Gene expression profiles predict emergence of psychiatric adverse events in HIV/HCV-coinfected patients on interferon-based HCV therapy

Meta-analysis approach as a gene selection method in class prediction: does it improve model performance? A case study in acute myeloid leukemia

BACKGROUND

Aggregating gene expression data across experiments via meta-analysis is expected to increase the precision of the effect estimates and to increase the statistical power to detect a certain fold change. This study evaluates the potential benefit of using a meta-analysis approach as a gene selection method prior to predictive modeling in gene expression data.

RESULTS

Six raw datasets from different gene expression experiments in acute myeloid leukemia (AML) and 11 different classification methods were used to build classification models to classify samples as either AML or healthy control. First, the classification models were trained on gene expression data from single experiments using conventional supervised variable selection and externally validated with the other five gene expression datasets (referred to as the individual-classification approach). Next, gene selection was performed through meta-analysis on four datasets, and predictive models were trained with the selected genes on the fifth dataset and validated on the sixth dataset. For some datasets, gene selection through meta-analysis helped classification models to achieve higher performance as compared to predictive modeling based on a single dataset; but for others, there was no major improvement. Synthetic datasets were generated from nine simulation scenarios. The effect of sample size, fold change and pairwise correlation between differentially expressed (DE) genes on the difference between MA- and individual-classification model was evaluated. The fold change and pairwise correlation significantly contributed to the difference in performance between the two methods. The gene selection via meta-analysis approach was more effective when it was conducted using a set of data with low fold change and high pairwise correlation on the DE genes.

CONCLUSION

Gene selection through meta-analysis on previously published studies potentially improves the performance of a predictive model on a given gene expression data.

Mental disorders in HIV/HCV coinfected patients under antiviral treatment for hepatitis C

This paper aims to review the epidemiology and management of mental disorders in human immunodeficiency virus (HIV)/hepatitis C virus (HCV) coinfected patients, the need for antiviral therapy in this specific population, and current treatment strategies for HIV/HCV patients with psychiatric and/or substance use disorders. This is a narrative review. Data was sourced from electronic databases and was not limited by language or date of publication. HIV infection has become a survivable chronic illness. Prevalence of HCV infection among HIV-infected patients is high ranging from 50% to 90%. Patients with psychiatric diseases have also an increased risk for HIV/HCV coinfection. The most effective strategy to decrease HCV-related morbidity and mortality in coinfection is to achieve viral eradication. Although psychiatric symptoms often appear during antiviral treatment and may be associated with the use of interferon-alpha (IFN-α), recent evidence suggests that many patients with comorbid mental and substance use disorders can be treated safely. Recent data indicate that IFNα-induced psychiatric side effects have a similar prevalence in HIV/HCV coinfected patients than in monoinfected patients and they can be managed and even prevented successfully with psychopharmacological strategies in the frame of a multidisciplinary team. New antivirals offer INF-free therapies for this specific population.

Microarrays-Enabled Hypothesis Generation: The Suspect Role of FNBP-1 in Neuropsychiatric Pathogenesis Associated with HIV and/or HCV Infection

OBJECTIVE

The spectrum of neuropsychiatric illness (NI) associated with the Human Immunodeficiency Virus (HIV) and/or the Hepatitis C Virus (HCV) is far reaching and significantly impacts the clinical presentation and outcome of infected persons; however, the etiological and pathophysiological background remains partially understood. The present work was aimed to investigate the potential significance of formin binding protein 1 (FNBP-1)-dependent pathways in NI-pathogenesis by elaborating on previous microarray-based research in HIV and/or HCV-infected patients receiving interferon-α (IFN-α) immunotherapy via a rigorous data mining procedure.

METHODS

Using microarray data of peripheral whole blood (PB) samples obtained from HCV mono-infected persons (n=25, Affymetrix® HG-U133A_2) 12 h before and after the 1st dose of pegylated IFN-α (PegIFN-α), we re-applied the same analytical algorithm that we had developed and published in an earlier study with HIV/HCV co-infected subjects (N=28, Affymetrix® HG-U133A), in order to evaluate reproducibility of potential NI-related molecular findings in an independent cohort.

RESULTS

Among 28 gene expression profiles (HIV/HCV: N=9 vs. HCV: N=19) selected by applying different thresholds (a Mean Fold Difference value (MFD) in gene expression of ≥ 0.38 (log2) and/or P value from <0.05 to ≤ 0.1) FNBP-1 was identified as the only overlapping marker, which also exhibited a consistent upregulation in association with the development of NI in both cohorts. Previous functional annotation analysis had classified FNBP-1 as molecule with significant enrichment in various brain tissues (P<0.01).

