WO2011109503A1

WO2011109503A1 - Novel csf biomarkers for alzheimer's disease and frontotemporal lobar degeneration

Info

Publication number: WO2011109503A1
Application number: PCT/US2011/026852
Authority: WO
Inventors: John Q. Trojanowski; Virginia M. Y. Lee; Leslie M. Shaw
Original assignee: The Trustees Of The University Of Pennsylvania
Priority date: 2010-03-02
Filing date: 2011-03-02
Publication date: 2011-09-09

Abstract

Protein and gene biomarkers that differentially discriminate between various neurodegenerative disorders are utilized in diagnosing a specific neurodegenerative disorder in a subject. Neurodegenerative disorders that can be distinguished from one another include Alzheimer's disease (AD), different forms of Frontotemporal lobar degeneration (FTLD), and different forms of dementia.

Description

TITLE OF THE INVENTION

Novel CSF Biomarkers for Alzheimer's Disease and Frontotemporal Lobar Degeneration

BACKGROUND OF THE INVENTION

Frontotemporal lobar degeneration (FTLD) represents a group of neurodegenerative disorders which lead to progressive behavioral and/or language abnormalities (McKhann et al., 2001 , Arch Neurol. 58: 1803- 1809; Cairns et al, 2007, Acta Netiropathol, 1 14:5-22; Mackenzie et al,, 2009, Acta Neuropathoi, 1 17: 15-18). Two major forms of FTLD account for about 90% of FTLD cases: FTLD-TDP is characterized by neuronal and glial inclusions that contain ubiquitinated TAR DNA binding protein of -43 kD (TDP-43), wiiiie FTLD-Tau contains the hallmark neuropathology of fibrillar and hyperphosphorylated tan inclusions (Cairns et at., 2007, Acta Neuropathoi. 1 14:5-22;

Mackenzie et al., 2009, Acta Neuropathoi. 1 17: 15-18). These pathologic lesions likely contribute to the development of FTLD, as mutations in the genes that encode TDP-43 (TARDP) (Benajiba et al, 2009, Ann Neurol. 65:470-473) or tan (MAPT) (Cairns et al., 2007, Acta Neuropathoi. 1 14:5-22) result in familial FTLD cases with lesions respectively containing TDP-43 or Tau. Accurate ante-mortem diagnosis of the underlying pathology in FTLD will be important in the appropriate pairing of clinical FTLD patients with TDP-43- or Tau-specific disease-modifying treatments.

Currently, there are no reliable and well validated ante-mortem biomarkers for FTLD-TDP or FTLD-Tau. in familial cases, the pathologic FTLD subtype can be determined by the associated mutations, such as progranulin (GRN), valosin containing protein (VCP) and TARDP in FTLD-TDP, and MAPT FTLD-Tau (Cairns et al., 2007, Acta Neuropathoi. 1 14:5-22). These cases are small in number, and the distinction between disease-causing mutations and polymorphisms can be challenging. In sporadic cases, clinical features have been proposed as biomarkers for FTLD subtypes. For example, patients presenting with the clinical features of FTLD in association with amyotrophic lateral sclerosis (ALS) nearly always show TDP-43 immunoreactive lesions as the underlying neuropathology U 2011/026852

(Hit et al., 2009, Arch Neurol. 66: 1359- 1364), while FTLD patients with clinical features typical of progressive supranuclear palsy (PSP) are almost always clue to FTLD-Tau (Josephs et al., 2003, Mov Disord, 18: 1018- 1026; Josephs et al, 2006, Neurology. 66:41 -48).

Among cases without ALS or prominent extrapyramid l findings, clinical FTLD subtypes with a form of Primary Progressive Aphasia (PPA) are also preferentially associated with a specific underlying pathology. For example, FTLD-TDP is the most common cause of semantic dementia (SemD), while FTLD-Tau is the more common cause for progressive non- fluent aphasia (PNFA) (Josephs et al., 2006, Neurology. 66: 1 -48). Nevertheless, these ciinicopathologic associations on the group level have been only modestly successful at predicting pathology at the individual patient level. Detailed neuropsychological analysis has also shown group-level differences between FTLD-TDP and FTLD-Tau cases (Grossman et al. 2008, Neurology. 70:2036-2045), although the predictive accuracy of neuropsychological testing on the individual level is modest. Finally, quantitative imaging studies appear to be only modestly reliable at distinguishing between FTLD-TDP and FTLD-Tau (Grossman et al, 2008, Neurology, 70:2036-2045). While clinical and imaging features alone are insufficient for the prediction of underlying FTLD pathologic subtype, chemical biomarkers may better predict the underlying pathology in patients with clinical features of FTLD. Analytes in cerebrospinal fluid (CSF) offer the potential for accurate and reproducible ante-mortem diagnosis. CSF levels of peptides related to Alzheimer's disease (AD), including total tau, phosphorylated at threonine 181 (p-taiim) and Αβ1 -42 (or Αβ42), represent the most established biomarkers in neurodegenerative disease research (Shaw et a!., 2007, Nat Rev Drug Discov. 6:295-303). Altered levels of these peptides are useful in confirming AD as the underlying pathology in dementia among elderly patients (Shaw et al., 2009, Ann Neurol. 65:403-4 1 3). These biomarkers can additionally identify patients with clinical features of FTLD that are due to an atypical presentation of pathological AD (Shaw et al., 2009, Ann Neurol. 65:403-413; Tapiola et al., 2009, Arch Neurol. 66:382-389). However, no CSF biomarker exists to distinguish between FTLD-TDP or FTLD-Tau although a small number of studies suggest that measures of TDP-43 in plasma and/or CSF look promising for the diagnosis of FTLD-TDP, but they need to be further validated.

Alzheimer's disease (AD), frontotemporal lobar degenerations (FTLD), and dementia with Lewy bodies (DLB) are major neurodegenerative disorders pathologically characterized by lesions composed of disease-specific misfolded proteins. Their clinical syndromes often have overlapping features, making antemoitem prediction of pathology challenging. Yet, as specific disease-modifying therapies become available, it is increasingly important that such diagnoses be made. Analytes in cerebrospi al fl id (CSF) or plasma offer the potential for more accurate diagnosis, especially those associated with AD pathology such as total tau, tan phosphorylated at threonine 181 (p-tau|₈i) and Αβ 1 -42 (or Αβ42) (Shaw, et al„ 2009, Ann Neurol. 65: 403-13; Tapiola et al., 2009, Arch Neurol. 66: 382-9). Recently, a targeted proteomic approach identified plasma biomarkers associated with the clinical diagnosis of AD (Ray et al., 2007, Nat Med. 13: 1359-62), but the absence of pathological confirmation makes these results difficult to interpret since 10- 20% of clinically diagnosed AD patients are found at autopsy to have a cause for dementia other than AD. In addition to tau and Αβ42, peptides in common inflammatory and apoptotic pathways, growth factors, and other analytes have been proposed as biomarkers for AD (Shaw et al., 2007, Nat Rev Drug Discov, 6: 295-303), Some of these analyte changes may be specifically associated with AD pathogenesis, while others reflect neurodegeneration irrespective of etiology. To establish the accuracy and utility of biomarkers of AD diagnosis thus requires studies of biofluids obtained during life from well -characterized AD patients longitudinally followed to autopsy confirmation. Beyond diagnostic classification, levels of CSF biomarkers may also track cognitive decline during disease progression. Characterization of a select panel of CSF biomarkers is therefore critical in diagnosis and prognosis, and alterations in their levels may be considered secondary endpoints in future therapeutic trials,

There exists a need in the art for a method of differentially diagnosing neurodegenerative disorders, including Alzheimer's Disease (AD), Frontotemporai lobar degeneration (FTLD), FTLD-TDP, FTLD-Tau, demetias, dementia with Lewy bodies (DLB), vascular dementias and the like, which displays good sensitivity and specificity so that it particularly enables discriminating between different neurodegenerative disorders. The present invention addresses this unmet need in the art.

SUMMARY OF THE INVENTION

The invention provides a method of differentially diagnosing a neurodegenerative disorder in a patient. In one embodiment, the method comprises determining the level of at least one biomarker in a biological sample obtained from the patient wherein the biomarker differentially discriminates between different

neurodegenerative disorders.

In one embodiment, the neurodegenerative disorder is selected from the group consisting of Alzheimer's disease, frontotemporai lobar degeneration (FTLD), frontotemporai lobar degeneration TDP-43 pathology (FTLD-TDP), frontotemporai lobar degeneration TDP 2 tan pathology (FTLD-Tau), dementias, dementia with Lewy bodies (DLB), vascular dementias, or any combinations thereof.

In one embodiment, the biological sample is a body fluid. Preferably, the body fluid is a cerebrospinal fluid (CSF).

In one embodiment, the biomarker is selected from the group consisting of the number of ApoE4 alleles, Αβ42 levels, tau, p-tauisi , C3, IL-23, NrCAM, IL-1 , and any combination thereof, further wherein the biomarker differentially discriminates between pathologically confirmed AD from cognitively normal patients.

In one embodiment, the biomarker is selected from the group consisting of Αβ42, tau, C3, Eotaxin-3, p-taii|_SS, agouti-related peptide (AgRP), angiotensinogen (AGT), hepatocyte growth factor (HQF), monocyte chemoattractant 1 (MCP- 1 ), von Wiliebrand factor (vWF), apolipoprotein H (ApoH), resistin, and any combination thereof, further wherein the biomarker differentially discriminates between pathologically confirmed

Alzheimer's disease from other neurodegenerative disorders.

In one embodiment, the biomarker is selected from the group consisting of C3, CgA, IL- 1 a, 1-309, NrCAM, and VEGF, further wherein the biomarker is an indication of severity of cognitive impairment

In one embodiment, the biomarker is selected from the group consisting of IL- la, TEC , and any combination thereof, further wherein the biomarker is an indication of cognitive decline in MCI,

In one embodiment, the biomarker is selected from the group consisting of Fas, agouti-related peptide (AgRP), adrenocortotropic hormone (ACTH), IL-23, IL- 17, Eotaxin-3, ApoB, and any combination thereof, further wherein the biomarker differentially discriminates between frontotemporal lobar degeneration TDP-43 pathology (FTLD-TDP) and FTLD tau pathology (FTLD-Tau).

in one embodiment, the biomarker is separately identified by logistic regression (LR) analysis, random forest (RF) classification, and predictive analysis of microarrays (PAM).

The present invention provides a kit or assay for a panel of biomarkers for differentially diagnosing a neurodegenerative disorder. In one embodiment, the kit comprises an agent designed to determine the level of at least one biomarker in a body fluid obtained from a patient wherein the biomarker differentially discriminates between different neurodegenerative disorders. The invention also provides a method for assessing progression of a neurodegenerative disorder in a patient. In one embodiment, the method comprises differentially diagnosing a neurodegenerative disorder comprising the steps of determining the level of at least one biomarker in a biological sample obtained from the patient wherein the biomarker differentially discriminates between different neurodegenerative disorders.

The invention also provides a method for staging a neurodegenerative disorder in a patient. In one embodiment, the method comprises differentially diagnosing a neurodegenerative disorder comprising tiie steps of determining the level of at least one biomarker in a biological sample obtained from the patient wherein the biomarker differentially discriminates between different neurodegenerative disorders.

The invention provides a method of diagnosing whether a patient has a neurodegenerative disorder. In one embodiment, the method comprises determining the level of at least one biomarker in a biological sample obtained from the patient wherein the biomarker differentially discriminates between different neurodegenerative disorders.

BRIEF DESCRIPTION OF THE DRAWINGS

The following detailed description of preferred embodiments of the invention will be better understood when read in conjunction with the appended drawings. For the purpose of illustrating the invention, there are shown in the drawings embodiments which are presently preferred. It should be understood, however, that the invention is not limited to the precise arrangements and instrumentalities of the embodiments shown in the drawings.

Figure 1 , comprising Figures 1A and IB is a series of images depicting a representation of biomarkers identified by multiple analytical strategies, Figure I A is a schematic of CSF analytes useful in the distinction between FTLD-TDP and FTLD-Tau as identified by the three analytical algorithms, Analytes in overlapping regions represent analytes identified by multiple algorithms. Figure I B is an image depicting levels of CSF analytes identified by multiple analytical strategies, Levels shown are normalized to levels from normal control subjects (±SEM).

Figure 2 is a graph depicting relative levels of CSF chemokines involved in the TL-23/IL- 17 axis in control subjects, and patients with FTLD-TDP, FTLD-Tau, and AD (±SEM). * p < 0.005 by Mann- Whitney U test compared to control subjects (FTLD-Tau and AD) or FTLD-TDP (FTLD-Tau); ** p < 0.02 compared to control subjects, FTLD-Tau, or AD. Figure 3, comprising Figures 3A through 3C, is a series of images depicting representative AD biomarkers from combined and MAP biomarker models. Figure 3A is a schematic depicting representative biomarkers identified in each analytical strategy by combining traditional AD biomarkers (e.g., tau, p-taiiiss, Αβ42, number of ApoE4 allele) and MAP biomarkers for AD versus NL. Figure 3B is a schematic depicting representative MAP and traditional AD biomarkers according to each analytical strategy for the distinction between AD and non-AD dementia. Figure 3C is an image depicting representative levels of AD biomarkers identified using at least two analytical strategies in autopsy-confirmed cases of AD and other non-AD dementias normalized to values in NL (± SEM).

Figure 4 is a graph depicting MAP analytes associated with AD in comparison to NL and non-AD neurodegenerative disorders identified by the three analytical strategies, Levels are normalized to values in NL (± SEM).

Figure 5 is an image depicting partial residual plots of MAP analytes versus rates of subsequent cognitive decline in MCI. Linear fit and 95% confidence interval for fit are shown for each graph. The overall model includes age, gender, education, IL- l a level, and TECK level.

Figure 6, comprising Figures 6A and 6B, is a series of boxplots depicting median values, quartiles, and outliers (circles) of traditional (i.e. tau and Ab42) and other candidate CSF biomarkers that differed in levels between subjects with normal cognition and AD. Values shown are normalized to mean values of cognitively normal subjects. Figure 6A depicts analytes elevated in AD as compared to cognitively normal subjects. Figure 6B depicts analytes decreased in AD as compared to cognitively normal subjects. Levels in patients with autopsyconfirmed non-AD neurodegeneration were also shown for comparison. White box corresponds to cognitively normal subjects; light shaded box corresponds to autopsy-confirmed cases of AD; dark shaded box corresponds to autopsy confirmed cases of non-AD neurodegenerative disorders. *I-309 was found to differ between AD and cognitively normal subjects by random forest and PAM, but not Mann-Whitney U test.

Figure 7, comprising Figures 7A and 7B, is a series of images depicting AD biomarkers identified by each of the three analytical strategies (logistic regression, random forest, and PAM). Figure 7A depicts biomarkers useful in distinguishing between subjects with AD and normal cognition. Figure 7B depicts biomarkers useful in distinguishing between subjects with AD and other non-AD neurodegenerative disorders. Analytes in overlapping regions were identified by multiple strategies as important biomarkers. 2

Figure 8 comprises boxplots showing median values, quartiles, and outliers (circles) of traditional and candidate bioniarkers that differed in levels between AD and other non-AD neurodegenerative disorders, Values shown are normalized to mean values of cognitively normal subjects. From left to right, the first box corresponds to cognitively normal subjects; the second box corresponds to AD, the third box corresponds to FTLD-TDP, the fourth box corresponds to FTLD-Tau; and the fifth box corresponds to dementia with Lewy bodies.

Figure 9 is an image depicting partial residual plots of MAP analytes versus rates of subsequent cognitive decline in MCI. Li ear fit and 95% confidence interval for fit are shown for each graph. The overall model includes age, gender, education, IL- l level and TECK level.

