The performance of interferon-gamma release assays in tuberculosis patients with type II diabetes

The sensitivity of Interferon-γ release assays for detection of Mycobacterium tuberculosis (MTB) infection or disease is affected by conditions that depress host immunity (such as HIV). It is critical to determine whether these assays are affected by diabetes and related conditions (i.e. hyperglycemia, chronic hyperglycemia, or being overweight/obese) given that immune impairment is thought to underline susceptibility to tuberculosis (TB) in people with diabetes. This is important for tuberculosis control due to the millions of type 2 diabetes patients at risk for tuberculosis worldwide.^ The objective of this study was to identify host characteristics, including diabetes, that may affect the sensitivity of two commercially available Interferon-γ (IFN-γ) release assays (IGRA), the QuantiFERON®-TB Gold (QFT-G) and the T-SPOT®.TB in active TB patients. We further explored whether IFN-γ secretion in response to MTB antigens (ESAT-6 and CFP-10) is associated with diabetes and its defining characteristics (high blood glucose, high HbA1c, high BMI). To achieve these objectives, the sensitivity of QFT-G and T-SPOT. TB assays were evaluated in newly diagnosed, tuberculosis confirmed (by positive smear for acid fast bacilli and/or positive culture for MTB) adults enrolled at Texas and Mexico study sites between March 2006 and April 2009. Univariate and multivariate models were constructed to identify host characteristics associated with IGRA result and level of IFN-γ secretion.^ QFT-G was positive in 68% of tuberculosis patients. Those with diabetes, chronic hyperglycemia or obesity were more likely to have a positive QFT-G result, and to secrete higher levels of IFN-γ in response to the mycobacterial antigens (p<0.05). Previous history of BCG vaccination was the only other host characteristic associated with QFT-G result, whereby a higher proportion of non-BCG vaccinated persons were QFT-G positive, in comparison to vaccinated persons. In a separate group of patients, the T-SPOT.TB was 94% sensitive, with similar performance in all tuberculosis patients, regardless of host characteristics.^ In summary, we have demonstrated the validity of QFT-G and T-SPOT. TB to support the diagnosis of TB in patients with a range of host characteristics, but most notably in patients with diabetes. We also confirmed that TB patients with diabetes and associated characteristics (chronic hyperglycemia or BMI) secreted higher titers of IFN-γ when stimulated with MTB specific antigens, in comparison to patients without these characteristics. Together, these findings suggest that the mechanism by which diabetes increases risk to TB may not be explained by the inability to secrete IFN-γ, a key cytokine for TB control.^

Interferon-gamma release assays for latent tuberculosis infection in child and young adult candidates or recipients of tumor necrosis factor inhibitor therapy

Background. Inhibition of tumor necrosis factor (TNF) is associated with progression of latent tuberculosis infection (LTBI) to active disease. LTBI screening prior to starting TNF inhibitor therapy is recommended. Blood tests, collectively known as interferon-gamma release assays (IGRAs), offer a means other than the tuberculin skin test (TST) of screening for LTBI. However, in the setting of immune compromise, anergy may limit the clinical utility of IGRAs. ^ Methods. A cross-sectional study was conducted in children and young adults ≤ 21 years of age who were cared for at Texas Children's Hospital in Houston, TX, during 2011 and who were candidates for, or were receiving, tumor necrosis factor (TNF)-inhibitor therapy. All subjects answered a risk factor questionnaire and were tested for LTBI by two commercially available IGRAs (QuantiFERON-Gold In-Tube assay and the T-SPOT.TB assay), along with the TST. T-cell phenotypes were evaluated through flow cytometry, both at baseline and after antigen stimulation. ^ Results. Twenty-eight subjects were enrolled. All were TST negative and none were IGRA positive. Results were negative for the 27 subjects who were tested with QuantiFERON-Gold In-Tube. However, 26% of subjects demonstrated anergy in the T-SPOT.T. Patients with T-SPOT. TB anergy had lower quantitative IFN-γ responses to mitogen in the QFT assay—the mean IFN-γ level to mitogen in patients without T-SPOT.TB anergy was 9.84 IU/ml compared to 6.91 IU/ml in patients with T-SPOT.TB anergy (P = 0.046). Age and use of TNF inhibitors, corticosteroids, or methotrexate use were not significantly associated with T-SPOT.TB anergy. Antigen stimulation revealed depressed expression of intracellular IFN-γ in subjects with T-SPOT. TB anergy. ^ Conclusions. The frequency of anergy in this population is higher than would be expected from studies in adults. There appears to be inappropriate IFN-γ responses to antigen in subjects with T-SPOT. TB anergy. This immune defect was detected by the T-SPOT. TB assay but not by the QuantiFERON-Gold In-Tube assay. Further data are needed to clarify the utility of IGRAs in this population.^

INTEGRATIVE BIOMARKER IDENTIFICATION AND CLASSIFICATION USING HIGH THROUGHPUT ASSAYS

It is well accepted that tumorigenesis is a multi-step procedure involving aberrant functioning of genes regulating cell proliferation, differentiation, apoptosis, genome stability, angiogenesis and motility. To obtain a full understanding of tumorigenesis, it is necessary to collect information on all aspects of cell activity. Recent advances in high throughput technologies allow biologists to generate massive amounts of data, more than might have been imagined decades ago. These advances have made it possible to launch comprehensive projects such as (TCGA) and (ICGC) which systematically characterize the molecular fingerprints of cancer cells using gene expression, methylation, copy number, microRNA and SNP microarrays as well as next generation sequencing assays interrogating somatic mutation, insertion, deletion, translocation and structural rearrangements. Given the massive amount of data, a major challenge is to integrate information from multiple sources and formulate testable hypotheses. This thesis focuses on developing methodologies for integrative analyses of genomic assays profiled on the same set of samples. We have developed several novel methods for integrative biomarker identification and cancer classification. We introduce a regression-based approach to identify biomarkers predictive to therapy response or survival by integrating multiple assays including gene expression, methylation and copy number data through penalized regression. To identify key cancer-specific genes accounting for multiple mechanisms of regulation, we have developed the integIRTy software that provides robust and reliable inferences about gene alteration by automatically adjusting for sample heterogeneity as well as technical artifacts using Item Response Theory. To cope with the increasing need for accurate cancer diagnosis and individualized therapy, we have developed a robust and powerful algorithm called SIBER to systematically identify bimodally expressed genes using next generation RNAseq data. We have shown that prediction models built from these bimodal genes have the same accuracy as models built from all genes. Further, prediction models with dichotomized gene expression measurements based on their bimodal shapes still perform well. The effectiveness of outcome prediction using discretized signals paves the road for more accurate and interpretable cancer classification by integrating signals from multiple sources.