WO2008049175A1 - High discriminating power biomarker diagnosing - Google Patents

High discriminating power biomarker diagnosing Download PDF

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Publication number
WO2008049175A1
WO2008049175A1 PCT/BE2007/000115 BE2007000115W WO2008049175A1 WO 2008049175 A1 WO2008049175 A1 WO 2008049175A1 BE 2007000115 W BE2007000115 W BE 2007000115W WO 2008049175 A1 WO2008049175 A1 WO 2008049175A1
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WIPO (PCT)
Prior art keywords
endometriosis
disorder
biomarker
patient
vitro
Prior art date
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PCT/BE2007/000115
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French (fr)
Inventor
Jaak Billen
Norbert Blanckaert
Bart De Moor
Frank De Smet
Thomas D'hooghe
Olivier Gevaert
Cleophas Kyama
Christel Meuleman
Attila Mihalyi
Nathalie Pochet
Peter Simsa
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Katholieke Universiteit Leuven
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Priority claimed from GB0621086A external-priority patent/GB0621086D0/en
Priority claimed from GB0700424A external-priority patent/GB0700424D0/en
Priority claimed from GB0700425A external-priority patent/GB0700425D0/en
Priority claimed from GB0700423A external-priority patent/GB0700423D0/en
Priority claimed from GB0712747A external-priority patent/GB0712747D0/en
Application filed by Katholieke Universiteit Leuven filed Critical Katholieke Universiteit Leuven
Publication of WO2008049175A1 publication Critical patent/WO2008049175A1/en

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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
    • G16B20/20—Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
    • G16B20/30—Detection of binding sites or motifs
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
    • G16B20/50—Mutagenesis
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B25/00—ICT specially adapted for hybridisation; ICT specially adapted for gene or protein expression
    • G16B25/10—Gene or protein expression profiling; Expression-ratio estimation or normalisation
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
    • G16B40/20—Supervised data analysis
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/36—Gynecology or obstetrics
    • G01N2800/364—Endometriosis, i.e. non-malignant disorder in which functioning endometrial tissue is present outside the uterine cavity
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B25/00—ICT specially adapted for hybridisation; ICT specially adapted for gene or protein expression

Definitions

  • the present invention relates generally to a diagnostic method, model and apparatus to identify the condition of a disorder which is associated with the female's menstrual cycle and is a disorder selected from the group consisting of premenstrual syndrome, migraine headache, endometriosis, psoriasis, acne, dysmenorrhoea, neurosia, asthma and premenstrual cramps and more particularly to a diagnostic system comprising models of multiple biomarker analysis values to diagnose the presence, absence and progression of such disorder and to distinguish between early and advanced stages of endometriosis.
  • An high discriminating power biomarker diagnosing model and a diagnostic model for a disorder which is associated with the female's menstrual cycle such as selected from the group consisting of premenstrual syndrome, migraine headache, endometriosis, psoriasis, acne, dysmenorrhoea, neurosia, asthma and premenstrual cramps has been elaborated.
  • Such models also allow determining the biomarker relevance of new bioactive proteins or gene expression products for such disorders which are associated with the female's menstrual cycle.
  • system, method and diagnostic model test fof present invention is for high sensitivity testing of such disoders and especially for minimal to mild endometriosis, if particularly, a SELDI-TOF -MS ProteinChip technollogy tor an alike technology for sensitive protein, polypeptide or peptide detection is combined with bioinformatics tools of present invention for diagnostic testing for minimal to mild endometriosis with a high sensitivity.
  • a SELDI-TOF -MS ProteinChip technollogy tor an alike technology for sensitive protein, polypeptide or peptide detection is combined with bioinformatics tools of present invention for diagnostic testing for minimal to mild endometriosis with a high sensitivity.
  • Endometriosis is a common, frequently progressive, gynecological disease, defined as the presence of endometrial tissue outside the uterus.
  • the disease is associated with chronic pelvic pain, dysmenorrhea, dyspareunia and infertility, and may lead to pelvic organ dysfunction, often requiring extensive surgery.
  • Scientific reports clearly indicate that endometriosis represents a major financial burden to healthcare systems and to the society, consuming EUR millions each year in Europe as well as in the USA in the forms of health care costs and loss of working capacity.
  • the estimated prevalence of endometriosis is 6-10% in the general population and 13 - 33% in infertile women and more than 50% in women with severe dysmenorrhea, dyspareunia or chronic pelvic pain.
  • noninvasive diagnostic approaches such as ultrasound, MRI or blood tests for CA-125 do not have sufficient diagnostic power; thus, the only way to conclusively diagnose endometriosis is laparoscopic surgery with histological confirmation.
  • the lack of a noninvasive diagnostic test or an accurate blood test is the major reason why the delay between the onset of symptoms and a diagnosis is often as long as 8 to 11 years.
  • Development of a clinically useful, noninvasive diagnostic test will have a technological impact on the patients' quality of life, on the efficacy of the available treatments as well as on the financial aspects of the disease. Due to the enormous financial impact of endometriosis, the pharmaceutical industry makes significant efforts to develop new medications for endometriosis or to improve the already existing ones.
  • Plasma levels of several immunological and inflammatory factors are significantly altered in women with endometriosis.
  • these changes have not yet been studied sufficiently to allow a distinction between women with and without endometriosis or between early and advanced stages of the disease.
  • most of the studies in this area showed one or more features in study design which made it impossible to accurately evaluate their potential diagnostic value (e.g. limited number of patients, poorly defined study groups, insufficient statistical analysis etc).
  • a further embodiment of present invention is a system or apparatus comprising a surface enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-
  • TOF MS TOF MS
  • another analyzer that combines chromatography and mass spectrometry to provide a protein expression profile from a variety of biological and clinical samples and to input signals corresponding to the height of a peak, the area under the curve of such peak, or the position of such peak that represent a molecular weight in a chromatograph for each biomarker in said sample and further comprising a signal processor for processing the input signals such comprising a combined LOO-SVM algorithm ranking and logistic regression classification (LOO - CV) mathematic model that produces output signals that determine the presence or absence of the disorder, the seriousness of the disorder or the progress of the disorder in the patient.
  • LOO-SVM algorithm ranking and logistic regression classification LOO-SVM algorithm ranking and logistic regression classification (LOO - CV) mathematic model that produces output signals that determine the presence or absence of the disorder, the seriousness of the disorder or the progress of the disorder in the patient.
  • the present invention concerns a diagnostic system for the presence and/or progression of endometriosis that significantly reduces the time from the onset of pain and/or subfertility symptoms to diagnosis. Such earlier diagnosis and treatment will improve health related quality of life and allow prevention of the natural progression of endometriosis.
  • the diagnostic system of present invention provides a tool for a conveniently applicable noninvasive diagnostic test for endometriosis without requiring costly laparoscopy and hospitalization.
  • the noninvasive diagnostic tool to conclusively determine the presence or absence of endometriosis will have a major impact on the treatment of women with pelvic pain and/or subfertility, and result in significant improvements on several levels of the society.
  • Yet another advantage of the present system is that it will - indirectly - reduce the economic loss related to endometriosis by enabling early treatment and thus preventing the progression of the disease which is frequently seriously decreasing working capacity.
  • the present invention solves the problems of the related art of accurately predicting the presence, extent and progress of endometriosis in the patient in a non-invasive manner.
  • the invention is broadly drawn to an in vitro diagnosis system for determining whether a patient is affected by endometriosis and if so defining the stages of endometriosis progress.
  • One aspect of the invention is to rapidly and accurately diagnose and/or prognose the absence, presence and/or progression of endometriosis.
  • Another aspect of the invention is a method of prediction of the severity of endometriosis or endometriosis progress in the patient by in vitro diagnosing, characterised in that the methods comprise processing of several biomarker variables obtainable from in vitro assaying of at least one fluid sample or at least one cell containing sample of said patient.
  • the invention of in vitro diagnosing can involve an assay system that produces input signals into a signal processor corresponding with activity or presence of each biomarker in said sample and whereby the input signals are processed in the signal processor comprising a mathematical model that produces output signals that are predictive for the presence or extent of endometriosis or for endometriosis progress in the patient.
  • the method or system can comprise that the output signals are compared to reference signals.
  • stage l-ll endometriosis also referred to as minimal-mild endometriosis
  • stage I H-IV endometriosis also referred to as moderate-severe endometriosis
  • Yet another aspect of the invention can be that the in vitro assaying of the samples is carried out by an Surface Enhanced Laser Desorption/lonisation Time Of Flight Mass Spectrometry, immunoprecipitation, a radioimmunoassay, an enzyme immunoassay, a fluorescent immunoassay, a chemiluminescent immunoassay, a competitive binding assay, an ELISA or a homogeneous immunoassay described herein or in laboratory manuals available in the art such as The ELISA Guidebook (Methods in Molecular Biology) by John R. Crowther; Elisa: Theory and Practice (Methods in Molecular Biology, VoI 42) by John R. Crowther; Immunoassays: A Practical Approach (Practical Approach Series) by James P. Gosling (Editor) and The Immunoassay Handbook, Third Edition by David Wild (Editor)
  • the samples are analysed by homogenous binding assays.
  • biomarker variables are corresponding to substances of the group consisting of immunological factors, inflammatory factors, Intercellular adhesion molecules and cystine-knot growth factors of the PDGF/VEGF growth factor family such as vascular endothelial growth factor (VEGF) and Placental Growth Factor
  • Such biomarker value may also be peak the that represents a molecular weight or the area under the curve of such peak in a chromatograph which is obtainable form a surface enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS) or another analyzer that combines chromatography and mass spectrometry.
  • SELDI-TOF MS surface enhanced laser desorption/ionization time-of-flight mass spectrometry
  • the method and system of in vitro diagnosing of present invention is particularly suitable for predicting responsiveness to a medicament.
  • Yet another embodiment of present invention is a method to test a biomarker for its relevance in the diagnosis or prognosis of a selected disorder, by in vitro assaying at least one biological sample per patient from a plurality of patients comprising patient groups without the disorder and patient groups with the disorder or with a known stage of the disorder characterised in that the method comprises the steps of 1) producing a first input signal on the level or activity of at least one relevant biomarker for that disorder and a second input signal on the level or activity of a biomarker to be tested for its relevance to that selected disorder 2) processing the first input signal in the signal processor to construct a first mathematical model that produces output signals which are indicative for the disorder, the seriousness of disorder or the progress of disorder in the affected patient, 3) processing both the first input signal and the second input signal in the signal processor to construct a second mathematical model that produces output signals which are indicative for the disorder, the seriousness of disorder or the progress of disorder in the affected patient, 4) selecting the test biomarker as relevant in the diagnosis or prognosis of that selected disorder if
  • the first and the second mathematical model can be selected of the group consisting of Stepwise Logistic Regression Model and LS-SVM model.
  • the method of present invention can for instance been used for a disorder is associated with the female's menstrual cycle and is a disorder selected from the group consisting of premenstrual syndrome, migraine headache, endometriosis, psoriasis, acne, dysmenorrhoea, neurosia, asthma and premenstrual cramps.
  • the biomarker variables are preferably obtained from in vitro assaying of at least one biological sample of a patient in a specific phase of the menstrual cycle and most preferably the biomarker variables are obtained from in vitro assaying of at least one biological sample of a patient in the secretory phase of the menstrual cycle.
  • Yet another embodiment of present invention is an operating system for operating the methods of method to test a biomarker for its relevance in the diagnosis or prognosis of a selected disorder of present invention
  • which operating system controls the allocation of an in vitro essay system to generate biomarker values of a patient and which feeds the input signals from the in vitro essay system into signal processor comprising a mathematical model that is described on the relationship of a plurality of biomarker variables and a plurality of disorder variables from in vitro assaying of biological samples of plurality of patients with no disorder, affected with disorder, affected with a defined seriousness or with defined progress of disorder.
  • Such operating system can be used for testing the relevance of a specific biomarker for diagnosing or prognosing of a disorder, determining the presence or absence of a disorder, the seriousness of the disorder or the progress of the disorder in a patient. Furthermore this operating system can also control usage of the in vitro essay system and it may include a user interface that to enable the user to interact with the functionality of the computer.
  • the operating system of present invention includes a graphical user interface whereby the operating system controls the ability to generate graphics on the computer's display device that can be displayed in a variety of manners representative for or associated with the relevance of a specific biomarker for diagnosing or prognosing of a disorder, determining the presence or absence of a disorder or for the seriousness of the disorder or the progress of the disorder in a patient.
  • Yet another embodiment of present invention is a computer-executable code, stored in a computer-readable medium, the computer executable code adapted, when running on a computer system to run the operating system of present invention or to execute the mathematical model of present invention and to direct a processing means- to produce out put signals that are representative for the relevance of a specific biomarker for diagnosing or prognosing of a disorder, determining the presence or absence of a disorder or for the seriousness of the disorder or the progress of the disorder in a patient.
  • Yet another embodiment is a computer system for operating the operating system of present invention comprising a file system for storing files and a processor for analysing the content of biomarker value files stored in said file system to wherein said processor clusters said files in accordance with the mathematical model of present inventionto determine the relevance of a specific biomarker for diagnosing or prognosing of a disorder or for determining the presence or absence of a disorder, the seriousness of the disorder or the progress of the disorder in a patient.
  • Yet another embodiment of present invention is an apparatus comprising an in vitro diagnosis system for determining the relevance of a specific biomarker for diagnosing or prognosing of a disorder or for determining the presence or absence of a disorder, the seriousness of the disorder or the progress of the disorder in a patient, whereby the apparatus comprises or is interrelating with the computers system.
  • biomarker that has been identified by methods of present invention to be relevant for a specific medical condition, is used in the manufacture of a diagnostic to diagnose for the medical condition
  • the present invention concerns a method of in vitro diagnosing an endometriosis, characterised in that the method comprises processing of several biomarker variables obtained from in vitro assaying of at least one biological sample of a patient in a determined phase of the menstrual cycle, whereby the assay system produces an input signal into a signal processor corresponding with level, activity or presence of each biomarker in said sample and whereby the input signal is processed in the signal processor comprising a mathematical model that produces output signals that determine the presence or absence of endometriosis, the seriousness of endometriosis or the progress of endometriosis in the patient.
  • the biological samples can be of the same patient but in different phases selected of the menstruation phase, the follicular phase, the ovulation phase or the proliferation phase of the menstrual cycle are assayed in vitro for biomarkers.
  • the biomarker variables for diagnosing are obtained from in vitro assaying of at least one biological sample of patients when they were identified to be in the secretory phase of the menstrual cycle.
  • the mathematical model in this method of in vitro diagnosing can be described on the relationship of biomarker variables and disorder variables from in vitro assaying of biological samples of plurality of patients with no endometriosis, affected with endometriosis, affected with a defined seriousness or with defined progress of endometriosis.
  • the mathematical model method of in vitro diagnosing of present invention is build on in vitro assaying the presence of, the level of or the activity of a plurality of different biomarkers values, which are presumed relevant for the endometriosis in at least one biological sample from a plurality of patients with a known stage of the endometriosis and patients without the selected endometriosis.
  • Such mathematical model can for instance be a Stepwise Logistic Regression model or it can be mathematical model that is a trained Least Squares Support Vector Machine model (LS-SVM model).
  • LS-SVM model is further construed by testing the discriminating power for indicating presence or absence of the endometriosis of the specific biomarkers by performing a stepwise logistic regression analysis on said biomarker values and training the LS-SVM using only those biomarkers that show discriminating power.
  • Such method of in vitro diagnosing an endometriosis of present invention can further involve classifying the endometriosis extent or endometriosis progress in stages of endometriosis free, stage l-ll endometriosis, stage I H-IV endometriosis or it can involve classifying the endometriosis extent or endometriosis progress in stages of endometriosis free, minimal endometriosis, mild endometriosis, moderate endometriosis and severe endometriosis. Furthermore the output signals can be compared to reference signals.
  • the method of in vitro diagnosing of present invention can be characterized in that the in vitro assaying of the samples is carried out by an immunoprecipitation, a radioimmunoassay, an enzyme immunoassay, a fluorescent immunoassay, a chemiluminescent immunoassay, a competitive binding assay, an ELISA or a homogeneous immunoassay or it can be characterized in that the samples are analysed by homogenous binding assays or multi phase assays.
  • biomarker variables are corresponding to substances of the group consisting of immunological factors, inflammatory factors, Intercellular adhesion molecules and cystine-knot growth factors of the PDGF/VEGF growth factor family.
  • biomarker variables are corresponding to substances of the group consisting of interleukin (IL)-6, interleukin IL-8, tumor necrosis factor (TNF)- ⁇ , CA-125, CA-19-9, C-reactive protein (CRP), intercellular adhesion molecule-1 (slCAM-1) and vascular endothelial growth factor (VEGF) and Placental Growth Factor (PIGF).
  • IL interleukin
  • IL-8 tumor necrosis factor
  • TNF tumor necrosis factor
  • CBP C-reactive protein
  • slCAM-1 intercellular adhesion molecule-1
  • VEGF vascular endothelial growth factor
  • PIGF Placental Growth Factor
  • the method of in vitro diagnosing of present invention can be characterized in that the output signal identifies endometriosis with a sensitivity of > 60 % and a specificity of > 40 % as compared to a non endometriosis or control condition, preferably the output signal identifies endometriosis with a sensitivity of > 70 % and a specificity of > 50 % as compared to a non endometriosis or control condition, preferably the output signal identifies endometriosis with a sensitivity of > 70 % and a specificity of >60 % as compared to a non endometriosis or control condition.
  • the analysis is done by a combination of the Stepwise Logistic Regression model and the Least Squares Support Vector Machine model.
  • the biological sample can be chosen from serum, blood, and plasma
  • Present invention also involves a method for detecting or testing a endometriosis modulating or preventing agent in a non human mammalian endometriosis model, the method comprising: (a) administration of the modulation agent to said endometriosis model; (b) providing a biological sample from an endometriosis model (c) carrying out the diagnostic method of present invention and (e) comparing output signals form samples if the endometriosis model treated with said agent, with output signals form samples of the non treated endometriosis and/or with output signals form control biological samples.
  • present invention also involves a method of optimising the discriminating power of the in vitro diagnosing method of present invention by processing of a biomarker variable which has been validated to be relevant for endometriosis diagnosis or prognosis and is obtainable from in vitro assaying of at least one biological sample of a patient in a determined phase of the menstrual cycle, whereby the assay system produces input signals corresponding to concentration, activity or presence of each biomarker in said sample and whereby the input signals are processed in a signal processor comprising a mathematical model that produces output signals that determine the presence of the emdometriosis, the seriousness of the disorder or the progress of the disorder in the patient and wherein said mathematical model is a LS- SVM model which is constructed according to the following method:
  • the present invention can involve an operating system for operating the methods of the in vitro diagnosis method of present which operating system controls the allocation of an in vitro essay system to generate biomarker values of a patient and which feeds the input signals from the in vitro essay system into signal processor comprising a mathematical model that is described on the relationship of a plurality of biomarker variables and a plurality of endometriosis disorder variables from in vitro assaying of biological samples of plurality of patients with no endometriosis, affected with endometriosis, affected with a defined seriousness or with defined progress of endometriosis.
  • Such method can be used for determining the presence or absence of endometriosis, the seriousness of endometriosis or the progress of endometriosis in the patient.
  • the operating system also controls usage of the in vitro essay system.
  • Such operating system may include a user interface that to enable the user to interact with the functionality of the computer.
  • the operating system can include a graphical user interface whereby the operating system controls the ability to generate graphics on the computer's display device that can be displayed in a variety of manners representative for or associated with the condition of endometriosis in a selected patient or a group of patients to allow a user to distinguish between the absence of endometriosis, the seriousness of endometriosis or the progress of endometriosis in identified patients or patient groups.
  • Yet another aspect of present invention is a computer-executable code, stored in a computer-readable medium, the computer executable code adapted, when running on a computer system to run the operating system of present invention or to execute the mathematical models present invention and to direct a processing means to produce out put signals that are representative for a condition of endometriosis or a modifying condition of endometriosis.
  • Another aspect of present invention is a computer system for operating the operating system of present invention comprising a file system for storing files and a processor for analysing the content of biomarker value files stored in said file system to wherein said processor clusters said files in accordance with mathematical models of the invention as described previously in this application to establish plural levels of clusters that represent the presence or absence of endometriosis, the seriousness of endometriosis or the progress of endometriosis in the patient.
  • Yet another embodiment is an apparatus comprising an in vitro diagnosis system for generating the biomarker values for identifying a condition of endometriosis or any modification of such condition, whereby the apparatus comprises or is interrelating with the computers system.
  • a particular embodiment of present invention is a method of in vitro diagnosing with high discriminating power of a disorder, characterized in that the method comprises processing of a plurality of biomarker variables obtainable from in vitro assaying of at least one biological sample of a patient in a determined phase of the menstrual cycle, whereby the assay system produces input signals corresponding to concentration, activity or presence of each biomarker in said sample and whereby the input signals are processed in a signal processor comprising a mathematical model that produces output signals that determine the presence of the disorder, the seriousness of the disorder or the progress of the disorder in the patient, and wherein said mathematical model is a LS-SVM model which is constructed according to the following method a) in vitro assaying presence, level or activity of several specific biomarkers values which are presumed relevant for the disorder in at least one biological sample from a plurality of patients comprising patient groups without the disorder and patient groups with the disorder or with a known stage of the disorder and producing input signals thereof; b) performing a stepwise logistic regression analysis on the input
  • Such method of in vitro diagnosing with high discriminating power of a disorder of present invention can comprise that the biomarker variables for diagnosing are obtained from in vitro assaying of at least one biological sample of a patient in a specific phase of the menstrual cycle, preferably the biomarker variables for diagnosing are obtained from in vitro assaying of at least one biological sample of a patient in the secretory phase of the menstrual cycle.
  • the method of present invention can be used to diagnose a disorder which is associated with the menstrual cycle and is a disorder selected from the group consisting of premenstrual syndrome, migraine headache, endometriosis, psoriasis, acne, dysmenorrhoea, neurosia, asthma and premenstrual cramps.
  • present invention may comprise an operating system for operating the methods of in vitro diagnosing of present invention which controls the allocation of an in vitro essay system to generate biomarker values of a patient and which feeds the input signals from the in vitro essay system into signal processor comprising a mathematical model that is described on the relationship of a plurality of biomarker variables and a plurality of disorder variables from in vitro assaying of biological samples of plurality of patients with no disorder, affected with disorder, affected with a defined seriousness or with defined progress of disorder.
  • Such operating system can be used for determining the presence or absence of disorder, the seriousness of disorder or the progress of disorder in the patient.
  • the operating system also controls usage of the in vitro essay system.
  • the operating system can include a user interface that to enable the user to interact with the functionality of the computer.
  • the operating system includes a graphical user interface whereby the operating system controls the ability to generate graphics on the computer's display device that can be displayed in a variety of manners representative for or associated with the condition of disorder in a selected patient or a group of patients to allow a user to distinguish between the absence of disorder, the seriousness of disorder or the progress of disorder in identified patients or patient groups.
  • Yet another embodiment of present invention is a computer-executable code, stored in a computer-readable medium, the computer executable code adapted, when running on a computer system to run the operating system of present invention or to execute the mathematical model of present invention, and to direct a processing means to produce out put signals that are representative for a condition of disorder or a modifying condition of disorder.
  • the computer system for operating the operating system of present can comprise a file system for storing files and a processor for analysing the content of biomarker value files stored in said file system to wherein said processor clusters said files in accordance with the mathematical model of present invention, to establish plural levels of clusters that represent the presence or absence of disorder, the seriousness of disorder or the progress of disorder in the patient.
  • Yet another embodiment of present invention is an apparatus comprising an in vitro diagnosis system for generating the biomarker values for identifying a condition of disorder or any modification of such condition, whereby the apparatus comprises or is interrelating with the computers system of present invention.
  • the present invention relates further to a system and method for a diagnostic test with high sensitivity especially for minimal to mild endometriosis for instance a SELDI-TOF - MS ProteinChip technology can be combined with bioinformatics tools in a diagnostic model test with a high sensitivity especially for minimal to mild endometriosis.
  • the present invention also solves the problems of the related art on lack of early detection of endometriosis which is so crucial for its ultimate prevention. This can be done by applying a fast protein/peptides expression profiling of body fluids of women during distinct secretory phases. This method allows fast and non invasive early detection of endometriosis and indiction of the specific stages of the disease (minimal, mild, moderate or severe).
  • the method or system of present invention for instance involved SELDI-TOF -MS ProteinChip technology combined with bioinformatics analysis tools and this study demonstrated it to have sensitivity especially for minimal to mild endometriosis.
  • the invention is also broadly drawn to a diagnostic model test that thus not need the full structural characterization of specific biomarkers and binding of specific ligands to specific biomarkers, such as for instance antibodies.
  • a particular embodiment of present invention is a method suitable for use in the diagnosis of an early, minimal or mild endometriosis by identifying in isolated body fluid of a patient mass peaks of gene expressing products whereby the expression signature allows to differentiate a condition of endometriosis or no endometriosis is a peak in the 8.648 to 8.652 kDa molecular weight range, the 8.658 to 8.661 kDa molecular weight range, the 13.89 to 13.93 kDa molecular weight range, the 5.181 to 5.185 kDa molecular weight range and/or the 1.947 to1.951 kDa molecular weight range, the endometriosis condition is being indicated by down regulation of the expression products identified by these peaks.
  • a particular embodiment of present invention is a method suitable for use in the diagnosis of an early, minimal or mild endometriosis by identifying in isolated body fluid of a patient mass peaks of gene expressing products whereby the expression signature can differentiate a condition of endometriosis or no endometriosis by a peak indicating 8.65OkDa molecular weight, 8.659kDa molecular weight, 13.91 kDa molecular weight, 5.183kDa molecular weight and/or 1.949kDa molecular weight expressing agent.
  • a particular embodiment of present invention is a method suitable for use in the diagnosis of an early, minimal or mild endometriosis by identifying in isolated body fluid of a patient mass peaks of gene expressing products whereby the expression signature can differentiate a condition of endometriosis or no endometriosis by a peak indicating an about 8.65OkDa molecular weight, an about 8.659kDa molecular weight, an about 13.91 kDa molecular weight, an about 5.183kDa molecular weight and/or an about 1.949kDa molecular weight or combination of such peaks, the endometriosis condition being indicated by down regulation of the expression products identified by these peaks.
  • a particular embodiment of present invention is a method suitable for use diagnosis of an early, minimal or mild endometriosis by identifying in an isolated body fluid of a patient mass peaks of gene expressing products whereby the expression signature allows to differentiate a condition of endometriosis or no endometriosis.
