Introduction
Infection by hepatitis C virus (HCV) is commonly complicated by coinfection with HIV due to shared routes of transmission. Coinfection is found in up to 30% of HIV-infected individuals overall and up to 95% of HIV-infected injection drugs users [1]. HCV/HIV coinfection has increased morbidity and mortality relative to infection by either HIV or HCV alone. Compared with HCV infection alone, coinfection with HIV increases HCV levels in plasma and is associated with a 50% accelerated rate of liver fibrosis progression [2].
Given the significant adverse event profile of current HCV therapy, it is important to balance the likelihood of liver disease progression with the risks of these side effects when deciding whether to initiate therapy. The degree of liver fibrosis is a determinant for the initiation of therapy in HIV/HCV-coinfected patients [3]. Liver biopsy is the gold standard for the assessment of fibrosis, but is invasive, expensive, and associated with complications such as pain and bleeding that may be life-threatening. Additionally, because it samples only one in 10 000 to one in 50 000 of the liver, it is highly variable and subject to sampling error [4]. Liver biopsy is not an ideal test and is poorly suited to repeated follow-up, which may be particularly relevant in the setting of HIV/HCV coinfection, in which the progression of fibrosis is accelerated and more frequent monitoring of the degree of fibrosis may be required [5].
There has been considerable interest in generating a noninvasive test that would allow simple repeated testing to more easily monitor fibrosis progression and provide a more global estimate of fibrosis. Several new assays to measure fibrosis have been generated that involve the use of panels of serum markers. These markers include routine clinical tests that measure liver function and inflammation such as aspartate aminotransferase (AST), alanine aminotransferase (ALT), γ-glutamyltransferase (GGT), prothrombin time international normalized ratio (PT-INR), and total bilirubin [6]. Less commonly used markers have also been proposed and incorporated into panels. These include components of the extracellular matrix (ECM) such as hyaluronic acid, procollagen I, and collagen IV, enzymes that regulate the ECM such as YKL-40 (human cartilage glycoprotein 39), regulators of matrix metalloproteases, such as tissue inhibitor of metalloproteinase 1 (TIMP-1), as well as acute-phase proteins such as α2-macroglobulin (A2M) and haptoglobin [7,8].
Several of the panels of currently available biomarkers have demonstrated good accuracy with respect to differentiating mild from severe fibrosis. However, the ability to differentiate more finely between different stages of fibrosis is limited [9]. Thus, it is important to develop new and more robust biomarkers that increase the accuracy and sensitivity of noninvasive models of fibrosis. The use of DNA microarray may allow the identification of novel biomarkers by comparing differences in gene expression in the peripheral blood mononuclear cells of individuals with different stages of fibrosis. The aims of this study were first to validate existing markers of fibrosis in a cohort of HIV/HCV-coinfected patients and second to determine whether DNA microarray analysis could be used to discover novel biomarkers that could be added to currently available clinical and biochemical markers to develop a more accurate model of liver fibrosis, which can be validated in larger number of patients.
Study subjects
Data from all 100 liver biopsies performed in HIV/HCV-coinfected patients at the Warren Grant Magnusson Clinical Research Center during the past 7 years for whom biochemical data were available were obtained from 68 patients. Biochemical data was available on all patients at the time of liver biopsy. Hepatic fibrosis was assessed by the modified Ishak fibrosis score as described elsewhere [10]. An Ishak fibrosis score greater than or equal to 3 was considered advanced, whereas a score less than 3 was considered mild. Nine serum markers (AST, ALT, GGT, α-fetoprotein [AFP], total cholesterol, total bilirubin, A2M, haptoglobin, and apolipoprotein A1 [Apo A1]), one cell marker (platelet count), and one plasma marker (PT-INR) were assayed from samples collected within 12 weeks of biopsy. Hyaluronic acid was measured by an enzyme-linked protein-binding assay on a 96-well microplate (Corgenix Inc., Denver, Colorado, USA). Sex, age at biopsy, and CD4+ T-cell count were assessed. Cumulative months of antiretroviral therapy (ART) and nevirapine (NVP) use were determined by chart review, and ART experience was additionally calculated as a dichotomous (yes/no) value (Table 1). Patients were not receiving interferon therapy at the time of either biopsy or serum collection. All patients were urged to abstain from alcohol consumption; however, a comprehensive history of alcohol consumption was not collected. The study was reviewed and approved by the Institutional Review Board of the National Institute of Allergy and Infectious Diseases.
