Project description:Differentially methylated oral squamous cell carcinoma (OSCC) biomarkers, identified in-vitro and validated in well-characterized surgical specimens, have shown poor clinical correlation in cohorts with different risk profiles. To overcome this lack of relevance we used the HumanMethylation27 BeadChip, publicly available methylation and expression array data, and Quantitative Methylation Specific PCR to uncover differential methylation in OSCC clinical samples with heterogeneous risk profiles. A two stage-design consisting of Discovery and Prevalence screens was used to identify differential promoter methylation and deregulated pathways in patients diagnosed with OSCC and head and neck squamous cell carcinoma. Promoter methylation of KIF1A (k=0.64), HOXA9 (k=0.60), NID2 (k=0.60), and EDNRB (k=0.60) had a moderate to substantial agreement with clinical diagnosis in the Discovery screen. HOXA9 had 68% sensitivity, 100% specificity and a 0.81 AUC. NID2 had 71% sensitivity, 100% specificity and a 0.79 AUC. In the Prevalence screen HOXA9 (k=0.82) and NID2 (k=0.80) had an almost perfect agreement with histologic diagnosis. HOXA9 had 85% sensitivity, 97% specificity and a 0.95 AUC. NID2 had 87% sensitivity, 95% specificity and a 0.91 AUC. A HOXA9 and NID2 gene panel had 94% sensitivity, 97% specificity and a 0.97 AUC. In saliva from OSCC cases and controls HOXA9 had 75% sensitivity, 53% specificity and a 0.75 AUC. NID2 had 87% sensitivity, 21% specificity and a 0.73 AUC. This Phase I Biomarker Development Trial identified a panel of differentially methylated genes in normal and OSCC clinical samples from patients with heterogeneous risk profiles. This panel may be useful for early detection and cancer prevention studies. The 12 samples analyzed comprise equal numbers of three tissue-types: Primary oral squamous cell carcinoma, Oral leukoplakia and Normal oral mucosa. Gender, age and smoking-status were approximately equally represented in these goups. Normal oral mucosa served as a control.

Project description:Background: Systemic inflammation is a whole body reaction that can have an infection-positive (i.e. sepsis) or infection-negative origin. It is important to distinguish between septic and non-septic presentations early and reliably, because this has significant therapeutic implications for critically ill patients. We hypothesized that a molecular classifier based on a small number of RNAs expressed in peripheral blood could be discovered that would: 1) determine which patients with systemic inflammation had sepsis; 2) be robust across independent patient cohorts; 3) be insensitive to disease severity; and 4) provide diagnostic utility. The overall goal of this study was to identify and validate such a molecular classifier. Methods and Findings: We conducted an observational, non-interventional study of adult patients recruited from tertiary intensive care units (ICU). Biomarker discovery was conducted with an Australian cohort (n = 105) consisting of sepsis patients and post -surgical patients with infection-negative systemic inflammation. Using this cohort, a four-gene classifier consisting of a combination of CEACAM4, LAMP1, PLA2G7 and PLAC8 RNA biomarkers was identified. This classifier, designated SeptiCyteÂ® Lab, was externally validated using RT-qPCR and receiver operating characteristic (ROC) curve analysis in five cohorts (n = 345) from the Netherlands. Cohort 1 (n=59) consisted of unambiguous septic cases and infection-negative systemic inflammation controls; SeptiCyteÂ® Lab gave an area under curve (AUC) of 0.96 (95% CI: 0.91-1.00). ROC analysis of a more heterogeneous group of patients (Cohorts 2-5; 249 patients after excluding 37 patients with infection likelihood possible) gave an AUC of 0.89 (95% CI: 0.85-0.93). Disease severity, as measured by Sequential Organ Failure Assessment (SOFA) score or the Acute Physiology and Chronic Health Evaluation (APACHE) IV score, was not a significant confounding variable. The diagnostic utility o f SeptiCyteÂ® Lab was evaluated by comparison to various clinical and laboratory parameters that would be available to a clinician within 24 hours of ICU admission. SeptiCyteÂ® Lab was significantly better at differentiating sepsis from infection-negative systemic inflammation than all tested parameters, both singly and in various logistic combinations. SeptiCyteÂ® Lab more than halved the diagnostic error rate compared to PCT in all tested cohorts or cohort combinations. Conclusions: SeptiCyteÂ® Lab is a rapid molecular assay that may be clinically useful in the management of ICU patients with systemic inflammation. SIRS and Sepsis ICU patients, admission samples Retrospective, mutli-site sutdy using retrospective physician adjudication as a comparator

