Special Issue on Digital Pathology, Tissue Image Analysis, Artificial Intelligence, and Machine Learning: Approximation of the Effect of Novel Technologies on Toxicologic Pathology.

For decades, it has been postulated that digital pathology is the future. By now it is safe to say that we are living that future. Digital pathology has expanded into all aspects of pathology, including human diagnostic pathology, veterinary diagnostics, research, drug development, regulatory toxicologic pathology primary reads, and peer review. Digital tissue image analysis has enabled users to extract quantitative and complex data from digitized whole-slide images. The following editorial provides an overview of the content of this special issue of Toxicologic Pathology to highlight the range of key topics that are included in this compilation. In addition, the editors provide a commentary on important current aspects to consider in this space, such as accessibility of publication content to the machine learning-novice pathologist, the importance of adequate test set selection, and allowing for data reproducibility.

HistoNet: A Deep Learning-Based Model of Normal Histology.

We introduce HistoNet, a deep neural network trained on normal tissue. On 1690 slides with rat tissue samples from 6 preclinical toxicology studies, tissue regions were outlined and annotated by pathologists into 46 different tissue classes. From these annotated regions, we sampled small 224 × 224 pixels images (patches) at 6 different levels of magnification. Using 4 studies as training set and 2 studies as test set, we trained VGG-16, ResNet-50, and Inception-v3 networks separately at each magnification level. Among these model architectures, Inception-v3 and ResNet-50 outperformed VGG-16. Inception-v3 identified the tissue from query images, with an accuracy up to 83.4%. Most misclassifications occurred between histologically similar tissues. Investigation of the features learned by the model (embedding layer) using Uniform Manifold Approximation and Projection revealed not only coherent clusters associated with the individual tissues but also subclusters corresponding to histologically meaningful structures that had not been annotated or trained for. This suggests that the histological representation learned by HistoNet could be useful as the basis of other machine learning algorithms and data mining. Finally, we found that models trained on rat tissues can be used on non-human primate and minipig tissues with minimal retraining.

Society of Toxicologic Pathology Digital Pathology and Image Analysis Special Interest Group Article*: Opinion on the Application of Artificial Intelligence and Machine Learning to Digital Toxicologic Pathology.

Toxicologic pathology is transitioning from analog to digital methods. This transition seems inevitable due to a host of ongoing social and medical technological forces. Of these, artificial intelligence (AI) and in particular machine learning (ML) are globally disruptive, rapidly growing sectors of technology whose impact on the long-established field of histopathology is quickly being realized. The development of increasing numbers of algorithms, peering ever deeper into the histopathological space, has demonstrated to the scientific community that AI pathology platforms are now poised to truly impact the future of precision and personalized medicine. However, as with all great technological advances, there are implementation and adoption challenges. This review aims to define common and relevant AI and ML terminology, describe data generation and interpretation, outline current and potential future business cases, discuss validation and regulatory hurdles, and most importantly, propose how overcoming the challenges of this burgeoning technology may shape toxicologic pathology for years to come, enabling pathologists to contribute even more effectively to answering scientific questions and solving global health issues. [Box: see text].

Docking analysis and resistance evaluation of clinically relevant mutations associated with the HIV-1 non-nucleoside reverse transcriptase inhibitors nevirapine, efavirenz and etravirine.

An integrated computational and statistical approach was used to determine the association of non-nucleoside reverse transcriptase inhibitors (NNRTIs) nevirapine, efavirenz and etravirine with resistance mutations that cause therapeutic failure and their impact on NNRTI resistance. Mutations detected for nevirapine virological failure with a prevalence greater than 10% in the used patient set were: K103N, Y181C, G190A, and K101E. A support vector regression model, based on matched genotypic/phenotypic data (n=850), showed that among 6365 analyzed mutations, K103N, Y181C and G190A have the first, third, and sixth greatest significance for nevirapine resistance, respectively. The most common indicator of treatment failure for efavirenz was K103N mutation present in 56.7% of the patients where the drug failed, followed by V108I, L100I, and G190A. For efavirenz resistance, K103N, G190, and L100I have the first, fourth, and eighth greatest significance, respectively, as determined in support vector regression model. No positive interactions were observed among nevirapine resistance mutations, while a more complex situation was observed with treatment failure of efavirenz and etravirine, characterized by the accumulation of multiple mutations. Docking simulations and free energy analysis based on docking scores of mutated human immunodeficiency virus (HIV) RT complexes were used to evaluate the influence of selected mutations on drug recognition. Results from support vector regression were confirmed by docking analysis. In particular, for nevirapine and efavirenz, a single mutation K103N was associated with the most unfavorable energetic profile compared to the wild-type sequence. This is in line with recent clinical data reporting that diarylpyrimidine etravirine, a very potent third generation drug effective against a wide range of drug-resistant HIV-1 variants, shows increased affinity towards K103N/S mutants due to its high conformational flexibility.

