Cellular fibronectin as a diagnostic marker in stroke and methods of use thereof

• US 20050130230A1
• Filed: 01/29/2005
• Published: 06/16/2005
• Est. Priority Date: 09/23/2003
• Status: Active Grant

• Current Assignee: Antoni Davalos, Cornelius Allen Diamond, Jose Castillo, Mar Castellanos
• Original Assignee: Antoni Davalos, Cornelius Allen Diamond, Jose Castillo, Mar Castellanos
• Inventors: Castellanos, Mar, Castillo, Jose, Diamond, Cornelius Allen, Davalos, Antoni
• Granted Patent: US 7,634,360 B2
• Time in Patent Office: Days
• US Class Current: 435/7.100
• CPC Class Codes: G01N 2333/78 Connective tissue peptides, e.g. collagen, elastin, laminin, fibronectin, vitronectin, cold insoluble globulin [CIG]
• CPC Class Codes: G01N 2800/2871 Cerebrovascular disorders, e.g. stroke, cerebral infarct, cerebral haemorrhage, transient ischemic event
• CPC Class Codes: G01N 33/6848 Methods of protein analysis involving mass spectrometry
• CPC Class Codes: G01N 33/6893 related to diseases not provided for elsewhere
• CPC Class Codes: G16B 20/00 ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
• CPC Class Codes: G16B 20/20 Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
• CPC Class Codes: G16B 40/00 ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
• CPC Class Codes: G16B 40/10 Signal processing, e.g. from mass spectrometry [MS] or from PCR
• CPC Class Codes: G16B 40/20 Supervised data analysis
• CPC Class Codes: Y02A 90/10 Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Abstract

The present invention relates to methods for the diagnosis and evaluation of stroke and stroke sub-type. A variety of bio-markers are disclosed for assembling a panel for such diagnosis and evaluation. Methods are disclosed for selecting markers and correlating their combined levels with a clinical outcome of interest. In various aspects: the invention provides methods for early detection and differentiation of stroke subtypes, for determining the prognosis of a patient presenting with stroke symptoms, and identifying a patient at risk for hemorrhagic transformation after thrombolyic therapy. Methods are disclosed that provide rapid, sensitive and specific assays to greatly increase the number of patients that can receive beneficial stroke treatment and therapy, and reduce the costs associated with incorrect stroke diagnosis.

Claims

1. A method of determining presence or risk of hemorrhage in a human subject, said method comprising:

obtaining a test sample from a human subject;

analyzing the obtained test sample for the presence or amount of (1) cellular fibronectin and (2) one or more additional markers both proteomic and non-proteomic for, or mass spectrometry peak levels of, any of apoptosis, cellular adhesion, cellular injury, coagulation, glial activation, inflammatory mediation, myelin breakdown, thrombosis, vascular damage, and specific and non-specific markers of cerebral injury; and

correlating (1) the presence or amount of said cellular fibronectin and said or more additional markers or peak levels, and (2) clinical patient information, other than the cellular fibronectin and additional makers or peak levels, for cerebral injury, in order to deduce a probability of present or future risk of a hemorrhage for the subject.

2. The method according to claim 1, wherein said correlating step comprises:

determining the expression levels or mass spectrometry peak levels of one or more proteomic marker(s) or mass-to-charge ratio(s) and the numerical quantity of one or more non-proteomic marker(s) or mass-to-charge ratio(s) from humans suspected or known to have some form of hemorrhage; and

comparing said determined levels and numerical values to humans known to have said matched type of hemorrhage; and

training an algorithm to identify patterns of differences in said humans which correlate with the prescience or absence of said matched type of hemorrhage, respectively.