  • a particular embodiment of present invention is a method suitable for use in the diagnosis of Stage l-ll endometriosis by identifying in isolated body fluid of a patient mass peaks of gene expressing products, whereby the expression signature allows to differentiate a condition of Stage l-ll endometriosis or no endometriosis by peaks indicating the 8.65OkDa, 8.659kDa, 13.91kDa 5.183kDa and/or 1.949kDa expression product, the endometriosis condition being indicated by down regulation of the expression products identified by these peaks.
  • a particular embodiment of present invention is a method suitable for use in the diagnosis of Stage l-ll endometriosis by identifying in isolated body fluid of a patient mass peaks of gene expressing products, whereby the expression signature allows to differentiate a condition of Stage l-ll endometriosis or no endometriosis by peaks indicating the about 8.65OkDa, about 8.659kDa, about 13.91kDa, about 5.183kDa and/or about 1.949kDa expression product and the endometriosis condition being indicated by down regulation of the expression products identified by these peaks.
  • the method may comprise (a) comparing a proteomic profile of a test sample of a biological fluid (i) a proteomic profile of a normal sample, or (ii) a reference proteomic profile comprising at least one unique expression signature characteristip of said condition, wherein the test sample proteomic profile and the normal sample proteomic profile or the reference proteomic profile comprise information of the expression of gene expression factors in the 8.648 to 8.652 kDa molecular weight range, the 8.658 to 8.661 kDa molecular weight range , the 13.89 to 13.93 kDa molecular weight range, the 5.181 to 5.185 kDa molecular weight range and/or the 1.947 to1.951 kDa molecular weight range or preferably of 8.65OkDa molecular weight, 8.659kDa molecular weight, 13.91 kDa molecular weight, 5.183kDa molecular weight and/or 1.949kDa molecular weight or about 8.65Ok
  • the proteomic profiles comprise information on the expression of all these factors the 8.65OkDa molecular weight and the 8.659kDa molecular weight and the 13.91 kDa molecular weight and the 5.183kDa molecular weight and the 1.949kDa molecular weight factor.
  • proteomic profiles are produced by mass spectrum analysis.
  • it comprises at least one unique expression signature in the 8.648 to 8.652 kDa range, the 8.658 to 8.661 kDa range, the 13.89 to 13.93 kDa range, the 5.181 to 5.185 kDa range or the 1.947 to1.951 kDa range of the mass spectrum.
  • the proteomic profiles are produced by Western blot analysis.
  • the body fluid may be selected from the group consisting of amniotic fluid, serum and vaginal fluid plasma, endometrial fluid obtained from a patient for instance a mammalian subject and preferably a human.
  • the method may comprise (a) comparing a proteomic profile of a test sample of a biological fluid (i) a proteomic profile of a normal sample, or (ii) a reference proteomic profile comprising at least one unique expression signature characteristic of said condition, wherein the test sample proteomic profile and the normal sample proteomic profile or the reference proteomic profile comprise information of the expression of gene expression factors in the 90.673 - 90.677 kDa range, the 35.92 - 35.94 kDa range , the 1.922 - 1.926kDa range and/or the 2.502 - 2.506 kDa range of the mass profile or preferably of 90.675kDa molecular weight, 35.95kDa molecular weight, 1.924kDa molecular weight and/or 2.504kDa molecular weight (b) if the test proteomic profile is essentially the same as the normal sample proteomic profile the subject is determined to not possess the endometriosis, while if the test
  • the proteomic profiles comprise information on the expression of all these factors the 90.675kDa molecular weight and the 35.95kDa molecular weight and the 1.924kDa molecular weight and the 2.504kDa molecular weight factor.
  • proteomic profiles are produced by mass spectrum analysis.
  • it comprises at least one unique expression signature in the 90.673 - 90.677 kDa range, the 35.92 - 35.94 kDa range , the 1.922 - 1.926kDa range, the 2.502 - 2.506 kDa range of the mass profile, where by the factor in the 90.673 - 90.677 kDa range or the 35.92 - 35.94 kDa range of the mass spectrum has to be upregulated to quote for Stage l-ll endometriosis and the factor in the 1.922 - 1.926kDa range or the 2.502 - 2.506 kDa of the mass spectrum has tob e down regulated to quote for Stage l-ll endometriosis.
  • the proteomic profiles are produced by Western blot analysis.
  • the body fluid may be selected from the group consisting of amniotic fluid, serum and vaginal fluid plasma, endometrial fluid obtained from a patient for instance a mammalian subject and preferably a human.
  • the method of present invention may further involve an analysis model for instance based on a Support Vector Machine (SVM) algorithm, logistic regression classification models with Leave-One-Out -Cross Validation (LOO - CV) and Ranking the significant mass peaks according to their classification power
  • SVM Support Vector Machine
  • LEO - CV Leave-One-Out -Cross Validation
  • Present invention thus involves a method of in vitro diagnosing and determining early stage endometriosis, characterised in that the methods comprises processing of several biomarker variables obtainable from in vitro assaying of a mass spectrum profiles of expression products in at least one fluid sample or at least one cell containing sample of said patient, whereby the assay system produces input signals into a signal processor corresponding with activity or presence of each biomarker in said sample and whereby the input signals are processed in the signal processor comprising a mathematical model that produce output signals that identifies if said patient has been affected by endometriosis and the seriousness of endometriosis or the progress of endometriosis in the affected patient. Is such diagnosis the output signals are compared to reference signals.
  • Another embodiment of present invention is a method to test a biomarker which is a molecular weight hit on a molecular mass profile for its relevance in the diagnosis or prognosis of a selected disorder, by in vitro assaying at least one biological sample per patient from a plurality of patients comprising patient groups without the disorder and patient groups with the disorder or with a known stage of the disorder characterized in that the method comprises the steps of 1) producing a first input signal on the level or activity of at least one relevant biomarker for that disorder and a second input signal on the level or activity of a biomarker to be tested for its relevance to that selected disorder 2) processing the first input signal in the signal processor to construct a first mathematical model that produces output signals which are indicative for the disorder, the seriousness of disorder or the progress of disorder in the affected patient, 3) processing both the first input signal and the second input signal in the signal processor to construct a second mathematical model that produces output signals which are indicative for the disorder, the seriousness of disorder or the progress of disorder in the affected patient, 4) selecting the test biomarker
  • the first and the second mathematical model are selected of the group consisting of Stepwise Logistic Regression Model and LS-SVM model and the biomarker variables may obtained from in vitro assaying of at least one biological sample of a patient in a specific phase of the menstrual cycle.
  • the operating system for operating such methods of controls the allocation of an in vitro essay system to generate biomarker values of a patient and which feeds the input signals from the in vitro essay system into signal processor comprising a mathematical model that is described on the relationship of a plurality of biomarker variables and a plurality of disorder variables from in vitro assaying of biological samples of plurality of patients with no disorder, affected with disorder, affected with a defined seriousness or with defined progress of disorder.
  • the operating system can include a user interface that to enable the user to interact with the functionality of the computer.
  • the operating system may include a graphical user interface whereby the operating system controls the ability to generate graphics on the computer's display device that can be displayed in a variety of manners representative for or associated with the relevance of a specific biomarker for diagnosing or prognosing of a disorder, determining the presence or absence of a disorder or for the seriousness of the disorder or the progress of the disorder in a patient.
  • the present invention may also comprise a computer-executable code, stored in a computer-readable medium, the computer executable code adapted, when running on a computer system to run the operating system of any of present invention to execute the mathematical model described herein and to direct a processing means to produce out put signals that are representative for the relevance of a specific biomarker for diagnosing or prognosing of a disorder, determining the presence or absence of a disorder or for the seriousness of the disorder or the progress of the disorder in a patient.
  • the present invention van be a computer system for operating the operating system of present invention which comprises a file system for storing files and a processor for analysing the content of biomarker value files stored in said file system to wherein said processor clusters said files in accordance with the mathematical model of present invention to determine the relevance of a specific biomarker for diagnosing or prognosing of a disorder or for determining the presence or absence of a disorder, the seriousness of the disorder or the progress of the disorder in a patient.
  • the present invantion concerns an apparatus comprising an in vitro diagnosis system for determining the relevance of a specific biomarker for diagnosing or prognosing of a disorder or for determining the presence or absence of a disorder, the seriousness of the disorder or the progress of the disorder in a patient, whereby the apparatus comprises or is interrelating with the computers of present invention
  • a particular embodiment of present invention is a method of in vitro diagnosing an endometriosis, characterised in that the method comprises processing of several biomarker variables obtained from in vitro assaying a mass profile of gene expression products of at least one biological sample of a patient in a determined phase of the menstrual cycle, whereby the assay system produces an input signal into a signal processor corresponding with level, activity or presence of each biomarker in said sample and whereby the input signal is processed in the signal processor comprising a mathematical model that produces output signals that determine the presence or absence of endometriosis, the seriousness of endometriosis or the progress of endometriosis in the patient.
  • the biological samples can be of the same patient but in different phases selected of the menstruation phase, the follicular phase, the ovulation phase or the proliferation phase of the menstrual cycle are assayed in vitro for biomarkers.
  • the method may be characterised in that the biomarker variables for diagnosing are obtained from in vitro assaying of at least one biological sample of patients when they were identified to be in the secretory phase of the menstrual cycle and the mathematical model can be described on the relationship of biomarker variables and disorder variables from in vitro assaying of biological samples of plurality of patients with no endometriosis, affected with endometriosis, affected with a defined seriousness or with defined progress of endometriosis.
  • the mathematical model is build on in vitro assaying the presence of, the level of or the activity of a plurality of different biomarkers values, which are presumed relevant for the endometriosis in at least one biological sample from a plurality of patients with a known stage of the endometriosis and patients without the selected endometriosis.
  • the mathematical model is a Stepwise Logistic Regression model.
  • the mathematical model can be a trained Least Squares Support Vector Machine model (LS-SVM model) and this LS-SVM model can be further construed by testing the discriminating power for indicating presence or absence of the endometriosis of the specific biomarkers by performing a stepwise logistic regression analysis on said biomarker values and training the LS-SVM using only those biomarkers that show discriminating power, lnr this method the output signals can be compared to reference signals.
  • LS-SVM model Least Squares Support Vector Machine model
  • the method for detecting or testing a endometriosis modulating or preventing agent in a non human mammalian endometriosis model comprises : (a) administration of the modulation agent to said endometriosis model; (b) providing a biological sample from an endometriosis model (c) carrying out the presviously descibed diagnostic method; and (e) comparing output signals form samples if the endometriosis model treated with said agent, with output signals form samples of the non treated endometriosis and/or with output signals form control biological samples.
  • the method for optimising the discriminating power of the in vitro diagnosing method of present invention comprises processing of a biomarker variable which has been validated to be relevant for endometriosis diagnosis or prognosis and is obtainable from in vitro assaying of at least one biological sample of a patient in a determined phase of the menstrual cycle, whereby the assay system produces input signals corresponding to concentration, activity or presence of each biomarker in said sample and whereby the input signals are processed in a signal processor comprising a mathematical model that produces output signals that determine the presence of the emdometriosis, the seriousness of the disorder or the progress of the disorder in the patient and wherein said mathematical model is a LS-SVM model which is constructed according to the following method:
  • the operating system for operating the methods of diagnosis of present invention controls the allocation of an in vitro essay system to generate biomarker values of a patient and which feeds the input signals from the in vitro essay system into signal processor comprising a mathematical model that is described on the relationship of a plurality of biomarker variables and a plurality of endometriosis disorder variables from in vitro assaying of biological samples of plurality of patients with no endometriosis, affected with endometriosis, affected with a defined seriousness or with defined progress of endometriosis.
  • the operating system may also control usage of the in vitro essay system.
  • the operating system may include a user interface that to enable the user to interact with the functionality of the computer.
  • the operating system may include a graphical user interface whereby the operating system controls the ability to generate graphics on the computer's display device that can be displayed in a variety of manners representative for or associated with the condition of endometriosis in a selected patient or a group of patients to allow a user to distinguish between the absence of endometriosis, the seriousness of endometriosis or the progress of endometriosis in identified patients or patient groups.
  • Yet another embodiment is a method of in vitro diagnosing with high discriminating power of a disorder, characterized in that the method comprises processing of a plurality of biomarker variables from a mass profile of expression products obtainable from in vitro assaying of at least one biological sample of a patient in a determined phase of the menstrual cycle, whereby the assay system produces input signals corresponding to concentration, activity or presence of each biomarker in said sample and whereby the input signals are processed in a signal processor comprising a mathematical model that produces output signals that determine the presence of the disorder, the seriousness of the disorder or the progress of the disorder in the patient, and wherein said mathematical model is a LS-SVM model which is constructed according to the following method a) in vitro assaying presence, level or activity of several specific biomarkers values which are presumed relevant for the disorder in at least one biological sample from a plurality of patients comprising patient groups without the disorder and patient groups with the disorder or with a known stage of the disorder and producing input signals thereof; b) performing a stepwise logistic
  • Endometriosis is currently defined as the presence of endometrial-like tissue outside the uterus, is associated with a chronic inflammatory reaction in the pelvis and results often in subfertility and pain. Endometriosis occurs mainly in women of reproductive age (16 to 50 years), is estrogen sensitive, and has a progressive character in at least 50%, but the rate and risk factors for progression are unpredictable and unknown, respectively. . The estimated prevalence of endometriosis is 6-10% in the general population, 13 - 33% in infertile women and more than 50% in women with severe dysmenorrhea, dyspareunia or chronic pelvic pain. In total, endometriosis affects approximately 14 million women in Europe only [Mihalyi A, et al.
  • endometriosis can be suspected in women with a history of dysmenorrhea, deep dyspareunia, chronic pelvic pain with or without subfertility, although it is possible that endometriosis remains asymptomatic.
  • endometriosis In speculo inspection of cervix and vagina may show a small cervical diameter, lateral cervical displacement and rarely blue discoloration suggestive for cervical or vaginal endometriosis.
  • Vaginal ultrasound is an adequate diagnostic method to detect ovarian endometriotic cysts, less accurate to detect deeply infiltrating endometriotic nodules, and useless to diagnose peritoneal endometriosis or endometriosis-associated adhesions.
  • the gold standard to diagnose endometriosis is visual pelvic inspection by laparoscopy, preferably with histological confirmation (Kennedy S. et al. Hum Reprod. 2005.
  • Endometriosis can be present as minimal to mild (Stage l-ll), mostly present on the pelvic peritoneal or ovarian surface, but can also present as moderate to severe disease (Stage III-IV; ASRM, 1997). More advanced endometriosis can be deeply invasive behind the cervix and invade into the rectovaginal septum, obliterating the pouch of Douglas partially or completely, or can present as ovarian endometriotic cysts (endometrioma). All presentations of endometriosis can be associated with filmy or dense adhesions.
  • stage of endometriosis is positively correlated with the degree of subfertility, but not or not as clearly with the degree of pelvic pain (Fauconnier A, Chapron C. Hum Reprod Update 2005; 11:595-606; Kennedy S. et al. Hum Reprod. 2005. 20(10):2698-704).
  • Surgical excision of endometriosis is an effective treatment for both endometriosis-associated subfertility and pain.
  • the spontaneous pregnancy rate following surgery is negatively correlated with the degree of endometriosis (D'Hooghe TM et al. Sem Reprod Med. March 2003; 21 :243-254).
  • the probability of endometriosis is so high that many gynaecologists will offer the patient a laparoscopy combined with excision of all visible endometriotic lesions and histological examination of at least 1 implant to confirm the presence of endometrial glands and stroma or will start medical treatment based on the assumption that the patient has endometriosis.
  • a non-invasive diagnostic test is important to confirm the presence of endometriosis, especially for those who will not be diagnosed or treated surgically.
  • Endometriosis also has a tremendous impact on the quality of life of the sufferers not only physically but also mentally and emotionally. 30-40% of the women with endometriosis were infertile, 72% had their relationships affected by endometriosis,
  • endometriosis 78% were at times unable to carry out day-to-day activities due to endometriosis, 36% had their job affected (41% having lost their job, 37% had to reduce their working hours and 23% changed their job). Furthermore, endometriosis is also associated with negative self image, anxiety, poor sleep, disturbances in partner relationships and social contact [Mihalyi A, et al. Expert Opin Emerg Drugs. 2006. 11(3):503-24.].
  • the present invention is based on a diagnostic model of using the plasma or serum concentration values of endometriosis related biomarkers as parameter values which are integrated in a non-invasive diagnostic system for determining the presence and the stages of endometriosis.
  • the system of present invention has been tested on a panel of 6 endometriosis related biomarkers interleukin (IL)-6, IL-8, tumor necrosis factor (TNF)- ⁇ , cancer antigen (CA)-125, carbohydrate antigen (CA)-19-9 and C-reactive protein (CRP), and is being further extended with soluble intercellular adhesion molecule-1 (slCAM-1) and vascular endothelial growth factor (VEGF) of which the parameter values have been statistically analyzed using stepwise logistic regression and Least Squares Support Vector Machines (LS-SVM) and demonstrated a sensitive and specific tool for diagnosing endometriosis and distinguishing between the phases of endometriosis stage l-ll or endometriosis stage I H-IV.
  • IL interleukin
  • TNF tumor necrosis factor
  • CA cancer antigen
  • CA carbohydrate antigen
  • CRP C-reactive protein
  • slCAM-1 soluble intercellular adhesion molecule-1
  • This system allows diagnostic testing of tissue or body fluid parameters and allows early diagnosis, also in cases when endometriosis expertise is limited or not available (e.g. rural areas, patients with limited mobility to attend hospital).
  • the availability of this system decreases the number of laparoscopic operations and related hospitalizations as well as the consequential costs, allows the selection of appropriate treatment modalities, reduces unnecessary medications and procedures, improves the efficacy of the available treatments for endometriosis-associated pain and subfertility and improves the patients' quality of life by preventing misdiagnosis and hit-and-miss treatments thus decreasing the time till symptom relief.
  • a non-invasive diagnostic test for instance by analysis of a fluid of a patient or from a cell or cells removed from patients but preferably a body fluid sample such as blood, or a plasma or a serum sample for women with pelvic pain and/or subfertility should have as most important goal that no women with endometriosis or other significant pelvic pathology who might benefit from surgery are missed.
  • a test with a high sensitivity is needed, with a low number of false negative results, ie a low number of patients who have a negative test but who do have endometriosis or other significant pelvic pathology justifying surgery.
  • a high specificity implies a low number of false positive results, ie a low number of patients who have a positive test but who do not have endometriosis or other pelvic pathology requiring surgery. This is less important in daily clinical practice, since a laparoscopy in this subset of women with subfertility will not only be useful to diagnose and treat endometriosis, but also to assess tubal patency and uterine function via hysteroscopy and possibly endometrial biopsy. Taking into account this clinical perspective, a diagnostic test with a sensitivity as high as 100% will be ideal, even if the specificity will be only 50%. Such test was not yet available in the art.
  • IL-6 for the meaning of this application is interleukin 6. Its characteristics and nucleotide and protein sequences has been disclosed by Hirano T., et al. Nature
  • IL-8 in this application is one of the several N-terminal processed forms are produced by proteolytic cleavage after secretion from at least peripheral blood monocytes, leukocytes and endothelial cells. It can be IL-8(1-77) but also IL-8(6-77) which is the most prominent form, L-8(5-77) or IL-8(7-77) or any isoform or splicing variant. Its characteristics and nucleotide and protein sequences have been disclosed by Matsushima K. et al. J. Exp. Med. 167:1883-1893(1988); Schmid J. and Weissmann C. J. Immunol. 139:250-256(1987); Kowalski J. And Denhardt D.T.
  • Tumor necrosis factor (TNF)- ⁇ is a protein that belongs to the tumor necrosis factor family. Its characteristics and nucleotide and protein sequences have been disclosed by Nedospasov S.A. et al. Cold Spring Harb. Symp. Quant. Biol. 51:611-624(1986); Pennica D. et al. Nature 312:724-729(1984); Shirai T. et al. Nature 313:803-806(1985); Nedwin G.E. et al. Nucleic Acids Res. 13:6361-6373(1985); Wang A.M. et al.Science 228:149-154(1985); Marmenout A. Et al. Eur. J. Biochem.
  • NHLBI HL66682 program for genomic applications UW-FHCRC, Seattle, WA (URL: http://pga.gs.washington.edu).” Submitted (DEC-2001) to the EMBL/GenBank/DDBJ databases; Rieder M.J. et al. "NIEHS-SNPs, environmental genome project, NIEHS ES15478, Department of Genome Sciences, Seattle, WA (URL: http://egp.gs.washington.edu).”; Submitted (JAN-2003) to the EMBL/GenBank/DDBJ databases; The MGC Project Team; Genome Res. 14:2121-2127(2004); Jang J.S. and Kim B.E.
  • CA-125 or cancer antigen 125 is a tumor marker or biomarker that may be elevated in the blood of some people with specific types of cancers (e.g. ovarian cancer).
  • CA-125 is a mucinous glycoprotein and the product of the MUC16 gene the sequence of the protein product has been disclosed in O'Brien T.J.et al. Tumour Biol. 22:348-366(2001) and O'Brien T.J. et al. Submitted (OCT-2002) to the EMBL/GenBank/DDBJ databases
  • CA-19-9 for this application is in the meaning of the carbohydrate antigen CA-19-9 which is an oncofetal antigen, expressed by several different cancers, but especially carcinomas of the gastrointestinal tract (Tolliver BA, O'Brien BL. South Med J 1997 Jan;90(1):89-90 and Reiter W. et al. Anticancer Res 2000 Nov-Dec;20(6D):5195-8
  • C-reactive protein is a protein that belongs to the pentaxin family. Its characteristics and nucleotide and protein sequences have been disclosed by Lei K.-J. et al. J. Biol. Chem. 260:13377-13383(1985); Woo P. et al. J. Biol. Chem. 260:13384- 13388(1985); Murphy T.M. et al. "Extrahepetic transcription of human C-reactive protein.” Submitted (NOV-1990) to the EMBL/GenBank/DDBJ databases; Tenchini M. L. et al.
  • Intercellular adhesion molecule-1 is a protein that belongs to the immunoglobulin superfamily, the ICAM family. Its characteristics and nucleotide and protein sequences have been disclosed by Simmons D.et al. Nature 331:624- 627(1988); Staunton D.E. et al. Cell 52:925-933(1988) ; Tomassini J. E. et al.Proc. Natl. Acad. Sci. U.S.A. 86:49' ⁇ 7-4911 (1989); Voraberger G.F. et al. J. Immunol. 147:2777- 2786(1991); Kalnine N. et al.
  • Placental Growth Factor is a protein that belongs to the PDGF/VEGF growth factor family. Its characteristics and nucleotide and protein sequences has been disclosed by Maglione D. et al. Proc. Natl. Acad. Sci. U.S.A. 88:9267-9271(1991);
  • VEGF Vascular endothelial growth factor
  • vascular endothelial growth factor isoform VEGF165 Cloning and identification of vascular endothelial growth factor isoform VEGF165.” Submitted (FEB-2002) to the EMBL/GenBank/DDBJ databases; Koul S. et al. "Cloning and characterization of VEGF from LnCAP cells, a line of prostate cancer cells.” Submitted (SEP-2004) to the EMBL/GenBank/DDBJ databases; Mungall A.J., et al. Nature 425:805-811(2003); The MGC Project Team, Genome Res. 14:2121- 2127(2004); Rieder M.J. et al. "SeattleSNPs.
  • NHLBI HL66682 program for genomic applications UW-FHCRC, Seattle, WA (URL: http://pga.gs.washington.edu)." Submitted (OCT-2001) to the EMBL/GenBank/DDBJ databases; Fiebich B.L. et al. Eur. J. Biochem. 211:19-26(1993); Zhang Z. and Henzel W.J. Protein Sci. 13:2819- 2824(2004) and Muller Y.A. et al. Proc. Natl. Acad. Sci. U.S.A. 94:7192-7197(1997).
  • subject or “patient” refers to any human or animal mammals.
  • biological sample can be fluid sample or at least one cell containing sample or it can be a sample chosen from serum, blood, plasma, biopsy sample, tissue sample, cell suspension, saliva, oral fluid, cerebrospinal fluid, amniotic fluid, milk, colostrum, mammary gland secretion, lymph, urine, sweat, lacrimal fluid, gastric fluid, synovial fluid, and mucus.
  • phases of the menstrual cycle refer to known in that art that identification of the phases of distinct endocrine and/or physiological activity : the menstruation phase, the follicular phase or proliferation phase, the event dividing phase, ovulation, and the luteal or secretory phase secretory phase which also can be described as the transition phase, fertile phase, ovulation, or infertile phase of a menstrual cycle
  • biomarkers for the disease.
  • Current diagnostic and prognostic methods for endometriosis depend on the identification and evaluation of these biomarkers, both individually and as they relate to one another.
  • biomarkers includes all types of biological data from a patient.
  • biomarkers may include, but are not limited to, data derived from the presence of substance of the body of a subject including, but not limited to endocrine substances such as hormones, exocrine substances such as enzymes, and neurotransmitters, electrolytes, proteins, carbohydrates, growth factors, cytokines, chemokines, monokines, fatty acids, triglycerides, and cholesterol.
  • endocrine substances such as hormones, exocrine substances such as enzymes, and neurotransmitters, electrolytes, proteins, carbohydrates, growth factors, cytokines, chemokines, monokines, fatty acids, triglycerides, and cholesterol.
  • endocrine substances such as hormones, exocrine substances such as enzymes, and neurotransmitters, electrolytes, proteins, carbohydrates, growth factors, cytokines, chemokines, monokines, fatty acids, triglycerides, and cholesterol.
  • the data are preferably generated from protein substances.
  • Biological data may be derived from analysis of fluids of a patient but it also may be derived from cells removed from patients (e.g. a from a blood sample) and grown in culture. Various characteristics of these cells may be examined histologically and biochemically. For example, cells removed from a patient and placed in culture may be examined for the presence of specific markers associated with the presence of a disease. Cells may be examined for their metabolic activity or for the products made and released into the culture medium.
  • Biological data about a patient includes results from genetic and molecular biological analysis of the nuclear and cytoplasmic molecules associated with transcription and translation such as various forms of ribonucleic acid, deoxyribonucleic acid and other transcription factors, and the end product molecules resulting from the translation of such ribonucleic acid molecules.
  • antibody refers to an intact antibody, or a binding fragment thereof that competes with the intact antibody for specific binding. Binding fragments are produced by recombinant DNA techniques, or by enzymatic or chemical cleavage of intact antibodies. Binding fragments include Fab, Fab 1 , F(ab')2, Fv, and single-chain antibodies.
