EP3265810A1 - Procédés pour classer des populations comprenant des populations atteintes de la maladie d'alzheimer - Google Patents

Procédés pour classer des populations comprenant des populations atteintes de la maladie d'alzheimer

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Publication number
EP3265810A1
EP3265810A1 EP16714094.6A EP16714094A EP3265810A1 EP 3265810 A1 EP3265810 A1 EP 3265810A1 EP 16714094 A EP16714094 A EP 16714094A EP 3265810 A1 EP3265810 A1 EP 3265810A1
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Prior art keywords
data points
assay
disease
alzheimer
samples
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German (de)
English (en)
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Florin V. CHIRILA
Daniel L. Alkon
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Blanchette Rockefeller Neuroscience Institute
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Blanchette Rockefeller Neuroscience Institute
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/5005—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving human or animal cells
    • G01N33/5091—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving human or animal cells for testing the pathological state of an organism
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/68—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids
    • G01N33/6893—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids related to diseases not provided for elsewhere
    • G01N33/6896—Neurological disorders, e.g. Alzheimer's disease
    • 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
    • G16B25/00—ICT specially adapted for hybridisation; ICT specially adapted for gene or protein expression
    • 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
    • G16B25/00—ICT specially adapted for hybridisation; ICT specially adapted for gene or protein expression
    • G16B25/10—Gene or protein expression profiling; Expression-ratio estimation or normalisation
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
    • G16H10/40—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for data related to laboratory analysis, e.g. patient specimen analysis
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H30/00—ICT specially adapted for the handling or processing of medical images
    • G16H30/40—ICT specially adapted for the handling or processing of medical images for processing medical images, e.g. editing
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/28—Neurological disorders
    • G01N2800/2814—Dementia; Cognitive disorders
    • G01N2800/2821—Alzheimer
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
    • G16H10/20—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for electronic clinical trials or questionnaires

Definitions

  • AD Alzheimer's disease
  • AD is a neurodegenerative disorder characterized by the progressive decline of memory and cognitive functions. It is estimated that over five million Americans are living with this progressive and fatal disease. Alzheimer's destroys brain cells, causing memory loss and problems with thinking and behavior that decrease quality of life. AD has no known cure, but treatments for symptoms can improve the quality of life of the millions of people, and their families, suffering from AD.
  • An early diagnosis of AD gives the patient time to make choices that maximize quality of life, reduces anxiety about unknown problems, gives more time to plan for the future, and provides a better chance of benefiting from treatment.
  • Biomarker assays for AD are affected by the input variables related to the status of the cells before using in the assay.
  • F.V. Chirila et al. J. Alzheimer's Disease 33, 165-176 (2013)
  • W.Q. Zhao et al. Neurobiol. Dis. 1 1 , 166-183 (2002)
  • T.K. Khan et al. Proc. Natl. Acad. Sci. U.S.A. 103(35), 13203-13207 (2006)
  • T.K. Khan et al. Neurobiol Aging 2, ⁇ o), 889-900 (2008)
  • Such input variables include cell density (cell-cell interaction), age of the patients, passage number (number of cell population duplication), as well as other assay ingredients such as MatrigelTM, Dulbecco's Modified Eagle Medium (DMEM), or Fetal Bovine Serum (FBS).
  • cell density cell-cell interaction
  • age of the patients include age of the patients, passage number (number of cell population duplication), as well as other assay ingredients such as MatrigelTM, Dulbecco's Modified Eagle Medium (DMEM), or Fetal Bovine Serum (FBS).
  • DMEM Dulbecco's Modified Eagle Medium
  • FBS Fetal Bovine Serum
  • FBS lot-to-lot variation often changes assay outputs and shifts cutoffs. Such shifting is shown in Figures 1 (A), (C), and Figures 2(A) - (B), where the dependence of the human skin fibroblast aggregation, measured by the natural logarithm of the unit aggregate area, Ln(Area/Number), on the Ln(Cell Density) is shown.
  • F.V. Chirila et al. J Alzheimer's Disease 33, 165-176 (201 3); F.V. Chirila et al., J. Alzheimer's Disease 42, 1279-94 (2014).
  • five FBS lots were used that originated from three different companies (i.e., Gemini Bio Products, Gibco Laboratories, and Atlanta Biologicals, Inc.).
  • the assay is already available commercially, then getting the results for patients will be delayed whenever the FBS lot is changed due to the functional quality control (QC) required for choosing another FBS lot. Furthermore, the lot-to-lot FBS variation might make impossible the use of a fixed cutoff in the case when the assay is used to discriminate between different classes of patients, and the cutoff will have to be readjusted each time a new lot is used.
  • QC functional quality control
  • Another strategy through the method disclosed herein, provides the same cutoff regardless of the FBS lot, making the comparison between FBS lots easier.
  • the first variable, x is the status of the cells before the experiment, measured by the natural logarithm of cell density.
  • the second variable, y is one of the ingredients of the media feeding the human skin fibroblast cells called fetal bovine serum (FBS).
  • FBS fetal bovine serum
  • the dependence on the first variable, Ln(Cell density), is linear, while the second variable, FBS lot, alters the linear parameters of the data classes in a discreet manner ( Figure 1 (A), (C) and Figures 2(A) - (B)).
  • the present inventors developed new methods for classifying two or more AD populations, such as AD class 1 (C1 ) and Age-matched control class 2 (C2), based on one or more biomarkers.
  • AD class 1 C1
  • C2 Age-matched control class 2
  • the present inventors surprisingly discovered a novel two-stage method that accounts for the dependence of the diagnostic assay measurements, Ln(Area/Number) (see F.V. Chirila et al., J. Alzheimer's Disease 33, 165-176 (2013); F.V. Chirila et al., J Alzheimer's Disease 42, 1279-94 (2014)), on cell density before the experiment and FBS.
  • those new methods also can be applied to other fields of study such as machine learning, neural networks, data mining, gene expression, pattern or face recognition, cognitive psychology, or astronomy.
  • a method for classifying an Alzheimer's disease (AD) population from an age-matched control (AC) population and/or a non-Alzheimer's disease demented (non-ADD) population based on a biomarker assay comprising:
  • the method results in the separation of the AD population from the AC population and/or the non-ADD population.
  • Also disclosed herein is a method for classifying a subject in need thereof into an Alzheimer's disease (AD) population based on a biomarker comprising:
  • AD Alzheimer's disease
  • AC age-matched control
  • non-ADD non-Alzheimer's disease demented
  • biomarker assay chosen from cell aggregation, fractal dimension, protein kinase C epsilon, and Alzheimer's disease specific molecular biomarkers (ASDMB) on the cells cultured with Fetal Bovine Serum to generate a plurality of data points, wherein the assay comprises an input variable and an output variable and the assay output variable depends linearly on the assay input variable;
  • the method results in the separation of the AD population from the AC population and/or the non-ADD population.
  • a method for classifying a subject in need thereof into an Alzheimer's disease (AD) population based on a biomarker comprising
  • biomarker assay chosen from cell aggregation, fractal dimension, protein kinase C epsilon, and Alzheimer's disease specific molecular biomarkers (ASDMB) on the cells cultured with Fetal Bovine Serum to generate a plurality of data points, wherein the assay comprises an input variable and an output variable and the assay output variable depends linearly on the assay input variable;
  • FBS Fetal Bovine Serum
  • FBS Fetal Bovine Serum
  • FBS Fetal Bovine Serum
  • a method for classifying a subject in need thereof into a population based on a diagnostic system comprising:
  • Figure 1 (A) shows the dependence of the human skin fibroblast aggregation in an assay for AD skin samples compared to AC samples, as measured by the natural logarithm of the unit aggregate area, Ln(Area/Number), on the Ln(Cell Density);
  • Figure 1 (C) shows the noise added to the slope and intercepts of fit lines from Figure 1 (A);
  • Figure 1 (D) shows a normalized assay for the noisy data classes from Figure 1 (C).
