WO2014144605A1

WO2014144605A1 - Biomarkers for major depressive disorder

Info

Publication number: WO2014144605A1
Application number: PCT/US2014/029084
Authority: WO
Inventors: Andre Tadic; Klaus Lieb; Stefanie Wagner; Konrad SCHLICHT
Original assignee: Myriad Genetics, Inc.; University Medical Center Of Johannes Gutenberg University Mainz
Priority date: 2013-03-15
Filing date: 2014-03-14
Publication date: 2014-09-18

Abstract

Biomarkers and methods for diagnosis of MDD and predicting treatment outcome in patients with MDD are disclosed.

Description

BIOMARKERS FOR MAJOR DEPRESSIVE DISORDER

RELATED APPLICATIONS This application claims priority to U.S. provisional application number 61/789,159, filed March 15, 2013, the entire contents of which is hereby incorporated by reference.

FIELD OF TH E INVENTION

This disclosure relates to a method of diagnosing or monitoring major depressive disorder, in particular but not exclusively to a method of diagnosing or monitoring major depressive disorder in male and female subjects and also methods for predicting treatment outcome in male and female patients with MDD.

BACKGROUND OF THE INVENTION

Major depressive disorder ("MDD") is a mental disorder characterized by a pervasive low mood, low self-esteem, and loss of interest or pleasure in normally enjoyable activities. The term "major depressive disorder" (which is also known as clinical depression, major depression, unipolar depression, or unipolar disorder) was selected by the American Psychiatric Association for this symptom cluster under mood disorders in the 1980 version of the Diagnostic and

Statistical Manual of Mental Disorders (DSM-III) classification, and has become widely used since.

The general term depression is often used to describe the disorder, but as it is also used to describe a depressed mood, more precise terminology is preferred in clinical and research use. Major depression is a disabling condition which adversely affects a person's family, work or school life, sleeping and eating habits, and general health. In the United States, approximately 3.4% of people with major depression commit suicide, and up to 60% of all people who commit suicide have depression or another mood disorder. The diagnosis of major depressive disorder is based on the patient's self- reported experiences, behaviour reported by relatives or friends, and a mental status exam. There is no laboratory test for major depression, although physicians generally request tests for physical conditions that may cause similar symptoms. The most common time of onset is between the ages of 30 and 40 years, with a later peak between 50 and 60 years. Major depression is reported about twice as frequently in women as in men, although men are at higher risk for suicide.

Most patients are treated in the community with antidepressant medication and some with psychotherapy or counseling. Hospitalization may be necessary in cases with associated self-neglect or a significant risk of harm to self or others. A minority are treated with electroconvulsive therapy (ECT), under a short- acting general anaesthetic.

The course of the disorder varies widely, from one episode lasting months to a lifelong disorder with recurrent major depressive episodes. Depressed individuals have shorter life expectancies than those without depression, in part because of greater susceptibility to medical illnesses. Current and former patients may be stigmatized.

The understanding of the nature and causes of depression has evolved over the centuries, though many aspects of depression remain incompletely understood and are the subject of discussion and research.

SUMMARY OF THE INVENTION

According to a first aspect of this disclosure, there is provided the use of Complement Factor H Related Protein (CFHRP) as a biomarker for diagnosis of major depressive disorder or predisposition thereto.

According to a second aspect of this disclosure, there is provided a method of diagnosing major depressive disorder, or predisposition thereto, in an individual, comprising :

(a) obtaining a biological sample from an individual; (b) quantifying the amounts of the analyte biomarkers as defined herein;

(c) comparing the amounts of the analyte biomarkers in the biological sample with the amounts present in a normal control biological sample from a normal subject, such that a difference in the level of the analyte biomarkers in the biological sample is indicative of major depressive disorder, or predisposition thereto.

According to a third aspect of this disclosure, there is provided a method of monitoring efficacy of a therapy in a subject having, suspected of having, or of being predisposed to major depressive disorder, comprising detecting and/or quantifying, in a sample from said subject, one or more of the analyte biomarkers defined herein. According to a fourth aspect of this disclosure, there is provided a method of determining the efficacy of therapy for major depressive disorder in an individual, comprising :

(a) obtaining a biological sample from an individual;

(b) quantifying the amounts of the analyte biomarkers as defined herein;

(c) comparing the amounts of the analyte biomarkers in the biological sample with the amounts present in a sample obtained from the individual on a previous occasion, such that a difference in the level of the analyte biomarkers in the biological sample is indicative of a beneficial effect of the therapy.

A further aspect of this disclosure provides ligands, such as naturally occurring or chemically synthesised compounds, capable of specific binding to the peptide biomarker. A ligand according to this disclosure may comprise a peptide, an antibody or a fragment thereof, or an aptamer or oligonucleotide, capable of specific binding to the peptide biomarker. The antibody can be a monoclonal antibody or a fragment thereof capable of specific binding to the peptide biomarker. A ligand according to this disclosure may be labelled with a detectable marker, such as a luminescent, fluorescent or radioactive marker; alternatively or additionally a ligand according to this disclosure may be labelled with an affinity tag, e.g. a biotin, avidin, streptavidin or His (e.g. hexa-His) tag. A biosensor according to this disclosure may comprise the peptide biomarker or a structu ral/shape mimic thereof capable of specific binding to an antibody against the peptide biomarker. Also provided is an array comprising a ligand or mimic as described herein.

Also provided by this disclosure is the use of one or more ligands as described herein, which may be natu rally occu rring or chemically synthesised, and is suitably a peptide, antibody or fragment thereof, aptamer or oligonucleotide, or the use of a biosensor of this disclosu re, or an array of this disclosu re, or a kit of this disclosure to detect and/or quantify the peptide. In these uses, the detection and/or quantification can be performed on a biological sample such as from the group consisting of CSF, whole blood, blood serum, plasma, urine, saliva, or other bodily fluid, breath, e.g . as condensed breath, or an extract or purification therefrom, or dilution thereof.

Diagnostic or monitoring kits are provided for performing methods of this disclosu re. Such kits will suitably comprise a ligand according to this disclosu re, for detection and/or quantification of the peptide biomarker, and/or a biosensor, and/or an array as described herein, optionally together with instructions for use of the kit.

A further aspect of this disclosu re is a kit for monitoring or diagnosing major depressive disorder, comprising a biosensor capable of detecting and/or quantifying one or more of the biomarkers as defined herein.

Biomarkers for major depressive disorder are essential targets for discovery of novel targets and drug molecules that retard or halt progression of the disorder. As the level of the peptide biomarker is indicative of disorder and of drug response, the biomarker is useful for identification of novel therapeutic compounds in in vitro and/or in vivo assays. Biomarkers of this disclosure can be employed in methods for screening for compounds that modu late the activity of the peptide. Thus, in a further aspect of this disclosure, there is provided the use of a ligand, as described, which can be a peptide, antibody or fragment thereof or aptamer or oligonucleotide according to this disclosu re; or the use of a biosensor according to this disclosure, or an array according to this disclosu re; or a kit according to this disclosure, to identify a substance capable of promoting and/or of su ppressing the generation of the biomarker.

Also there is provided a method of identifying a substance capable of promoting or su ppressing the generation of the peptide in a subject, comprising administering a test substance to a subject animal and detecting and/or quantifying the level of the peptide biomarker present in a test sample from the subject.

Also provided herein are methods for predicting treatment outcome in patients with MDD.

BRIEF DESCRIPTION OF THE FIGURES

Figure 1 describes a graphical overview of the sensitivity-specificity-profile of each molecular marker in the early course of antidepressant treatment for final response in men.

Figure 2 describes a graphical overview of the sensitivity-specificity-profile of each molecular marker in the early course of antidepressant treatment for final response in women.

Figure 3 shows the distribution of observed and permuted AUCs for both sexes for week 1 in plasma .

Figure 4 shows the distribution of observed and permuted AUCs for both sexes for week 2 in plasma .

Figure 5 is a box plot of Complement Factor H Related Protein (CFHRP) a nalyte in serum versus response in males and females. Figure 6 is a profile plot of Complement Factor H Related Protein (CFHRP) analyte in serum versus visit number.

Figure 7 shows the distribution of observed and permuted AUCs for week 1 in serum.

Figure 8 shows the distribution of observed and permuted AUCs for week 2 in serum. Figure 9 shows observed versus permuted AUCS in serum for week 1 based on the reanalysis of data in Example 4.

Figure 10 shows observed versus permuted AUCS in plasma for week 1 based on the reanalysis of data in Example 4.

Figure 11 shows observed versus permuted AUCS in serum for week 2 based on the reanalysis of data in Example 4.

Figure 12 shows observed versus permuted AUCS in plasma for week 2 based on the reanalysis of data in Example 4.

DETAILED DESCRIPTION OF THE INVENTION

Data is presented herein which shows that CFHRP was surprisingly identified to be a sensitive and specific biomarker for major depressive disorder in both male and female subjects.

In an embodiment where diagnosis of major depressive disorder or predisposition thereto is of a male subject, the use of the first aspect of this disclosure additionally comprises one or more further analytes selected from : Amphiregulin, Apolipoprotein E (Apo E), Calcitonin, CD5 ligand, Thymus- Expressed Chemokine (TECK), Creatine Kinase-MB (CK-MB), C-Reactive Protein (CRP), Granulocyte Colony-Stimulating Factor (G-CSF), Intercellular Adhesion Molecule 1 (ICAM- 1), Interleukin-5 (IL-5), Interleukin-8 (IL-8), Lectin-Like Oxidized LDL Receptor 1 (LOX-1), Matrix Metalloproteinase-9 (MMP-9), Matrix Metalloproteinase- 10 (MMP- 10), Myoglobin, Tissue Inhibitor of Metalloproteinases 1 (TIMP- 1), Vascular Cell Adhesion Molecule- 1 (VCAM- 1) and Thyroid-Stimulating Hormone (TSH).

In one embodiment, the analytes comprise Apolipoprotein E (Apo E), Lectin-Like Oxidized LDL Receptor 1 (LOX- 1) and Myoglobin. Data is presented herein which demonstrates that the levels of these 3 analytes increased at an early stage in male final responders.

In an alternative embodiment, the analytes comprise Amphiregulin, Calcitonin, CD5 ligand, Complement Factor H Related Protein (CFHRP), Creatine Kinase-MB (CK-MB), C-Reactive Protein (CRP), Granulocyte Colony-Stimulating Factor (G- CSF), Intercellular Adhesion Molecule 1 (ICAM- 1), Interleukin-5 (IL-5), Interleukin-8 (IL-8), Matrix Metalloproteinase-9 (MMP-9), Matrix Metalloproteinase- 10 (MMP- 10), Myoglobin, Thymus-Expressed Chemokine (TECK), Tissue Inhibitor of Metalloproteinases 1 (TIMP- 1) and Thyroid- Stimulating Hormone (TSH). Data is presented herein which demonstrates that the levels of these 16 analytes decreased at an early stage in male final responders.

According to a further aspect of this disclosure, there is provided the use of Thymus-Expressed Chemokine (TECK), Amphiregulin, Apolipoprotein E (Apo E), Calcitonin, CD5 ligand, Complement Factor H Related Protein (CFHRP), Creatine Kinase-MB (CK-MB), C-Reactive Protein (CRP), Granulocyte Colony-Stimulating Factor (G-CSF), Intercellular Adhesion Molecule 1 (ICAM-1), Interleukin-5 (IL-5), Interleukin-8 (IL-8), Lectin-Like Oxidized LDL Receptor 1 (LOX-1), Matrix Metalloproteinase-9 (MMP-9), Matrix Metalloproteinase-10 (MMP-10), Myoglobin, Tissue Inhibitor of Metalloproteinases 1 (TIMP-1), Vascular Cell Adhesion Molecule- 1 (VCAM- 1) and Thyroid-Stimulating Hormone (TSH) as a specific panel of analyte biomarkers for major depressive disorder, or predisposition thereto in a male subject. Data is presented herein which shows that this sex-specific serum biomarker profile, which was observable 7-14 days after the initiation of treatment and each marker, was able to disentangle final responders from non-responders to antidepressant treatment in males.

In an embodiment where diagnosis of major depressive disorder or predisposition thereto is of a female subject, the use of the first aspect of this disclosure additionally comprises one or more further analytes selected from : Alpha-2-Macroglobulin (Alpha-2-Macro), Alpha-Fetoprotein (AFP), Apolipoprotein B (Apo B), Beta-2-Microglobulin (B2M), Clusterin (CLU), Cystatin-C, Eotaxin- 1, Glucagon-like Peptide 1, total (GLP- 1 total), Hepatocyte Growth Factor (HGF), Interleukin-2 (IL-2), Interleukin-7 (IL-7), Interleukin-15 (IL-15), Monocyte Chemotactic Protein 1 (MCP- 1), Macrophage Inflammatory Protein- 1 beta (MIP- 1 beta), Osteopontin, Proinsulin Intact, Proinsulin Total, T-Cell-Specific Protein RANTES (RANTES), S100 calcium-binding protein B (S100-B), Thrombospondin- 1, Tumor Necrosis Factor Receptor-Like 2 (TNFR2) and Vitamin K-Dependent Protein S (VKDPS).

In one embodiment, the analytes comprise Alpha-2-Macroglobulin (Alpha-2- Macro), Apolipoprotein B (Apo B), Beta-2-Microglobulin (B2M), Clusterin (CLU), Cystatin-C, Macrophage Inflammatory Protein- 1 beta (MIP- 1 beta) and Tumor Necrosis Factor Receptor-Like 2 (TNFR2). Data is presented herein which demonstrates that the levels of these 7 analytes increased at an early stage in female final responders.

In an alternative embodiment, the analytes comprise Amphiregulin, Calcitonin, CD5 ligand, Complement Factor H Related Protein (CFHRP), Creatine Kinase-MB (CK-MB), C-Reactive Protein (CRP), Granulocyte Colony-Stimulating Factor (G- CSF), Intercellular Adhesion Molecule 1 (ICAM- 1), Interleukin-5 (IL-5), Interleukin-8 (IL-8), Matrix Metalloproteinase-9 (MMP-9), Matrix Metalloproteinase- 10 (MMP- 10), Myoglobin, Thymus-Expressed Chemokine (TECK), Tissue Inhibitor of Metalloproteinases 1 (TIMP- 1) and Thyroid- Stimulating Hormone (TSH). Data is presented herein which demonstrates that the levels of these 16 analytes decreased at an early stage in female final responders. According to a further aspect of this disclosure, there is provided the use of Thymus-Expressed Chemokine (TECK), Alpha-2-Macroglobulin (Alpha-2-Macro), Alpha-Fetoprotein (AFP), Apolipoprotein B (Apo B), Beta-2-Microglobulin (B2M), Clusterin (CLU), Cystatin-C, Eotaxin- 1, Glucagon-like Peptide 1, total (GLP-1 total), Hepatocyte Growth Factor (HGF), Interleukin-2 (IL-2), Interleukin-7 (IL- 7), Interleukin- 15 (IL- 15), Monocyte Chemotactic Protein 1 (MCP- 1), Macrophage Inflammatory Protein- 1 beta (MIP- 1 beta), Osteopontin, Proinsulin Intact, Proinsulin Total, T-Cell-Specific Protein RANTES (RANTES), S100 calcium- binding protein B (S100-B), Thrombospondin-1, Tumor Necrosis Factor Receptor-Like 2 (TNFR2) and Vitamin K-Dependent Protein S (VKDPS) as a specific panel of biomarkers for the diagnosis of major depressive disorder or predisposition thereto in a female subject. Data is presented herein which shows that this sex-specific serum biomarker profile, which was observable 7-14 days after the initiation of treatment and each marker was able to disentangle final responders from non-responders to antidepressant treatment in females. The term "biomarker" means a distinctive biological or biologically derived indicator of a process, event, or condition. Peptide biomarkers can be used in methods of diagnosis, e.g. clinical screening, and prognosis assessment and in monitoring the results of therapy, identifying patients most likely to respond to a particular therapeutic treatment, drug screening and development. Biomarkers and uses thereof are valuable for identification of new drug treatments and for discovery of new targets for drug treatment.

