WO2007139777A2 - Procédé permettant de diagnostiquer et prédire le pronostic de la maladie d'alzheimer par profilage de protéines csf - Google Patents

Procédé permettant de diagnostiquer et prédire le pronostic de la maladie d'alzheimer par profilage de protéines csf Download PDF

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WO2007139777A2
WO2007139777A2 PCT/US2007/012155 US2007012155W WO2007139777A2 WO 2007139777 A2 WO2007139777 A2 WO 2007139777A2 US 2007012155 W US2007012155 W US 2007012155W WO 2007139777 A2 WO2007139777 A2 WO 2007139777A2
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features
group
disease
samples
csf
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PCT/US2007/012155
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WO2007139777A3 (fr
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Ronald C. Hendrickson
Jeffrey L. Seeburger
Matthew Wiener
Nathan A. Yates
Qinghua Song
Andy Liaw
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Merck & Co., Inc.
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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
    • G16B40/10—Signal processing, e.g. from mass spectrometry [MS] or from PCR

Definitions

  • the present invention relates generally to the diagnosis and prognosis in the field of Alzheimer's disease. More specifically, it relates to biomarkers that can be used to classify Alzheimer's disease or to determine the efficacy of drugs given to treat Alzheimer's disease.
  • AD Alzheimer's disease
  • Basal forebrain cholinergic neurons The degeneration of these cells leads to a secondary loss of neurons in the limbic system and cortex that control learning and memory.
  • the consequent symptoms of the disease include a progressive loss of memory, the loss of the ability to communicate and the loss of other cognitive functions which occur over a course of approximately eight years.
  • patients often become bedridden and completely unable to care for themselves.
  • Aricept donepezil
  • Aricept the clinical effects of these are modest and none are able to significantly alter the course of the disease.
  • AD age-related disorders
  • Requisite to improving the treatment of AD is improving the ability of clinicians to accurately diagnose the disease early in its course and to accurately monitor the progression of the disease.
  • a diagnosis of possible or probable AD is typically made based on clinical symptoms.
  • a definitive diagnosis of AD can only be made post-mortem and requires a pathological examination of the affected brain tissue.
  • the key pathological hallmarks of the disease are plaques consisting of deposited amyloid beta (A / 8) protein and tangles consisting of degenerated neuronal cells and their cytoskeletal elements (neurofibrillary tangles).
  • a / 8 amyloid beta
  • tangles consisting of degenerated neuronal cells and their cytoskeletal elements (neurofibrillary tangles).
  • CSF cerebrospinal fluid
  • the CSF proteins that have received the most attention are those thought to reflect key features of the disease pathogenesis, including A ⁇ deposition and neuronal degeneration.
  • a ⁇ 42 is a cleavage product of the amyloid precursor protein (APP) and is thought to be a major constituent of the senile plaque.
  • APP amyloid precursor protein
  • Biomarkers can be used for multiple purposes including as diagnostic markers to classify and identify patients, as drug response markers to confirm target engagement, as disease predictive markers to predict who is likely to develop the disease and as disease progression markers to reflect the progression of the pathophysiology.
  • a recent review article describes not only the status of biochemical biomarkers but also imaging biomarkers and their use in longitudinal clinical trials (Thai, L. J., et al, Alzheimer Pis. Assoc. Disord.. 20(1): 6-15 (2006)).
  • Mass spectrometry is capable of detecting large numbers of analytes in complex mixtures. A wide range of different analytes can be detected including those of environmental and biological importance.
  • Peptides are an example of biologically important analytes.
  • Peptides and peptides derived from proteins interact in complex ways to regulate cellular functions. Small changes in the abundance of particular proteins or their modifications can significantly alter the functioning of a cell, can impact on the overall health of an animal and can provide an indication as to the health of a cell or animal. Proteomic studies measuring peptide expression are increasingly making use of mass spectrometry, Smith, Richard D., Trends in Biotechnology. Vol. 20, No. 12 (SuppL), S3-S7 (2002).
  • the present invention relates to compositions and methods for classifying disease states in Alzheimer's disease using mass spectrometry data analysis techniques that can be employed to selectively identify analytes differing in abundance between different sample sets.
  • the sample sets comprise CSF samples from ante-mortem confirmed cases of Alzheimer's disease (AD) and normal or non-AD controls used to build a classifier that can be used to analyze additional unknown samples.