Genomics
Peripheral blood mononuclear cells (PBMCs) were collected by Ficoll-Hypaque separation technique for DNA microarray analysis from a subset of 28 HCV/HIV patients at the same time of their liver biopsy (total 51 liver biopsies). This subset was selected on the basis of the availability of DNA microarray and proteomics data, which were performed only in this subset. Otherwise, there was no statistically significant difference between the subset and the larger set of patients. Total RNA and labeled cRNA synthesis and hybridization to the Affymetrix U133A human microarray, representing approximately 14 500 genes, were performed according to the manufacturer's recommended protocol as described elsewhere [11]. One-way analysis of variance (ANOVA) analysis was used to identify significantly modulated genes (P < 0.05) that additionally had an absolute fold change difference more than 1.3. These genes were included in modeling.
Model
Logistic regression analysis was performed to develop a predictive model from the 68 subjects with 100 biopsies. To account for repeated diagnostic tests (biopsies) within a subject, a generalized estimating equation [12] was adopted. Backward elimination was used for model selection: we started with the feature set of all factors significantly correlated with fibrosis (P < 0.05) and sequentially deleted the factors until the performance of the model began to decline. The performance of the predictive model was evaluated by area under the receiver operating characteristic curve (AUROC), and the positive and negative predictive values (Fig. 1). Bootstrap method [13] was used to correct the overestimation of AUROC caused by the fact that the model was both trained and tested on the same dataset.
Over the subset of 28 subjects with 51 biopsies, two predictive models were generated from logistic regression: one contained only clinical and biochemical markers, the other contained two biochemical markers and two genes (Fig. 2). The two models were compared in terms of AUROC by the method of DeLong et al. [14].
Results
Patient characteristics
The characteristics of the patients at time of biopsy and of the genomics subset broken out are shown in Table 1. The patients in the larger set were predominantly male (81.5%) and the majority (65%) had a mild stage (Ishak score <3) of liver fibrosis. Comparisons between the groups with mild and advanced fibrosis are shown in Table 2. The two groups differed significantly at all markers except for sex, CD4+ T-cell count, nevirapine use, ALT, AST/ALT ratio, serum cholesterol, and Apo A1, though the failure of sex to reach statistical significance may have been influenced by the low number of females overall. The significant differences observed with respect to platelet counts and hyaluronic acid level might be due to the low platelet and high hyaluronic acid level among the participants with advanced fibrosis in this study.
Univariate analyses of the association between the clinical and biochemical markers with fibrosis stage are shown in Table 3. Eleven of the 17 markers evaluated were significantly correlated with fibrosis stage. These were considered for inclusion in the logistic regression for the predictive model. Increased A2M, decreased platelets, and increased hyaluronic acid were most closely related to increased stage of fibrosis. The other significant factors, in order of decreasing significance were AFP, age, haptoglobin, GGT, cumulative ART experience, AST/platelet ratio, PT-INR, and bilirubin.
Model generation
Through model selection by backward elimination, eight out of the 11 markers that were significantly related to fibrosis stage were included in the predictive model. In addition, ART experience (yes/no), instead of the cumulative ART experience (which was continuous), was used in the model, as information about this binary factor was easier to obtain, and it also led to a slightly better performance than the cumulative ART experience. The resultant diagnostic score was S = 6.281 + 0.370 × (ART experience) - 3.739 × ln(platelets) + 0.063 × ln(GGT) + 0.058 × (age) + 0.054 × (AFP) + 0.236 × (total bilirubin) + 2.000 × ln(A2M) - 0.663 × ln(haptoglobin).
