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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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).

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.

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.

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.

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.

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.

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.

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.

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).

HIV-1 gp120 V3 loop for structure-based drug design.

HIV-1 cell entry is mediated by sequential interactions of the envelope protein gp120 with the receptor CD4 and a coreceptor, usually CCR5 or CXCR4, depending on the individual virion. Considerable efforts on exploiting the HIV coreceptors as drug targets have led to the new class of coreceptor antagonists. While these antiretroviral drugs aim at preventing virus/coreceptor interaction by binding to host proteins, neutralizing antibodies directed against the coreceptor-binding sites on gp120 have attracted attention as possible vaccine candidates.

Computational methods for the design of effective therapies against drug resistant HIV strains.

The development of drug resistance is a major obstacle to successful treatment of HIV infection. The extraordinary replication dynamics of HIV facilitates its escape from selective pressure exerted by the human immune system and by combination drug therapy. We have developed several computational methods whose combined use can support the design of optimal antiretroviral therapies based on viral genomic data.

ROCR: visualizing classifier performance in R.

Unlabelled: ROCR is a package for evaluating and visualizing the performance of scoring classifiers in the statistical language R. It features over 25 performance measures that can be freely combined to create two-dimensional performance curves. Standard methods for investigating trade-offs between specific performance measures are available within a uniform framework, including receiver operating characteristic (ROC) graphs, precision/recall plots, lift charts and cost curves.

Estimating HIV evolutionary pathways and the genetic barrier to drug resistance.

Background: The evolution of drug-resistant viruses challenges the management of human immunodeficiency virus (HIV) infections. Understanding this evolutionary process is important for the design of effective therapeutic strategies.

Methods: We used mutagenetic trees, a family of probabilistic graphical models, to describe the accumulation of resistance-associated mutations in the viral genome.