Biomarkers for monitoring pre-analytical quality variation of mRNA in blood samples.

There is an increasing need for proper quality control tools in the pre-analytical phase of the molecular diagnostic workflow. The aim of the present study was to identify biomarkers for monitoring pre-analytical mRNA quality variations in two different types of blood collection tubes, K2EDTA (EDTA) tubes and PAXgene Blood RNA Tubes (PAXgene tubes). These tubes are extensively used both in the diagnostic setting as well as for research biobank samples. Blood specimens collected in the two different blood collection tubes were stored for varying times at different temperatures, and microarray analysis was performed on resultant extracted RNA. A large set of potential mRNA quality biomarkers for monitoring post-phlebotomy gene expression changes and mRNA degradation in blood was identified. qPCR assays for the potential biomarkers and a set of relevant reference genes were generated and used to pre-validate a sub-set of the selected biomarkers. The assay precision of the potential qPCR based biomarkers was determined, and a final validation of the selected quality biomarkers using the developed qPCR assays and blood samples from 60 healthy additional subjects was performed. In total, four mRNA quality biomarkers (USP32, LMNA, FOSB, TNRFSF10C) were successfully validated. We suggest here the use of these blood mRNA quality biomarkers for validating an experimental pre-analytical workflow. These biomarkers were further evaluated in the 2nd ring trial of the SPIDIA-RNA Program which demonstrated that these biomarkers can be used as quality control tools for mRNA analyses from blood samples.

Prediction of mild cognitive impairment that evolves into Alzheimer's disease dementia within two years using a gene expression signature in blood: a pilot study.

BACKGROUND: The focus on Alzheimer's disease (AD) is shifting from dementia to the prodromal stage of the disorder, to a large extent due to increasing efforts in trying to develop disease modifying treatment for the disorder. For development of disease-modifying drugs, a reliable and accurate test for identification of mild cognitive impairment (MCI) due to AD is essential. OBJECTIVE: In the present study, MCI progressing to AD will be predicted using blood-based gene expression. MATERIAL AND METHODS: Gene expression analysis using qPCR was performed on blood RNA from a cohort of patients with amnestic MCI (aMCI; n = 66). Within the aMCI cohort, patients progressing to AD within 1 to 2 years were grouped as MCI converters (n = 34) and the patients remaining at the MCI stage after 2 years were grouped as stable MCI (n = 32). AD and control populations were also included in the study. RESULTS: Multivariate statistical method partial least square regression was used to develop predictive models which later were tested using leave-one-out cross validation. Gene expression signatures that identified aMCI subjects that progressed to AD within 2 years with a prediction accuracy of 74%-77% were identified for the complete dataset and subsets thereof. CONCLUSION: The present pilot study demonstrates for the first time that MCI that evolves into AD dementia within 2 years may be predicted by analyzing gene expression in blood. Further studies will be needed to validate this gene signature as a potential test for AD in the predementia stage.

A gene expression pattern in blood for the early detection of Alzheimer's disease.

A whole genome screen was performed using oligonucleotide microarray analysis on blood from a large clinical cohort of Alzheimer's disease (AD) patients and control subjects as clinical sample. Blood samples for total RNA extraction were collected in PAXgene tubes, and gene expression analysis performed on the AB1700 Whole Genome Survey Microarrays. When comparing the gene expression of 94 AD patients and 94 cognitive healthy controls, a Jackknife gene selection based method and Partial Least Square Regression (PLSR) was used to develop a disease classifier algorithm, which gives a test score indicating the presence (positive) or absence (negative) of AD. This algorithm, based on 1239 probes, was validated in an independent test set of 63 subjects comprising 31 AD patients, 25 age-matched cognitively healthy controls, and 7 young controls. This algorithm correctly predicted the class of 55/63 (accuracy 87%), including 26/31 AD samples (sensitivity 84%) and 29/32 controls (specificity 91%). The positive likelihood ratio was 8.9 and the area under the receiver operating characteristic curve (ROC AUC) was 0.94. Furthermore, the algorithm also discriminated AD from Parkinson's disease in 24/27 patients (accuracy 89%). We have identified and validated a gene expression signature in blood that classifies AD patients and cognitively healthy controls with high accuracy and show that alterations specific for AD can be detected distant from the primary site of the disease.

A novel blood test for the early detection of Alzheimer's disease.

Despite a variety of testing approaches, it is often difficult to make an accurate diagnosis of Alzheimer's disease (AD), especially at an early stage of the disease. Diagnosis is based on clinical criteria as well as exclusion of other causes of dementia but a definitive diagnosis can only be made at autopsy. We have investigated the diagnostic value of a 96-gene expression array for detection of early AD. Gene expression analysis was performed on blood RNA from a cohort of 203 probable AD and 209 cognitively healthy age matched controls. A disease classification algorithm was developed on samples from 208 individuals (AD = 103; controls = 105) and was validated in two steps using an independent initial test set (n = 74; AD = 32; controls = 42) and another second test set (n = 130; AD = 68; controls = 62). In the initial analysis, diagnostic accuracy was 71.6 ± 10.3%, with sensitivity 71.9 ± 15.6% and specificity 71.4 ± 13.7%. Essentially the same level of agreement was achieved in the two independent test sets. High agreement (24/30; 80%) between algorithm prediction and subjects with available cerebrospinal fluid biomarker was found. Assuming a clinical accuracy of 80%, calculations indicate that the agreement with underlying true pathology is in the range 85%-90%. These findings suggest that the gene expression blood test can aid in the diagnosis of mild to moderate AD, but further studies are needed to confirm these findings.

Gene expression profiling of peripheral blood cells for early detection of breast cancer.

INTRODUCTION: Early detection of breast cancer is key to successful treatment and patient survival. We have previously reported the potential use of gene expression profiling of peripheral blood cells for early detection of breast cancer. The aim of the present study was to refine these findings using a larger sample size and a commercially available microarray platform. METHODS: Blood samples were collected from 121 females referred for diagnostic mammography following an initial suspicious screening mammogram. Diagnostic work-up revealed that 67 of these women had breast cancer while 54 had no malignant disease. Additionally, nine samples from six healthy female controls were included. Gene expression analyses were conducted using high density oligonucleotide microarrays. Partial Least Squares Regression (PLSR) was used for model building while a leave-one-out (LOO) double cross validation approach was used to identify predictors and estimate their prediction efficiency. RESULTS: A set of 738 probes that discriminated breast cancer and non-breast cancer samples was identified. By cross validation we achieved an estimated prediction accuracy of 79.5% with a sensitivity of 80.6% and a specificity of 78.3%. The genes deregulated in blood of breast cancer patients are related to functional processes such as defense response, translation, and various metabolic processes, such as lipid- and steroid metabolism. CONCLUSIONS: We have identified a gene signature in whole blood that classifies breast cancer patients and healthy women with good accuracy supporting our previous findings.