3. The method according to claim 2, where training said algorithm on characteristic protein patterns comprises the steps of

obtaining numerous examples of (i) said proteomic and non-proteomic data, and (ii) historical clinical results corresponding to this proteomic and non-proteomic data;

constructing an algorithm suitable to map (i) said protein expression levels or mass spectrometry peak mass-to-charge ratio(s) and said non-proteomic values as inputs to the algorithm, to (ii) the historical clinical results as outputs of the algorithm;

exercising the constructed algorithm to so map (i) the said protein expression levels or mass spectrometry peak mass-to-charge ratio(s) and said non-proteomic values as inputs to (ii) the historical clinical results as outputs; and

conducting an automated procedure to vary the mapping function, inputs to outputs, of the constructed and exercised algorithm in order that, by minimizing an error measure of the mapping function, a more optimal algorithm mapping architecture is realized;

wherein realization of the more optimal algorithm mapping architecture, also known as feature selection, means that any irrelevant inputs are effectively excised, meaning that the more optimally mapping algorithm will substantially ignore said protein expression levels or mass spectrometry peak mass-to-charge ratio(s) and said non-proteomic values that are irrelevant to output clinical results; and

wherein realization of the more optimal algorithm mapping architecture, also known as feature selection, also means that any relevant inputs are effectively identified, making that the more optimally mapping algorithm will serve to identify, and use, those input protein expression levels or mass spectrometry peak mass- to-charge ratio(s) and said non-proteomic values that are relevant, in combination, to output clinical results that would result in a clinical detection of disease, disease diagnosis, disease prognosis, or treatment outcome or a combination of any two, three or four of these actions.

4. The method according to claim 3, where the constructed algorithm is drawn from the group consisting essentially of:

linear or nonlinear regression algorithms;

linear or nonlinear classification algorithms;

ANOVA;

neural network algorithms;

genetic algorithms;

support vector machines algorithms;

hierarchical analysis or clustering algorithms;

hierarchical algorithms using decision trees;

kernel based machine algorithms such as kernel partial least squares algorithms, kernel matching pursuit algorithms, kernel fisher discriminate analysis algorithms, or kernel principal components analysis algorithms;

Bayesian probability function algorithms;

Markov Blanket algorithms;

a plurality of algorithms arranged in a committee network; and

forward floating search or backward floating search algorithms.

5. The method according to claim 3, where the feature selection process employs an algorithm drawn from the group consisting essentially of:

linear or nonlinear regression algorithms;

linear or nonlinear classification algorithms;

ANOVA;

neural network algorithms;

genetic algorithms;

support vector machines algorithms;

hierarchical analysis or clustering algorithms;

hierarchical algorithms using decision trees;

kernel based machine algorithms such as kernel partial least squares algorithms, kernel matching pursuit algorithms, kernel fisher discriminate analysis algorithms, or kernel principal components analysis algorithms;

Bayesian probability function algorithms;

Markov Blanket algorithms;

recursive feature elimination or entropy-based recursive feature elimination algorithms;

a plurality of algorithms arranged in a committee network; and

forward floating search or backward floating search algorithms.

6. The method according to claim 3, wherein a tree algorithm is trained to reproduce the performance of another machine-learning classifier or regressor by enumerating the input space of said classifier or regressor to form a plurality of training examples sufficient (1) to span the input space of said classifier or regressor and (2) train the tree to emulate the performance of said classifier or regressor.

7. The method according to claim 1, wherein the risk of hemorrhage is risk of hemorrhage following thrombolytic therapy.

8. The method of claim 7 expanded for diagnosing the risk of hemorrhage following thrombolytic therapy, the expanded method further comprising:

measuring the level of cellular fibronectin (c-Fn) alone;

evaluating the patient'"'"'s risk of hemorrhage following thrombolytic therapy from said measured level of c-Fn; and

administering stroke therapy as appropriate to the evaluated risk of hemorrhage.

9. The method according to claim 7, wherein said one or more additional markers includes, in addition to cellular fibronectin (c-Fn), the proteomic marker MMP-9.

10. The method according to claim 7, wherein said one or more additional markers includes, in addition to cellular fibronectin (c-Fn), a proteomic marker of endothelial injury.