DETAILED DESCRIPTION

The present invention relates generally to diagnostic methods and markers, prognostic methods and markers, and therapy evaluators for neurodegenerative disorders. Non-limiting examples of neurodegenerative disorders include, but are not limited to

Alzheimer's Disease (AD), Frontotemporal lobar degeneration (FTLD), FTLD-TDP, FTLD- Tau, demetias, dementia with Lewy bodies (DLB), vascular dementias and the like. In one embodiment, the biomarkers of the invention are useful for discriminating between different neurodegenerative disorders.

In one embodiment, the present invention relates to biomarkers of A lzheimer's Disease, methods for diagnosis of Alzheimer's Disease, methods of determining

predisposition to Alzheimer's Disease, methods of monitoring progression/regression of Alzheimer's Disease, methods of assessing efficacy of compositions for treating Alzheimer's Disease, methods of screening compositions for activity in modulating biomarkers of Alzheimer's Disease, methods of treating Alzheimer's Disease, as well as other methods based on biomarkers of Alzheimer's Disease.

In another embodiment, the invention relates to biomarkers that distinguish pathologically confirmed AD from cognitively normal subjects and patients with other neurodegenerative disorders. Preferably, the biomarkers correlate with cognition in AD and mild cognitive impairment (MCI).

In another embodiment, the invention provides biomarkers that can distinguish between the two main causes of frontotemporal lobar degeneration (FTLD), including FTLD with TDP-43 pathology (FTLD-TDP) and FTLD with tau pathology (FTLD-Tau), According to the present invention, certain biomarkers are associated with an elevated risk of having or developing a neurodegenerative disorder. Persons so identified have an elevated risk of having or developing a neurodegenerative disorder. Therefore, the present invention provides methods of assessing risk of AD, FTLD, FTLD-TDP, FTLD-Tau, dementias, dementia with Lewy bodies (DLB), vascular dementias, or any combinations thereof, in an individual. Kits useful in practicing embodiments of the inventive methods are also provided.

Armed with the present disclosure, a skilled artisan would appreciate that the biomarkers of the invention can be used to differentially diagnose dementias, including vascular dementias and/or frontotemporai lobe degenerations, which display good sensitivity and specificity so that it particularly enables discriminating vascular dementias and/or frontotemporai lobe degenerations from other kinds of dementia like, for example,

Alzheimer's disease.

The invention also provides a method for permitting refinement of disease diagnosis, disease risk prediction, and clinical management of patients associated with a neurodegenerative disorder. That is, the biomarkers of the invention can be used as a marker for the disease state or disease risk. For example, the presence of the selective biomarkers of the invention permits refinement of disease diagnosis, disease risk prediction, and clinical management of patients being treated with agents that are associated with a particular neurod egene at i ve d i sorde .

In still further embodiments, the invention provides methods of monitoring a particular bioniarker to evaluate the progress of a therapeutic treatment of a

neurodegenerative disorder.

The invention also provides methods for screening an individual to determine if the individual is at increased risk of having a neurodegenerative disorder. Individuals found to be at increased risk can be given appropriate therapy and monitored using the methods of the invention.

Definitions

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Although any methods and materials similar or equivalent to those described herein can be used in the practice or testing of the present invention, the preferred methods and materials are described. 6852

As used herein, each of the following terms has the meaning associated with it in this section.

The articles "a" and "an" are used herein to refer to one or to more than one (i.e., to at least one) of the grammatical object of the article. By way of example, "an element" means one element or more than one element.

"About" as used herein when referring to a measurable value such as an amount, a temporal duration, and the like, is meant to encompass variations of ±20% or ±10%, more preferably ±5%, even more preferably ±1%, and still more preferably ±0.1 % from the specified value, as such variations are appropriate to perform the disclosed methods.

The term "abnormal" when used in the context of organisms, tissues, cells or components thereof, refers to those organisms, tissues, cells or components thereof that differ in at least one observable or detectable characteristic (e.g., age, treatment, time of day, etc.) from those organisms, tissues, cells or components thereof that display the "normal"

(expected) respective characteristic. Characteristics which are normal or expected for one cell or tissue type, might be abnormal for a different cell or tissue type.

"Amplification" refers to any means by which a polynucleotide sequence is copied and thus expanded into a larger number of polynucleotide sequences, e.g., by reverse transcription, polymerase chain reaction or ligase chain reaction, among others.

The term "biomarker" is a characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathological processes, or pharmacological responses to a therapeutic intervention. The biomarker can for example describe a substance whose detection indicates a particular disease state. The biomarker may be a peptide that causes disease or is associated with susceptibility to disease. In some instances, the biomarker may be a gene that causes disease or is associated with susceptibility to disease. In other instances, the biomarker is a metabolite, In any event, the biomarker can be differentially present (i.e., increased or decreased) in a biological sample from a subject or a group of subjects having a First phenotype (e.g., having a disease) as compared to a biological sample from a subject or group of subjects having a second phenotype (e.g., not having the disease). A biomarker is preferably differentially present at a level that is statistically significant (i.e., a p-value less than 0.05 and/or a q-value of less than 0, 1 0 as determined using either Welch's T-test or Wilcoxon's rank-sum Test).

The term "body fluids" includes any fluids which can be obtained from a mammalian body. Thus, the term "body fluids" also includes hoiiiogenates of any tissues and other body matter. More particularly, however, the term "body fluids" includes fluids that 11 026852 are normally or abnormally secreted by or excreted from the body. The respective fluids may include, but are not limited to: blood, plasma, lymph, urine, and cerebrospinal fluid, blood, plasma, and cerebrospinal fluid.

A "disease" is a state of health of an animal wherein the animal cannot maintain homeostasis, and wherein if the disease is not ameliorated then the animal's health continues to deteriorate.

In contrast, a "disorder" in an animal is a state of health in which the animal is able to maintain homeostasis, but in which the animal's state of health is less favorable than it would be in the absence of the disorder. Left untreated, a disorder does not necessarily cause a further decrease in the animal's state of health.

A disease or disorder is "alleviated" if the severity of a symptom of the disease or disorder, the frequency with which such a symptom is experienced by a patient, or both, is reduced.

An "effective amount" or "therapeutically effective amount" of a compound is that amount of compound which is sufficient to provide a beneficial effect to the subject to which the compound is administered. An "effective amount" of a delivery vehicle is that amount sufficient to effectively bind or deliver a compound.

As used herein, an "instructional material" includes a publication, a recording, a diagram, or any other medium of expression which can be used to communicate the usefulness of a compound, composition, vector, or delivery system of the invention in the kit for effecting alleviation of the various diseases or disorders recited herein. Optionally, or alternately, the instructional material can describe one or more methods of alleviating the diseases or disorders in a cell or a tissue of a mammal. The instructional material of the kit of the invention can, for example, be affixed to a container which contains the identified compound, composition, vector, or delivery system of the invention or be shipped together with a container which contains the identified compound, composition, vector, or delivery system. Alternatively, the instructional material can be shipped separately from the container with the intention that the instructional material and the compound be used cooperatively by the recipient,

"Isolated" means altered or removed from the natural state. For example, a nucleic acid or a peptide naturally present in a living animal is not "isolated," but the same nucleic acid or peptide partially or completely separated from the coexisting materials of its natural state is "isolated." An isolated nucleic acid or protein can exist in substantially purified form, or can exist in a non-native environment such as, for example, a host cell or a test tube.

The term "microarray" refers broadly to both "DNA mic oarrays" and "DNA chip(s)," and encompasses all art-recognized solid supports, and all art-recognized methods for affixing nucleic acid molecules thereto or for synthesis of nucleic acids thereon.

"Naturally occurring" as used herein describes a composition that can be found in nature as distinct from being artificially produced. For example, a nucleotide sequence present in an organism , winch can be isolated from a source in nature and which has not been intentionally modified by a person in the laboratory, is naturally occurring.

As used herein, "phenotypically distinct" is used to describe organisms, tissues, cells or components thereof, which can be distinguished by one or more

characteristics, observable and/or detectable by current technologies. Each of such characteristics may also be defined as a parameter contributing to the definition of the phenotype. Wherein a phenotype is defined by one or more parameters an organism that does not conform to one or more of the parameters shall be defined to be distinct or distinguishable from organisms of the phenotype.

The terms "patient," "subject," "individual," and the like are used interchangeably herein, and refer to any animal, or cells thereof whether in vitro or in situ, amenable to the methods described herein. In certain non-limiting embodiments, the patient, subject or individual is a human.

As used herein, "phenotypically distinct" is used to describe organisms, cells or components thereof, which can be distinguished by one or more characteristics, observable and/or detectable by current technologies. Each of such characteristics may also be defined as a parameter contributing to the definition of the phenotype. Wherein a phenotype is defined by one or more parameters an organism that does not conform to one or more of the parameters shall be defined to be distinct or distinguishable from organisms of the said phenotype.

A "prophylactic" treatment is a treatment administered to a subject who does not exhibit signs of a disease or exhibits only early signs of the disease for the purpose of decreasing the risk of developing pathology associated with the disease.

The term "protein" typically refers to large polypeptides.

"Sample" or "biological sample" as used herein means a biological material isolated from a subject. The biological sample may contain any biological material suitable for detecting the desired biomarkers, and may comprise cellular and/or non-cellular material

I I from the subject. The sample can be isoiated from any suitable biological tissue or fluid such as, for example, blood, blood plasma, urine, or cerebral spinal fluid (CSF).

A "therapeutic" treatment is a treatment administered to a subject who exhibits signs of pathology, for the purpose of diminishing or eliminating those signs.

The terms "marker" and "epigenetic marker" are used interchangeably herein to refer to a distinguishing or characteristic substance that may be found in a biological material. The substance may, for example, be a protein, an enzyme, an RNA molecule or a DNA molecule. Non-limiting examples of such a substance include a kinase, a methylase, and an acetyiase. The terms also refer to a specific characteristic of the substance, such as, but not limited to, a specific phosphorylation, methylation, or acetylation event or pattern, making the substance distinguishable from otherwise identical substances. The terms further refer to a specific modification, event or step occurring in a signaling pathway or signaling cascade, such as, but not limited to, the deposition or removal of a specific phosphate, methyl, or acetyl group.

The term to "treat," as used herein, means reducing the frequency with which symptoms are experienced by a patient or subject or administering an agent or compound to reduce the frequency with which symptoms are experienced,

As used herein, "treating a disease or disorder" means reducing the frequency with which a symptom of the disease or disorder is experienced by a patient. Disease and disorder are used interchangeably herein.

Ranges: throughout this disclosure, various aspects of the invention can be presented in a range format. It should be understood that the description in range format is merely for convenience and brevity and should not be construed as an inflexible limitation on the scope of the invention. Accordingly, the description of a range should be considered to have specifically disclosed all the possible subranges as well as individual numerical values within that range. For example, description of a range such as from 1 to 6 should be considered to have specifically disclosed subranges such as from 1 to 3, from 1 to 4, from 1 to 5, from 2 to 4, from 2 to 6, from 3 to 6 etc., as well as individual numbers within that range, for example, 1 , 2, 2.7, 3, 4, 5, 5.3, and 6. This applies regardless of the breadth of the range.

Description

The present invention relates to the identification of biomarkers that are associated with neurodegenerative disorders. In one embodiment, the biomarkers of the invention are useful for discriminating between different neurodegenerative disorders. In one embodiment, the biomarkers can distinguish between the two main causes of frontotemporai lobar degeneration (FTLD), including FTLD with TDP-43 pathology (FTLD-TDP) and FTLD with tau pathology (FTLD-Tau). In another embodiment, the biomarkers can distinguish between pathologically confirmed Alzheimer's disease (AD) from cognitively normal (NL) subjects and patients with other neurodegenerative disorders. In another embodiment, the biomarkers are associated with cognition in AD and mild cognitive impairment (MCI).

Such biomarkers could be used for neurodegenerative disorder screening and diagnosis, as well as potentially for assessing response to new therapies. Given the probability of multiple underlying pathogenic mechanisms of some neurodegenerative disorders, the present invention provides novel biomarkers present in the bodily fluid of a subject. The biomarkers of the invention allow a more accurate diagnosis or prognosis of a neurodegenerative disorder. For example, the biomarkers are useful for distinguishing between FTLD-TDP and FTLD-Tau, distinguishing between pathologically confirmed Alzheimer's disease from cognitively normal subjects, distinguishing between pathologically confirmed Alzheimer's disease from other neurodegenerative disorders, and assessing cognition in AD and mild cognitive impairment (MCI).

The biomarkers of the invention may also allow the monitoring of a neurodegenerative disorder, such that a comparison of biomarker levels allows an evaluation of disease progression in subjects that have been diagnosed with a neurodegenerative disorder, or that do not yet show any clinical signs of the neurodegenerative disorder.

Moreover, the biomarkers of the invention may be used in concert with known biomarkers such that a more accurate diagnosis or prognosis of the neurodegenerative disorder may be made.

Generally, the invention provides that biomarkers are determined for biological samples from human subjects diagnosed with a neurodegenerative disorder, for example Alzheimer's Disease, as well as from one or more other groups of human subjects (e.g., healthy control subjects not diagnosed with Alzheimer's Disease), The biomarkers for a particular neurodegenerative disorder are compared to the biomarkers for biological samples from the one or more other groups of subjects. Those molecules differentially present, including those molecules differentially present at a level that is statistically significant, in the profile of a neurodegenerative disorder sample as compared to another group (e.g., healthy control subjects not diagnosed with Alzheimer's Disease) are identified as biomarkers to distinguish those groups.

The biomarkers disclosed herein may be used in combination with existing clinical diagnostic measures of Alzheimer's Disease and/or other neurodegenerative diseases. Combinations with clinical diagnostics may facilitate the disclosed methods, or confirm results of the disclosed methods (for example, facilitating or confirming diagnosis, monitoring progression or regression, and/or determining predisposition to Alzheimer's Disease and/or neurodegenerative disorders).

Generally, any suitable method may be used to analyze the biological sample in order to determine the level(s) of the one or more biomarkers in the sample. Suitable methods include those disclosed in the Examples section as well as chromatography (e.g., HPLC, gas chromatography, liquid chromatography), mass spectrometry (e.g., MS, MS-MS), enzyme-linked immunosorbent assay (EL1SA), antibody linkage, other immunochemical techniques, and combinations thereof. Further, the level(s) of the one or more biomarkers may be measured indirectly, for example, by using an assay that measures the level of a compound (or compounds) that correlates with the level of the biomarker(s) that are desired to be measured.

The biomarkers of the invention can be used to facilitate the optimum selection of treatment protocols, and open new venues for the development of effective therapy for a desired neurodegenerative disorder. Biomarkers of the invention can be used to guide treatment selection for individual patients, as well as to guide the development of new therapies specific to each type of neurodegenerative disorder.

I. Biomarkers to distinguish between FTLD-TDP and FTLD-Tau

In one embodiment, the invention relates to the identification of ante-mortem cerebrospinal fluid (CSF) diagnostic biomarkers that can distinguish between the two main causes of frontotemporal lobar degeneration (FTLD), including FTLD with TDP-43 pathology (FTLD-TDP) and FTLD with tau pathology (FTLD-Tau).

FTLD-TDP and FTLD-Tau can each lead to clinical FTLD syndromes, although the underlying pathologic substrate is difficult to predict on clinical grounds alone. The invention provides novel CSF biomarkers that improve the distinction between FTLD-TDP an FTLD-Tau. in one embodiment, biomarkers that differ between FTLD-TDP and FTLD- Tau, include but are not limited to Fas, neuropeptides (agouti-related peptide and

adrenocortotropic hormone), and cheniokines (1L-23, 1L- 17).