  • An antibody other than a "bispecific” or “bifu notional” antibody is understood to have each of its binding sites identical.
  • An antibody substantially inhibits adhesion of a receptor to a counter-receptor or to a ligand when an excess of antibody reduces the quantity of receptor bound to counter-receptor or ligand by at least about 20%, 40%, 60% or 80%, and more usually greater than about 85% (as measured in an in vitro competitive binding assay).
  • Recombinant proteins formed by gene fusion of light and heavy chain antibody regions are also included in the definition of "antibody.”
  • Antibodies having changes in amino acid sequence from particular antibodies exemplified herein can be used in the method of the invention.
  • the sequences can have "substantial identity", meaning the sequence of the original and changed sequence, when optimally aligned, such as by the programs GAP or BESTFIT using default gap weights, share at least 80 percent sequence identity, preferably at least 90 percent sequence identity, more preferably at least 95 percent sequence identity, and most preferably at least 99 percent sequence identity in the sequence of the entire antibody, the variable regions, the framework regions, or the CDR regions.
  • residue positions which are not identical differ by conservative amino acid substitutions.
  • Conservative amino acid substitutions refer to the interchangeability of residues having similar side chains.
  • a group of amino acids having aliphatic side chains is glycine, alanine, valine, leucine, and isoleucine; a group of amino acids having aliphatic-hydroxyl side chains is serine and threonine; a group of amino acids having amide-containing side chains is asparagine and glutamine; a group of amino acids having aromatic side chains is phenylalanine, tyrosine, and tryptophan; a group of amino acids having basic side chains is lysine, arginine, and histidine; and a group of amino acids having sulfur-containing side chains is cysteine and methionine.
  • Preferred conservative amino acid substitution groups are: valine-leucine-isoleucine, phenylalanine-tyrosine, lysine-arginine, alanine-valine, glutamic-aspartic, and asparagine-glutamine.
  • valine-leucine-isoleucine phenylalanine-tyrosine
  • lysine-arginine alanine-valine
  • glutamic-aspartic glutamic-aspartic
  • asparagine-glutamine a preferred conservative amino acid substitution groups.
  • Fragments or analogs of antibodies or immunoglobulin molecules can be readily prepared by those of ordinary skill in the art. Preferred amino-and carboxy-termini of fragments or analogs occur near boundaries of functional domains. Structural and functional domains can be identified by comparison of the nucleotide and/or amino acid sequence data to public or proprietary sequence databases. Preferably, computerized comparison methods are used to identify sequence motifs or predicted protein conformation domains that occur in other proteins of known structure and/or function. Methods to identify protein sequences that fold into a known three-dimensional structure are known. [Bowie et al. Science 253:164 (1991)]. Thus, the foregoing examples demonstrate that those of skill in the art can recognize sequence motifs and structural conformations that may be used to define structural and functional domains in accordance with the invention.
  • Preferred amino acid substitutions are those which: (1) reduce susceptibility to proteolysis, (2) reduce susceptibility to oxidation, (3) alter binding affinity for forming protein complexes, (4) alter binding affinities, and (4) confer or modify other physicochemical or functional properties of such analogs.
  • Analogs can include various muteins of a sequence other than the naturally-occurring peptide sequence. For example, single or multiple amino acid substitutions (preferably conservative amino acid substitutions) may be made in the naturally-occurring sequence (preferably in the portion of the polypeptide outside the domain(s) forming intermolecular contacts).
  • a conservative amino acid substitution should not substantially change the structural characteristics of the parent sequence (e.g., a replacement amino acid should not tend to break a helix that occurs in the parent sequence, or disrupt other types of secondary structure that characterizes the parent sequence).
  • Examples of art-recognized polypeptide secondary and tertiary structures are described in Proteins, Structures and Molecular Principles (Creighton, Ed., W. H. Freeman and Company, New York (1984)); Introduction to Protein Structure (C. Branden and J. Tooze, eds., Garland Publishing, New York, N.Y. (1991)); and Thornton et at. Nature 354: 105 (1991).
  • any suitable technique for determining levels of endometriosis protein biomarkers such as interleukin (IL)-6, IL-8, tumor necrosis factor (TNF)- ⁇ , CA-125, CA-19-9 and C- reactive protein (CRP) or others in body fluids such as plasma may be used in the method of the invention.
  • suitable techniques include those based on determining protein activity and/or those based on determining the presence or levels of the protein biomarkers. Latent or active biomarker may thus be measured but the protein biomarker quantification can even be based on the quantification of their mRNA.
  • Bispecific antibodies can be generated that comprise (i) two antibodies: one with a specificity for a first biomarker and the other for a second molecule (ii) a single antibody that has one chain specific for first biomarker and a second chain specific for a second molecule, or (iii) a single chain antibody that has specificity for first biomarker and the other molecule.
  • Such bispecific antibodies can be generated using well known techniques, e.g., Fanger et al. Immunol Methods 4:72-81 (1994), Wright and Harris, supra, and Traunecker et al. Int. J. Cancer (Suppl.)7:51-52 (1992).
  • Antibodies for use in the invention also include “kappabodies” (III et al. "Design and construction of a hybrid immunoglobulin domain with properties of both heavy and light chain variable regions” Protein Engl 0:949-57 (1997)), “minibodies” (Martin et al. "The affinity-selection of a minibody polypeptide inhibitor of human interleukin-6" EMBO J13:5303-9 (1994)), “diabodies” (Holliger et al. '"Diabodies 1 : small bivalent and bispecific antibody fragments” PNAS USA90:6444-6448 (1993)).
  • Antibodies used in the present invention can be expressed in various cell lines. Sequences encoding the cDNAs or genomic clones for the particular antibodies can be used for transformation of suitable mammalian or nonmammalian host cells. Transformation can be by any known method for introducing polynucleotides into a host cell, including, for example packaging the polynucleotide in a virus (or into a viral vector) and transducing a host cell with the virus (or vector) or by transfection procedures known in the art, as exemplified by U.S. Patents 4,399,216, 4,912,040, 4,740,461, and 4,959, 455.
  • Methods for introduction of heterologous polynucleotides into mammalian cells include, but are not limited to, dextran-mediated transfection, calcium phosphate precipitation, polybrene mediated transfection, protoplast fusion, electroporation, particle bombardment, encapsulation of the polynucleotide(s) in liposomes, peptide conjugates, dendrimers, and direct microinjection of the DNA into nuclei.
  • Mammalian cell lines available as hosts for expression are well known in the art and include many immortalized cell lines available from the American Type Culture Collection (ATCC), including but not limited to Chinese hamster ovary (CHO) cells, NSOO, HeLa cells, baby hamster kidney (BHK) cells, monkey kidney cells (COS), and human hepatocellular carcinoma cells (e.g., Hep G2).
  • ATCC American Type Culture Collection
  • Non-mammalian cells can also be employed, including bacterial, yeast, insect, and plant cells.
  • Site directed mutagenesis of the antibody CH2 domain to eliminate glycosylation may be preferred in order to prevent changes in either the immunogenicity, pharmacokinetic, and/or effector functions resulting from non-human glycosylation.
  • the glutamine synthase system of expression is discussed in whole or part in connection with European Patents 216 846, 256 055, and 323 997 and European Patent Application 89303964.4.
  • Antibodies for use in the invention can also be produced transgenically through the generation of a mammal or plant that is transgenic for the immunoglobulin heavy and light chain sequences of interest and production of the antibody in a recoverable form therefrom.
  • Transgenic antibodies can be produced in, and recovered from, the milk of goats, cows, or other mammals. See, e.g., U.S. Patents 5,827,690, 5,756,687, 5,750,172, and 5,741 ,957.
  • Total biomarker protein levels may also be measured using methods such as ELISA, fluorometric assay, chemiluminescent assay, or radioimmunoassay.
  • ELISA or chemiluminescent assay methods are particularly preferred, since these are quick, sensitive, and specific, and are readily automated for large scale use. These methods also provide quantitative determinations. A number of appropriate methods for measuring these biomarker proteins are detailed in manuals on diagnosis of protein or (polypeptide biomarkers (The ELISA Guidebook (Methods in Molecular Biology) by John R. Crowther (Editor) Publisher: Humana Press; Spiral edition (March 2000); Elisa: Theory and Practice (Methods in Molecular Biology, VoI 42) by John R.
  • ⁇ assays conducted entirely in one liquid phase commonly called homogenous binding assays
  • assays which require the separation of a solid phase from a liquid phase herein called multi phase assays.
  • the traditional radioimmunoassay (RIA) is an example of a multi phase assay, as is the sandwich assay.
  • a binding component containing a signal producing species is bound to or converted into a solid phase (e. g., by precipitation).
  • the solid phase is separated from the liquid phase which contains signal that is not bound to the binding component.
  • the amount of bound signal is measured and used to determine analyte concentration.
  • the reliable noninvasive diagnostic tool of present invention can conclusively determine the presence or absence and severity of endometriosis and thus will have a major impact on the treatment of women with pelvic pain and/or subfertility, and result in significant improvements.
  • Many women suffering from chronic pelvic pain, dysmenorrhea, dyspareunia and/or infertility can get a correct diagnosis immediately at the onset of their symptoms with respect to the presence or absence of endometriosis.
  • these patients will not have to travel to a hospital or fertility center for the diagnosis as it can be sufficient to visit a general practitioner with access to blood analysis at a competent institution.
  • the general practitioner can assign patients with probable endometriosis immediately to an appropriate institution with the necessary expertise where gynecologists can immediately provide the most adequate, disease specific and personalized treatment for these women.
  • This will enable the health care specialists to start targeted therapies in early stages of the disease which can largely increase the success rates of the treatments which in turn can significantly improve the quality of life of the patients on the physical health levels as well as on the mental and emotional levels.
  • the patients found negative by the test will also benefit from the results as it can indicate that the symptoms are caused by condition(s) other than endometriosis which may require a different area of expertise (eg. gastroenterology, urology). This will shorten the time to a correct diagnosis also for symptomatic women without endometriosis.
  • the use of a noninvasive diagnostic test will also greatly decrease endometriosis related health care costs by decreasing the number of unnecessary diagnostic laparoscopic surgeries, and by preventing the use of medications ineffective in endometriosis but frequently prescribed when the diagnosis is unknown.
  • a test can indirectly reduce the indirect economic loss related to endometriosis by enabling early treatment and thus preventing the progression of the disease which is frequently seriously decreasing working capacity.
  • the use of a reliable noninvasive diagnostic test will allow an accurate estimation of the prevalence of endometriosis in general, its relation to pelvic pain and/or (in)fertility and its potential impact on the quality of life of the affected women population. Furthermore, it can help to develop population scale strategies to fight endometriosis more efficiently.
  • a particular advantage the presen invention is that it provides a method to test a biomarker for its relevance in the diagnosis or prognosis of a selected disorder which is associated with the female's menstrual cycle and is a disorder selected from the group consisting of premenstrual syndrome, migraine headache, endometriosis, psoriasis, acne, dysmenorrhoea, neurosia, asthma and premenstrual cramps, by in vitro assaying at least one biological sample per patient from a plurality of patients comprising patient groups without the disorder and patient groups with the disorder or with a known stage of the disorder characterised in that the method comprises the steps of 1) producing a first input signal on the level or activity of.
  • the first and the second mathematical model are selected of the group consisting of Stepwise Logistic Regression Model and LS-SVM model 2) processing the first input signal in the signal processor to construct a first mathematical model that produces output signals which are indicative for the disorder, the seriousness of disorder or the progress of disorder in the affected patient, 3) processing both the first input signal and the second input signal in the signal processor to construct a second mathematical model that produces output signals which are indicative for the disorder, the seriousness of disorder or the progress of disorder in the affected patient, 4) selecting the test biomarker as relevant in the diagnosis or prognosis of that selected disorder if the second mathematical model reveals a better performance than the first mathematical model.
  • biomarker variables are obtained from in vitro assaying of at least one biological sample of a patient in a specific phase of the menstrual cycle and in particular the biomarker variables are obtained from in vitro assaying of at least one biological sample of a patient in the secretory phase of the menstrual cycle.
  • Such method can be implemented in operating system which controls the allocation of an in vitro essay system to generate biomarker values of a patient and which feeds the input signals from the in vitro essay system into signal processor comprising a mathematical model that is described on the relationship of a plurality of biomarker variables and a plurality of disorder variables from in vitro assaying of biological samples of plurality of patients with no disorder, affected with disorder, affected with a defined seriousness or with defined progress of disorder.
  • An interesting use of such operating system is testing the relevance of a specific biomarker for diagnosing or prognosing of a disorder, determining the presence or absence of a disorder, the seriousness of the disorder or the progress of the disorder in a patient pr the use of the operating system also controls usage of the in vitro essay system.
  • Such operating system can include a user interface that to enable the user to interact with the functionality of the computer, whereby the operating system can include a graphical user interface whereby the operating system controls the ability to generate graphics on the computer's display device that can be displayed in a variety of manners representative for or associated with the relevance of a specific biomarker for diagnosing or prognosing of a disorder, determining the presence or absence of a disorder or for the seriousness of the disorder or the progress of the disorder in a patient.
  • the invention also provide a computer-executable code, stored in a computer-readable medium, the computer executable code adapted, when running on a computer system to run the operating system descibed here above or to execute the mathematical model of present invention and to direct a processing means to produce out put signals that are representative for the relevance of a specific biomarker for diagnosing or prognosing of a disorder, determining the presence or absence of a disorder or for the seriousness of the disorder or the progress of the disorder in a patient.
  • the invention comprises a computer system for operating the operating system of present invention comprising a file system for storing files and a processor for analysing the content of biomarker value files stored in said file system to wherein said processor clusters said files in accordance with the mathematical model of claim 1 to determine the relevance of a specific biomarker for diagnosing or prognosing of a disorder or for determining the presence or absence of a disorder, the seriousness of the disorder or the progress of the disorder in a patient.
  • the invention furthermore concerns an apparatus comprising an in vitro diagnosis system for determining the relevance of a specific biomarker for diagnosing or prognosing of a disorder or for determining the presence or absence of a disorder, the seriousness of the disorder or the progress of the disorder in a patient, whereby the apparatus comprises or is interrelating with the computers system of present invention.
  • a biomarker that has been identified by methods, the computer system and /or the apparatus of present invention which were found to be relevant for a specific medical condition can be in the manufacture of a diagnostic to diagnose for the medical condition"
  • the plasma concentrations of these biomarkers were determined in 315 plasma samples in 3 precisely defined study groups: 1) women without endometriosis (controls), 2), women with stage l-ll endometriosis, 3) women with stage I H-IV endometriosis.
  • the results confirmed that plasma levels of these markers can vary significantly between women with and without endometriosis, regardless of disease stage and menstrual cycle phase.
  • This study demonstrated that it is the secretory phase of the menstrual cycle when differences in terms of biomarkers are most explicitly expressed and that the secretory phase is the most suitable period to measure plasma concentrations of the selected biomarkers for diagnostic purposes.
  • tissue bank contains plasma samples of over 800 patients.
  • BMI body mass index
  • ASRM advanced surgery report with scoring and staging of endometriosis according to the classification of the ASRM (ASRM, 1997) and histological confirmation.
  • Sample collection Before surgery, about 4x4ml peripheral blood is collected from the patients. The blood samples are centrifuged at 10000 rpm for 10 minutes at 4°C, plasma is aliquoted and stored at -80 0 C till analysis. The maximum time period between sample collection and storage in the -80 0 C freezer is maximum 1 hour. Each aliquot is labeled and the related information (eg date of collection, location in the tissue-bank, clinical data) is entered in the electronic sample database.
  • Control group 38; Endometriosis Stage l-ll group: 47; Endometriosis Stage III-IV group: 31.
  • the selected 8 biomarkers - IL-6, IL-8, TNF- ⁇ , slCAM-1 , VEGF, CA-125, CA-19-9 and CRP- are determined in plasma samples collected in the secretory phase from women with or without endometriosis.
  • IL-6, IL-8 and TNF- ⁇ levels can be determined by various methods such as -but not limited to- commercially available OptEIA ELISA kits developed for the quantitative determination of the respective plasma markers by BD Biosciences-Pharmingen (Erembodegem, Belgium).
  • slCAM-1 levels will also be determined by using the commercially available OptEIA ELISA kits developed for slCAM-1 quantitation.
  • the method of the measurements is based on the following principle:
  • the BD OptEIATM test is a solid phase sandwich ELISA (Enzyme-Linked Immunosorbent Assay). It utilizes a monoclonal antibody specific for the target molecules coated on a 96-well plate. Standards and samples are added to the wells in duplicates, and any target molecule (eg. IL-6, IL-8, TNF- ⁇ or slCAM-1) present in the wells binds to the immobilized antibody. The wells are washed and an streptavidin- horseradish peroxidase conjugate mixed with a biotinylated antihuman antibody - specific for the target molecule of interest- is added, producing an antibody-antigen- antibody "sandwich”.
  • target molecule eg. IL-6, IL-8, TNF- ⁇ or slCAM-1
  • CA-125 and CA-19-9 can be measured by various methods such as -but not limited to- using automated electrochemiluminescence immunoassays (Roche Diagnostics, Belgium), while CRP levels were determined using an automated immunoturbidimetric assay (Roche Diagnostics, Belgium).
  • ROC Receiver Operating Characteristic
  • Multivariate analysis represents a far more complex statistical approach.
  • two methods have been used to analyze the data: stepwise logistic regression and Least Squares Support Vector Machines (LS-SVM).
  • Stepwise Logistic regression is a statistical model part of a group of statistical models called General Linear Models (GLM).
  • a logistic regression model describes the relationship between one or several independent or explanatory variables (xi,...,x n ) and a binary (a variable that can only take two values: 0 or 1) outcome variable Y.
  • x), which is the conditional probability of Y 1 given the explanatory variables, is represented by y(x): where g(x) is called the logit and is given by s( ⁇ ) - Po + P ⁇ ⁇ ⁇ + ⁇ 2 ⁇ 2 + - + ⁇ P x P .
  • ⁇ o, ⁇ i, ⁇ 2 ,---, ⁇ P are the parameters of the logistic regression model. These parameters indicate the influence that their corresponding explanatory variable has on the outcome.
  • the sign of the parameter corresponds to an increase of the odds if it is positive and a decrease of the odds if it is negative.
  • the magnitude of each parameter refers to the amount by which the dependent variable (i.e. the outcome) will increase if the independent variable changes by one unit.
  • Variables are iteratively added to the logistic regression model if their p-value is below the significance level for entry in the model (i.e. PE). After each iteration, the variable with the worst p-value is removed if this p-value is above the significance level P R for removal out of the model. This means that each forward selection step is followed by a backward selection step. The algorithm stops if no more variables can be included or removed.
  • logistic regression makes no assumption about the distribution of the independent variables. Moreover both discrete and continuous variables can be modeled at the same time without extra processing. In addition the logistic regression parameters can be easily interpreted.
  • Least Squares Support Vector Machines are a modified version of Support Vector Machines (SVM) where a linear problem is solved instead of the more complex quadratic programming problem. This makes LS-SVMs easier and much faster than SVM. Given a training set of data with the corresponding class labels, LS-SVMs can be used for binary classification.
  • the LS-SVM classifier takes the following form:
  • mapping function ⁇ (x) ⁇ (x)
  • K(r,s) ⁇ (r) ⁇ ⁇ (s) where r and s belong to the input space.
  • a kernel function satisfies certain conditions it can be used in LS-SVM.
  • Different types of kernels exist such as a linear kernel and RBF kernel.
  • LS-SVM Least Squares Support Vector Machine
  • MACBETH a publicly available web server can be used to build LS-SVM classification models [Pochet N.L.M.M., et al. Bioinformatics, vol. 21 , no. 14, JuI. 2005, pp. 3185-3186] and allows using LS-SVM in an easy way.
  • IL-6, IL-8, tumor necrosis factor (TNF)- ⁇ , CA-125, CA-19-9 and C-reactive protein (CRP)- are involved in the development and/or progression of endometriosis as autocrine/paracrine factors or as products of immunocompetent cells promoting vascularisation and/or supporting survival and proliferation of ectopic endometrial cells through various mechanisms.
  • Samples collected from women who at the time of collection were taking medications on a regular basis; Samples collected from women who had been operated within 6 months prior to the time of collection and Samples collected from women who had other disease(s) at the time of collection.
  • the plasma concentrations of IL-6, IL-8, TNF- ⁇ , CA-125, CA-19-9 and CRP were determined in the remaining 315 samples by using commercially available ELISA kits (for IL-6, IL-8, TNF- ⁇ , BD Biosciences, Erembodegem, Belgium) or by automated immunometric assays (for CA-125, CA-19-9, CRP, Roche, Belgium).
  • Statistical analysis of the data was carried out by using Kruskal-Wallis test, Mann-Whitney test, ROC analysis Multivariate Stepwise Logistic Regression analysis and LS-SVM as appropriate, to evaluate the diagnostic value of the molecules individually and in combination.
  • stepwise logistic regression analysis showed that combined measurement of IL-8 and CA 125 have the strongest discriminating ability compared to that of the individual molecules (Table I).
  • stepwise logistic regression analysis showed that combined measurement of IL-6 and TNF- ⁇ have the strongest discriminating ability compared to that of the individual molecules alone or in other combinations (Table III).
  • CTRL vs All secretory endometriosis: IL-8+ TNF alpha+CA 125
  • CTRL vs All secretory endometriosis: ezp( ⁇ O + ⁇ l *lL -B + ⁇ 2*7m- a+ ⁇ 3*CA-125) 7 ⁇ 1 + esp(£0 + ⁇ . *IL - 8 + ⁇ 2*TNF - a+ ⁇ i*CA- 125)
  • the parameters ⁇ , ⁇ 1, ⁇ 2 and ⁇ 3 are constants calculated during the stepwise logistic regression analysis, therefore these are known.
  • the symbols IL-6, IL-8, TNF- ⁇ and CA 125 represent the secretory phase plasma concentrations of these biomarkers in the patient in question. This way all parameters are known to calculate the "y" value. This will actually be the final diagnostic parameter.
  • the stepwise logistic regression analysis also provides an ROC curve with the possible cut-off values (y-values) and the respective sensitivity and specificity values for all comparisons.
  • y-value cutPositive Negative FaIs FaIs Sensitivity 1-Specificity Specificity Sum off
  • Positive Negative (Sens+Spec) 0.500473332 35 34 4 12 0.744680851 0.105263158 0.89473684 1.639418
  • cut-off level y-value
  • both the first (CTRL vs Stage l-ll) and the second (CTRL vs Stage l-ll) model are negative the patient is considered to be endometriosis free. If the first model (CTRL vs Stage l-ll) gives positive result but the second (CTRL vs Stage III-IV) is negative then the patient is considered to have stage l-ll endometriosis. If the second model is positive (regardless whether the first one is positive or negative) the patient is considered to have stage III-IV endometriosis. In this manner the investigator can classify the patient according to disease stage.
  • the third model (CTRL vs All secretory endometriosis) can serve as a backup test to verify the outcome of the others or as a general screening test to determine presence or absence of the disease.
  • This example uses the multivariate stepwise logistic regression method to classify unknown patients.
  • a LS-SVM method is used.
  • the system does not select certain biomarkers like the stepwise logistic regression method but on the contrary, it will use all parameters that are fed into the system (eg if we enter 6 biomarkers it will try to distinguish between the different groups by using all 6 parameters, if we enter 8 parameters it will use all 8 etc).
  • LS- SVMs have means to prevent the model from being sensitive to outliers in the data, resulting in a model that is capable of making even better predictions for prospective analyses as the generalization of this technique on an independent set of patients can be more optimal than is possible with logistic regression.
  • the training and test set for LS-SVM analysis 4-5 were determined as follows: all secretory phase control patients (no endometriosis), stage l-ll patients and stage III-IV patients were divided into a 2/3 and a 1/3 group. The 2/3 group was used as the training set and the 1/3 group was used as the test set. It means that 2/3 of all secretory phase control patients, stage l-ll patients and stage III-IV patients were included in the training set.
  • the plasma concentration values of the six biomarkers (IL- 6, IL-8, TNF- ⁇ , CA 125, CA 19-9 and CRP) measured in these patients (training set) were entered into the LS-SVM system and analyzed.
  • the plasma concentration values of the six biomarkers (IL-6, IL-8, TNF- ⁇ , CA 125, CA 19-9 and CRP) measured in the other 1 third of the patients (test set) was entered into the model resulting from the training analysis. This test could tell us how well the model can differentiate between the groups.
  • the LS-SVM method was trained and tested to differentiate between endometriosis free women and women with stage l-ll endometriosis. Both groups consisted exclusively of patients from the secretory phase of the menstrual cycle. Using six endometriosis related biomarkers (IL-6, IL-8, TNF- ⁇ , CA 125, CA 19-9 and CRP) the LS-SVM could differentiate between the different subgroups with the following parameters (Table Vl):
  • the LS-SVM method was also trained and tested to differentiate between endometriosis free women and women with stage MI-IV endometriosis. Both groups consisted exclusively of patients from the secretory phase of the menstrual cycle. Using six endometriosis related biomarkers (IL-6, IL-8, TNF- ⁇ , CA 125, CA 19-9 and CRP) the LS-SVM could differentiate between the different subgroups with the following parameters (Table VII):
  • the LS-SVM method was also trained and tested to differentiate between endometriosis free women and all women with endometriosis regardless of the disease stage. Both groups consisted exclusively of patients from the secretory phase of the menstrual cycle. Using six endometriosis related biomarkers (IL-6, IL-8, TNF- ⁇ , CA 125, CA 19-9 and CRP) the LS-SVM could differentiate between the different subgroups with the following parameters (Table VIII):
  • the practical application of the diagnostic model based on LS-SVM will also be convenient and easy to use by because the LS-SVM with a linear kernel can be written as a simple linear equation and even the LS-SVM with the RBF kernel can be implemented in any software package that allows calculations to be preformed (e.g. Microsoft Excel).
  • the examples, analyses and results given above clearly indicate that the determination of biomarker levels in the secretory phase of the menstrual cycle and subsequent analysis of the parameters (biomarker values) by either the stepwise logistic regression model or by the LS-SVM model developed and described in the present invention are enabling the diagnosing of the presence/absence and stage of endometriosis, to prognose the progression of the disease and determine responsiveness to medical or surgical treatment in a noninvasive manner.
  • the combination of the two data analysis methods can further improve the diagnostic power of the present invention.
