The diagnosis of Alzheimer's disease can be improved by the use of biological measures. Biomarkers of functional impairment, neuronal loss, and protein deposition that can be assessed by neuroimaging (ie, MRI and PET) or CSF analysis are increasingly being used to diagnose Alzheimer's disease in research studies and specialist clinical settings. However, the validation of the clinical usefulness of these biomarkers is incomplete, and that is hampering reimbursement for these tests by health insurance providers, their widespread clinical implementation, and improvements in quality of health care. We have developed a strategic five-phase roadmap to foster the clinical validation of biomarkers in Alzheimer's disease, adapted from the approach for cancer biomarkers. Sufficient evidence of analytical validity (phase 1 of a structured framework adapted from oncology) is available for all biomarkers, but their clinical validity (phases 2 and 3) and clinical utility (phases 4 and 5) are incomplete. To complete these phases, research priorities include the standardisation of the readout of these assays and thresholds for normality, the evaluation of their performance in detecting early disease, the development of diagnostic algorithms comprising combinations of biomarkers, and the development of clinical guidelines for the use of biomarkers in qualified memory clinics.

Novel diagnostic criteria for Alzheimer's disease (AD) incorporate biomarkers, but their maturity for implementation in clinical practice at the prodromal stage (mild cognitive impairment [MCI]) is unclear. Here, we evaluate cerebrospinal fluid (CSF) β-amyloid42 (Aβ42), total tau, and phosphorylated tau in the light of a 5-phase framework for biomarker development. Ample evidence is available for phase 1 (identifying useful leads) and phase 2 (assessing the accuracy for AD dementia versus controls) for CSF biomarkers. Phase 3 (utility in MCI) is partially achieved. In cohorts with long follow-up time, CSF Aβ42, total tau, and phosphorylated tau have high diagnostic accuracy for MCI due to AD. Phase 4 (performance in real world) is ongoing, and phase 5 studies (quantify impact and costs) are to come. Our results highlight priorities to pursue and to enable the proper use of CSF biomarkers in the clinic. Priorities are to reduce measurement variability by introduction of fully automated assay systems; to increase diagnostic specificity toward non-AD neurocognitive diseases at the MCI stage; and to clarify the role of CSF biomarkers versus other biomarker modalities in clinical practice and in design of clinical trials. These efforts are currently ongoing.

Cold-tolerant plants may endure subzero temperatures partially by inhibiting the development of ice crystals in the intercellular spaces and the xylem through the accumulation of antifreeze proteins (AFP) and the extra production of carbohydrates. Certain proteins associated with pathogen resistance in plants have the ability to bind and alter the growth of ice crystals. In this study, the accumulation of pathogenesis-related (PR) proteins and the development of freezing tolerance in seedlings of two latitudinal distinct Norway spruce (Picea abies L. Karst.) ecotypes were investigated. Despite freezing tolerance difference, timing of growth cessation and bud set variations, our results showed that there is no significant difference in the concentration of soluble carbohydrates between the two ecotypes. Immunoblots showed the presence of several β-1,3-glucanase and thaumatin PR proteins in the apoplastic fluid and the enzymatic assay showed an extra accumulation of several isoforms of PR chitinases in cold-treated Norway spruce needles. In addition to PR proteins, a presence of de novo protein in cold-treated needles was noticed. In contrary to mature plants, total proteins isolated from freezing-tolerant Norway spruce seedling did not show antifreeze activity. Our results suggest that the activity of the PR proteins and the accumulation of soluble carbohydrates that increased during cold acclimation may have an indirect impact on the freezing tolerance in Norway spruce, however, deciphering the direct mechanism behind freezing tolerance in Norway spruce seedlings growing under controlled environmental conditions require further investigation.

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.