Biomarkers identified by multiple analytical strategies have more discriminating value and potential biological significance. In one embodiment, biomarkers for FTLD-TDP that are identified using at least three analytical strategies include but are not limited to TL- 17 and Eotaxin-3. In another embodiment, biomarkers for FTLD-TDP that are identified using at least two analytical strategies include but are not limited to agouti-related peptide (AgRP), adrenocortotropic hormone (ACTH), Fas, Angiopoietin 2 (ANG-2), ApoB, and IL-23. Accordingly, the invention includes biomarkers that distinguish between FTLD- TDP an FTLD-Tau including, but are not limited to, TL- 17, Eotaxin-3, AgRP, ACTH, Fas, ANG-2, ApoB, IL-23, and any combination thereof. These biomarkers are associated with high sensitivity and modest specificity for FTLD-TDP. Therefore, these biomarkers are useful for differential diagnosis of FTLD-TDP versus FTLD-Tau.

In some instances, the invention provides biomarkers that can differentially discriminates between FTLD-TDP and FTLD-Tau. The biomarkers include, but are not limited to Fas, agouti-related peptide (AgRP), adrenocortotropic hormone (ACTH),

Angiopoietin 2 (ANG-2), IL-23 IL- 17, Eotaxin, ApoB, macrophage-derived chemokine (MDC), S 100 calcium binding protein b (S lOOb), TRAIL receptor 3, and (TRA1L-R3).

II. Biomarkers associated AD and mild cognitive impairment (MCI),

The invention relates to the identification of biomarkers that are associated with Alzheimer's disease. Two categories of biomarkers included: I ) biomarkers that specifically distinguished Alzheimer's disease from normal subjects and other

neurodegenerative disorders; and 2) biomarkers altered in multiple neurodegenerative diseases, but not in normal subjects.

in one embodiment, the invention provides biomarkers useful in improving the distinction between Alzheimer's disease from normal subjects, in another embodiment, the invention provides biomarkers useful in improving the classification between AD and non- AD dementia. In another embodiment, the invention provides biomarkers useful in determining the staging of AD. In yet another embodiment, the invention provides biomarkers associated with rates of cognitive decline in MCI.

in one embodiment, Alzheimer's disease is distinguished from NL by a combination of traditional AD biomarkers that confer sensitivity, and multiplex biomarkers that confer specificity relative to NL. Two categories of biomarkers include: I ) biomarkers that specifically distinguish AD (e.g., CSF Αβ42 levels) from NL and other

neurodegenerative disorders; and 2) biomarkers altered in multiple neurodegenerative diseases (e.g., C3, Eotaxin-3, IL- l a, PDGF), but not i NL subjects. Six biomarkers (e.g., C3, CgA, IL-l a, 1-309, NrCAM, and VEGF) correlate with severity of cognitive impairment at CSF collection, and two (e.g., TEC , 1L- l a) associate with subsequent cognitive decline in MCI.

In one embodiment, biomarkers associated with AD are separately identified by logistic regression (LR) analysis, random forest (RF) classification, and predictive analysis of microarrays (PAM). In some instances, biomarkers associated with AD are identified using Multi-Analyte Profile (MAP) analysis. The methods of the invention provides for high sensitivity and specificity with increased accuracy for detecting

Alzheimer's disease,

In one embodiment, the invention includes biomarkers that can differentially discriminate between pathologically confirmed Alzheimer's disease from cognitively normal patients. The biomarkers include, but are not limited to the number of ApoE4 alleles, Αβ42 levels, tau, p-tau_{1 8}i , C3, IL-23, NrCAM, IL- I , BMP6, and PDGF

In one embodiment, the invention provides biomarkers that reliably differentiate the major neurodegenerative disorders from one another. For example, the biomarkers of the invention can distinguish AD from other neurodegenerative disorders. In one embodiment, Αβ42 and total tau levels can be used to distinguish AD from non-AD neurodegenerative disorders. Other biomarkers that can distinguish AD from non-AD neurodegenerative disorders include but are not limited to C3, Eotaxin-3, p-tauisi , agouti- related peptide (AgRP), angiotensinogen (AGT), hepatocyte growth factor (HGF), monocyte chemoattractant 1 (MCP- 1), and von Willebrand factor (vWF), apolipoprotein H (ApoH), resistin, and any combination thereof.

III. Application of the biomarkers

The following disclosure discusses biomarkers of the invention in the context of AD. However, the disclosure is not limited to biomarkers of AD, but is applicable to any of the biomarkers of the invention. For example, the following disclosure is also applicable to biomarkers that distinguish between FTLD-TDP and FTLD-Tau, and biomarkers that correlate with cognition in AD and mild cognitive impairment (MCI). As such, use of AD in the disclosure that follows should be considered to exemplify other embodiments including biomarkers that are associated with neurodegenerative disorders; biomarkers useful for discriminating between different neurodegenerative disorders; biomarkers that distinguish between the two main causes of frontotemporal lobar degeneration (FTLD); biomarkers that distinguish between pathologically confirmed Alzheimer's disease (AD) from cognitively normal (NIL) subjects and patients with other neurodegenerative disorders; and biomarkers that are associated with cognition in AD and mild cognitive impairment (MCI); and biomarkers that differentially diagnose dementias.

In one embodiment, the biomarkers of the invention are useful for diagnosis of a neurodegenerative disorder and permits refinement of disease diagnosis, disease risk prediction, and clinical management of patients. Thus, the invention provides a method of improving the treatment options and prognosis of a patient having a neurodegenerative disorder. In some instances, the biomarkers are useful for evaluating the effectiveness of potential therapies.

The biomarkers of the invention provides a method of early diagnosis of a neurodegenerative disorder. In some instances, the biomarkers of the invention can distinguish between the possible underlying diseases responsible for neurodegeneration.

In still further embodiments, the invention provides methods of monitoring a particular biomarker to evaluate the progress of a therapeutic treatment of a

neurodegenerative disorder.

In one embodiment, a biomarker of the invention is typically a protein, found in a bodily fluid, whose level varies with disease state and may be readily quantified. The quantified level may then be compared to a known value. The comparison may be used for several different purposes, including but not limited to, diagnosis of a disorder, prognosis of a disorder, and monitoring treatment of a disorder.

Through screening performed as detailed in the examples, several novel biomarkers have been identified for AD as well as biomarkers that distinguish between FTLD-TDP and FTLD-Tau, and biomarkers that correlate with cognition in AD and mild cognitive impairment (MCI).

Each of the biomarkers identified herein may be used in concert with another biomarker for purposes including but not limited to diagnosis of a particular disorder (e.g., 2011/026852

AD), prognosis of a disorder (e.g., AD), and monitoring treatment of a disorder (e.g., AD). For instance, two or more, three or more, four or more, five or more, or six or more AD biomarkers may be used in concert.

Bodily Fluids

The levels of AD biomarkers of the invention as well as biomarkers that distinguish between FTLD-TDP and FTLD-Tau, and biomarkers that correlate with cognition in AD and mild cognitive impairment (MCI) may be quantified in several different bodily fluids. Non-limiting examples of bodily fluid include whole blood, plasma, serum, bile, lymph, pleural fluid, semen, saliva, sweat, urine, and CSF. In one embodiment, the bodily fluid is selected from the group comprising whole blood, plasma, and serum , In another embodiment, the bodily fluid is whole blood. In yet another embodiment, the bodily fluid is plasma. In still yet another embodiment, the bodily fluid is serum. In an exemplary embodiment, the bodily fluid is CSF.

As will be appreciated by a skilled artisan, the method of collecting a bodily fluid from a subject can and will vary depending upon the nature of the bodily fluid, Any of a variety of methods generally known in the art may be utilized to collect a bodily fluid from a subject. Generally speaking, the method preferably maintains the integrity of the biomarkers of the invention such that it can be accurately quantified in the bodily fluid. One method of collecting CSF is detailed in the examples.

A bodily fluid may be tested from any mammal known to suffer from a neurodegenerative disorder (e.g., Alzheimer's disease) or used as a disease model for a neurodegenerative disorder (e.g., Alzheimer's disease). In one embodiment, the subject is a rodent including, but is not limited to, mice, rats, and guinea pigs. In another embodiment, the subject is a primate including, but is not limited to monkeys, apes, and humans. In an exemplary embodiment, the subject is a human. In some embodiments, the subject has no clinical signs of a neurodegenerative disorder (e.g., AD). In other embodiments, the subject has mild clinical signs of a neurodegenerative disorder (e.g., AD). In yet other embodiments, the subject may be at risk for a neurodegenerative disorder (e.g., AD). In still other embodiments, the subject has been diagnosed with a neurodegenerative disorder (e.g., AD).

The level of the biomarker may encompass the level of protein concentration or the level of enzymatic activity. In either embodiment, the level is quantified, such that a value, an average value, or a range of values is determined. In one embodiment, the level of protein concentration of the AD biomarker is quantified. There are numerous known methods and kits for measuring the amount or concentration of a protein in a sample, including EL1SA, western blot, absorption

measurement, colorimetric determination, Lowry assay, Bicinchonmic acid assay, or a Bradford assay.

The amount or concentration of a protein in a sample can also be analyzed using the methods disclosed herein. For example, the biomarkers of the invention can be identified by logistic regression (LR) analysis, random forest (RF) classification, and predictive analysis of microarrays (PAM). In some instances, biomarkers of the invention can be identified using Multi-Ana!yte Profile (MAP) analysis. The methods of the invention provides for high sensitivity and specificity with increased accuracy for detecting

Alzheimer's disease as well as detecting biomarkers that distinguish between FTLD-TDP and FTLD-Tau, and biomarkers that correlate with cognition in AD and mild cognitive impairment (MCI).

In another embodiment, the level of enzymatic activity of the biomarker is quantified. Generally, enzyme activity may be measured by means known in the art, such as measurement of product formation, substrate degradation, or substrate concentration, at a selected point(s) or time(s) in the enzymatic reaction. There are numerous known methods and kits for measuring enzyme activity. For example, see U.S. Pat. No. 5,854, 152, Some methods may require purification of the AD biomarker prior to measuring the enzymatic activity of the biomarker. A pure biomarker constitutes at least about 90%, preferably, 95% and even more preferably, at least about 99% by weight of the total protein in a given sample. Biomarkers of the invention may be purified according to methods known in the art, including, but not limited to, ion-exchange chromatography, size-exciusion chromatography, affinity chromatography, differential solubility, differential centrifugation, and HPLC.

Using Biomarkers for the Diagnosis or Prognosis

The following disclosure discusses biomarkers of the invention in the context of AD. However, the disclosure is not limited to using biomarkers in the context of AD, but is applicable to any of the biomarkers of the invention. For example, the following disclosure is also applicable to biomarkers that distinguish between FTLD-TDP and FTLD-Tau, and biomarkers that correlate with cognition in AD and mild cognitive impairment (MCI). As such, use of AD in the disclosure that follows should be considered to exemplify other embodiments including biomarkers that are associated with neurodegenerative disorders; biomarkers useful for discriminating between different neurodegenerative disorders; 6852 biomarkers that distinguish between the two main causes of frontotemporat lobar

degeneration (FTLD); biomarkers that distinguish between pathologically confirmed

Alzheimer's disease (AD) from cognitively normal (NL) subjects and patients with other neurodegenerative disorders; and biomarkers that are associated with cognition in AD and mild cognitive impairment (MCI); and biomarkers that differentially diagnose dementias.

In one embodiment, the invention encompasses a method for detecting AD comprising quantifying the level of an AD biomarker in a bodily fluid of a subject and subsequently determining if the quantified level of the biomarker is elevated or depressed in comparison to the average level of the biomarker for an otherwise normal subject. The subject may have no clinical signs of AD, the subject might be at risk for AD, or

alternatively, the subject might show mild dementia.

An elevated or depressed biomarker level may lead to either a diagnosis or prognosis of AD. In one embodiment, an elevated biomarker level indicates a diagnosis of AD. In another embodiment, an elevated biomarker level indicates a prognosis of AD. In yet another embodiment, a depressed biomarker level indicates a diagnosis of AD. in still yet another embodiment, a depressed biomarker level indicates a prognosis of AD.

The percent elevation or depression of an AD biomarker compared to the average level of the biomarker for a normal subject is typically greater than 15% to indicate a diagnosis or prognosis of AD. in some instances, the percent elevation or depression is 15%, 16%, 17%, 1 8%, 19%, 20%, 21%, or 22%. In other instances, the percent elevation or depression is 23%, 24%, 25%, 26%, 27%, 28%, 29% or 30%. In still other instances, the percent elevation or depression is greater than 30%. In alternative instances, the percent elevation or depression is greater than 50%.

Another embodiment of the invention encompasses a method for monitoring AD comprising quantifying the level of an AD biomarker in a bodily fluid of a subject and comparing the quantified level of the biomarker to a previously quantified biomarker level of the subject to determine if the quantified level is elevated or depressed in comparison to the previous level. The subject may be diagnosed with AD, or alternatively, may have no clinical signs of AD. The comparison may give an indication of disease progression. Therefore, the comparison may serve to measure the effectiveness of a chosen therapy. Alternatively, the comparison may serve to measure the rate of disease progression.

In the context of monitoring AD, the percent elevation or depression of an AD biomarker compared to a previous level may be from 0% to greater than about 50%. In one embodiment, the percent elevation or depression is from about 1% to about 10%, In another embodiment, the percent eievation or depression is from about 10% to about 20%. In yet another embodiment, the percent elevation or depression is from about 20% to about 30%, In still another embodiment, the percent elevation or depression is from about 30% to about 40%. in yet still another embodiment, the percent elevation or depression is from about 40% to about 50%. In a further embodiment, the percent elevation or depression is greater than 50%.

Another aspect of the invention encompasses kits for detecting or monitoring AD in a subject. A variety of kits having different components are contemplated by the current invention. Generally speaking, the kit will include the means for quantifying one or more AD biomarkers in a subject, in another embodiment, the kit will include means for collecting a bodily fluid, means for quantifying one or more AD biomarkers in the bodily fluid, and instructions for use of the kit contents. In certain embodiments, the kit comprises a means for quantifying AD bioniarker enzyme activity. Preferably, the means for quantifying biomarker enzyme activity comprises reagents necessary to detect the biomarker enzyme activity, !n certain aspects, the kit comprises a means for quantifying the amount of AD biomarker protein. Preferably, the means for quantifying the amount of biomarker protein comprises reagents necessary to detect the amount of biomarker protein.

Kits

The invention provides kits for detecting biomarkers of the invention. A variety of kits having different components are contemplated by the current invention.

Generally, the invention provides a kit comprising a component for quantifying one or more biomarkers of the invention. In another embodiment, the kit comprises a component for collecting a bodily fluid. In another embodiment, the kit comprises a component for quantifying one or more biomarkers of the invention in a bodily fluid. In another

embodiment, the kit comprises instructions for use of the kit contents. In certain

embodiments, the kit comprises a component for quantifying enzyme activity of the biomarkers of the invention. Preferably, the component for quantifying enzyme activity of the biomarkers of the invention comprises reagents necessary to detect the bioniarker enzyme activity, In certain aspects, the kit comprises a component for quantifying the amount of biomarker protein. Preferably, the component for quantifying the amount of biomarker protein comprises a reagent necessary to detect the amount of biomarker protein.

In one embodiment, the kit comprises a means to quantify the level of biomarkei s that differ between FTLD-TDP and FTLD-Tau, include but are not limited to Fas, neuropeptides (agouti-related peptide and adrenocortotropic hormone), and chemokines (IL- 23, IL- 17).