  • Figures 2(A) - (B) show the dependence of Ln(Area/Number) on the Ln(Cell Density) and FBS lot for AD skin samples compared to AC samples;
  • Figure 2(C) illustrates the probability distribution for the raw data from Figure 2(B);
  • Figure 2(D) illustrates the probability distribution for the normalized data after the first stage of the method.
  • Figures 3(A) - (F) show a step-by-step description of the first stage of the analysis in the method for two noisy data classes of patients from Figure 1 (C).
  • Figure 4(A) shows the raw data ranked by the distance for the various FBS lots examined with the unknown
  • Figure 4(B) shows the average coefficient of variation normalized by the distance for the data Figure 4(A) including the unknown.
  • Figures 5(A) and (B) plot the CutOff(x) function for the FBS lot as a function of Ln(Cell Density).
  • Figure 6(A) shows the inverse slope versus the intercept for the fractal curves for AD samples, AC samples, and Non-Alzheimer's disease demented samples;
  • Figure 6(B) shows normalized data from Figure 6(A);
  • Figure 6(C) shows 120 randomly generated surrogate data;
  • Figure 6(D) shows the normalized data from Figure 6(C).
  • Figure 7(A) ranks distances between three pairs of randomly generated data;
  • Figures 8(A) and (B) show the signal to noise ratio in establishing the discrimination limit (d-limit), where the level of noise is 10%.
  • Figure 9(A) shows Ln(Area/Number) versus the Ln(Cell Density) for AD samples (Ci), AC samples (C 3 ), and a third group labeled M
  • Figure 9(B) maps the three classes from Figure 9(A) into horizontal and parallel data classes
  • Figure 9(C) shows the dependence of total Length(L) on the slope, where the location of the three classes from Figure 9(A) are shown with a square(Ci), circle(M), and triangle(C 3 );
  • Figure 9(D) shows the dependence of the Length(L) on the X Projection/X range from Figure 9(A) and on the slope;
  • Figure 9(E) shows the distance between pairs of segments from Figure 9(A) and Figure 9(B), where the solid black curves are the linear and exponential fits;
  • Figure 9(F) shows the ratio of the distances in Figure 9(E).
  • Figure 10(a) shows raw data for two cell cycle-regulated genes of the Yeast Saccharomyces cerevisiae. See P.T. Spellman et al., Molec. Biol, of the Cell 9, 3273-3297 (1 998);
  • Figure 10(b) shows rotation curves of low surface brightness galaxies. See K. de Naray et al., ApJS 1 65, 461 -479 (2006); K. de Naray et al., ApJS 676, 920-943 (2008);
  • Figure 10(c) shows optimized data for the classes in Figure 10(A);
  • Figure 1 0(d) shows optimized data for the classed in Figure 10(b).
  • Figures 1 1 (a) - (f) show the change in mini-mental state examination (MMSE) or Folstein test after three hours of administering Bryostatin or Placebo versus the change in Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) at 48 hours, where the dependence for both groups is linear.
  • MMSE mini-mental state examination
  • RBANS Neuropsychological Status
  • Figures 12(a) and (b) plot the raw data and algorithm data for the method of classifying the age dependence for an Alzheimer's disease diagnostic assay.
  • AD Alzheimer's disease
  • AC non-demented age-matched control
  • Non-ADD non-Alzheimer's dementia
  • S any diagnostic system
  • Ci C 2 , . . ., C n : two or more classes of data
  • D distance between data classes.
  • Alzheimer's disease population can mean an Alzheimer's disease patient population, an age-matched control (AC) population, and/or a non- Alzheimer's disease demented (non-ADD) population.
  • AC age-matched control
  • non-ADD non- Alzheimer's disease demented
  • the term "subject” generally refers an organism.
  • a subject can be a mammal or mammalian cell, including a human or human cell.
  • the term also refers to an organism, which includes a cell or a donor or recipient of such cell.
  • the term “subject” refers to any animal (e.g., a mammal), including, but not limited to humans, mammals and non-mammals, such as non- human primates, mice, rabbits, sheep, dogs, cats, horses, cows, chickens, amphibians, and reptiles, which is to be the recipient of a compound or pharmaceutical composition described herein.
  • the terms “subject” and “patient” are used interchangeably herein in reference to a human subject.
  • the present inventors discovered a new method for classifying an Alzheimer's disease (AD) population from an Age-matched control (AC) population and/or a non-Alzheimer's disease demented (non-ADD) population based on a biomarker assay comprising:
  • the method results in the separation of the AD population from the AC population and/or the non-ADD population.
  • a novel approach of normalization based on distance preserving (i.e., isometric transformations) of the two or more classes of patients by the intercepts is provided. See H.S.M. Coxeter, Introduction to Geometry, John Wiley & Sons. Inc., New York (1961 ); I.M. Yaglom, Geometric Transformations I, II, I II, Mathematical Association of America).
  • slope a fixed cutoff
  • the data classes are parallel, horizontal, symmetrical with respect to the fixed cutoff, and classified by the intercepts. Therefore, in some embodiments, the first stage of the method uses four isometric transformations, normalizes across the slope, sets a fixed cutoff, and increases exponentially the distance between classes of patients.
  • the first stage serves as a sorting procedure for the intercepts of the linear fits in a wide range of diagnostic systems (see Figures 3 and 10).
  • these diagnostic systems are chosen from: (1 ) AD diagnostic assays (F.V. Chirila et al., J. Alzheimer's Disease 33, 165-1 76 (2013); F.V. Chirila et al., J. Alzheimer's Disease 42, 1 279-94 (2014)); (2) machine learning (D. Elizondo, IEEE TRANSACTIONS ON NEURAL NETWORKS 17(2), 330-344 (2006)); (3) neural networks (F. Lavigne et al., Front Psychol. 5(842), 1 -24 (2014); D.L.
  • the present inventors further discovered a method for classifying a subject in need thereof into an Alzheimer's disease (AD) population based on a biomarker comprising:
  • the normalized data classes with a fixed cutoff that resulted from the first stage are compared with respect to the second variable, e.g., fetal bovine serum.
  • the second stage may use the distance between classes of patients, and the average coefficient of variation normalized by the distance, and rank them with the discreet variable, y, (e.g., fetal bovine serum lot).
  • y e.g., fetal bovine serum lot.
  • the methods disclosed herein comprise ranking the states of a system, S, that separates two data classes, (C1 , C2), which show a linear dependence in one of the variables, x, and for which the linear dependence changes discreetly with a second variable, y.
  • AD diagnostic assays disclosed herein establish a quantitative framework for diagnosing patients when dealing also with unexpected variables such as, cell density, FBS, age, etc.
  • This strategy for AD diagnostic assays includes significantly reducing the quality control (QC) duration when changing the FBS lot.
  • the disclosed method requires a smaller number of samples needed in each class of patients, ⁇ 5, for establishing the linear fits. The reduced QC duration increases the practical value of the assay, as it may help laboratories using cellular based assays and experiencing output shifts with FBS lots, or different varieties of FBS free media.
  • the method disclosed herein may be applied to AD diagnostic assays of the type , C 2 ,..., C n , x, y).
  • the methods described herein have a general applicability to any system showing a linear dependence or a linear input-output function.
  • the method has no restriction in terms of the number of data classes that need to be separated.
  • the two-stage procedure may be employed in fields such as machine learning, neural networks, data mining, gene expression, pattern or face recognition, cognitive psychology, or astronomy.
  • the two-stage analysis may be utilized by FBS and FBS free media vendors to provide a standardized method for correcting for the variation through isometric transformations.
  • a method for screening at least one FBS lot by employing the two-stage procedure is disclosed herein.
  • a method of establishing a ranked order for two or more Fetal Bovine Serum (FBS) lots using an assay with FBS comprising:
  • a first plurality of data points comprises controls and a second plurality of data points comprises AD samples.