  • the combination of the two analysis methods can be performed in the following manner: Firstly, the following preprocessing step is performed: the measured endometriosis related biomarker values can be entered into the stepwise logistic regression model which will select the best marker combinations which have more discriminating power than the individual markers alone or in other combination(s) regarding the discriminating ability between women without endometriosis and those with early (minimal-mild) and advanced (moderate-severe) stages of the disease. Secondly, these selected parameters can be entered into the LS-SVM which provides a diagnostic model. The obtained model can perform better than the model provided by the stepwise logistic regression analysis.
  • LS-SVMs by applying a radial basis function (RBF) kernel- are also able to identify possible nonlinear structures or correlations.
  • the applied analysis methods of present invention are the most adequate in terms of developing a clinically useful diagnostic test.
  • the use of stepwise logistic regression and the LS-SVM to analyze endometriosis related biomarker values (as parameters) in the secretory phase provide an accurate, reliable and convenient diagnostic and research tool.
  • the models developed and described in present invention are also more efficient in solving the problem of non-invasively diagnosing and prognosing endometriosis than decision trees or the nearest neighbor(s) approaches.
  • decision tree also referred to as classification tree or reduction tree
  • the nearest neighbor(s) method does not guarantee good generalization performance as it does not provide a model
  • new samples are classified based on there closeness to the already collected samples. Therefore the performance of nearest neighbor methods can be serioulsy degraded by the presence of noise.
  • the application of the system of present invention is a suitable research tool for at least five major innovative areas.
  • the analysis of large patient populations based not only on the stage of the disease but also on the actual menstrual cycle phase is an innovative feature.
  • Previous studies have shown that many molecules in the peripheral circulation show a menstrual cycle phase dependent variability in their expression/plasma concentration.
  • To present inventions is based on studies on studies aiming at identifying disease specific alterations in the different cycle phases.
  • authors either arbitrarily chose a specific phase of the cycle or analyzed a study-population containing individuals from all phases of the menstrual cycle.
  • the system of present invention concerns a tool for developing novel medicaments and for prediction of the responsiveness to a drug.
  • the statistical models used to analyze the experimental data are innovative in the development of a noninvasive diagnostic test for endometriosis.
  • biomarkers variables
  • a stepwise selection procedure a combination of forward and backward selection- to select the optimal set of variables in the logistic regression model can be used.
  • This approach makes no assumption about the distribution of the independent variables and the logistic regression parameters can be easily interpreted.
  • results can be analyzed by using Least Squares Support Vector Machines (LS-SVM). Given a training set of data with the corresponding class labels, LS-SVMs can be used for binary classification. By using LS-SVM non-linear relationships between the dependent and independent variables can be discovered and the number of variables that can be modeled is large.
  • LS-SVM Least Squares Support Vector Machines
  • present invention provides a tool for determining whether or not a known biomarker -which can be any known biomarker-, is relevant in the diagnosis and/or prognosis of endometriosis.
  • a body fluid for instance blood, plasma or serum samples from women with or without endometriosis in the secretory phase of the menstrual cycle.
  • the levels of the diagnostically relevant biomarkers and the candidate biomarker(s) that may or may not be relevant for diagnosis and/or prognosis can be measured.
  • Stepwise Logistic Regression analysis initially showed that simultaneous measurement and analysis of IL-6 and TNF-a in the secretory phase has better diagnostic/prognostic value than any of the six biomarkers (IL-6, IL-8, TNF- ⁇ , CA 125, CA 19-9 and CRP) alone or in any combination other than the combination of IL-6 and TNF-a. If someone wants to test whether VEGF is relevant in the diagnosis/prognosis of endometriosis he/or she needs to measure IL-6, TNF-a and VEGF concentrations in a set of secretory phase women without endometriosis or with stage l-ll endometriosis.
  • VEGF vascular endometriosis
  • LS-SVM can be used to determine whether or not a known biomarker is relevant in the diagnosis/prognosis of endometriosis. If someone wants to test whether VEGF is relevant in the diagnosis/prognosis of endometriosis he or she needs to measure all biomarker that are already included in the LS-SVM model (for example biomarkers IL-6, IL-8, TNF- ⁇ , CA 125, CA 19-9 and CRP as in analysis 4) and additionally he or she has to measure the VEGF concentrations also in a set of secretory phase women without endometriosis or with stage l-ll endometriosis.
  • biomarker for example biomarkers IL-6, IL-8, TNF- ⁇ , CA 125, CA 19-9 and CRP as in analysis
  • VEGF is relevant in the diagnosis/prognosis of endometriosis if the model does not perform better then the model without VEGF then this biomarker is not relevant.
  • the present invention can provide a better insight and understanding of the pathogenesis of the disease and will help to identify the so far poorly understood environmental, nutritional, professional and social risk factors that can promote the onset and development of endometriosis.
  • the present invention can provide new therapeutic targets for developing novel, more efficient medical (non-surgical) treatment modalities for endometriosis with less adverse effects. Additionally - by monitoring the biomarkers in the patients using the system of present invention- the patients' response to medical (non-surgical) or surgical treatment can be followed and evaluated.
  • Present invention allows simultaneous measurement of 6 or more potential plasma markers in precisely defined, large populations followed by sophisticated statistical analysis. This is a powerful diagnostic and research tool.
  • the system of present invention provides a first time simultaneous measurement of 6 or more potential plasma markers for. endometriosis in precisely defined, large populations followed by a model of sophisticated statistical analysis of the data and provides a tool for non-invasively diagnosing endometriosis at different stages.
  • the diagnostic method and tool of present invention can be used for development of new treatment strategies to prevent progression of the disease and furthermore it can be used to monitor the disease and/or its symptoms and determine response to certain treatment modalities which in turn can provide deeper understanding of the pathophysiology of endometriosis also.
  • a panel of biomarkers has been identified and at the date of the invention instructions have been provided for the man skilled in the art to enlarge that panel, which panel can be used to significantly reduce the time from the onset of pain symptoms to diagnosis of endometriosis. Such earlier diagnosis and treatment will improve health related quality of life and the disease's natural progression may also be impeded.
  • the system is particularly suitable for distinguishing patients being either negative - having no endometriosis- or positive -having stage l-ll or stage IH-IV endometriosis
  • ASRM American Society of Reproductive Medicine
  • Frozen tissues biopsies were weighed (100mg/ml lysis buffer) and immediately thawed in phosphate buffered saline (PBS) while on ice. Tissues were washed five times in PBS to rinse off any adhering haemoglobin (Hb). The tissue homogenisation was realised by addition of 500 ⁇ l (100 ⁇ l/10mg) of U9 lysis buffer ( 9M Urea, 2% CHAPS, 50 mM Tris-HCI pH 9.0) (Ciphergen Biosystems, Fremont, CA, USA). Tissues were homogenised using tissue sonicator while on ice, until they were completely dissolved in the lysis buffer.
  • U9 lysis buffer 9M Urea, 2% CHAPS, 50 mM Tris-HCI pH 9.0
  • the protein lysates were incubated on ice for 1 hour to allow optimal extraction from the membrane fraction.
  • the protein lysate was centrifuged at 13,000 rpm for 10 minutes at 4 0 C to remove cell membranes and other undissolved components. Samples were subjected to resin treatment.
  • Resin of 100 ⁇ l was added to Spin-column and spun down at high speed.
  • the IMAC resin Biopsera, Cergy, France
  • the 100m ZnSO 4 composition was centrifuged at high speed for approximately 30 seconds and discarded.
  • the spin column were washed twice with 20OuI of MiIIi-Q H 2 O. Further saturation was done by additional 200 ⁇ l of 10OmM ZnSO 4 to 100 ⁇ l of IMAC-resin and incubated for 15 minutes at room temperature (RT). The spin columns were washed twice with 200 ⁇ l MiIIi-Q H 2 O.
  • ProteinChip array spots were equilibrated with 150 ⁇ l of respective binding buffer (Ciphergen, Fremont, CA, USA) with shaking for five minutes at room temperature to pre-activate binding surfaces. Then 20 ⁇ l of sample lysates (10 ⁇ g per spot) with surface-type dependent binding buffer (Table IX) was loaded onto each spot in duplicate, and incubated for 60 minutes at room temperature (RT) while shaking. The proteins not retained on the ProteinChip array surfaces were washed away twice with appropriate buffer for five minutes, rinsed in 150 ⁇ l of distilled milli-Q water and air-dried.
  • Spectra of the retained proteins was obtained by ionising the proteins to gaseous phase using two types of energy absorbing molecules (EAM): alpha-cyano-4-hydroxy cinnamic acid (CHCA), recommended for small molecules ( ⁇ 15 kDa), and Sinnapinic acid (SPA) (both from Ciphergen, Fremont, CA, USA), recommended for all larger molecules.
  • CHCA alpha-cyano-4-hydroxy cinnamic acid
  • SPA Sinnapinic acid
  • Example 6 Data pre-processing We filtered background noise through baseline correction. Normalisation of variation in signal intensity between spectra was done through rescaling each spectrum by total ion current. Peak detection was done First S/N ratio 10, 7, 5 and 3 and minimum peak threshold were 30%, 25% and 20%. Peak cluster were completed using second pass peak selection (S/N ratio 3), within mass window of 0.3% mass error. All these were performed using ProteinChip Software 3.1 (Ciphergen, Fremont, CA, USA).
  • SVM is a new machine learning approach that is powerful tool for ranking mass peaks according to a support vector machines algorithm. This algorithm gives the average ranking of each feature when doing leave-one-out cross validation (LOO-CV).
  • LOO-CV leave-one-out cross validation
  • This approach randomly selected the (n-1) of all the samples to be the blinded training set, and the remaining 1/(n-1) samples to be the test set and repeated the procedure n times.
  • the power of each peak in discriminating different groups was estimated by logistic regression classification models with leave-one-out cross validation (LOO-CV).
  • LOO-CV leave-one-out cross validation
  • two models based on logistic regression were built (i.e LOO-Logistic regression model) and LOO-logistic ridge regression Odds ratio >2 were used.
  • Women with Stage I - Il endometriosis when compared to controls, generated 30 differentially expressed mass peaks.
  • two models based on logistic regression were built. The first model (LOO-Logistic Model) selected 3 mass peaks with a LOO-CV performance of %, while the second logistic regression (LOO-logistic ridge regression Odds ratio >2) selected 4 mass peaks with the same performance of % (LOO-CV).
  • LOO-CV logistic regression
  • Example 11 Mass peak expression in women with moderate-severe endometriosis compared with controls
  • Women with Stage III -IV endometriosis when compared to controls, generated 131 differentially expressed mass peaks.
  • two models based on logistic regression were built. The first model (LOO-Logistic Model) selected 5, while the second logistic regression (LOO-logistic ridge regression Odds ratio >2) was excluded because performance was poor. However there was some overlap between the model and the SVM ranking.
  • Immobilized metallic affinity capture surface (IMAC-30-Cu ) 0.
  • Table X Summary of sensitivity and specificity values of the best selected biomarker combination in women with endometriosis compared with controls during luteal phase.
  • Endometriosis clinically may manifest as peritoneal disease, ranging from sparse peritoneal implants to diffuse peritoneal disease, endometriotic ovarian cysts and/or deeply infiltrating deeply endometriosis affecting the rectum, sigmoid and other parts of the bowel, as well as the bladder and rectovaginal septum (Nisolle and Donnez 1997).
  • peritoneal disease ranging from sparse peritoneal implants to diffuse peritoneal disease, endometriotic ovarian cysts and/or deeply infiltrating deeply endometriosis affecting the rectum, sigmoid and other parts of the bowel, as well as the bladder and rectovaginal septum (Nisolle and Donnez 1997).
  • the diagnosis of endometriosis is through a laparoscopy with subsequent histological confirmation of endometrial stroma and glands of a biopsy.
  • transvaginal ultrasound TVS is only useful to diagnose an ovarian
  • bioinformatics analysis tools may help develop a diagnostic model test with a high sensitivity especially for minimal to mild endometriosis.
  • FIG. 1 is schematic view showing the ProteinChip array technology for biomarker discovery.
  • FIG. 2 s schematic view showing the expression difference mapping using chromatographic MS.
  • FIG. 3 demonstrates the ProteinChip Technology
  • FIG. 4 s a schematic diagram showing the experimental design
  • FIG. 5 is a schematic diagram providing a summary of results
  • FIG. 6 is a schematic diagram showing LOO-CV and SVM
  • FIG. 7 is a schematic diagram providing a summary of results

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Abstract

Lack of non-invasive method for diagnosis of endometriosis has weighed down efforts to control and study the aetiopathogenesis of the disease among women of reproductive age. The early detection of endometriosis is crucial for its ultimate control and prevention. In many cases endometriosis is not diagnosed and treated until the disease has established itself and caused pathological symptoms. A published poll revealed women have to wait an average of 11.7 years in USA and 8.0 years in UK [Hadfield R. et al. Hum Reprod. 1996; 11 : 878 - 80] to get a correct diagnosis after the initial onset of symptoms for endometriosis. The present invention concerns a system and a method of in vitro diagnosing which comprises processing of several biomarker variables obtainable from in vitro assaying of at least one fluid sample or at least one cell containing sample of said patient, whereby the assay system produces input signals into a signal processor corresponding with activity or presence of each biomarker in said sample and whereby the input signals are processed in the signal processor comprising a mathematical model that produce output signals that is predictive for the extent of endometriosis or the progress of endometriosis in the patient.

Description

HIGH DISCRIMINATING POWER BIOMARKER DIAGNOSING
Background and Summary
BACKGROUND OF THE INVENTION
A. Field of the Invention
The present invention relates generally to a diagnostic method, model and apparatus to identify the condition of a disorder which is associated with the female's menstrual cycle and is a disorder selected from the group consisting of premenstrual syndrome, migraine headache, endometriosis, psoriasis, acne, dysmenorrhoea, neurosia, asthma and premenstrual cramps and more particularly to a diagnostic system comprising models of multiple biomarker analysis values to diagnose the presence, absence and progression of such disorder and to distinguish between early and advanced stages of endometriosis.
An high discriminating power biomarker diagnosing model and a diagnostic model for a disorder which is associated with the female's menstrual cycle such as selected from the group consisting of premenstrual syndrome, migraine headache, endometriosis, psoriasis, acne, dysmenorrhoea, neurosia, asthma and premenstrual cramps has been elaborated. Such models also allow determining the biomarker relevance of new bioactive proteins or gene expression products for such disorders which are associated with the female's menstrual cycle.
Furthermore the system, method and diagnostic model test fof present invention is for high sensitivity testing of such disoders and especially for minimal to mild endometriosis, if particularly, a SELDI-TOF -MS ProteinChip technollogy tor an alike technology for sensitive protein, polypeptide or peptide detection is combined with bioinformatics tools of present invention for diagnostic testing for minimal to mild endometriosis with a high sensitivity. Several documents are cited throughout the text of this specification. Each of the documents, herein (including any manufacturer's specifications, instructions etc.) are hereby incorporated by reference; however, there is no admission that any document cited is indeed prior art of the present invention.
B. Description of the Related Art
Endometriosis is a common, frequently progressive, gynecological disease, defined as the presence of endometrial tissue outside the uterus. The disease is associated with chronic pelvic pain, dysmenorrhea, dyspareunia and infertility, and may lead to pelvic organ dysfunction, often requiring extensive surgery. Scientific reports clearly indicate that endometriosis represents a major financial burden to healthcare systems and to the society, consuming EUR millions each year in Europe as well as in the USA in the forms of health care costs and loss of working capacity. The estimated prevalence of endometriosis is 6-10% in the general population and 13 - 33% in infertile women and more than 50% in women with severe dysmenorrhea, dyspareunia or chronic pelvic pain.
So far, noninvasive diagnostic approaches such as ultrasound, MRI or blood tests for CA-125 do not have sufficient diagnostic power; thus, the only way to conclusively diagnose endometriosis is laparoscopic surgery with histological confirmation. The lack of a noninvasive diagnostic test or an accurate blood test is the major reason why the delay between the onset of symptoms and a diagnosis is often as long as 8 to 11 years. Development of a clinically useful, noninvasive diagnostic test will have a groundbreaking impact on the patients' quality of life, on the efficacy of the available treatments as well as on the financial aspects of the disease. Due to the enormous financial impact of endometriosis, the pharmaceutical industry makes significant efforts to develop new medications for endometriosis or to improve the already existing ones.
Plasma levels of several immunological and inflammatory factors are significantly altered in women with endometriosis. However, these changes have not yet been studied sufficiently to allow a distinction between women with and without endometriosis or between early and advanced stages of the disease. Indeed, most of the studies in this area showed one or more features in study design which made it impossible to accurately evaluate their potential diagnostic value (e.g. limited number of patients, poorly defined study groups, insufficient statistical analysis etc).
There is a need in the art for early detection of endometriosis, since this is crucial for its ultimate prevention and control. Currently, the diagnosis of endometriosis is through a laparoscopy with subsequent histological confirmation of endometrial stroma and glands in the biopsy that was taken. A non- invasive diagnosic test in serum or endometrium would be beneficial to both physicians and patients. 2-DE technique has high-resolution capacity but is labour intensive and requires large quantities of intact proteins and is thus not practical. In present study protein profiling had been carried out using Surface Enhanced Laser Desorption/lonisation Time Of Flight Mass Spectrometry (SELDI -TOF MS). This offers a complementary and efficient platform to measure the expression of low molecular weight peptides and proteins that are poorly detected by other analytical methods. Nevertheless, precautions should be taken while designing SELDI-TOF experiments to avoid biases during interpretation of data [Hu J, Coombes KR, Morris JS, Baggerly KA. Brief Funct Genomic Proteomic. 2005; 4:322- 331]. Our study tested to the hypothesis that specific protein/peptides are differentially expressed by eutopic endometrium of women with and without endometriosis and at specific stages of the disease (minimal, mild, moderate or severe) during luteal phase. Present invention addressed such needs also by tracing specific protein/peptides which are differentially expressed in eutopic endometrium of women with and without endometriosis and at specific stages of the disease (minimal, mild, moderate or severe) during the secretory phase. Furthermore SELDI-TOF -MS ProteinChip technology combined with bioinformatics analysis tools demonstrated diagnostic model test with a high sensitivity especially for minimal to mild endometriosis.
A further embodiment of present invention is a system or apparatus comprising a surface enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-
TOF MS) or another analyzer that combines chromatography and mass spectrometry to provide a protein expression profile from a variety of biological and clinical samples and to input signals corresponding to the height of a peak, the area under the curve of such peak, or the position of such peak that represent a molecular weight in a chromatograph for each biomarker in said sample and further comprising a signal processor for processing the input signals such comprising a combined LOO-SVM algorithm ranking and logistic regression classification (LOO - CV) mathematic model that produces output signals that determine the presence or absence of the disorder, the seriousness of the disorder or the progress of the disorder in the patient.
Taking into account the great need for a non-invasive diagnostic test for endometriosis and the limitations of the previously conducted studies in this area up till now, invention tests were set up and completed to evaluate whether simultaneous measurement and analysis of various potential biomarkers can offer a clinically useful diagnostic method for endometriosis. It involves a panel of relevant endometriosis related biomarkers which can be extended to a novel endometriosis diagnostic test.
The present invention concerns a diagnostic system for the presence and/or progression of endometriosis that significantly reduces the time from the onset of pain and/or subfertility symptoms to diagnosis. Such earlier diagnosis and treatment will improve health related quality of life and allow prevention of the natural progression of endometriosis.
The diagnostic system of present invention provides a tool for a conveniently applicable noninvasive diagnostic test for endometriosis without requiring costly laparoscopy and hospitalization. The noninvasive diagnostic tool to conclusively determine the presence or absence of endometriosis will have a major impact on the treatment of women with pelvic pain and/or subfertility, and result in significant improvements on several levels of the society.
Many women suffering from chronic pelvic pain, dysmenorrhea, dyspareunia and/or infertility can by the system of present invention get a correct diagnosis immediately at the onset of their symptoms with respect to the presence or absence of endometriosis. Moreover, these patients will not have to travel to a hospital or fertility center for the diagnosis as it can be sufficient to visit a general practitioner with access to blood analysis at a competent institution. Furthermore, the general practitioner can assign patients with probable endometriosis immediately to an appropriate institution with the necessary expertise where gynecologists can immediately provide the most adequate, disease specific and personalized treatment for these women. This will enable the health care specialists to start targeted therapies in early stages of the disease which can largely increase the success rates of the treatments which in turn can significantly improve the quality of life of the patients on the physical health levels as well as on the mental and emotional levels. Furthermore, the patients found negative by the test will also benefit from the results as it can indicate that their symptoms are caused by condition(s) other than endometriosis which may require a different area of expertise (eg. gastroenterology, urology). This will shorten the time to a correct diagnosis also for symptomatic women without endometriosis.
Furthermore the use of a noninvasive diagnostic test will also greatly decrease endometriosis related health care costs by decreasing the number of unnecessary diagnostic laparoscopic surgeries, and by preventing the use of medications ineffective in endometriosis but frequently prescribed when the diagnosis is unknown.
Yet another advantage of the present system is that it will - indirectly - reduce the economic loss related to endometriosis by enabling early treatment and thus preventing the progression of the disease which is frequently seriously decreasing working capacity.
Additionally, the use of a reliable noninvasive diagnostic test will allow an accurate estimation of the prevalence of endometriosis, its relation to pelvic pain and/or (in)fertility and its potential impact on the quality of life of women and it can help to develop population scale strategies to fight endometriosis more efficiently. SUMMARY OF THE INVENTION
The present invention solves the problems of the related art of accurately predicting the presence, extent and progress of endometriosis in the patient in a non-invasive manner.
In accordance with the purpose of the invention, as embodied and broadly described herein, the invention is broadly drawn to an in vitro diagnosis system for determining whether a patient is affected by endometriosis and if so defining the stages of endometriosis progress.
One aspect of the invention is to rapidly and accurately diagnose and/or prognose the absence, presence and/or progression of endometriosis.
Another aspect of the invention is a method of prediction of the severity of endometriosis or endometriosis progress in the patient by in vitro diagnosing, characterised in that the methods comprise processing of several biomarker variables obtainable from in vitro assaying of at least one fluid sample or at least one cell containing sample of said patient.
Moreover the invention of in vitro diagnosing can involve an assay system that produces input signals into a signal processor corresponding with activity or presence of each biomarker in said sample and whereby the input signals are processed in the signal processor comprising a mathematical model that produces output signals that are predictive for the presence or extent of endometriosis or for endometriosis progress in the patient. The method or system can comprise that the output signals are compared to reference signals.
By this method the endometriosis extent or endometriosis progress is classified in stages of endometriosis free, stage l-ll endometriosis (also referred to as minimal-mild endometriosis) or stage I H-IV endometriosis (also referred to as moderate-severe endometriosis). Yet another aspect of the invention can be that the in vitro assaying of the samples is carried out by an Surface Enhanced Laser Desorption/lonisation Time Of Flight Mass Spectrometry, immunoprecipitation, a radioimmunoassay, an enzyme immunoassay, a fluorescent immunoassay, a chemiluminescent immunoassay, a competitive binding assay, an ELISA or a homogeneous immunoassay described herein or in laboratory manuals available in the art such as The ELISA Guidebook (Methods in Molecular Biology) by John R. Crowther; Elisa: Theory and Practice (Methods in Molecular Biology, VoI 42) by John R. Crowther; Immunoassays: A Practical Approach (Practical Approach Series) by James P. Gosling (Editor) and The Immunoassay Handbook, Third Edition by David Wild (Editor)
In yet another embodiment of the invention the samples are analysed by homogenous binding assays.
In a particular embodiment the biomarker variables are corresponding to substances of the group consisting of immunological factors, inflammatory factors, Intercellular adhesion molecules and cystine-knot growth factors of the PDGF/VEGF growth factor family such as vascular endothelial growth factor (VEGF) and Placental Growth Factor
(PIGF). Such biomarker value may also be peak the that represents a molecular weight or the area under the curve of such peak in a chromatograph which is obtainable form a surface enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS) or another analyzer that combines chromatography and mass spectrometry.
The method and system of in vitro diagnosing of present invention is particularly suitable for predicting responsiveness to a medicament.
Yet another embodiment of present invention is a method to test a biomarker for its relevance in the diagnosis or prognosis of a selected disorder, by in vitro assaying at least one biological sample per patient from a plurality of patients comprising patient groups without the disorder and patient groups with the disorder or with a known stage of the disorder characterised in that the method comprises the steps of 1) producing a first input signal on the level or activity of at least one relevant biomarker for that disorder and a second input signal on the level or activity of a biomarker to be tested for its relevance to that selected disorder 2) processing the first input signal in the signal processor to construct a first mathematical model that produces output signals which are indicative for the disorder, the seriousness of disorder or the progress of disorder in the affected patient, 3) processing both the first input signal and the second input signal in the signal processor to construct a second mathematical model that produces output signals which are indicative for the disorder, the seriousness of disorder or the progress of disorder in the affected patient, 4) selecting the test biomarker as relevant in the diagnosis or prognosis of that selected disorder if the second mathematical model reveals a better performance than the first mathematical model. In such method the first and the second mathematical model can be selected of the group consisting of Stepwise Logistic Regression Model and LS-SVM model. The method of present invention can for instance been used for a disorder is associated with the female's menstrual cycle and is a disorder selected from the group consisting of premenstrual syndrome, migraine headache, endometriosis, psoriasis, acne, dysmenorrhoea, neurosia, asthma and premenstrual cramps. For this method the biomarker variables are preferably obtained from in vitro assaying of at least one biological sample of a patient in a specific phase of the menstrual cycle and most preferably the biomarker variables are obtained from in vitro assaying of at least one biological sample of a patient in the secretory phase of the menstrual cycle.
Yet another embodiment of present invention is an operating system for operating the methods of method to test a biomarker for its relevance in the diagnosis or prognosis of a selected disorder of present invention which operating system controls the allocation of an in vitro essay system to generate biomarker values of a patient and which feeds the input signals from the in vitro essay system into signal processor comprising a mathematical model that is described on the relationship of a plurality of biomarker variables and a plurality of disorder variables from in vitro assaying of biological samples of plurality of patients with no disorder, affected with disorder, affected with a defined seriousness or with defined progress of disorder. Such operating system can be used for testing the relevance of a specific biomarker for diagnosing or prognosing of a disorder, determining the presence or absence of a disorder, the seriousness of the disorder or the progress of the disorder in a patient. Furthermore this operating system can also control usage of the in vitro essay system and it may include a user interface that to enable the user to interact with the functionality of the computer.