CONCLUSION

Our current findings are strongly arguing for intensifying research into the FNBP-1-related mechanisms that may be conferring risk for or resistance to HIV- and/or HCV-related NI.

Factors affecting the accuracy of a class prediction model in gene expression data

BACKGROUND

Class prediction models have been shown to have varying performances in clinical gene expression datasets. Previous evaluation studies, mostly done in the field of cancer, showed that the accuracy of class prediction models differs from dataset to dataset and depends on the type of classification function. While a substantial amount of information is known about the characteristics of classification functions, little has been done to determine which characteristics of gene expression data have impact on the performance of a classifier. This study aims to empirically identify data characteristics that affect the predictive accuracy of classification models, outside of the field of cancer.

RESULTS

Datasets from twenty five studies meeting predefined inclusion and exclusion criteria were downloaded. Nine classification functions were chosen, falling within the categories: discriminant analyses or Bayes classifiers, tree based, regularization and shrinkage and nearest neighbors methods. Consequently, nine class prediction models were built for each dataset using the same procedure and their performances were evaluated by calculating their accuracies. The characteristics of each experiment were recorded, (i.e., observed disease, medical question, tissue/cell types and sample size) together with characteristics of the gene expression data, namely the number of differentially expressed genes, the fold changes and the within-class correlations. Their effects on the accuracy of a class prediction model were statistically assessed by random effects logistic regression. The number of differentially expressed genes and the average fold change had significant impact on the accuracy of a classification model and gave individual explained-variation in prediction accuracy of up to 72% and 57%, respectively. Multivariable random effects logistic regression with forward selection yielded the two aforementioned study factors and the within class correlation as factors affecting the accuracy of classification functions, explaining 91.5% of the between study variation.

CONCLUSIONS

We evaluated study- and data-related factors that might explain the varying performances of classification functions in non-cancerous datasets. Our results showed that the number of differentially expressed genes, the fold change, and the correlation in gene expression data significantly affect the accuracy of class prediction models.

Interferon stimulated exonuclease gene 20 kDa links psychiatric events to distinct hepatitis C virus responses in human immunodeficiency virus positive patients

Hepatitis C Virus (HCV) infection occurs frequently in patients with preexisting mental illness. Treatment for chronic hepatitis C using interferon formulations often increases risk for neuro-psychiatric symptoms. Pegylated-Interferon-α (PegIFN-α) remains crucial for attaining sustained virologic response (SVR); however, PegIFN-α based treatment is associated with psychiatric adverse effects, which require dose reduction and/or interruption. This study's main objective was to identify genes induced by PegIFN-α and expressed in the central nervous system and immune system, which could mediate the development of psychiatric toxicity in association with antiviral outcome. Using peripheral blood mononuclear cells from Human Immunodeficiency Virus (HIV)/HCV co-infected donors (N = 28), DNA microarray analysis was performed and 21 differentially regulated genes were identified in patients with psychiatric toxicity versus those without. Using these 21 expression profiles a two-way-ANOVA was performed to select genes based on antiviral outcome and occurrence of neuro-psychiatric adverse events. Microarray analysis demonstrated that Interferon-stimulated-exonuclease-gene 20 kDa (ISG20) and Interferon-alpha-inducible-protein 27 (IFI27) were the most regulated genes (P < 0.05) between three groups that were built by combining antiviral outcome and neuro-psychiatric toxicity. Validation by bDNA assay confirmed that ISG20 expression levels were significantly associated with these outcomes (P < 0.035). Baseline levels and induction of ISG20 correlated independently with no occurrence of psychiatric adverse events and non-response to therapy (P < 0.001). Among the 21 genes that were associated with psychiatric adverse events and 20 Interferon-inducible genes (IFIGs) used as controls, only ISG20 expression was able to link PegIFN-α related neuro-psychiatric toxicity to distinct HCV-responses in patients co-infected with HIV and HCV in vivo.

Evaluation of gene expression classification studies: factors associated with classification performance

Classification methods used in microarray studies for gene expression are diverse in the way they deal with the underlying complexity of the data, as well as in the technique used to build the classification model. The MAQC II study on cancer classification problems has found that performance was affected by factors such as the classification algorithm, cross validation method, number of genes, and gene selection method. In this paper, we study the hypothesis that the disease under study significantly determines which method is optimal, and that additionally sample size, class imbalance, type of medical question (diagnostic, prognostic or treatment response), and microarray platform are potentially influential. A systematic literature review was used to extract the information from 48 published articles on non-cancer microarray classification studies. The impact of the various factors on the reported classification accuracy was analyzed through random-intercept logistic regression. The type of medical question and method of cross validation dominated the explained variation in accuracy among studies, followed by disease category and microarray platform. In total, 42% of the between study variation was explained by all the study specific and problem specific factors that we studied together.