Exploiting HIV-1 protease and reverse transcriptase cross-resistance information for improved drug resistance prediction by means of multi-label classification
© Riemenschneider et al. 2016
Received: 30 November 2015
Accepted: 20 February 2016
Published: 29 February 2016
Antiretroviral therapy is essential for human immunodeficiency virus (HIV) infected patients to inhibit viral replication and therewith to slow progression of disease and prolong a patient’s life. However, the high mutation rate of HIV can lead to a fast adaptation of the virus under drug pressure and thereby to the evolution of resistant variants. In turn, these variants will lead to the failure of antiretroviral treatment. Moreover, these mutations cannot only lead to resistance against single drugs, but also to cross-resistance, i.e., resistance against drugs that have not yet been applied.
662 protease sequences and 715 reverse transcriptase sequences with complete resistance profiles were analyzed using machine learning techniques, namely binary relevance classifiers, classifier chains, and ensembles of classifier chains.
In our study, we applied multi-label classification models incorporating cross-resistance information to predict drug resistance for two of the major drug classes used in antiretroviral therapy for HIV-1, namely protease inhibitors (PIs) and non-nucleoside reverse transcriptase inhibitors (NNRTIs). By means of multi-label learning, namely classifier chains (CCs) and ensembles of classifier chains (ECCs), we were able to improve overall prediction accuracy for all drugs compared to hitherto applied binary classification models.
The development of fast and precise models to predict drug resistance in HIV-1 is highly important to enable a highly effective personalized therapy. Cross-resistance information can be exploited to improve prediction accuracy of computational drug resistance models.
KeywordsInfectious diseases Machine learning Retrovirus HIV therapy
According to estimations by the World Health Organization (WHO) around 35 million people are HIV infected in 2013 worldwide. Moreover, 2.1 million individuals were newly infected in 2013. Although antiretroviral therapy has been steadily improved in the last decades, resistance against antiretroviral drugs is still a serious clinical problem. Driving force of drug resistance is the genetic variation of the virus caused by the high mutation rate paired with a fast replication cycle .
An HIV-1 therapy typically contains a combination of three or even four active pharmaceutical ingredients from different drug classes, thus inhibiting different steps in the replication cycle of HIV. Classical therapies employ two nucleoside reverse transcriptase inhibitors (NRTIs) combined with one non-nucleoside reverse transcriptase inhibitor (NNRTI) or one protease inhibitor (PI). New drug classes, such as Integrase Inhibitors (INIs), and entry inhibitors, enable alternative therapies when resistance mutations are already present. PIs prevent viral replication by inhibiting the activity of HIV-1 protease, an enzyme used by the viruses to cleave nascent polypeptides into functional proteins. They are designed to have a high affinity to the catalytic center of the HIV protease, thereby hampering its enzymatic activity. NRTIs and NNRTIs inhibit the activity of the reverse transcriptase (RT). NRTIs are nucleoside analogs, and therefore compete for the RT with the natural nucleosides. An incorporation of an NRTI leads to a premature termination of the viral genome replication. In contrast, NNRTIs are non-competitive inhibitors of the RT. They inhibit the movement of protein domains of the RT that is needed to carry out the process of DNA synthesis.
A combination therapy is highly effective in suppressing viral replication, however, the emergence of resistant HIV-1 variants frequently occurs. An important aspect of resistance mutations, namely the occurrence of cross-resistance, has been addressed only recently. Cross-resistance has been frequently found in HIV, leading to resistance not only against a drug from the current treatment, but also to other not yet applied drugs from the same class. These cross-resistance mutations have been described for almost all drug classes, e.g. for PIs, NRTIs, and NNRTIs [2, 3].
In the recent years, machine learning algorithms have improved the development of mathematical models to predict drug resistance, ranging from simple mutation tables over decision trees , support vector machines , rule-based systems  to random forests . In another study, Brandt et al.  used multi-label approaches to predict therapy outcome without genotypic information of the virus. Today, the most widely applied tools for resistance prediction are geno2pheno  and HIVdb . Geno2pheno applies support vector machines to classify sequences as resistant or susceptible. The HIVdb algorithm uses penalty scores for each mutation within a sequence. The scores are summed up in order to reflect the level of resistance against a certain drug with levels ranging from susceptible to high-level resistance.
However, the use of cross-resistance profiles to improve resistance prediction was hitherto rather neglected and have been only applied in a few studies so far [11, 12]. We were the first to exploit cross-resistance information to improve computational drug resistance prediction by means of multi-label learning . We demonstrated an increased prediction accuracy for six nucleoside analogues by using multi-label classification (MLC) methods, namely classifier chains (CCs) and ensembles of classifier chains (ECCs) in combination with cross-resistance information. In the current study, we applied the MLC methods described in Heider et al.  on protease sequences and non-nucleoside reverse transcriptase sequences to investigate whether higher prediction capabilities compared to binary classification could be achieved.