  • AD Alzheimer's disease
  • the invention comprises a method for classifying disease states in Alzheimer's disease ("AD") comprising a) selecting a statistically relevant set of mass spectrometric features from human ante- mortem and healthy control fluid samples in which a plurality of features are differentially expressed to form a reference AD and control panel; b)conducting a linear discriminate analysis on the mass spectrometric feature data from step (a); c) obtaining a test fluid sample from a patient; d) gathering mass spectrometric data on the test sample including the features in the set of step (a); e) applying the results of step (d) to the linear discriminate analysis of step (b) to obtain an output; and f) determining from the output of step (e) the classification of the disease state, where the output is either AD or control, hi another aspect of this embodiment, the set of mass spectrometric features is a plurality of features selected from the group of features listed in Table 4. In a more preferred aspect, the set of features comprises features selected from the group consisting of group 2 and group
  • Another embodiment of the invention comprises a method for predicting cognition scores for Alzheimer's disease ("AD") patients comprising a) selecting a statistically relevant set of mass spectrometric features from human ante- mortem and healthy control fluid samples in which a plurality of features are differentially expressed to form a reference AD and control panel; b) conducting a random forest analysis on the multi-analyte data from step (a); c) obtaining a test fluid sample from a patient; d) gathering mass spectrometric data on the test sample including the features in the set of step (a); e) applying the results of step (d) to the random forest analysis of step (b) to obtain an output; and f) determining from the output of step (e), where the output is the assignment of the cognition score.
  • AD Alzheimer's disease
  • the set of mass spectrometric features is a plurality of features selected from the group of features listed in Table 4.
  • the set of features comprises biomarkers selected from the group consisting of the summed features from group 2, group 3, group 7, group 18, group 19, group 24, group 28, group 36, group 37, group 42, group 51 and group 52.
  • the invention comprises disease status biomarkers selected from the group consisting of SMEl (group 2) and SME2 (group 51).
  • Figure 1 is a graphical depiction of the validation of FTMS and dMS platform using human CSF.
  • the coefficient of variance (CV) was determined for 7,478 molecular ion features binned at 10 unit intervals from five technical replicate experiments. The average and median CV were 36.3% and 31.5%, respectively.
  • FIG. 1 shows the estimate of false positive rate and robustness of false positive estimate by receiver-operator-curve (ROC curve) analysis.
  • Figure 3 shows a subset of differentially expressed peptides identified by proteomic profiling of the OPTIMA pilot human CSF samples.
  • the first column of each pair represents the control, while the second column represents the AD sample.
  • Figures 4A and 4B show a linear discriminant classification of AD versus control samples (done on two samples each) based on two groups of MS features ("+" : control; "•” : AD).
  • Figure 4 A shows the separation of two MS features;
  • Figure 4B shows the separation of A ⁇ 40 and A ⁇ 42 done by ELISA.
  • Figures 5A and 5B show the cross-validated error rates verus the number of features for random forest classifiers.
  • Figure 5A shows the cross-validated error rates verus number of features for random forest classifiers built from the irnmunodepletion data from the Blennow cross-sectional cohort.
  • Figure 5B shows the cross-validated error rates versus number of features for random forest classifiers built from the combined immunodepletion and ultrafiltration data from the Blennow cross-sectional cohort.
  • Figures 6 A and 6B show the estimated trends over time for two Subject Matter Expert (“SME") features in the longitudinal data: SMEl (Fig. 6A) and SME2 (Fig. 6B).
  • SME Subject Matter Expert
  • the left panel shows the AD group as compared to the control group in the right panel.
  • the lines represent the "population average" trends estimated from linear mixed effects models.
  • classifier refers to a computational method that takes as input features and yields as output a state marker.
  • a state marker may be binary (for example, "Alzheimer's disease” or “control”), have multiple discrete states (for example, the integers from 1 to 100 inclusive), or be drawn from a continuous range (for example, any number between 0 and 1).
  • feature refers to the mass spectrometric signal characterized by a particular mass-to-charge ratio (m/z) or a range of such ratios and a range of retention times from a liquid chromatography column.
  • m/z mass-to-charge ratio
  • features may be grouped together based on evidence that they arise from a single analyte (as multiple isotopes and/or charge states).
  • the ion intensity of a particular feature in a particular sample can be measured by a mass spectrometer.