The AUROC for the above diagnostic score was 0.904. To achieve 90% sensitivity, the cutoff for S needs to be no more than -1.3; to achieve 90% specificity, the cutoff needs to be no less than 0.1. At cutoff of -1.3, the sensitivity was 91.4%, with specificity 72.3%, positive predictive value 64%, and negative predictive value 94%. At cutoff of 0.1, the specificity was 90.8%, with sensitivity 74.3%, the positive predictive value 81.2% and negative predictive value 86.8%. Eighty-three percent of the biopsies fell either above or below the cutoffs and could thus be classified with 90% sensitivity and specificity. Of these 83% of biopsies, 87.9% were correctly identified. The receiver operating characteristic curve is shown in Fig. 2.
The above AUROC and the model performance evaluation could be too optimistic, due to the fact that the diagnostic score was both trained and tested on the same data. The validation by bootstrap, which compared favorably with the standard cross-validation techniques especially for sample sizes, was applied to correct the possible overestimation of the diagnostic score performance. The following were results from the validation: the validated AUROC was 0.8154. At the cutoff of -1.3, the sensitivity was 76.2%, the specificity was 69.9%, the positive predictive value (PPV) was 58.4%, and the NPV was 85.2%; at the cutoff of 0.1, the sensitivity was 64.2%, specificity was 86.9%, PPV was 72.9% and NPV was 82.0%.
Genomic analysis
Twenty-two genes were identified as significantly modulated between biopsies with mild and advanced fibrosis. The two most significantly modulated genes were the cell surface marker alanine aminopeptidase N (AAN) (P < 0.005), also known as CD13, and mitogen-activated protein kinase kinase 3 (MKK3) (P < 0.006).
Generation and comparison of models in the genomics subset
The eight markers identified as predictive in the larger set were included in logistic regression modeling in the 51-biopsy subset. The resulting eight-marker diagnostic score was S = 2.66 + 0.41 × (ART experience) + 0.04 × (age) + 0.11 × (AFP) - 1.11 × (bilirubin) - 2.83 × ln(platelets) - 0.16 × ln(GGT) + 2.16 × log(A2M) - 0.40 × log(haptoglobin). The AUROC for the diagnostic score was 0.856.
Microarray data from the 22 genes previously identified by ANOVA analysis were then included with the eight clinical markers and logistic regression modeling was again performed to yield a second model. Four markers (A2M, haptoglobin, AAN, MKK3) were retained as significantly predictive in this model. The resulting four-marker diagnostic score was S = -20.6 + 0.79 × AAN + 1.03 × MKK3 + 1.65 × ln(A2M) - 0.36 × ln(haptoglobin). The AUROC was 0.852 (Fig. 2).
Although the eight-parameter model seemed to have slightly higher AUROC than the four-parameter model, the difference between the two was not statistically significant (P = 0.96). It was thus reasonable to expect that the four-parameter model should have a similar performance as the eight-parameter model if validated over the full data of 100 biopsies. In fact, over the 51 biopsies, the validated AUROC for the four-parameter model was 0.796.
Discussion
In this study, we describe a novel set of four markers (A2M, haptoglobin, MAP MKK3 gene, and AAN gene), which accurately predicted the stage of liver fibrosis in HIV-infected individuals. This set of four markers includes two novel genetic markers expressed in PBMCs functioning as surrogate markers of hepatic fibrosis. Our findings suggest that the novel four-biomarker set had the same accuracy in predicting the degree of liver fibrosis when compared with that of a more elaborate eight-biomarker set. Our study also illustrates the validity of utilizing novel approaches such as whole genome wide analysis of transcriptional profiling in PBMC to identify novel biomarkers.