In yet another aspect of present invention the operating system of present invention includes a graphical user interface whereby the operating system controls the ability to generate graphics on the computer's display device that can be displayed in a variety of manners representative for or associated with the relevance of a specific biomarker for diagnosing or prognosing of a disorder, determining the presence or absence of a disorder or for the seriousness of the disorder or the progress of the disorder in a patient.
Yet another embodiment of present invention is a computer-executable code, stored in a computer-readable medium, the computer executable code adapted, when running on a computer system to run the operating system of present invention or to execute the mathematical model of present invention and to direct a processing means- to produce out put signals that are representative for the relevance of a specific biomarker for diagnosing or prognosing of a disorder, determining the presence or absence of a disorder or for the seriousness of the disorder or the progress of the disorder in a patient.
Yet another embodiment is a computer system for operating the operating system of present invention comprising a file system for storing files and a processor for analysing the content of biomarker value files stored in said file system to wherein said processor clusters said files in accordance with the mathematical model of present inventionto determine the relevance of a specific biomarker for diagnosing or prognosing of a disorder or for determining the presence or absence of a disorder, the seriousness of the disorder or the progress of the disorder in a patient.
Yet another embodiment of present invention is an apparatus comprising an in vitro diagnosis system for determining the relevance of a specific biomarker for diagnosing or prognosing of a disorder or for determining the presence or absence of a disorder, the seriousness of the disorder or the progress of the disorder in a patient, whereby the apparatus comprises or is interrelating with the computers system.
In a particular embodiment the biomarker that has been identified by methods of present invention to be relevant for a specific medical condition, is used in the manufacture of a diagnostic to diagnose for the medical condition
The present invention concerns a method of in vitro diagnosing an endometriosis, characterised in that the method comprises processing of several biomarker variables obtained from in vitro assaying of at least one biological sample of a patient in a determined phase of the menstrual cycle, whereby the assay system produces an input signal into a signal processor corresponding with level, activity or presence of each biomarker in said sample and whereby the input signal is processed in the signal processor comprising a mathematical model that produces output signals that determine the presence or absence of endometriosis, the seriousness of endometriosis or the progress of endometriosis in the patient. In this method of in vitro diagnosing the biological samples can be of the same patient but in different phases selected of the menstruation phase, the follicular phase, the ovulation phase or the proliferation phase of the menstrual cycle are assayed in vitro for biomarkers. In a preferred embodiment the biomarker variables for diagnosing are obtained from in vitro assaying of at least one biological sample of patients when they were identified to be in the secretory phase of the menstrual cycle. The mathematical model in this method of in vitro diagnosing can be described on the relationship of biomarker variables and disorder variables from in vitro assaying of biological samples of plurality of patients with no endometriosis, affected with endometriosis, affected with a defined seriousness or with defined progress of endometriosis. In a preferred embodiment the mathematical model method of in vitro diagnosing of present invention is build on in vitro assaying the presence of, the level of or the activity of a plurality of different biomarkers values, which are presumed relevant for the endometriosis in at least one biological sample from a plurality of patients with a known stage of the endometriosis and patients without the selected endometriosis. Such mathematical model can for instance be a Stepwise Logistic Regression model or it can be mathematical model that is a trained Least Squares Support Vector Machine model (LS-SVM model). In a particular embodiment such LS-SVM model is further construed by testing the discriminating power for indicating presence or absence of the endometriosis of the specific biomarkers by performing a stepwise logistic regression analysis on said biomarker values and training the LS-SVM using only those biomarkers that show discriminating power. Such method of in vitro diagnosing an endometriosis of present invention can further involve classifying the endometriosis extent or endometriosis progress in stages of endometriosis free, stage l-ll endometriosis, stage I H-IV endometriosis or it can involve classifying the endometriosis extent or endometriosis progress in stages of endometriosis free, minimal endometriosis, mild endometriosis, moderate endometriosis and severe endometriosis. Furthermore the output signals can be compared to reference signals.
In a particular embodiment the method of in vitro diagnosing of present invention can be characterized in that the in vitro assaying of the samples is carried out by an immunoprecipitation, a radioimmunoassay, an enzyme immunoassay, a fluorescent immunoassay, a chemiluminescent immunoassay, a competitive binding assay, an ELISA or a homogeneous immunoassay or it can be characterized in that the samples are analysed by homogenous binding assays or multi phase assays.
In yet another embodiment of present invention the method of in vitro diagnosing of present invention is characterized in that biomarker variables are corresponding to substances of the group consisting of immunological factors, inflammatory factors, Intercellular adhesion molecules and cystine-knot growth factors of the PDGF/VEGF growth factor family.
In yet another embodiment of present invention the method of in vitro diagnosing of present invention is characterized in that biomarker variables are corresponding to substances of the group consisting of interleukin (IL)-6, interleukin IL-8, tumor necrosis factor (TNF)-α, CA-125, CA-19-9, C-reactive protein (CRP), intercellular adhesion molecule-1 (slCAM-1) and vascular endothelial growth factor (VEGF) and Placental Growth Factor (PIGF). The method of present invention is further suitable for predicting responsiveness to a medicament or to a surgery.
Further the method of in vitro diagnosing of present invention can be characterized in that the output signal identifies endometriosis with a sensitivity of > 60 % and a specificity of > 40 % as compared to a non endometriosis or control condition, preferably the output signal identifies endometriosis with a sensitivity of > 70 % and a specificity of > 50 % as compared to a non endometriosis or control condition, preferably the output signal identifies endometriosis with a sensitivity of > 70 % and a specificity of >60 % as compared to a non endometriosis or control condition.
In yet another embodiment of the method of present invention the analysis is done by a combination of the Stepwise Logistic Regression model and the Least Squares Support Vector Machine model.
For the analysis the biological sample can be chosen from serum, blood, and plasma
Present invention also involves a method for detecting or testing a endometriosis modulating or preventing agent in a non human mammalian endometriosis model, the method comprising: (a) administration of the modulation agent to said endometriosis model; (b) providing a biological sample from an endometriosis model (c) carrying out the diagnostic method of present invention and (e) comparing output signals form samples if the endometriosis model treated with said agent, with output signals form samples of the non treated endometriosis and/or with output signals form control biological samples.
Furthermore present invention also involves a method of optimising the discriminating power of the in vitro diagnosing method of present invention by processing of a biomarker variable which has been validated to be relevant for endometriosis diagnosis or prognosis and is obtainable from in vitro assaying of at least one biological sample of a patient in a determined phase of the menstrual cycle, whereby the assay system produces input signals corresponding to concentration, activity or presence of each biomarker in said sample and whereby the input signals are processed in a signal processor comprising a mathematical model that produces output signals that determine the presence of the emdometriosis, the seriousness of the disorder or the progress of the disorder in the patient and wherein said mathematical model is a LS- SVM model which is constructed according to the following method:
• in vitro assaying the presence, the level or the activity of a plurality specific biomarkers values which are presumed relevant for the disorder in at least one biological sample from a plurality of patients, wherein said plurality of patients comprising patients with a known stage of the disorder and patients without the selected disorder whereby the assay system produces input signals; • testing the discriminating power for indicating presence or absence of the disorder of the specific biomarkers by performing a stepwise logistic regression analysis on said input signals;
• training the LS-SVM using only those biomarkers that show discriminating power.
Furthermore the present invention can involve an operating system for operating the methods of the in vitro diagnosis method of present which operating system controls the allocation of an in vitro essay system to generate biomarker values of a patient and which feeds the input signals from the in vitro essay system into signal processor comprising a mathematical model that is described on the relationship of a plurality of biomarker variables and a plurality of endometriosis disorder variables from in vitro assaying of biological samples of plurality of patients with no endometriosis, affected with endometriosis, affected with a defined seriousness or with defined progress of endometriosis. Such method can be used for determining the presence or absence of endometriosis, the seriousness of endometriosis or the progress of endometriosis in the patient. In a particular embodiment the operating system also controls usage of the in vitro essay system. Furthermore such operating system may include a user interface that to enable the user to interact with the functionality of the computer. Furthermore the operating system can include a graphical user interface whereby the operating system controls the ability to generate graphics on the computer's display device that can be displayed in a variety of manners representative for or associated with the condition of endometriosis in a selected patient or a group of patients to allow a user to distinguish between the absence of endometriosis, the seriousness of endometriosis or the progress of endometriosis in identified patients or patient groups.
Yet another aspect of present invention is a computer-executable code, stored in a computer-readable medium, the computer executable code adapted, when running on a computer system to run the operating system of present invention or to execute the mathematical models present invention and to direct a processing means to produce out put signals that are representative for a condition of endometriosis or a modifying condition of endometriosis.
Another aspect of present invention is a computer system for operating the operating system of present invention comprising a file system for storing files and a processor for analysing the content of biomarker value files stored in said file system to wherein said processor clusters said files in accordance with mathematical models of the invention as described previously in this application to establish plural levels of clusters that represent the presence or absence of endometriosis, the seriousness of endometriosis or the progress of endometriosis in the patient.
Yet another embodiment is an apparatus comprising an in vitro diagnosis system for generating the biomarker values for identifying a condition of endometriosis or any modification of such condition, whereby the apparatus comprises or is interrelating with the computers system.
A particular embodiment of present invention is a method of in vitro diagnosing with high discriminating power of a disorder, characterized in that the method comprises processing of a plurality of biomarker variables obtainable from in vitro assaying of at least one biological sample of a patient in a determined phase of the menstrual cycle, whereby the assay system produces input signals corresponding to concentration, activity or presence of each biomarker in said sample and whereby the input signals are processed in a signal processor comprising a mathematical model that produces output signals that determine the presence of the disorder, the seriousness of the disorder or the progress of the disorder in the patient, and wherein said mathematical model is a LS-SVM model which is constructed according to the following method a) in vitro assaying presence, level or activity of several specific biomarkers values which are presumed relevant for the disorder in at least one biological sample from a plurality of patients comprising patient groups without the disorder and patient groups with the disorder or with a known stage of the disorder and producing input signals thereof; b) performing a stepwise logistic regression analysis on the input signals to test the discriminating power of the specific biomarkers for indicating the presence or absence of the disorder or the severity of the disorder; c) training the LS-SVM using only those biomarkers that show discriminating power. Such method of in vitro diagnosing with high discriminating power of a disorder of present invention can comprise that the biomarker variables for diagnosing are obtained from in vitro assaying of at least one biological sample of a patient in a specific phase of the menstrual cycle, preferably the biomarker variables for diagnosing are obtained from in vitro assaying of at least one biological sample of a patient in the secretory phase of the menstrual cycle. The method of present invention can be used to diagnose a disorder which is associated with the menstrual cycle and is a disorder selected from the group consisting of premenstrual syndrome, migraine headache, endometriosis, psoriasis, acne, dysmenorrhoea, neurosia, asthma and premenstrual cramps.
Furthermore present invention may comprise an operating system for operating the methods of in vitro diagnosing of present invention which controls the allocation of an in vitro essay system to generate biomarker values of a patient and which feeds the input signals from the in vitro essay system into signal processor comprising a mathematical model that is described on the relationship of a plurality of biomarker variables and a plurality of disorder variables from in vitro assaying of biological samples of plurality of patients with no disorder, affected with disorder, affected with a defined seriousness or with defined progress of disorder. Such operating system can be used for determining the presence or absence of disorder, the seriousness of disorder or the progress of disorder in the patient. In a particular aspect of present invention the the operating system also controls usage of the in vitro essay system. Furthermore the operating system can include a user interface that to enable the user to interact with the functionality of the computer. In a particular embodiment of present invention the operating system includes a graphical user interface whereby the operating system controls the ability to generate graphics on the computer's display device that can be displayed in a variety of manners representative for or associated with the condition of disorder in a selected patient or a group of patients to allow a user to distinguish between the absence of disorder, the seriousness of disorder or the progress of disorder in identified patients or patient groups.
Yet another embodiment of present invention is a computer-executable code, stored in a computer-readable medium, the computer executable code adapted, when running on a computer system to run the operating system of present invention or to execute the mathematical model of present invention, and to direct a processing means to produce out put signals that are representative for a condition of disorder or a modifying condition of disorder.
The computer system for operating the operating system of present can comprise a file system for storing files and a processor for analysing the content of biomarker value files stored in said file system to wherein said processor clusters said files in accordance with the mathematical model of present invention, to establish plural levels of clusters that represent the presence or absence of disorder, the seriousness of disorder or the progress of disorder in the patient.
Yet another embodiment of present invention is an apparatus comprising an in vitro diagnosis system for generating the biomarker values for identifying a condition of disorder or any modification of such condition, whereby the apparatus comprises or is interrelating with the computers system of present invention.
The present invention relates further to a system and method for a diagnostic test with high sensitivity especially for minimal to mild endometriosis for instance a SELDI-TOF - MS ProteinChip technology can be combined with bioinformatics tools in a diagnostic model test with a high sensitivity especially for minimal to mild endometriosis. The present invention also solves the problems of the related art on lack of early detection of endometriosis which is so crucial for its ultimate prevention. This can be done by applying a fast protein/peptides expression profiling of body fluids of women during distinct secretory phases. This method allows fast and non invasive early detection of endometriosis and indiction of the specific stages of the disease (minimal, mild, moderate or severe). The method or system of present invention for instance involved SELDI-TOF -MS ProteinChip technology combined with bioinformatics analysis tools and this study demonstrated it to have sensitivity especially for minimal to mild endometriosis.
In accordance with the purpose of the invention, as embodied and broadly described herein, the invention is also broadly drawn to a diagnostic model test that thus not need the full structural characterization of specific biomarkers and binding of specific ligands to specific biomarkers, such as for instance antibodies.
An additional object of the presented study was to test the hypothesis that specific protein/peptides are differentially expressed in eutopic endometrium of women with and without endometriosis and at specific stages of the disease (minimal, mild, moderate or severe) during the secretory phase. The study design concerned establishment of luteal endometrial protein pattern for screening endometriosis using SELDI-TOF-MS. A total of 29 patients all from secretory phase days 16 - 26 of a 28-day menstrual cycle were selected for this study based on cycle phase and presence or absence of endometriosis. Common characteristics of groups of gene expression products were identified and validated in to usable biomarkers for early detection of endometriosis, as summarized hereafter: Interventions: A total of 29 tissues samples which included secretory phase endometrium from women with (n = 19) and without (n =10) endometriosis. All women with endometriosis had minimal to mild (Ml, n= 9) and moderate to severe (HI-IV, n= 10) endometriosis, according to the classification system of the American Society of Reproductive Medicine. Main Outcome Measure (s): More than 200 mass peaks were differentially expressed in the above comparisons, representing both up-regulation and down-regulation in gene expression products. Results: Women with endometriosis had 73 mass peaks detected with significant differences when compared to controls. Using leave-one-out SVM (LOOSVM) algorithm ranking and logistic regression classification models (LOO - CV), 5 downregulated mass peaks ( 8.65OkDa, 8.659kDa, 13.91kDa, 5.183kDa and 1.949kDa) were selected as endometrial biomarkers for the diagnosis of endometriosis with a high sensitivity (89.5%) and specificity (90 %). Women with Stage I - Il endometriosis, when compared to controls, generated 30 differentially expressed mass peaks. Furthermore, using LOO-SVM algorithm ranking and logistic regression classification models (LOO - CV), 4 mass peaks (2 upregulated: 90.675kDa and 35.956kDa) and 2 downregulated: 1.924kDa and 2.504kDa) were selected as biomarkers for the diagnosis of Stage I - Il endometriosis with maximal sensitivity (100%) and specificity (100%). Women with Stage III-IV endometriosis, when compared to controls, generated 131 differentially expressed mass peaks. A total of 103 qualified mass peaks were upregulated or downregulated in endometrium from women with endometriosis (stages I-IV, ASRM classification 1997) when compared to endometrium from controls. Women with endometriosis had 73 mass peaks detected with significant differences when compared to controls. Using LOO-SVM algorithm ranking and logistic regression classification models (LOO - CV), 5 downregulated mass peaks ( 8.65OkDa, 8.659kDa, 13.91kDa, 5.183kDa and 1.949kDa) were selected as endometrial biomarkers for the diagnosis of endometriosis with a high sensitivity (89.5%) and specificity (90 %). Women with Stage I - Il endometriosis, when compared to controls, generated 30 differentially expressed mass peaks. Using LOO-SVM algorithm ranking and logistic regression classification models (LOO - CV), 4 mass peaks (2 upregulated: 90.675kDa and 35.956kDa) and 2 downregulated: 1.924kDa and 2.504kDa) were selected as biomarkers for the diagnosis of Stage I - Il endometriosis with maximal sensitivity (100%) and specificity (100%). Conclusions: SELDI-TOF -MS ProteinChip technology combined with bioinformatics delivered a diagnostic model test with a high sensitivity especially for minimal to mild endometriosis.
A particular embodiment of present invention is a method suitable for use in the diagnosis of an early, minimal or mild endometriosis by identifying in isolated body fluid of a patient mass peaks of gene expressing products whereby the expression signature allows to differentiate a condition of endometriosis or no endometriosis is a peak in the 8.648 to 8.652 kDa molecular weight range, the 8.658 to 8.661 kDa molecular weight range, the 13.89 to 13.93 kDa molecular weight range, the 5.181 to 5.185 kDa molecular weight range and/or the 1.947 to1.951 kDa molecular weight range, the endometriosis condition is being indicated by down regulation of the expression products identified by these peaks.
A particular embodiment of present invention is a method suitable for use in the diagnosis of an early, minimal or mild endometriosis by identifying in isolated body fluid of a patient mass peaks of gene expressing products whereby the expression signature can differentiate a condition of endometriosis or no endometriosis by a peak indicating 8.65OkDa molecular weight, 8.659kDa molecular weight, 13.91 kDa molecular weight, 5.183kDa molecular weight and/or 1.949kDa molecular weight expressing agent.
A particular embodiment of present invention is a method suitable for use in the diagnosis of an early, minimal or mild endometriosis by identifying in isolated body fluid of a patient mass peaks of gene expressing products whereby the expression signature can differentiate a condition of endometriosis or no endometriosis by a peak indicating an about 8.65OkDa molecular weight, an about 8.659kDa molecular weight, an about 13.91 kDa molecular weight, an about 5.183kDa molecular weight and/or an about 1.949kDa molecular weight or combination of such peaks, the endometriosis condition being indicated by down regulation of the expression products identified by these peaks.
A particular embodiment of present invention is a method suitable for use diagnosis of an early, minimal or mild endometriosis by identifying in an isolated body fluid of a patient mass peaks of gene expressing products whereby the expression signature allows to differentiate a condition of endometriosis or no endometriosis. Down regulation of the expression products with a molecular weight of about 8.65OkDa, 8.659kDa, 13.91kDa, 5.183kDa and/or 1.949kDa or about 8.65OkDa, 8.659kDa, 13.91kDa 5.183kDa and/or 1.949kDa or combinations thereof, is indicative for a condition of endometriosis.
A particular embodiment of present invention is a method suitable for use in the diagnosis of Stage l-ll endometriosis by identifying in isolated body fluid of a patient mass peaks of gene expressing products, whereby the expression signature allows to differentiate a condition of Stage l-ll endometriosis or no endometriosis by peaks indicating the 8.65OkDa, 8.659kDa, 13.91kDa 5.183kDa and/or 1.949kDa expression product, the endometriosis condition being indicated by down regulation of the expression products identified by these peaks.
A particular embodiment of present invention is a method suitable for use in the diagnosis of Stage l-ll endometriosis by identifying in isolated body fluid of a patient mass peaks of gene expressing products, whereby the expression signature allows to differentiate a condition of Stage l-ll endometriosis or no endometriosis by peaks indicating the about 8.65OkDa, about 8.659kDa, about 13.91kDa, about 5.183kDa and/or about 1.949kDa expression product and the endometriosis condition being indicated by down regulation of the expression products identified by these peaks.
The method may comprise (a) comparing a proteomic profile of a test sample of a biological fluid (i) a proteomic profile of a normal sample, or (ii) a reference proteomic profile comprising at least one unique expression signature characteristip of said condition, wherein the test sample proteomic profile and the normal sample proteomic profile or the reference proteomic profile comprise information of the expression of gene expression factors in the 8.648 to 8.652 kDa molecular weight range, the 8.658 to 8.661 kDa molecular weight range , the 13.89 to 13.93 kDa molecular weight range, the 5.181 to 5.185 kDa molecular weight range and/or the 1.947 to1.951 kDa molecular weight range or preferably of 8.65OkDa molecular weight, 8.659kDa molecular weight, 13.91 kDa molecular weight, 5.183kDa molecular weight and/or 1.949kDa molecular weight or about 8.65OkDa molecular weight, 8.659kDa molecular weight, 13.91kDa molecular weight, 5.183kDa molecular weight and/or 1.949kDa molecular weight (b) if the test proteomic profile is essentially the same as the normal sample proteomic profile the subject is determined to not possess the endometriosis, while if the test proteomic profile shows a unique expression signature with lower expression of said the the expression of gene expression factors in the 8.648 to 8.652 kDa molecular weight range, the 8.658 to 8.661 kDa molecular weight range , the 13.89 to 13.93 kDa molecular weight range, the 5.181 to 5.185 kDa molecular weight range and/or the 1.947 to1.951 kDa molecular weight range or preferably of 8.65OkDa molecular weight, 8.659kDa molecular weight, 13.91 kDa molecular weight, 5.183kDa molecular weight and/or 1.949kDa molecular weight or about 8.65OkDa molecular weight, 8.659kDa molecular weight, 13.91 kDa molecular weight, 5.183kDa molecular weight and/or 1.949kDa molecular weight relative to the normal sample proteomic profile the subject is determined to possess the maternal condition
In a particular embodiment the proteomic profiles comprise information on the expression of all these factors the 8.65OkDa molecular weight and the 8.659kDa molecular weight and the 13.91 kDa molecular weight and the 5.183kDa molecular weight and the 1.949kDa molecular weight factor.
In a particular embodiment of present invention the proteomic profiles are produced by mass spectrum analysis.
In a particular embodiment it comprises at least one unique expression signature in the 8.648 to 8.652 kDa range, the 8.658 to 8.661 kDa range, the 13.89 to 13.93 kDa range, the 5.181 to 5.185 kDa range or the 1.947 to1.951 kDa range of the mass spectrum.
In a particular embodiment the the proteomic profiles are produced by Western blot analysis. The body fluid may be selected from the group consisting of amniotic fluid, serum and vaginal fluid plasma, endometrial fluid obtained from a patient for instance a mammalian subject and preferably a human.
The method may comprise (a) comparing a proteomic profile of a test sample of a biological fluid (i) a proteomic profile of a normal sample, or (ii) a reference proteomic profile comprising at least one unique expression signature characteristic of said condition, wherein the test sample proteomic profile and the normal sample proteomic profile or the reference proteomic profile comprise information of the expression of gene expression factors in the 90.673 - 90.677 kDa range, the 35.92 - 35.94 kDa range , the 1.922 - 1.926kDa range and/or the 2.502 - 2.506 kDa range of the mass profile or preferably of 90.675kDa molecular weight, 35.95kDa molecular weight, 1.924kDa molecular weight and/or 2.504kDa molecular weight (b) if the test proteomic profile is essentially the same as the normal sample proteomic profile the subject is determined to not possess the endometriosis, while if the test proteomic profile shows a unique expression signature with lower expression of said the expression of gene expression factors for instance that the 90.675kDa molecular weight factor and/or the 35.95kDa molecular weight is up regulated and the 1.924kDa molecular weight factor and/or the 2.504kDa molecular weight factor is down regulated.
In a particular embodiment the proteomic profiles comprise information on the expression of all these factors the 90.675kDa molecular weight and the 35.95kDa molecular weight and the 1.924kDa molecular weight and the 2.504kDa molecular weight factor.
In a particular embodiment of present invention the proteomic profiles are produced by mass spectrum analysis.
In a particular embodiment it comprises at least one unique expression signature in the 90.673 - 90.677 kDa range, the 35.92 - 35.94 kDa range , the 1.922 - 1.926kDa range, the 2.502 - 2.506 kDa range of the mass profile, where by the factor in the 90.673 - 90.677 kDa range or the 35.92 - 35.94 kDa range of the mass spectrum has to be upregulated to quote for Stage l-ll endometriosis and the factor in the 1.922 - 1.926kDa range or the 2.502 - 2.506 kDa of the mass spectrum has tob e down regulated to quote for Stage l-ll endometriosis.
In a particular embodiment the the proteomic profiles are produced by Western blot analysis.
The body fluid may be selected from the group consisting of amniotic fluid, serum and vaginal fluid plasma, endometrial fluid obtained from a patient for instance a mammalian subject and preferably a human.
The method of present invention may further involve an analysis model for instance based on a Support Vector Machine (SVM) algorithm, logistic regression classification models with Leave-One-Out -Cross Validation (LOO - CV) and Ranking the significant mass peaks according to their classification power
Present invention thus involves a method of in vitro diagnosing and determining early stage endometriosis, characterised in that the methods comprises processing of several biomarker variables obtainable from in vitro assaying of a mass spectrum profiles of expression products in at least one fluid sample or at least one cell containing sample of said patient, whereby the assay system produces input signals into a signal processor corresponding with activity or presence of each biomarker in said sample and whereby the input signals are processed in the signal processor comprising a mathematical model that produce output signals that identifies if said patient has been affected by endometriosis and the seriousness of endometriosis or the progress of endometriosis in the affected patient. Is such diagnosis the output signals are compared to reference signals.
Another embodiment of present invention is a method to test a biomarker which is a molecular weight hit on a molecular mass profile for its relevance in the diagnosis or prognosis of a selected disorder, by in vitro assaying at least one biological sample per patient from a plurality of patients comprising patient groups without the disorder and patient groups with the disorder or with a known stage of the disorder characterized in that the method comprises the steps of 1) producing a first input signal on the level or activity of at least one relevant biomarker for that disorder and a second input signal on the level or activity of a biomarker to be tested for its relevance to that selected disorder 2) processing the first input signal in the signal processor to construct a first mathematical model that produces output signals which are indicative for the disorder, the seriousness of disorder or the progress of disorder in the affected patient, 3) processing both the first input signal and the second input signal in the signal processor to construct a second mathematical model that produces output signals which are indicative for the disorder, the seriousness of disorder or the progress of disorder in the affected patient, 4) selecting the test biomarker as relevant in the diagnosis or prognosis of that selected disorder if the second mathematical model reveals a better performance than the first mathematical model. The first and the second mathematical model are selected of the group consisting of Stepwise Logistic Regression Model and LS-SVM model and the biomarker variables may obtained from in vitro assaying of at least one biological sample of a patient in a specific phase of the menstrual cycle.