Samples Many embodiments of the present disclosure involve taking or analyzing or quantifying in a biological sample from an individual or patient. In some embodiments the individual or patient is human. In some embodiments the biological sample is taken from a patient or individual.

In some embodiments the sample is blood. In some embodiments the sample from is plasma. In some embodiments the sample from the patient may include blood, plasma, buffy coat, saliva or buccal swabs. In some embodiments, the sample is urine. In some embodiments, the sample is saliva.

Methods of Diagnosis, Monitoring Therapy and Predicting Response to Treatment

In one embodiment, the disclosure provides for a method of diagnosing diagnosing major depressive disorder, or predisposition thereto, comprising :

(a) quantifying the amounts of a panel of analyte biomarkers in a biological sample from an individual;

(b) comparing the amount of the panel of analyte biomarkers in the biological sample to one or more control samples; and

(c) diagnosing the individual with depression based at least in part on a difference between the amount of the panel of analyte biomarkers between the biological sample and the one or more control samples.

In a group of related embodiments, the panel of analyte biomarkers comprises, respectively, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, or 15 of the biomarkers disclosed in Tables 1-21. In one embodiment, the panel of analytes comprises Complement Factor H Related Protein.

In one embodiment, the disclosure provides a method of diagnosing major depressive disorder, or predisposition thereto, comprising :

(a) quantifying the amount of Complement Factor H Related Protein in a biological sample from the individual;

(b) comparing the amount of Complement Factor H Related Protein in the biological sample to one or more control samples; and

(c) diagnosing the individual with depression based at least in part on a difference between the amount of Complement Factor H Related Protein in the biological sample and the one or more control samples.

In a related embodiment, the method further comprises:

(a) quantifying the amount of one or more additional analytes in the biological sample from the individual; (b) comparing the amount of the one or more additional analytes in the biological sample to the one or more control samples; and

(c) diagnosing the individual with depression based in part on a difference between the amount of the one or more additional analytes in the biological sample and the one or more control samples.

In a related embodiment, the one or more additional analytes are selected from : Angiopoietin 2, Apolipoprotein H, Beta 2 Microglobulin, Betacellulin, Brain Derived Neurotrophic Factor, C Reactive Protein, CD5, Clusterin, ComplementC3, CreatineKinase MB, Cystatin C, Eotaxin 1, Epithelial Derived Neutrophil

Activating Protein 78 , Fibrinogen, Granulocyte Colony Stimulating Factor, Haptoglobin, Immunoglobulin A, Interleukin 13 , Interleukin 16, Interleukin 5, Lectin Like Oxidized LDL Receptor 1, Macrophage Derived Chemokine,

Macrophage Inflammatory Protein lbeta, Matrix Metalloproteinase 10, Serum Amyloid P Component, Sex Hormone Binding Globulin, Sortilin, Tenascin C, Tissue Inhibitor of Metalloproteinases 1, Transthyretin and Vitronectin.

(a) quantifying the amount of one or more antibody-antigen interactions in a biological sample from the individual;

(b) comparing the amount of the one or more antibody-antigen interactions in the biological sample to one or more control samples; and

(c) diagnosing the individual based at least in part on a difference between the amount of the one or more antibody-antigen interactions between the biological sample and the one or more control samples. In a related embodiment, the one or more antibody-antigen interactions comprise a C18-antigen interaction.

According to a further aspect of this disclosure, there is provided a method of diagnosing major depressive disorder, or predisposition thereto, in a male subject, comprising (a) obtaining a biological sample from a male subject;

(b) quantifying the amounts of a panel of analyte biomarkers in the biological sample, wherein the panel of analyte biomarkers comprises Apolipoprotein E (Apo E), Lectin-Like Oxidized LDL Receptor 1 (LOX- 1) and Myoglobin; and

(c) comparing the amounts of the panel of analyte biomarkers in the biological sample with the amounts present in a normal control biological sample from a normal male subject, wherein a higher level of any one of the analyte biomarkers in the biological sample is indicative of major depressive disorder, or predisposition thereto.

In one embodiment, the higher level is at least a 5% increase relative to the control sample, such as at least a 10%, 15%, 20%, 25% or 30% increase relative to the control sample. In one embodiment, the higher level is above at least 15% to 30% relative to the control sample.

According to a further aspect of this disclosure, there is provided a method of diagnosing major depressive disorder, or predisposition thereto, in a male subject, comprising

(a) obtaining a biological sample from a male subject;

(b) quantifying the amounts of a panel of analyte biomarkers in the biological sample, wherein the panel of analyte biomarkers comprises Amphiregulin, Calcitonin, CD5 ligand, Complement Factor H Related Protein (CFHRP), Creatine Kinase-MB (CK-MB), C-Reactive Protein (CRP), Granulocyte Colony-Stimulating Factor (G-CSF), Intercellular

Adhesion Molecule 1 (ICAM-1), Interleukin-5 (IL-5), Interleukin-8 (IL- 8), Matrix Metalloproteinase-9 (MMP-9), Matrix Metalloproteinase- 10 (MMP- 10), Myoglobin, Thymus-Expressed Chemokine (TECK), Tissue Inhibitor of Metalloproteinases 1 (TIMP-1) and Thyroid-Stimulating Hormone (TSH); and

(c) comparing the amounts of the panel of analyte biomarkers in the biological sample with the amounts present in a normal control biological sample from a normal male subject, wherein a lower level of any one of the analyte biomarkers in the biological sample is indicative of major depressive disorder, or predisposition thereto. In one embodiment, the lower level is at least a 5% decrease relative to the control sample, such as at least a 5%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 100%, 150%, 200%, 250%, 300%, 350%, 400%, 450% or 500% decrease relative to the control sample.

According to a further aspect of this disclosure, there is provided a method of diagnosing major depressive disorder, or predisposition thereto, in a female subject, comprising

(a) obtaining a biological sample from a female subject;

(b) quantifying the amounts of a panel of analyte biomarkers in the biological sample, wherein the panel of analyte biomarkers comprises Alpha-2-Macroglobulin (Alpha-2-Macro), Apolipoprotein B (Apo B), Beta-2-Microglobulin (B2M), Clusterin (CLU), Cystatin-C, Macrophage Inflammatory Protein-1 beta (MIP- 1 beta) and Tumor Necrosis Factor

Receptor-Like 2 (TNFR2); and

(c) comparing the amounts of the panel of analyte biomarkers in the biological sample with the amounts present in a normal control biological sample from a normal female subject, wherein a higher level of any one of the analyte biomarkers in the biological sample is indicative of major depressive disorder, or predisposition thereto.

In one embodiment, the higher level is at least a 1% increase relative to the control sample, such as at least a 3%, 10% or 15% increase relative to the control sample. In one embodiment, the higher level is above at least 3% to 15% relative to the control sample.

(a) obtaining a biological sample from a female subject;

(b) quantifying the amounts of a panel of analyte biomarkers in the biological sample, wherein the panel of analyte biomarkers comprises Complement Factor H Related Protein (CFHRP); and (c) comparing the amounts of the panel of analyte biomarkers in the biological sample with the amounts present in a normal control biological sample from a normal female subject, wherein a lower level of any one of the analyte biomarkers in the biological sample is indicative of major depressive disorder, or predisposition thereto.

In one embodiment, the lower level is at least a 5% decrease relative to the control sample, such as at least a 5%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 100%, 150%, 200%, 250%, 300%, 350%, 400%, 450%, 500%, 550%, 600%, 650%, 700%, 750%, 800%, 850%, 900%, 950% or 1000% decrease relative to the control sample.

In one embodiment, the disclosure provides for a method of predicting response to treatment of major depressive disorder, comprising :

(d) quantifying the amounts of a panel of analyte biomarkers in a biological sample from an individual;

(e) comparing the amount of the panel of analyte biomarkers in the biological sample to one or more control samples; and

(f) predicting the individual's response to treatment based at least in part on a difference between the amount of the panel of analyte biomarkers between the biological sample and the one or more control samples.

(d) quantifying the amount of Complement Factor H Related Protein in a biological sample from the individual;

(e) comparing the amount of Complement Factor H Related Protein in the biological sample to one or more control samples; and

(f) classifying the individual as a likely responder to therapy based at least in part on a difference between the amount of Complement Factor H Related Protein in the biological sample and the one or more control samples.

In a related embodiment, the method further comprises:

(d) quantifying the amount of one or more additional analytes in the biological sample from the individual;

(e) comparing the amount of the one or more additional analytes in the biological sample to the one or more control samples; and

(f) classifying the individual as a likely responder to therapy based in part on a difference between the amount of the one or more additional analytes in the biological sample and the one or more control samples.

Macrophage Inflammatory Protein lbeta, Matrix Metalloproteinase 10, Serum Amyloid P Component, Sex Hormone Binding Globulin, Sortilin, Tenascin C, Tissue Inhibitor of Metalloproteinases 1, Transthyretin and Vitronectin. In one embodiment, the disclosure provides for a method of predicting response to treatment of major depressive disorder, comprising :

(d) quantifying the amount of one or more antibody-antigen interactions in a biological sample from the individual;

(e) comparing the amount of the one or more antibody-antigen interactions in the biological sample to one or more control samples; and

(f) classifying the individual as a likely responder to therapy based at least in part on a difference between the amount of the one or more antibody-antigen interactions between the biological sample and the one or more control samples. In a related embodiment, the one or more antibody-antigen interactions comprise a C18-antigen interaction. As used herein, the term "biosensor" means anything capable of detecting the presence of the biomarker. Examples of biosensors are described herein.

In one embodiment, one or more of the biomarkers defined hereinbefore may be replaced by a molecule, or a measurable fragment of the molecule, found upstream or downstream of the biomarker in a biological pathway.

Biosensors according to this disclosure may comprise a ligand or ligands, as described herein, capable of specific binding to the peptide biomarker. Such biosensors are useful in detecting and/or quantifying a peptide of this disclosure.

Diagnostic kits for the diagnosis and monitoring of major depressive disorder are described herein. In one embodiment, the kits additionally contain a biosensor capable of detecting and/or quantifying a peptide biomarker. Monitoring methods of this disclosure can be used to monitor onset, progression, stabilisation, amelioration and/or remission.

In methods of diagnosing or monitoring according to this disclosure, detecting and/or quantifying the peptide biomarker in a biological sample from a test subject may be performed on two or more occasions. Comparisons may be made between the level of biomarker in samples taken on two or more occasions. Assessment of any change in the level of the peptide biomarker in samples taken on two or more occasions may be performed. Modulation of the peptide biomarker level is useful as an indicator of the state of major depressive disorder or predisposition thereto. An increase in the level of the biomarker, over time is indicative of onset or progression, i.e. worsening of this disorder, whereas a decrease in the level of the peptide biomarker indicates amelioration or remission of the disorder, or vice versa. A method of diagnosis of or monitoring according to this disclosure may comprise quantifying the peptide biomarker in a test biological sample from a test subject and comparing the level of the peptide present in said test sample with one or more controls.

The control used in a method of this disclosure can be one or more control(s) selected from the group consisting of: the level of biomarker peptide found in a normal control sample from a normal subject, a normal biomarker peptide level ; a normal biomarker peptide range, the level in a sample from a subject with major depressive disorder, or a diagnosed predisposition thereto; major depressive disorder biomarker peptide level, or major depressive disorder biomarker peptide range.

In one embodiment, there is provided a method of diagnosing major depressive disorder, or predisposition thereto, which comprises:

(a) quantifying the amount of the peptide biomarker in a test biological sample; and

(b) comparing the amount of said peptide in said test sample with the amount present in a normal control biological sample from a normal subject.

For biomarkers which are increased in patients with major depressive disorder, a higher level of the peptide biomarker in the test sample relative to the level in the normal control is indicative of the presence of major depressive disorder, or predisposition thereto; an equivalent or lower level of the peptide in the test sample relative to the normal control is indicative of absence of major depressive disorder and/or absence of a predisposition thereto. For biomarkers which are decreased in patients with major depressive disorder, a lower level of the peptide biomarker in the test sample relative to the level in the normal control is indicative of the presence of major depressive disorder, or predisposition thereto; an equivalent or lower level of the peptide in the test sample relative to the normal control is indicative of absence of major depressive disorder and/or absence of a predisposition thereto. The term "diagnosis" as used herein encompasses identification, confirmation, and/or characterisation of major depressive disorder, or predisposition thereto. By predisposition it is meant that a subject does not currently present with the disorder, but is liable to be affected by the disorder in time. Methods of monitoring and of diagnosis according to this disclosure are useful to confirm the existence of a disorder, or predisposition thereto; to monitor development of the disorder by assessing onset and progression, or to assess amelioration or regression of the disorder. Methods of monitoring and of diagnosis are also useful in methods for assessment of clinical screening, prognosis, choice of therapy, evaluation of therapeutic benefit, i.e. for drug screening and drug development.

Efficient diagnosis and monitoring methods provide very powerful "patient solutions" with the potential for improved prognosis, by establishing the correct diagnosis, allowing rapid identification of the most appropriate treatment (thus lessening unnecessary exposure to harmful drug side effects), reducing "downtime" and relapse rates.

Also provided is a method of monitoring efficacy of a therapy for major depressive disorder in a subject having such a disorder, suspected of having such a disorder, or of being predisposed thereto, comprising detecting and/or quantifying the peptide present in a biological sample from said subject. In monitoring methods, test samples may be taken on two or more occasions. The method may further comprise comparing the level of the biomarker(s) present in the test sample with one or more control(s) and/or with one or more previous test sample(s) taken earlier from the same test subject, e.g. prior to commencement of therapy, and/or from the same test subject at an earlier stage of therapy. The method may comprise detecting a change in the level of the biomarker(s) in test samples taken on different occasions.

This disclosure provides a method for monitoring efficacy of therapy for major depressive disorder in a subject, comprising :

(a) quantifying the amount of the peptide biomarker; and

(b) comparing the amount of said peptide in said test sample with the amount present in one or more control(s) and/or one or more previous test sample(s) taken at an earlier time from the same test subject.

