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Mattia CF Prosperi, PhD ahnven@yahoo.it Clinic of Infectious Diseases Catholic University of Sacred Heart Largo F. Vito, 1 – 00168 - Rome, Italy
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HIV-1 biology, treatment and resistance Epidemiology Surveillance of resistance trends Phylogenetics, HIV-1 evolution Clustering of HIV-1 mutations Intra-host analysis HIV-1 replication, natural genetic drift and selective drug pressure Differential equation modelling Optimising treatments with machine learning Prediction of HIV-1 co-receptor usage Prediction of in vivo HIV-1 virologic response to treatments Genotype-based models Treatment history-based models Perspectives Modelling time to viral rebound, and resistance emergence Modelling epidemic with complex networks
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Usually focus on specific problems Incidence of new infections Temporal trends of treatment efficacy Determinants of virologic or immunologic failure … Standard models Univariable analysis (chi-square, t-test, rank-sum) Linear, logistic regression Survival analysis (Kaplan-Meier, Cox proportional hazard) Often limited when considering predictive ability of the models Complex Network models HIV-1 is peculiar! not only sexually transmitted, long asymptomatic stage, high rate of evolution, integration into host genome, no natural eradication, rapid development of drug resistance… SIR-like models, adjusted for MANY other factors like the drug resistance emergence (Smith, Blower et al., Science, 2010)
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Several epidemiological studies carried out at our institute Temporal trends of drug resistance in Europe, considering different inhibition classes We assessed the temporal trends of resistance by fitting a linear model, adjusting for potential confounders such as age, gender, mode of HIV transmission, introduction of new drugs…
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Several scales of analysis Subtype evolution Transmission events (and drug resistance transmission) Not always the phylogenetic reconstruction is able to trace the epidemiological evidence, due to sparse sampling (intra-host evolution does not proceed at a constant rate) Note: phylogenetic analysis constructs a hierachy of sequences that represents the evolution from an hypothetical ancestor. Several techniques are available, from distance-based clustering to maximum likelihood, to bayesian clustering
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Probability of DR transmission intrinsic efficiency viral load frequency and modality of exposition
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≈12,000 group M subtype B HIV-1 polymerase sequences collected from Italian ARCA DB Maximum-likelihood parallel phylogeny, computationally intensive There is no methodology for automatic cluster identification New technique for partitioning a phylogenetic tree Depth first visit with constraints on node reliability and intra/inter-cluster patristic distance distributions Validated on a set of known transmission events
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11,541 sequences 9,855 patients Tree rooted on outgroup subtype J (ancient differentiation) 3D-hyperbolic geometry view Fractal dimension ≈1.6 Sustained level of differentiation
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A more recent calendar year of sequencing Patients from central-Italy vs those from northern- or southern-Italy Heterosexuals and homosexuals vs injecting drug users Younger patients Patients with more recent infections ( =14 years) Presence of resistance mutations in the protease gene
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Similar to phylogenetic analysis, but performed on transposed sequence alignments Useful to find associations among mutations under particular drug pressures Basis for structural analysis (Prosperi et al, ARHR 2009)
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Description of patient’s viral dynamics immune response Suitable for control theory (if equations could be treated analytically) Difficulties in dealing with prediction of therapy outcomes (see the constant η values, indeed they should change!) Difficulties modelling resistance outbreak (stochasticity, multi-strain models) System of differential equations
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New approach to account both for treatment administration and viral evolution Stochastic model for viral natural evolution in absence of treatment Calculation of instant resistance at different time points using in-vitro known drug susceptibility Usage of time-varying resistance [] in the differential equations and approximation with numerical solutions Usage of time-varying resistance [η= η(t)] in the differential equations and approximation with numerical solutions Calculation of number of virions in the next replication Selection of resistant strains with roulette wheel procedure (from genetic algorithm theory)
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Different combination treatments evaluated, along with therapy sequencing policies Although theoretically complex and sound, the model was not suitable for clinical practice (Prosperi et al, Bioinformatics 2008)
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We do not attempt to define an explicit model Extensive use of machine learning Linear and non-linear models Feature selection Robust validation In-vitro: prediction of HIV-1 co-receptor usage In-vivo: prediction of virological response to combination antiretroviral therapy (cART) With viral genotypic information Without viral genotypic information Designed for low/middle income countries