Project description:Melioidosis, caused by Gram negative bacteria Burkholderia pseudomallei, is a major type of community-acquired septicemia in Southeast Asia and Northern Australia with high mortality and morbidity rate. More accurate and rapid diagnosis is needed for improving the management of septicemic melioidosis. We previously identified 37-gene candidate signature to distinguish septicemic melioidosis from sepsis due to other pathogens. The aims of this current study were to independently validate our previous biomarker and consolidate gene selection from each of our microarray data set for establishing a targeted assay for the differential diagnosis of melioidosis. Blood samples were collected from patients who presented with severe inflammatory response syndromes from 3 provincial hospitals in Northeast of Thailand during September 2009 and November 2011. Only culture-confirmed sepsis were included in the study (n=166). We generated a new microarray dataset comprising of 29 patients with septicemic melioidosis and 54 patients with sepsis due to other pathogens. Validation of the 37-gene signature using this new dataset demonstrated the prediction accuracy of approximately 80% for detecting type of sepsis. In order to develop a nanoliter-scale high throughput PCR technology, we further identified additional gene signature from this new microarray dataset and by revisiting our published data. Altogether 85 genes including 6 housekeeping genes were selected. Using multi-steps iteration approach we could reduce the number of biomarkers to 12 genes while the performance is comparable to that of the full panel. The high performance (accuracy >70%) of this 12-gene signature could be validated in a second independent set of samples. The 12-gene panel identified by our study provides high performance for the differential diagnosis of septicemic melioidosis. This finding will be useful for improving the management of septicemic melioidosis in term of diagnosis, treatment and follow up. Total RNA from whole blood obtained from patients with sepsis caused by B.pseudomallei (n=29) or other pathogens (n=54) and uninfected controls (28 healthy and 27 subjects with type 2 diabetes mellitus) were collected. In order to validate the published signature, microarray data were generated from these samples. This dataset was also used for an independent selection of signature for septicemic melioidosis. The same RNA samples were used for validation by a high throughput real-time PCR technique, Fluidigm.

Project description:Melioidosis, caused by Gram negative bacteria Burkholderia pseudomallei, is a major type of community-acquired septicemia in Southeast Asia and Northern Australia with high mortality and morbidity rate. More accurate and rapid diagnosis is needed for improving the management of septicemic melioidosis. We previously identified 37-gene candidate signature to distinguish septicemic melioidosis from sepsis due to other pathogens. The aims of this current study were to independently validate our previous biomarker and consolidate gene selection from each of our microarray data set for establishing a targeted assay for the differential diagnosis of melioidosis. Blood samples were collected from patients who presented with severe inflammatory response syndromes from 3 provincial hospitals in Northeast of Thailand during September 2009 and November 2011. Only culture-confirmed sepsis were included in the study (n=166). We generated a new microarray dataset comprising of 29 patients with septicemic melioidosis and 54 patients with sepsis due to other pathogens. Validation of the 37-gene signature using this new dataset demonstrated the prediction accuracy of approximately 80% for detecting type of sepsis. In order to develop a nanoliter-scale high throughput PCR technology, we further identified additional gene signature from this new microarray dataset and by revisiting our published data. Altogether 85 genes including 6 housekeeping genes were selected. Using multi-steps iteration approach we could reduce the number of biomarkers to 12 genes while the performance is comparable to that of the full panel. The high performance (accuracy >70%) of this 12-gene signature could be validated in a second independent set of samples. The 12-gene panel identified by our study provides high performance for the differential diagnosis of septicemic melioidosis. This finding will be useful for improving the management of septicemic melioidosis in term of diagnosis, treatment and follow up.

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Integrative ‘omic analysis of experimental bacteremia identifies a metabolic signature that distinguishes human sepsis from SIRS

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