  • a first plurality of data points comprises controls, a second plurality of data points comprises AD samples, and a third plurality of data points comprises non-ADD samples.
  • the non-ADD samples may be chosen from Huntington disease samples and Parkinson's disease samples.
  • the samples comprise human skin fibroblast cells.
  • the dynamic range is based on the distance between the two or more different populations. In various embodiments, the dynamic range is based on the average coefficient of variation normalized by the distance.
  • Also disclosed herein is a method of ranking at least one untested Fetal Bovine Serum (FBS) lot using an assay with FBS comprising:
  • a first plurality of data points comprises controls and a second plurality of data points comprises AD samples.
  • a first plurality of data points comprises controls, a second plurality of data points comprises AD samples, and a third plurality of data points comprises non-ADD samples.
  • the non-ADD samples may be chosen from Huntington disease samples and Parkinson's disease samples.
  • the samples comprise human skin fibroblast cells.
  • the dynamic range is based on the distance between the two or more different populations. In various embodiments, the dynamic range is based on the average coefficient of variation normalized by the distance.
  • a method of ranking at least one untested Fetal Bovine Serum (FBS) lot using an assay with FBS comprising:
  • the assay is a diagnostic assay, such as a diagnostic assay for Alzheimer's disease.
  • the isometric transformations in the method may be used for automation.
  • the normalization of data classes across the slope and establishment of a fixed cutoff allows for comparison and classification with respect to the second variable, y (e.g., FBS lot).
  • the rank of an untested FBS lot can be determined immediately if the new y-state (e.g., FBS lot), has a good dynamic range, D, and a small level of noise by the normalized coefficient of variation, ⁇ CV>/D, based on the location on the linear standard curve (see Figure 4).
  • the sample comprises at least one cell obtained from a human subject.
  • the at least one cell is a peripheral cell (i.e., a cell obtained from non-CNS tissue).
  • the at least one cell is a fibroblast cell.
  • the fibroblast cell is a skin fibroblast cell.
  • Cell precursors of fibroblasts such as induced pluripotent stem cells (IPSC) may also be used.
  • ISC induced pluripotent stem cells
  • recent techniques for obtaining IPSC from human skin fibroblasts permitted differentiation of IPSC in cells such as neurons and showed imbalances in ⁇ in both skin fibroblasts and IPSC differentiated neurons.
  • the sample comprises at least one cell chosen from skin cells, blood cells (lymphocytes), and buccal mucosal cells.
  • the non-Alzheimer's disease cells may be chosen, e.g., from an age- matched control.
  • the age-matched control is chosen from a non-AD non-demented population.
  • the age-matched control is chosen from a non-AD demented population, such as patients with Huntington disease or Parkinson's disease.
  • the at least one cell may be cultured in a media for growth.
  • the media comprises FBS.
  • the cells are cultured in media that is a protein mixture, such as a gelatinous protein mixture.
  • a protein mixture such as a gelatinous protein mixture.
  • a non-limiting exemplary gelatinous protein mixture is MatrigelTM.
  • MatrigelTM is the trade name for a gelatinous protein mixture secreted by the Engelbreth-Holm-Swarm (EHS) mouse sarcoma cells and marketed by BD Biosciences. This mixture resembles the complex extracellular environment found in many tissues and is used by cell biologists as a substrate for cell culture.
  • the at least one cell is cultured in a preparation comprising extracellular matrix proteins.
  • the preparation comprises laminin, collagen, heparin sulfate proteoglycans, entactin/nidogen, and/or combinations thereof.
  • the preparation is extracted from a tumor, such as the EHS mouse sarcoma.
  • the preparation may further comprise a growth factor, such as TGF-beta, epidermal growth factor, insulin-like growth factor, fibroblast growth factor, tissue plasminogen activator, and/or other growth factors or combinations thereof.
  • the growth factors occur naturally in the EHS mouse sarcoma.
  • Extracellular matrix proteins may also contain numerous other proteins.
  • the at least one cell is cultured in a basement membrane preparation.
  • the preparation is solubilized.
  • the basement membrane preparation is extracted from a tumor, such as the EHS mouse sarcoma— a tumor rich in extracellular matrix proteins. Its major component is laminin, collagen IV, heparin sulfate proteoglycans, and entactin/nidogen.
  • the preparation contains TGF-beta, epidermal growth factor, insulin-like growth factor, fibroblast growth factor, tissue plasminogen activator, and/or other growth factors which may or may not occur naturally in the EHS tumor.
  • BD Matrigel Matrix Growth Factor Reduced (GFR) is found to be particularly well suited for applications requiring a more highly defined basement membrane preparation.
  • measurable cellular networks form. This time period may vary in view of, for example, cell type and conditions, but generally, this time period ranges from about 1 hour or less, ranging from about 10 minutes to about 60 minutes, such as from about 10 minutes to about 45 minutes or any time in between. After a time, for example, approximately 5 hours, these networks start to degenerate and edges retract to leave behind measurable "clumps" or aggregates.
  • the time period for culturing the at least one cell is chosen from about 1 hour to about 72 hours, such as from about 12 hours to about 72 hours or from about 24 hours to about 48 hours. In various embodiments, the time period is about 48 hours or any one hour increment subdivision thereof.
  • the method(s) may further comprise imaging the cultured cells at the end of the time period. Images may be captured according to techniques known in the art. For example, images of the cellular networks may be captured with an inverted microscope, such as Western Digital AMID Model 2000, and controlled by a computer via image acquisition software at a desired magnification. Appropriate imaging techniques include, but are not limited to, confocal microscopy, phase contrast, bright field, fluorescence, differential interference contrast, and robotic systems.
  • Biomarker assays can be any AD diagnostic assay.
  • AD diagnostic assays include, but are not limited to, cell aggregation, fractal dimension, Protein Kinase C (PKC) epsilon, Alzheimer's disease specific biomarkers (ADSMB), lacunarity, cell migration, PKC isozyme index, and Alzheimer's disease Neuroimagining Initiative (ADNI) biomarkers.
  • PKC Protein Kinase C
  • ADSMB Alzheimer's disease specific biomarkers
  • ADNI Alzheimer's disease Neuroimagining Initiative
  • the AD diagnostic assay is cell aggregation.
  • the area of aggregates can be determined by any suitable method, e.g., by fitting an ellipse across the aggregate.
  • the counting of aggregates as well as aggregate area determination can be performed manually or can be automated, e.g., by image processing techniques known in the art.
  • cell density is measured based on the number of cells per ⁇ 2 or per field of view. In certain embodiments, cell density is measured by measuring the number of cells per 10 ⁇ image. In certain embodiments, the rate of change of the average area per number of aggregates as a function of cell density is evaluated within the boundaries of 320 to 550 cells/1 Ox image or such as, of 330 to 500 cells/1 Ox image.
  • Cell aggregation rate is determined by evaluating the rate of change of the average area per number of aggregates as a function of cell density. In some embodiments, the rate of change of the average area per number of aggregates as a function of cell density is evaluated by determining the slope of a linear fit between the average area per number of aggregates and cell density.
  • the aggregation rate of the cultured cells obtained from the human subject is compared to the aggregation rate determined using non-Alzheimer's disease control cells.
  • the diagnosis is positive for Alzheimer's disease if the aggregation rate of the cultured cells from the human subject is increased compared to the aggregation rate determined using the non-Alzheimer's disease control cells.
  • the AD diagnostic assay is fractal dimensions.
  • complexity of human skin fibroblast networks can be quantified by computing their fractal dimensions.
  • Fractal analysis utilizes the complexity of the networks as means for distinguishing AD, AC, and non-ADD cells. Fibroblast cells obtained from patients suffering from AD have a statistically significant lower fractal dimension than AC cells when grown in tissue culture. The complexity of the networks measured by fractal dimension is also markedly different for fibroblasts taken from AD as compared to AC and non-ADD fibroblasts. Thus, a reduced complexity of human skin fibroblast networks AD cases provides distinctions from AC and non-ADD cases.