Protein sequences of the HIV-1 protease (PR) and reverse transcriptase (RT) originated from subtype B strains with data for seven PIs (RTV: Ritonavir, IDV: Indinavir, SQV: Saquinavir, NFV: Nelfinavir, APV: Amprenavir, ATV: Atazanavir, LPV: Lopinavir) and three NNRTIs (NVP: Nevirapine, EFV: Efavirenz, DLV: Delavirdine) with IC50 ratios were collected from the HIV Drug Resistance Database . The data was separated into susceptible and resistant by drug-specific cutoffs according to Rhee et al. . We removed sequences from the datasets for which no resistance information was available and excluded ATV and LPV from our classification approach, since too many sequences lacked IC50 information, resulting in 662 PR sequences and 715 RT sequences with complete resistance profiles. The protein sequences were then encoded and normalized by Interpol  with default settings. The sequences can be found in Additional file 1.
In the current study, we used classifier chains (CCs) and ensembles of classifier chains (ECCs)  according to Heider et al. . The CC method learns m binary classifiers linked along a chain, each time extending the feature space by all previous labels in the chain. Realizing that the order of labels in the chain may influence the performance of the classifier, and that an optimal order is hard to anticipate, Read et al.  propose the use of an ensemble of CC classifiers. This approach combines the predictions of different random orders and, moreover, uses a different sample of the training data to train each member of the ensemble. ECCs have been shown to increase prediction performance over CCs by effectively using a simple voting scheme to aggregate predicted relevance sets of the individual chains. For MLC we applied random forests  and logistic regression models as base classifiers. Classifiers were evaluated by the F-measure, the classification rate and the AUC (Area Under the receiver operating characteristic Curve) obtained by five-times 10-fold cross-validation. Moreover, we applied permutation tests on the AUC values [17, 18]. The methodological set up of binary and multi-label classification prediction is shown in Additional file 2. The phi coefficient, as well as the variable importance measurements, i.e., the mean decrease in gini impurity, were calculated according to Heider et al. .
Results and discussion
Phi coefficients of NNRTIs
Phi coefficients of PIs
Taken together, we were able to demonstrate that cross-resistance information can be exploited to improve drug resistance prediction of PIs and NNRTIs by applying MLC techniques, i.e., ECCs. To the best of our knowledge, this is the first time information about NNRTI and PI cross-resistance has been explicitly integrated in HIV-1 drug resistance prediction models. Since we found promising results using MLC methods, the concept could be enhanced in future work by applying alternative MLC methods, including the probabilistic variant of CCs proposed by Dembczynski et al. , but also approaches that are not based on the idea of chaining, such as multi-instance learning (MIL) on sequence and structural information to further improve resistance prediction accuracy. A few studies have already reported the use of structural information for drug resistance prediction [21–23], also for data from next-generation-sequencing [24–26]. However, these models neither make use of MIL techniques nor were combined with multi-label approaches yet. Moreover, instead of modeling binary relevance problems, the class membership representation could be expanded to susceptible, intermediate resistance, and resistance, network based approaches , or multi-objective optimization  could be employed, which might further contribute to refined prediction performance.
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- Smyth RP, Davenport MP, Mak J. The origin of genetic diversity in HIV-1. Virus Res. 2012; 169(2):415–29.View ArticlePubMedGoogle Scholar
- Melikian GL, Rhee SY, Varghese V, Porter D, White K, Taylor J, Towner W, Troia P, Burack J, DeJesus E, Robbins GK, Razzeca K, Kagan R, Liu TF, Fessel WJ, Israelski D, Shafer RW. Non-nucleoside reverse transcriptase inhibitor (NNRTI) cross-resistance: implications for preclinical evaluation of novel nnrtis and clinical genotypic resistance testing. Antimicrob Chemother. 2014; 69(1):12–20.View ArticleGoogle Scholar
- Sluis-Cremer N. The emerging profile of cross-resistance among the nonnucleoside HIV-1 reverse transcriptase inhibitors. Viruses. 2014; 6(8):2960–73.PubMed CentralView ArticlePubMedGoogle Scholar
- Beerenwinkel N, Schmidt B, Walter H, Kaiser R, Lengauer T, Hoffmann D, Korn K, Selbig J. Diversity and complexity of HIV-1 drug resistance: A bioinformatics approach to predicting phenotype from genotype. Proc Nat Acad Sci. USA. 2002; 99(12):85:8271–6.View ArticleGoogle Scholar