In a particular embodiment the operating system for operating such methods of controls the allocation of an in vitro essay system to generate biomarker values of a patient and which feeds the input signals from the in vitro essay system into signal processor comprising a mathematical model that is described on the relationship of a plurality of biomarker variables and a plurality of disorder variables from in vitro assaying of biological samples of plurality of patients with no disorder, affected with disorder, affected with a defined seriousness or with defined progress of disorder.
The operating system can include a user interface that to enable the user to interact with the functionality of the computer. The operating system may include a graphical user interface whereby the operating system controls the ability to generate graphics on the computer's display device that can be displayed in a variety of manners representative for or associated with the relevance of a specific biomarker for diagnosing or prognosing of a disorder, determining the presence or absence of a disorder or for the seriousness of the disorder or the progress of the disorder in a patient.
The present invention may also comprise a computer-executable code, stored in a computer-readable medium, the computer executable code adapted, when running on a computer system to run the operating system of any of present invention to execute the mathematical model described herein and to direct a processing means to produce out put signals that are representative for the relevance of a specific biomarker for diagnosing or prognosing of a disorder, determining the presence or absence of a disorder or for the seriousness of the disorder or the progress of the disorder in a patient. Or the present invention van be a computer system for operating the operating system of present invention which comprises a file system for storing files and a processor for analysing the content of biomarker value files stored in said file system to wherein said processor clusters said files in accordance with the mathematical model of present invention to determine the relevance of a specific biomarker for diagnosing or prognosing of a disorder or for determining the presence or absence of a disorder, the seriousness of the disorder or the progress of the disorder in a patient.
In a particular embodiment the present invantion concerns an apparatus comprising an in vitro diagnosis system for determining the relevance of a specific biomarker for diagnosing or prognosing of a disorder or for determining the presence or absence of a disorder, the seriousness of the disorder or the progress of the disorder in a patient, whereby the apparatus comprises or is interrelating with the computers of present invention
A particular embodiment of present invention is a method of in vitro diagnosing an endometriosis, characterised in that the method comprises processing of several biomarker variables obtained from in vitro assaying a mass profile of gene expression products of at least one biological sample of a patient in a determined phase of the menstrual cycle, whereby the assay system produces an input signal into a signal processor corresponding with level, activity or presence of each biomarker in said sample and whereby the input signal is processed in the signal processor comprising a mathematical model that produces output signals that determine the presence or absence of endometriosis, the seriousness of endometriosis or the progress of endometriosis in the patient. The biological samples can be of the same patient but in different phases selected of the menstruation phase, the follicular phase, the ovulation phase or the proliferation phase of the menstrual cycle are assayed in vitro for biomarkers. The method may be characterised in that the biomarker variables for diagnosing are obtained from in vitro assaying of at least one biological sample of patients when they were identified to be in the secretory phase of the menstrual cycle and the mathematical model can be described on the relationship of biomarker variables and disorder variables from in vitro assaying of biological samples of plurality of patients with no endometriosis, affected with endometriosis, affected with a defined seriousness or with defined progress of endometriosis. Furthermore the mathematical model is build on in vitro assaying the presence of, the level of or the activity of a plurality of different biomarkers values, which are presumed relevant for the endometriosis in at least one biological sample from a plurality of patients with a known stage of the endometriosis and patients without the selected endometriosis. In a particular embodiment the mathematical model is a Stepwise Logistic Regression model. For instance the mathematical model can be a trained Least Squares Support Vector Machine model (LS-SVM model) and this LS-SVM model can be further construed by testing the discriminating power for indicating presence or absence of the endometriosis of the specific biomarkers by performing a stepwise logistic regression analysis on said biomarker values and training the LS-SVM using only those biomarkers that show discriminating power, lnr this method the output signals can be compared to reference signals.
In a particular embodiment the method for detecting or testing a endometriosis modulating or preventing agent in a non human mammalian endometriosis model, the method comprises : (a) administration of the modulation agent to said endometriosis model; (b) providing a biological sample from an endometriosis model (c) carrying out the presviously descibed diagnostic method; and (e) comparing output signals form samples if the endometriosis model treated with said agent, with output signals form samples of the non treated endometriosis and/or with output signals form control biological samples.
In yet another embodiment the method for optimising the discriminating power of the in vitro diagnosing method of present invention is characterised in that the method comprises processing of a biomarker variable which has been validated to be relevant for endometriosis diagnosis or prognosis and is obtainable from in vitro assaying of at least one biological sample of a patient in a determined phase of the menstrual cycle, whereby the assay system produces input signals corresponding to concentration, activity or presence of each biomarker in said sample and whereby the input signals are processed in a signal processor comprising a mathematical model that produces output signals that determine the presence of the emdometriosis, the seriousness of the disorder or the progress of the disorder in the patient and wherein said mathematical model is a LS-SVM model which is constructed according to the following method:
• in vitro assaying the presence, the level or the activity of a plurality specific biomarkers values which are presumed relevant for the disorder in at least one biological sample from a plurality of patients, wherein said plurality of patients comprising patients with a known stage of the disorder and patients without the selected disorder whereby the assay system produces input signals; • testing the discriminating power for indicating presence or absence of the disorder of the specific biomarkers by performing a stepwise logistic regression analysis on said input signals;
• training the LS-SVM using only those biomarkers that show discriminating power.
In a particular embodiment the operating system for operating the methods of diagnosis of present invention controls the allocation of an in vitro essay system to generate biomarker values of a patient and which feeds the input signals from the in vitro essay system into signal processor comprising a mathematical model that is described on the relationship of a plurality of biomarker variables and a plurality of endometriosis disorder variables from in vitro assaying of biological samples of plurality of patients with no endometriosis, affected with endometriosis, affected with a defined seriousness or with defined progress of endometriosis. The operating system may also control usage of the in vitro essay system. Furthermore the operating system may include a user interface that to enable the user to interact with the functionality of the computer. Furthermore , the operating system may include a graphical user interface whereby the operating system controls the ability to generate graphics on the computer's display device that can be displayed in a variety of manners representative for or associated with the condition of endometriosis in a selected patient or a group of patients to allow a user to distinguish between the absence of endometriosis, the seriousness of endometriosis or the progress of endometriosis in identified patients or patient groups.
Yet another embodiment is a method of in vitro diagnosing with high discriminating power of a disorder, characterized in that the method comprises processing of a plurality of biomarker variables from a mass profile of expression products obtainable from in vitro assaying of at least one biological sample of a patient in a determined phase of the menstrual cycle, whereby the assay system produces input signals corresponding to concentration, activity or presence of each biomarker in said sample and whereby the input signals are processed in a signal processor comprising a mathematical model that produces output signals that determine the presence of the disorder, the seriousness of the disorder or the progress of the disorder in the patient, and wherein said mathematical model is a LS-SVM model which is constructed according to the following method a) in vitro assaying presence, level or activity of several specific biomarkers values which are presumed relevant for the disorder in at least one biological sample from a plurality of patients comprising patient groups without the disorder and patient groups with the disorder or with a known stage of the disorder and producing input signals thereof; b) performing a stepwise logistic regression analysis on the input signals to test the discriminating power of the specific biomarkers for indicating the presence or absence of the disorder or the severity of the disorder; c) training the LS-SVM using only those biomarkers that show discriminating power. Such biomarker variables for diagnosing may be obtained from in vitro assaying of at least one biological sample of a patient in a specific phase of the menstrual cycle.
Detailed Description
DETAILED DESCRIPTION OF EMBODIMENTS OF THE INVENTION
It will be apparent to those skilled in the art that various modifications and variations can be made in the present invention using various reagents and materials of the present invention and in construction of the system and method without departing from the scope or spirit of the invention. Examples of such modifications have been previously provided.
Other embodiments of the invention will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein.
Endometriosis is currently defined as the presence of endometrial-like tissue outside the uterus, is associated with a chronic inflammatory reaction in the pelvis and results often in subfertility and pain. Endometriosis occurs mainly in women of reproductive age (16 to 50 years), is estrogen sensitive, and has a progressive character in at least 50%, but the rate and risk factors for progression are unpredictable and unknown, respectively. . The estimated prevalence of endometriosis is 6-10% in the general population, 13 - 33% in infertile women and more than 50% in women with severe dysmenorrhea, dyspareunia or chronic pelvic pain. In total, endometriosis affects approximately 14 million women in Europe only [Mihalyi A, et al. Expert Opin Emerg Drugs. 2006. 11 (3): 503-24]. In Belgium the estimated prevalence of endometriosis is 20% amongst reproductive age women [Heilier JF et al. Toxicol Lett. 2004. 1;154(1- 2):89-93], indicating that Belgium is in the upper half of the international statistics with regard to endometriosis related problems.
There is a clear need in the art for efficient, low invasive and sensitive endometriosis assays. The prevalence of endometriosis is > estimated to 10% among women of reproductive age > 13%-33% in infertile women (D'Hooghe and Hill, 2002)
> 33% in women with CPP (Guo and Wang 2006)
Moreover the Economic burden of endometriosis related hospitalisation costs (US) is
> $540 million for 1991 and $579 million for 1992 (Zhao et al., 1998) > annually attained $22 billion in US in 2002 (Simoens et al, 2007).
An,d the CPP related costs (UK):
> £158 million direct and £24 million indirect
Moreover early detection of endometriosis is crucial for its ultimate control and prevention. But early diagnosis and treatments of endometriosis have been weighed- down by lack of proper methods to study and manage the disease. Alterations in protein expression can act as useful indicators of pathological abnormalities of endometriosis prior to development of clinical symptoms of a disease. Present invention developed a a system and method for and a diagnostic model test with a high sensitivity especially for minimal to mild endometriosis and, more particularly to a SELDI-TOF -MS ProteinChip technology combined with bioinformatics tools could help develop a diagnostic model test with a high sensitivity especially for minimal to mild endometriosis
The diagnosis of endometriosis can be suspected in women with a history of dysmenorrhea, deep dyspareunia, chronic pelvic pain with or without subfertility, although it is possible that endometriosis remains asymptomatic. In speculo inspection of cervix and vagina may show a small cervical diameter, lateral cervical displacement and rarely blue discoloration suggestive for cervical or vaginal endometriosis.
Clinical abdominal and bimanual pelvic examination, preferably during the menstrual period, may reveal a painful lower abdomen, painful adnexal masses, painful uterosacral ligaments, and painful mobilisation of the uterus. Vaginal ultrasound is an adequate diagnostic method to detect ovarian endometriotic cysts, less accurate to detect deeply infiltrating endometriotic nodules, and useless to diagnose peritoneal endometriosis or endometriosis-associated adhesions. The gold standard to diagnose endometriosis is visual pelvic inspection by laparoscopy, preferably with histological confirmation (Kennedy S. et al. Hum Reprod. 2005. 20(10):2698-704) Endometriosis can be present as minimal to mild (Stage l-ll), mostly present on the pelvic peritoneal or ovarian surface, but can also present as moderate to severe disease (Stage III-IV; ASRM, 1997). More advanced endometriosis can be deeply invasive behind the cervix and invade into the rectovaginal septum, obliterating the pouch of Douglas partially or completely, or can present as ovarian endometriotic cysts (endometrioma). All presentations of endometriosis can be associated with filmy or dense adhesions. The stage of endometriosis is positively correlated with the degree of subfertility, but not or not as clearly with the degree of pelvic pain (Fauconnier A, Chapron C. Hum Reprod Update 2005; 11:595-606; Kennedy S. et al. Hum Reprod. 2005. 20(10):2698-704). Surgical excision of endometriosis, even in mild stage disease, is an effective treatment for both endometriosis-associated subfertility and pain. The spontaneous pregnancy rate following surgery is negatively correlated with the degree of endometriosis (D'Hooghe TM et al. Sem Reprod Med. March 2003; 21 :243-254). The degree of pain relief following surgery is more pronounced in women with mild to severe endometriosis than in women with minimal endometriosis (Kennedy S. et al. Hum Reprod. 2005. 20(10):2698-704). Current medical therapy of endometriosis is based on hormonal suppression and is effective for pain but useless for endometriosis-associated subfertility (Kennedy S. et al. Hum Reprod. 2005. 20(10):2698-704)
Since many women with endometriosis are of reproductive age, the active or passive desire to become pregnant later in life is an important issue. Early diagnosis of endometriosis in women with pelvic pain or in women who try to conceive should enable gynaecologists to detect and excise endometriosis before the disease has progressed to a moderate to severe stage, in order to preserve or improve fertility as much as possible. In the presence of subfertility with a history of cyclic or chronic pelvic pain, combined with a clinical examination that is positive for pain, and an ultrasound positive for ovarian endometriotic cysts or deep endometriotic nodules, the probability of endometriosis is so high that many gynaecologists will offer the patient a laparoscopy combined with excision of all visible endometriotic lesions and histological examination of at least 1 implant to confirm the presence of endometrial glands and stroma or will start medical treatment based on the assumption that the patient has endometriosis. For this population, a non-invasive diagnostic test is important to confirm the presence of endometriosis, especially for those who will not be diagnosed or treated surgically.
For a population of women with mainly subfertility, the situation is different. Indeed, if these women have a regular cycle, a partner with a normal sperm examination, and if they have been unsuccessful in trying to conceive for more than 1 year without moderate to severe cyclic or chronic pelvic pain (requiring at least cyclic or chronic use of pain killers), combined with a normal clinical examination and a normal pelvic ultrasound, most gynaecologists are not sure if endometriosis is present and if it is useful to do a diagnostic laparoscopy. From a clinical perspective, it is unlikely that these women will have moderate to severe endometriosis, but they may have extensive peritoneal endometriosis with or without adhesions associated with subfertility and possibly mild pain. For this population, a non-invasive diagnostic test will be useful to rule out those without endometriosis and to rule in those with endometriosis, most likely minimal to mild disease, who are known to benefit from surgical therapy for both subfertility and pain and from controlled ovarian stimulation in combination with intrauterine insemination for subfertility. It will be an additional advantage if the test will also be diagnostic for women with pelvic adhesions or chronic PID since these women will also benefit from laparoscopic diagnosis and possibly surgical treatment.
In women presenting with pelvic pain only (dysmenorrhea, dyspareunia, chronic pelvic pain, pain during micturition or defecation) without subfertility, a non-invasive diagnostic test would allow accurate diagnosis and early treatment.
So far, it is only possible to suspect ovarian endometriotic cysts by ultrasound (Kennedy S. et al. Hum Reprod. 2005. 20(10):2698-704), but all other presentations of endometriosis cannot be reliably diagnosed by ultrasound, MRI or blood test for CA 125. The only way to conclusively diagnose endometriosis is laparoscopic inspection with histological confirmation. Positive histology confirms the diagnosis of endometriosis; however, negative histology does not exclude it [Kennedy S. et al. Hum Reprod. 2005. 20(10):2698-704]. The diagnosis of endometriosis may be suspected based on pain symptoms. However, frequently these symptoms are similar or identical to those of other gynaecological or gastrointestinal disorders. Endometriosis does not always provide a visible handicap, despite its often crippling effects, and thus is not widely and sufficiently recognised by the general public, many first line practitioners and some gynaecologists.
A recent survey completed by 7,025 women with endometriosis (European Endometriosis Alliance, 2006) demonstrated that 65% of the women with endometriosis were misdiagnosed with another condition, and 46% had to see five doctors or more before they were correctly diagnosed. Since endometriosis is a progressive disease in at least 50% (DΗooghe TM, Debrock S. Hum Reprod Update. 2002. 8(1):84-8. Review.), early diagnosis is vital. However, the above survey showed an average diagnostic delay of 8 years from the time they present with symptoms. The lack of an accurate noninvasive diagnostic test is the major reason for this enormous delay.
Scientific reports clearly indicate that endometriosis represents a major financial burden to healthcare systems and to the society, consuming EUR millions each year in Europe as well as in the USA in the forms of health care costs. In the US, the estimated total hospitalization costs, for women with endometriosis as the primary diagnosis were $540 million in 1991 and $579 million in 1992 [Zhao SZ et al. Am. J. Manag. Care (1998) 4(8):1127-1134]. Similarly, the cost of drugs, surgery, infertility treatments, and so on, for chronic pelvic pain in the UK alone is estimated at £158 million/year, with indirect costs of £24 million [Mihalyi A, et al. Expert Opin Emerg Drugs. 2006. 11(3):503-24.]. Furthermore, additional economic loss due to days taken off work is estimated around €30 billion per year for endometriosis patients in Europe only.
Endometriosis also has a tremendous impact on the quality of life of the sufferers not only physically but also mentally and emotionally. 30-40% of the women with endometriosis were infertile, 72% had their relationships affected by endometriosis,
78% were at times unable to carry out day-to-day activities due to endometriosis, 36% had their job affected (41% having lost their job, 37% had to reduce their working hours and 23% changed their job). Furthermore, endometriosis is also associated with negative self image, anxiety, poor sleep, disturbances in partner relationships and social contact [Mihalyi A, et al. Expert Opin Emerg Drugs. 2006. 11(3):503-24.].
There is thus a need in the art for an easy and minimally or non invasive diagnosis system that allows early diagnosis of a subject for endometriosis. The present invention is based on a diagnostic model of using the plasma or serum concentration values of endometriosis related biomarkers as parameter values which are integrated in a non-invasive diagnostic system for determining the presence and the stages of endometriosis. The system of present invention has been tested on a panel of 6 endometriosis related biomarkers interleukin (IL)-6, IL-8, tumor necrosis factor (TNF)- α, cancer antigen (CA)-125, carbohydrate antigen (CA)-19-9 and C-reactive protein (CRP), and is being further extended with soluble intercellular adhesion molecule-1 (slCAM-1) and vascular endothelial growth factor (VEGF) of which the parameter values have been statistically analyzed using stepwise logistic regression and Least Squares Support Vector Machines (LS-SVM) and demonstrated a sensitive and specific tool for diagnosing endometriosis and distinguishing between the phases of endometriosis stage l-ll or endometriosis stage I H-IV.
This system allows diagnostic testing of tissue or body fluid parameters and allows early diagnosis, also in cases when endometriosis expertise is limited or not available (e.g. rural areas, patients with limited mobility to attend hospital). The availability of this system decreases the number of laparoscopic operations and related hospitalizations as well as the consequential costs, allows the selection of appropriate treatment modalities, reduces unnecessary medications and procedures, improves the efficacy of the available treatments for endometriosis-associated pain and subfertility and improves the patients' quality of life by preventing misdiagnosis and hit-and-miss treatments thus decreasing the time till symptom relief.
A non-invasive diagnostic test for instance by analysis of a fluid of a patient or from a cell or cells removed from patients but preferably a body fluid sample such as blood, or a plasma or a serum sample for women with pelvic pain and/or subfertility should have as most important goal that no women with endometriosis or other significant pelvic pathology who might benefit from surgery are missed. To achieve this, a test with a high sensitivity is needed, with a low number of false negative results, ie a low number of patients who have a negative test but who do have endometriosis or other significant pelvic pathology justifying surgery. A high specificity implies a low number of false positive results, ie a low number of patients who have a positive test but who do not have endometriosis or other pelvic pathology requiring surgery. This is less important in daily clinical practice, since a laparoscopy in this subset of women with subfertility will not only be useful to diagnose and treat endometriosis, but also to assess tubal patency and uterine function via hysteroscopy and possibly endometrial biopsy. Taking into account this clinical perspective, a diagnostic test with a sensitivity as high as 100% will be ideal, even if the specificity will be only 50%. Such test was not yet available in the art.
The performance of CA-125 measurement in the detection of endometriosis has been reviewed (MoI BWJ, et al. Fertil Steril. 1998 Dec;70(6): 1101-8). At a cut-off level of 35 IU/mL, the specificity was 90% and the sensitivity was only 28% to detect minimal to severe endometriosis, and the specificity was 89% and the sensitivity 47% to detect moderate to severe endometriosis (MoI BWJ, et al. Fertil Steril. 1998 Dec; 70(6): 1101- 8). Lowering the specificity did only result in a marginal increase in sensitivity (MoI et al, 1998). Clinically, this implies that a normal CA 125 level in subfertile women will reduce the probability of moderate to severe endometriosis to 2%, whereas a positive test can indicate 13% of the subfertile couples whose probability of minimal to severe endometriosis is 31% and whose probability of moderate to severe endometriosis is 15%. (MoI BWJ, et al. Fertil Steril. 1998 Dec;70(6):1101-8). In this meta-analysis, it was impossible to analyse how lowering the threshold might result in a higher sensitivity at the cost of a lower specificity (MoI BWJ, et al. Fertil Steril. 1998 Dec;70(6):1101-8). Based on these data, serum CA 125 determination on its own is not a good diagnostic test for endometriosis (Kennedy S. et al. Hum Reprod. 2005. 20(10):2698-704). A large body of evidence shows that endometriosis is associated with pelvic inflammation [D'Hooghe TM and Debrock S. Hum Reprod Update. 2002. 8(1):84-8. Review] and plasma levels of several immunological and inflammatory factors - including various cytokines, chemokines and growth factors such as IL-1, IL-6, IL-8, IL- 12, TNF-α, VEGF, RANTES - are significantly altered in patients suffering from endometriosis [Bedaiwy MA, et al. Hum Reprod 2002; 17:426-321.; Berkkanoglu M and Arid A., Am J Reprod Immunol. 2003. 50(1):48-59. Review; Gazvani R and Templeton A. Reproduction. 2002. 123(2):217-26. Review.; Agio A, et al; Gynecol Obstet Invest. 2006 May 4;62(3): 139-147]. In parallel, several tumor markers such as the already mentioned ovarian cancer antigen CA 125 or the pancreatic tumor marker CA 19-9 have also been suggested to have a diagnostic potential in endometriosis [Chen FP et al. Acta Obstet Gynecol Scand. 1998. 77(6):665-70, Harada T et al Fertil Steril. 2002. 78(4):733-9,].
Numerous attempts have been made trying to develop a reliable, clinically meaningful, non-invasive diagnostic test based on the observed differences, but none of these attempts can deliver a diagnostic method capable of providing clinically acceptable diagnostic power to differentiate between women with and without the disease [Chen
FP, et al. Acta Obstet Gynecol Scand. 1998. 77(6):665-70]. In fact, so far no laboratory findings have been helpful in making or excluding a diagnosis of endometriosis [Winkel CA.: Evaluation and management of women with endometriosis. Obstet Gynecol. 2003.
102(2):397-408. Review.].
However, the unsuccessful outcome of these attempts is likely to be the consequence of inappropriate evaluation and/or analysis of the reported differences. The most common features of these studies that can have biased the final results were the following:
1) Investigators measured plasma levels of one or more biomarkers but only evaluated the discriminating power (e.g. sensitivity, specificity etc) of the individual markers, not their concomitant combinations [Harada T et al. Fertil Steril. 2002. 78(4):733-9, O'Shaughnessy A et al. Obstet Gynecol. 1993. 81(1):99-103]. 2) Several biomarkers are known to have a menstrual cycle phase dependent expression profile [Xavier P. et al. Arch Gynecol Obstet. 2006. 273(4):227-31 , Tabibzadeh S. et al. Hum Reprod. 1995. 10(10):2793-9, Tabibzadeh S et al. Hum Reprod. 1995. 10(2):277-86, Bon GG, et al. Hum Reprod. 1999. 14(2):566-70]. However, in previous studies investigators either did not take into account these cyclic changes [Harada T et al. Fertil Steril. 2002. 78(4):733-9, Somigliana E, et al. Hum Reprod. 2004. 19(8):1871-6] or studied only one specific phase [Xavier P et al. Eur J Obstet Gynecol Reprod Biol. 2005. 1;123(2):254-5, Abrao MS, Podgaec S, Filho BM, Ramos LO, Pinotti JA, de Oliveira RM. Hum Reprod. 1997. 12(11):2523-7]. 3) The applied statistical methods were in many cases limited to methods that were inappropriate to incorporate the results of two or more variables into the diagnostic model [Somigliana E, et al. Hum Reprod. 2004. 19(8):1871-6, Xavier P et al. Eur J Obstet Gynecol Reprod Biol. 2005. 1 ;123(2):254-5, Harada T et alFertil Steril. 2002. 78(4):733-9]. 4) Most studies had only a limited number of patients.
IL-6 for the meaning of this application is interleukin 6. Its characteristics and nucleotide and protein sequences has been disclosed by Hirano T., et al. Nature
324:73-76(1986); Yasukawa K. et al. EMBO J. 6:2939-2945(1987); May LT. et al. Proc. Natl. Acad. Sci. U.S.A. 83:8957-8961(1986); Zilberstein A. et al. EMBO J.
5:2529-2537(1986); Brakenhoff J.P.J, et al. J. Immunol. 139:4116-4121(1987);
Tonouchi N. et al. Biochem. Biophys. Res. Commun. 163:1056-1062(1989);
Haegeman G. et al. Eur. J. Biochem. 159:625-632(1986), Wong G. et al. Behring Inst.
Mitt. 83:40-47(1988; Chen Q.Y. Zhonghua Zhong Liu Za Zhi 14:340-344(1992); van Damme J et al.J. Immunol. 140:1534-1541(1988); Ming J. E., et al. J. MoI. Cell.
Immunol. 4:203-211(1989); May L.T., Shaw J. E. et al. Cytokine 3:204-211(1991);
Breton J. et al. Eur. J. Biochem. 227:573-581(1995) and Nishimura C. et al;
Biochemistry 35:273-281(1996).
