Strategic roadmap for an early diagnosis of Alzheimer's disease based on biomarkers.

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.

Clinical validity of cerebrospinal fluid Aß42, tau, and phospho-tau as biomarkers for Alzheimer's disease in the context of a structured 5-phase development framework.

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) ß-amyloid42 (Aß42), 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 Aß42, 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.

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.

The future of blood-based biomarkers for Alzheimer's disease.

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.

Found in transcription: accurate Parkinson's disease classification in peripheral blood.

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.

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.

Microarray-based prediction of Parkinson's disease using clinical data as additional response variables.

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.

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.

Found in transcription: gene expression and other novel blood biomarkers for the early detection of breast cancer.

Early detection of a growing breast tumor is of key importance for patient survival. Despite limitations, mammography screening has improved the detection of breast tumors, however many tumors are not detected. This is especially true for younger women and women with high breast density. Novel diagnostic blood biomarkers either generated by the tumor and released into the blood, or generated by nontumor cells as a response to the tumor presence, can now potentially help improve the accuracy of early-stage breast cancer detection. They include multicomponent biomarkers, circulating tumor cells and RNA expression of peripheral blood. These novel biomarkers and their potential use will be presented and discussed in this review, with special emphasis on gene expression-based markers.

Biomarkers for Alzheimer disease in cerebrospinal fluid, urine, and blood.

Alzheimer disease is the most common cause of dementia, yet its clinical diagnosis remains uncertain until an eventual postmortem histopathology examination. Currently, therapy for patients with Alzheimer disease only treats the symptoms; however, it is anticipated that new disease-modifying drugs will soon become available.Diagnostic tools for detecting Alzheimer disease at an incipient stage that can reliably differentiate the disease from other forms of dementia are of key importance for optimal treatment. Biomarkers have the potential to aid in a correct diagnosis, and great progress has been made in the discovery and development of potentially useful biomarkers in recent years. This includes single protein biomarkers in the cerebrospinal fluid, as well as multi-component biomarkers, and biomarkers based on gene expression. Novel biomarkers that use blood and urine, the more easily available clinical samples, are also being discovered and developed. The plethora of potential biomarkers currently being investigated may soon provide biomarkers that fulfill different functions, not only for diagnostic purposes but also for drug development and to follow disease progression.

Early detection of breast cancer based on gene-expression patterns in peripheral blood cells.

INTRODUCTION: Existing methods to detect breast cancer in asymptomatic patients have limitations, and there is a need to develop more accurate and convenient methods. In this study, we investigated whether early detection of breast cancer is possible by analyzing gene-expression patterns in peripheral blood cells. METHODS: Using macroarrays and nearest-shrunken-centroid method, we analyzed the expression pattern of 1,368 genes in peripheral blood cells of 24 women with breast cancer and 32 women with no signs of this disease. The results were validated using a standard leave-one-out cross-validation approach. RESULTS: We identified a set of 37 genes that correctly predicted the diagnostic class in at least 82% of the samples. The majority of these genes had a decreased expression in samples from breast cancer patients, and predominantly encoded proteins implicated in ribosome production and translation control. In contrast, the expression of some defense-related genes was increased in samples from breast cancer patients. CONCLUSION: The results show that a blood-based gene-expression test can be developed to detect breast cancer early in asymptomatic patients. Additional studies with a large sample size, from women both with and without the disease, are warranted to confirm or refute this finding.

Effects of Rhizoctonia infection and drought on peroxidase and chitinase activity in Norway spruce (Picea abies).

Seedlings of Norway spruce were exposed to fungal infection and drought in order to investigate differences in their stress responses on the enzymatic level. Six-week-old seedlings were infected with the root rot fungus Rhizoctonia, or subjected to drought, respectively. Changes at the enzymatic level were more rapid and significantly higher in infected plants in comparison with drought-stressed spruce plants. Rhizoctonia infection resulted in early local and systemic increase in peroxidase and chitinase activity. The most prominent isoforms responding were highly basic peroxidases and chitinases (pI 9-9.5) and several acidic chitinases (pI3-4). An increased intensity of similar peroxidase isoforms was found in drought-affected plants. Two peroxidase isoforms (with pI < 9) accumulated exclusively in response to drought. These results suggest that at an early stage of infection and drought stress, the two stresses can be distinguished by the temporal appearance and isoform profile of peroxidases and chitinases. Changes in enzyme activity appeared before changes in physiological parameters, thus these isoform profiles could be used as early markers of stress conditions in spruce.

Induced responses to pathogen infection in Norway spruce phloem: changes in polyphenolic parenchyma cells, chalcone synthase transcript levels and peroxidase activity.

Polyphenolic parenchyma cells (PP cells) in Norway spruce (Picea abies (L.) Karst.) stem phloem play important roles in constitutive and inducible defenses. To determine whether anatomical and molecular changes in PP cells are correlated with tree resistance, we infected two Norway spruce clones with the pathogenic fungus Ceratocystis polonica (Siem.) C. Moreau. The fungus induced significantly different lesion lengths in the two clones, indicating that one clone was more resistant to the fungus (short lesions) than the other (long lesions). After infection, the cross-sectional area of PP cells and their vacuolar polyphenol bodies increased in the three most recent annual rings of PP cells in both clones. The more resistant clone had larger PP cells with denser polyphenol bodies than the less resistant clone, whereas the less resistant clone accumulated relatively more polyphenols after infection. Compared with the less resistant clone, the more resistant clone contained higher starch concentrations before infection that were reduced more quickly after infection before returning to original values. Low transcript levels of chalcone synthase were detected in uninfected tissues of both clones, but the levels increased dramatically after infection. Transcript levels were higher and peaked 6 days earlier in the more resistant clone than in the less resistant clone. The activity of at least one highly basic peroxidase isoform was greatly enhanced after infection, and this increase occurred earlier in the more resistant clone.

The putative gymnosperm plant defensin polypeptide (SPI1) accumulates after seed germination, is not readily released, and the SPI1 levels are reduced in Pythium dimorphum-infected spruce roots.

The putative plant defensin SPI1 cDNA from the conifer Norway spruce (Picea abies) is the only known plant defensin-like sequence from a gymnosperm. The predicted translational product SPI1 was not detected in the embryo or other parts of the seed by means of antibodies, but it accumulated in the root cortex after germination. In roots of seedlings infected with the root pathogenic oomycete Pythium dimorphum and the blue stain fungus Ceratocystis polonica, variable levels of SPI1 was detected during the first day as a response to the infection, however a significant increase was seen as an initial response to the root-rot fungus Heterobasidion annosum. After the first day of infection, the amount of SPI1 polypeptide was dramatically reduced in response to either of the pathogens, but not in response to the ectomycorrhizal fungus Laccaria bicolor. During the same time of infection, extensive damage to cortical root cells resulted from the infecting pathogens, but not from the mycorrhiza. These results indicate that pathogens may reduce the level of SPI1 by suppressing its expression, but may also reduce the SPI1 level by invading and disrupting the root cortical cells or by a combination of these mechanisms.