Molecular dynamics and free energy studies on the wild-type and mutated HIV-1 protease complexed with four approved drugs: mechanism of binding and drug resistance.

The current strategy to improve the quality of life of Human Immunodeficiency Virus (HIV) infected individuals through suppressing viral replication and maintaining the virus at low to undetectable levels is based on highly active antiretroviral therapy (HAART). Protease inhibitors are essential components of most HAART protocols and are often used as the first line of treatment. However, a considerable percentage of new HIV-1 infections are caused by viruses carrying antiretroviral drug-resistant mutations. In this paper molecular dynamics, docking simulations, and free energy analysis of mutated HIV protease complexes were used to estimate the influence of different drug resistance-associated mutations in lopinavir, amprenavir, saquinavir, and atazanavir protease recognition. In agreement with virological and clinical data, the structural analysis showed that the single mutations V82A, I84V, and M46I are associated with higher energetic values for all analyzed complexes with respect to wild-type, indicating their decreased stability. Interestingly, in atazanavir complexes, in the presence of the L76V substitution, the drug revealed a more productive binding affinity, in agreement with hypersusceptibility data.

Advantages of predicted phenotypes and statistical learning models in inferring virological response to antiretroviral therapy from HIV genotype.

BACKGROUND: Inferring response to antiretroviral therapy from the viral genotype alone is challenging. The utility of an intermediate step of predicting in vitro drug susceptibility is currently controversial. Here, we provide a retrospective comparison of approaches using either genotype or predicted phenotypes alone, or in combination. METHODS: Treatment change episodes were extracted from two large databases from the USA (Stanford-California) and Europe (EuResistDB) comprising data from 6,706 and 13,811 patients, respectively. Response to antiretroviral treatment was dichotomized according to two definitions. Using the viral sequence and the treatment regimen as input, three expert algorithms (ANRS, Rega and HIVdb) were used to generate genotype-based encodings and VircoTYPE() 4.0 (Virco BVBA, Mechelen, Belgium) was used to generate a predicted -phenotype-based encoding. Single drug classifications were combined into a treatment score via simple summation and statistical learning using random forests. Classification performance was studied on Stanford-California data using cross-validation and, in addition, on the independent EuResistDB data. RESULTS: In all experiments, predicted phenotype was among the most sensitive approaches. Combining single drug classifications by statistical learning was significantly superior to unweighted summation (P<2.2x10(-16)). Classification performance could be increased further by combining predicted phenotypes and expert encodings but not by combinations of expert encodings alone. These results were confirmed on an independent test set comprising data solely from EuResistDB. CONCLUSIONS: This study demonstrates consistent performance advantages in utilizing predicted phenotype in most scenarios over methods based on genotype alone in inferring virological response. Moreover, all approaches under study benefit significantly from statistical learning for merging single drug classifications into treatment scores.

Dynamics of NRTI resistance mutations during therapy interruption.

Abstract To date, very little information is available regarding the evolution of drug resistance mutations during treatment interruption (TI). Using a survival analysis approach, we investigated the dynamics of mutations associated with resistance to nucleoside analogue reverse transcriptase inhibitors (NRTIs) during TI. Analyzing 132 patients having at least two consecutive genotypes, one at last NRTI-containing regimen failure, and at least one during TI, we observed that the NRTI resistance mutations disappear at different rates during TI and are lost independently of each other in the majority of patients. The disappearance of the K65R and M184I/V mutations occurred in the majority of patients, was rapid, and was associated with the reemergence of wild-type virus, thus showing their negative impact on viral fitness. Overall, it seems that the loss of NRTI drug resistance mutations during TI is not an ordered process, and in the majority of patients occurs without specific interaction among mutations.