For biomarkers which are increased in patients with major depressive disorder, a decrease in the level of the peptide biomarker in the test sample relative to the level in a previous test sample taken earlier from the same test subject is indicative of a beneficial effect, e.g. stabilisation or improvement, of said therapy on the disorder, suspected disorder or predisposition thereto. For biomarkers which are decreased in patients with major depressive disorder, an increase in the level of the peptide biomarker in the test sample relative to the level in a previous test sample taken earlier from the same test subject is indicative of a beneficial effect, e.g. stabilisation or improvement, of said therapy on the disorder, suspected disorder or predisposition thereto. Methods for monitoring efficacy of a therapy can be used to monitor the therapeutic effectiveness of existing therapies and new therapies in human subjects and in non-human animals (e.g. in animal models). These monitoring methods can be incorporated into screens for new drug substances and combinations of substances.

Suitably, the time elapsed between taking samples from a subject undergoing diagnosis or monitoring will be 3 days, 5 days, a week, two weeks, a month, 2 months, 3 months, 6 or 12 months. Samples may be taken prior to and/or during and/or following an anti-depressant therapy. Samples can be taken at intervals over the remaining life, or a part thereof, of a subject.

The term "detecting" as used herein means confirming the presence of the peptide biomarker or antibody-antigen interaction present in a sample. Quantifying the amount of the biomarker present in a sample may include determining the concentration of the peptide biomarker present in the sample. Quantifying an antibody-antigen interaction in a sample may include determining the strength of the interaction or the K_d of the interaction. Quantifying the amount of an antibody-antigen interaction in a sample may include determining the concentration of antibody-antigen complexes in the sample, or determining the amount of bound antibody in the sample. Detecting and/or quantifying may be performed directly on the sample, or indirectly on an extract therefrom, or on a dilution thereof.

In alternative aspects of this disclosure, the presence of the peptide biomarker is assessed by detecting and/or quantifying antibody or fragments thereof capable of specific binding to the biomarker that are generated by the subject's body in response to the peptide and thus are present in a biological sample from a subject having major depressive disorder or a predisposition thereto. Measuring Biomarkers

Those skilled in the art are familiar with various techniques for measuring the levels of biomarkers in a sample. Useful techniques include, but are not limited to antibody tests, Western Blot, ELISA, PCR, and others as outlined below.

Detecting and/or quantifying can be performed by any method suitable to identify the presence and/or amount of a specific protein in a biological sample from a patient or a purification or extract of a biological sample or a dilution thereof. In methods of this disclosure, quantifying may be performed by measuring the concentration of the peptide biomarker in the sample or samples. Biological samples that may be tested in a method of this disclosure include cerebrospinal fluid (CSF), whole blood, blood serum, plasma, urine, saliva, or other bodily fluid (stool, tear fluid, synovial fluid, sputum), breath, e.g. as condensed breath, or an extract or purification therefrom, or dilution thereof. Biological samples also include tissue homogenates, tissue sections and biopsy specimens from a live subject, or taken post-mortem. The samples can be prepared, for example where appropriate diluted or concentrated, and stored in the usual manner.

Detection and/or quantification of peptide biomarkers may be performed by detection of the peptide biomarker or of a fragment thereof, e.g. a fragment with C-terminal truncation, or with N-terminal truncation. Fragments are suitably greater than 4 amino acids in length, for example 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 amino acids in length.

The biomarker may be directly detected, e.g. by SELDI or MALDI-TOF. Alternatively, the biomarker may be detected directly or indirectly via interaction with a ligand or ligands such as an antibody or a biomarker-binding fragment thereof, or other peptide, or ligand, e.g. aptamer, or oligonucleotide, capable of specifically binding the biomarker. The ligand may possess a detectable label, such as a luminescent, fluorescent or radioactive label, and/or an affinity tag.

For example, detecting and/or quantifying can be performed by one or more method(s) selected from the group consisting of: SELDI (-TOF), MALDI (- TOF), a 1-D gel-based analysis, a 2-D gel-based analysis, Mass spec (MS), reverse phase (RP) LC, size permeation (gel filtration), ion exchange, affinity, HPLC, UPLC and other LC or LC MS-based techniques. Appropriate LC MS techniques include ICAT® (Applied Biosystems, CA, USA), or iTRAQ® (Applied Biosystems, CA, USA). Liquid chromatography (e.g. high pressure liquid chromatography (HPLC) or low pressure liquid chromatography (LPLC)), thin- layer chromatography, NMR (nuclear magnetic resonance) spectroscopy could also be used.

Methods of diagnosing or monitoring according to this disclosure may comprise analysing a sample of cerebrospinal fluid (CSF) by SELDI TOF or MALDI TOF to detect the presence or level of the peptide biomarker. These methods are also suitable for clinical screening, prognosis, monitoring the results of therapy, identifying patients most likely to respond to a particular therapeutic treatment, for drug screening and development, and identification of new targets for drug treatment. Detecting and/or quantifying the peptide biomarkers may be performed using an immunological method, involving an antibody, or a fragment thereof capable of specific binding to the peptide biomarker. Suitable immunological methods include sandwich immunoassays, such as sandwich ELISA, in which the detection of the peptide biomarkers is performed using two antibodies which recognize different epitopes on a peptide biomarker; radioimmunoassays (RIA), direct, indirect or competitive enzyme linked immunosorbent assays (ELISA), enzyme immu noassays (EIA), Fluorescence immunoassays (FIA), western blotting, immu noprecipitation and any particle-based immu noassay (e.g . using gold, silver, or latex particles, magnetic particles, or Q-dots). Immu nological methods may be performed, for example, in microtitre plate or strip format.

Immunological methods in accordance with this disclosure may be based, for example, on any of the following methods. Immu noprecipitation is the simplest immunoassay method ; this measu res the quantity of precipitate, which forms after the reagent antibody has incubated with the sample and reacted with the target antigen present therein to form an insolu ble aggregate. Immunoprecipitation reactions may be qualitative or quantitative.

In particle immu noassays, several antibodies are linked to the particle, and the particle is able to bind many antigen molecules simu ltaneously. This greatly accelerates the speed of the visible reaction . This allows rapid and sensitive detection of the biomarker.

In immunonephelometry, the interaction of an antibody and target antigen on the biomarker results in the formation of immune complexes that are too small to precipitate. However, these complexes will scatter incident light and this can be measured using a nephelometer. The antigen, i .e. biomarker, concentration can be determined within minutes of the reaction.

Radioimmunoassay (RIA) methods employ radioactive isotopes such as I¹²⁵ to label either the antigen or antibody. The isotope used emits gamma rays, which are usually measured following removal of unbound (free) radiolabel . The major advantages of RIA, compared with other immunoassays, are higher sensitivity, easy signal detection, and well-established, rapid assays. The major disadvantages are the health and safety risks posed by the use of radiation and the time and expense associated with maintaining a licensed radiation safety and disposal program. For this reason, RIA has been largely replaced in routine clinical laboratory practice by enzyme immu noassays. Enzyme (EIA) immunoassays were developed as an alternative to radioimmu noassays (RIA) . These methods use an enzyme to label either the antibody or target antigen . The sensitivity of EIA approaches that for RIA, without the danger posed by radioactive isotopes. One of the most widely used EIA methods for detection is the enzyme-linked immu nosorbent assay (ELISA) . ELISA methods may use two antibodies one of which is specific for the target antigen and the other of which is coupled to an enzyme, addition of the substrate for the enzyme resu lts in production of a chemiluminescent or fluorescent signal .

Fluorescent immunoassay (FIA) refers to immu noassays which utilize a fluorescent label or an enzyme label which acts on the su bstrate to form a fluorescent product. Fluorescent measurements are inherently more sensitive than colorimetric (spectrophotometric) measurements. Therefore, FIA methods have greater analytical sensitivity than EIA methods, which employ absorbance (optical density) measurement.

Chem ilu minescent immunoassays utilize a chemilu minescent label, which produces light when excited by chemical energy; the emissions are measured using a light detector.

Immu nological methods according to this disclosure can thus be performed using well-known methods. Any direct (e.g . , using a sensor chip) or indirect procedure may be used in the detection of peptide biomarkers of this disclosu re.

The Biotin-Avidin or Biotin-Streptavidin systems are generic labelling systems that can be adapted for use in immu nological methods of this disclosure. One binding partner (hapten, antigen, ligand, aptamer, antibody, enzyme etc) is labelled with biotin and the other partner (su rface, e.g . well, bead, sensor etc) is labelled with avidin or streptavidin . This is conventional technology for immunoassays, gene probe assays and (bio)sensors, but is an indirect immobilisation route rather than a direct one. For example a biotinylated ligand (e.g . antibody or aptamer) specific for a peptide biomarker of this disclosu re may be im mobilised on an avidin or streptavidin surface, the immobilised ligand may then be exposed to a sample containing or suspected of containing the peptide biomarker in order to detect and/or quantify a peptide biomarker of this disclosure. Detection and/or quantification of the immobilised antigen may then be performed by an immunological method as described herein.

The term "antibody" as used herein includes, but is not limited to: polyclonal, monoclonal, bispecific, humanised or chimeric antibodies, single chain antibodies, Fab fragments and F(ab')₂ fragments, fragments produced by a Fab expression library, anti-idiotypic (anti-Id) antibodies and epitope-binding fragments of any of the above. The term "antibody" as used herein also refers to immunoglobulin molecules and immunologically-active portions of immunoglobulin molecules, i.e., molecules that contain an antigen binding site that specifically binds an antigen. The immunoglobulin molecules of this disclosure can be of any class (e. g., IgG, IgE, IgM, IgD and IgA) or subclass of immunoglobulin molecule.

The identification of key biomarkers specific to a disease is central to integration of diagnostic procedures and therapeutic regimes. Using predictive biomarkers appropriate diagnostic tools such as biosensors can be developed, accordingly, in methods and uses of this disclosure, detecting and quantifying can be performed using a biosensor, microanalytical system, microengineered system, microseparation system, immunochromatography system or other suitable analytical devices. The biosensor may incorporate an immunological method for detection of the biomarker(s), electrical, thermal, magnetic, optical (e.g. hologram) or acoustic technologies. Using such biosensors, it is possible to detect the target biomarker(s) at the anticipated concentrations found in biological samples.

Thus, according to a further aspect of this disclosure there is provided an apparatus for diagnosing or monitoring major depressive disorder which comprises a biosensor, microanalytical, microengineered, microseparation and/or immunochromatography system configured to detect and/or quantify any of the biomarkers defined herein. The biomarker(s) of this disclosure can be detected using a biosensor incorporating technologies based on "smart" holograms, or high frequency acoustic systems, such systems are particularly amenable to "bar code" or array configu rations.

In smart hologram sensors (Smart Holograms Ltd, Cambridge, UK), a holographic image is stored in a thin polymer film that is sensitised to react specifically with the biomarker. On exposure, the biomarker reacts with the polymer leading to an alteration in the image displayed by the hologram . The test resu lt read-out can be a change in the optical brightness, image, colou r and/or position of the image. For qualitative and sem i-quantitative applications, a sensor hologram can be read by eye, thus removing the need for detection equipment. A simple colou r sensor can be used to read the signal when quantitative measurements are required . Opacity or colou r of the sample does not interfere with operation of the sensor. The format of the sensor allows mu ltiplexing for simu ltaneous detection of several su bstances. Reversible and irreversible sensors can be designed to meet different requirements, and continuous monitoring of a particular biomarker of interest is feasible. Suitably, biosensors for detection of one or more biomarkers of this disclosure combine biomolecular recognition with appropriate means to convert detection of the presence, or quantitation, of the biomarker in the sample into a signal . Biosensors can be adapted for "alternate site" diagnostic testing, e.g . in the ward, outpatients' department, surgery, home, field and workplace.

Biosensors to detect one or more biomarkers of this disclosure include acoustic, plasmon resonance, holographic and microengineered sensors. Imprinted recognition elements, thin film transistor technology, magnetic acoustic resonator devices and other novel acousto-electrical systems may be employed in biosensors for detection of the one or more biomarkers of this disclosure.

Methods involving detection and/or quantification of one or more peptide biomarkers of this disclosu re can be performed on bench-top instruments, or can be incorporated onto disposable, diagnostic or monitoring platforms that can be used in a non-laboratory environment, e.g . in the physician's office or at the patient's bedside. Suitable biosensors for performing methods of this disclosure include "credit" cards with optical or acoustic readers. Biosensors can be configured to allow the data collected to be electronically transmitted to the physician for interpretation and thus can form the basis for e-neuromedicine.

Any suitable animal may be used as a subject non-human animal, for example a non-human primate, horse, cow, pig, goat, sheep, dog, cat, fish, rodent, e.g. guinea pig, rat or mouse; insect (e.g. Drosophila), amphibian (e.g. Xenopus) or C. elegans.

The test substance can be a known chemical or pharmaceutical substance, such as, but not limited to, an anti-depressive disorder therapeutic; or the test substance can be novel synthetic or natural chemical entity, or a combination of two or more of the aforesaid substances.

There is provided a method of identifying a substance capable of promoting or suppressing the generation of the peptide biomarker in a subject, comprising exposing a test cell to a test substance and monitoring the level of the peptide biomarker within said test cell, or secreted by said test cell.

The test cell could be prokaryotic, however a eukaryotic cell will suitably be employed in cell-based testing methods. Suitably, the eukaryotic cell is a yeast cell, insect cell, Drosophila cell, amphibian cell (e.g. from Xenopus), C. elegans cell or is a cell of human, non-human primate, equine, bovine, porcine, caprine, ovine, canine, feline, piscine, rodent or murine origin.

In methods for identifying substances of potential therapeutic use, non-human animals or cells can be used that are capable of expressing the peptide. Screening methods also encompass a method of identifying a ligand capable of binding to the peptide biomarker according to this disclosure, comprising incubating a test substance in the presence of the peptide biomarker in conditions appropriate for binding, and detecting and/or quantifying binding of the peptide to said test substance. High-throughput screening technologies based on the biomarker, uses and methods of this disclosure, e.g. configured in an array format, are suitable to monitor biomarker signatures for the identification of potentially useful therapeutic compounds, e.g. ligands such as natural compounds, synthetic chemical compounds (e.g. from combinatorial libraries), peptides, monoclonal or polyclonal antibodies or fragments thereof, which may be capable of binding the biomarker.

Methods of this disclosure can be performed in array format, e.g. on a chip, or as a multiwell array. Methods can be adapted into platforms for single tests, or multiple identical or multiple non-identical tests, and can be performed in high throughput format. Methods of this disclosure may comprise performing one or more additional, different tests to confirm or exclude diagnosis, and/or to further characterise a condition.

This disclosure further provides a substance, e.g. a ligand, identified or identifiable by an identification or screening method or use of this disclosure. Such substances may be capable of inhibiting, directly or indirectly, the activity of the peptide biomarker, or of suppressing generation of the peptide biomarker. The term "substances" includes substances that do not directly bind the peptide biomarker and directly modulate a function, but instead indirectly modulate a function of the peptide biomarker. Ligands are also included in the term substances; ligands of this disclosure (e.g. a natural or synthetic chemical compound, peptide, aptamer, oligonucleotide, antibody or antibody fragment) are capable of binding, suitably specific binding, to the peptide.