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HIV-1 can use two different co-receptors (CCR5/CXCR4) Entry inhibitors block only the CCR5 co- receptor The model helps to decide if a patient can be given an entry inhibitor or not, given his viral sequence Analysis using whole envelope region and other patient’s characteristics Logistic regression is a suitable model Not inferior to complex non-linear models Performance (with robust validation) up to 93% accuracy 0.77 sensitivity 0.93 AUC (Prosperi et al, ARHR 2009)
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Predicting the actual viral load changes following treatment switches is a challenging task Individual variability of immune response to infections add noise to the system Large number of possible therapeutic combinations leads to complex viral evolutionary pathways Other treatment-related factors such as pharmacokinetics and patient adherence to therapy play a crucial role in the control of virus replication and the development of resistance We focused on fixed patient’s follow up times (n-weeks of therapy)
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EuResist is a no-profit foundation (formerly EU-funded project), a consortium were hospitals, biology labs and universities cooperate Karolinska institute, University of Siena, University of Cologne, Max Planck Institute, IBM… It is the largest data base in the world comprising clinical, demographic and genomic data of HIV+ patients from national cohorts of Western Europe (at now Belgium, Italy, Germany, Sweden, Spain, Luxembourg) ≈34’000 patients ≈500’000 CD4 and ≈400’000 HIV-RNA measurements ≈100’000 antiretroviral therapies ≈31’000 HIV sequences (polymerase) Open to any kind of collaboration and data exchange
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Treatment Change Episodes (TCE) with a new cART Baseline HIV RNA load, CD4+ T cell counts Baseline HIV polymerase genotype and subtype Patient’s demographics (age, gender, ethnicity, mode of HIV transmission…) Previous drug usages (>1 year usage) for each drug class and each single drug 8-weeks and 24-weeks HIV RNA response Success defined as the achievement of 2 Log decrease from baseline at 8-weeks)
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The EuResist repository was queried and generated more than 3,000 TCE that were used for training and validating a prediction engine
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Statistical learning models Logistic Regression with higher-order interactions (LR) AIC stepwise selection Random Forests (RF) Feature importance evaluation with Strobl’s method Bayesian networks (BN) Three independent models were merged improving performance Extra sample error estimation Multiple ten-fold cross validation (MCV) Adjusted t-test on MCV performance distributions for model comparison External independent test set evaluation Comparison against human experts Comparison against rule-based algorithms (Stanford, Rega, ANRS)
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Statistical models Web-service Customised cART sequencing Patient’s Age, gender HIV RNA CD4 Experienced drugs HIV genotype
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The EuResist model outperforms the whole set of state-of-the-art techniques (i.e. rule-bases) and is as good as the world’s best human experts The average accuracy on validation is 76%, and AUC is 0.77 (Prosperi et al, Antivir Ther 2009)
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In high-income countries, guidelines recommend genotypic resistance testing (GRT) both before starting antiretroviral therapy (ART) and at ART failure Appropriate funding and/or facilities to perform GRTs may be not available in low-middle income countries (LMIC), leaving physicians to switch therapy based solely on the clinical/immunological conditions (sometimes even without virological monitoring) Treatment history (TH) is one of the most crucial factors to play a role in the response to a new treatment. Other important factors are virologic and immunologic monitoring
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GRT-based vs TH-based models were compared to see if there were sensible loss in performance Performance of the model were tested in extra-EU-like scenarios Tests on a larger set of TCE without the mandatory GRT baseline attribute were carried out No statistically significant differences found by comparing GRT and TH models
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We want to design and test a model that predicts viral load rebound over time using Patient’s viral genotypic information Patient’s clinical and demographic background Suitable models: Cox regression, random survival forests Need to define an appropriate goodness of fit Preliminary inquire on the EuResist DB gave a considerable number of training instances
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Also, we might be interested in a model that predicts drug resistance emergence
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Design and test an epidemic model for HIV-1 using complex networks Start from Science paper and from other models presented in literature New insights Capability to handle dynamics at a regional, national and international level Effective description of Infection incidence over different risk group strata Homogeneous vs heterogeneous mixing? Drug resistance trends Prevision of trends with the introduction of new inhibition classes Prevision of HIV-1 evolution with respect to drug resistance prevalence in the treatment-naive population Account for transmitted drug resistance from treatment-naive and treatment-experienced patients How much shall we go into details as concerns the intra/inter-host genetic HIV-1 evolution?
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