Gene expression profiles differentiating between breast cancers clinically responsive or resistant to letrozole.

PURPOSE: Endocrine agents, such as letrozole, are established in the treatment of hormone-dependent breast cancer. However, response rates are only 50% to 70% in the neoadjuvant setting and lower in advanced disease. Thus there is a need to identify novel markers predicting for response and to understand molecular mechanisms of resistance. PATIENTS AND METHODS: Sequential tumor biopsies were taken before and after 10 to 14 days of neoadjuvant treatment with letrozole in patients with estrogen receptor-rich breast cancer. Expression profiles on high-density microarray chips were then related to clinical responses as assessed from tumor volume measurements after 3 months of treatment. RESULTS: Of 52 patients, 37 (71%) were classified as having a clinical response to letrozole and 15 being clinically resistant. Bioinformatic analysis identified 205 covariables (69 baseline expression, 45 day 14 expression, and 91 change in expression with treatment) which differentiated between clinical responders and nonresponders. Hierarchical clustering using the variables separated responders and nonresponders into two distinct groups. Ontological assessment indicated that discriminating genes were enriched toward cellular biosynthetic processes. In particular, functional gene assessment showed ribosomal protein probes to have higher baseline expression in tumors responsive to letrozole and increased expression with treatment in nonresponding cases. CONCLUSION: To our knowledge, this is the first study to describe genetic covariables and molecular processes discriminating between tumors clinically responsive and resistant to an aromatase inhibitor. The understanding of such molecular phenotypes will be important in optimizing the clinical use of aromatase inhibitors, both in terms of identifying responsive breast cancers and developing new agents to target resistance pathways.

CD4-dependent characteristics of coreceptor use and HIV type 1 V3 sequence in a large population of therapy-naive individuals.

We investigated the associations between coreceptor use, V3 loop sequence, and CD4 count in a cross-sectional analysis of a large cohort of chronically HIV-infected, treatment-naive patients. HIV coreceptor usage was determined in the last pretherapy plasma sample for 977 individuals initiating HAART in British Columbia, Canada using the Monogram Trofile Tropism assay. Relative light unit (RLU) readouts from the Trofile assay, as well as HIV V3 loop sequence data, were examined as a function of baseline CD4 cell count for 953 (97%) samples with both phenotype and genotype data available. Median CCR5 RLUs were high for both R5 and X4-capable samples, while CXCR4 RLUs were orders of magnitude lower for X4 samples (p < 0.001). CCR5 RLUs in R5 samples (N = 799) increased with decreasing CD4 count (p < 0.001), but did not vary with plasma viral load (pVL) (p = 0.74). In X4 samples (N = 178), CCR5 RLUs decreased with decreasing CD4 count (p = 0.046) and decreasing pVL (p = 0.097), while CXCR4 RLUs increased with decreasing pVL (p = 0.0008) but did not vary with CD4 (p = 0.96). RLUs varied with the presence of substitutions at V3 loop positions 11, 25, and 6-8. The prevalence and impact of substitutions at codons 25 and 6-8 were CD4 dependent as was the presence of amino acid mixtures in the V3; substitutions at position 11 were CD4 independent. Assay RLU measures predictably vary with both immunological and virological parameters. The ability to predict X4 virus using genotypic determinants at positions 25 and 6-8 of the V3 loop is CD4 dependent, while position 11 appears to be CD4 independent.

Predicting HIV coreceptor usage on the basis of genetic and clinical covariates.