In another embodiment, the kit comprises a means to quantify biomarkers for FTLD-TDP including, but are not limited to, IL- 17 and Eotaxin-3, In another embodiment, the kit comprises a means to quantify biomarkers for FTLD-TDP including, but are not limited to, AgRP, ACTH, Fas, ANG-2, ApoB, and IL-23. In another embodiment, the kit comprises a means to quantify biomarkers that distinguish between FTLD-TDP an FTLD- Tau including, but are not limited to, IL- 17, Eotaxin-3, AgRP, ACTH, Fas, ANG-2, ApoB, IL-23, and any combination thereof. These biomarkers are associated with high sensitivity and modest specificity for FTLD-TDP. Therefore, these biomarkers are useful for differential diagnosis of FTLD-TDP versus FTLD-Tau.

In another embodiment, the kit comprises a means to quantify the level of biomarkers that are associated with Alzheimer's disease. In one embodiment, the kit comprises a means to quantify biomarkers that specifically distinguish a patient having AD from a normal patient, such as Αβ42. In another embodiment, the kit comprises a means to quantify biomarkers that distinguish AD from other neurodegenerative disorders including, but are not limited to Αβ42 and total tail levels. In another embodiment, the kit comprises a means to quantify biomarkers that distinguish AD from non-AD neurodegenerative disorders, including but are not limited to C3, Eotaxin-3, ρ-tauisj _» agouti-related peptide (AgRP), angiotensinogen (AGT), hepatocyte growth factor (HGF), monocyte chemoattractant I (MCP- I ), and von Willebrand factor (vWF), apolipoprotein H (ApoH), and resistin.

In one embodiment, the kit comprises a means to quantify biomarkers altered in multiple neurodegenerative diseases but not in normal subjects including, but are not limited to C3, Eotaxin-3, IL- l , and PDGF). In another embodiment, the kit comprises a means to quantify biomarkers that correlate with severity of cognitive impairment including, but are not limited to C3, CgA, IL- l a, 1-309, NrCAM, and VEGF. In another embodiment, the kit comprises a means to quantify biomarkers that are associated with subsequent cognitive decline i MCI including, but are not limited to TECK and IL- l a).

EXPERIMENTAL EXAMPLES

The invention is further described in detail by reference to the following experimental examples. These examples are provided for purposes of illustration only, and are not intended to be limiting unless otherwise specified. Thus, the invention should in no way be construed as being limited to the following examples, but rather, should be construed to encompass any and all variations which become evident as a result of the teaching provided herei .

Without further description, it is believed that one of ordinary skill in the art can, using the preceding description and the following illustrative examples, make and utilize the compounds of the present invention and practice the claimed methods. The following working examples therefore, specifically point out the preferred embodiments of the present invention, and are not to be construed as limiting in any way the remainder of the disclosure.

Example 1 : Novel CSF Biomarkers for Frontotemporal Lobar Degenerations

The following experiments were designed to identify ante-mortem cerebrospinal fluid (CSF) diagnostic biomarkers that can potentially distinguish between the two main causes of frontotemporal lobar degeneration (FTLD), including FTLD with TDP-43 pathology (FTLD-TDP) and FTLD with tau pathology (FTLD-Tau).

Briefly, CSF samples were collected ante-mortem from 24 FTLD patients who had autopsy confirmation of their diagnosis to form a training set as part of a comparative biomarker study that additionally included 33 living cognitive!)' normal subjects and 66 patients with autopsy-confirmed Alzheimer's disease (AD). CSF samples were also collected from 80 patients clinically diagnosed with frontotemporal dementia (FTD) without autopsy, and 8 patients with amyotrophic lateral sclerosis (ALS), Levels of 151 novel analytes were measured via a targeted multiplex panel enriched in neuropeptides, cytokines, and growth factors, along with levels of CSF biomarkers for AD.

The results presented herein demonstrate that CSF levels of multiple analytes differed between FTLD-TDP and FTLD-Tau, including Fas, neuropeptides (agouti-related peptide and adrenocortotropic hormone), and chemokines (IL-23, IL- 17). Classification by either random forest or predictive analysis of microarrays using these analytes achieved high sensitivity for FTLD-TDP (> 86%) with modest specificity (77.8%) in the training set.

Neuropsychological analysis of living patients with behavioral variant of FTD predicted to have FTLD-TDP or FTLD-Tau showed relative cognitive performance in each group consistent with previous findings,

Without wishing to be bound by any particular theory, it is believed that clinical cases with FTLD-TDP and FTLD-Tau pathology can be identified ante-mortem by assaying levels of specific analytes that are well known and readily measureable in CSF.

The materials and methods employed in these experiments are now described. Materials and Methods

Participants

Patients and control subjects were recruited and longitudinally followed at Perm in specialty services dedicated to the evaluation and management of neurodegenerative diseases. All protocols were approved by the Penn Institutional Review Board, and alt CSF was obtained from patients with informed consent as described (Shaw et al._} 2009, Ann Neurol. 65:403-413), The autopsy cohort was previously described. Briefly, each patient in the autopsy cohort had undergone detailed cognitive, neurological, neuroimaging and laboratory examinations to ensure the accuracy of clinical diagnosis according to established criteria for AD (Dubois et al., 2007, Lancet Neurol. 6:734-746), FTLD (Neary et a!., 1 98, Neurology. 5 1 : 1546- 1554), ALS (Ross et a!., 1998, Neurology. 50:768-772). Autopsy- confirmed cases (n=84) were characterized neuropathologically with detailed

immunohistochemicaf analysis (Neumann et al., 2009, Acta Neuropathol. 1 17: 137- 149). Clinical history was reviewed in a blinded manner to confirm FTLD syndromic diagnosis according to consensus criteria as behavioral variant FTD (bv-FTD) (McKhann et at., 2001 , Arch Neurol, 58: 1803-1809; Neary et al„ 1998, Neurology. 51 : 1546- 1554), SemD (Neary et al., 1998, Neurology. 5 1 : 1546- 1554), PNFA (Neary et al., 1998, Neurology. 51 : 1546-1554), and corticobasal syndrome (CBS) (Litvan et al., 2003, Mov Disord. 1 8:467-486). Seven patients with clinical FTLD and ALS but no autopsy were added to the FTLD-TDP group, as these cases nearly always have TDP-43 pathology (Hu et al., 2009, Arch Neurol, 66: 1359- 1364). Clinical characteristics are provided in Table 1 .

Table 1 . Demographic and clinical features of patients in the training set.

Group FTLD-TDP FTLD-Tau

n ( Female) 14 (50%) 9 (44%)

Age at onset, yr (SD) 57.21 (7.46)* 61.44 (9.25)

Age at CSF, yr (SD) 59.57 64.1 1 (9.14)

(6.91 )**

Disease duration at CSF, yr 2,29 (1 .64) 3.22 ( 1.48)

(SD)

Age at death, yr (SD) 62.57 (8.46)* 67.56 (9.94)

Disease duration, yr (SD) 5.36 (3. 15)* 6, 1 1 (2.47)

Education, yr (SD) 16.29 (2.92) 13.56 ( 1 ,94)

MMSE (SD) 21 .42 (6.44) 24.40 (5.18)

FTD Diagnosis

bv-FTD 4 4

CBS 1 3 PNFA 2

SD 0

FTD-ALS 0

Eighty patients clinically diagnosed with bv-FTD or PPA without autopsy along with eight patients clinically diagnosed with probable ALS without autopsy were recruited to form a test cohort. A subset of these living patients (n=56) had

neuropsychological analysis including category naming fluency and confrontational naming, and these patients' relative performance on each subtest (converted to Z-scores) were analyzed. These measures were selected because of their putative usefulness at

distinguishing patients with FTLD-Tau from patients with FTLD-TDP (Grossman et al., 2008 Neurology 70: 2036-2045). ApoE genotyping was performed for ail subjects.

Procedures

Baseline CSF samples were obtained during routine diagnostic lumbar puncture as previously described (Shaw et al., 2009, Ann Neurol. 65:403- 13). Briefly, lumbar puncture was performed with a 20- or 24-guage spinal needle, and CSF was transferee! into polypropylene tubes. Aliquots (0.5 mL) were prepared, bar-coded, and then stored in polypropylene vials at -80°C. Samples were interrogated by Rules-Based Medicine, Inc. ( Austin, TX) to assay levels of 15 1 analytes using the Human DiscoveryMAP^rM panel and a Luminex 100 platform. Measures of CSF Αβ42, total tau, and p-tauisi, were performed using the multiplex xMAP Luminex platform (Luminex Corp, Austin, TX) with Innogenetics (ΓΝΝΟ-ΒΙΑ AlzBio3, Ghent, Belgium;) immunoassay kit-based reagents as described in Mackenzie et al„ (2009 Acta Neuropathol 2009 1 17: 1 5- 18).

Statistical Analysis

Statistical analysis was performed in SPSS 12,0 except for classification. Mann- Whitney U test was used to identify analytes that differed between autopsy-confirmed FTLD-TDP and FTLD-lau at the univariate level. Given the high dimensional data with a small sample size, random forest was used for classification in the autopsy cohort, Analytes were entered with nodes optimized for best classification of FTLD-TDP vs FTLD-tau. Out- of-box error rate was used to derive diagnostic accuracy, with sensitivity and specificity derived from the confusion matrix. Receiver operating characteristics curves were used to derive cutoff values for each individual analyte. The established random forests structure was then used to classify each patient in the living cohort as likely to have FTLD-TDP or FTLD-tau. For neuropsychological analysis, Z score was calculated for each neuropsychological subtest according to cognitively normal control subjects. A relative performance score was calculated between confrontational naming and category naming fluency (ΔΖ = Z score of category naming fluency - Z score of confrontational naming). A positive relative performance score was taken as suggestive of predicted FTLD-TDP.

Statistical analysis was performed in SPSS 12.0, Random Forests (RF, http;//www. stat.berkeley.edu/~breiman/RandomForests/), and predictive analysis of microarrays (PAM). Given the high dimensional data with a small sample size, RF and PAM were used for classification, For each predictive analysis, the autopsy cohort served as a training set, while the living cohort served as a test set. in RF analysis, analytes were entered with nodes optimized for best classification of FTLD-TDP versus FTLD-Tau. Out-of-box error (OOB) rate was used to derive diagnostic accuracy, with sensitivity and specificity derived from the confusion matrix. In PAM, analytes that significantly differentiated FTLD- TDP from FTLD-Tau were identified for diagnostic accuracy. Effects from age and gender were included in both RF and PAM analysis.

The results of the experiments are now described.

Training Set (Autopsy cohert)

Not all analytes were sufficiently abundant to be reliably measured and here 106 of the 1 5 1 analytes in the MAP had measurable levels for analysis. To determine the best biomarkers of FTLD-TDP, three independent analytical strategies were used to identify MAP analytes associated with FTLD-TDP versus FTLD-Tau.

Patients with FTLD-TDP were younger than those with FTLD-Tau, but the two groups were otherwise similar in gender, disease duration to CSF, and cognitive performance measured by Mini-Mental Status Examination (Table 1 ), Mann-Whitney U-test identified 10 analytes that differ between FTLD-TDP and FTLD-Tau (Table 2), including interleukin-17 (IL-17), interIeukin-23 (1L-23), Eotaxin-3, adrenocorticotropic hormone (ACTH), Fas, angiopoietin-2 (ANG-2), apo!ipoprotein B (ApoB), macrophage-derived chemokine (MDC), S 100 calcium binding protein b (Sl OOb), and TRAIL receptor 3 (TRAIL- R3), As the number of analytes is significantly larger than the number of cases in the training set, additional classification algorithms were performed using RF and PAM to identify putative biomarkers for each FTLD subtype and to classify patients in the test set. Table 2. Candidate biomarkers for FTLD-TDP according to Mann-Whitney U-test, random forest (RF) analysis, or predictive analysis o microarrays (PAM). Analytes identified by multiple strategies are shown in bold.

Mann- P RF Z-score PAM

Whitney

IL-17 0.012 IL-17 22.633 <0.00 Age at CSF

I

MDC 0.015 Eotaxin- 16.916 <0.00 IL-17

3 1

ACTH 0.016 ACTH 15.065 <0.00 GRO- 1

FAS 0.023 FAS 9.033 <0.00 ApoB

1

1L-23 0.025 AgRP 6.872 <0.00 Pancreatic Polypeptide

1

ApoB 0.028 AgRP

Eotaxin-3 0.037 TIMP I

S I 00b 0.037 lL- l ra

TRA1L-R3 0.04 PAl- 1

ANG2 0.05 ! Eotaxin3

HB-EGF

IL-23

ApoH

BMP6

Total Tau

ANG2

Thrombospondin- 1 Complement Factor H

Cortisol

# ApoE4 allele

CTGF

Lpa

PYY

RF identified a list of analytes that differentiated between FTLD-TDP and FTLD-Tau through a tree-based classification algorithm. Optimal classification was achieved by using the top 5 analytes identified by RF, including IL-17, Eotaxin-3, ACTH, Fas, and Aguti-related protein (AgRP) (Table 2). These biomarkers were associated with a diagnostic accuracy of 82.6%, with 85.7% sensitivity and 77.8% specificity for FTLD-TDP.

To determine the relative performance of individual analyte alone, cutoff values for each analyte using receiver operating characteristic curves were derived: 0.1350 ng/mL for ACTH (sensitivity 71.4%, specificity 77.8%), 53.0 for AgRP pg/mL (sensitivity 57.1 %, specificity 88.9%), 52.5 pg/mL for Eotaxin-3 (sensitivity 78.6%, specificity 88.9%), 0.455 for FAS ng/mL (sensitivity 64.3%, specificity 77.8%), and 9.25 pg/mL for IL- 17 (14.3% sensitivity, 77.8% specificity). While some biomarkers identified by Mann- Whitney test contributed little to the distinction of FTLD-TDP from FTLD-tau by random forests, it was observed that FTLD-TDP and FTLD-tau cases differed in both IL-23 and IL- 17 levels. This was suggestive of an altered IL-23 pathway, as IL-23 induces the differentiation of na^'i^'ve T-cells into iL- 17-releasing helper T cells. Therefore, experiments were conducted to additionally analyze levels of IL-23 and IL-17 in FTLD-TDP and FTLD-tau cases to determine the direction and magnitude of these changes. When levels of IL-23 and IL- 17 were normalized to average levels in cognitively healthy control subjects (n = 33) previously reported, patients with FTLDTDP (p = 0.074) and FTLD-tau (p = 0.069) both had a relative increase in IL-23 levels compared to IL- 17. With this increase in IL-23, FTLD-tau cases were found to have similar levels of IL-17 as control subjects, while FTLD-TDP had lower IL- 17 levels than control subjects (p = 0.002).

A separate analysis using PAM identified analytes that distinguish between FTLD-TDP and FTLD-Tau through a nearest shrunken centroid method (Table 2), including analytes previously identified by Mann- Whitney U-test (IL- 17, IL-23, Eotaxin-3, ANG2, ApoB), and analytes identified by RF (IL- 17, Eotaxin-3, AgRP). The diagnostic accuracy associated with PAM for FTLD-TDP was 87.0% in the original set (92.9% sensitivity, 77.8% specificity), and 65.2% in the cross validation model (71 .4% sensitivity, 55.5% specificity).

Biomarkers identified by multiple analytical strategies are more likely to have discriminating value and potential biological significance. Among candidate biomarkers for FTLD-TDP, IL- 17 and Eotaxin-3 were identified by all three algorithms as candidate biomarkers for FTLD-TDP, while AgRP, ACTH, Fas, ANG-2, ApoB, and IL-23 were identified using at least two strategies as candidate biomarkers (Figure I ). Coupled changes in IL- 17 and IL-23 levels were suggestive of an affected IL-23 pathway, as IL-23 induces the differentiation of naive T-cells into IL- 17 releasing helper T-cells (Annunziato et al., 2009, Nat Rev Rheumatol. 5:325-33 1 ). Levels of IL-23 and IL- 17 in FTLD-TDP and FTLD-Tau cases relative to control subjects and patients with autopsy-confirmed AD were additionally analyzed (Figure 2). Post-hoc analysis by Mann-Whitney U-test showed that FTLD-Tau cases had elevated iL-23 levels compared to control subjects (p = 0.002) but normal IL-17 levels. Whereas a similar trend was observed in 66 AD cases, FTLD-TDP cases instead showed normal IL-23 levels with decreased IL- 17 levels (p = 0.002) compared to control subjects.