11. The method of claim 1, when said markers in addition to cellular-fibronectin, are selected from the group consisting of two or more of the following:

Glial fibrillary acidic protein, apolipoprotein CI (ApoC-I), apolipoprotein CII (ApoC-III), serum amyloid A (SAA), Platelet factor 4 (PF4), platelet-derived growth factor, antithrombin-III fragment (AT-III fragment), bradykinin, renin, haptoglobin, Creatine kinase brain band (CK-BB), adenylate kinase, lactate dehydrogenase, troponin I, troponin T, Brain Derived Neurotrophic Factor, CPK, LDH Isoenzymes, Thrombin-Antithrombin III, calcitonin, procalcitonin, c-tau, Protein C, Protein S, fibrinogen, Factor VII, activated Protein C resistance, E-selectin, P- selectin, von Willebrand factor (vWF), platelet-derived microvesicles (PDM), plasminogen activator inhibitor-I (PAI-I), angiotensin I, angiotensin II, angiotensin III, annexin V, arginine vasopressin, B-type natriuretic peptide (BNP), pro-BNP, atrial natriuretic peptide (ANP), N-terminal pro-ANP, pro-ANP, C-type natriuretic peptide, (CNP), c-fos, c-jun, ubiquitin, cytochrome C, beta-enolase, cardiac troponin I, cardiac troponin T, urotensin II, creatine kinase-MB, glycogen phosphorylase-BB, KL-6, endothelin-1, endothelin-2, and endothelin-3, A-, F-, and H-Fatty acid binding protein (A-, F-, H-FABP), phosphoglyceric acid mutase-MB, aldosterone, S-100beta (S100b), myelin basic protein, NR2A or NR2B NMDA receptor or fragment thereof (a subtype of N-methyl-D-aspartate (NMDA) receptors), Intracellular adhesion molecule (ICAM or CD54), Neuronal cell adhesion molecule, (NCAM or CD56), C-reactive protein, caspase-3, cathepsin D, hemoglobin alpha.sub.2, human lipocalin-type prostaglandin D synthase, interleukin-1 beta, interleukin-1 receptor angonist, interleukin 2, interleukin 2 receptor, interleukin-6, IL-1, IL-8, IL-10, monocyte chemotactic protein-1, soluble intercellular adhesion molecule-1, soluble vascular cell adhesion molecule-1, MMP-2, MMP-3, MMP-9, MMP-12, MMP-9, tissue factor (TF), TRAIL, TWEAK, fibrin D-dimer (D-dimer), total sialic acid (TSA), TpP, heat shock protein 60, heat shock protein 70, tumor necrosis factor alpha, tumor necrosis factor receptors 1 and 2, VEGF, Calbindin-D, Proteolipid protein RU Malendialdehyde, neuron-specific enolase gamma gamma isoform (NSE gg isoform), thrombus precursor protein, Fibrinopeptide A (FPA), plasmin-a 2AP complex (PAP), plasmin inhibitory complex (PIC), beta-thromboglobulin (b-TG), Prothrombin fragment 1+2, PGI2, Creatinine phosphokinase brain band, neurotrophin-3 (NT-3), neurotrophin4/5 (NT4/5), neurokinin A, neurokinin B, neurotensin, neuropeptide Y, Lactate dehydrogenase (LDH), soluble thrombomodulin (sTM), Insulin-like growth factor-1 (IGF-1), protein kinase C gamma (PKC-g), PGE2,8-epi PGF.sub.2alpha and Transforming growth factor beta (TGFb) or markers related thereto.

12. The method of claim 11 when the determination of diagnostic or prognostic outcome is made in accordance with an algorithm drawn from the group consisting essentially of:

linear or nonlinear regression algorithms;

linear or nonlinear classification algorithms;

ANOVA;

neural network algorithms;

genetic algorithms;

support vector machines algorithms;

hierarchical analysis or clustering algorithms;

hierarchical algorithms using decision trees;

kernel based machine algorithms such as kernel partial least squares algorithms, kernel matching pursuit algorithms, kernel fisher discriminate analysis algorithms, or kernel principal components analysis algorithms;

Bayesian probability function algorithms;

Markov Blanket algorithms;

recursive feature elimination or entropy- based recursive feature elimination algorithms;

a plurality of algorithms arranged in a committee network; and

forward floating search or backward floating search algorithms.