This disclosure further provides a substance according to this disclosure for use in the treatment of major depressive disorder, or predisposition thereto. Also provided is the use of a substance according to this disclosure in the treatment of major depressive disorder, or predisposition thereto.

Also provided is the use of a substance according to this disclosure as a medicament. Yet further provided is the use of a substance according to this disclosure in the manufacture of a medicament for the treatment of major depressive disorder, or predisposition thereto. A kit for diagnosing or monitoring major depressive disorder, or predisposition thereto is provided. Suitably a kit according to this disclosure may contain one or more components selected from the group: a ligand specific for the peptide biomarker or a structural/shape mimic of the peptide biomarker, one or more controls, one or more reagents and one or more consumables; optionally together with instructions for use of the kit in accordance with any of the methods defined herein.

The identification of biomarkers for major depressive disorder permits integration of diagnostic procedures and therapeutic regimes. Currently there are significant delays in determining effective treatment and hitherto it has not been possible to perform rapid assessment of drug response. Traditionally, many anti-depressant therapies have required treatment trials lasting weeks to months for a given therapeutic approach. Detection of a peptide biomarker of this disclosure can be used to screen subjects prior to their participation in clinical trials. The biomarkers provide the means to indicate therapeutic response, failure to respond, unfavourable side-effect profile, degree of medication compliance and achievement of adequate serum drug levels. The biomarkers may be used to provide warning of adverse drug response. Biomarkers are useful in development of personalized brain therapies, as assessment of response can be used to fine-tune dosage, minimise the number of prescribed medications, reduce the delay in attaining effective therapy and avoid adverse drug reactions. Thus by monitoring a biomarker of this disclosure, patient care can be tailored precisely to match the needs determined by the disorder and the pharmacogenomic profile of the patient, the biomarker can thus be used to titrate the optimal dose, predict a positive therapeutic response and identify those patients at high risk of severe side effects.

Biomarker-based tests provide a first line assessment of 'new' patients, and provide objective measures for accurate and rapid diagnosis, in a time frame and with precision, not achievable using the current subjective measures. Furthermore, diagnostic biomarker tests are useful to identify family members or patients at high risk of developing major depressive disorder. This permits initiation of appropriate therapy, or preventive measures, e.g. managing risk factors. These approaches are recognised to improve outcome and may prevent overt onset of the disorder.

Biomarker monitoring methods, biosensors and kits are also vital as patient monitoring tools, to enable the physician to determi ne whether relapse is due to worsening of the disorder, poor patient compliance or substance abuse. If pharmacological treatment is assessed to be inadequate, then therapy can be reinstated or increased ; a change in therapy can be given if appropriate. As the biomarkers are sensitive to the state of the disorder, they provide an indication of the impact of drug therapy or of substance abuse.

In a final aspect of this disclosure, methods of diagnosing MDD by detecting the binding of an antigen to a specific antibody. In an general embodiment of this final aspect, this disclosure provides a method of diagnosing an individual with MDD, comprisi ng :

(a) quantifying the amount of one or more antibody-antigen interactions in a biological sample from the individual ;

(b) comparing the amou nt of the one or more antibody-antigen interactions in the biological sample to one or more control samples; and

(c) diagnosing the individual based at least in part on a difference between the amount of the one or more antibody-antigen interactions between the biological sample and the one or more control samples.

In this embodiment, detection of an interaction between a specific antibody and an antigen is sufficient to diagnose the individual - identification of the antigen is not necessary. Control samples may be biological samples from healthy control subjects. Control samples may also be samples from i ndividuals not suffering from M DD. In one embodiment, the method of diagnosing comprises quantifying the amount of an antibody-antigen interaction between an antigen and a specific antibody disclosed in Jozsi et. al., Anti-factor H Autoantibodies Block C-terminal Recognition Function of Factor H in Hemolytic Uremic Syndrome, 110 BLOOD 1516, 1517 (2007), which is hereby incorporated by reference in its entirety. In a related embodiment, the method of diagnosing comprises quantifying the amount of an a antibody-antigen interaction between an antigen and the C18 antibody of Jozsi et al. All of the methods and uses described herein contemplate that an individual will be treated for MDD after diagnosis, or that the individual's treatment will be modified based at least in part on the level of analyte in the sample from the individual. Reference Standards for Treatment

In many embodiments, the levels of one or more analyte biomarkers or the levels of a specific panel of analyte biomarkers in a sample are compared to a reference standard ("reference standard" or "reference level") in order to direct treatment decisions. The reference standard used for any embodiment disclosed herein may comprise average, mean, or median levels of the one or more analyte biomarkers or the levels of the specific panel of analyte biomarkers in a control population. The reference standard may additionally comprise cutoff values or any other statistical attribute of the control population, such as a standard deviations from the mean levels of the one or more analyte biomarkers or the levels of the specific panel of analyte biomarkers.

In some embodiments, comparing the level of the one or more analyte biomarkers is performed using a cutoff value. In related embodiments, if the level of the one or more analyte biomarkers is greater than the cutoff value, the individual may be diagnosed as having, or being at risk of developing depression. In other distinct embodiments, if the level of the one or more analyte biomarkers is less than the cutoff value, the individual may be diagnosed as having, or being at risk of developing depression. Cutoff values may be determined by statistical analysis of the control population to determine which levels represent a high likelihood that an individual does or does not belong to the control population. In some embodiments, comparing the level of the one or more analyte biomarkers is performed using other statistical methods. In related embodiments, comparing comprises logistic or linear regression. In other embodiments, comparing comprises computing an odds ratio.

In some embodiments, the control population may comprise healthy individuals, or individuals with depression. In some embodiments, individuals with levels of one or more analyte biomarkers or levels of a specific panel of analyte biomarkers greater than the reference levels would be more likely to have depression. Therefore, an individual presenting with levels of the one or more analyte biomarkers or levels of the specific panel of analyte biomarkers greater than the reference standard would be a candidate for treatment with antidepressant therapy, or with more aggressive therapy. On the other hand, an individual presenting with levels of the one or more analyte biomarkers or levels of the specific panel of analyte biomarkers less than or equal to the reference standard would be less likely to have depression and therefore be a candidate for no antidepressant therapy, delayed antidepressant therapy or less aggressive antidepressant therapy.

In other embodiments, individuals with levels of one or more analyte biomarkers or levels of a specific panel of analyte biomarkers less than the reference levels would be more likely to have depression. Therefore, an individual presenting with levels of the one or more analyte biomarkers or levels of the specific panel of analyte biomarkers less than the reference standard would be a candidate for treatment with antidepressant therapy, or with more aggressive therapy. On the other hand, an individual presenting with levels of the one or more analyte biomarkers or levels of the specific panel of analyte biomarkers greater than or equal to the reference standard would be less likely to have depression and therefore be a candidate for no antidepressant therapy, delayed antidepressant therapy or less aggressive antidepressant therapy.

Reference Therapy for Treatment In some embodiments, a patient is treated more or less aggressively than a reference therapy. A reference therapy is any therapy that is the standard of care for depression. The standard of care can vary temporally and geographically, and a skilled person can easily determine the appropriate standard of care by consulting the relevant medical literature.

In some embodiments, based on a determination that levels of a panel of biomarkers is a) greater than, b) less than, c) equal to, d) greater than or equal to, or e) less than or equal to a reference standard, treatment will be either 1) more aggressive, or 2) less aggressive than a standard therapy.

In some embodiments, a more aggressive therapy than the standard therapy comprises beginning treatment earlier than in the standard therapy. In some embodiments, a more aggressive therapy than the standard therapy comprises administering additional treatments than in the standard therapy. In some embodiments, a more aggressive therapy than the standard therapy comprises treating on an accelerated schedule compared to the standard therapy. In some embodiments, a more aggressive therapy than the standard therapy comprises administering additional treatments not called for in the standard therapy. In some embodiments, a less aggressive therapy than the standard therapy comprises delaying treatment relative to the standard therapy. In some embodiments, a less aggressive therapy than the standard therapy comprises administering less treatment than in the standard therapy. In some embodiments, a less aggressive therapy than the standard therapy comprises administering treatment on a decelerated schedule compared to the standard therapy. In some embodiments, a less aggressive therapy than the standard therapy comprises administering no treatment.

Treatment of Depression

Health practitioners treat depression by taking actions to ameliorate the causes or symptoms of the disorder in a patient. Treatment may comprise drug-based or non-drug-based therapies. Drug-based therapies may include: selecting and administering one or more antidepressant drugs to the patient, adjusting the dosage of an antidepressant drug, adjusting the dosing schedu le of an antidepressant drug, and adjusting the length of the therapy with an antidepressant drug . Antidepressant drugs are selected by practitioners based on the natu re of the symptoms and the patient's response to any previous treatments. The dosage of an antidepressant drug can be adjusted as well by the practitioner based on the nature of the drug, the natu re of the patient's symptoms, the patient's response to previous treatment, and the patient's response to the drug . The dosing schedu le can also be adjusted by the practitioner based on the nature of the drug, the nature of the patient's symptoms, the patient's response to previous treatment, and the patient's response to the drug . Also, the length of the therapy can be adjusted by the practitioner based on the nature of the drug, the nature of the patient's symptoms, the patient's response to previous treatment, and the patient's response to the drug . Additionally, the practitioner can select between a single drug therapy, a dual drug therapy, or a triple drug therapy. In some embodiments, a practitioner may optionally treat the patient with a com bination of one or more antidepressant drugs and one or more non-drug-based therapies. In one embodiment, the practitioner begins antidepressant therapy based on a comparison between a reference level and the levels of one or more analyte biomarkers or the levels of a specific panel of analyte biomarkers in a sample from a patient. In one embodiment, therapy comprises the selection and administration of an antidepressant drug to the patient by the practitioner. In another embodiment, therapy comprises the selection and administration of two antidepressant drugs to the patient by the practitioner as part of dual therapy. In another embodiment, therapy comprises the selection and administration of three antidepressant drugs to the patient by the practitioner as part of triple therapy.

Antidepressant drugs are commonly used by medical practitioners, and a skilled person may identify the appropriate antidepressant drug to administer based on the medical literature. In some embodiments, treatment comprises administering to an individual a selective serotonin reuptake inhibitor ("SSRI") . In some embodiments, the SSRI is citalopram. In some embodiments, the SSRI is escitalopram . In some embodiments, the SSRI is fluoxetine. In some embodiments, the SSRI is paroxetine. In some embodiments, the SSRI is sertraline. In other embodiments, treatment com prises administering to an individual a serotonin-norepinephrine reuptake inhibitors ("SNRI") . In some embodiments, the SNRI is venlafaxine. In other embodiments, the SNRI is duloxetine.

In other embodiments, treatment comprises administering to an individual a norepinephrine and dopamine reuptake inhibitor ("NDRI"). In one embodiment, the N DRI is bupropion .

In other embodiments, treatment comprises administering to an individual a tetracyclic antidepressant ("tetracyclic") . In some embodiments, the tetracyclic is amoxapine. In some embodiments, the tetracyclic is maprotiline. In some embodiments, the tetracyclic is mazindol . In some embodiments, the tetracyclic is mirtazapine.

In other embodiments, treatment comprises administering to an individual a tricyclic antidepressant ("tricyclic"). In some embodiments, the tricyclic is amitriptyline. In some embodiments, the tricyclic is imipramine. In some embodiments, the tricyclic is nortriptyline.

In other embodiments, treatment comprises administering to an individual a monoamine oxidase inhibitor ("MAOI"). In some embodiments, the MAOI is selegiline. In some embodiments, the MAOI is isocarboxazid. In some embodiments, the MAOI is phenelzine. In some embodiments, the MAOI is tranylcypromine. In addition to or in lieu of drug-based therapies, in some embodiments a practitioner may also treat an individual with non-drug-based antidepressant therapies. In some embodiments, the non-drug based therapy comprises cognitive-behavioral therapy. In some embodiments, the non-drug based therapy comprises psychotherapy. In a related embodiment, the non-drug based therapy comprises psychodynamic therapy. In some embodiments, the non-drug based therapy comprises electroconvulsive therapy. In some embodiments, the non-drug based therapy comprises hospitalization and residential treatment programs. In some embodiments, the non-drug based therapy comprises vagus nerve stimulation. In some embodiments, the non- drug based therapy comprises transcranial magnetic stimulation. In some embodiments, the non-drug based therapy comprises regular, vigorous exercise.

In one embodiment, the practitioner adjusts the antidepressant therapy based on a comparison between a reference level and the levels of one or more analyte biomarkers or the levels of a specific panel of analyte biomarkers in a sample from a patient. In one embodiment, the practitioner adjusts the therapy by selecting and administering a different drug . In one embodiment, the practitioner adjusts the therapy by selecting and administering a different combination of drugs. In one embodiment, the practitioner adjusts the therapy by adjusting drug dosage. In one embodiment, the practitioner adjusts the therapy by adjusting dose schedule. In one embodiment, the practitioner adjusts the therapy by adjusting length of therapy. In one embodiment, the practitioner adjusts the therapy by selecting and administering a different drug combination and adjusting drug dosage. In one embodiment, the practitioner adj usts the therapy by selecting and administering a different drug combination and adjusting dose schedule. In one embodiment, the practitioner adjusts the therapy by selecting and administering a different drug combination and adjusting length of therapy. In one embodiment, the practitioner adjusts the therapy by adjusting drug dosage and dose schedule. In one embodiment, the practitioner adjusts the therapy by adjusting drug dosage and adjusting length of therapy. In one embodiment, the practitioner adjusts the therapy by adjusting dose schedule and adjusting length of therapy. In one embodiment, the practitioner adjusts the therapy by selecting and administering a different drug, adjusting drug dosage, and adjusting dose schedule. In one embodiment, the practitioner adjusts the therapy by selecting and administering a different drug, adjusting drug dosage, and adjusting length of therapy. In one embodiment, the practitioner adjusts the therapy by selecting and administering a different drug, adjusting dose schedule, and adjusting length of therapy. In one embodiment, the practitioner adjusts the therapy by adjusting drug dosage, adjusting dose schedule, and adjusting length of therapy. In one embodiment, the practitioner adjusts the therapy by selecting and administering a different drug, adjusting drug dosage, adjusting dose schedu le, and adjusting length of therapy.