Test Set (Living cohort) The next set of experiments was designed to determine the likelihood of FTLD-TDP versus FTLD-Tau as the underlying pathology in an independent living cohort consisting of 80 patients with FTLD and 8 patients with ALS (Table 3), bv-FTD was the most common syndromic diagnosis (11=37, 46%), and PPA (n=22) and CBS (n=21 ) cases were similar in number. Among these 80 patients, 54 were predicted by RF to have FTLD- TDP, 57 were predicted by PAM to have FTLD-TDP, and 45 patients were predicted by both RF and PAM to have FTLD-TDP, Among these 45 patients, 62% of bv-FTD, 48% of CBS, and 55% of PPA cases were predicted to have FTLD-TDP (Table 3). By comparison, among 23 patients who underwent neuropathologic analysis in the training set, 1 1 of 14 patients identified by both RF and PAM to have TDP-43 pathology had FTLD-TDP, and 7 of 8 patients predicted by both RF and PAM to have Tan pathology had FTLD-Tau.

Table 3, Clinical and neuropsychological features of patients predicted to have FTLD- bv-FTD CBS PNFA SD ALS

(n=37) (n=21) (n=10) (n= 12) (n=8)

Female 26 (70%) 8 (38%) 3 (30%) 9 (75%) 3 (38%)

Age 61 .6 (7.83) 62.5 (8.23) 65.9 ( 10.2) 66.7 (8.33)

MMSE 23.6 (5.41) 24,8 (4.61) 24.5 (4.34) 24.9 (4.18) N.D.

FTLD-TDP predicted

by

RF 26 (70%) 12 (57%) 5 (50%) 10 (83%) 3 (38%)

PAM 28 (76%) 14 (67%) 6 (60%) 8 (67%) 8

( 100%)

RF and PAM 23 (62%) 10 (48%) 5 (50%) 7 (58%) 3 (38%)

Neuropsych Analysis 26 14 8 7 0

Z, category fluency - 1 .74 - 1 ,83 ( 1.25) - 1.98 ( 1 .26) -2.25 (0.78)

( 1 .10)

Z, confrontation - 1.93 (2.29) - 1.43 ( 1 .57) - 1.56 (2.02) -4.79 (2.60) naming

Relative Z-score 0.19 (1 .81) -0.40 (1 .53) -0.42 ( 1 .30) 2.54 (2.47)

FTLD-TDP 14 7 4 5

Relative Z 0,73 ( 1.92) -0.59 ( 1 .77) -0.88 (0.73) 2.85 (2.91 )

Relative Z > 0 9 (64%) 3 (43%) 2 (50%) 4 (80%)

FTLD-Tau 6 3 4 1

Relative Z - 1 .17 (0.89) -0.53 (1.20) 0.053 (1.69) 2.45

Relative Z < 0 6 (100%) 2 (67%) 2 (50%) 0

TDP or FTLD-Tau in the test set.

Fifty-five of the 80 FTLD patients underwent neuropsychological evaluation for performance in category naming fluency and confrontational naming. When these 55 patients were analyzed according to their clinical FTLD syndromes, 54% of bv-FTD, 50% of CBS, 50% of PNFA, and 71 % of SemD patients were predicted by both classification algorithms to have FTLD-TDP. Conversely, 23.1 % of bv-FTD, 21 % of CBS, 50% of PNFA, and 14% of SemD patients were predicted by both algorithms to have FTLD-Tau. In view of (he fact that FTLD-TDP cases were observed to have worse performance in confrontation naming compared to category naming fluency and the opposite pattern in FTLD-Tau, experiments were designed to analyze whether such trends existed between cases predicted to be FTLD-TDP or FTLD-Tau. Only bv-FTD cases demonstrated divergent patterns of cognitive performance between the two predicted pathology groups (p = 0.014 by Fisher's exact test).

CSF biomarkers for FTLD subtypes

FTLD-TDP and FTLD-Tau can each lead to clinical FTLD syndromes, although the underlying pathologic substrate is difficult to predict on clinical grounds alone. Using autopsy-confirmed cases of FTLD-TDP and FTLD-Tau as the training set, the results presented herein have identified novel CSF biomarkers that may improve the distinction between FTLD-TDP an FTLD-Tau, Multiple analytical approaches identified levels of IL- 1 7, Eotaxin-3, and AgRP to differ statistically between FTLD-TDP and FTLD-Tau, and combinations of novel biomarkers were associated with high sensitivity and modest specificity for FTLD-TDP. While the potentially pathogenic roles of these candidate biomarkers remain to be determined in FTLD, these analytes offer promise in the antemortem differential diagnosis of FTLD-TDP versus FTLD-Tau.

Although plasma levels of progranulin have been measured as a surrogate chemical biomarker for GRN mutations which are pathogenic exclusively for FTLD-TDP, only TDP-43 itself has been examined as a potential biomarker for FTLD-TDP. One study showed elevated plasma TDP-43 levels in 46% of clinical FTLD cases and 22% of clinical AD cases, but the lack of pathologic confirmation in these groups limited confident interpretation of results (Foulds et al.₍ 2008, Acta Neuropathol. 1 16: 141 - 146). Levels of TDP-43 also appeared to be elevated in CSF samples from patients with ALS and FTD-ALS (Steinacker et al., 2008, Arch Neurol. 65: 1481 -1487). However, interpretation was clouded by significant overlap in TDP-43 levels between patients and controls, emphasizing the need for improved assays of TDP-43 such as disease-specific TDP-43 phospho-epitopes or cleavage products. Other studies have also sought to identify biomarkers in disorders associated with FTLD, including ALS (associated with TDP-43 pathology) and clinical PSP (characterized pathologically by FTLD-Tau). Potential biomarkers of ALS have included elevated levels of TDP-43 (Steinacker et al„ 2008, Arch Neurol. 65: 1481 -1487),

inflammatory proteins (GM-CSF (Mitchell et al„ 2009, Neurology 72: 14- 19), G-CSF (Mitchell et al., 2009, Neurology 72: 14- 19), MCP- 1 (Mitchell et al., 2009, Neurology 72: 14- 19; Kuhle et a!„ 2009, Eur J Neurol. 16:771 -774), MlP- l a/b (Mitchell et al., 2009,

Neurology 72: 14- 19), and multiple interleukins including IL-2, IL-6, 1L-8, IL- 15, and IL- 1 (Mitchell et al., 2009, Neurology 72: 14- 19; Kuhle et al., 2009, Eur J Neurol. 16:771 -774), axonal structural proteins (neurofilament light chain (Zetterberg et al., 2007, Eur J Neurol. 14: 1329- 1333), and growth factors (FGF basic protein and VEGF (Mitchell et ai., 2009, Neurology 72: 14- 19); and decreased levels of cystatin C (Pasinetti et al, 2006, Neurology 66: 1218- 1222), insulin-like growth factor 1 (Bilic et al., 2006, Eur J Neurol. 13: 1 340- 1345), lL- 10 (Mitchell et al., 2009, Neurology 72: 14- 19), interferon γ (Mitchell et al„ 2009, Neurology 72: 1 - 19), erythropoietin (Brettschneider et al., 2006, Neurosci Lett. 404:347- 35 1 ), and angiotensin 11 (Kawajiri et al., 2009, Acta Neurol Scand. 1 19:341 -344). While some of these biomarkers are likely specific to ALS or even FTLD-TDP spectrum disorders, many may also represent inflammatory or structural changes that can occur in FTLD-TDP or FTLD-Tau. For example, neurofilament light chains were proposed as a biomarker for ALS (Zetterberg et al., 2007, Eur J Neurol. 14: 1329-1333), but elevated levels were independently found in PSP and CBS (Constantinescu et al., Parkinsonism Relat Disord. 2009 Jul 30. [Epub ahead of print]). Conversely, candidate CSF biomarkers for FTLD-Tau such as low orexin (Yasui et ai., 2006, J Neurol Sci, 250: 120-123) were mostly found in patients with prominent Parkinsonian syndromes with little or no dementia, and have not been examined in ALS or FTLD-TDP cases. Hence, any discovery or validation biomarker work in FTLD-TDP or FTLD-Tau needs to incorporate both disorders with neuropathologic confirmation.

Some of the proposed ALS biomarkers above were specifically evaluated in the multiplex panel presented herein, including 1L-8, insul in-like growth factor 1 , and multiple proteins from one study of ALS (Mitchell et al., 2009, Neurology 72: 14- 19). Levels of GM-CSF, G-CSF, IL-2, IL-6, or IL- 15 were undetectable using standard protocols, and more sensitive platforms are necessary to determine their association with FTLD-TDP.

Among analytes with detectable CSF levels in the current study, MCP- 1 was found by two previous studies (Mitchell et al._t 2009, Neurology 72: 14-19; Kuhle et al., 2009, Eur J Neurol. 16:771 -774) to be elevated in ALS, and MCP- 1 was increased in the FTLD-TDP cases compared to control subjects (p=0.01 ) and FTLD-Tau (p=0.089). It was not observed that levels of FGF basic protein, IP- l a, MIP- l b, and VEGF differed between FTLD-TDP and FTLD-Tau, although a separate analysis using ALS patients without dementia showed some of these markers to differ between ALS and normal subjects. Thus, without wishing to be bound by any particular theory, it is believed that some ALS biomarkers are specific to the presence of motor neuron disease but insufficient to distinguish between FTLD-TDP and FTLD-Tau cases. Similarly, biomarkers useful for the distinction between FTLD-TDP and FTLD-Tau may be insufficient to detect all ALS cases, which may account for the algorithm only detecting about 40% of the ALS cases in the test set.

Even though MCP- 1 distinguished between FTLD-TDP and FTLD-Tau, MCP- 1 did not emerge as a reliable biomarker for FTLD-TDP in the current study. Further analysis showed that MCP- 1 levels were also highly correlated with Fas levels (R=0.698, p=0.005), and Fas was identified by multiple methods as a key discriminator between FTLD- TDP and FTLD-Tau. The multiplex approach disclosed herein therefore identified Fas as a more robust proxy biomarker for FTLD-TDP than MCP- 1 for a similar underlying biological process. Among the remaining analytes that distinguished FTLD-TDP from FTLD-Tau, AgRP and ACTH are both hypothalamic neuropeptides, and their elevation in the CSF may reflect hypothalamic dysfunction. No specific hypothalamic dysfunction has been previously described in FTLD-TDP, but disinhibited behaviors common i bv-FTD and hypothalamic dysfunction such as an eating disorder can both be linked to amygdala abnormalities (Kling et a!., 1993, Behav Brain Res, 56: 161 - 170). Clinically, elevated AgRP may account for the common hyperoral behavior in clinical FTD patients through its appetite promoting effect. Other biomarkers of FTLD-TDP of potential biological significance include IL-23 and IL- 17, as IL-23 promotes the development of helper T-celis that release TL- 17 (Annunziato et al., 2009, Nat Rev Rheumatol. 5:325-331). These T-helper 17 cells have been implicated in multiple sclerosis (Kebir et al., 2009, Ann Neurol. 66:390-402), and microglia can themselves release IL-17 in the presence of IL-23 (Kawanokuchi et al., 2008, J

Neuroimmunol, 194:54-61 ). The relative reduction in IL-23 and IL- 17 in FTLD-TDP compared to FTLD-Tau suggests an impaired 1L-23/IL- 17 axis in TDP-retated disorders, although normal levels of IL-17 in the presence of elevated IL-23 in FTLD-Tau may reflect additional alterations common to FTLD. As treatments targeting IL- 1 7 and IL-23 become available (Chen et al„ 2006, J Clin Invest. 1 16: 1317- 1326), elucidating their roles in FTLD- TDP and FTLD-Tau may herald new therapeutic directions in FTLD.

In summary, the novel exploratory study of FTLD biomarkers discussed herein identified potential CSF biomarkers that may improve the ante-mortem distinction between FTLD-TDP and FTLD-Tau. Without wishing to be bound by any particular theory, if alterations in these CSF biomarkers are confirmed, they would suggest investigations pursuing dysfunction in the hypothalamus and the IL-23/IL- 17 axis in FTLD-TDP. The observations discussed herein suggest a stepwise work-up of patients clinically diagnosed with FTLD spectrum disorders. As CSF biomarkers for AD have been validated in independent studies, AD biomarkers (including CSF p-taujsi , total tau, and Αβ42 levels) should be used to exclude cases of clinical FTLD due to atypical AD, This can be followed by measurements of novel FTLD biomarkers like those reported herein to further distinguish between FTLD-TDP and FTLD-Tau.

Example 2: Novel CSF Biomarkers for Alzheimer's Disease and Mild Cognitive Impairment

The following experiments were designed to identify antemortem cerebrospinal fluid (CSF) diagnostic biomarkers that distinguish pathologically confirmed Alzheimer's disease (AD) from cognitively normal (NL) subjects and patients with other neurodegenerative disorders, and to identify CSF biomarkers that correlate with cognition in AD and mild cognitive impairment (MCI).

Briefly, CSF samples were collected antemortem from 66 AD patients with AD and 25 patients with other neurodegenerative dementias followed longitudinally to neuropathologic confirmation, 42 longitudinally followed MCI patients, and 33 NL, Levels of 151 novel analytes were measured via a targeted multiplex panel enriched in cytokines, chemokines, and growth factors as well as established AD biomarkers (ApoE4 allele, and levels of Αβ42, tau, and p-tauisi).

It was observed that AD is best distinguished from NL by a combination of traditional AD biomarkers that confer sensitivity, and multiplex biomarkers that confer specificity relative to NL. Two categories of biomarkers were identified: 1 ) analytes that specifically distinguish AD (especially CSF Αβ42 levels) from NL and other

neurodegenerative disorders; and 2) analytes altered in multiple neurodegenerative diseases (C3, Eotaxin-3, IL- la, PDGF), but not in NL subjects. Six analytes (C3, CgA, IL-la, 1-309, NrCAM, and VEGF) were correlated with severity of cognitive impairment at CSF collection, and two (TECK , IL- l a) were associated with subsequent cognitive decline in MCI.

The targeted proteomic screen presented herein revealed novel CSF biomarkers that distinguished AD from NL and other neurodegenerative disorders, and subsets of biomarkers that correlated with cognition and subsequent cognitive decline.

Without wishing to be bound by any particular theory, it is believed that a multiplex panel of CSF biomarkers can improve the antemortem diagnostic and prognostic classification of AD and MCI.

The materials and methods employed in these experiments are now described.