- Rhee SY, Taylor J, Wadhera G, Ben-Hur A, Brutlag DL, Shafer RW. Genotypic predictors of human immunodeficiency virus type 1 drug resistance. Proc Nat Acad Sci USA. 2006; 103(46):17355–60.PubMed CentralView ArticlePubMedGoogle Scholar
- Kierczak M, Ginalski K, Dramiński M, Koronacki J, Rudnicki W, Komorowski J. A Rough Set-Based Model of HIV-1 Reverse Transcriptase Resistome. Bioinform Biol Insights. 2009; 3:109–27.PubMed CentralPubMedGoogle Scholar
- Heider D, Verheyen J, Hoffmann D. Predicting bevirimat resistance of hiv-1 from genotype. BMC bioinformatics. 2010; 11(1):37.PubMed CentralView ArticlePubMedGoogle Scholar
- Brandt P, Moodley D, Pillay AW, Seebregts CJ, de Oliveira T. An Investigation of Classification Algorithms for Predicting HIV Drug Resistance without Genotype Resistance Testing In: Gibbons J, MacCaull W, editors. Foundations of Health Information Engineering and Systems. Lecture Notes in Computer Science. Macau, China: Springer: 2014. p. 236–53.Google Scholar
- Lengauer T, Sing T. Bioinformatics-assisted anti-hiv therapy. Nat Rev Microbiol. 2006; 4(10):790–7.View ArticlePubMedGoogle Scholar
- Liu TF, Shafer RW. Web resources for hiv type 1 genotypic-resistance test interpretation. Clinical Infectious Dis. 2006; 42(11):1608–18.View ArticleGoogle Scholar
- Heider D, Senge R, Cheng W, Hüllermeier E. Multilabel classification for exploiting cross-resistance information in HIV-1 drug resistance prediction. Bioinformatics. 2013; 29(16):1946–52.View ArticlePubMedGoogle Scholar
- Goenen M, Margolin AA. Drug susceptibility prediction against a panel of drugs using kernelized bayesian multitask learning. Bioinformatics. 2014; 30(17):556–63.View ArticleGoogle Scholar
- Rhee SY, Gonzales MJ, Kantor R, Betts BJ, Ravela J, Shafer RW. Human immunodeficiency virus reverse transcriptase and protease sequence database. Nucleic Acids Res. 2003; 31(1):298–303.PubMed CentralView ArticlePubMedGoogle Scholar
- Heider D, Hoffmann D. Interpol: An R package for preprocessing of protein sequences. BioData Min. 2011; 4:16.PubMed CentralView ArticlePubMedGoogle Scholar
- Read J, Pfahringer B, Holmes G, Frank E. Classifier chains for multi-label classification. Mach Learn. 2011; 85(3):333–59.View ArticleGoogle Scholar
- Breiman L. Random forests. Mach Learn. 2001; 45(1):5–32.View ArticleGoogle Scholar
- Sowa JP, Heider D, Bechmann LP, Gerken G, Hoffmann D, Canbay A. Novel algorithm for non-invasive assessment of fibrosis in nafld. PloS one. 2013; 8(4):62439.View ArticleGoogle Scholar
- Barbosa E, Röttger R, Hauschild AC, Azevedo V, Baumbach J. On the limits of computational functional genomics for bacterial lifestyle prediction. Brief Funct Genomics. 2014; 13(5):398–408.View ArticlePubMedGoogle Scholar
- Davey NE, Satagopam VP, Santiago-Mozos S, Villacorta-Martin C, Bharat TA, Schneider R, Briggs JA. The HIV mutation browser: A resource for human immunodeficiency virus mutagenesis and polymorphism data. PLoS Comput Biol. 2014; 10(12):1003951.View ArticleGoogle Scholar
- Dembczynski K, Cheng W, Hüllermeier E. Bayes optimal multilabel classification via probabilistic classifier chains. In: Proceedings of the 27th International Conference on Machine Learning (ICML). Haifa, Israel: 2010. p. 223–30.Google Scholar
- Dybowski JN, Heider D, Hoffmann D. Prediction of co-receptor usage of HIV-1 from genotype. PLoS Comput Biol. 2010; 6(4):1000743.View ArticleGoogle Scholar
- Dybowski JN, Riemenschneider M, Hauke S, Pyka M, Verheyen J, Hoffmann D, Heider D. Improved bevirimat resistance prediction by combination of structural and sequence-based classifiers. BioData Min. 2011; 4:26.PubMed CentralView ArticlePubMedGoogle Scholar
- Heider D, Dybowski JN, Wilms C, Hoffmann D. A simple structure-based model for the prediction of HIV-1 co-receptor tropism. BioData mining. 2014; 7(1):14.PubMed CentralView ArticlePubMedGoogle Scholar
- Dybowski JN, Heider D, Hoffmann D. Structure of HIV-1 quasi-species as early indicator for switches of co-receptor tropism. AIDS Res Ther. 2010; 7:41.PubMed CentralView ArticlePubMedGoogle Scholar
- Ramos RTJ, Carneiro AR, Baumbach J, Azevedo V, Schneider MPC, Silva A. Analysis of quality raw data of second generation sequencers with quality assessment software. BMC Res Notes. 2011; 4:130.PubMed CentralView ArticlePubMedGoogle Scholar
- Olejnik M, Steuwer M, Gorlatch S, Heider D. gCUP: rapid GPU-based HIV-1 co-receptor usage prediction for next-generation sequencing. Bioinformatics. 2014; 30(22):3272–3.View ArticlePubMedGoogle Scholar
- Rosa MJ, Portugal L, Hahn T, Fallgatter AJ, Garrido MI, Shawe-Taylor J, Mourao-Miranda J. Sparse network-based models for patient classification using fMRI. Neuroimage. 2015; 105:493–506.PubMed CentralView ArticlePubMedGoogle Scholar
- Rosenthal S, Borschbach M. Impact of Population Size, Selection and Multi-Parent Recombination within a Customized NSGA-II for Biochemical Optimization. Int J Adv Life Sci. 2014; 6(3):310–24.Google Scholar