In some embodiments, treatment comprises a less aggressive therapy than a reference therapy. In one embodiment a less aggressive therapy comprises not administering drugs and taking a "watchful waiting" approach . In one embodiment a less aggressive therapy comprises delaying treatment. In one embodiment a less aggressive therapy comprises selecting and administering less potent drugs. In one embodiment a less aggressive therapy comprises decreasing dosage of antidepressant drugs. In one embodiment a less aggressive therapy comprises decreasing the frequency treatment. In one embodiment a less aggressive therapy comprises shortening length of therapy. In one embodiment, less aggressive therapy comprises selecting and administering less potent drugs and decreasing drug dosage. In one embodiment, less aggressive therapy comprises selecting and administering less potent drugs and decelerating dose schedule. In one embodiment, less aggressive therapy comprises selecting and administering less potent drugs and shortening length of therapy. In one embodiment, less aggressive therapy comprises decreasing drug dosage and decelerating dose schedule. In one embodiment, less aggressive therapy comprises decreasing drug dosage and shortening length of therapy. In one embodiment, less aggressive therapy comprises decelerating dose schedule and shortening length of therapy. In one embodiment, less aggressive therapy comprises selecting and administering less potent drugs, decreasing drug dosage, and decelerating dose schedule. In one embodiment, less aggressive therapy comprises selecting and administering less potent drugs, decreasing drug dosage, and shortening length of therapy. In one embodiment, less aggressive therapy comprises selecting and administering less potent drugs, decelerating dose schedu le, and shortening length of therapy. In one embodiment, less aggressive therapy comprises decreasing drug dosage, decelerating dose schedule, and shortening length of therapy. In one embodiment, less aggressive therapy comprises selecting and administering less potent drugs, decreasing drug dosage, decelerating dose schedule, and shortening length of therapy. In some embodiments, a less aggressive therapy comprises administering only non-drug-based therapies. In another aspect of the present application, treatment comprises a more aggressive therapy than a reference therapy. In one embodiment a more aggressive therapy comprises earlier administration of antidepressant drugs. In one embodiment a more aggressive therapy comprises increased dosage of antidepressant drugs. In one embodiment a more aggressive therapy comprises increased length of therapy. In one embodiment a more aggressive therapy comprises increased frequency of the dose schedu le. In one embodiment, more aggressive therapy comprises selecting and administering more potent drugs and increasing drug dosage. In one embodiment, more aggressive therapy comprises selecting and administering more potent drugs and accelerating dose schedule. In one embodiment, more aggressive therapy comprises selecting and administering more potent drugs and increasing length of therapy. In one embodiment, more aggressive therapy comprises increasing drug dosage and accelerating dose schedu le. In one embodiment, more aggressive therapy comprises increasing drug dosage and increasing length of therapy. In one embodiment, more aggressive therapy comprises accelerating dose schedule and increasing length of therapy. In one embodiment, more aggressive therapy comprises selecting and administering more potent drugs, increasing drug dosage, and accelerating dose schedule. In one embodiment, more aggressive therapy comprises selecting and ad ministering more potent drugs, increasing drug dosage, and increasing length of therapy. In one embodiment, more aggressive therapy com prises selecting and administering more potent drugs, accelerating dose schedu le, and increasing length of therapy. In one embodiment, more aggressive therapy comprises increasing drug dosage, accelerating dose schedu le, and increasing length of therapy. In one embodiment, more aggressive therapy comprises selecting and administering more potent drugs, increasing drug dosage, accelerating dose schedule, and increasing length of therapy. In some embodiments, a more aggressive therapy comprises administering a combination of drug-based and non-drug-based therapies.

Systems for Diagnosing and Treating Depression

The results of any analyses according to the invention will often be communicated to physicians and/or patients (or other interested parties such as researchers) in a transmittable form that can be communicated or transmitted to any of the above parties. Such a form can vary and can be tangible or intangible. The results can be embodied in descriptive statements, diagrams, photographs, charts, images or any other visual forms. The statements and visual forms can be recorded on a tangible medium such as papers, computer readable media such as hard disks, compact disks, etc., or on an intangible medium, e.g., an electronic medium in the form of email or website on internet or intranet. In addition, results can also be recorded in a sound form and transmitted through any suitable medium, e.g., analog or digital cable lines, fiber optic cables, etc., via telephone, facsimile, wireless mobile phone, internet phone and the like.

Thus, the information and data on a test result can be produced anywhere in the world and transmitted to a different location. As an illustrative example, when an assay is conducted outside the United States, the information and data on a test result may be generated, cast in a transmittable form as described above, and then imported into the United States. Accordingly, the present invention also encompasses a method for producing a transmittable form of information on levels of one or more analyte biomarkers or levels of a specific panel of analyte biomarkers for at least one patient sample. The method comprises the steps of ( 1) determining levels of one or more analyte biomarkers or levels of a specific panel of analyte biomarkers for at least one patient sample according to methods of the present invention; and (2) embodying the result of the determining step in a transmittable form. The transmittable form is the product of such a method.

Techniques for analyzing levels of one or more analyte biomarkers or levels of a specific panel of analyte biomarkers for at least one patient sample will often be implemented using hardware, software or a combination thereof in one or more computer systems or other processing systems capable of effectuating such analysis.

Thus, the present invention further provides a system for determining whether an individual suffers from depression, comprising : (1) a sample analyzer for determining the levels of one or more analyte biomarkers or levels of a specific panel of analyte biomarkers for at least one patient sample, wherein the sample analyzer contains the patient sample; (2) a first computer program for (a) receiving data regarding the levels of one or more analyte biomarkers or the levels of a specific panel of analyte biomarkers; and optionally (3) a second computer program for comparing the test value to one or more reference standards each associated with a predetermined degree of risk of depression.

The sample analyzer can be any instruments useful in determining the levels of biomarkers in a sample, as described herein.

The computer-based analysis function can be implemented in any suitable language and/or browsers. For example, it may be implemented with C language and preferably using object-oriented high-level programming languages such as Visual Basic, SmallTalk, C++, and the like. The application can be written to suit environments such as the Microsoft WindowsTM environment including WindowsTM 98, WindowsTM 2000, WindowsTM NT, and the like. In addition, the application can also be written for the MacIntoshTM, SUNTM, UNIX or LINUX environment. In addition, the functional steps can also be implemented using a universal or platform-independent programming language. Examples of such multi-platform programming languages include, but are not limited to, hypertext markup language (HTML), JAVATM, JavaScriptTM, Flash programming language, common gateway interface/structured query language (CGI/SQL), practical extraction report language (PERL), AppleScriptTM and other system script languages, programming language/structured query language (PL/SQL), and the like. JavaTM- or JavaScriptTM-enabled browsers such as HotJavaTM, MicrosoftTM ExplorerTM, or NetscapeTM can be used. When active content web pages are used, they may include JavaTM applets or ActiveXTM controls or other active content technologies. The analysis function can also be embodied in computer program products and used in the systems described above or other computer- or internet-based systems. Accordingly, another aspect of the present invention relates to a computer program product comprising a computer-usable medium having computer-readable program codes or instructions embodied thereon for enabling a processor to carry out disease risk analysis. These computer program instructions may be loaded onto a computer or other programmable apparatus to produce a machine, such that the instructions which execute on the computer or other programmable apparatus create means for implementing the functions or steps described above. These computer program instructions may also be stored in a computer-readable memory or medium that can direct a computer or other programmable apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory or medium produce an article of manufacture including instructions which implement the analysis. The computer program instructions may also be loaded onto a computer or other programmable apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions or steps described above.

Thus one aspect of the present invention provides a system for determining whether a patient has depression. Generally speaking, the system comprises (1) computer program for receiving, storing, and/or retrieving data regarding levels of biomarkers in a patient's sample and optionally clinical parameter data (e.g., disease-related symptoms); (2) computer program for querying this patient data; (3) computer program for concluding whether an individual suffers from depression based on this patient data; and optionally (4) computer program for outputting/displaying this conclusion. In some embodiments this computer program for outputting the conclusion may comprise a computer program for informing a health care professional of the conclusion.

The practice of the present invention may also employ conventional biology methods, software and systems. Computer software products of the invention typically include computer readable media having computer-executable Instructions for performing the logic steps of the method of the invention. Suitable computer readable medium include floppy disk, CD-ROM/DVD/DVD- ROM, hard-disk drive, flash memory, ROM/RAM, magnetic tapes and etc. Basic computational biology methods are described in, for example, Setubal et al., INTRODUCTION TO COMPUTATIONAL BIOLOGY METHODS (PWS Publishing Company, Boston, 1997); Salzberg et al. (Ed.), COMPUTATIONAL METHODS IN MOLECULAR BIOLOGY, (Elsevier, Amsterdam, 1998); Rashidi & Buehler, BIOINFORMATICS BASICS: APPLICATION IN BIOLOGICAL SCIENCE AND MEDICINE (CRC Press, London, 2000); and Ouelette & Bzevanis, Attorney Docket No. 3330-01- lP Page 38 of 64 BIOINFORMATICS : A PRACTICAL GUIDE FOR ANALYSIS OF GENE AND PROTEINS (Wiley & Sons, Inc., 2nd ed., 2001); see also, U.S. Pat. No. 6,420,108.

The following Examples further illustrate the disclosed invention. EXAMPLE 1

METHODOLOGY

Patients

The design of the study has been reported in detail previously (Tadic A et al, Prog Neuropsychopharmacol Biol Psychiatry 2010; epub ahead of print 20 August 2010; doi : 10.1016/j. pnpbp.2010.08.011). In brief, men and women subsequently hospitalised at the Department of Psychiatry and Psychotherapy at the University Medical Center Mainz (UMCM) for the treatment of MDD, participated in the present study. After a complete oral and written description of the study, all patients gave their written informed consent. The study was approved by the local ethics committee of the Landesarztekammer Rheinland- Pfalz and is compliant with the Code of Ethics of the World Medical Association (Declaration of Helsinki). In order to investigate a patient sample that is highly representative for inpatients with MDD, broad in- and exclusion criteria were used.

Inclusion criteria were:

i) Major Depressive Episode according to the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV) (American Psychiatric Association. Diagnostic and Statistical Manual of Mental disorders. 4th Edition Text Revision : DSM-IV-TR (2000). ISBN : 0890420256);

ii) age between 18-65 years and ≤ 60 years at the beginning of the first depressive episode;

iii) no treatment with an antidepressant medication or insufficient treatment response to an eventually existing antidepressant pre-medication (treatment duration > 14 days); thus, all patients were about to be commenced or changed an antidepressant pharmacotherapy at the time of inclusion;

iv) written informed consent to study participation.

Exclusion criteria were:

i) lifetime diagnosis of dementia, schizophrenia, schizoaffective disorder, or bipolar disorder according to DSM-IV;

ii) current diagnosis of alcohol dependency (DSM-IV) requiring acute detoxification;

iii) depression due to organic factors;

iv) pregnancy or breast-feeding;

v) cognitive impairment, which precludes a correct psychometric assessment.

Diagnosis of MDD according to DSM-IV criteria was ascertained by at least one specialist in psychiatry One of two research assistants, who had been trained in several trainings according to standard procedures established for clinical trials prior to the start of the study, additionally applied the German Version of the MINI International Neuropsychiatric Interview (Shehaan DV, Lecrubier Y: M .I. N .I. International Neuropsychiatric Interview. USA Tampa, 1998). The antidepressant medication during the study period followed the doctor's choice (see results section). Depression severity was assessed by the HAMD (Szegedi A et al J Clin Psychiatry 2003; 64: 413-420) from baseline to max. day 42 in weekly intervals, which was applied by one of two trained (Wagner S et al Psychopathology 2011 ; 44: 68-70; Muller MJ, Dragicevic A. J Affect Disord 2003; 77: 65-69) and supervised research assistants.

Immunoassay Profiling

For serum sampling blood was obtained in a serum separator tube from the antecubital vein between 08.00 and 11.00 am from baseline to max. day 42 in weekly intervals. After 30 minutes of clotting time, whole blood was centrifuged at 1000 x g for 15 minutes to isolate serum. Serum was collected and kept at - 80°C until proteomic profiling. For this study, a total of 160 from 190 analytes were analysed in 120 serum samples from 41 male and female MDD patients (baseline, day 7 and day 14 for each patient; material for day 7 and day 14 was missing for two and one patients, respectively) in a Clinical Laboratory Improved Amendments (CLIA)-certified laboratory at Rules Based Medicine Inc. (Austin, TX, USA) with the Multi Analyte Profiling Human Discovery MAP vl .O, which is a quantitative, multiplexed immunoassay based on Luminex xMAP technology. This platform measures a battery of analytes including chemokines, cytokines, hormones, growth factors, antigens and other protein markers and has already been applied successfully in clinical studies (Domenici E et al PLoS ONE 2010, 5: e9166; Schwarz E et al, Biomark Insights 2010 5: 39-47; Schwarz E et al, Mol Psychiatry 2010, epub ahead of print 28 September 2010, doi : 10.1038/mp.2010.102; Bertenshaw GP et al, Cancer Epidemiol Biomarkers Prev 2008; 17: 2872-2881). Analytes were measured in multiplexes in 25-50 ml of serum samples. The assays were calibrated using standard curves and raw intensity measurements converted to protein concentrations using proprietary software. Analyses were conducted under blind conditions with respect to sample identities and the samples were analyzed randomly to avoid any sequential bias because of the presence or absence of diagnosis, participant age, or age of material. Statistical Analysis

Due to the naturalistic setting of the study, there were changes in antidepressant treatment (i.e., a medication switch or the initiation of an antidepressant combination following monotherapy) during the study period in some patients. An analysis of the relation between early molecular changes and final treatment outcome in a sample with medication changes during the study period has to recognize the risk to investigate the relation between an early molecular change under treatment A with a final treatment outcome, which however has been reached after the initiation of treatment B. Therefore, data analysis was performed on those data, which were obtained from baseline (BL) until the last visit before a treatment change (=endpoint, EP). Patients with observed data at BL, day 7 and at least one data between day 21 and 42 were eligible for analysis. Statistical analyses were performed exclusively on observed clinical data. Differences between men and women in baseline clinical and demographic data were analysed by t-tests for independent variables or Chi²-tests, when required. First, differences in mean biomarker changes from BL-d7 and BL-14, respectively, between responders and non-responders were analysed by ANCOVA with response at EP as independent variable, the relative change of biomarkers from BL-d7 and BL-d l4, respectively, as dependent variables. Potential covariates were included in ANCOVA according to the results of correlation analysis between the relative molecular change (BL-d7 and BL-d l4, respectively, for each marker) and the covariates age, body mass index (BMI), duration of current episode, antidepressant class and "medication in therapeutic range". Second, logistic regression analysis was conducted in order to identify the percentage of variance of response (defined as HAMD-21 sum score decrease from baseline to endpoint >50%) explained by early molecular changes, i.e. from BL-d7 and BL-d l4, respectively. Third, ROC analyses were performed to calculate the area under the curve (ROC-AUC) and to determinate the most appropriate cut-off for the predictive value of a molecular change from BL-d7 and BL-d l4 on treatment response at EP. This calculated most appropriate cut-off value for a molecular change disentangling final responders and non- responders was compared with intra- and inter-assay CV of each marker; in order to minimize the risk of false-positive predictions, the highest among the three values was used as threshold for the determination whether a marker increased or decreased after the initiation of antidepressant treatment in the individual patient; after determination of increase/decrease for each marker and individual patient, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of an early molecular change for final outcome as well as Odds ratios (ORs) were all calculated. Significance was set at p≤0.050. P-values are stated without adjustment for multiple hypothesis correction as the necessity of this procedure remains controversial (Rothman KJ. Epidemiology 1990; 1 : 43-6; Perneger TV. Br Med J 1998; 316: 1236-8). All analyses were done using Predictive Analysis Software (PASW) 18.0.