  • the ion intensity of a group is the sum of ion intensities for the features making up the group.
  • features or “groups of features” maybe determined from proteins, peptides, metabolites, lipids, or other analytes in a sample being analyzed. “Features” or “groups of features” are used to define the panel of analytes that distinguish, on a statistical basis, a sample from an AD patient from that of a non-AD, control sample or to measure disease progression.
  • feature extraction algorithm is a computational tool that measures the AUC of "features” present in one or more mass spectrometric files.
  • Two examples of feature extraction algorithms used in this work include dMS (U.S. Patent 6,906,320) and PeakTeller (PCT/US06/044166 and U.S.S.N. 11/599,185 filed on November 13, 2006).
  • the term "attribute” refers to a biological, chemical or physical property of a feature that is quantifiable, such as mass to charge ratio (m/z), charge state, elution time, or ion intensity.
  • immunodepletion refers to a biochemical process used to isolate a sub-population of peptides, proteins, or metabolites from a biological fluid.
  • a example of a suitable immunodepletion method for use herein is describe in Example 1.
  • the term “ultrafiltration” refers to a biochemical process used to isolate a sub-population of peptides, proteins or metabolites from a biological fluid. Ultrafiltration was performed by Digilab-BioVisioN, Hannover, Germany under a master fee-for- service agreement as described in Example 6.
  • LC-MS refers to the tandem use of liquid chromatography (“LC”) and mass spectrometyr ("MS”) for detecting and quantifying one or more peptides or molecules from a biological sample for potential use as a biomarker.
  • LC-MS profiling As used herein, the term “profiling”, “LC-MS profiling” or “protein profiling” refers to the measurement of analyte abundance in one or more samples.
  • AUC values refers to area under the curve or other measure of the abundance of a feature.
  • sensitivity refers to the ability of an individual marker or a composite of markers to correctly identify patients with the disease, i.e. Alzheimer's disease, which is the probability that the test is positive for a patient with the disease.
  • the current clinical criterion on patients having a probability of AD is about 85% sensitive when compared to autopsy confirmed cases.
  • the term "specificity" refers to the ability of an individual marker or a composite of markers to correctly identify patients that do not have the disease, that is, the probability that the test is negative for a patient without disease.
  • the current clinical criterion is that the test should be 75% specific.
  • the term “accuracy” refers to the overall ability of an individual marker or a composite of markers to correctly identify those patients with the disease and those without the disease.
  • the term “estimated effect of AD” refers to the estimated percentage change in a feature per year in the disease population. The current standard for dementia is a decrease of about 6% per year.
  • CSF cerebrospinal fluid
  • the invention described herein relates to the use of proteomics to profile human CSF and to identify human CSF markers for Alzheimers Disease (AD).
  • AD Alzheimers Disease
  • Applicants have obtained access to a well annotated longitudinal cohort of human CSF samples, OPTIMA (Oxford Project to Investigate Memory and Ageing).
  • OPTIMA Olford Project to Investigate Memory and Ageing
  • Applicants used archived OPTIMA CSF samples from clinically diagnosed AD patients and age-matched non-demented controls.
  • the object of the study herein was to determine if unbiased proteomic profiling could identify protein markers that discriminate the samples from the ante-mortem diagnosed AD patients versus those from non-AD/non-demented (control) patients.
  • biochemical processing and proteomic profiling of the CSF samples on a fourier transform mass spectrometer (Finnigan LTQ-FTTM, Thermo Electron Corporation, Waltham, MA) and differential mass spectrometry (dMS) platform supervised differential mass spectrometry analysis (dMS) was performed to reveal approximately 250 distinct peptide signals that exhibit a statistically significant difference in abundance between AD and controls. Fifteen (15) peptide signals are increased in AD and 235 peptide signals are decreased in AD as compared to the control group.
  • a selected set of the 250 peptides was targeted for protein identification. Proteins identified include prostaglandin-h2D isomerase, amyloid-like protein 1 precursor and ApoE.
  • a classifier using twelve proteomics markers was created that distinguishes between the AD and control samples with high accuracy in cross validation tests. To date, nine of the twelve peptides in this classifier have been identified by tandem mass spectrometric analysis, and these are derived from four proteins, including, neurosecretory protein VGF (5 peptides), secretograninin-1 (2 peptides), neuronal pentraxin receptor (1 peptide), and cadherin-13 (1 peptide).