Determination of the stage of liver fibrosis is necessary to assess the stage of liver disease, monitor disease progression and also as a determinant for treatment of HCV and for follow-up of response to therapy in HIV/HCV-coinfected patients. Liver biopsy is the gold standard. However, it is invasive, associated with significant morbidity and is highly variable due to the small sample size relative to the entire liver. Several noninvasive models of liver fibrosis have thus been proposed, most using various combinations of serum markers representing inflammation and ECM breakdown and formation. Although these models have generally performed well with regard to the differentiation of fibrosis, it remains important to identify novel biomarkers that may prove more robust. Furthermore, many of the existing biomarkers were developed retrospectively using laboratory data routinely collected for monitoring liver disease. Therefore, identification of novel biomarkers will not only enable us to differentiate degrees of liver fibrosis but also to understand the systemic pathophysiology of liver fibrosis in these patients.
The use of '-omics' technology has not been widely applied to the creation of noninvasive models of liver fibrosis. Utsunomiya et al. [15] found that a model based on hepatocyte gene-expression signatures accurately predicted fibrosis, though this technique was invasive. Poon et al. [16] combined serum proteomics and clinical data to create a noninvasive model of fibrosis in chronic hepatitis B. However, no study to our knowledge has thus far used genomic analysis to discover new serum biomarkers of liver fibrosis.
In our study, we first created a model based on clinical and biochemical markers. We found that a model consisting of six serum markers (A2M, platelets, haptoglobin, GGT, AFP, and bilirubin) and two clinical markers (age, ART experience) accurately predicted fibrosis, with an AUROC of 0.904 (bootstrapped AUROC = 0.8154), which compared favorably with previous models. Using cutoffs set to 90% sensitivity and specificity, 83% of the biopsies were classifiable and the model correctly identified 87.9% of these biopsies. Based on our model, 83% of all liver biopsies would thus have been avoidable with high accuracy. The accuracy of this model may approach the limit of accuracy when compared with biopsy. Mathematical modeling by Afdhal et al. has indicated that assuming an 80-90% accuracy of biopsy largely secondary to sampling error, noninvasive models cannot achieve an AUROC better than 0.9 [17,18].
Interestingly, the inclusion of ART experience improved the prediction of fibrosis in this study, whereas cumulative ART use was not sufficiently independently predictive to be included in the model. There are conflicting reports regarding the role of ART in liver fibrosis in HIV/HCV-coinfected patients. Some studies [19] have found that ART therapy slows the progression of liver fibrosis, whereas others have found that ART use accelerates fibrosis. The stronger predictive performance for the dichotomous versus continuous variable with fibrosis in our study is difficult to interpret, but may indicate an initial hepatotoxic response followed by a long-term protective response. A second interpretation may be that initiation of ART was the result of HIV disease progression and associated with HCV progression, though CD4+ T-cell count was not associated with fibrosis in our study. Nevirapine, in particular, has been associated with liver fibrosis [20]; however, our study did not find an association, though this may be due to the small number of patients on NVP.
Two models were then created using data from a subset of biopsies that had associated genomics data. One model contained the same eight clinical and biochemical markers as the first model. The second additionally contained data from genomic analysis of PBMCs and resulted in a model combining two biochemical markers and two genomic markers. This four-marker model performed equivalently to the eight-marker model, with AUROCs of 0.852 and 0.856, respectively, a difference that was statistically insignificant. Given the smaller number of parameters, this model is more likely to be robust than the eight-marker model when applied to a validation set of HIV/HCV-coinfected patients, due to a smaller risk of 'over-fitting'. Additionally, because it lacks an ART parameter, the four-marker model is more likely to be applicable to predicting fibrosis in a more diverse array of chronic liver disease, if this biomarker set is validated in a larger clinical cohort of HIV/HCV-coinfected patients. The approach of using DNA microarrays is to generate a model, which could then be validated by using less expensive methods such as multiplex bDNA amplification or real time PCR along with the two clinical parameters. This methodology is much more cost effective and adaptable in many centers.