Treatment of Alzheimer's disease (AD) is significantly hampered by the lack of easily accessible biomarkers that can detect disease presence and predict disease risk reliably. Fluid biomarkers of AD currently provide indications of disease stage; however, they are not robust predictors of disease progression or treatment response, and most are measured in cerebrospinal fluid, which limits their applicability. With these aspects in mind, the aim of this article is to underscore the concerted efforts of the Blood-Based Biomarker Interest Group, an international working group of experts in the field. The points addressed include: (1) the major challenges in the development of blood-based biomarkers of AD, including patient heterogeneity, inclusion of the "right" control population, and the blood brain barrier; (2) the need for a clear definition of the purpose of the individual markers (e.g., prognostic, diagnostic, or monitoring therapeutic efficacy); (3) a critical evaluation of the ongoing biomarker approaches; and (4) highlighting the need for standardization of preanalytical variables and analytical methodologies used by the field. (C) 2014 The Alzheimer's Association. All rights reserved.

BACKGROUNDThe 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.OBJECTIVEIn the present study, MCI progressing to AD will be predicted using blood-based gene expression.MATERIAL AND METHODSGene 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.RESULTSMultivariate 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.CONCLUSIONThe 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.

BACKGROUND A blood-based test for the early detection of Parkinson's disease (PD) would be an important diagnostic tool and useful for patient selection when developing novel drugs or treatments for the disease. OBJECTIVE Here, we aimed to identify potential biomarkers associated with PD. METHODS We applied gene expression profiling to the study of peripheral blood from 75 healthy control subjects and 79 PD patients at different stages of the disease. Healthy control subjects were matched for age and gender with PD subjects, and the diagnosis of patients was based on clinical evaluation by specialists in movement disorders. RNA was extracted from the blood samples and the gene expressions were measured using the Illumina HumanHT-12 v4.0 Expression BeadChip. RESULTS Our results support previous studies that gene expression in blood may be instrumental in the search for molecular biomarkers for PD. Single cross-validation results show that PD can be correctly classified from healthy controls with an agreement of 88% to clinical diagnosis. De novo PD patients are classified with a sensitivity of 87%, which is close to what was achieved for the patients having a confirmed PD diagnosis with disease duration <5 and >5 years (93% and 88%). A double cross-validation procedure showed that using a selected set of around 650 informative genes, similar results are achieved. Functional analysis of the selected genes showed genes significantly associated to mitochondrial dysfunction, protein ubiquitination, gene expression and cell death. CONCLUSIONS PD affects gene expression in blood, suggesting the potential for the development of a blood-based gene expression test.

A blood based test for prodromal Alzheimer's disease (AD) will allow for early initiation of treatment before clinical symptoms of dementia are evident. Such a test may also support in drug development by enabling homogenous cohorts in the clinical trials reducing trial size and costs. In a proof of concept (PoC) study we identified a gene expression signature in blood that detected MCI progressing to AD (prodromal AD) in an MCI population. We have now undertaken additional studies to further investigate and develop this gene expression signature. Blood samples were obtained from the DiaGenic biobank that previously had been collected from DiaGenic's multi-center blood collection studies in EU and the US. Total RNA was isolated from blood sampled in PAXgene“¢ tubes. Gene expression in blood was investigated using RT-qPCR on ViiA7 Dx (Life Technologies) for 384 TaqMan assays (384-assay cards) resulting in a quality approved dataset. Data modelling and analysis were performed using multivariate statistical methods. In the previous PoC study we developed the first blood based prodromal AD gene expression biomarker based on an algorithm (model) using the gene expression information from 20 assays to predict MCI progressing to AD in an MCI population. The prediction accuracy of detecting prodromal AD was 74%, sensitivity 74% and specificity 75%. Now we present new results from additional studies on developing and evaluating this gene expression signature for prodromal AD. We have demonstrated that it is possible to detect individuals with MCI progressing to AD (prodromal AD) within 2 years from AD diagnosis based on analysis of gene expression in blood. The new results will increase our understanding of the utility of this potential diagnostic biomarker for prodromal AD.