  • proteomics that is the systematic analysis of all of the proteins in a tissue or cell, to find biomarkers is based upon an assumption that protein markers of disease exist in plasma of cerebrospinal fluid ("CSF").
  • CSF cerebrospinal fluid
  • a pattern analysis approach typically uses two dimensions to describe a peptide ion, such as chromatographic elution time and mass.
  • the advantage of such an approach is that all the features that are detected by mass spectrometry can be quantified.
  • the shotgun approach only quantifies identified peptides.
  • sample preparation methods such as for sample collection, fractionation, and digestion
  • LC-MS liquid chromatograph-mass spectrometry
  • dMS differential mass spectrometry
  • OPTIMA is a longitudinal cohort of human volunteers who participated in annual assessment of their memory and cognitive status and who also underwent a series of neuropsychological, radiological (CT and SPECT scans) and various biochemical tests on their blood at regular intervals. CSF samples were obtained from a subset of patients who consented specifically for this procedure. After death, an autopsy was performed and the brains were examined by a neuropathologist for clinical diagnosis. To date, the autopsy rate is 94%.
  • Applicants have obtained access to a limited volume of cerebrospinal fluid (1-3 ml) collected at each assessment with associated clinical and radiological data in approximately 100 AD cases and a similar number of controls.
  • a dMS analysis between spiked (3 ⁇ g/ml) and non-spiked CSF yielded 54 differences at p 0.05.
  • the limits of detection for this experiment have been estimated at 1 ⁇ g/ml based on this study and previous studies on Rhesus CSF by Applicants (data not shown) although a precise titration with horse myoglobin has not as yet been carried out.
  • OPTIMA samples are archived samples and hence there was no opportunity to modify the collection protocol, Applicants set out to determine the amount of plasma contamination and red blood cell contamination due to hemolysis by performing ELISA measurement of alpha-2 macroglobin, IgM, and hemoglobin. This information allowed Applicants to perform a retrospective analysis to look for samples that may be outlyers and to look for any systematic bias that may be present in the archived samples due to uncontrolled pre- analytical variables.
  • Alpha 2-macroglobin and IgM are major proteins known to be present in plasma that are not removed by use of an immunodepletion column and high-levels of each was evidence of plasma contamination.
  • Hemoglobin is the major protein in red blood cell and high levels of hemoglobin was indicative of hememolysis that occurred prior to removal of any unwanted cells.
  • a 150 ⁇ l aliquot of archived CSF from each sample was thawed on ice and the three commercially available ELISA assays were performed. Each assay was performed in duplicate with 15 ⁇ l for total protein measurement (BCA), 50 ⁇ l for IgM, 25 ⁇ l for hemoglobin, 25 ⁇ l for alpha-2 microglobulin. Results of these assays are shown in Table 2.
  • Hemoglobin data are expressed as the mean value of absorbance at 450 nm (OD450) and samples with an "*" exceed 10 RBC/ ⁇ l.
  • dMS analysis was performed to reveal approximately 250 peptides quantitatively different between the AD and control samples: 15 peptides which were increased in the AD samples and 235 peptides which were decreased in the AD samples as compared to the control group. A selected subset of these 250 peptides was targeted for protein identification. Targeted analysis was compared with the original dMS data to align and link the targeted MS/MS spectra with the dMS feature data.
  • various alternative methods or protocols can be used to augment the detection of features that are useful for characterizing Alzheimer's disease as described herein.
  • Methods that allow for the reproducible collection and processing of biological specimens may allow for the detection of features from various body fluids or tissue samples.
  • Those skilled in the art would recognize that various methods for processing these samples for use in the methods described herein can produce a range of analyses and determines the value of the m/z and retention time for these analytes.
  • the use of protein and peptide isolation steps can shift the m/z ratios and retention time of the observed feature.
  • the use of alternative digestive enzymes and/or chemical reaction that modify the chemical composition of the analytes can result in different m/z and retention time values.
  • Chemical separations and methods of chromatographic analysis can alter the m/z and retention time value of an analyte.
  • Various methods of ionization, detection and mass analysis also impact the absolute value of a features m/z and retention time. Examples include Matrix assisted laser desorption ionization, Electrospray ionization, Desorption electrospray ionization, atmospheric pressure chemical ionization, electron impact ionization, chemical ionization, tandem mass spectrometric analysis, time-of-flight mass analysis, ion-mobility analysis, quadrupole mass analysis, and Fourier transform mass analysis.