RESULTS

The ITT sample consisted of 46 patients; five patients had to be excluded from analysis because of missing data, resulting in 41 (49% men, 51% women) eligible subjects. Of those, 24% had the endpoint (EP) at day (d) 21, 32% at d28, 12% at d35 and 32% at d42. Men and women did not differ significantly in demographical and clinical characteristics (see Table 1).

Table 1 : Mean (± SD) baseline demographical and clinical characteristics of men and women

^-test for independent variables; ²Chi² tests;

³ Medication during the study period consisted of: escitalopram (10-20 mg/d), sertraline (50-150 mg/d), fluoxetine (20 mg/d), venlafaxine (150-375 mg/d), duloxetine (90-120 mg/d), mirtazapine (30-45 mg/d), tranylcypromine (30 mg/d), amitriptyline (225 mg/d), clomipramine (150 mg/d), trimipramine (100 mg/d).

Abbreviations: SSRI: Selective Serotonin Reuptake inhibitor, SSNRI: Selective Serotonin Noradrenalin Reuptake inhibitor; TCA: tricyclic antidepressants.

Men: Age and BMI correlated with most molecular changes, whereas the study medication, the duration of the current episode and the factor "medication in therapeutic range" were associated only with some single molecular markers. ANCOVA led to the identification of 21 out of 160 biomarkers showing a significantly different molecular change in the early treatment course between final responders and non-responders either at d7, day 14 or at both time points. Regression analysis revealed that 19 of these 21 molecules explained at least 25.5% (interleukin-8) and up to 74.1% (TIMP-1) of the variance of response at EP (p< 0.05 for each analysis). A detailed analysis of the molecular changes revealed that 16 markers displayed a decrease, and three molecules increased at an early stage in final responders (see Table 2).

The ROC-AUC for the prediction of a relative molecular change from BL-d7 and BL-dl4, respectively, on response at EP ranged for single markers between 74.2-94.5%. Molecular changes from BL-d7 or BL-dl4 predicted final response with 25-86% sensitivity and 38-100% specificity. The PPV ranged from 43- 100%, the NPV from 67-92%. The ORs ranged between 2-33 or were even not assignable because of 100% specificity of some markers (see Table 3 and Figure 1 for a graphical overview of the sensitivity-specificity profiles of each marker).

Women: Like in men, age and BMI were correlated with several molecular changes, whereas the other potential covariates were correlated with few molecular changes. ANCOVA revealed that 33 serum markers had significantly different changes between final responders and non-responders in the early treatment course. Of those, 23 markers explained a relevant percentage (R² > 25.9%; p < 0.05 for each analysis) of the variance of final response. Of those, 16 markers decreased, and 7 increased at an early stage in final responders (see Table 4). The ROC-AUC for the prediction of a molecular change from BL-d7 or BL-d l4 on response at endpoint ranged between 70-87.5%. In three markers (Clusterin, TNFR-II, VKDPS), the intra- or inter-assay CV exceeded the calculated most appropriate cut-off, making it impossible to reliably detect a molecular signal in these markers. For the remaining 20 markers, a molecular change from BL-7 or BL-d l4 predicted final response with 40- 100% sensitivity and 69-100% specificity. The PPV across markers ranged between 29- 100%, the NPV between 79- 100%. The ORs ranged from 1.47-28 or were even not assignable because of 100% specificity of some markers, (see Table 5 as well as Figure 2 for a graphical overview of the sensitivity-specificity profiles of each marker).

In summary, the enclosed analysis resulted in the identification of 19 biomarkers in men which reflected the onset of antidepressant action in the first 7 or 14 days, validated by their predictive power for final response to antidepressant treatment in patients with MDD. Surprisingly, the sole marker which overlapped between male and female subjects was Thymus-Expressed Chemokine (TECK) which was found to be significant in both sexes.

Table 2: Association between relative biomarker changes from baseline to day 7 and 14 on Response at endpoint in men with MDD

A) ANCOVA B) Regression analysis C) Molecular change duri interval [%; mean ± SD]

Serum Interval Main Age BMI Nagelkerke P value Responder Non- marker³ (days) Effect at EP Responde

R² (%) EP

Amphiregulin 0.037 0.137 0.697 50.5 0.002 ^■124 ± 203 +20 ± 3

Apo E 14 0.028 0.175 0.885 42.7 0.006 + 22 ± 18 ^■5 ± 28

Calcitonin 0.044 0.307 0.812 34.6 0.016 -81 ± 157 + 25 ± 4

CD 5L 14 0.008 0.538 0.057 41.4 0.007 -7 ± 11 +7 ± 1

CF H 14 0.027 0.781 0.176 30.3 0.026 -7 ± 10 + 5 ± 1

CK. MB 0.006 0.055 0.166 49.9 0.003 -35 ± 65 + 24 ± 2

CK.MB 14 0.046 0.227 0.259 27.3 0.036 -29 ± 59 + 17 ± 3

CRP 0.008 0.236 0.511 42.3 0.007 ^■148 ± 162 +4 ± 6

G-CSF 14 0.034 0.351 0.842 29.3 0.029 -63 ± 54 -4 ± 54

ICAM 14 0.004 0.178 0.553 61.5 0.001 -22 ± 30 + 13 ± 1

IL 5 14 0.007 0.422 0.900 45.1 0.005 ^■187 ± 148 -9 ± 98

Table 2 shows the results for those markers of the RBM Human Discovery MAP vl.O, which were significantly associated with treatment response in men in both the univariate and the regression analysis. Apart from the markers displayed in Table 2, the relative changes between BL and day 7 of the markers BTC and S100B were significantly associated with treatment response at EP in the univariate analysis, but not in the regression analysis (data not shown).

²ANCOVA: independent variable: relative biomarker change from baseline to day 7 or day 14; dependent variable: response at endpoint; covariates: age, BMI; other covariates (duration of current episode; study medication; medication in therapeutic range) were additionally included in case of a significant correlation between these variables and the relative molecular change in the early course of treatment.

³Logistic regression analyses: predictor: relative biomarker change from baseline to day 7/14; criterion : response at endpoint. - There are no symbols in the Table for 1-3

⁴Covariate "medication in therapeutic range" included in the ANCOVA (p=0.966).

Covariate "study medication" included in the ANCOVA (p=0.663)

Covariate "study medication" included in the ANCOVA (p=0.503)

Table 3: ROC-AUC, sensitivity, specificity, PPV, NPV and OR of relative biomarker changes from baseline to day 7 and 14 on response at endpoint in men with MDD

Abbreviations: ROC-AUC: Area under the curve; PPV: positive predictive value; NPV: negative predictive value; OR: Odds Ratio; n.a. : not assignable.

¹ In order to preclude false-negative predictions, the cut-off value used for the calculation of predictive values has been selected according to the highest figure comparing the most appropriate cut-off derived from the ROC analysis, the intra-assay coefficient of variation (CV) or the inter-assay CV.

Precision (intra- and inter-run) was determined by measuring 3 levels of control in duplicate over five runs and provides information concerning random error expected in a test result caused by person, instrument, and day variations. In table 3, the maximum intra- and inter-assay CV is presented.

³ In markers, where an increase is associated with treatment response, sensitivity means the proportion of responders who are correctly identified by an increase of the biomarker concentration from baseline to day 7/14 higher than the selected cut-off (which could be either the calculated cut-off, the intra- or the inter-assay CV). Specificity in this context means the proportion of non-responders who are correctly identified by a decrease of the biomarker (or an increase below the identified cut-off). PPV means the rate of patients with a biomarker increase above the selected cut-off value) who become responder * 100, and NPV means the rate of patients with a biomarker decrease (or increase below the selected cut-off value) who become non-responder * 100. In markers, where a decrease is associated with treatment response, sensitivity means the proportion of responders who are correctly identified by a decrease of the biomarker concentration from baseline to day 7/14, which is greater than the selected cut-off (e.g. cut-off= -15%; a greater decrease would be -20%). Specificity in this context means the proportion of non-responders who are correctly identified by an increase of the biomarkers (or a decrease smaller than the selected cut-off (e.g. cut-off value: = -15%; a smaller decrease would be -10%). PPV means the rate of patients with a biomarker decrease greater than the selected cut-off who become responder * 100, and NPV means the rate of patients with a biomarker increase (or decrease smaller than the selected cut-off value) who become non-responder* 100.

Table 4: ANCOVA of relative biomarker changes from baseline to day 7 and 14 on Response at endpoint in women with MDD

¹ Table 4 shows the results for markers of the RBM Human Discovery MAP, which were significantly associated with treatment response in women in both the univariate and the regression analysis. Apart form the markers displayed in table 3, the markers C_3, CD 5L, CgA, FAS, Haptoglobin, Thrombospondin, Thrombopoietin, TN-C at day 7 as well as the markers Amphiregulin, Complement Factor H Related Protein (CFHRP), CgA were significantly associated with treatment response at EP in the univariate analysis, but not in the regression analysis (data not shown).

⁴Covariate "study medication" included in the ANCOVA (p=0.885).

5Covariate "duration of current episode" included in the ANCOVA (p=0.985).

⁶Covariate "study medication" included in the ANCOVA (p=0.421).

Table 5: Sensitivity and Specificity of relative biomarker changes from baseline to day 7 and 14 on Response at endpoint in women with MDD

² Precision (intra- and inter-run) was determined by measuring 3 levels of control in duplicate over five runs and provides information concerning random error expected in a test result caused by person, instrument, and day variations. In table 3, the maximum intra- and inter-assay CV is presented.

³ In markers, where an increase is associated with treatment response, case of an increase of the biomarker concentration, sensitivity means the proportion of responders who are correctly identified by an increase of the biomarker concentration from baseline to day 7/14 higher than the selected cut-off (which could be either the calculated cut-off, the intra- or the inter-assay CV). Specificity in this context means the proportion of non-responders who are correctly identified by a decrease of the biomarker (or an increase below the identified cut-off). PPV means the rate of patients with a biomarker increase above the selected cut-off value) who become responder * 100, and NPV means the rate of patients with a biomarker decrease (or increase below the selected cut-off value) who become non-responder * 100. In markers, where a decrease is associated with treatment response, sensitivity means the proportion of responders who are correctly identified by a decrease of the biomarker concentration from baseline to day 7/14, which is greater than the selected cut-off (e.g. cut-off= -15%; a greater decrease would be -20%). Specificity in this context means the proportion of non-responders who are correctly identified by an increase of the biomarkers (or a decrease smaller than the selected cut-off (e.g. cut-off value: = -15%; a smaller decrease would be - 10%). PPV means the rate of patients with a biomarker decrease greater than the selected cut-off who become responder * 100, and NPV means the rate of patients with a biomarker increase (or decrease smaller than the selected cut-off value) who become non-responder*100.

⁴ Range of relative molecular change below the highest value of inter-/intra-assay CV: Clusterin : -26.22% - + 11.16%; TNFR 2: -32.59% - 44.34%; VKDPS : -13.62% - 15.4%.

EXAMPLE 2

BACKGROUND

We analyzed the RBM Human MAP data in the plasma to identify candidate sex-specific serum biomarkers for the onset of action of antidepressant drugs revealed in patients with Major Depressive Disorder.

The 21-item Hamilton-Depression Rating Scale (HAMD-21) score is the dependent variable for all the analyses presented here, specifically the change in HAMD from baseline to endpoint of antidepressant treatment. For the purpose of these analyses, HAMD is analyzed as a binary categorical variable. The binary categorical variable "response" is defined as a HAMD decrease from baseline to endpoint > 50%. The goal is to identify biomarkers predictive of the dichotomous treatment response.

DATA

The data set comprises 41 subjects (20 male, 21 female) who underwent treatment with antidepressant drugs. During the study, depression severity was measured in weekly intervals from baseline to week 6 with the 21-item Hamilton-Depression Rating Scale (HAMD-21); plasma markers were measured in weekly intervals from baseline to week two.

The analyte values were processed as follows.

1. Analytes with more than 30% of < LOW> values were not analyzed .

2. < LOW> values were replaced by half of the lowest observed value of that analyte.

3. Analyte values were log transformed.

4. Analyte values from step 3 were adjusted by regressing analyte ~ age + BMI. The residuals from this regression are the adjusted analyte values for subsequent analysis.

Data Analysis Methods

For the data analysis presented here, the dependent variable is the HAMD endpoint response (binary response).

The independent (predictor) variables used in the analyses are as follows. The values for the analytes are age and BMI adjusted and log transformed .

• analyte change from baseline to week 1

• analyte change from baseline to week 2

• baseline analyte level

• age

• BMI

• sex

• sex * analyte change interaction terms

• HAMD sum score at baseline

The sex * analyte interaction terms are included to identify biomarkers of response that differ between males and females.

The statistical methods used in the analysis are as follows.

Logistic Regression

Using the changes from baseline: weekl_change, week2_change. In the logistic regression analysis we performed with R, the p-value of the interaction sex*analyte_change was missing . Therefore, we transformed the log transformed as well as age and BMI adjusted value into SPSS and performed the logistic regression with SPSS.

Formulas

Using sex analyte interaction :

End_point response ~ age + BMI + sex + analyte_change + sex*analyte_change + analyte_baseline + HAMD_BL

Results Tables:

The results Tables have the following values for weekl and week2 :

• p-values for the values in the logistic regression with sex analyte interaction.

• Area under the ROC curve (AUCs) of the models with sex analyte interaction, or separately for sex.

• The mean values of groups defined by sex and response.

RESULTS

Logistic Regression with Sex * Analyte Interaction Terms

We used logistic regression to test for an association of the binary response (HAMD decrease from baseline to endpoint ^50%.) with analyte values, including interaction terms for sex * analyte, and also including baseline analyte, BMI, age and baseline HAMD in the models. Analyses using baseline HAMD showed that that variable was not significant in the models, so it was dropped from further analyses.

The models using sex analyte interaction were of the following form :

End_point response ~ age + BMI + sex + analyte_change + sex*analyte_change + analyte_baseline + HAMD BL

Because of the small sample size and the small number of responders of each gender, analytes may not show a significant sex* analyte interaction, even if such an interaction actually exists. Because the sample size is further reduced when the analytes are examined separately for males and females, power is very low to detect significant associations. Thus, a separate analysis for males and females was omitted.

The results have the following values for weekl and week2 :

• P-values for the variables age, BMI, sex, analyte, baseline analyte, and sex * analyte interaction in the logistic regression

• Area under the ROC curve (AUCs) of the models with sex*analyte interaction, or separately for sex.

• The mean values of the analyte for the four groups defined by sex and response:

o male responder

o male non-responder

o female responder

o female non-responder

Table 6 shows the p-values for the plasma analytes with significant sex*analyte interactions on Week 1.

Table 6

There were no analytes with significant p-values for the analytes change, which had not a significant sex*analyste_change interaction

Table 7 shows the p-values for the plasma analytes with significant baseline_analytes from the Week 1 analysis.