  • the fractal dimension may be calculated using a standard box counting procedure after raw images (e.g., digital images) are filtered through an edge detection procedure that uses, for example, the difference of two Gaussians.
  • Edge detection is a term used in the field of image processing, particularly in the areas of feature detection and feature extraction, to refer to algorithms which aim at identifying points in a digital image at which, for example, the image brightness changes sharply or has other discontinuities.
  • discontinuities in image brightness are likely to correspond to one or more of discontinuities in depth, discontinuities in surface orientation, changes in material properties and variations in scene illumination.
  • Applying an edge detector to an image may lead to a set of connected curves that indicate the boundaries of objects, the boundaries of surface markings as well curves that correspond to discontinuities in surface orientation.
  • applying an edge detector to an image may significantly reduce the amount of data to be processed, and may therefore filter out information that may be regarded as less relevant while preserving the important structural properties of an image. If the edge detection step is successful, the subsequent task of interpreting the information content in the original image may therefore be substantially simplified.
  • Methods for edge detection can generally be grouped into two categories: search-based and zero-crossing based.
  • the search-based methods detect edges by first computing a measure of edge strength, usually a first-order derivative expression such as the gradient magnitude, and then searching for local directional maxima of the gradient magnitude using a computed estimate of the local orientation of the edge, usually the gradient direction.
  • the zero-crossing based methods search for zero crossings in a second-order derivative expression computed from the image in order to find edges, usually the zero-crossings of the Laplacian or the zero crossings of a nonlinear differential expression.
  • a smoothing stage for example Gaussian smoothing, may be applied. In other embodiments noise filtering algorithms may be employed.
  • edge detection methods that have been published mainly differ in the types of smoothing filters that are applied and the way the measures of edge strength are computed. As many edge detection methods rely on the computation of image gradients, they also differ in the types of filters used for computing gradient estimates in the x- and y-directions.
  • the fractal dimension is determined using a box counting procedure wherein the image is covered with boxes, for example, by a computer.
  • the box counting procedure is implemented on a computer using digital images of cell samples.
  • a positive diagnosis for AD is made if the fractal dimension for ⁇ -treated test cells is less than the fractal dimension for non-treated test cells.
  • a positive diagnosis for AD is made if the difference in fractal dimension between ⁇ -treated test cells and AD control cells is statistically significant.
  • the AD diagnostic assay is lacunarity.
  • Lacunarity is a complementary measure for complexity discrimination that quantifies the gaps in cellular networks.
  • AD cell lines show an increased average lacunarity when compared with cell lines from AC and non-ADD individuals.
  • the lacunarity analysis method presently disclosed quantifies gaps of the fibroblast patterns as a complementary measure of complexity used as a second level of discrimination.
  • the average lacunarity is higher for AD fibroblasts in comparison to AC and non-ADD fibroblasts.
  • lacunarity increases and peaks when the network degeneration is maximized, i.e., only isolated aggregates are visible. Lacunarity drops as network regeneration starts.
  • a positive diagnosis for AD is made if the lacunarity for A. beta. -treated test cells is greater than the lacunarity for non-treated test cells. In some embodiments, a positive diagnosis for AD is made if the difference in lacunarity between ⁇ treated test cells and AD control cells is statistically significant.
  • the AD diagnostics assay is cell aggregation.
  • Cell migration allows for distinguishing between AD, AC, and non-ADD cells.
  • the number of freely migrating cells may be counted about 24 hours after plating, such as about 36 hours, about 48 hours, about 50 hours, about 52 hours, about 55 hours, about 57 hours, or about 60 hours after plating.
  • the initial cell density is controlled. In at least one embodiment, the initial cell density is controlled to about 50 cells/mm 3 .
  • a positive diagnosis for AD is made if the number of migrating cells is less than the number of migrating cells for age-matched controls (AC) or non-Alzheimer's disease demented (Non-ADD) patients.
  • a positive diagnosis for AD is made if the difference in number of migrating cells between AD and AC or between AD and Non-ADD cells is less than one standard deviation from the mean.
  • a migration rate lower than about 0.3 hr "1 is indicative of AD.
  • the AD diagnostic assay is PKC epsilon.
  • protein kinase C(PKC) isozymes particularly -a and - ⁇ play a critical role in regulating major aspects of AD pathology including the loss of synapses, the generation of ⁇ and amyloid plaques, and the GSK ⁇ -mediated hyperphosphorylation of tau in neurofibrilliary tangles.
  • PKC- ⁇ is an accurate AD Biomarker in AD skin fibroblasts.
  • a method of diagnosing Alzheimer's Disease in a human subject comprises the steps of: a) determining the PKC epsilon level in said human subject; and b) comparing the PKC epsilon level in said human subject to the PKC epsilon level in a control subject; wherein said method is indicative of Alzheimer's Disease in said human subject if the PKC epsilon level in said human subject is lower than the PKC epsilon level in said control subject.
  • AD Alzheimer's Disease subjects
  • AC age matched controls
  • ADSMB Alzheimer's disease specific molecular biomarker
  • the AD diagnostic assay is Alzheimer's disease specific molecular biomarker (ADSMB) or AD-index.
  • ADSMB Alzheimer's disease specific molecular biomarker
  • AD-index See e.g., U.S. Patent No. 7,595, 167 and U.S. Patent Application Publication No. 2014/0031245, which the contents for each is incorporated herein by reference.
  • diagnostic methods and methods of screening compounds useful for treating Alzheimer's disease are based on a unique molecular biomarker for Alzheimer's disease.
  • ADSMB Alzheimer's disease-specific molecular biomarker
  • the Alzheimer's disease-specific molecular biomarker may be measured by determining the ratio of phosphorylated Erk1 to phosphorylated Erk2 in cells that have been stimulated by Bradykinin, which is an inflammatory mediator, and subtracting from this the ratio of phosphorylated Erk1 to phosphorylated Erk2 in cells that have been stimulated with a control solution (vehicle) that lacks the Bradykinin (i.e., an inflammatory mediator).
  • a control solution i.e., an inflammatory mediator.
  • the difference is greater than zero, i.e. a positive value, this is diagnostic of Alzheimer's disease. If the difference is less than or equal to zero, this is indicative of the absence of Alzheimer's disease.
  • the Alzheimer's disease-specific molecular biomarkers are measured by determining the ratio of two phosphorylated MAP kinase proteins after stimulation of cells with Bradykinin, which is an inflammatory mediator.
  • the molecular biomarker may be measured by determining the ratio of a first phosphorylated MAP kinase protein to a phosphorylated second MAP kinase protein in cells that have been stimulated by Bradykinin (i.e., an inflammatory mediator) and subtracting from this the ratio of phosphorylated first MAP kinase protein to phosphorylated second MAP kinase protein in cells that have been stimulated with a control solution (vehicle) that lacks Bradykinin.
  • a control solution that lacks Bradykinin.
  • the Alzheimer's disease-specific molecular biomarker is a positive numerical value in cell samples taken from patients diagnosed with Alzheimer's disease (AD cells). When the Alzheimer's disease-specific molecular biomarker is measured by determining ratios of phosphorylated Erk1 to phosphorylated Erk2 in AD cells that have been stimulated with bradykinin, the positive numerical values for the Alzheimer's disease-specific molecular biomarker in AD cells may range from about zero to about 0.5. [0099] The Alzheimer's disease-specific molecular biomarker is a negative numerical value when measured in cells taken from subjects diagnosed with non- Alzheimer's disease dementia (non-ADD cells), such as, for example, Parkinson's disease or Huntington's disease or Clinical Schizophrenia.
  • non-ADD cells non- Alzheimer's disease dementia
  • the negative numerical values may range from about zero to about -0.2 or about -0.3.