IL-8 in this application is one of the several N-terminal processed forms are produced by proteolytic cleavage after secretion from at least peripheral blood monocytes, leukocytes and endothelial cells. It can be IL-8(1-77) but also IL-8(6-77) which is the most prominent form, L-8(5-77) or IL-8(7-77) or any isoform or splicing variant. Its characteristics and nucleotide and protein sequences have been disclosed by Matsushima K. et al. J. Exp. Med. 167:1883-1893(1988); Schmid J. and Weissmann C. J. Immunol. 139:250-256(1987); Kowalski J. And Denhardt D.T. MoI. Cell. Biol. 9:1946-1957(1989); Mukaida N. et al. J. Immunol. 143:1366-1371(1989); Kalnine N. et al. Cloning of human full-length CDSs in BD Creator(TM) system donor vector." Submitted (MAY-2003) to the EMBL/GenBank/DDBJ databases; Halleck A.et al. "Cloning of human full open reading frames in Gateway(TM) system entry vector (pDONR201)."; Submitted (JUN-2004) to the EMBL/GenBank/DDBJ databases; The MGC Project Team Genome Res. 14:2121-2127(2004); King CH. et al. "cDNA cloning of human mesangial cell interleukin 8 by polymerase chain reaction." Submitted (FEB- 1992) to the EMBL/GenBank/DDBJ databases; Yoshimura T. et al. MoI. Immunol. 26:87-93(1989); Suzuki K. Et al. J. Exp. Med. 169:1895-1901(1989); Gregory H. et al. Biochem. Biophys. Res. Commun. 151 :883-890(1988); WaIz A. Et al. Biochem. Biophys. Res. Commun. 149:755-761(1987); Yoshimura T. et al. Proc. Natl. Acad. Sci. U.S.A. 84:9233-9237(1987); van Damme J. et al. Eur. J. Immunol. 20:2113- 2118(1990); van Damme J. et al. Eur. J. Biochem. 181 :337-344(1989); Hebert CA. et al. J. Immunol. 145:3033-3040(1990); Clark-Lewis I. et al. Biochemistry 30:3128- 3135(1991); Van den Steen P.E. et al. Blood 96:2673-2681(2000); Schutyser E. Et al. J. Biol. Chem. 277:24584-24593(2002); Baggiolini M. And Clark-Lewis I. FEBS Lett. 307:97-101(1992); Struyf S. et al. Adv. Immunol. 81:1-44(2003); Clore G. M. et al. J. Biol. Chem. 264:18907-18911(1989); Clore G. M. et al. Biochemistry 29:1689- 1696(1990); Sticht H. Et al. Eur. J. Biochem. 235:26-35(1996); Skelton N.J. et al. Structure 7:157-168(1999); Baldwin E.T. et al. J. Biol. Chem. 265:6851-6853(1990); Clore G.M. and Gronenborn A.M. J. MoI. Biol. 217:611-620(1991) and Baldwin ET. et al. Proc. Natl. Acad. Sci. U.S.A. 88:502-506(1991).
Tumor necrosis factor (TNF)-α is a protein that belongs to the tumor necrosis factor family. Its characteristics and nucleotide and protein sequences have been disclosed by Nedospasov S.A. et al. Cold Spring Harb. Symp. Quant. Biol. 51:611-624(1986); Pennica D. et al. Nature 312:724-729(1984); Shirai T. et al. Nature 313:803-806(1985); Nedwin G.E. et al. Nucleic Acids Res. 13:6361-6373(1985); Wang A.M. et al.Science 228:149-154(1985); Marmenout A. Et al. Eur. J. Biochem. 152:515-522(1985); Iris F.J. M. et al. Nat. Genet. 3:137-145(1993); Neville M.J., Campbell R.D.J. Immunol. 162:4745-4754(1999); Xie T. et al. Genome Res. 13:2621-2636(2003); Shiina S. Et al. Submitted (SEP-1999) to the EMBL/GenBank/DDBJ databases; Shiina T. Et al. Submitted (JUL-2002) to the EMBL/GenBank/DDBJ databases; Rieder M.J. et al. "SeattleSNPs. NHLBI HL66682 program for genomic applications, UW-FHCRC, Seattle, WA (URL: http://pga.gs.washington.edu)." Submitted (DEC-2001) to the EMBL/GenBank/DDBJ databases; Rieder M.J. et al. "NIEHS-SNPs, environmental genome project, NIEHS ES15478, Department of Genome Sciences, Seattle, WA (URL: http://egp.gs.washington.edu)."; Submitted (JAN-2003) to the EMBL/GenBank/DDBJ databases; The MGC Project Team; Genome Res. 14:2121-2127(2004); Jang J.S. and Kim B.E. Submitted (JAN- 1998) to the EMBL/GenBank/DDBJ databases; Shao C, et al. Submitted (MAR-2000) to the EMBL/GenBank/DDBJ databases; Pocsik E., et al. J. Inflamm. 45:152- 160(1995); Watts A.D. et al. EMBO J. 18:2119-2126(1999); Ostade X.V. et al. EMBO J. 10:827-836(1991); Stevenson FT., et al. J. Exp. Med. 176:1053-1062(1992); Moss M.L., et al. Nature 385:733-736(1997); Jones E.Y. et al. Nature 338:225-228(1989); Jones E.Y. et al. J. Cell Sci. Suppl. 13:11-18(1990); Eck M.J. and Sprang S.R.; J. Biol. Chem. 264:17595-17605(1989); Reed C, et al. Protein Eng. 10:1101-1107(1997) and Cha S.S., et al. J. Biol. Chem. 273:2153-2160(1998)
CA-125 or cancer antigen 125 is a tumor marker or biomarker that may be elevated in the blood of some people with specific types of cancers (e.g. ovarian cancer). CA-125 is a mucinous glycoprotein and the product of the MUC16 gene the sequence of the protein product has been disclosed in O'Brien T.J.et al. Tumour Biol. 22:348-366(2001) and O'Brien T.J. et al. Submitted (OCT-2002) to the EMBL/GenBank/DDBJ databases
CA-19-9 for this application is in the meaning of the carbohydrate antigen CA-19-9 which is an oncofetal antigen, expressed by several different cancers, but especially carcinomas of the gastrointestinal tract (Tolliver BA, O'Brien BL. South Med J 1997 Jan;90(1):89-90 and Reiter W. et al. Anticancer Res 2000 Nov-Dec;20(6D):5195-8
C-reactive protein (CRP) is a protein that belongs to the pentaxin family. Its characteristics and nucleotide and protein sequences have been disclosed by Lei K.-J. et al. J. Biol. Chem. 260:13377-13383(1985); Woo P. et al. J. Biol. Chem. 260:13384- 13388(1985); Murphy T.M. et al. "Extrahepetic transcription of human C-reactive protein." Submitted (NOV-1990) to the EMBL/GenBank/DDBJ databases; Tenchini M. L. et al. Submitted (MAY-1992) to the EMBL/GenBank/DDBJ databases; Harraghy N."Controlled gene expression using acute phase response elements." Submitted (DEC-2001) to the EMBL/GenBank/DDBJ databases; Rieder M.J. et al. "SeattleSNPs. NHLBI HL66682 program for genomic applications, UW-FHCRC, Seattle, WA (URL: http://pga.gs.washington.edu)." Submitted (NOV-2001) to the EMBL/GenBank/DDBJ databases; Gregory S.G., Barlow K.F., McLay K.E., Kaul R. Nature 441 :315-321 (2006); The MGC Project Team Genome Res. 14:2121-2127(2004); Tucci A. et al. J. Immunol. 131:2416-2419(1983); Whitehead A.S. et al. Science 221:69-71(1983); Oliveira E.B. et al. J. Biol. Chem. 254:489-502(1979); Osmand A.P. et al. Proc. Natl. Acad. Sci. U.S.A. 74:1214-1218(1977); Kiernan U.A. et al. Clin. Proteomics 1 :7-16(2004); Srinivasan N. et al. Structure 2:1017-1027(1994); Shrive A.K. et al. Nat. Struct. Biol. 3:346-353(1996) and Thompson D. et al. Structure 7:169-177(1999).
Intercellular adhesion molecule-1 (slCAM-1) is a protein that belongs to the immunoglobulin superfamily, the ICAM family. Its characteristics and nucleotide and protein sequences have been disclosed by Simmons D.et al. Nature 331:624- 627(1988); Staunton D.E. et al. Cell 52:925-933(1988) ; Tomassini J. E. et al.Proc. Natl. Acad. Sci. U.S.A. 86:49'θ7-4911 (1989); Voraberger G.F. et al. J. Immunol. 147:2777- 2786(1991); Kalnine N. et al. "Cloning of human full-length CDSs in BD Creator(TM) system donor vector." Submitted (MAY-2003) to the EMBL/GenBank/DDBJ databases; Rieder M.J.et al. "SeattleSNPs. NHLBI HL66682 program for genomic applications, UW-FHCRC, Seattle, WA (URL: http://pga.gs.washington.edu)." Submitted (JAN-2003) to the EMBL/GenBank/DDBJ databases; The MGC Project Team Genome Res. 14:2121-2127(2004); Stade B.G. et al. lmmunobiology 182:79-87(1990); Greve J.M. et al. Cell 56:839-847(1989); Liu T.et al. J. Proteome Res. 4:2070-2080(2005); Casasnovas J.M.et al. Proc. Natl. Acad. Sci. U.S.A. 95:4134-4139(1998); Bella J. Et al. Proc. Natl. Acad. Sci. U.S.A. 95:4140-4145(1998); Kolatkar P.R. et al. EMBO J. 18:6249-6259(1999); Vora D.K. et al. Genomics 21:473-477(1994); Wenzel K. Et al. Hum. Genet. 97:15-20(1996); Fernandez-Reyes D. et al. Hum. MoI. Genet. 6:1357- 1360(1997) and Halushka M.K. et al. Nat. Genet. 22:239-247(1999).
Placental Growth Factor (PIGF) is a protein that belongs to the PDGF/VEGF growth factor family. Its characteristics and nucleotide and protein sequences has been disclosed by Maglione D. et al. Proc. Natl. Acad. Sci. U.S.A. 88:9267-9271(1991);
Hauser S.D. and Weich H.A. Growth Factors 9:259-268(1993); Maglione D. Et al.
Oncogene 8:925-931(1993); Cao Y. et a(. Biochem. Biophys. Res. Commun. 235:493-
498(1997); Heilig R. Et al. Nature 421:601-607(2003); The MGC Project Team, Genome Res. 14:2121-2127(2004); Park J.E. et al. J. Biol. Chem. 269:25646-
25654(1994) and Iyer S. et al. J. Biol. Chem. 276:12153-12161(2001).
Vascular endothelial growth factor (VEGF) is a protein that belongs to the PDGF/VEGF growth factor family. Its characteristics and nucleotide and protein sequences has been disclosed by Leung D.W. et al. Science 246:1306-1309(1989) ; Keck PJ. et al. Science 246:1309-1312(1989) ; Tischer E. Et al. J. Biol. Chem. 266:11947-11954(1991); Houck K.A. et al. MoI. Endocrinol. 5:1806-1814(1991); Weindel K. et al. Biochem. Biophys. Res. Commun. 183:1167-1174(1992); Poltorak Z. et al. J. Biol. Chem. 272:7151- 7158(1997); Lei J. et al. Biochim. Biophys. Acta 1443:400-406(1998); Claffey K.P., et al. MoI. Biol. Cell 9:469-481(1998); Whittle CJ. , et al. Clin. Sci. 97:303-312(1999) ; Bates D.O., et al. Cancer Res. 62:4123-4131(2002) ; Murata H. et al. Human cDNA for the vascular endothelial growth factor isoform VEGF165." Submitted (DEC-1998) to the EMBL/GenBank/DDBJ databases; Sato J. D. and Whitney R.G.; "Human cDNA for vascular endothelial growth factor isoform VEGF121." Submitted (DEC-1999) to the EMBL/GenBank/DDBJ databases; Liu J. et al. Cloning of vascular endothelial growth factor (VEGF) cDNA."; Submitted (JUL-2001) to the EMBL/GenBank/DDBJ databases; Shan Z.X. et al. Cloning and identification of vascular endothelial growth factor isoform VEGF165." Submitted (FEB-2002) to the EMBL/GenBank/DDBJ databases; Koul S. et al. "Cloning and characterization of VEGF from LnCAP cells, a line of prostate cancer cells." Submitted (SEP-2004) to the EMBL/GenBank/DDBJ databases; Mungall A.J., et al. Nature 425:805-811(2003); The MGC Project Team, Genome Res. 14:2121- 2127(2004); Rieder M.J. et al. "SeattleSNPs. NHLBI HL66682 program for genomic applications, UW-FHCRC, Seattle, WA (URL: http://pga.gs.washington.edu)." Submitted (OCT-2001) to the EMBL/GenBank/DDBJ databases; Fiebich B.L. et al. Eur. J. Biochem. 211:19-26(1993); Zhang Z. and Henzel W.J. Protein Sci. 13:2819- 2824(2004) and Muller Y.A. et al. Proc. Natl. Acad. Sci. U.S.A. 94:7192-7197(1997).
The term "subject" or "patient" refers to any human or animal mammals.
The term "biological sample" as used herein can be fluid sample or at least one cell containing sample or it can be a sample chosen from serum, blood, plasma, biopsy sample, tissue sample, cell suspension, saliva, oral fluid, cerebrospinal fluid, amniotic fluid, milk, colostrum, mammary gland secretion, lymph, urine, sweat, lacrimal fluid, gastric fluid, synovial fluid, and mucus.
The term phases of the menstrual cycle refer to known in that art that identification of the phases of distinct endocrine and/or physiological activity : the menstruation phase, the follicular phase or proliferation phase, the event dividing phase, ovulation, and the luteal or secretory phase secretory phase which also can be described as the transition phase, fertile phase, ovulation, or infertile phase of a menstrual cycle
The practice of the present invention will employ, unless otherwise indicated, conventional techniques of chemistry, molecular biology, microbiology, recombinant DNA and immunology, which are within the capabilities of a person of ordinary skill in the art. Such techniques are explained in the literature. See, for example, J. 10 Sambrook, E. F. Fritsch, and T. Maniatis, 1989, Molecular Cloning: A Laboratory Manual, Second Edition, Books 1-3, Cold Spring Harbor Laboratory Press; Ausubel, F. M. et al. (1995 and periodic supplements; Current Protocols in Molecular Biology, ch. 9, 13, and 16, John Wiley & Sons, New York, N. Y.); B. Roe, J. Crabtree, and A. Kahn, 1996, DNA Isolation and Sequencing: Essential Techniques, John Wiley & 15 Sons; J. M. Polak and James O1D. McGee, 1990, In Situ Hybridization: Principles and Practice; Oxford University Press; M. J. Gait (Editor), 1984, Oligonucleotide Synthesis: A Practical Approach, IrI Press; and, D. M. J. Lilley and J. E. Dahlberg, 1992, Methods of Enzymology: DNA Structure Part A: Synthesis and Physical Analysis of DNA Methods in Enzymology, Academic Press. Each of these general 20 texts is herein incorporated by reference.
For purposes of this application, the quantifiable signs, symptoms and/or analytes in biological fluids and tissues characteristic of endometriosis or a particular progression stage of endometriosis are defined as "biomarkers" for the disease. Current diagnostic and prognostic methods for endometriosis depend on the identification and evaluation of these biomarkers, both individually and as they relate to one another. The term "biomarkers" includes all types of biological data from a patient. Such biomarkers may include, but are not limited to, data derived from the presence of substance of the body of a subject including, but not limited to endocrine substances such as hormones, exocrine substances such as enzymes, and neurotransmitters, electrolytes, proteins, carbohydrates, growth factors, cytokines, chemokines, monokines, fatty acids, triglycerides, and cholesterol. For present invention the data are preferably generated from protein substances.
Biological data may be derived from analysis of fluids of a patient but it also may be derived from cells removed from patients (e.g. a from a blood sample) and grown in culture. Various characteristics of these cells may be examined histologically and biochemically. For example, cells removed from a patient and placed in culture may be examined for the presence of specific markers associated with the presence of a disease. Cells may be examined for their metabolic activity or for the products made and released into the culture medium. Biological data about a patient includes results from genetic and molecular biological analysis of the nuclear and cytoplasmic molecules associated with transcription and translation such as various forms of ribonucleic acid, deoxyribonucleic acid and other transcription factors, and the end product molecules resulting from the translation of such ribonucleic acid molecules. The term "antibody" as used herein refers to an intact antibody, or a binding fragment thereof that competes with the intact antibody for specific binding. Binding fragments are produced by recombinant DNA techniques, or by enzymatic or chemical cleavage of intact antibodies. Binding fragments include Fab, Fab1, F(ab')2, Fv, and single-chain antibodies. An antibody other than a "bispecific" or "bifu notional" antibody is understood to have each of its binding sites identical. An antibody substantially inhibits adhesion of a receptor to a counter-receptor or to a ligand when an excess of antibody reduces the quantity of receptor bound to counter-receptor or ligand by at least about 20%, 40%, 60% or 80%, and more usually greater than about 85% (as measured in an in vitro competitive binding assay). Recombinant proteins formed by gene fusion of light and heavy chain antibody regions are also included in the definition of "antibody."
Antibodies having changes in amino acid sequence from particular antibodies exemplified herein can be used in the method of the invention. For example, the sequences can have "substantial identity", meaning the sequence of the original and changed sequence, when optimally aligned, such as by the programs GAP or BESTFIT using default gap weights, share at least 80 percent sequence identity, preferably at least 90 percent sequence identity, more preferably at least 95 percent sequence identity, and most preferably at least 99 percent sequence identity in the sequence of the entire antibody, the variable regions, the framework regions, or the CDR regions. Preferably, residue positions which are not identical differ by conservative amino acid substitutions. Conservative amino acid substitutions refer to the interchangeability of residues having similar side chains. For example, a group of amino acids having aliphatic side chains is glycine, alanine, valine, leucine, and isoleucine; a group of amino acids having aliphatic-hydroxyl side chains is serine and threonine; a group of amino acids having amide-containing side chains is asparagine and glutamine; a group of amino acids having aromatic side chains is phenylalanine, tyrosine, and tryptophan; a group of amino acids having basic side chains is lysine, arginine, and histidine; and a group of amino acids having sulfur-containing side chains is cysteine and methionine. Preferred conservative amino acid substitution groups are: valine-leucine-isoleucine, phenylalanine-tyrosine, lysine-arginine, alanine-valine, glutamic-aspartic, and asparagine-glutamine. For example, it is reasonable to expect that an isolated replacement of a leucine with an isoleucine or valine, an aspartate with a glutamate, a threonine with a serine, or a similar replacement of an amino acid with a structurally related amino acid will not have a major effect on the binding or properties of the resulting molecule, especially if the replacement does not involve an amino acid within a framework site. Whether an amino acid change results in a functional peptide can readily be determined by assaying the specific activity of the polypeptide derivative.
Fragments or analogs of antibodies or immunoglobulin molecules can be readily prepared by those of ordinary skill in the art. Preferred amino-and carboxy-termini of fragments or analogs occur near boundaries of functional domains. Structural and functional domains can be identified by comparison of the nucleotide and/or amino acid sequence data to public or proprietary sequence databases. Preferably, computerized comparison methods are used to identify sequence motifs or predicted protein conformation domains that occur in other proteins of known structure and/or function. Methods to identify protein sequences that fold into a known three-dimensional structure are known. [Bowie et al. Science 253:164 (1991)]. Thus, the foregoing examples demonstrate that those of skill in the art can recognize sequence motifs and structural conformations that may be used to define structural and functional domains in accordance with the invention.
Preferred amino acid substitutions are those which: (1) reduce susceptibility to proteolysis, (2) reduce susceptibility to oxidation, (3) alter binding affinity for forming protein complexes, (4) alter binding affinities, and (4) confer or modify other physicochemical or functional properties of such analogs. Analogs can include various muteins of a sequence other than the naturally-occurring peptide sequence. For example, single or multiple amino acid substitutions (preferably conservative amino acid substitutions) may be made in the naturally-occurring sequence (preferably in the portion of the polypeptide outside the domain(s) forming intermolecular contacts). A conservative amino acid substitution should not substantially change the structural characteristics of the parent sequence (e.g., a replacement amino acid should not tend to break a helix that occurs in the parent sequence, or disrupt other types of secondary structure that characterizes the parent sequence). Examples of art-recognized polypeptide secondary and tertiary structures are described in Proteins, Structures and Molecular Principles (Creighton, Ed., W. H. Freeman and Company, New York (1984)); Introduction to Protein Structure (C. Branden and J. Tooze, eds., Garland Publishing, New York, N.Y. (1991)); and Thornton et at. Nature 354: 105 (1991).
Any suitable technique for determining levels of endometriosis protein biomarkers such as interleukin (IL)-6, IL-8, tumor necrosis factor (TNF)-α, CA-125, CA-19-9 and C- reactive protein (CRP) or others in body fluids such as plasma may be used in the method of the invention. Examples of suitable techniques include those based on determining protein activity and/or those based on determining the presence or levels of the protein biomarkers. Latent or active biomarker may thus be measured but the protein biomarker quantification can even be based on the quantification of their mRNA. Those skilled in the art will readily be able to determine whether or not a technique is suitable, if necessary using positive and negative control samples.
Bispecific antibodies can be generated that comprise (i) two antibodies: one with a specificity for a first biomarker and the other for a second molecule (ii) a single antibody that has one chain specific for first biomarker and a second chain specific for a second molecule, or (iii) a single chain antibody that has specificity for first biomarker and the other molecule. Such bispecific antibodies can be generated using well known techniques, e.g., Fanger et al. Immunol Methods 4:72-81 (1994), Wright and Harris, supra, and Traunecker et al. Int. J. Cancer (Suppl.)7:51-52 (1992).
Antibodies for use in the invention also include "kappabodies" (III et al. "Design and construction of a hybrid immunoglobulin domain with properties of both heavy and light chain variable regions" Protein Engl 0:949-57 (1997)), "minibodies" (Martin et al. "The affinity-selection of a minibody polypeptide inhibitor of human interleukin-6" EMBO J13:5303-9 (1994)), "diabodies" (Holliger et al. '"Diabodies1: small bivalent and bispecific antibody fragments" PNAS USA90:6444-6448 (1993)).
Antibodies used in the present invention can be expressed in various cell lines. Sequences encoding the cDNAs or genomic clones for the particular antibodies can be used for transformation of suitable mammalian or nonmammalian host cells. Transformation can be by any known method for introducing polynucleotides into a host cell, including, for example packaging the polynucleotide in a virus (or into a viral vector) and transducing a host cell with the virus (or vector) or by transfection procedures known in the art, as exemplified by U.S. Patents 4,399,216, 4,912,040, 4,740,461, and 4,959, 455. Methods for introduction of heterologous polynucleotides into mammalian cells are well known in the art and include, but are not limited to, dextran-mediated transfection, calcium phosphate precipitation, polybrene mediated transfection, protoplast fusion, electroporation, particle bombardment, encapsulation of the polynucleotide(s) in liposomes, peptide conjugates, dendrimers, and direct microinjection of the DNA into nuclei.
Mammalian cell lines available as hosts for expression are well known in the art and include many immortalized cell lines available from the American Type Culture Collection (ATCC), including but not limited to Chinese hamster ovary (CHO) cells, NSOO, HeLa cells, baby hamster kidney (BHK) cells, monkey kidney cells (COS), and human hepatocellular carcinoma cells (e.g., Hep G2). Non-mammalian cells can also be employed, including bacterial, yeast, insect, and plant cells. Site directed mutagenesis of the antibody CH2 domain to eliminate glycosylation may be preferred in order to prevent changes in either the immunogenicity, pharmacokinetic, and/or effector functions resulting from non-human glycosylation. The glutamine synthase system of expression is discussed in whole or part in connection with European Patents 216 846, 256 055, and 323 997 and European Patent Application 89303964.4.
Antibodies for use in the invention can also be produced transgenically through the generation of a mammal or plant that is transgenic for the immunoglobulin heavy and light chain sequences of interest and production of the antibody in a recoverable form therefrom. Transgenic antibodies can be produced in, and recovered from, the milk of goats, cows, or other mammals. See, e.g., U.S. Patents 5,827,690, 5,756,687, 5,750,172, and 5,741 ,957. Total biomarker protein levels may also be measured using methods such as ELISA, fluorometric assay, chemiluminescent assay, or radioimmunoassay. ELISA or chemiluminescent assay methods are particularly preferred, since these are quick, sensitive, and specific, and are readily automated for large scale use. These methods also provide quantitative determinations. A number of appropriate methods for measuring these biomarker proteins are detailed in manuals on diagnosis of protein or (polypeptide biomarkers (The ELISA Guidebook (Methods in Molecular Biology) by John R. Crowther (Editor) Publisher: Humana Press; Spiral edition (August 2000); Elisa: Theory and Practice (Methods in Molecular Biology, VoI 42) by John R. Crowther Publisher: Humana Press; Spiral edition (May 1995) ; The Immunoassay Handbook, Third Edition by David Wild (Editor) Elsevier Science; 3 edition (June 20, 2005); Immunoassay by Eleftherios P. Diamandis (Editor), Theodore K. Christopoulos (Editor) Publisher: Academic Press (June 5, 1996) and Chemosensitivity: In Vitro Assays (Methods in Molecular Medicine) by Rosalyn D. Blumenthal (Editor) Publisher: Humana Press (March 2005).
Numerous methods for conducting competitive binding assays are known in the art. These methods can be divided into two classes: assays conducted entirely in one liquid phase, commonly called homogenous binding assays; and assays which require the separation of a solid phase from a liquid phase, herein called multi phase assays. The traditional radioimmunoassay (RIA) is an example of a multi phase assay, as is the sandwich assay. In these assays, a binding component containing a signal producing species is bound to or converted into a solid phase (e. g., by precipitation). The solid phase is separated from the liquid phase which contains signal that is not bound to the binding component. The amount of bound signal is measured and used to determine analyte concentration.
Methods are currently available for determining the concentration of different biomarkers in a body fluid, this is for instance achievable by homogenous binding assays or assays that can be read without a phase separation step, for instance as described in EP494509 A1. Such methods can be incorporated in present invention for accurate, quantitative information without the use of complex laboratory instruments or experiments.
The reliable noninvasive diagnostic tool of present invention can conclusively determine the presence or absence and severity of endometriosis and thus will have a major impact on the treatment of women with pelvic pain and/or subfertility, and result in significant improvements. Many women suffering from chronic pelvic pain, dysmenorrhea, dyspareunia and/or infertility can get a correct diagnosis immediately at the onset of their symptoms with respect to the presence or absence of endometriosis. Moreover, these patients will not have to travel to a hospital or fertility center for the diagnosis as it can be sufficient to visit a general practitioner with access to blood analysis at a competent institution. Furthermore, the general practitioner can assign patients with probable endometriosis immediately to an appropriate institution with the necessary expertise where gynecologists can immediately provide the most adequate, disease specific and personalized treatment for these women. This will enable the health care specialists to start targeted therapies in early stages of the disease which can largely increase the success rates of the treatments which in turn can significantly improve the quality of life of the patients on the physical health levels as well as on the mental and emotional levels. Furthermore, the patients found negative by the test will also benefit from the results as it can indicate that the symptoms are caused by condition(s) other than endometriosis which may require a different area of expertise (eg. gastroenterology, urology). This will shorten the time to a correct diagnosis also for symptomatic women without endometriosis. Secondly, the use of a noninvasive diagnostic test will also greatly decrease endometriosis related health care costs by decreasing the number of unnecessary diagnostic laparoscopic surgeries, and by preventing the use of medications ineffective in endometriosis but frequently prescribed when the diagnosis is unknown. Thirdly, such a test can indirectly reduce the indirect economic loss related to endometriosis by enabling early treatment and thus preventing the progression of the disease which is frequently seriously decreasing working capacity. Finally, last but not least, the use of a reliable noninvasive diagnostic test will allow an accurate estimation of the prevalence of endometriosis in general, its relation to pelvic pain and/or (in)fertility and its potential impact on the quality of life of the affected women population. Furthermore, it can help to develop population scale strategies to fight endometriosis more efficiently.