BACKGROUND: We compared several statistical learning methods for the prediction of HIV coreceptor use from clonal HIV third hypervariable (V3) loop sequences, and evaluated and improved their effectiveness on clinical samples. METHODS: Support vector machines (SVM), artificial neural networks, position-specific scoring matrices (PSSM) and mixtures of localized rules were estimated and tested using 10x ten-fold cross-validation on a clonal dataset consisting of 1,100 matched clonal genotype-phenotype pairs from 332 patients. Different SVMs were also trained and tested on a clinically derived dataset, representing 920 patient samples from British Columbia, Canada. Methods were evaluated using receiver operating characteristic (ROC) curves. RESULTS: In the clonal analysis, the sensitivity of the 11/25 rule at 92.5% specificity was 59.5%. PSSMs and SVMs increased sensitivity to 71.9% and 76.4%, respectively, at the same specificity (P < < 0.05). In clinical samples, the sensitivity of the 11/25 rule and SVM decreased to 25.9% (specificity 93.9%) and 39.8% (specificity 93.5%), respectively. However, the integration of clinical data resulted in a further 2.4-fold increase in sensitivity over the 11/25 rule (63%). Univariate analyses identified 41 V3 mutations significantly associated with coreceptor usage. CONCLUSION: For all methods tested, a substantial sensitivity decrease is observed on clinical data, probably owing to the heterogeneity of the viral population in vivo. In response to these complications, we present an SVM-based approach that integrates sequence information with clinical and host data, resulting in improved performance and sensitivity compared with purely sequence-based approaches.

Fifteen years of env C2V3C3 evolution in six individuals infected clonally with human immunodeficiency virus type 1.

The study of the evolution of human immunodeficiency virus type 1 (HIV-1) requires blood samples collected longitudinally and data on the approximate time point of infection. Although these requirements were fulfilled in several previous studies, the infectious sources were either unknown or heterogeneous genetically. In the present study, HIV-1 env C2V3C3 (nt 7029-7315) evolution was examined retrospectively in a cohort of hemophiliacs. Compared to other cohorts, the area of interest here was the infection of six hemophiliacs by the same virus strain, that is, the infecting viruses shared an identical genome. As expected, divergence from the founder sequence as well as interpatient divergence of the predominant virus strains increased significantly over time. Based on the V3 nucleotide sequences, CCR5 usage was predicted exclusively throughout the whole period of infection in all patients. Interestingly, common patterns of viral evolution were detected in the patients of the cohort. Four amino acid substitutions within the V3 loop emerged and persisted subsequently in five (positions 305 and 308 of the HXB2 gp120 reference sequence) and six patients (positions 325 and 328 in HXB2 gp120), respectively. These common changes within the V3 loop are likely to be enforced by HIV-1 specific immune response.

Current V3 genotyping algorithms are inadequate for predicting X4 co-receptor usage in clinical isolates.

OBJECTIVE: Integrating CCR5 antagonists into clinical practice would benefit from accurate assays of co-receptor usage (CCR5 versus CXCR4) with fast turnaround and low cost. DESIGN: Published HIV V3-loop based predictors of co-receptor usage were compared with actual phenotypic tropism results in a large cohort of antiretroviral naive individuals to determine accuracy on clinical samples and identify areas for improvement. METHODS: Aligned HIV envelope V3 loop sequences (n = 977), derived by bulk sequencing were analyzed by six methods: the 11/25 rule; a neural network (NN), two support vector machines, and two subtype-B position specific scoring matrices (PSSM). Co-receptor phenotype results (Trofile Co-receptor Phenotype Assay; Monogram Biosciences) were stratified by CXCR4 relative light unit (RLU) readout and CD4 cell count. RESULTS: Co-receptor phenotype was available for 920 clinical samples with V3 genotypes having fewer than seven amino acid mixtures (n = 769 R5; n = 151 X4-capable). Sensitivity and specificity for predicting X4 capacity were evaluated for the 11/25 rule (30% sensitivity/93% specificity), NN (44%/88%), PSSM(sinsi) (34%/96%), PSSM(x4r5) (24%/97%), SVMgenomiac (22%/90%) and SVMgeno2pheno (50%/89%). Quantitative increases in sensitivity could be obtained by optimizing the cut-off for methods with continuous output (PSSM methods), and/or integrating clinical data (CD4%). Sensitivity was directly proportional to strength of X4 signal in the phenotype assay (P < 0.05). CONCLUSIONS: Current default implementations of co-receptor prediction algorithms are inadequate for predicting HIV X4 co-receptor usage in clinical samples, particularly those X4 phenotypes with low CXCR4 RLU signals. Significant improvements can be made to genotypic predictors, including training on clinical samples, using additional data to improve predictions and optimizing cutoffs and increasing genotype sensitivity.