  • the Alzheimer's disease-specific molecular biomarker may be a negative numerical value, zero or very low positive numerical value in cell samples from age- matched control cells (AC cells) taken from patients who do not have Alzheimer's disease.
  • AC cells age- matched control cells
  • the Alzheimer's disease-specific molecular biomarker in AC cells may range from less than about 0.05 to about -0.2.
  • Protein kinase C activators that are specifically contemplated include, but are not limited to: Bradykinin; .alpha.-APP modulator; Bryostatin 1 ; Bryostatin 2; DHI; 1 ,2-Dioctanoyl-sn-glycerol; FTT; Gnidimacrin, Stellera chamaejasme L; (-)- Indolactam V; Lipoxin A.sub.4; Lyngbyatoxin A, Micromonospora sp.; Oleic acid; 1 - Oleoyl-2-acetyl-sn-glycerol; 4 .alpha.-Phorbol; Phorbol-1 2, 1 3-dibutyrate; Phorbol-12, 13-didecanoate; 4a-Phorbol-12, 13-didecanoate; Phorbol-12-myristate-13-acetate; L- a-Phosphatidylinosito
  • Bryologues are described, for example, in Wender et al. Organic letters (United States) May 12, 2005, 7 (10) p1995-8; Wender et al. Organic letters (United States) Mar. 17, 2005, 7 (6) p1 1 77-80; Wender et al. Journal of Medicinal Chemistry (United States) Dec. 1 6, 2004, 47 (26) p6638-44.
  • a protein kinase C activator may be used alone or in combination with any other protein kinase C activator in the diagnostic methods, kits and methods of screening compounds disclosed herein.
  • the AD diagnostic assay is PKC isozyme index.
  • the PKC isozyme index comprises measuring levels of steady state or phosphorylated PKC isozymes in peripheral cells from a candidate subject and, optionally, from a non-AD control subject (AC). See e.g., U.S. Patent Application Publication No. 201 1 /0212474, which is incorporated by reference herein.
  • steady levels of a first PKC isozyme are measured in peripheral cells from the AD and AC subjects both in the absence of, and in the presence of, an ⁇ peptide to generate a first ratio of the PKC isozyme level (PKC isozyme level in the absence of ⁇ peptide/level in the presence of ⁇ peptide).
  • a second PKC isozyme ratio is also obtained by measuring steady state or phosphorylated levels of a second PKC isozyme in peripheral cells from a subject, again in the absence of and in the presence of an ⁇ peptide.
  • Results of these measurements are then used to construct a third ratio, in which the first ratio (level of the first PKC isozyme obtained in cells not contacted with the ⁇ peptide/level of the first PKC isozyme obtained in cells contacted with the ⁇ peptide) is divided by the second ratio (level of the second PKC isozyme in cells not contacted with the ⁇ peptide/level of the second PKC isozyme in cells contacted with the ⁇ peptide) to generate a PKC Isozyme Index.
  • the input variable is an independent variable that causes a change of the output or dependent variable (e.g., for an engine, the fuel-air ratio is an independent/input variable, while exhaust or power is the dependent/output variable).
  • the output or dependent variable e.g., for an engine, the fuel-air ratio is an independent/input variable, while exhaust or power is the dependent/output variable.
  • x is an independent/input variable for a linear function f, which is the dependent/output variable.
  • the input variable when diagnosing Alzheimer's disease, includes, but is not limited to, cell density (cell-cell interaction), age of the patients, passage number (number of cell population duplication), as well as other assay ingredients such as MatrigelTM, Dulbecco's Modified Eagle Medium (DMEM), Fetal Bovine Serum (FBS), feeding time, or any variable in the cell cycle.
  • cell density cell-cell interaction
  • age of the patients passage number
  • passage number number of cell population duplication
  • other assay ingredients such as MatrigelTM, Dulbecco's Modified Eagle Medium (DMEM), Fetal Bovine Serum (FBS), feeding time, or any variable in the cell cycle.
  • DMEM Dulbecco's Modified Eagle Medium
  • FBS Fetal Bovine Serum
  • the output variable is the dependent variable that changes as a result of the input/independent variable.
  • the output variable includes, but is not limited to, the natural logarithm of (Area/Number) (e.g., when the biomarker is fibroblast aggregation and the input variable is the natural logarithm of cell density and fetal bovine serum), and the inverse slope (e.g., when the biomarker is fractal dimension, the input variable is the intercept for the fractal curves).
  • AD Alzheimer's disease
  • AC Age-matched control
  • non-ADD non-Alzheimer's disease demented
  • the method results in the separation of the AD population from the AC population and/or the non-ADD population.
  • a first plurality of data points comprises controls and a second plurality of data points comprises AD samples.
  • a first plurality of data points comprises controls, a second plurality of data points comprises AD samples, and a third plurality of data points comprises non-ADD samples.
  • the non-ADD samples may be chosen from Huntington disease samples and Parkinson's disease samples.
  • the samples comprise human skin fibroblast cells, which, may be cultured in fetal bovine serum.
  • the biomarker is chosen from cell aggregation, fractal dimension, protein kinase C epsilon, and Alzheimer's disease specific molecular biomarkers (ASDMB).
  • ASDMB Alzheimer's disease specific molecular biomarkers
  • the output variable is the natural logarithm of (Area/Number) and the input variable is the natural logarithm of cell density and fetal bovine serum.
  • the input variable is the intercept for the fractal curves and the output variable is the inverse slope.
  • the method further comprises establishing a universal cut-off value.
  • the performing step (c) comprises establishing a discrimination limit having a cut-off value based on the signal-to-noise ratio, and the plurality of data points generated from the AD population, AC population and/or the non-ADD population do not cross over the cut-off value.
  • a method for classifying a subject in need thereof into an Alzheimer's disease (AD) population based on a biomarker comprising:
  • a first plurality of data points comprises controls and a second plurality of data points comprises AD samples.
  • a first plurality of data points comprises controls, a second plurality of data points comprises AD samples, and a third plurality of data points comprises non-ADD samples.
  • the non-ADD samples may be chosen from Huntington disease samples and Parkinson's disease samples.
  • the samples comprise human skin fibroblast cells, which, may be cultured in fetal bovine serum.
  • the biomarker is chosen from cell aggregation, fractal dimension, protein kinase C epsilon, and Alzheimer's disease specific molecular biomarkers (ASDMB).
  • ASDMB Alzheimer's disease specific molecular biomarkers
  • the output variable is the natural logarithm of (Area/Number) and the input variable is the natural logarithm of cell density and fetal bovine serum.
  • the input variable is the intercept for the fractal curves and the output variable is the inverse slope.
  • the method further comprises establishing a universal cut-off value.
  • the performing step (c) comprises establishing a discrimination limit having a cut-off value based on the signal-to-noise ratio, and the plurality of data points generated from the AD population, AC population and/or the non-ADD population do not cross over the cut-off value.
  • AD Alzheimer's disease
  • AC age-matched control
  • non-ADD non- Alzheimer's disease demented
  • biomarker assay chosen from cell aggregation, fractal dimension, protein kinase C epsilon, Alzheimer's disease specific molecular biomarkers (ASDMB), lacunarity, cell migration, PKC isozyme index, and Alzheimer's disease Neuroimagining initiative (ADNI) on the cells cultured with Fetal Bovine Serum to generate a plurality of data points, wherein the assay comprises an input variable and an output variable and the assay output variable depends linearly on the assay input variable;
  • a biomarker assay chosen from cell aggregation, fractal dimension, protein kinase C epsilon, Alzheimer's disease specific molecular biomarkers (ASDMB), lacunarity, cell migration, PKC isozyme index, and Alzheimer's disease Neuroimagining initiative (ADNI)
  • a first plurality of data points comprises controls and a second plurality of data points comprises AD samples.
  • a first plurality of data points comprises controls, a second plurality of data points comprises AD samples, and a third plurality of data points comprises non-ADD samples.