Materials and Methods

Participants

Patients and control subjects were recruited and longitudinally followed at Penn in specialty services dedicated to the evaluation and management of neurodegenerative diseases (Table 4). All protocols were approved by the Penn Institutional Review Board. Each patient in the autopsy cohort had undergone detailed cognitive, neurological, neuroimaging and laboratory examinations to ensure the accuracy of clinical diagnosis according to established criteria for AD (Dubois et al,_} 2007, Lancet Neurol. 6: 734-46), frontotempora! dementia (FTD) (Neary et al., 1998, Neurology 5 1 : 1546-54), amyotrophic lateral sclerosis (ALS) (Ross et al., 1998, Neurology 50: 768-728), and DLB (Lippa et al., 2007, Neurology 2007; 68: 812-9), Auto psy-confi rm ed cases of AD (n=66), FTLD (n= 16), and DLB (n=2) were characterized neuropathologically with detailed immunohistochemical analysis for pathology associated with each major neurodegenerative disorder, including Αβ42, hyperphosphorylated tau, hyperphosphorylated TDP-43, and alpha-synuclein as described by Neumann et al, (2009, Acta Neuropathol. 1 17: 137-49). Seven patients with clinical FTD and ALS but no autopsy were added to the FTLD-TDP group, as these cases nearly always have TDP-43 pathology. Thirty-eight patients with MCI were also recruited to assess predictors of cognitive decline. Each MCI patient was diagnosed by modified Petersen criteria (Winblad et al., 2004, Journal of Internal Medicine 256: 240-6), and followed longitudinally with serial cognitive and neurological examination. ApoE genotyping was performed for all subjects as discussed elsewhere herein,

Table 4. Patient demographics included in the CSF analysis, Mean values are shown for continuous variables, with standard deviations shown in parentheses. MMSE=Mini-Mental Status Examination score (out of 30) at time of CSF. * p < 0.05 compared with AD. ** p < 0,05 compared with AD and NL.

Group NL AD FTLD-TDP FTLD-Tau DLB n (% Female) 33 (58%) 66 (53%) 14 (50%) 9 (44%) 2 (0)

Age at onset, yr (SD) - 67. 1 1 (9.94) 57.2 1 (7.46)* 61 .44 68.00 ) )

Subjects

Patients with clinical AD, FTD, and DLB were longitudinally followed in the Alzheimer Disease Core Center and Froiitotemporal Dementia Clinic, and patients with ALS with and without dementia were longitudinally followed in the ALS Center and

Froiitotemporal Dementia Clinic, Patient demographics were similar, except patients with FTLD-TDP were younger at age of CSF than other patients (Table 4).

Procedures

Baseline CSF samples were obtained during routine diagnostic lumbar puncture as previously described (Shaw, et al., 2009, Ann Neurol. 65: 403-13; Clark et ah, 2003, Arch Neurol.60: 1696-702). Briefly, lumbar puncture was performed with a 20- or 24- guage spinal needle, and CSF was transfered into polypropylene tubes. Aliquots (0,5 mL) were prepared, bar-coded, and then stored in polypropylene viats at -80°C. Samples were interrogated by Rules-Based Medicine, Inc. (Austin, TX) to assay levels of 151 analytes using the Human DiscoveryMAPTM panel and a Luminex 100 platform, Measures of CSF Αβ42, total tau, and p-taiiiss , were performed using the multiplex xMAP Luminex platform (Luminex Corp, Austin, TX) with Innogenetics (ΓΝΝΟ-ΒΙΑ AlzBio3, Ghent, Belgium;) immunoassay kit-based reagents as described in Mackenzie et al., (2009 Acta Neuropathol 2009 1 17: 15-18).

ApoE Genotpying

APOE genotyping was performed for all subjects using EDTA blood samples collected at the time of lumbar puncture. TaqMan quantitative PCR assays were used for genotyping ^/OS nucleotides 334 T/C and 472 CT with an ABI 7900 real-time thermocycler using DNA freshly prepared from EDTA whole blood.

CSF Analysis

At RBM, each sample was thawed at room temperature, vortexed, spun at 13,000 x g for 5 minutes for clarification and 40 uL was removed for Muiti-Analyte Profile (MAP) analysis into a master microtiter plate. Using automated pipetting, an aliquot of each sample was introduced into one of the capture microsphere multiplexes of the

DiscoveryMAP ^{f M}. These mixtures of sample and capture microspheres were thoroughly mixed and incubated at room temperature for 1 hour. Multiplexed cocktails of biotinylated, reporter antibodies for each multiplex were then added robotica!ly and after thorough mixing, were incubated for an additional hour at room temperature. Multiplexes were developed using an excess of streptavidin-phycoerythrin solution which was thoroughly mixed into each multiplex and incubated for 1 hour at room temperature. The volume of each multiplexed reaction was reduced by vacuum filtration and the volume increased by dilution into matrix buffer for analysis. Analysis was performed in a Luminex 100 instrument and the resulting data stream was interpreted using proprietary data analysis software developed at Rules- Based Medicine and licensed to Qiagen Instruments. For each multiplex, both calibrators and controls were included on each microtiter plate. Eight-point calibrators were run in the first and last column of each plate and 3-levei controls were included in duplicate. Testing results were determined first for the high, medium and low controls for each multiplex to ensure proper assay performance. Unknown values for each of the analytes localized in a specific multiplex were determined using 4 and 5 parameter, weighted and non-weighted curve fitting algorithms included in the data analysis package.

Analyte levels from RBM HumamMAP multiplex and tau or Αβ42 levels were analyzed for linear correlations before univariate and multiple variate analyses. Many analytes correlated with CSF tau in NL, AD, or both (Table 5), but Αβ42 levels only weakly correlated with PAM (R=-0.422), Eotaxin (R=-0.402), and Cortisol (R=-0.441 ) in AD and with Endothelin- 3 (R=0,552) in cognitively normal subjects.

Table 5. Lists of MAP analytes analyzed in CSF samples. * Marks analytes that correlated with CSF tau levels in AD or NL with Pearson correlation coefficient greater than 0.500. <LDD=analyte levels were below lowest detectable dose for greater than 90% of the samples tested, AD NL

Alpha-1 Antitrypsin -0.028 0.146

ACE (CD143) Angiotensin Converting Enzyme 0.309 0.751 *

ACTH {Adrenocorticotropic Hormone) -0,052 0.033

Adiponectin 0.193 -0.063

Agouti-Related Protein (AgRP) 0.182 -0.026

AIpha-2 Macroglobulin 0.369 0.438

Alpha-Fetoprotein < LDD < LDD

Amphiregulin < LDD < LDD

Angiopoietin 2 (ANG-2) 0,781 * 0.753*

Angiotensinogen -0,019 -O. f 16

Apolipoprotein A1 0.031 0.073

Apolipoprtein B 0.328 -0.542*

Apolipoprotein Clll 0.269 0.082

Apolipoprotein D 0.065 0.267

Apolipoprotein E 0,536* 0.625*

Apolipoprotein H 0.336 0.091

AXL 0.584* 0.741 *

Beta-2 Microglobulin 0.732* 0.664*

Betacellulin < LDD < LDD

B-Lymphocyte Chemoattractant <BLC) < LDD < LDD

BMP-6 -0.035 -0.33 1

Brain-Derived Neurotrophic Factor < LDD < LDD

Complement 3 0.126 0,245

Cancer Antigen 125 < LDD < LDD

Cancer Antigen 19-9 < LDD < LDD

Calcitonin 0.172 -0, 149

CD40 0.750* 0.601 *

CD40 Ligand 0.723* 0.144

Carcinoembryonic Antigen < LDD < LDD

CgA 0,347 0.499

Complement Factor H 0.072 0,071

Creatine Kinase-MB < LDD < LDD

Ciliary Neurotrophic Factor (CNTF) < LDD < LDD

Cortisol 0.743* 0.479

C Reactive Protein < LDD < LDD

CTGF < LDD < LDD

EGF < LDD < LDD

EGF-R 0.186 0,702*

ENA-78 < LDD < LDD

Endothelin-1 -0.166 -0.221

EN-RAGE 0.037 -0.025

Eotaxin 0.790* -0.128

Eotaxin-3 0,561 * 0.284

Epiregulin < LDD < LDD

Erythropoietin < LDD < LDD

Fatty Acid Binding Protein 0.695* 0.809*

Factor VII < LDD < LDD

26852

TNF-β < LDD < LDD

Thrombopoietin < LDD < LDD

TRAIL-R3 0.53 I * 0.586*

Thyroid Stimulating Hormone 0.441 -0.098

Thrombospondin-1 0.01 -0.101

VCAM-1 0.439 0.393

VEGF 0.424 0,776* von Willebrand Factor 0.401 0.693*

LDD was established using blank samples within each multiplex and summing the mean value of blank samples and 3 times the standard deviations of blank samples. For the current cohort, any value below t!ie LDD was adjusted to the LDD value to avoid over- interpretation of values derived from standard curves in the range of blank samples. Analyte values were analyzed non-paraniet ically, as many analytes did not demonstrate normal distribution even after transformation. In the stage-wise LR model, 22 analytes differed between AD and NL by Mann Whitney U-test (p < 0.01 ); alpha- 1 -antitrypsin, adiponection, aipha-2-macroglobuim, BMP-6, C3, eotaxin-3, Fabp, ferritin, HCC4, IgA, IL- Ι α, IL-23, 1L- 7, MIP- l a, myoglobin, NrCAM, pancreatic polypeptide, PDGF, prolactin, resistin, thyroxine binding globulin, tluOmbospondm- 1 ; 6 analytes differed between AD and non-AD dementias (p<0.01): AgRP, angiotensinogen, eotaxin-3, HGF, resistin, and vWF.

Statistical Analysis

Statistical analysis was performed in SPSS 12.0, Random Forests (RF, http://www.stat.berkeley.edii/~breiman/RandomForests/), and SAM/PAM, In LR analysis, the Mann Whitney U test identified 22 analytes to differ significantly between NL and autopsy-confirmed AD (nominal p < 0.01). Analytes that differed significantly between NL and AD were entered into LR models for AD identification, adjusting for age and gender. Sensitivity and specificity of LR models were obtained by the leave-one-out (LOO) approach in discriminant analysis. In RF analysis, analytes were entered into the analysis with nodes optimized for best classification of AD versus NL. Out-of-box error (OOB) rate was used to derive diagnostic accuracy, with sensitivity and specificity derived from the confusion matrix. In PAM, analytes that significantly differentiated AD from NL were identified, and diagnostic accuracy was derived through internal cross-validation. A similar three-approach strategy was employed to determine biomarkers that distinguished between AD and non-AD neu ro de generat i ve d i sord ers . For cross-sectional association between novel AD biomarker levels and severity of cognitive impairment at time of CSF collection, Pearson's correlation coefficient was used to relate levels of newly identified CSF AD biomarkers with cognitive performance characterized by Mini-Mental Status Examination (MMSE) in autopsy-confirmed AD cases. For correlation of CSF biomarker levels and rates of cognitive decline following CSF collection in MCI, rates of cognitive decline were first estimated by the slope of MMSE score linear regression over time. Pearson's correlation coefficient was then determined for CSF biomarker levels and rates of cognitive decline. Effects from age and gender were adjusted for all diagnostic and progression models.

The results of the experiments are now described.

AD versus NL

The experiments partly were performed to test the hypothesis that distinct sets of CSF peptides and proteins are associated with AD in contrast to normal (NL) subjects and other common neu odegenerative disorders, including FTLD with neuronal inclusions immunoreactive (ir) to TDP-43 (FTLD-TDP), FTLD with tau-ir inclusions (FTLD-Tau), and DLB. CSF samples antemortem from a total of 162 subjects were collected and analyzed, and the concentrations of 151 analytes in the Rules Based Medicine Human

DiscoveryMAP™ panel (referred to below here as MAP) were measured by a Ltiminex based multiplex platform. Analytes associated with AD were separately identified by logistic regression (LR) analysis, random forest (RF) classification, and predictive analysis of microarrays (PAM) (Ray et al., 2007, Nat Med.13: 1 359-62; Tusher et al., 2001 , Proc Natl Acad Sci U S A. 98: 5 1 16-21), and the diagnostic accuracy of novel analytes was assessed. AD biomarkers identified by these methods were then assessed for their relationship to cognitive functioning in AD subjects, and rates of subsequent cognitive decline in patients with mild cognitive impairment (MCI).

All CSF was obtained from patients with informed consent as described (Shaw, et a!., 2009, Ann Neurol. 65: 403-13; Grossman et al, 2005, Ann Neurol. 57: 721 -9, Clark et al., 2008, Neurosignais 16: 1 1 -8). Levels of 151 analytes in the MAP were measured in the CSF, with 106 analytes having measurable levels for analysis (Table 5). To determine the best biomarkers of AD, three independent analytical strategies were used to identify MAP analytes associated with AD, and combined traditional AD biomarkers and MAP analytes to identify complementary AD biomarkers. 1 026852

In the LR model, top analytes associated with AD versus NL by Mann Whitney U-test (at p <0.0I) were entered into a forward stepwise LR model (Table 6). LOO discriminant analysis using the six resultant MAP analytes achieved 84.8% sensitivity and 93.9%) specificity, with overall 87.8% accuracy. By comparison, traditional AD biomarkers Αβ42 and total tau yielded greater sensitivity (93.9%) but less specificity (81 .8%) for a similar overall accuracy of 89.9%. Combining MAP analytes and traditional AD biomarkers resulted in a combined model differentiating AD from NL by the number of ApoE4 alleles and Αβ42 levels, in addition to C3, 1L-23, and NrCAM levels (Table 6). This combined model has high sensitivity (97.0%) and specificity (87,9%) with 93,9% accuracy, and improved upon the traditional AD models by correctly reclassifying up to 5 NL subjects with pathologic CSF levels of tau and Αβ42, and 3 AD subjects with non-pathologic levels of CSF tau and Αβ42, Interestingly, including tau and p-tautsi levels in the combined model did not improve the classification.

Table 6. Factors predictive of AD compared with NL according to LR modeling. Traditional AD model incorporated Αβ42 and tau levels along with number of ApoE4 alleles.

Coefficient (B) and p-value for each factor as part of the overall model are shown. Age and gender were entered into first block of LR, while analytes identified to be different between AD and NL were then entered in a forward step-wise fashion, with p<0.05 for entry and p>0.10 for removal,

AD versus NL B P

MAP Model

Age -0.076 0.227

Male Gender 2.826 0.061

BMP6 -7.921 0.009

C3 1 .565 0.007

Fabp 1 .013 0.006

1L-23 24.944 0.012

NrCAM -0.071 0.001

Prolactin 7.1 17 0.043

Traditional AD Model

Age -0.024 0.558

Male Gender 0.001 0.999

Αβ42 -0.035 <0.00 l

Tau 0.019 0.051

Combined Model

Age -0.1 4 0.134

Male Gender 4.093 0.05 1 2

C3 3.085 0.01 1

IL-23 35.391 0.04

NrCAM -0.046 0.018

Number of ApoE4 2.941 0.040

allele

Αβ42 -0.08 0.002

Feature p e-selection and lack of an independent validation set may bias the classification res lts. Hence, a similar analysis of AD versus NL through RF and PAM (both with internal cross validation) using age, gender, and levels of 4 traditional biomarkers and 106 MAP analytes was performed. RF analysis identified analytes useful in AD

classification previously selected by LR, including C3, IL-23, and NrCAM, in addition to other analytes (including IL- l (x, Table 7, Figure 3 A). The OOB error rate of traditional AD biomarkers was 12.12%, which reduced to 8.08% when MAP analytes were introduced, Using PAM, some analytes previously identified by LR or RF were also identified (such as BMP6, NrCAM, PDGF; Table 8, Figure 3A). Diagnostic accuracy obtained through cross validation was 91 ,2%. A summary of analytes important in distinguishing between AD and NL are summarized in Figure 3A.

Table 7. Analytes differentiating AD from NL according to RF analysis.

AD versus NL

MAP Model Z- score

PDGF 28.452

IL- l a 24.019

BMP6 22.05 1

IL-23 17.95

C3 16.005

NrCAM 15.585

Fabp 13.873

VEGF 12.107

Prolactin 9.545

Eotaxin-3 9.069

IL17 8.731

Thyroxine binding

globulin 8.015

1-309 6.863

ApoH 5.63

Ferriti 5.452

A2M 4.167

Adiponectin 3.696

IgA 3.681 Traditional AD Mode!