13. The method of claim 1 when said clinical patient information is selected from a group consisting of Complete blood count (CBC), Coagulation test, Blood chemistry (glucose, serum electrolytes {Na, Ca, K}), Leukocyte and Neutrophil counts, platelet count, and Blood lipids tests.

14. The method of claim 1 when said clinical patient information is selected from a group consisting of age, weight, height, body mass index, computed tomography scan information, gender, time from onset of stroke-like symptoms, time to recanalization, ethnicity, heart rate, blood pressure, respiration rate, blood oxygenation, previous personal and/or familial history of cardiac events, recent cranial trauma and unequal eye dilation.

15. The method of claim 1 when the determination of diagnostic or prognostic outcome where both proteomic markers and non-proteomic markers are used.

16. The method of claim 1 wherein the test sample obtained from the subject is selected from the group consisting of blood, urine, blood plasma, blood serum, cerebrospinal fluid, saliva, perspiration or brain tissue, or a derivative thereof.

17. A method according to claim 1,

wherein the obtaining of the test sample from the subject is within a specific time window from onset of symptoms;

and

wherein the correlating is between (1) proteomic and non-marker marker values, and (2) the probability of present or future risk of a hemorrhage for the subject, for said specific time window from onset of symptoms.

18. The method of claim 17 wherein the correlating is in accordance with an algorithm drawn from the group consisting essentially of:

linear or nonlinear regression algorithms;

linear or nonlinear classification algorithms;

ANOVA;

neural network algorithms;

genetic algorithms;

support vector machines algorithms;

hierarchical analysis or clustering algorithms;

hierarchical algorithms using decision trees;

kernel based machine algorithms such as kernel partial least squares algorithms, kernel matching pursuit algorithms, kernel fisher discriminate analysis algorithms, or kernel principal components analysis algorithms;

Bayesian probability function algorithms;

Markov Blanket algorithms;

recursive feature elimination or entropy-based recursive feature elimination algorithms;

a plurality of algorithms arranged in a committee network; and

forward floating search or backward floating search algorithms.

19. A kit for determining presence or risk of hemorrhage in a human subject comprising:

a device having reagents at each of a plurality of discrete locations, each reagent and corresponding location configured and arranged to immobilize for detection one of said plurality of subject-derived markers, the device supporting the analysis of cellular fibronectin and additional markers; and

a computer algorithm residing on a computer for calculating in consideration of analyzed cellular fibronectin and additional markers a probability of present or future risk of hemorrhage for the subject.

20. The kit according to claim 19 wherein the device each of said plurality of discrete locations comprises a poly- or monoclonal antibody specific for an individual protein or protein fragment and that binds one of said plurality of subject-derived markers.

21. The kit according to claim 19 wherein the device is an immunoassay.

22. The kit according to claim 19 wherein the device is capable of determining the levels of cellular fibronectin and said additional markers within 20 minutes.

24. The method according to claim 22 wherein said predicting is that hemorrhage is more probable than not when the determining is of an amount of cellular fibronectin greater than or equal to 2.8 μ

g/mL in serum.

23. A method of determining presence or risk of hemorrhage in a human subject, said method comprising:

determining the presence or amount of cellular fibronectin in the human subject; and

predicting the risk of hemorrhage following thrombolyic therapy in accordance with the determined presence or amount.

25. A panel comprising a plurality of markers selected to selectively identify the occurrence or nonoccurrence of a stroke in a subject exhibiting one or more symptoms associated with the diagnosis of stroke, wherein said marker(s) selected to distinguish the occurrence of a stroke in said subject from one or more stroke mimic conditions include cellular fibronectin (c-Fn).