  • the non-ADD samples may be chosen from Huntington disease samples and Parkinson's disease samples.
  • AD Alzheimer's disease
  • biomarker assay chosen from cell aggregation, fractal dimension, protein kinase C epsilon, Alzheimer's disease specific molecular biomarkers (ASDMB), lacunarity, cell migration, PKC isozyme index, and Alzheimer's disease Neuroimagining initiative (ADNI) on the cells cultured with Fetal Bovine Serum to generate a plurality of data points, wherein the assay comprises an input variable and an output variable and the assay output variable depends linearly on the assay input variable;
  • a biomarker assay chosen from cell aggregation, fractal dimension, protein kinase C epsilon, Alzheimer's disease specific molecular biomarkers (ASDMB), lacunarity, cell migration, PKC isozyme index, and Alzheimer's disease Neuroimagining initiative (ADNI)
  • a first plurality of data points comprises controls and a second plurality of data points comprises AD samples.
  • a first plurality of data points comprises controls, a second plurality of data points comprises AD samples, and a third plurality of data points comprises non-ADD samples.
  • the non-ADD samples may be chosen from Huntington disease samples and Parkinson's disease samples.
  • a method for classifying two or more different populations based on a diagnostic system comprising:
  • the diagnostic system is chosen from Alzheimer's diagnostic assays, machine learning, neural networks, data mining, gene expressions, pattern or face recognition, cognitive psychology, and astronomy.
  • Also disclosed herein is a method for classifying a subject in need thereof into a population based on a diagnostic system comprising:
  • the diagnostic system is chosen from Alzheimer's diagnostic assays, machine learning, neural networks, data mining, gene expressions, pattern or face recognition, cognitive psychology, and astronomy.
  • the fibroblast cells were plated on a thick layer (-1 .8 mm) of 3-D matrix (Matrigel, BD Biosciences, San Jose, CA) on 1 2 well plates. See Chirila F.V. et al., J. Alzheimer's Disease, 33, 165-176 (201 3). The available patient information is posted on Coriell web site (http://ccr.coriell.org/). The cell lines analyzed (40) were based on a number of criteria including autopsy and genetic family history. A significant number of samples (7) were studied under double-blind conditions (Table 1 ), and a further sample confirmed the diagnostic differentiation on freshly obtained skin samples. The age-matched control (AC) samples were not demented at the date of skin biopsy extraction.
  • AC age-matched control
  • Freshly taken fibroblasts were obtained as follows. Punch-biopsies (2-3 mm, upper arm) skin tissues from patients and controls were obtained. Cells with passages between 5 and 15 were used (Table 2).
  • the initial cell density was controlled to be 50 cells/mm 3 , and was homogenized with 1 .5 ml Dulbecco's Modified Eagle Medium with 10% fetal bovine serum and 1 % penicillin/streptomycin (PS) for each well. Cells were kept in a C0 2 water-jacket incubator (Forma Scientific) up to 7 days after plating.
  • the shadows in the four corners should have equal areas for an aligned microscope; b) mark the center with a needle; c) use gridded plates (Pioneer scientific; Shrewsbury, MA) where the central square is always the 6th, in the central row or column.
  • gridded plates Pierisbury, MA
  • the target number of cells per 10x image was 41 7 which corresponded to an initial cell concentration of 50 cells/ ⁇ . A variation of cell concentration between 45 and 60 cells/ ⁇ was permitted. To minimize heterogeneity of the cell distribution in the image, images outside of the range 195-650 cells per 1 0x image were eliminated. For cellular aggregates at 48 hours, manual ellipse fitting with the Micron software was used.
  • Fractal dimension and lacunarity For fractal and lacunarity analyzes FracLac_2.5 plug-in (http://rsbweb.nih.gov/ij/plugins/fraclac/fraclac.html) using the "box counting" method was used. The recovery slope and intercept were monitored by fitting a line in the range 20-80% of the min-max difference. The average lacunarity was calculated between 0 and 120 hours.
  • Cell density is a hidden variable that can influence the output of the assay.
  • This cell density is in T25 flasks before using the cells in the assay.
  • the cell density was calculated based on the average cell number measured with the hemocytometer.
  • the total number of cells was first estimated based on the volume of the medium used after trypsinization, then this total number of cells was divided by the surface of the flask 25 cm 2 .
  • the cell density is also an indirect measure of the cell confluence.
  • Gnuplot 4.4 was used. Gnuplot 4.4 is a freely available software (http://www.gnuplot.info). For fitting of the raw data points, a built in fit function from Gnuplot was used, which uses an implementation of the nonlinear least-squares (NLLS) Marquardt-Levenberg algorithm. Unless otherwise specified, the error-bars are standard errors of the mean (SEM). Gnuplot was chosen because every step can be visualized and saved as a source code in ".gnu" file format, making the recovery and visualization easy. However, this method can be easily implemented in other software of choice such as C, or C++. All the steps are standard because they depend on the slopes and intercepts of the data classes therefore prone to automation.
  • the objectives of the study disclosed herein include validation for the peripheral biomarkers for AD using skin fibroblasts.
  • the detailed experimental methods are briefly described here, and in detail in F.V. Chirila et al., J. Alzheimer's Disease 33, 165-176 (2013); F.V. Chirila et al., J. Alzheimer's Disease 42, 1 279-94 (2014).
  • a goal of the validation study was to find the variables (e.g., hidden or unexpected variables) that may influence the diagnosis of AD, and then to quantify their dependence.
  • Another goal was to apply corrective procedures that may remove an assay's dependence on those variables.
  • Example 1 Alzheimer's disease diagnostic assay for different FBS lots
  • Figure 1 (A) and Figures 2(A) - (B) show the input-output function for an AD diagnostic assay for different FBS lots.
  • the FBS A14 is from Gemini Bio Products (Triangles)
  • the FBS 941 from Gibco Laboratories (Squares)
  • the FBS D1 1 from Atlanta Biologicals (Circles).
  • the FBS lot A92 is from Gemini Bio Products (Figure 2(A))
  • the FBS lot 692 is from Gibco Laboratories ( Figure 2(B)).
  • n represents the number of patients.
  • the assay output, Ln(A/N) depends linearly on assay input, Ln(Cell Density), for the two classes of patients, class 1 -(AD; empty symbols) and class 2-(AC; filled symbols), and for the five FBS lots.
  • Ln(Cell Density) Ln(Cell Density)
  • class 1 -(AD; empty symbols) class 1 -(AD; empty symbols)
  • class 2-(AC; filled symbols) for the five FBS lots.
  • Figure 1 (A) and Figures 2(A) - (B) the dependence of the assay output on the assay input is linear regardless of the FBS lot used. However, with each FBS lot there is a slight difference in the linear dependence, i.e., a difference in the slope and the intercept for each class of patients. This dependence on the FBS lot has practical implications because of the change in the cut-off, and therefore, in the diagnostic assay.
  • Example 2A The first stage of the method
  • the first stage in a method disclosed herein comprises five steps ⁇ see, e.g., Figure 3).
  • Step 1 includes finding the representation in which the dependence/input- output function is linear and fitting the classes of patients with linear functions. For the two classes of patients, this representation is in a Ln(A/N) versus Ln(Cell Density) plot (see Figures 1 (A) and Figures 2(A) - (B)). To emphasize the generality of the method, three pairs of data classes were generated based on the fit lines of the raw data from Figure 1 (A). Then, noise was added to the slopes and intercepts (see Figure 1 (C)). Out of the three different conditions studied, one condition was picked (see the two lines corresponding to FBS lot 941 in Figure 1 (C)) to show as an example of the first stage of the method.
  • Step 2 includes translating the data classes to the origin i.e., by subtracting the intercepts i.e., "-b" and "-d.” (See Figure 3(B)).
  • Step 3 includes rotating the data classes by the minus angles of the fit lines, i. e. "-atan(a),” and “-atan(c)” (see Figure 3(C)).