Characterization and structural analysis of novel mutations in human immunodeficiency virus type 1 reverse transcriptase involved in the regulation of resistance to nonnucleoside inhibitors.

Resistance to antivirals is a complex and dynamic phenomenon that involves more mutations than are currently known. Here, we characterize 10 additional mutations (L74V, K101Q, I135M/T, V179I, H221Y, K223E/Q, and L228H/R) in human immunodeficiency virus type 1 (HIV-1) reverse transcriptase which are involved in the regulation of resistance to nonnucleoside reverse transcriptase inhibitors (NNRTIs). These mutations are strongly associated with NNRTI failure and strongly correlate with the classical NNRTI resistance mutations in a data set of 1,904 HIV-1 B-subtype pol sequences from 758 drug-naïve patients, 592 nucleoside reverse transcriptase inhibitor (NRTI)-treated but NNRTI-naïve patients, and 554 patients treated with both NRTIs and NNRTIs. In particular, L74V and H221Y, positively correlated with Y181C, were associated with an increase in Y181C-mediated resistance to nevirapine, while I135M/T mutations, positively correlated with K103N, were associated with an increase in K103N-mediated resistance to efavirenz. In addition, the presence of the I135T polymorphism in NNRTI-naïve patients significantly correlated with the appearance of K103N in cases of NNRTI failure, suggesting that I135T may represent a crucial determinant of NNRTI resistance evolution. Molecular dynamics simulations show that I135T can contribute to the stabilization of the K103N-induced closure of the NNRTI binding pocket by reducing the distance and increasing the number of hydrogen bonds between 103N and 188Y. H221Y also showed negative correlations with type 2 thymidine analogue mutations (TAM2s); its copresence with the TAM2s was associated with a higher level of zidovudine susceptibility. Our study reinforces the complexity of NNRTI resistance and the significant interplay between NRTI- and NNRTI-selected mutations. Mutations beyond those currently known to confer resistance should be considered for a better prediction of clinical response to reverse transcriptase inhibitors and for the development of more efficient new-generation NNRTIs.

Improved prediction of response to antiretroviral combination therapy using the genetic barrier to drug resistance.

BACKGROUND: The outcome of antiretroviral combination therapy depends on many factors involving host, virus, and drugs. We investigate prediction of treatment response from the applied drug combination and the genetic constellation of the virus population at baseline. The virus's evolutionary potential for escaping from drug pressure is explored as an additional predictor. METHODS: We compare different encodings of the viral genotype and antiretroviral regimen including phenotypic and evolutionary information, namely predicted phenotypic drug resistance, activity of the regimen estimated from sequence space search, the genetic barrier to drug resistance, and the genetic progression score. These features were evaluated in the context of different statistical learning procedures applied to the binary classification task of predicting virological response. Classifier performance was evaluated using cross-validation and receiver operating characteristic curves on 6,337 observed treatment change episodes from the Stanford HIV Drug Resistance Database and a large US clinic-based patient population. RESULTS: We find that the choice of appropriate features affects predictive performance more profoundly than the choice of the statistical learning method. Application of the genetic barrier to drug resistance, which combines phenotypic and evolutionary information, outperformed the genetic progression score, which uses exclusively evolutionary knowledge. The benefit of phenotypic information in predicting virological response was confirmed by using predicted fold changes in drug susceptibility. Moreover, genetic barrier and predicted phenotypic drug resistance were found to be the best encodings across all datasets and statistical learning methods examined. AVAILABILITY: THEO (THErapy Optimizer), a prototypical implementation of the best performing approach, is freely available for research purposes at http://www.geno2pheno.org.

Structural descriptors of gp120 V3 loop for the prediction of HIV-1 coreceptor usage.