  • classifiers were built as described in the Examples that follow. Classifiers were based on mass spectrometric features determined to be significantly different between samples from AD and control samples in a single run using all samples.
  • a feature is specified by an m/z and a time range, and for each LC-MS data set, the feature's value is the area under the curve for the feature, that is, the sum of measured intensities for that m/z in that time range. .
  • Some signals are believed to arise as multiple charge states and/or isotopes of a single underlying analyte; these signals are said to form a group. For this analysis, area under curve was added together for all features in a group to create "group features.”
  • One classifier comprising twelve protein markers, distinguished between AD and control samples with high accuracy in cross validation test in this study (Table 3). To date, nine of the twelve peptides in the classifier have been identified by tandem mass spectrometric analysis. A total of four proteins have been identified that are associated with the twelve peptides, including, neurosecretory protein VGF (5 peptides), secretograninin-1 (2 peptides), neuronal pentraxin receptor (1 peptide), and cadherin-13 (1 peptide). Table 3.
  • mass spectrometry based proteomics does not have the sensitivity of classical ELISA or immunoassay techniques, it does provide an advantage as to an unbiased approach to identify and quantify protein changes without the need for antibody reagents.
  • a classifier was developed using the twelve protein markers that distinguished between AD and control samples with high accuracy in a cross validation test in this study.
  • the proteins identified in these studies include Apo E, amyloid-like protein 1 precursor, and neuroendocrine factors, such as VGF and chromogranin.
  • a classifier uses the values of a set of features from a sample as input and transform them into a prediction as to which group (or class) the sample belong to (or how likely that the sample belong to a group).
  • Such a classifier is usually constructed by a "training" step, where a set of samples with known values of the input features and their group membership is given to an algorithm and the algorithm builds a mathematical relationship between the input features and the group membership by "learning" from the data.
  • Algorithms in common use for building classifiers include, but are not limited to, random forests, support vector machines, neural networks, logistic regression, linear discriminant analysis, and the like.
  • Table 4 could be used as input to construct a classifier.
  • exemplary feature selection algorithms include principal components (The Element of Statistical Learning, Hastie T, Tibshirani R, and Friedman J, 2001, Springer), genetic algorithm (Pattern Classification, 2 nd Ed., Duda R, Hart P, Stork D, 2001, John Wiley & Sons), and the like.
  • a preferred feature selection procedure, and the one used herein, is the method proposed in Svetnik et al., "Application of Breiman's Random Forest to Modeling Structure- Activity Relationships of Pharmaceutical Molecules," Multiple Classifier Systems, F. RoIi, J.
  • Example 6 Applicants used a second independent cohort of clinical CSF samples to demonstrate markers of disease state and disease status.
  • the diagnosis for the patients in this cohort were provided by Dr. Kaj Blennow (Sahlgrenska University Hospital, G ⁇ teborg, Sweden) using standard methodology and included a follow up visit approximately two years after the initial visit.
  • Patients were categorized into five diagnostic categories: control (CTL), Alzheimer's (AD), stable mild cognitive impairment (MCI-MCI), mild cognitive impairment converted to Alzheimer's (MCI-AD) and vascular dementia (VaD).
  • CTL control
  • AD Alzheimer's
  • MCI-MCI stable mild cognitive impairment
  • MCI-AD mild cognitive impairment converted to Alzheimer's
  • VaD vascular dementia
  • Applicants also processed 1 ml of CSF by ultrafiltration as described in Example 6. From each LC-MS data set Applicants extracted the AUCs for features found to differentiate AD from control CSF via the dMS feature extraction algorithm. In the immunodepletion set, 633 dMS groups were extracted having at least two features among the top 1000 dMS groups. From the ultrafiltration set, 267 groups were extracted having at least two features among the top 1000 dMS groups. From each extracted dMS group, the feature with the minimum mean log likelihood (product of p-values) was chosen as the most representative.
  • the random forest classifier generates an importance ranking for the features.
  • the features having importance measures deemed to be above noise are kept and a final classifier built from them.
  • the methods described in Svetnik et al., ibid., were used to determine the number of features to retain in the final random forest classifier. From the 633 features for immunodepletion, 158 features were retained.