  • Step 4 includes reversing the shifting of the data with the intercepts from step 2 by translating the data by the intercepts "+b" and "+d” (see Figure 3(D)).
  • Step 5 includes-setting up the fixed cut-off in the middle of the gap "(b+d)/2,” therefore translating the data by the difference [8-(b+d)/2] (see Figure 3(E)).
  • the final step is depicted in Figure 3(F) and can be compared with the raw data classes from Figure 3(A).
  • the first stage comprises a linearization and fitting step and four isometric transformations: translation, rotation, translation, translation.
  • step 5 The last step in Figure 3 (step 5), has and arbitrary value for the Fixed Cutoff, which was assigned a value of 8. However, as long as the Fixed Cutoff is fixed for all of the different conditions in the second variable, y, herein the different FBS lots, the ranking and classification with respect to the second variable will not depend on the choice of the Fixed Cutoff.
  • Example 2B Results of the first stage of the method
  • the parameters used for the first three isometric transformations are set by the slopes and intercepts of the linear fits for each data class.
  • the fourth isometric transformation has an arbitrary, but fixed cutoff.
  • the classification with respect to this second variable does not depend on the arbitrary cutoff. Due to the standard nature of this first stage of the method, the result is a measurable distance, D, between the classes of patients ( Figures 1 (B), (D)).
  • the output of the assay may be sorted as a function of the second variable, y, which may be, e.g., the FBS lot used ( Figure 4).
  • Example 3A Description of the second stage in the method
  • Example 3B Results of the second stage in the method
  • Figures 4(A) and (C) show the raw data ranked by the distance, D, and the average coefficient of variation normalized by the distance, ⁇ CV>/D.
  • Figures 4(B) and (D) show the same measures, the distance, D, and the average coefficient of variation normalized by the distance, ⁇ CV>/D, for the noisy data from Figure 1 (C).
  • Example 4 Classification of an unknown y-state (e.g., FBS lot)
  • the distance between data classes, D, in their normalized form, after the first stage of the method depends linearly on the rank of second variable y (e.g., FBS lot). See, e.g., Figures 4(A) - (B).
  • an untested FBS lot can be classified by using the reference standard curve from Figure 4.
  • a range of 3 to 5 samples/patients per data class can be used in order to determine linear dependence.
  • the optimum y-state/FBS lot has a good dynamic range, large D, and low noise, small ⁇ CV>/D, i.e., low Rank.
  • CutOff (xH ⁇ c+a[(c?+ 1)/(c?+ 1)] 1/2 ⁇ x+d+b[(c 2 + 1)/(a 2 + 1)] 1/2 (Thick lines Figure 5).
  • variants of this approach may be used by defining an area near the CutOff(x) line as a band of a certain percent formed of parallel lines shifted upward and downward with a percentage of the intercept, inside which the classes will be undetermined (see Dashed lines Figure 5(B)).
  • this strategy depends on the dynamic range, D, of the two data classes needed to be compared, on the signal-to-noise ratio (SNR), ⁇ CV>/D, and on how close to the intersection point of the fit lines the two data classes are (see Example 10).
  • the distance between the unknown sample located at (x u , y u ), and the fit line for the AC class is given by the perpendicular segment line to the AC fit line (see Dotted lines in Figure 5(A)).
  • the distance from the unknown sample, (x u , y u ), and the fit line for the AD class is given by the perpendicular segment line to the AD fit line (see Dashed lines in Figure 5(A)). If the shortest distance is dAc, i.e., dAc ⁇ dAD, then the case is a class 2-AC (Circle in Figure 5(A)). If the shortest distance is dAD, i.e., dAD ⁇ dAc, then the case is a class1 -AD (Square in Figure 5(A)). The distances described herein, dAc and dAD, have an analytical description. The classification of an unknown sample can be up to the discrimination limit (see Example 10).
  • the five FBS lots from three different companies are regarded as discreet values for the second variable, y"53 ⁇ 4 ⁇ yi , y 2 , y3, y 4 , ys ⁇ .
  • Figures 1 (A) and (C) illustrate the FBS lot variability for the three FBS lots.
  • the unbiased classification with respect to this second variable is a result of this study (see Figure 4).
  • the first stage of the method allows for standardization by establishing a fixed cutoff (e.g., 8), which permits a fair and quantitative comparison of different FBS lots.
  • the second stage of the method allows the five FBS lots to be ranked (see Figure 4) based on the distance between data classes, D, and based on the normalized coefficient of variation, ⁇ CV>/D.
  • this linear dependence is also followed for three pairs of randomly generated data sets (See Figure 7).
  • the method disclosed herein offers an advantage by reducing the QC time. Practically, the FBS lot-to-lot variation issue is removed. Moreover, any lot within the range described herein can be used after establishing the linear dependence within the data classes with a small number of samples, e.g., typically less than 5 samples per data class. In some embodiments, the signal to noise ratio may limit the operation range up to discrimination limit (see Example 10).
  • the first stage of the analysis of the method was validated with a different AD diagnostic assay that quantifies the skin fibroblast network complexity with fractal dimension for different time points (F.V. Chirila et al., J Alzheimer's Disease 33, 1 65-176 (2013)).
  • the fractal curves were constructed, and then the linear part of the curve was fitted with a line from which the slope and intercepts were extracted.
  • Three classes of patients were studied with this assay, class 1 -AD, class 2-age-matched non-demented control (AC), and class 3-non Alzheimer's disease demented (Non-ADD), which includes Huntington disease (HD) and Parkinson's disease (PD) patients (see Figures 6(A) - (B)).
  • the linear representation is presented as 1 /slope versus the intercept of the linear part of the fractal curves. See, e.g., Figure 6(A) (showing raw data for AD samples (squares), AC samples (circles), and Non-ADD samples (triangles)). As shown in Figure 6(B), when using the first stage of the method for the three classes AD, AC, Non-ADD, the normalization and setting of a fixed cutoff are achieved. Furthermore, 120 randomly surrogate data were generated in this plane 1 /slope versus intercept ( Figure 6(C)). These surrogate data play the role of unknown samples that may be tested with this assay.
  • the method disclosed herein is general and has applicability to other diagnostic systems, S, with 2 or more classes of patients, (Ci , C 2 ,..., Cn ) to be separated, and which show a linear dependence of the assay output on one of the variables, x, with discreet changes with a second variable, y"53 ⁇ 4 ⁇ yi , y 2 , y3, y4, ys ⁇ -
  • the first stage of the method has no restrictions in terms of the number of data classes that need to be normalized.
  • For the second stage of the method in some embodiments, if more than two data classes need to be separated, then a decision must be made about the two most important classes that need to be separated.
  • Figure 7 illustrates the first and second stages of the method for randomly generated data classes C1 and C2.
  • two randomly generated data classes as represented by circles, squares, and triangles, were considered from exponential functions of the type a * exp(-x/b), where a and b are parameters (see Figure 7(A); empty symbols represent the upper data sets and filled symbols represent the lower data sets).
  • These randomly generated data classes show a linear dependence (Figure 7(A)).
  • the reference standard found for the AD diagnostic assay may be found for other systems requiring data classification and which have a change in the cutoff with hidden variables, linear in x, and changing the linear fit parameters with the second variable y.
  • Example 10 The noise level establishes the discrimination limit/d-limit
  • the level of noise with respect to the signal in the data may become important near the intersection point of the two linear classes of patients (see Figures 8(A) - (B)) where the two classes cross over the separation boundary/cutoff.
  • the dashed vertical line in Figure 8(A) defines the discrimination limit (d-limit).
  • the signal-to-noise ratio (SNR) is poor.
  • SNR signal-to-noise ratio
  • 1 1 out of the 51 data points from class 1 and 18 out of 51 data points from class 2
  • the vertical dashed line from Figure 8(A) is located where the signal plus the noise intersects the cutoff line.