Recent discoveries and developments in the field of genomics have led to the commercialization of novel diagnostic devices for studying disease or estimating therapeutic outcomes in individual patients. With this emerging field, the emphasis is shifting to integration of clinical research into product development. Data acquisition is primary in the initial exploratory phase of product development, and during the process of sample collection and data generation in clinical microarray studies, great amounts of additional information, such as demographic, clinical, and study design variables associated with the data, are often accumulated and made available. Including additional information in classification has been addressed in many different ways. However, in previous studies, the additional information have consistently been treated as extra predictors, which can be a problem for future prediction if such information are not available or collectable for the new samples. We instead propose to adopt a method called canonical partial least squares, which for our purpose, only uses the additional information at the model building stage to stabilize the construction of a classifier for disease status from microarray data. The canonical partial least squares method is compared with regular partial least squares for the classification of Parkinson's disease from gene expression in peripheral blood samples and also through computer simulations. The present study showed that including clinical data in the model building produces simpler and more stable models for prediction of Parkinson's disease from gene expression data.

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.

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 ADpatients, 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.

There is clear evidence that the Alzheimer's disease (AD) develops several years before clinical symptoms. The successful development of disease-modifying therapies will heavily depend on an early intervention and there is an unmet need for biomarkers that are able to identify with good accuracy subjects with mild cognitive impairment (MCI) progressing to AD. Booij et al. [1] identified 1239 informative gene probes that are able to predict AD. The primary objective of this proof of concept study is to identify a gene expression signature distinguishing individuals with stable MCI from individuals with MCI developing AD. The signatures will be investigated using a selection of gene probes from Booij et al. [1] and from the literature and 3 x 384 gene assays including references were distributed on TaqMan micro-fluidic cards (MFC) for real-time PCR analysis using ViiA7. RNA was extracted from blood collected in PAXgene tubes obtained from study studies in Norway and Sweden. The RNA samples are stored in the DiaGenic Biobank. Both subjects with stable MCI and subjects with MCI converting to AD were included, with 30 subjects in each group. All subjects were diagnosed with MCI at baseline, and subjects with confirmed MCI at follow-up after 2 years were defined as subjects with stable MCI. Subjects with confirmed AD diagnosis at follow-up after 2 years were defined as MCI converting to AD. Blood samples collected at baseline were used for gene expression analysis. >Gene expression data will be analyzed to allow gene selection and classifier definition. Various techniques will be investigated, including principal component analysis, partial least squares with or without jack-knife selection, single gene probe classifiers and support vector machines. The expression profile differences between subjects with stable MCI and subjects with MCI converting to AD will be presented for the entire assay set and subsets of assays. Conclusions from the present ongoing study will be presented. Reference: [1] Booij et al., J Alzheimers Dis, 2011, 23, 109-119.

A blood-based gene expression test for the early detection of Alzheimer's disease (AD) has been developed [[1],[1]] intended to aid in the diagnosis of mild to moderate AD. The test detects systemic effects of the disease as a specific gene expression signature in peripheral blood. Algorithmic evaluation of the gene expression signature results in a prediction value indicating the presence or absence of AD. The gene expression signature was derived from a whole genome screening study. The gene expression signature was selected based on the predictive value of the algorithm and not on a presumed association with AD pathology. The presentation aims at demonstrating the biological significance of the 96 gene expression signature of ADtect® test. The 96 gene expression signature was investigated, including database and literature search. Although the 96 gene expression signature was selected based on predictive value of the algorithm and not on a presumed association with AD pathology, still there are more than 30 genes encoding proteins with a biological function associated with AD, brain or neuronal function [[1]]. The presentation will investigate the biological significance of the ADtect® gene expression signature. In the ADtect® 96 gene expression signature, more than 30 genes encode proteins with biological functions associated with AD pathology. These include processing of APP, amyloid-beta, tau, and mitochondria as well as inflammation, calcium regulation and ubiquitin-associated protein processing suggesting an association between the genes included in ADtect® and the biology of AD.

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.