  • the classifier has prediction accuracies 93.3% (CTL), 96.7% (AD) 5 54.2% (MCI-MCl), 42.9% (MCI-AD), and 60% (VaD).
  • the classifier built on the combined data has prediction accuracies 100% (CTL), 96.7% (AD), 83.3% (MCI-MCI), 85.7% (MCI-AD), and 60% (VaD).
  • Figures 5 A and 5B show the cross-validated error rates versus the number of features for the random forest classifier of the immunodepletion data (Fig. 5A) and the combined immunodepletion and ultrafiltration data (Fig. 5B).
  • Table 5 A shows the cross-validated confusion matrix of a random forest classifier from the cross-sectional Blennow cohort, in which the samples were processed by immunodepletion prior to LC-MS analysis. Each row represents the true diagnosis and each of the first five columns represents the diagnosis predicted by the classifier. The last column shows the prediction accuracy of the classifier for each diagnosis group. Table 5A
  • the classifier has prediction accuracies of 100% for CTL, 96.7% for AD, 83.3% for MCI-MCI, 85.7% for MCI-AD and 60% for VaD.
  • Table 5B shows the cross-validated confusion matrix of a random forest classifier from the cross-sectional Blennow cohort, based on data from the combined immunodepletion and ultrafiltration analysis. Each row represent the true diagnosis, and each of the first five columns the diagnosis predicted by the classifier. The last column shows the prediction accuracy of the classifier for each diagnosis group.
  • Example 7 Applicants used the identified features as disease status markers in a longitudinal CSF study to determine whether they were a valid measure of disease progression.
  • the intensities (AUCs) of these two features (SME1/SME2) in the longitudinal CSF samples were quantified.
  • a linear mixed effects model is fitted to the log AUC of each feature as described in Example 7.
  • Table 6 shows the result of the mixed model analysis. The results indicate that both features (SMEl and SME2) show a statistically significant decline (- 8.8% to -12.3% change per year) in the AD patients (p-values in the fourth column all smaller than 0.05). The rate of change is statistically significantly different between the AD and control patients (p-values in the fifth column are all smaller than 0.05).
  • Example 8 To identify potential markers for use in disease progress, Applicants extracted features from the longitudinal CSF samples as described in Example 8. The features from the longitudinal samples derived from dMS underwent the selection process described in Example 8. Those features that met the selection criteria are shown in Table 7. These features show statistically significant change in magnitude over time (as the disease progresses) for the patients in the AD group, but not for patients in the control group.
  • proteomic features of the invention can be used in other way for Alzheimer's disease.
  • the proteomic features described herein can be used to screen patients and characterize their disease state or for a primary diagnosis of AD. Disease state information from longitudinal studies with the features could be used for understanding as to how to treat the disease, for the enrolling and monitoring patients in clinical trials, or to correct an errant disease diagnosis.
  • Those skilled in the art would understand that various measurement techniques can also be envisioned that would allow the proteomics features to be measured in a variety of research, clinical and day-to-day settings. For example, the development of antibody based tests or hand held analytical devices can be envisioned that would allow for routine AD screening, for improved diagnosis accuracy, and for the selection of appropriate treatments that the patient may respond to.
  • Peptides were analyzed by a fourier transform mass spectrometer (FTMS) using a 65 minute gradient.
  • the gradient had four distinct sections; a) 100% A [H2O 0.1 M acetic acid] at a flow rate of 3 ⁇ l/rain from 0 to 4 minutes, b) binary gradient from 0% solvent B [90% acetonitrile, 0.1 M acetic acid] to 30% B from 4.01 minutes to 29 minutes at a flow rate of 1 ⁇ l/min, c) 29.01 minutes to 39 minutes to 90% B at 1 ⁇ l /min, and d) 40 minutes to 65 minutes 100% A at 1 ⁇ l /min.
  • FTMS Fourier transform mass spectrometer
  • Results are further analyzed by looking for features that have consecutive statistically significant differences over a sufficiently long time range.
  • myoglobin was added into neat CSF at concentrations ranging from 1 ⁇ g/mL to 30 ⁇ g/mL before immunodepletion.
  • ELISA kits for hemoglobin and A2M were obtained from ALPCO ( Windham, NH); the IgM ELISA kit was obtained from Bethyl Inc (Austin, TX).