Αβ42 68.401

Tau 21.826

Male gender 10.052

Combined Model

Ap42 5 1 .148

PDGF 26.244

Tau 23.071

IL1 -alpha 20.098

BMP6 1 8. 186

NrCAM 15,675

C3 15. 123

VEGF 1 3, 188

1L-23 12.352

Prolactin 10.17

Fabp 9.442

TRA1L-R3 9. 198

Eotaxin-3 6.424

Adipottectin 4.735

Table 8. Factors differentiating AD from NL according to PAM using MAP analytes, traditional AD analytes, or combination of MAP and traditional AD analytes. Threshold was set for each model through internal cross- validation.

AD versus NL

Traditional AD Combined

MAP Mode! Model Model

PDGF Αβ42 Αβ42

VEGF Tau Tau

NrCAM p-taiiisi PDGF

CgA # ApoE4 allele VEGF

Fabp Age NrCAM

Eotaxin-3 CgA

1-309 Fabp

BMP6 Eotaxin-3

IL-7 1-309

Myoglobin BMP6

AGT Myoglobin

Myeloperoxidase 1L-7

EN-RAGE

Thrombospondinl

GRO-

Pancreatic polypeptide

TGFc

Tissue Factor Ferritin

Age

Stem ceil factor

AD versus Other Neurodegenerative Disorders

With the emergence of substrate-specific therapeutic interventions, it is critically important to identify biomarkers that reliably differentiate the major

neurodegenerative disorders from one another. To this end, experiments were designed to assess which CSF biomarkers best differentiated AD from other neurodegenerative disorders using a similar series of analytical strategies.

Among traditional AD biomarkers, Αβ42 and total tau levels were specifically altered in AD (Figure 3C) and were useful in discriminating between AD and non-AD neurodegenerative disorders. Among MAP biomarkers for AD, C3 and Eotaxin-3 were also useful in discriminating between AD and non-AD disorders (Figure 3B), although these distinctions were most robust in differentiating between AD and FTLD-TDP (p=0.039 for C3, p=0.001 for Eotaxin-3 by Mann Whitney U test, Figure 3C) and less so for FTLD-Tau and DLB. Additional analytes that discriminated between AD and non-AD disorders include p-tautsi , agouti-related peptide (AgRP, altered in FTLD-TDP but preserved in AD), angiotensinogen (AGT), hepatocyte growth factor (HGF), monocyte chemoattractant 1 (MCP- 1), and von Wil!ebrand factor (vWF, Figure 3B), Analytes uniquely associated with AD in univariate analyses compared to NL and other neurodegenerative disorders include AGT, apolipoprotein H (ApoH), and resistin in addition to those identified in the multivariate prediction models (Αβ42, tau, C3, Eotaxin-3, Figure 3). The remainder of MAP biomarkers in AD versus NL were also altered in other neurodegenerative disorders, and did not distinguish between AD and other neurodegenerative disorders.

Biomarker Associations with Cognitive Function and Decline

Some diagnostic biomarkers may reflect severity of cognitive impairment and thus be useful in disease staging. To assess this, CSF biomarker levels were correlated with MMSE scores at time of CSF collection as a general measure of cognitive impairment. Among CSF biomarkers for AD identified using at least one approach, six (C3, CgA, IL- 1 , 1-309, NrCAM, and VEGF) were correlated with MMSE score, and levels of these analytes did not correlate with MMSE scores in the other neurodegenerative disorders. A multivariate linear regression analysis adjusting for age, gender, and education showed C3, IL- Ι α, and 309 levels were independently associated with MMSE scores in autopsy-confirmed cases of AD.

To further test the value of these CSF biomarkers in predicting cognitive decline, experiments were designed to determine the correlation between levels of these six biomarkers and rates of subsequent MMSE decline in MCI subjects following CSF collection. The 38 living MCI patients were similar to AD patients in age (71.39 vs. 70.79 yo, P=0.674), education ( 15.66 vs. 14,64 yo, p=0.143), and gender (42.1 % vs 53.0% women), but MCI patients had higher MMSE scores (mean 26.16, S.D =2.00) compared to AD (mean 17.55, S.D.=8.57, pO.01). The MCI patients had a median follow-up of 52 months (range 30- 129 mo), and a median rate of MMSE decline of 1 .2 points per year (mean 2.0, S.D =2.0). Among analytes associated with cognitive performance at time of CSF collection in AD, IL- ! a levels were correlated with the rates of MMSE decline (p=0.003), although it only had modest effect on rates of MMSE decline (R=0.498 for model). A search across 4 traditional and 1 06 MAP analytes additionally identified TECK to be significantly associated with rate of cognitive decline in MCI (p<0,00 l adjusting for age, gender, and education), and had a stronger effect on the rate of decline (R=0.745 for model, Figure 5).

CSF biomarkers for AD and MCI

The search for accurate CSF and plasma biomarkers in neurodegenerative diseases has intensified with the increasing need for informative biomarkers in clinical trials of disease modifying therapies for AD, and has been facilitated by high throughput multiplex platforms (Shaw, et al„ 2009, Ann Neurol, 65: 403- 13; Ray et al„ 2007, Nat Med.13: 1359- 62). Few CSF or plasma biomarker studies in AD include postmortem neuropathology confirmation of probable clinical diagnoses (Clark et at., 2003, Arch Neurol, 60: 1696-702; Finehout et al., 2007, Ann Neurol. 61 : 120-9; Castano et ai., 2006, Neurol Res 28: 155-63), and a diagnostic panel based on agreement with clinical AD diagnosis is often confounded by bias (referral or diagnostic) and intrinsic clinicopathologic heterogeneity. Using clinically and pathologically well-characterized cases of AD and FTLD, novel biomarkers useful in improving the distinction between AD and NL, and biomarkers associated with other disorders that improved the classification between AD and non-AD dementia were identified. In addition, biomarkers helpful in the staging of AD, and biomarkers associated with rates of cognitive decline in MCI were identified. Although the disease pathways involving these other analytes remain to be determined, the pathogenic roles of Αβ42 and tan in AD led to the hypothesis that there exist potential pathogenic roles for one or more of these novel biomarkers.

MAP biomarkers for AD complemented traditional AD biomarkers in two ways, First, while decreased Αβ42 and increased total/phosphorylated-tau levels are strongly linked to AD, altered levels of MAP biomarkers improved the classification of NL subjects with altered AD CSF Αβ levels but no dementia. One such biomarker is C3, which was found in AD neuritic plaques (Yasojima et al., 1999, Am J Pathol . 154: 927-36) and possibly is involved in plaque clearance (Wyss-Coray et ah, 2002, Proc Natl Acad Sci U S A, 99: 10837-42; Maie et al., 2008, J Neurosci 28: 6333-41 ). In the tested cohort, C3 levels were increased in AD and non-AD dementias, suggesting that complement activation is a common feature of neurodegeneration regardless of etiology, However, C3 activation is less in FTLD- TDP, and it may be preferentially involved in disorders associated with hyerphosphorylated tau (AD, FTLD-Tau, and DLB with co-existing AD pathology), Another example is PDGF, previously identified as a plasma AD biomarker by Ray et al. (2007, Nat Med.1 : 1359-62), PDGF-receptor activation can promote AB precursor protein processing in vitro (Gianni et al., 2003, J Biol Chem. 278: 9290-7), and inhibition of PDGF-receptor activation with imatinib mesylate (Matei et al., 2004, Clin Cancer Res 10: 681-90; Abouantoun et al., Moi Cancer Ther. 2009 May 5. [Epub ahead of print]) can decrease Αβ40 and Αβ42 secretion (Netzer et al., 2003, Proc Natl Acad Sci U S A. 100: 12444-9). in the tested cohort, PDGF was found to be elevated in multiple neurodegenerative disorders, and its constitutive expression by neurons (Fruttiger et al., 2000, Curr Biol 10: 1283-6) suggests elevated PDGF levels may reflect neuronal loss rather than an AD specific process. Despite alterations in multiple forms of dementia, however, changes in biomarkers associated with neuronal loss can improve the distinction between AD and NL with age-associated amyloidosis by traditional AD biomarkers alone as confirmatory tests for abnormal levels of CSF Αβ42). They may also serve as common targets or secondary endpoints in therapy of neurodegeneration in general.

Novel MAP biomarkers also represent candidate biomarkers of disease staging and prediction of progression. Cross-sectional ly, six CSF diagnostic biomarkers of AD correlated with cognitive deficits at the time of CSF collection. Since changes in some of these analytes likely mirror severity of neurodegeneration, correlations between levels of these analytes and cognitive performance should be expected. Additionally, as most of these MAP biomarkers are not correlated with tau or Αβ42 levels in AD, alterations in these analytes may provide novel utility in tracking disease progression if CSF Αβ42 and p-taum are successfully altered by disease-modifying therapies. Furthermore, not only were IL-l a levels associated with degree of cognitive dysfunction in AD, they also were associated with rates of decline in MCI. IL- la immunoreactive microglia in AD ne ritic plaques have been implicated in plaque evolution (Griffin et al., 1995, J Neuropathol Exp Neurol. 54: 276-81 ), although increased IL-la levels in non AD dementias was also observed. The difference in IL- la levels between fast and slow MCI decliners may represent differences in cognitive deficits that MMSE alone is not sensitive enough to detect. Alternatively, fast MCI decliners may have more cognitive reserve despite more severe neuronal loss, and the accelerated cognitive decline in these patients may occur as they become more susceptible to increasing pathologic burden,

TECK was also identified as being a robust predictor for the rate of cognitive decline among MCi patients, even though TECK itself was not a robust classifying biomarker between AD and NL. TECK (CCL25) is best understood as a strong chemo-attractant for thymocytes and intestinal T-cells (Moser et ai., 2004, Trends Immunol. 25: 75-84). TECK is a ligand to CCR9 which is predominantly expressed in mucosal epithelial tissues, but also a ligand to atypical chemokine receptor CCX-CKR that is found in the human brain (Youn et al., 2002, Apoptosis 7: 271 -6; Townson et al., 2002, Eur J Immunol. 32: 1230-41). The role of TECK in AD pathogenesis or neurodegeneration has never been investigated, and its role as a robust predictor of cognitive decline in MCI should prompt further examination of its involvement in AD pathogenesis and cognitive decline.

Some analytes were identified by only one analytical strategy as a potential AD biomarker due to the non-uniqueness of multiple analytical strategies, begging the question of whether such analytes are "true" biomarkers. Notably, the number of ApoE4 alleles was only identified by one analytical strategy (LR) to be a significant predictor of AD versus NL, despite its known association with increased AD risk (Shaw et al., 2007, Nat Rev Drug Dtscov. 6: 295-303). Similarly, IL- l a was identified only by RF to be a significant predictor of AD, but it appears to be an important biomarker for staging. Several explanations are possible. First, levels of some analytes may correlate strongly with others, and each strategy may select different proxy analytes to reflect a group of correlated analytes representing the same underlying biological process. Second, different analytical strategies may have various strengths and weaknesses for detecting particular effects. This was the reason three analytical strategies was chosen to identify putative AD biomarkers, and analytes identified by multiple strategies may be most reliable. Third, some analytes identified by only one analytical strategy may be associated with chance difference at the population level not directly associated with dementia or AD. These speculations notwithstanding, each putative novel biomarker's value in diagnosis and prognosis needs independent validation in another single- or a multi-center study, and their biological significance should be assessed independently whether it possesses sufficient classification power.

In summary, the results presented herein have identified novel biomarkers associated with pathologically confirmed AD. Some analytes were specifically associated with AD including Αβ42 and resistin, while others were associated with multiple

neurodegenerative disorders examined in this study. In addition, some diagnostic biomarkers mirrored the severity of cognitive impairment at time of CSF collection, while TEC and IL- la reflected the rate of cognitive decline among clinically diagnosed MCI subjects. Without wishing to be bound by any particular theory, it is believed that diagnostic and prognostic biomarkers are to be included in a composite AD biomarker panel (Table 9). Given the variability of each candidate biomarker across individuals, their collective classifying power should be definitively determined in a large, preferably multi-center, cohort with detailed clinical and pathologic characterization such as the Alzheimer Disease Neuroimaging Initiative. The biological relevance of each individual and set of biomarkers should be investigated for potential targets of therapeutic developments.

Table 9. Comparative diagnostic performance of biomarker panels according to RF versus PAM analysis for AD versus NL, Traditional models included established AD biomarkers (Αβ42, tan and/or p-taiiisi, number of ApoE4 alleles). MAP models included novel biomarkers from the MAP 151 analyte panel. Combined models included traditional and MAP biomarkers.

Example 3: Novel CSF biomarkers

Altered levels of CSF peptides related to AD are associated with pathologic AD diagnosis, although cognitively normal subjects can also have abnormal levels of these AD biomarkers. To identify novel CSF biomarkers that distinguish pathologically confirmed AD from cognitively normal subjects and patients with other neurodegenerative disorders, 2011/026852 experiments were designed to collect antemortem CSF samples from 66 AD patients and 25 patients with other neurodegenerative dementias followed longitudinally to neuropathologic confirmation, plus CSF from 33 cognitively normal subjects. Levels of 151 novel analytes were measured via a targeted multiplex panel enriched in cytokines, chemokines and growth factors, as well as established AD CSF biomarkers (levels of Ab42, tan and p-tau l 81). Two categories of biomarkers were identified: (I ) analytes that specifically distinguished AD (especially CSF Ab42 levels) from cognitively norma! subjects and other disorders; and (2) analytes altered in multiple diseases (NrCAM, PDGF, C3, IL- l a), but not in cognitively normal subjects. A multiprong analytical approach showed AD patients were best distinguished from non-AD cases (including cognitively normal subjects and patients with other neurodegenerative disorders) by a combination of traditional AD biomarkers and novel multiplex biomarkers. Six novel biomarkers (C3, CgA, IL- l , 1-309, NrCAM and VEGF) were correlated with the severity of cognitive impairment at CSF collection, and altered levels of IL- l a and TECK associated with subsequent cognitive decline in 38 longitudinally followed subjects with mild cognitive impairment. In summary, the targeted proteomic screen revealed novel CSF biomarkers that can improve the distinction between AD and non- AD cases by established biomarkers alone.

The materials and methods employed in these experiments are now described.

Materials and Methods

Participants

Patients and control subjects were recruited and longitudinally followed at Penn in specialty services dedicated to the evaluation and management of neurodegenerative diseases (Table 10). All protocols were approved by the Penn Institutional Review Board. Each patient in the autopsy cohort had undergone detailed cognitive, neurological, neuroimaging and laboratory examinations to ensure the accuracy of clinical diagnosis according to established criteria for AD, frontotemporal dementia (FTD), amyotrophic lateral sclerosis (ALS) and DLB as discussed elsewhere herein. Autopsy-confirmed cases of AD (n = 66), FTLD (n = 16) and DLB (n = 2) were characterized neuropathologically with detailed immunohistochemical analysis for pathology associated with each major neurodegenerative disorder, including Ab42, hyperphosphorylated tau, hyperphosphorylated TDP-43 and alpha-synuclein as discussed elsewhere herein. Seven patients with clinical FTD-ALS, but no autopsy was added to the FTLD-TDP group, as these cases nearly always have TDP-43 pathology. Thirty-eight patients with MCI were also recruited to assess predictors of cognitive decline. Each MCI patient was diagnosed by modified Petersen criteria as discussed elsewhere herein, and followed longitudinally with serial cognitive and neurological examination. Cognitively normal subjects were evaluated at the time of CSF collection, and continued to undergo annual testing to confirm their cognitive status.