HIV-1 cell entry commonly uses, in addition to CD4, one of the chemokine receptors CCR5 or CXCR4 as coreceptor. Knowledge of coreceptor usage is critical for monitoring disease progression as well as for supporting therapy with the novel drug class of coreceptor antagonists. Predictive methods for inferring coreceptor usage based on the third hypervariable (V3) loop region of the viral gene coding for the envelope protein gp120 can provide us with these monitoring facilities while avoiding expensive phenotypic tests. All simple heuristics (such as the 11/25 rule) as well as statistical learning methods proposed to date predict coreceptor usage based on sequence features of the V3 loop exclusively. Here, we show, based on a recently resolved structure of gp120 with an untruncated V3 loop, that using structural information on the V3 loop in combination with sequence features of V3 variants improves prediction of coreceptor usage. In particular, we propose a distance-based descriptor of the spatial arrangement of physicochemical properties that increases discriminative performance. For a fixed specificity of 0.95, a sensitivity of 0.77 was achieved, improving further to 0.80 when combined with a sequence-based representation using amino acid indicators. This compares favorably with the sensitivities of 0.62 for the traditional 11/25 rule and 0.73 for a prediction based on sequence information as input to a support vector machine and constitutes a statistically significant improvement. A detailed analysis and interpretation of structural features important for classification shows the relevance of several specific hydrogen-bond donor sites and aliphatic side chains to coreceptor specificity towards CCR5 or CXCR4. Furthermore, an analysis of side chain orientation of the specificity-determining residues suggests a major role of one side of the V3 loop in the selection of the coreceptor. The proposed method constitutes the first approach to an improved prediction of coreceptor usage based on an original integration of structural bioinformatics methods with statistical learning.

Determining human immunodeficiency virus coreceptor use in a clinical setting: degree of correlation between two phenotypic assays and a bioinformatic model.

Two recombinant phenotypic assays for human immunodeficiency virus (HIV) coreceptor usage and an HIV envelope genotypic predictor were employed on a set of clinically derived HIV type 1 (HIV-1) samples in order to evaluate the concordance between measures. Previously genotyped HIV-1 samples derived from antiretroviral-naïve individuals were tested for coreceptor usage using two independent phenotyping methods. Phenotypes were determined by validated recombinant assays that incorporate either an approximately 2,500-bp ("Trofile" assay) or an approximately 900-bp (TRT assay) fragment of the HIV envelope gp120. Population-based HIV envelope V3 loop sequences ( approximately 105 bp) were derived by automated sequence analysis. Genotypic coreceptor predictions were performed using a support vector machine model trained on a separate genotype-Trofile phenotype data set. HIV coreceptor usage was obtained from both phenotypic assays for 74 samples, with an overall 85.1% concordance. There was no evidence of a difference in sensitivity between the two phenotypic assays. A bioinformatic algorithm based on a support vector machine using HIV V3 genotype data was able to achieve 86.5% and 79.7% concordance with the Trofile and TRT assays, respectively, approaching the degree of agreement between the two phenotype assays. In most cases, the phenotype assays and the bioinformatic approach gave similar results. However, in cases where there were differences in the tropism results, it was not clear which of the assays was "correct." X4 (CXCR4-using) minority species in clinically derived samples likely complicate the interpretation of both phenotypic and genotypic assessments of HIV tropism.

Stable coreceptor usage of HIV in patients with ongoing treatment failure on HAART.

BACKGROUND: Disease progression in HIV infection has been associated with switch of viral coreceptor usage from CCR5 to CXCR4. OBJECTIVES: To investigate the relationship between HIV-coreceptor tropism and clinical and virological outcome in 40 heavily pretreated patients over time. METHODS: Coreceptor phenotype was predicted after sequencing the V3 loop of the HIV glycoprotein 120. RESULTS: Coreceptor use was stable during observation time in 87% of patients, and CCR5 tropism was predominant. Viral mutations in the pol gene and clinical parameters were not predictive for coreceptor switching. CONCLUSIONS: Even in patients with repeated HAART failure, CCR5 antagonists might be a valuable treatment option.

Bioinformatics-assisted anti-HIV therapy.

Highly active antiretroviral therapy (HAART), in which three or more drugs are given in combination, has substantially improved the clinical management of HIV-1 infection. Still, the emergence of drug-resistant variants eventually leads to therapy failure in most patients. In such a scenario, the high diversity of resistance-associated mutational patterns complicates the choice of an optimal follow-up regimen. To support physicians in this task, a range of bioinformatics tools for predicting drug resistance or response to combination therapy from the viral genotype have been developed. With several free and commercial software services available, computational advice is rapidly gaining acceptance as an important element of rational decision-making in the treatment of HIV infection.