  • the quantitative sandwich ELISA for hemoglobin, IgM, and A2M were based on manufacturer's instruction with modifications on standard and sample dilution, HRP-conjugates dilution, and incubation condition.
  • assay volumes of 100 ⁇ l per well were used, with the exception of 100 ⁇ l of stopping solution for IgM and 50 ⁇ L of stopping solution for - hemoglobin.
  • Standard, CSF sample, capture antibody and HRP conjugated antibody were diluted in a dilution buffer containing 1% BSA and then directly applied to the designed wells.
  • A2M ELISA 10 ⁇ l of pre-diluted standard and CSF samples were transferred into 200 ⁇ l per well of 0.9% NaCl solution in a pre-coated plate; the HRP-conjugated antibody was diluted in wash buffer (PBS containing 0.05% Tween 20) without BSA. Except for the aforementioned specifications, the assay procedure for all ELISA was the same. CSF samples were thawed on ice and centrifuged at 4000 rpm for 4 minutes at 4°C prior to assay.
  • dMS label free LC-MS method for finding statistically significant differences in complex peptide and protein mixtures as previously described (Wiener et al., 2004). Differences were identified by performing a 10 x 10 group wise comparison of the disease samples versus control samples. To estimate the number of false positives that might be included in the detected differences, Applicants randomly divided the data into two groups of ten, with five control and five disease samples in each group, and performed a similar calculation. For a given confidence threshold (similar to ap-value), one can determine the number of differences (statistically significant features) in the original comparison and the estimated number of false positive results ⁇ from the second comparison) at that confidence threshold.
  • the random forest procedure produces a measure of each variable's importance.
  • a variable is considered important if randomly shuffling that variable substantially decreases the random forest's ability to correctly classify. Because eliminating less important variables has been shown to improve classifier performance in some cases, we reduced the number of variables by half several times, training and testing a new random forest based on the remaining variables.
  • a random forest classifier using only twelve features (the summed features from group 2, group 19, group 51, group 7, group 28, group 18, group 52, group 36, group 3, group 24, group 42 and group 37, in order of importance as estimated by random forest) automatically selected from the available features was able to perfectly distinguish control from AD samples in the training set, and also performed well in re-sampling tests (Table 3).
  • a random forest classifier based on the six features measured by ELISA performed similarly (data not shown).
  • a simple linear discriminant analysis using only two features (group 2 and group 51, for the mass spectrometry data, and A ⁇ -40 and A/3-42 measured by ELISA) could perfectly distinguish the two groups if given all the data for training.
  • the cross- validation revealed that classifiers do not always generalize perfectly even on data collected at the same time; a totally separate data set would present an even greater challenge.
  • Figures 4A and 4B show that the separation provided by two of the MS features in a linear discriminant analysis was similar to that provided by A/340 and Aj842 measured by ELISA.
  • CTL control
  • AD Alzheimer's
  • MCI-MCI stable mild cognitive impairment
  • VaD vascular dementia
  • 633 dMS groups were extracted having at least two features among the top 1000 dMS groups.
  • 267 groups were extracted having at least two features among the top 1000 dMS groups.
  • the feature with the minimum mean log likelihood (product of p-values) was chosen as the most representative.
  • the selected features (633 for immunodepletion and 261 for ultrafiltration) were then used to build a random forest classifier for the five diagnostic categories: control (CTL), Alzheimer's (AD), stable mild cognitive impairment (MCI-MCI), mild cognitive impairment converted to Alzheimer's (MCI-AD) and vascular dementia (VaD).
  • the random forest classifier generates an importance ranking for the features.
  • the features having importance measures deemed to be above noise are kept and a final classifier built from them.
  • the methods described in Svetnik et al., supra, were used to decide on the number of features to retain in the final random forest classifier. From the 633 features for immunodepletion, 158 features were retained. From the 267 features for the combined immunodepletion and ultrafiltration set, 67 features were retained. Combining the two data sets, 225 features were retained out of the 900.
  • Progression of disease state The methods and the proteomic features described herein were used to measure the progression of Alzheimer's disease.
  • SMEl was selected and is feature ID 8993803 (Table 4).
  • SME2 was selected and is feature 3D 8994035 (Table 4).
  • the CSF samples were collected at approximately one year intervals to establish the change of disease status over time or longitudinally.