  • the SNR ratio is closely related with the average coefficient of variation normalized by the distance, ⁇ CV>/D.
  • the d-limit suggests that high cell densities are not desirable for this diagnostic assay based on cell aggregation.
  • L the total Length
  • the three values for the three data classes were plotted with a square, circle, and triangle on the right hand side in Figure 9(B). The length (L) depends first on the slope ( Figure 9(C)), but also on the X projections/X range ( Figure 9(D)).
  • the points e.g., square, circle, triangle
  • the distance along this curve between data classes C1 (square) and C3 (circle) is proportional with the dynamic range, D, which is defined in the second stage of the method (see Figure 1 (D) and Figures 4(A) - (B)), and which was used for ranking the five FBS lots.
  • the separability between the classes of patients, as defined in the methods section as a Sep function, increases at least two orders of magnitude as a result of exponential amplification produced by the first stage of the method.
  • the percent increase of the Sep function, after applying the first stage of the method for the five FBS lots is in the range 200 to 2500%.
  • the overlap probability between classes of patients as measured by the t-test decreases several orders of magnitude after applying the first stage of the method.
  • Figure 1 1 shows the change in mini-mental state examination (MMSE) or Folstein test after three hours of administering Bryostatin or Placebo versus the change in Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) at 48 hours.
  • Figure 1 1 (A) shows the dependence is linear for both Bryostatin and Placebo.
  • the isometric transformations disclosed herein may be applied to clinical trial data to change the two groups as shown in Figure 1 1 (B).
  • Example 13 Classification of Genetics and Astronomy
  • the method disclosed herein may also be used to enhance the separation of e.g., raw data for two cell cycle-regulated genes of yeast Saccharomyces cerevisaie and rotation curves of low surface brightness galaxies.
  • Spellman P.T. et al. Comprehensive Identification of Cell Cycle-Regulated Genes of the Yeast Saccharomyces cervisiae by Microarray Hybridization, Mole. Biol. Of the Cell 9, 3273-3297 (1998); Kuzio de Naray, T., McGaugh, S.S. deBlok, W.J.G.
  • Figure 10(a) plots the rate data for the two cell cycle- regulated genes of yeast Saccharomyces cerevisaie;
  • Figure 10(c) is the algorithm optimized data for two cell cycle-regulated genes of yeast Saccharomyces cerevisaie.
  • Figure 10(b) plots the rotation curves of low surface brightness galaxies the velocity versus the radius for eight low surface brightness galaxies shows a linear dependence near the origin for eight data classes (C 1-8 );
  • Figure 10(d) is the algorithm optimized data that shows what the observed velocity of the galaxies would be if the radius of the galaxies would be 70 arsec.
  • Figure 12 illustrates the validation of the method for resolving the age dependence for Alzheimer's disease diagnostic assay.
  • the raw data is presented showing the linear dependence of the assay output, Ln(A/N), for the two data classes, claims 1 -AD-squares and class 2-AC circles, the age of the patient.
  • Figure 12(b) shows the algorithm normalized data for the same two classes of patients.
  • the method disclosed herein applies to increasing diagnostic separation for two or more linear classes of patients, (Ci , C 2 ,..., C n ), which change with respect to a second discreet variable, y, and therefore change the cutoff values.
  • the second stage of the method ranks the output with respect to the second variable, y, e.g., FBS lot.
  • the methods disclosed herein may be used with a diagnostic assay requiring ranking of the separation, D, e.g., between AD and age- matched controls, when the second variable (e.g., FBS lot) changes this separation.
  • D Fetal Bovine Serum
  • the reference standard may be used for classification of any unknown FBS lot.
  • the first stage of the method was also validated with a different assay measuring the network complexity and generalized for three data classes AD patients, Age-matched controls (AC), and Non-ADD patients. However, in some embodiments, the same first stage can be generalized to a larger number of data classes.
  • the method was also tested with randomly generated samples and with three classes of patients ( Figure 6 and Figures 7(A) - (B)).
  • the first stage of the procedure applies to separating any classes of patients, (C 1 ; C 2 ,..., C n ) (see Figure 7), depending linearly on variables (e.g., hidden or unexpected).
  • These data classes may be chosen from machine learning, neural networks, data mining, gene expressions, pattern or face recognition, cognitive psychology, or astronomy.
  • two randomly generated data classes as represented by circles, squares, and triangles, were considered from exponential functions of the type a * exp(-x/b), where a and b are parameters (see Figure 7(A) ; empty symbols represent the upper data sets and filled symbols represent the lower data sets).
  • the method disclosed herein produces certain results as long as the intercepts of the data classes to be separated are statistically significant. In various embodiments, the method also produces certain results as long as the signal to noise ratio (SNR) is large enough not cross the separation boundary/cutoff line (see Figure 8(A)), i.e., on the left hand side of the discrimination limit, (d-limit). For a small signal to noise ratio, i.e., on the right hand side of the discrimination limit, (d-limit) (see Figure 8(B)), the method can be applied only for estimation purposes.
  • SNR signal to noise ratio
  • the linear dependence on the first variable, x needs to be established. From the practical and validation point of view, a linear dependence may be manipulated via isometric transformations, if compared with a nonlinear dependence. Therefore, this method determines in which representation the dependence on the first variable, x, is linear. If the initial dependence is nonlinear and can be assumed exponential, then through transformations (e.g., a natural logarithmic plot), the dependence can be linearized. This process is often called "linearization.” For one of the AD diagnostic assays disclosed herein, the plot of the Ln(A/N) rather than A/N reached this linearization when represented versus Ln(Cell Density).
  • the second AD diagnostic assay disclosed herein also has a linear dependence (see Figure 6).
  • a linear dependence/input-output function is also desirable for the predictability of any system in general because a small perturbation in the independent variable/input of the system may produce a predictable and linearly correlated small perturbation in the dependent variable/output.
  • the two-stage procedure of the method disclosed herein may be applied to an AD diagnostic assay for which the output depends on two variables (e.g., cell density the experiment and FBS).
  • the dependence of the assay output on one of the variables is linear.
  • the second variable changes the parameters, i.e., slope and intercept, of the linear dependencies for each class of patients/data.
  • slope 0
  • equidistant with respect to a fixed Cutoff.
  • the signal-to-noise ratio establishes the discrimination limit (d-limit) as the location where the two classes of patients are crossing over the Cutoff boundary, defined analytically herein as the equation of the angle bisector.
  • the first stage of the method is also applied to an AD diagnostic assay that distinguishes between three classes of patients: Alzheimer's Disease, Age-matched controls, and Non-Alzheimer's Disease Demented patients.
  • the second stage of the method ranks the normalized classes of patients by the distance and the normalized coefficient of variation.
  • D D(Rank y )
  • Table 1 In-depth demographic, genetic/family history and clinical history of the Banked patients.

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Abstract

La présente invention concerne des procédés pour classer deux populations différentes ou plus, telle qu'une population atteinte de la maladie d'Alzheimer. Le ou les procédés de l'invention utilisent une nouvelle analyse en deux étapes pour améliorer la séparation, par exemple, avec des biomarqueurs associés à des patients atteints de la maladie d'Alzheimer à partir de patients témoins appariés suivant l'âge et de patients atteints de démence sans maladie d'Alzheimer. L'analyse tient compte de la dépendance linéaire de variables de sortie sur des variables d'entrée qui sont modifiées par, par exemple, des variables telles que le sérum fœtal bovin, l'âge des patients, et/ou la concentration protéique.
EP16714094.6A 2015-03-06 2016-03-06 Procédés pour classer des populations comprenant des populations atteintes de la maladie d'alzheimer Withdrawn EP3265810A1 (fr)

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US7595167B2 (en) 2005-10-11 2009-09-29 Blanchette Rockefeller Neurosciences Institute Alzheimer's disease-specific alterations of the Erk1/Erk2 phosphorylation ratio
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