Table 7

Table 8 shows i) the AUCs and ii) the mean values of plasma analyte change from baseline to week 1.

Table 8

Table 9 shows i) the AUCs and ii) the mean values of plasma analyte change with significant baseline_analytes at week 1.

Table 9

Table 10 shows the p-values for the plasma analytes with i) a significant sex*analyte interactions and ii) significant analyte_changes at Week 2.

Table 10

Analyte. .name p_age P_BI\ II p_sex2 p_analyte p_analyte p_sex2:analyt

baseline e

A) sex* analyte interactions

Tissue. Ir hibitor.of.Metalloproteinases. l..TIMP.l._log_adj

delta 0.142 0.70 5 0.704 0.672 0.024 0.023

Complement. Factor.H Related Protein

(CFHRP)_log_adj_delta 0.110 0.86 0 0.302 0.996 0.045 0.024

Alpha. l.Antichymotrypsin..AACT._log_adj_delta 0.057 0.87 6 0.448 0.176 0.045 0.027

Prolactin..PRL._log_adj_delta 0.097 0.23 0 0.261 0.058 0.036 0.030 lnterleukin.5..IL.5._log_adj_delta 0.037 0.40 4 0.120 0.751 0.042 0.036

Fibrinogen_log_adj_delta 0.351 0.32 1 0.720 0.221 0.037 0.050

B) Anal /te_change ¹¹ p_age p_SEv /II p_sex2 p_analyte p_analyte p_sex2:analyt baseline e

Angiotensin. Converting. Enzyme.. ACE. _log_adj_delta 0.160 0.78 1 0.205 0.797 0.008 0.059

Vascular. Endothelial. Growth. Factor.. EGF._log_adj_delta 0.203 0.14 6 0.190 0.111 0.049 0.061

Serum. Glutamic. Oxaloacetic. Transaminase.. SGOT._log_adj

_delta 0.743 0.76 5 0.408 0.206 0.050 0.095

' Analytes with significant p-value (p < 0.050) for analyste change without having a significant p-value for sex*analyte_change interaction

Table 11 shows i) the AUCs for men women and both and ii) the mean values of analyte change from baseline to week 2 for the analytes in Table 10.

Table 11

Analyte_name auc_wkl_mal auc_wkl_fen 1 auc_wkl_bot non_respond responder_m non_ .respond responder_fe e ale h_sex er_male ale er_ female male

A) sex'analyte interactions

Tissue. Inhibitor, of. Metal loproteinases. l

..TIMP. l._log_adj_delta 0.5980901328 0.642271040 1 0.7788481993 -0.003662681 -0.097479026 -0.42 9332823 0.1441274183

Complement. Factor.H Related Protein

(CFHRP)_log_adj_delta 0.6715716875 0.744463573 3 0.7879971200 0.0092531347 -0.060159333 -0.04 3053729 0.0800162615

Alpha. l.Antichymotrypsin..AACT._log_a

dj_delta 0.6324301453 0.714458288' 3 0.7189431658 0.0210610938 -0.013536755 -0.03 0436871 0.1373301963

Prolactin..PRL._log_adj_delta 0.7419202103 0.746360935 5 0.7909898433 0.8876398482 -0.063583389 -0.76 0847482 172.313.162.7 lnterleukin.5..IL.5._log_adj_delta 0.8182971471 0.586916884 7 0.7750796148 -145.768.775. 0.2608035782 -0.49 4306172 -179.412.116.

Fibrinogen_log_adj_delta 0.7643745934 0.658918032' \ 0.8204483916 0.0022937123 0.1388900962 0.30Ξ 0346340 -0.552431484

B) Analyte_change

Angiotensin. Converting. Enzyme.. ACE. J

og_adj_delta 0.5820514295 0.623042721' \ 0.6829175647 -0.020369469 -0.061489283 -0.11 0338166 0.0068227832

Vascular. Endothelial. Growth. Factor.. E

GF._log_adj_delta 0.7604124188 0.628678856 I 0.8874682714 -0.006832518 -0.017844034 -0.08 2296523 0.2363596007

Table 12 shows the p-values for the analytes with significant baseline_analytes from the Week 2 analysis.

Table 12

Table 13 shows i) the AUCs and ii) the mean values the analyte concentration at baseline as well as the AUCs for the four groups defined by sex and response, for the analytes in the table above.

Table 13

Multiple Comparisons

For the analytes with significant interaction, it is likely that the AUCs for the analytes over-estimate the performance on future samples from the same population, because we have selected the best among the analytes. Therefore, we wish to know the AUCs that we would expect to see by chance, if there is no relationship between the biomarkers and the response. We made this estimate using the following procedure, in which we use random permutations of the data and calculate the AUC using the same procedure as for the original data.

1000 Permutations to assess the significance of the observed results relative to random chance:

1. Separate the original data for each patient into two parts:

a. Clinical data for one patient, consisting of the endpoint HAMD score, response status (responder/not), age, sex and BMI

b. REM analyte data for that patient.

2. Randomly permute the assignment of patients to RBM analyte data.

Permutation of the patient labels (and response status) maintains the temporal relationship and the proportions of responders, age, sex the same as in the original data.

3. Do the same analysis as for the original data, but now using the randomly permuted data.

4. Plot the AUCs of the observed results and the permuted results. These are in the PowerPoint slides: "auc_permute. ppt" and shown in Figures 3 and 4.

Figure 3 shows the distribution of observed and permuted AUCs of analyte values for both sexes at Week 1. Figure 4 shows the distribution of observed and permuted AUCs of analyte values for both sexes at Week 2.

These graphs indicate that there is a slight excess of high AUCs in the observed data compared to the permuted data. However, the graphs also indicate that a large proportion of the high AUCs could be attributed to chance. CONCLUSIONS

All analyses are based on the R Codes, Jing Shi and Michael Walker kindly sends us in order to reanalyze the data. The analyses indicate that there are 19 analytes with significant interactions of sex by analyte in the prediction of HAMD Response following treatment. Thus, males and females appear to differ in the pattern of early post-treatment biomarker changes that predict response to treatment. Because of the small sample size and the small number of responders of each gender, analytes may not show a significant sex* analyte interaction, even if such an interaction actually exists. Because the sample size is further reduced when the analytes are examined separately for males and females, we did not perform the analyses separately for men and women. Permutation analysis indicates that there is a slight excess of high AUC's in the observed data compared to the permuted data. However, the graphs also indicate that a large proportion of the high AUC's could be attributed to chance. Further studies with larger numbers of patients are required to determine AUC's reliably, and to determine which interactions are reproducible.

EXAMPLE 3

BACKGROUND

We analyzed the RBM Human MAP data to identify candidate sex-specific serum biomarkers for the onset of action of antidepressant drugs revealed in patients with Major Depressive Disorder.

The 21-item Hamilton-Depression Rating Scale (HAMD-21) score is the dependent variable for all the analyses presented here, specifically the change in HAMD from baseline to a particular week post-baseline. For the purpose of these analyses, HAMD is analyzed both as a quantitative variable and as a binary categorical variable. The quantitative measurement is the HAMD score. The binary categorical response variable is defined as a HAMD decrease from baseline to endpoint >50%. The goal is to identify biomarkers predictive of the dichotomous or quantitative treatment responses.

DATA

The data set comprises 41 subjects (20 male, 21 female) who underwent treatment with antidepressant drugs. Depression severity was measured in weekly intervals from baseline to week 6 with the 21-item Hamilton-Depression Rating Scale (HAMD-21); serum markers were measured in weekly intervals from baseline to week two.

The analyte values were processed as follows.

1. Analytes with more than 30% of < LOW> values were not analyzed.

2. <LOW> values were replaced by half of the lowest observed value of that analyte.

3. Analyte values were log transformed.

4. Analyte values from step 3 were adjusted by regressing analyte ~ age + BMI. The residuals from this regression are the adjusted analyte values for subsequent analysis. Data Analysis Methods

The independent (predictor) variables used in the analyses are as follows. The values for the analytes are age and BMI adjusted and log transformed.

• analyte change from baseline to week 1

• analyte change from baseline to week 2

• baseline analyte level

• age

· BMI

• sex

• sex * analyte change interaction terms

The sex * analyte interaction terms are included to identify biomarkers of response that differ between males and females. The statistical methods used in the analysis are as follows. The output from each analysis is in the named file.

a. Logistic regression : using the changes from baseline: weekl_change, week2_change

i. Formulas

1. Using sex analyte interaction:

a. End_point response ~ age + BMI + sex*analyte_change + analyte_baseline

2. Analyze sex separately:

a. End_point response ~ age + BMI + analyte_change + analyte_baseline

ii. Results:

1. The results have the following values for weekl and week2 :

a. p-values for the values in the logistic regression with sex analyte interaction.

b. Area under the ROC curve (AUCs) of the models with sex analyte interaction, or separately for sex.

c. The mean values of groups defined by sex and response.

RESULTS

Graphs

An example is shown in Figure 5, for the analyte Complement Factor H Related Protein (CFHRP). In this boxplot, the y axis shows the change in analyte from baseline (using log transformed analyte values adjusted for BMI and age). The x axis show the gender and response status. On the left, in green, are the male non-responders (M. NR) and female non-responders (F. NR). On the right are the male responders (M. RR) and female responders (F.RR). Response is defined to be a 50% drop in HAMD from baseline to the endpoint. In Figure 5 for CFH at week 1, we see that, in male responders (M .RR), CFH values generally increase between baseline and week 1, while in male non- responders (M .NR), CFH values decrease between baseline and week 1. The pattern is reversed in females. In female responders, CFH values generally go down between baseline and week 1, while in female non-responders CFH values generally go up between baseline and week l .This is an example of a sex * analyte interaction. We will show shortly that the particular interaction in this graph is statistically significant. It is interactions of this type that we seek.

Profile plots for adjusted log transformed analytes were prepared. An example is shown in Figure 6, for the analyte Complement Factor H Related Protein (CFHRP), the same analyte as in the boxplots above. In this profile plot, there are four boxes: female non-responders, female responders, male non- responders, and male responders. Within each box, there is a graph of analyte values versus timepoints. The y axis shows the analyte value at each timepoint (using log transformed analyte values adjusted for BMI and age). The x axis shows timepoints ( 1, 2, 3, or 4).

Consistent with what we see in the boxplot, in this profile plot, we see that, in male responders CFH values generally increase between baseline and week 1, while in male non-responders CFH value decrease between baseline and week 1. The pattern is reversed in females. In female responders, CFH values generally go down between baseline and week 1, while in female non-responders CFH values generally go up between baseline and week 1.

Logistic Regression with Sex * Analyte Interaction Terms

We used logistic regression to test for an association of the binary response (HAMD decrease from baseline to endpoint >50%.) with analyte values, including interaction terms for sex * analyte, and also including baseline analyte, BMI, and age in the models. Analyses using baseline HAMD showed that that variable was not significant in the models, so it was dropped from further analyses.

The models using sex analyte interaction were of the following form :

End_point response ~ age + BMI + sex*analyte_change + analyte_baseline The models are of the following form separately for males and females:

End_point response ~ age + BMI + analyte_change + analyte_baseline

Because of the small sample size and the small number of responders of each gender, analytes may not show a significant sex* analyte interaction, even if such an interaction actually exists. Because the sample size is further reduced when the analytes are examined separately for males and females, power is very low to detect significant associations. The results have the following values for weekl and week2 :

· The mean values the analyte for the four groups defined by sex and response:

o male responder

o male non-responder

o female responder

o female non-responder

Table 14 below shows the p-values for the analytes with significant sex*analyte interactions at Week 1. Notice that Complement Factor H Related Protein (CFHRP), for which graphs are shown above, is at the top of the list.

Table 14: Analytes with significant sex*analyte interactions in week 1

Eotaxin.l_log_adj_delta 0.766 0.945 0.043 0.448 0.043 0.019

Serum Amyloid P. Component.SAP._ 0.510 0.713 0.181 0.075 0.289 0.021 log_adj_delta

SexHormone.BindingGlobulin.SHB 0.946 0.863 0.265 0.103 0.573 0.024 G._log_adj_delta

GranulocyteColony.StimulatingFac 0.941 0.927 0.824 0.177 0.086 0.026 tor.G.CSF._log_adj_delta

Haptoglobin_log_adj_delta 0.118 0.867 0.107 0.076 0.186 0.027

C. eactiveProtein.C P._log_adj_de 0.562 0.336 0.630 0.066 0.109 0.028 Ita

Tenascin.C.TN.C._log_adj_delta 0.278 0.640 0.102 0.579 0.033 0.039

CreatineKinase.MB.CK.MB._log_ad 0.177 0.340 0.109 0.023 0.771 0.042 j_delta

lmmunoglobulinA.lgA._log_adj_del 0.310 0.322 0.102 0.149 0.207 0.043 ta

Macrophagel nflammatoryProtein. 0.279 0.703 0.091 0.184 0.193 0.046 lbeta.MIP. lbeta._log_adj_delta

Clusterin.CLU._log_adj_delta 0.419 0.224 0.271 0.350 0.596 0.048

There are 16 analytes with significant interaction term between sex and analyte_change . The false discovery rate is 0.022 assessed from 1000 permutations.

Table 15 below shows the mean values the analyte change from baseline to week 1 for the four groups defined by sex and response, for the analytes in the table above.

Table 15: AUCs for analytes with significant sex*analyte interactions in week 1

CD5.CD5L._log_adj_delta 0.687 0.653 0.821 0.015 -0.056 -0.038 0.181

MatrixMetalloproteinase 0.801 0.784 0.883 0.010 -0.070 -0.031 0.093 .10.MMP.10._log_adj_de

Ita

ComplementC3.C3._log_ 0.750 0.604 0.802 0.028 -0.010 0.008 0.102 adj_delta

Eotaxin.l_log_adj_delta 0.843 0.590 0.854 -0.036 0.086 -0.020 -0.347

Serum Amyloid P. Compon 0.739 0.661 0.793 0.038 -0.090 0.029 0.236 en t.SAP .J og_a dj_d e Ita

SexHormone.BindingGlo 0.745 0.732 0.771 -0.057 0.084 0.077 -0.120 bulin.SHBG._log_adj_del

ta

GranulocyteColony.Stim 0.871 0.627 0.821 0.028 -0.303 -0.010 0.301 ulatingFactor.G.CSF.Jog

_adj_delta

Haptoglobin_log_adj_del 0.803 0.599 0.842 0.063 -0.075 0.009 0.219 ta

CReactiveProtein.CRP._l 0.818 0.744 0.925 0.262 -0.360 -0.178 1.096 og_adj_delta

Tenascin.C.TN.C._log_adj NA 0.557 0.939 -0.040 -0.062 -0.051 0.274 _delta

CreatineKinase.MB.CK.M 0.899 0.536 0.829 0.044 -0.029 0.046 0.012 B._log_adj_delta

ImmunoglobulinA.lgA.J 0.656 0.587 0.739 0.020 -0.010 0.007 0.072 og_adj_delta

Macrophagelnflammator 0.706 0.453 0.730 0.039 -0.053 -0.033 0.149 yProtein.lbeta.MIP.lbet

a._log_adj_delta

Clusterin.CLU.J og_a d j_d 0.545 0.527 0.738 -0.008 -0.031 -0.060 0.042 elta

There are 10 analytes with significant interaction term between sex and analyte_change. The false discovery rate is 0.201 assessed from 1000 permutations. Table 16 below shows the p-values for the analytes with significant sex*analyte interactions.