Involvement of novel human immunodeficiency virus type 1 reverse transcriptase mutations in the regulation of resistance to nucleoside inhibitors.

We characterized 16 additional mutations in human immunodeficiency virus type 1 (HIV-1) reverse transcriptase (RT) whose role in drug resistance is still unknown by analyzing 1,906 plasma-derived HIV-1 subtype B pol sequences from 551 drug-naïve patients and 1,355 nucleoside RT inhibitor (NRTI)-treated patients. Twelve mutations positively associated with NRTI treatment strongly correlated both in pairs and in clusters with known NRTI resistance mutations on divergent evolutionary pathways. In particular, T39A, K43E/Q, K122E, E203K, and H208Y clustered with the nucleoside analogue mutation 1 cluster (NAM1; M41L+L210W+T215Y). Their copresence in this cluster was associated with an increase in thymidine analogue resistance. Moreover, treatment failure in the presence of K43E, K122E, or H208Y was significantly associated with higher viremia and lower CD4 cell count. Differently, D218E clustered with the NAM2 pathway (D67N+K70R+K219Q+T215F), and its presence in this cluster determined an increase in zidovudine resistance. In contrast, three mutations (V35I, I50V, and R83K) negatively associated with NRTI treatment showed negative correlations with NRTI resistance mutations and were associated with increased susceptibility to specific NRTIs. In particular, I50V negatively correlated with the lamivudine-selected mutation M184V and was associated with a decrease in M184V/lamivudine resistance, whereas R83K negatively correlated with both NAM1 and NAM2 clusters and was associated with a decrease in thymidine analogue resistance. Finally, the association pattern of the F214L polymorphism revealed its propensity for the NAM2 pathway and its strong negative association with the NAM1 pathway. Our study provides evidence of novel RT mutational patterns that regulate positively and/or negatively NRTI resistance and strongly suggests that other mutations beyond those currently known to confer resistance should be considered for improved prediction of clinical response to antiretroviral drugs.

Compensatory mutations at the HIV cleavage sites p7/p1 and p1/p6-gag in therapy-naive and therapy-experienced patients.

BACKGROUND: Mutations in the genome of HIV conferring drug resistance are a major reason for the failure of antiretroviral therapy, but they often compromise viral fitness. Protease (PR) cleavage site (CS) mutations could compensate for impaired replication capacity of drug-resistant viruses. PATIENTS AND METHODS: We analysed the cleavage sites p1/p7 and p1/p6-gag of 500 HIV-1 subtype B infected patients. The collective consists of 275 therapy-naive and 225 therapy-experienced patients with at least one primary PR mutation, from whom eight underwent therapy-interruption in different clinical settings. RESULTS: Multiple mutations within the CS p7/p1 and p1/p6-gag accumulated in therapy-experienced isolates (p7/p1: A431V-K436R-I437V and p1/p6-gag: L449F/V-P452S-P453L/A). Further rare CS mutations were totally absent in therapy-naive viruses. Sixty percent of all therapy-experienced viruses exhibited at least one therapy-associated CS mutation, but so did 10% of therapy-naive viruses. The analysis of CS and PR mutations in therapy-experienced viruses revealed several positive correlations--A431V with L24I-M46I/L-I54V-V82A; I437V with I54V-V82F/T/S; L449V with I54M/L/S/T/A; and L449F/R452S/P453L: with D30N-I84V--whereas P453L and V82A were negatively correlated. Mutagenetic trees constructed form this cross-sectional data showed an ordered accumulation of the most prominent CS mutations along two pathways L90M-I84V-P453L and I54-V82-A431V followed by either M46L or L24I. Furthermore, eight viruses with at least one therapy-associated mutation at each CS displayed an outstanding maintenance of major PR mutations during therapy interruption. CONCLUSIONS: These findings emphasize the relevance of CS mutations in the evolution of HIV resistance to PR inhibitors. Therefore, therapy-associated CS mutations should be considered in HIV resistance tests to estimate viral fitness in different clinical settings.