ApoE genotyping was performed for all subjects as follows. APOE genotyping was performed for all subjects using EDTA blood samples collected at the time of lumbar puncture. TaqMan quantitative PCR assays were used for genotyping APOE nucleotides 334 T/C and 472 CT with an ABI 7900 real-time thermocycler using DNA freshly prepared from EDTA whole blood.

Table 10. Patient demographics included in the CSF analysis. Mean values are shown for continuous variables, with standard deviations shown in parentheses. MMSE=Mini-Mental Status Examination score (out of 30) at time of CSF. * p < 0.05 compared with AD. ** p < 0.05 compared with AD and NL.

Age at onset, yr (SD) - 67.1 1 (9.94) 57.21 (7.46)* 61 .44 68.00

(9.25) (5.66)

Age at CSF, yr (SD) 71 .79 70.79 (9.26) 59.57 64. 1 1 71.00

(9.26) (6.91)** (9.14) (5.66)

Disease duration at - 3.65 (2.14) 2.29 ( 1.64) 3.22 (1.48) 2.50 (0.71 )

CSF, yr (SD)

Age at death, yr (SD) - 76.08 62,57 (8.46)* 67,56 75.50

(10.17) (9.94) (9.19)

Disease duration, yr - 8.97 (3.44) 5.36 (3, 1 5)* 6.1 1 (2.47) 7.50 (3.54)

(SD)

Education, yr (SD) 15.97 14.64 (3. 1 5) 16.29 (2.92) 1 .56 16.00 (0)

(3.77) ( 1 .94)

MMSE (SD) 29.55 17,55 (8.57) 21.42 (6.44) 24.40 23.00

(0.71) (5. 18) (7.07)

Statistical analysis

Statistical analysis was performed in SPSS 12.0, Random Forests and

SAM/PAM. For testing of stability associated with each analyte, Pearson's correlation analysis was performed between analyte levels and time in -80_C storage. For each analytical strategy, diagnostic performance (sensitivity, specificity, accuracy) was determined using traditional AD biomarkers alone (tau, p-ta 1 S 1 , Ab42), MAP biomarkers alone or both traditional and MAP biomarkers. For each model, performance characteristics reported were based on the cross-validation. In the first model (Model 1 ), analytes that differed significantly between cognitive!)' normal and AD by Mann-Whitney U test (nominal P\0.01 ) were entered into logistic regression models for AD identification, adjusting for age and gender. Sensitivity and specificity of Model 1 were obtained by leave-one-out approach in discriminant analysis. In random forest analysis, analytes were entered into the analysis with nodes optimized for best classification of AD versus cognitive!)' normal (Model 2). Out-of- box error rate was used to derive diagnostic accuracy, with sensitivity and specificity derived from the confusion matrix. In PAM, analytes tiiat significantly differentiated AD from cognitively normal were identified, and diagnostic accuracy was derived through internal cross-validation (Model 3). Given the number of analytes relative to the number of subjects, interaction terms were not entered in the logistic regression model (Model 1). Random forest analysis (Model 2) and PAM (Model 3) each relies less on the assumption of normal distribution and takes into account possible correlations between analytes, although each algorithm can derive different analytes to account for variations in the respective

classification model. Thus, to expand the analysis beyond the strengths and constraints of any one algorithm, experiments were designed to identify biomarkers determined by at least two of these three well-established analytical strategies as key novel biomarkers. A similar three-approach strategy was employed to determine biomarkers that distinguished between AD and non-AD neurodegenerative disorders. For cross-sectional association between novel AD biomarker levels and severity of cognitive impairment at the time of CSF collection, Pearson's correlation coefficient was used to relate levels of newly identified CSF AD biomarkers with cognitive performance characterized by Mini-Mental Status Examination (MMSE) in autopsyconfirmed AD cases. For correlation of CSF biomarker levels and rates of cognitive decline following CSF collection in MCI, rates of cognitive decline were first estimated by the slope of MMSE score linear regression over time. Pearson's correlation coefficient was then determined for CSF biomarker levels and rates of cognitive decline. Effects from age and gender were adjusted for all diagnostic and progression models.

The results of the experiments are now described.

AD versus cognitively normal

All CSF was obtained from patients with informed consent as described elsewhere herein. Levels of 151 analytes in the MAP were measured in the CSF, with 106 analytes having measurable levels for analysis (Table 1 1 ). Four analytes (angiotensinogen, BMP-6, endotheliii- 1 , SGOT) demonstrated level changes that corresponded to time stored in

52 -80°C freezer and were excluded from the analysis because of their apparent instability with increasing length of storage. To determine the best biomarkers of AD, three independent analytical strategies were used to identify MAP analytes associated with AD, and combined traditional AD biomarkers and MAP analytes to identify complementary AD biomarkers.

Table 11. Lists of MAP analytes analyzed in CSF samples. * Marks analytes that correlated with CSF tan levels in AD or NL with Pearson correlation coefficient greater than 0.500. <LDD=analyte levels were below lowest detectable dose for greater than 90% of the samples tested.

AD NL

Alpha-1 Antitrypsin -0.028 0. 146

ACE (CD143) Angiotensin Converting Enzyme 0.309 0.75 i *

ACTH (Adrenocorticotropic Hormone) -0.052 0.033

Adiponectin 0.1 3 -0.063

Agouti-Related Protein (AgRP) 0.182 -0.026

Alpha-2 Macroglobulin 0.369 0.438

Alpha-Fetoprotein < LDD < LDD

Amphireguiin < LDD < LDD

Angiopoietin 2 (ANG-2) 0.781 * 0.753*

Angiotensinogen -0.01 9 -0, 1 16

Apolipoprotein A1 0.03 1 0.073

Apolipoprtein B 0.328 -0,542*

Apolipoprotein CUE 0.269 0.082

Apolipoprotein D 0.065 0.267

Apolipoprotein E 0.536* 0,625*

Apolipoprotein H 0.336 0.091

AXL 0.584* 0.741 *

Beta -2 Microglobulin 0.732* 0.664*

Betacellulin < LDD < LDD

B-Lyttiphocyte Chemoattractant (BLC) < LDD < LDD

BMP-6 -0.035 -0.33 1

Brain-Derived Neurotrophic Factor < LDD < LDD

Complement 3 0.126 0.245

Cancer Antigen 125 < LDD < LDD

Cancer Antigen 19-9 < LDD < LDD

Calcitonin 0.172 -0.149

CD40 0.750* 0.601 *

CD40 Ligand 0.723* 0.144

Carcinoembryonic Antigen < LDD < LDD

CgA 0.347 0.499

Complement Factor H 0.072 0.071

Creatine Kinase-MB < LDD < LDD

Ciliary Neurotrophic Factor (CNTF) < LDD < LDD

Cortisol 0.743* 0.479

C Reactive Protein < LDD < LDD

CTGF < LDD < LDD

26852

In Model 1 , 21 MAP analytes were found to differ between cognitively normal subjects and AD (Fig. 6) by Mann- Whitney U test at P\0.01, and only a minority of these were specifically changed in AD, including resistin and thrombospondin-1. MAP analytes alone, but not traditional AD biomarkers, were entered into a forward stepwise logistic regression model. Leave-oneout discriminant analysis using the five resultant MAP analytes achieved 84.8% sensitivity and 87.9% specificity, with overall 85.9% accuracy. By comparison, traditional AD biomarkers Ab42 and total tau yielded greater sensitivity

(92.4%), but less specificity (81 .8%) for overall accuracy of 88.9%. Combining MAP analytes and traditional AD biomarkers resulted in a model differentiating AD from cognitively normal subjects by the following biomarkers: levels of tau, Ab42, complement 3 (C3), neuron-glia-CAM-related cell adhesion molecule (NrCAM) and platelet-derived growth factor (PDGF). This combined model has high sensitivity (97.0%) and specificity (93.9%) with 96,0% accuracy, and improved upon the traditional AD model by correctly reclassifying up to four cognitively normal subjects with pathologic CSF levels of tau and Ab42, and three AD subjects with nonpathologic levels of CSF tau and Ab42.

Feature pre-selection and the lack of an independent validation set may bias the classification results. Hence, a similar analysis of AD versus cognitively normal through random forest (Model 2) and PAM (Model3) was used using age, gender and levels of 3 traditional biomarkers and 106 MAP analytes, as each analysis incorporates internal cross- U 2011/026852 validation that is more objective than leaveone-out analysis. Model 2 using MAP anaiytes alone identified some anaiytes from Model 1 , including C3, fatty acid-binding protein (Fabp), 1L-23, NrCAM and PDGF, among others (Fig. 7a). The out-of-box error rate of traditional AD biomarkers was 12.1 %, which reduced to 6.1% when MAP anaiytes were introduced with 93.9% accuracy, Model 3 also identified NrCAM and PDGF as important biomarkers useful in distinguishing between AD and cognitive!)' norma! subjects (Fig. 7a), Diagnostic accuracy obtained through cross-validation was 93.9% in Model 3. A summary of anaiytes important in distinguishing between AD and cognitive normal subjects is shown in Fig. 7a, including Ab42, tau, NrCAM and PDGF identified by all three algorithms.

AD versus other neurodegenerative disorders

neurodegenerative disorders from one another. To this end, we assessed which CSF biomarkers best differentiated AD from other neurodegenerative disorders using a similar series of analytical strategies.

Among traditional AD biomarkers, Ab42 and p-tau 181 levels discriminated between AD and non-AD neurodegenerative disorders in all models. Among novel MAP anaiytes, agouti-related peptide (AgRP) was identified by all algorithms to distinguish between AD and non-AD disorders (Fig. 7B), Post hoc analysis showed AgRP as most altered in FTLD-TDP (Fig. 8) and its classification power may rest in identifying FTLD-TDP cases. Tau, eotaxin-3 and hepatocyte growth factor (HGF) were additionally identified by both RF and PAM to be important in distinguishing between AD and non-AD disorders (Fig. 2B). Similar to the classification ro!e of AgRP, eotaxin-3 was most different between AD and FTLD-TDP (P = 0.001 ), and HGF was most different between AD and FTLD-Tau (P = 0.002, both comparisons byMann-Whitney U test; Fig. 8). Thus, biomarkers more specifically associated with other neurodegenerative disorders can also aid in the diagnosis of AD.

Biomarkers associated with cognitive function and decline

Some diagnostic biomarkers may reflect severity of cognitive impairment and thus be useful in disease staging. To assess this, experiments were performed to correlate CSF biomarker levels with MMSE scores at the time of CSF collection as a general measure of cognitive impairment. Among CSF biomarkers for AD identified by at least one approach, six (C3, CgA, IL- l a, 1-309, NrCAM and VEGF) were correlated with MMSE score, and levels of these anaiytes did not correlate with MMSE scores in the other neurodegenerative disorders. A multivariate linear regression analysis adjusting for age, gender and education showed C3, IL- l a and 1-309 levels were independently associated with MMSE scores in autopsy-confirmed cases of AD.

To further test the value of these CSF biomarkers in predicting cognitive decline, experiments were conducted to determine the correlation between levels of these six biomarkers and rates of subsequent MMSE decline in MCI subjects following CSF collection. The 38 living MCI patients were similar to AD patients in age (71.39 vs. 70.79 yo, P = 0.674), education ( 1 5.66 vs. 14.64 yo, P = 0.143) and gender (42.1 vs 53.0% women), but MCI patients had higher MMSE scores (mean 26.16, SD = 2.00) as compared to AD (mean 17.55, SD - 8.57, P < 0.01 ). The MCI patients had a median follow-up of 52 months (range 30-129 months) and a median rate of MMSE decline of 1.2 points per year (mean 2,0, SD = 2.0). Among anaiytes associated with cognitive performance at the time of CSF collection in AD, IL-l a levels correlated with the rates of MMSE decline (P = 0.003), although with modest effect on decline rates (R = 0.498 for model), A search across 4 traditional and 106 MAP anaiytes additionally identified thymus-expressed chemokine (TECK) as significantly associated with rates of cognitive decline in MCI (P < 0,00 1 adjusting for age, gender and education) and had a stronger effect on the rate of decline (R = 0.745 for model, Fig. 9).

The disclosures of each and every patent, patent application, and publication cited herein are hereby incorporated herein by reference in their entirety. While this invention has been disclosed with reference to specific embodiments, it is apparent that other embodiments and variations of this invention may be devised by others skilled in the art without departing from the true spirit and scope of the invention. The appended claims are intended to be construed to include all such embodiments and equivalent variations.

Claims

1 . A method of differentially diagnosing a neurodegenerative disorder in a patient, the method comprising determining the level of at least one biomarker in a biological sample obtained from said patient wherein said biomarker differentially discriminates between different neurodegenerative disorders.

2. The method of claim 1 , wherein said neurodegenerative disorder is selected from the group consisting of AD, FTLD, FTLD-TDP, FTLD-Tati, dementias, dementia with Lewy bodies (DLB), vascular dementias, or any combinations thereof.

3. The method of claim I , wherein said biological sample is a body fluid.

4. The method of claim 3, wherein said body fluid is a cerebrospinal fluid

(CSF).

5. The method of claim 1 , wherein said biomarker is selected from the group consisting of the number of ApoE4 alleles, Αβ42 levels, tau, p-taiim. C3, TL-23, NrCAM, IL- l , and any combination thereof, further wherein said biomarker differentially discriminates between pathologically confirmed AD from cognitively normal patients.

6. The method of claim I , wherein said biomarker is selected from the group consisting of Αβ42, tau, C3, Eotaxin-3, p-tauisi, agouti-related peptide (AgRP), angiotensinogen (AGT), hepatocyte growth factor (HGF), monocyte chemoattractant 1 (MCP-1 ), von Willebrand factor (vWF), apolipoprotein H (ApoH), resistin, and any combination thereof, further wherein said biomarker differentially discriminates between pathologically confirmed Alzheimer's disease from other neurodegenerative disorders.

7. The method of claim 1, wherein said biomarker is selected from the group consisting of C3, CgA, IL- la, 1-309, NrCAM, and VEGF, further wherein said biomarker is an indication of severity of cognitive impairment

8. The method of claim 1 , wherein said biomarker is selected from the group consisting of IL-l , TECK, and any combination thereof, further wherein said biomarker is an indication of cognitive decline in MCI.

9. The method of claim 1 , wherein said biomarker is selected from the group consisting of Fas, agouti-related peptide (AgRP), adrenocortotropic hormone (ACTH), IL-23, IL- 1 7, Eotaxin-3, ApoB, and any combination thereof, further wherein said biomarker differentially discriminates between frontotemporal lobar degeneration TDP-43 pathology (FTLD-TDP) and FTLD tau pathology (FTLD-Tau).

10. The method of claim 1, wherein said biomarker is separately identified by logistic regression (LR) analysis, random forest (RP) classification, and predictive analysis of microarrays (PAJV1),

1 1 . A kit for differentially diagnosing a neurodegenerative disorder, the kit comprising an agent designed to determi e the level of at least one biomarker in a biological sample obtained from a patient wherein said biomarker differentially discriminates between different neurodegenerative disorders.

12. A method for assessing progression of a neurodegenerative disorder in a patient, the method comprising differentially diagnosing a neurodegenerative disorder comprising the steps of determining the level of at least one biomarker in a biological sample obtained from said patient wherein said biomarker differentially discriminates between different neurodegenerative disorders.

13. A method for staging a neurodegenerative disorder in a patient, the method comprising differentially diagnosing a neurodegenerative disorder comprising the steps of determi ing the level of at least one biomarker in a biological sample obtained from said patient wherein said biomarker differentially discriminates between different neurodegenerative disorders.

14. A method of diagnosing whether a patient has a neurodegenerative disorder, the method comprising determining the level of at least one biomarker in a biological sample obtained from said patient wherein said biomarker differentially discriminates between different neurodegenerative disorders.