The two genes identified to be most significantly positively associated with fibrosis and to best predict fibrosis in this analysis of PBMCs were mitogen-associated protein MKK3 and AAN, which has been shown to be identical to CD13 [21]. These genes are both biologically plausible with regard to the pathogenesis of liver fibrosis and are thus likely to be robust markers. AAN is a membrane-associated metalloproteinase expressed by a range of cells, including macrophages, neutrophils, fibroblasts, and epithelial cells. It has been shown to both be involved in the chemotaxis of T lymphocytes [22], as well in the degeneration of the ECM, particularly in the setting of tumor angiogenesis [23]. Although it has not been previously implicated in liver fibrosis, increased expression of AAN has been shown to be associated with poor outcome in biliary atresia, also an inflammatory process of the liver [24].
MKK3 is an upstream component of the p38 mitogen-associated protein kinase (MAPK) signal transduction pathway, which is activated predominantly by cytokines and by environmental stresses such as osmotic changes and inflammation. Transforming growth factor-β1 (TGF-β1) has been shown to potently induce the p38 MAPK pathway, inducing ECM synthesis and fibrotic change in renal mesangial cells and hepatic stellate cells (HSCs) [25]. Genomic analysis of HSCs in response to the fibrogenic agent CCl4 has indicated that upregulation of the p38 MAPK pathway is strongly associated with fibrosis progression [26]. Finally, a selective inhibitor of the p38 MAPK pathway has been shown to improve cirrhosis in a rat model [27].
In conclusion, our model, incorporating eight markers (six biochemical and two clinical), accurately predicts fibrosis in HIV/HCV-coinfected patients and correctly classifies biopsies as containing either mild or advanced fibrosis. Thus, this model could spare biopsy for 83% of these patients by accurately predicting their status of fibrosis. In a subset of biopsies with genomic data, a four-marker model containing two novel candidate genetic markers, AAN and MKK3, performed equivalently compared with the eight-marker model in predicting fibrosis. Although AAN and MKK3 are biologically plausible markers of fibrosis, this is the first study that has linked their upregulation in PBMCs to liver fibrosis. The association between these genes and the role of PBMCs in fibrogenesis are thus deserving of further investigation. It is highly plausible that the gene expression profile in PBMCs serves as a surrogate for the inflammatory response in the liver. It will also be important to validate our models in larger clinical trials and compare how they perform prospectively and in relation to previously published models. It is also necessary to test the validity of this model and novel biomarkers in other subsets of patients that include HIV/hepatitis B virus (HBV)-coinfected, HIV, HCV, and HBV-monoinfected individuals. Such studies will have to be performed prospectively given the lack of availability of stored samples that is needed to quantify levels of expression of AAN and MKK3. These studies will help us determine whether the clinical utility of our model is restricted to a subset of patients coinfected with HCV and HIV or a global biomarker for liver fibrosis irrespective of the causality of chronic liver disease. Nevertheless, this study indicates that use of DNA microarray technology is a viable approach to the development of biomarkers for noninvasive modeling of liver fibrosis.
Acknowledgements
The present research was supported in whole by the Intramural Research Program of the NIH (National Institute of Allergy and Infectious Diseases). D.S. conducted the study, M.M. was the study coordinator, Z.H. performed statistical analysis, D.K. was the hepatopathologist, B.W. performed liver biopsies, R.L. performed assays, A.S., M.A.P., and H.M. designed the study and S.K. was the principal investigator. We would like to thank Ms Mary Vailati for her assistance with co-ordinating laboratory assays for this study.
The content of this publication does not necessarily reflect the views of policies of the Department of Health and Human Services, nor does mention of trade names, commercial products or organization imply endorsement by the US Government.
The authors have no conflicts of interest.
Presented at the 14th Conference on Retroviruses and Opportunistic infections at Los Angeles, CA, 25-28 February 2007.
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