walker et al., 2011; Mouquet et al., 2012) Glycans 45-46G54W (CD4bs) (Diskin et al., 2011) HIV-1 vaccine research: HIV-1 treatment: Induce bnab development bnab immunotherapy 4E10 (MPER) (Cardoso et al., 2005) Burton et al., Science

7 bnabs for HIV-1 Treatment 4

8 bnabs for HIV-1 Treatment 4

9 bnabs for HIV-1 Treatment 4

10 bnabs for HIV-1 Treatment Challenges 1. Resistance to bnabs (Will it work?) 2. Optimal Personalized Treatment (Which will work best?) Goal Given HIV-1 variants of the patient and a bnab Predict susceptibility or resistance 5

11 Data Neutralization assay data covering 4 major epitopes: V1/V2-loop: PG9, PG16 gp41-gp120: 35O22 V3-loop: CD4bs: PGT128, PGT121, , VRC01, VRC-PG04, NIH45-46, 3BNC117 Doria-Rose et al., J.Virol. 2009; Mouquet et al., PNAS 2012; Huang et al., Nature

12 Data Neutralization assay data covering 4 major epitopes: V1/V2-loop: PG9, PG envelope (Env) sequences with corresponding IC50 values per bnab gp41-gp120: 35O22 V3-loop: CD4bs: PGT128, PGT121, , VRC01, VRC-PG04, NIH45-46, 3BNC117 Doria-Rose et al., J.Virol. 2009; Mouquet et al., PNAS 2012; Huang et al., Nature

13 Building Prediction Model 1. Learning 6

14 Building Prediction Model 1. Learning 6

15 Building Prediction Model 1. Learning Binarize label into resistant ( ), if IC50 50μg/mL susceptible (+), if IC50 < 50μg/mL 6

16 Building Prediction Model 1. Learning Binarize label into resistant ( ), if IC50 50μg/mL susceptible (+), if IC50 < 50μg/mL 6

17 Building Prediction Model 1. Learning 2. Predicting 6

18 Prediction Performance Model performance was tested in a 10 times nested crossvalidation Overall high prediction performance (up to 0.84 AUC) Classifiers for the same epitope achieve similar performances 7

19 Prediction Performance Questions: Can we interpret the models? Can we interpret the classification result? 7

20 Understanding the Classifier Learnt discriminant positions of the classifiers susceptible resistant 8

21 Understanding the Result AA susceptible aa resistant Residues of the test sequence that contributed the most (strongest 5%) to the classification result of the PG9 classifier. 9

22 bnabs for HIV-1 Treatment Challenges 1. Resistance to bnabs (Will it work?) 2. Optimal Personalized Treatment (Which will work best?) Goal Given HIV-1 variants of the patient and a bnab Predict susceptibility or resistance 10

23 bnabs for HIV-1 Treatment Challenges 1. Resistance to bnabs (Will it work?) 2. Optimal Personalized Treatment (Which will work best?) Goal Given HIV-1 variants of the patient and a bnab Predict the corresponding IC50 value 10

24 Building Regression Models Setup: Same input data Instead of binarization, log transformation used Instead of classification, the corresponding IC50 value is predicted using support vector regression 11

25 Building Regression Models Setup: Same input data Instead of binarization, log transformation used Instead of classification, the corresponding IC50 value is predicted using support vector regression Result: Positive correlations of for all bnabs apart from 35O22 11

26 Continuous Drift Towards Resistance Studied population: 40 Caucasian men having sex with men, subtype B similar distribution of viral loads and CD4-T cell counts b12, VRC01, VRC03, NIH45-46G54W, PG9, PG16, PGT121, PGT128, PGT145 Over 20 years ( / / ) French ANRS PRIMO and SEROCO cohorts 12

27 Continuous Drift Towards Resistance Studied population: 40 Caucasian men having sex with men, subtype B Questions: Only for subtype B? Does it hold for global viral population? What about other time periods? similar distribution of viral loads and CD4-T cell counts b12, VRC01, VRC03, NIH45-46G54W, PG9, PG16, PGT121, PGT128, PGT145 Over 20 years ( / / ) French ANRS PRIMO and SEROCO cohorts 12

28 Time Analysis over LANL Env Seqs Setup: Predicted IC50 value using support vector regression models before ART ART cocktail (NRTIs) ART monotherapy LPV/r HAART cocktail with PIs Time covered: Paper vs our time partitioning 1987 ~ Env Seqs from LANL, different subtypes 1981 Maraviroc/ Raltegravir Doria-Rose et al., J.Virol. 2009; Mouquet et al., PNAS 2012; Huang et al., Nature

29 Time Analysis over LANL Env Seqs NIH45-46 Continuous trend towards resistance for all antibodies but PG9 and PG16 (Bonferroni correction threshold 0.05/22=~0.002, umbrella test) Considering non-b subtype (vs B): similar trend, but PGT121, PGT128 not significant anymore (Bonferroni correction threshold 0.05/22=~0.002, umbrella test) PG9 log(ic50) Over the whole available time period Time periods 14

30 Conclusion Well performing classification models for HIV-1 resistance to bnabs Reliable classifiers identifying potential binding site residues Visualization of data relationships and motif logos improve biological understanding of the classification result Regression models provide more fine-grained predictions Useful as recommendation device for bnab combination therapy Extendable to new HIV-1 bnabs or HCV bnabs 15

31 Thanks to... Max Planck Institute for Informatics, Saarbrücken Thomas Lengauer Nico Pfeifer Alejandro Pironti Nora Speicher and Rolf Kaiser 16

32 Thanks to... Max Planck Institute for Informatics, Saarbrücken Thomas Lengauer Nico Pfeifer Alejandro Pironti Nora Speicher you for listening! you for listening. Questions? and Rolf Kaiser 16