Table 16: Analytes with significant sex*analyte interactions in week 2

Table 17 below shows the mean values the analyte change from baseline to week 2 for the four groups defined by sex and response, for the analytes in Table 16. Table 17: AUCs for analytes with significant sex*analyte interactions in week 2

Vitronectin log adj

delta 0.811 0.622 0.710 0.054 -0.006 -0.010 0.045

ComplementFactorH

Related Protein

(CFHRP) log adj delta 0.764 0.626 0.773 0.052 -0.047 -0.008 0.049

GranulocyteColony.Sti

mulatingFactor.G.CSF.

log adj delta 0.820 0.631 0.826 0.070 -0.350 -0.046 0.417 lnterleukin.5.IL5. log

adj delta 0.848 0.533 0.809 0.135 -0.635 -0.218 -0.083

Lectin. LikeOxidizedLD

LReceptorl.LOX.l. log

adj delta NA 0.614 0.770 -0.030 0.328 -0.018 -0.015

Multiple Comparisons

For the analytes with significant interaction, it is likely that the AUC's for the analytes over-estimate the performance on future samples from the same population, because we have selected the best among the analytes. Therefore, we wish to know the AUC's that we would expect to see by chance, if there is no relationship between the biomarkers and the response. We made this estimate using the following procedure, in which we use random permutations of the data and calculate the AUC using the same procedure as for the original data.

1. Separate the original data for each patient into two parts:

b. RBM analyte data for that patient.

2. Randomly permute the assignment of patients to RBM analyte data. Permutation of the patient labels (and response status) maintains the temporal relationship and the proportions of responders, age, sex the same as in the original data.

3. Do the same analysis as for the original data, but now using the randomly permuted data. 4. Plot the AUC's of the observed resu lts and the permuted results. These are in the PowerPoint slides : "auc permute. ppt" and shown here in Figures 7 and 8. Figure 7 shows the distribution of observed and permuted AUCs of analyte values at Week 1. Figu re 8 shows the distribution of observed and permuted AUCs of analyte values at Week 2.

These graphs indicate that there is a slight excess of high AUC's in the observed data compared to the permuted data . However, the graphs also indicate that a large proportion of the high AUC's could be attributed to chance.

CONCLUSIONS

The analyses indicate that there are analytes with significant interactions of sex by analyte in the prediction of HAMD following treatment. Thus, males and females appear to differ in the pattern of early post-treatment biomarker changes that predict response to treatment.

Because of the small sample size and the small nu mber of responders of each gender, analytes may not show a significant sex* analyte interaction, even if such an interaction actually exists. Because the sample size is further reduced when the analytes are examined separately for males and females, power is very low to detect significant associations. Permutation analysis indicates that there is a slight excess of high AUC's in the observed data compared to the permuted data . However, the graphs also indicate that a large proportion of the high AUC's could be attributed to chance. Fu rther studies with larger nu mbers of patients are required to determine AUC's reliably, and to determine which interactions are reproducible. EXAMPLE 4: Comparison of REM results in serum vs. plasma for MDD HAMD subjects

SUMMARY

We re-analyzed the RBM data in serum and plasma samples for the MDD HAMD subjects. While the results for serum and plasma are similar overall, it appears that serum samples identify slightly more significant analytes, and have a slightly greater difference between the observed AUC's and the AUC's expected by chance. Serum appears to be the preferable sample by these criteria.

ANALYSIS

Because of problems with the r script used originally, we replicated the results for plasma at Week 1, but get different results for plasma at Week 2. We have not identified the reason for this difference at Week 2. However, whichever results we use, the conclusion with regard to plasma vs. serum is largely the same. The results below are from our analyses.

RESULTS

Tables 18, 19, 20 and 21 provide the raw results of the analysis for each analyte.

The columns in the 4 tables have the following interpretation.

The columns that begin with the letter "p" are p-values that indicate if the named covariate is significant in the regression model to the response (HAMD) at week 1 or week 2.

For example, p. age indicates if age is significantly associated with the response (HAMD) in the model including the named analyte (on that row in the table) and the other covariates.

Covariate Interpretation

p. X. Intercept. p-value that the intercept is significant

p. age p-value that age is significant

p. BMI p-value that BMI is significant

p.sex2 p-value that sex is significant p. analyte p-value that the analyte is significant p.analyte_baseline p-value that the analyte at baseline is significant p. HAMD_baseline p-value that HAMD at baseline is significant

p. sex2. analyte p-value that the sex*analyte interaction is significant

The three columns beginning "auc" give the area under the receiver operating characteristic (ROC) curve for the analyte for three groups: males only, females only, and combined.

auc_wkl_male

auc_wkl_female

auc_wkl_both_sex

The last 4 columns give the mean values the named analyte (on that row in the table) changes from baseline for the four groups defined by sex and response non_responder_male

responder_male

non_responder_female

responder_female

TABLE 18: Results from Plasma Week 1

TABLE 19: Results from Plasma Week 2

TABLE 20: Results from Serum Week 1

TABLE 21: Results from Serum Week 2

Table 22 below shows the number of analytes with significant sex*analyte interaction term in logistic models predicting depression remission using serum or plasma samples, at Week 1 and Week 2. Table 22 includes the estimated false discovery rate (FDR). It appears that serum samples identify slightly more significant analytes, with FDR comparable to the FDR for plasma.

Table 22

Figures 9-12 show the observed and permuted AUCs of logistic models predicting end-point depression remission (including the sex*analyte interaction term), using serum and plasma samples, at Week 1 and Week 2. It appears that, compared to plasma samples, the serum samples have a slightly greater difference between the observed AUC's and the AUC's expected by chance. Serum appears to be the preferable sample by this criterion.

CONCLUSIONS

Serum appears to be the preferable sample by these criteria. However, the difference between serum and plasma by these criteria is small, and other factors such as cost or reproducibility may alter the choice.

Claims

1. Use of Complement Factor H Related Protein (CFHRP)Thymus-Expressed Chemokine (TECK) as a biomarker for diagnosis of major depressive disorder or predisposition thereto.

2. Use as defined in claim 1, in the diagnosis of major depressive disorder or predisposition thereto in a male subject which additionally comprises the use of one or more further analytes selected from : Amphiregulin, Apolipoprotein E (Apo E), Calcitonin, CD5 ligand, Creatine Kinase-MB (CK-MB), C-Reactive Protein (CRP), Granulocyte Colony-Stimulating Factor (G-CSF), Intercellular Adhesion Molecule 1 (ICAM- 1), Interleukin-5 (IL-5), Interleukin-8 (IL-8), Lectin-Like Oxidized LDL Receptor 1 (LOX-1), Matrix Metalloproteinase-9 (MMP-9), Matrix Metalloproteinase- 10 (MMP- 10), Myoglobin, Thymus-Expressed Chemokine (TECK), Tissue Inhibitor of Metalloproteinases 1 (TIMP-1), Vascular Cell Adhesion Molecule-1 (VCAM- 1) and Thyroid-Stimulating Hormone (TSH).

3. Use of Thymus-Expressed Chemokine (TECK), Amphiregulin, Apolipoprotein E (Apo E), Calcitonin, CD5 ligand, Complement Factor H Related Protein (CFHRP), Creatine Kinase-MB (CK-MB), C-Reactive Protein (CRP), Granulocyte Colony-Stimulating Factor (G-CSF), Intercellular Adhesion Molecule 1 (ICAM-1), Interleukin-5 (IL-5), Interleukin-8 (IL-8), Lectin-Like Oxidized LDL Receptor 1 (LOX- 1), Matrix Metalloproteinase-9 (MMP-9), Matrix Metalloproteinase- 10 (MMP- 10), Myoglobin, Tissue Inhibitor of Metalloproteinases 1 (TIMP- 1) as a specific panel of biomarkers for the diagnosis of major depressive disorder or predisposition thereto in a male subject.

4. Use as defined in claim 1, in the diagnosis of major depressive disorder or predisposition thereto in a female subject which additionally comprises the use of one or more further analytes selected from : Alpha-2-Macroglobulin (Alpha-2- Macro), Alpha-Fetoprotein (AFP), Apolipoprotein B (Apo B), Beta-2-Microglobulin (B2M), Clusterin (CLU), Cystatin-C, Eotaxin-1, Glucagon-like Peptide 1, total (GLP- 1 total), Hepatocyte Growth Factor (HGF), Interleukin-2 (IL-2), Interleukin-7 (IL-7), Interleukin- 15 (IL- 15), Monocyte Chemotactic Protein 1 (MCP-1), Macrophage Inflammatory Protein- 1 beta (MIP-1 beta), Osteopontin, Proinsulin Intact, Proinsulin Total, T-Cell-Specific Protein RANTES (RANTES), SlOO calcium-binding protein B (S100-B), Thrombospondin-1, Tumor Necrosis Factor Receptor-Like 2 (TNFR2) and Vitamin K-Dependent Protein S (VKDPS).

5. Use of Thymus-Expressed Chemokine (TECK), Alpha-2-Macroglobulin (Alpha-2-Macro), Alpha-Fetoprotein (AFP), Apolipoprotein B (Apo B), Beta-2-

Microglobulin (B2M), Clusterin (CLU), Cystatin-C, Eotaxin- 1, Glucagon-like Peptide 1, total (GLP-1 total), Hepatocyte Growth Factor (HGF), Interleukin-2 (IL-2), Interleukin-7 (IL-7), Interleukin- 15 (IL- 15), Monocyte Chemotactic Protein 1 (MCP- 1), Macrophage Inflammatory Protein- 1 beta (MIP-1 beta), Osteopontin, Proinsulin Intact, Proinsulin Total, T-Cell-Specific Protein RANTES (RANTES), SlOO calcium-binding protein B (S100-B), Thrombospondin- 1, Tumor Necrosis Factor Receptor-Like 2 (TNFR2) and Vitamin K-Dependent Protein S (VKDPS) as a specific panel of biomarkers for the diagnosis of major depressive disorder or predisposition thereto in a female subject.

6. Use as defined in any preceding claims, wherein one or more of the biomarkers may be replaced by a molecule, or a measurable fragment of the molecule, found upstream or downstream of the biomarker in a biological pathway.

7. A method of diagnosing major depressive disorder or predisposition thereto, in an individual, comprising :

(a) obtaining a biological sample from an individual;

(b) quantifying the amounts of the analyte biomarkers as defined in any of claims 1 to 5;

8. A method of monitoring efficacy of a therapy in a subject having, suspected of having, or of being predisposed to major depressive disorder, comprising detecting and/or quantifying, in a sample from said subject, the analyte biomarkers as defined in any of claims 1 to 5.

9. A method as defined in claim 7 or claim 8, which is conducted on samples taken on two or more occasions from a test subject.

10. A method as defined in any of claims 7 to 9, further comprising comparing the level of the biomarker present in samples taken on two or more occasions.

11. A method as defined in any of claims 7 to 10, comprising comparing the amount of the biomarker in said test sample with the amount present in one or more samples taken from said subject prior to commencement of therapy, and/or one or more samples taken from said subject at an earlier stage of therapy.

12. A method as defined in any of claims 7 to 11, further comprising detecting a change in the amount of the biomarker in samples taken on two or more occasions.

13. A method as defined in any of claims 7 to 12, comprising comparing the amount of the biomarker present in said test sample with one or more controls.

14. A method as defined in claim 13, comprising comparing the amount of the biomarker in a test sample with the amount of the biomarker present in a sample from a normal subject.

15. A method as defined in any of claims 7 to 14, wherein samples are taken prior to and/or during and/or following therapy for major depressive disorder.

16. A method as defined in any of claims 7 to 15, wherein samples are taken at intervals over the remaining life, or a part thereof, of a subject.

17. A method as defined in any of claims 7 to 16, wherein quantifying is performed by measuring the concentration of the analyte biomarker in the or each sample.

18. A method as defined in any of claims 7 to 17, wherein detecting and/or quantifying is performed by one or more methods selected from SELDI (-TOF), MALDI (-TOF), a 1-D gel-based analysis, a 2-D gel-based analysis, Mass spec (MS), reverse phase (RP) LC, size permeation (gel filtration), ion exchange, affinity, HPLC, UPLC or other LC or LC-MS-based technique.

19. A method as defined in any of claims 7 to 18, wherein detecting and/or quantifying is performed using an immunological method.

20. A method as defined in any of claims 7 to 19, wherein the detecting and/or quantifying is performed using a biosensor or a microanalytical, microengineered, microseparation or immunochromatography system.

21. A method as defined in any of claims 7 to 20, wherein the biological sample is cerebrospinal fluid, whole blood, blood serum, plasma, urine, saliva, or other bodily fluid, or breath, condensed breath, or an extract or purification therefrom, or dilution thereof.

22. A kit for monitoring or diagnosing major depressive disorder, comprising a biosensor capable of detecting and/or quantifying the analyte biomarkers as defined in any of claims 1 to 5.

23. A method of diagnosing an individual with major depressive disorder, comprising :

24. The method of claim 23, wherein the one or more antibody-antigen interactions comprise a C18-antigen interaction.

25. A method of predicting treatment outcome in an individual suffering from Major Depressive Disorder comprising :

(g) quantifying the amount of Complement Factor H Related Protein in a biological sample from the individual;

(h) comparing the amount of Complement Factor H Related Protein in the biological sample to one or more control samples; and

(i) classifying the individual as a likely responder to therapy based at least in part on a difference between the amount of Complement Factor H Related Protein in the biological sample and the one or more control samples.

The method of claim 25, further comprising :

(g) quantifying the amount of one or more additional analytes in the biological sample from the individual;

(h) comparing the amount of the one or more additional analytes in the biological sample to the one or more control samples; and

(i) classifying the individual as a likely responder to therapy based in part on a difference between the amount of the one or more additional analytes in the biological sample and the one or more control samples.

27. The method of claim 26, wherein the one or more additional analytes are selected from Angiopoietin 2, Apolipoprotein H, Beta 2 Microglobulin, Betacellulin, Brain Derived Neurotrophic Factor, C Reactive Protein, CD5, Clusterin, ComplementC3, CreatineKinase MB, Cystatin C, Eotaxin 1, Epithelial Derived Neutrophil Activating Protein 78 , Fibrinogen, Granulocyte Colony Stimulating Factor, Haptoglobin, Immunoglobulin A, Interleukin 13 , Interleukin 16, Interleukin 5, Lectin Like Oxidized LDL Receptor 1, Macrophage Derived Chemokine, Macrophage Inflammatory Protein lbeta, Matrix Metalloproteinase 10, Serum Amyloid P Component, Sex Hormone Binding Globulin, Sortilin, Tenascin C, Tissue Inhibitor of Metalloproteinases 1 , Transthyretin and Vitronectin.