  • Immunodepletion biochemical sample processing was performed on the CSF longitudinal samples using the method described in Example 1.
  • LC-MS profiling was performed using the method described in Example 2, features were identified, and AUC values extracted from the LC-MS data using the feature extraction algorithm described previously.
  • a linear mixed effects model that assumed a linear relationship between log AUC and time and was fitted to the data for each feature. The model allows for a "population average" relationship between log AUC and time and for trends between AD and control groups to be different (the fixed effects), as well as allowing each patient to have his/her own trend over time (random effects).
  • Applicants used dMS to obtain a list of features and their corresponding AUC values in the OPTIMA longitudinal clinical data set as described in Example 7. From these features, selection was made based on those having at least 80% positive AUC values in both AD and control samples. A linear mixed effect model similar to those described in Example 7 was fitted to each of the features. Those features having Q-values for time effect in the AD group and the Q-values for the difference in time effect between the AD and control groups smaller than 0.1 are shown in Table 7.

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Abstract

La présente invention concerne un moyen nouveau et sensible de surveiller la maladie d'Alzheimer. Ledit procédé consiste à construire une classification valide sur le plan statistique comprenant une pluralité de caractéristiques, grâce à l'analyse par spectrométrie de masse différentielle de biomarqueurs individuels, de façon à évaluer de façon plus juste et objective l'état d'un individu, dans le but de classifier la maladie et de prédire les points de fin dudit individu sur le plan cognitif.
PCT/US2007/012155 2006-05-26 2007-05-22 Procédé permettant de diagnostiquer et prédire le pronostic de la maladie d'alzheimer par profilage de protéines csf WO2007139777A2 (fr)

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP2287604A1 (fr) * 2008-06-13 2011-02-23 Eisai R&D Management Co., Ltd. Procédé d'examen de la maladie d'alzheimer
EP2304431A1 (fr) * 2008-07-25 2011-04-06 Merck & Co., Inc. Biomarqueurs de liquide céphalorachidien (csf) pour la prédiction d'un déclin cognitif chez des patients souffrant de la maladie d'alzheimer

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107202833A (zh) * 2017-06-21 2017-09-26 佛山科学技术学院 一种水体中铜离子污染程度的快速检测方法

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6114175A (en) * 1994-07-19 2000-09-05 University Of Pittsburgh Compound for the antemortem diagnosis of Alzheimer's Disease and in vivo imaging and prevention of amyloid deposition
US6875434B1 (en) * 1997-12-02 2005-04-05 Neuralab Limited Methods of treatment of Alzheimer's disease
US20050244890A1 (en) * 2003-11-07 2005-11-03 Davies Huw A Biomarkers for Alzheimer's disease

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6114175A (en) * 1994-07-19 2000-09-05 University Of Pittsburgh Compound for the antemortem diagnosis of Alzheimer's Disease and in vivo imaging and prevention of amyloid deposition
US6875434B1 (en) * 1997-12-02 2005-04-05 Neuralab Limited Methods of treatment of Alzheimer's disease
US20050244890A1 (en) * 2003-11-07 2005-11-03 Davies Huw A Biomarkers for Alzheimer's disease

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
LUNETTA K.L. ET AL.: 'Screening Large-Scale Association Study Data: Exploiting Interactions Using Random Forests' BMC GENETICS vol. 5, December 2004, pages 1 - 3 *

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP2287604A1 (fr) * 2008-06-13 2011-02-23 Eisai R&D Management Co., Ltd. Procédé d'examen de la maladie d'alzheimer
EP2287604A4 (fr) * 2008-06-13 2011-06-08 Eisai R&D Man Co Ltd Procédé d'examen de la maladie d'alzheimer
JP5106631B2 (ja) * 2008-06-13 2012-12-26 エーザイ・アール・アンド・ディー・マネジメント株式会社 アルツハイマー病の検査方法
EP2304431A1 (fr) * 2008-07-25 2011-04-06 Merck & Co., Inc. Biomarqueurs de liquide céphalorachidien (csf) pour la prédiction d'un déclin cognitif chez des patients souffrant de la maladie d'alzheimer
EP2304431A4 (fr) * 2008-07-25 2011-11-02 Merck & Co Inc Biomarqueurs de liquide céphalorachidien (csf) pour la prédiction d'un déclin cognitif chez des patients souffrant de la maladie d'alzheimer

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