A particular adavantage the presen invention is that it provides a method to test a biomarker for its relevance in the diagnosis or prognosis of a selected disorder which is associated with the female's menstrual cycle and is a disorder selected from the group consisting of premenstrual syndrome, migraine headache, endometriosis, psoriasis, acne, dysmenorrhoea, neurosia, asthma and premenstrual cramps, by in vitro assaying at least one biological sample per patient from a plurality of patients comprising patient groups without the disorder and patient groups with the disorder or with a known stage of the disorder characterised in that the method comprises the steps of 1) producing a first input signal on the level or activity of. at least one relevant biomarker for that disorder and a second input signal on the level or activity of a biomarker to be tested for its relevance to that selected disorder whereby the first and the second mathematical model are selected of the group consisting of Stepwise Logistic Regression Model and LS-SVM model 2) processing the first input signal in the signal processor to construct a first mathematical model that produces output signals which are indicative for the disorder, the seriousness of disorder or the progress of disorder in the affected patient, 3) processing both the first input signal and the second input signal in the signal processor to construct a second mathematical model that produces output signals which are indicative for the disorder, the seriousness of disorder or the progress of disorder in the affected patient, 4) selecting the test biomarker as relevant in the diagnosis or prognosis of that selected disorder if the second mathematical model reveals a better performance than the first mathematical model. Such biomarker variables are obtained from in vitro assaying of at least one biological sample of a patient in a specific phase of the menstrual cycle and in particular the biomarker variables are obtained from in vitro assaying of at least one biological sample of a patient in the secretory phase of the menstrual cycle. Such method can be implemented in operating system which controls the allocation of an in vitro essay system to generate biomarker values of a patient and which feeds the input signals from the in vitro essay system into signal processor comprising a mathematical model that is described on the relationship of a plurality of biomarker variables and a plurality of disorder variables from in vitro assaying of biological samples of plurality of patients with no disorder, affected with disorder, affected with a defined seriousness or with defined progress of disorder. An interesting use of such operating system is testing the relevance of a specific biomarker for diagnosing or prognosing of a disorder, determining the presence or absence of a disorder, the seriousness of the disorder or the progress of the disorder in a patient pr the use of the operating system also controls usage of the in vitro essay system. Such operating system can include a user interface that to enable the user to interact with the functionality of the computer, whereby the operating system can include a graphical user interface whereby the operating system controls the ability to generate graphics on the computer's display device that can be displayed in a variety of manners representative for or associated with the relevance of a specific biomarker for diagnosing or prognosing of a disorder, determining the presence or absence of a disorder or for the seriousness of the disorder or the progress of the disorder in a patient. Furthermore the invention also provide a computer-executable code, stored in a computer-readable medium, the computer executable code adapted, when running on a computer system to run the operating system descibed here above or to execute the mathematical model of present invention and to direct a processing means to produce out put signals that are representative for the relevance of a specific biomarker for diagnosing or prognosing of a disorder, determining the presence or absence of a disorder or for the seriousness of the disorder or the progress of the disorder in a patient. Furthermore the invention comprises a computer system for operating the operating system of present invention comprising a file system for storing files and a processor for analysing the content of biomarker value files stored in said file system to wherein said processor clusters said files in accordance with the mathematical model of claim 1 to determine the relevance of a specific biomarker for diagnosing or prognosing of a disorder or for determining the presence or absence of a disorder, the seriousness of the disorder or the progress of the disorder in a patient. The invention furthermore concerns an apparatus comprising an in vitro diagnosis system for determining the relevance of a specific biomarker for diagnosing or prognosing of a disorder or for determining the presence or absence of a disorder, the seriousness of the disorder or the progress of the disorder in a patient, whereby the apparatus comprises or is interrelating with the computers system of present invention. A biomarker that has been identified by methods, the computer system and /or the apparatus of present invention which were found to be relevant for a specific medical condition can be in the manufacture of a diagnostic to diagnose for the medical condition"
EXAMPLES
Example A
Material and methods
Six biomarkers that were suggested to potentially have a significant role in the onset and/or development of endometriosis were analyzed in the initial study. These molecules - IL-6, IL-8, TNF-α, CA-125, CA-19-9 and C-reactive protein (CRP) - are considered to be Involved in the development and/or progression of endometriosis as autocrine/paracrine factors or as products of immunocompetent cells promoting vascularisation and/or supporting survival and proliferation of ectopic endometrial cells through various mechanisms. The plasma concentrations of these biomarkers were determined in 315 plasma samples in 3 precisely defined study groups: 1) women without endometriosis (controls), 2), women with stage l-ll endometriosis, 3) women with stage I H-IV endometriosis. The results confirmed that plasma levels of these markers can vary significantly between women with and without endometriosis, regardless of disease stage and menstrual cycle phase. This study demonstrated that it is the secretory phase of the menstrual cycle when differences in terms of biomarkers are most explicitly expressed and that the secretory phase is the most suitable period to measure plasma concentrations of the selected biomarkers for diagnostic purposes. When the patients were analyzed during the secretory phase only -comparing all women with endometriosis to women without endometriosis-, multiple logistic regression analysis revealed a sensitivity of 90% and a specificity of 71%, suggesting that a noninvasive diagnostic test can be developed for endometriosis if patients are tested in the secretory phase of the menstrual cycle.
Samples
Blood samples had been collected -after obtaining a signed informed consent- from every women undergoing laparoscopic surgery at the Leuven University Fertility Center (LUFC). At present the tissue bank contains plasma samples of over 800 patients. For each patient, detailed clinical information is available in the electronic file of the patient and includes age, body mass index (BMI), cycle phase at surgery, detailed surgery report with scoring and staging of endometriosis according to the classification of the ASRM (ASRM, 1997) and histological confirmation. Sample collection: Before surgery, about 4x4ml peripheral blood is collected from the patients. The blood samples are centrifuged at 10000 rpm for 10 minutes at 4°C, plasma is aliquoted and stored at -800C till analysis. The maximum time period between sample collection and storage in the -800C freezer is maximum 1 hour. Each aliquot is labeled and the related information (eg date of collection, location in the tissue-bank, clinical data) is entered in the electronic sample database.
The numbers of secretory phase samples in the different study groups of the preliminary study were as follows: Control group: 38; Endometriosis Stage l-ll group: 47; Endometriosis Stage III-IV group: 31.
Measurement of the plasma biomarkers
The selected 8 biomarkers - IL-6, IL-8, TNF-α, slCAM-1 , VEGF, CA-125, CA-19-9 and CRP- are determined in plasma samples collected in the secretory phase from women with or without endometriosis. Identical to the preliminary study, IL-6, IL-8 and TNF-α levels can be determined by various methods such as -but not limited to- commercially available OptEIA ELISA kits developed for the quantitative determination of the respective plasma markers by BD Biosciences-Pharmingen (Erembodegem, Belgium). In addition to the markers measured in the preliminary study slCAM-1 levels will also be determined by using the commercially available OptEIA ELISA kits developed for slCAM-1 quantitation. The method of the measurements is based on the following principle: The BD OptEIA™ test is a solid phase sandwich ELISA (Enzyme-Linked Immunosorbent Assay). It utilizes a monoclonal antibody specific for the target molecules coated on a 96-well plate. Standards and samples are added to the wells in duplicates, and any target molecule (eg. IL-6, IL-8, TNF-α or slCAM-1) present in the wells binds to the immobilized antibody. The wells are washed and an streptavidin- horseradish peroxidase conjugate mixed with a biotinylated antihuman antibody - specific for the target molecule of interest- is added, producing an antibody-antigen- antibody "sandwich". The wells are again washed and TMB substrate solution is added, which produces a blue color directly proportional to the amount of the target molecule present in the initial sample. The Stop Solution changes the color from blue to yellow, and the microwell absorbances are read at 450 nm. Since up till now BD does not produce VEGF ELISA kits, plasma levels of VEGF can bedetermined by using a human VEGF quantification ELISA kit manufactured by RayBio. The basic principle and method of the application is identical to that described above. The measurement of the plasma markers CA-125, CA-19-9 and CRP were carried out in the central laboratories of the University Hospitals Leuven (Laboratoriumgeneeskunde, UZ Gasthuisberg, Leuven). CA-125 and CA-19-9 can be measured by various methods such as -but not limited to- using automated electrochemiluminescence immunoassays (Roche Diagnostics, Belgium), while CRP levels were determined using an automated immunoturbidimetric assay (Roche Diagnostics, Belgium).
Statistical analysis of the experimental results, development of the diagnostic model
Appropriate statistical analysis of the experimental results is the cornerstone of the present invention. Univariate analysis is carried out using adequate software packages such as -but not limited to- the Prizm 4.0 software package (GraphPad, USA) and consists of performing the following statistical test on the experimental data:
A) Mann-Whitney test or Kruskal-Wallis test with the Bonferroni post hoc test or Dunn's multiple comparison test can be performed-as appropriate- to identify significant differences between groups.
B) Receiver Operating Characteristic (ROC) curves are constructed for each of the individual plasma markers to identify the discriminative power of each marker alone.
Multivariate analysis represents a far more complex statistical approach. In this part of the data-analysis two methods have been used to analyze the data: stepwise logistic regression and Least Squares Support Vector Machines (LS-SVM).
Stepwise Logistic regression Logistic regression is a statistical model part of a group of statistical models called General Linear Models (GLM). A logistic regression model describes the relationship between one or several independent or explanatory variables (xi,...,xn) and a binary (a variable that can only take two values: 0 or 1) outcome variable Y. This outcome variable Y has a binomial distribution where the probability P(Y=I |x), which is the conditional probability of Y= 1 given the explanatory variables, is represented by y(x):
Figure imgf000056_0001
where g(x) is called the logit and is given by s(χ) - Po + P\χ\ +β2 χ2 +-+ βP x P . where βo,βi,β2,---,βP are the parameters of the logistic regression model. These parameters indicate the influence that their corresponding explanatory variable has on the outcome. The sign of the parameter corresponds to an increase of the odds if it is positive and a decrease of the odds if it is negative. The magnitude of each parameter refers to the amount by which the dependent variable (i.e. the outcome) will increase if the independent variable changes by one unit. When the optimal set of variables is not known model selection will have to be used. Therefore a stepwise selection procedure can be used to select the optimal set of variables in the logistic regression model. Stepwise selection is a combination of forward and backward selection. The basic scheme is the same as for forward selection. Variables are iteratively added to the logistic regression model if their p-value is below the significance level for entry in the model (i.e. PE). After each iteration, the variable with the worst p-value is removed if this p-value is above the significance level PR for removal out of the model. This means that each forward selection step is followed by a backward selection step. The algorithm stops if no more variables can be included or removed.
The advantage of logistic regression is that it makes no assumption about the distribution of the independent variables. Moreover both discrete and continuous variables can be modeled at the same time without extra processing. In addition the logistic regression parameters can be easily interpreted.
Least Squares Support Vector Machines
Least Squares Support Vector Machines (LS-SVM) are a modified version of Support Vector Machines (SVM) where a linear problem is solved instead of the more complex quadratic programming problem. This makes LS-SVMs easier and much faster than SVM. Given a training set of data with the corresponding class labels, LS-SVMs can be used for binary classification. The LS-SVM classifier takes the following form:
Figure imgf000057_0001
Where the input data is mapped to a higher dimensional feature space (which can be infinite dimensional) by a mapping function φ(x). This means that, conceptually, the classification is done in a high dimensional feature space. The mapping itself (i.e. φ(x)) does not have to be constructed explicitly. Only the inner product in the feature space has to be specified. This inner product is represented by a kernel function:
K(r,s) = φ(r)τφ(s) where r and s belong to the input space. When a kernel function satisfies certain conditions it can be used in LS-SVM. Different types of kernels exist such as a linear kernel and RBF kernel.
The advantages of Least Squares Support Vector Machine (LS-SVM) are that by using more complex kernels non-linear relationships between the dependent and independent variables can be discovered. Moreover the number of variables that can be modeled is large since LS-SVM work well with high-throughput data sets. LS-SVM and SVM related methods are more frequently being used as an alternative for logistic regression in clinical data analysis. MACBETH a publicly available web server can be used to build LS-SVM classification models [Pochet N.L.M.M., et al. Bioinformatics, vol. 21 , no. 14, JuI. 2005, pp. 3185-3186] and allows using LS-SVM in an easy way.
Results In view of the great need for a non-invasive diagnostic test for endometriosis and the the limitations of the previously conducted studies in this area, we have set up and completed a series of experiments on simultaneous measurement and analysis of various potential biomarkers. This offers powerful tools for clinically useful diagnostic method for endometriosis. Relevant biomarkers that potentially have a significant role in the onset and/or development of endometriosis have been incorporated. These molecules -interleukin (IL)-6, IL-8, tumor necrosis factor (TNF)-α, CA-125, CA-19-9 and C-reactive protein (CRP)- are involved in the development and/or progression of endometriosis as autocrine/paracrine factors or as products of immunocompetent cells promoting vascularisation and/or supporting survival and proliferation of ectopic endometrial cells through various mechanisms.
From a tissue bank of the Leuven University Fertility Center more than 300 plasma samples collected from women had been identified with or without endometriosis. Each of the samples were complete with clinicopathological documentation including the patients' age, revised American Fertility Society (rAFS) classification stage and score of endometriosis and menstrual cycle phase, medications and previous operations allowing for precise study group definitions.
Following careful revision of the clinicopathological data files the following samples were excluded: Samples collected from women who at the time of collection were taking medications on a regular basis; Samples collected from women who had been operated within 6 months prior to the time of collection and Samples collected from women who had other disease(s) at the time of collection.
After exclusion of the samples that fell into one or more of the above categories the plasma concentrations of IL-6, IL-8, TNF-α, CA-125, CA-19-9 and CRP were determined in the remaining 315 samples by using commercially available ELISA kits (for IL-6, IL-8, TNF-α, BD Biosciences, Erembodegem, Belgium) or by automated immunometric assays (for CA-125, CA-19-9, CRP, Roche, Belgium). Statistical analysis of the data was carried out by using Kruskal-Wallis test, Mann-Whitney test, ROC analysis Multivariate Stepwise Logistic Regression analysis and LS-SVM as appropriate, to evaluate the diagnostic value of the molecules individually and in combination.
Analysis 1 : Statistical analysis based only on stage of the disease (disregarding cycle phase) using stepwise logistic regression.,
In stage l-ll endometriosis, stepwise logistic regression analysis showed that combined measurement of IL-8 and CA 125 have the strongest discriminating ability compared to that of the individual molecules (Table I).
Table I.
Figure imgf000059_0001
Figure imgf000060_0001
For more advanced stages of endometriosis the regression model revealed that simultaneous IL-6 and CA 125 analysis can distinguish significantly better between control patients and women with stage MI-IV endometriosis than any of the individual molecules alone or in other combinations. (Table II).
Table II.
Figure imgf000060_0002
Analysis of the experimental results according to disease stage AND menstrual cycle phase revealed that the differences in terms of levels of the different biomarkers in the different groups are the most explicit in the secretory phase, therefore we performed another stepwise logistic regression analysis, but this time comparing controls and cases only from the secretory phase of the menstrual cycle.
Analysis 2: Statistical analysis of only secretory phase samples based on stage of the disease using stepwise logistic regression
When comparing secretory phase control women with secretory phase women having stage l-ll endometriosis, stepwise logistic regression analysis showed that combined measurement of IL-6 and TNF-α have the strongest discriminating ability compared to that of the individual molecules alone or in other combinations (Table III).
Table III.
Figure imgf000061_0001
Comparing the secretory phase samples of controls and women with stage I H-IV endometriosis the regression model revealed that a triple combination of CA 125, IL-6 and TNF-α can distinguish significantly better between control and diseased patients than any of the individual molecules alone or in other combinations (Table IV).
Table IV.
Figure imgf000061_0002
Analysis 3: Statistical analysis of only secretory phase samples regardless of the disease stage using stepwise logistic regression^
Comparing the secretory phase samples of women without endometriosis with the samples of all secretory phase women with endometriosis the stepwise logistic regression model identified the triple combination of CA 125, IL-8 and TNF-α as the best combination to differentiate between diseased and control women (Table V).
Table V.
Plasma Marker Sensitivity Specificity AUC W
Figure imgf000062_0001
One way of carrying out present invention is given below. The present invention will become more fully understood from this example given herein below by way of illustration only. This example is thus not limitative of the present invention.
The analysis 1-3 showed that the following biomarker combinations of the secretory phase values have the greatest diagnostic power in the different disease stages: CTRL vs Stage Nl: IL-6+TNF alpha
CTRL vs Stage NI-IV: IL-6+TNF alpha+CA 125
CTRL vs All secretory endometriosis: IL-8+ TNF alpha+CA 125
The above described analysis didn't only give the best marker combinations but also the model which can be used to classify unknown patients: - CTRL vs Stage Nl: exp(^0 + βl *IL - 6 + β2*TNF - a) y = l + eiφ(βθ+ β *IL- 6 + β2 *TNF - a)
CTRL vs Stage HI-IV: eκp(^0 + βl *IL - 6 + β2 *TNF - a+ β3 *CA - 125) y = l + exp(βO + βl*lL - 6 + β2*mF- a+ β3*CA-125)
CTRL vs All secretory endometriosis: ezp(βO + βl *lL -B + β2*7m- a+ β3*CA-125) 7 ~ 1 + esp(£0 + β. *IL - 8 + β2*TNF - a+ βi*CA- 125) The parameters βθ, β1, β2 and β3 are constants calculated during the stepwise logistic regression analysis, therefore these are known. The symbols IL-6, IL-8, TNF-α and CA 125 represent the secretory phase plasma concentrations of these biomarkers in the patient in question. This way all parameters are known to calculate the "y" value. This will actually be the final diagnostic parameter.
The stepwise logistic regression analysis also provides an ROC curve with the possible cut-off values (y-values) and the respective sensitivity and specificity values for all comparisons. Eg: y-value (cutPositive Negative FaIs FaIs Sensitivity 1-Specificity Specificity Sum off) Positive Negative (Sens+Spec) 0.500473332 35 34 4 12 0.744680851 0.105263158 0.89473684 1.639418
0.47244672 35 33 5 12 0.744680851 0.131578947 0.86842105 1.613102
0.458024867 36 33 5 11 0.765957447 0.131578947 0.86842105 1.634378
0.455011796 36 32 6 11 0.765957447 0.157894737 0.84210526 1.608063
0.445877277 36 31 7 11 0.765957447 0.184210526 0.81578947 1.581747
0.41652235 37 31 7 10 0.787234043 0.184210526 0.81578947 1.603024
0.405663347 37 30 8 10 0.787234043 0.210526316 0.78947368 1.576708
0.403378628 37 29 9 10 0.787234043 0.236842105 0.76315789 1.550392
0.402445475 41 28 10 6 0.872340426 0.263157895 0.73684211 1.609183
0.401238291 41 27 11 6 0.872340426 0.289473684 0.71052632 1.582867
0.400964972 41 26 12 6 0.872340426 0.315789474 0.68421053 1.556551
0.396426271 41 25 13 6 0.872340426 0.342105263 0.65789474 1530235
0.378181755 41 24 14 6 0.872340426 0.368421053 0.63157895 1.503919
0.369596852 41 23 15 6 0.872340426 0.394736842 0.60526316 1.477604
0.368448936 41 22 16 6 0.872340426 0.421052632 0.57894737 1.451288
0.366550179 41 21 17 6 0.872340426 0.447368421 0.55263158 1.424972
0.362757409 41 20 18 6 0.872340426 0.473684211 0.52631579 1.398656
0.359835711 41 19 19 6 0.872340426 0.5 0.5 1.37234
After selecting the clinically necessary sensitivity and/or specificity levels the investigator can see which cut-off level (y-value) will be the most useful to the clinical question. In this example we selected to have as few false negatives as possible (highest sensitivity) with acceptable specificity. Thus in this example we chose the cutoff level y equal to 0.402445475 highlighted in yellow. Any patients with test results below this value will be considered as negative, all that are equal or higher are considered as positive. 1) So to diagnose a new patient, we can measure the plasma levels of these (at least) four biomarkers in the secretory phase. Substitute these values in the models given above and check whether the final value (y) is higher or lower then the selected cut-off level. If both the first (CTRL vs Stage l-ll) and the second (CTRL vs Stage l-ll) model are negative the patient is considered to be endometriosis free. If the first model (CTRL vs Stage l-ll) gives positive result but the second (CTRL vs Stage III-IV) is negative then the patient is considered to have stage l-ll endometriosis. If the second model is positive (regardless whether the first one is positive or negative) the patient is considered to have stage III-IV endometriosis. In this manner the investigator can classify the patient according to disease stage.
2) The third model (CTRL vs All secretory endometriosis) can serve as a backup test to verify the outcome of the others or as a general screening test to determine presence or absence of the disease.
This example uses the multivariate stepwise logistic regression method to classify unknown patients.
In a particular embodiment of present invention a LS-SVM method is used. In the LS- SVM method the system does not select certain biomarkers like the stepwise logistic regression method but on the contrary, it will use all parameters that are fed into the system (eg if we enter 6 biomarkers it will try to distinguish between the different groups by using all 6 parameters, if we enter 8 parameters it will use all 8 etc). LS- SVMs have means to prevent the model from being sensitive to outliers in the data, resulting in a model that is capable of making even better predictions for prospective analyses as the generalization of this technique on an independent set of patients can be more optimal than is possible with logistic regression. The training and test set for LS-SVM analysis 4-5 were determined as follows: all secretory phase control patients (no endometriosis), stage l-ll patients and stage III-IV patients were divided into a 2/3 and a 1/3 group. The 2/3 group was used as the training set and the 1/3 group was used as the test set. It means that 2/3 of all secretory phase control patients, stage l-ll patients and stage III-IV patients were included in the training set. The plasma concentration values of the six biomarkers (IL- 6, IL-8, TNF-α, CA 125, CA 19-9 and CRP) measured in these patients (training set) were entered into the LS-SVM system and analyzed. The plasma concentration values of the six biomarkers (IL-6, IL-8, TNF-α, CA 125, CA 19-9 and CRP) measured in the other 1 third of the patients (test set) was entered into the model resulting from the training analysis. This test could tell us how well the model can differentiate between the groups.
Example: Let's say we had 99 secretory phase patients. 33 controls, 33 stage l-ll and 33 stage III-IV. We divided them in 2/3 - 1/3 ratio to create a training set and a test set for all comparisons, so we ended up with the following groups: Training set: 22 controls, 22 Stage l-ll patients, 22 stage IM-IV patients Tets set: 11 controls, 11 Stage l-ll patients, 11 stage III-IV patients
All the patients had six biomarker values (plasma concentrations) (IL-6, IL-8, TNF-α, CA 125, CA 19-9 and CRP) .
To create a model for diagnosing Stage l-ll endometriosis (analysis 4) we entered all six biomarker (IL-6, IL-8, TNF-α, CA 125, CA 19-9 and CRP) values of 22 controls and 22 Stage l-ll patients into the LS-SVM system. The LS-SVM system was trained on these values. When the model was ready, we entered all six biomarker values of the remaining 11 controls and 11 Stage l-ll into the created model and tested the model.
The same was done for the other comparisons, namely controls vs Stage III-IV (analysis 4) endometriosis and controls vs All endometriosis (analysis 5). Analysis 4: LS-SVM analysis of only secretory phase samples based on stage of the disease.
Controls vs Stage l-ll (secretory phase):
The LS-SVM method was trained and tested to differentiate between endometriosis free women and women with stage l-ll endometriosis. Both groups consisted exclusively of patients from the secretory phase of the menstrual cycle. Using six endometriosis related biomarkers (IL-6, IL-8, TNF-α, CA 125, CA 19-9 and CRP) the LS-SVM could differentiate between the different subgroups with the following parameters (Table Vl):
Table Vl.
Figure imgf000066_0001
Controls vs Stage IH-IV (secretory phase):
The LS-SVM method was also trained and tested to differentiate between endometriosis free women and women with stage MI-IV endometriosis. Both groups consisted exclusively of patients from the secretory phase of the menstrual cycle. Using six endometriosis related biomarkers (IL-6, IL-8, TNF-α, CA 125, CA 19-9 and CRP) the LS-SVM could differentiate between the different subgroups with the following parameters (Table VII):
Table VII.
Figure imgf000066_0002
Analysis 5: LS-SVM analysis of only secretory phase samples regardless of the disease stage.
Controls vs All endometriosis (secretory phase):
The LS-SVM method was also trained and tested to differentiate between endometriosis free women and all women with endometriosis regardless of the disease stage. Both groups consisted exclusively of patients from the secretory phase of the menstrual cycle. Using six endometriosis related biomarkers (IL-6, IL-8, TNF-α, CA 125, CA 19-9 and CRP) the LS-SVM could differentiate between the different subgroups with the following parameters (Table VIII):
Table VIII.
Figure imgf000067_0001
The practical application of the diagnostic model based on LS-SVM will also be convenient and easy to use by because the LS-SVM with a linear kernel can be written as a simple linear equation and even the LS-SVM with the RBF kernel can be implemented in any software package that allows calculations to be preformed (e.g. Microsoft Excel). The examples, analyses and results given above clearly indicate that the determination of biomarker levels in the secretory phase of the menstrual cycle and subsequent analysis of the parameters (biomarker values) by either the stepwise logistic regression model or by the LS-SVM model developed and described in the present invention are enabling the diagnosing of the presence/absence and stage of endometriosis, to prognose the progression of the disease and determine responsiveness to medical or surgical treatment in a noninvasive manner. In a particular embodiment the combination of the two data analysis methods can further improve the diagnostic power of the present invention. The combination of the two analysis methods can be performed in the following manner: Firstly, the following preprocessing step is performed: the measured endometriosis related biomarker values can be entered into the stepwise logistic regression model which will select the best marker combinations which have more discriminating power than the individual markers alone or in other combination(s) regarding the discriminating ability between women without endometriosis and those with early (minimal-mild) and advanced (moderate-severe) stages of the disease. Secondly, these selected parameters can be entered into the LS-SVM which provides a diagnostic model. The obtained model can perform better than the model provided by the stepwise logistic regression analysis. This is due to the fact that LS-SVMs -by applying a radial basis function (RBF) kernel- are also able to identify possible nonlinear structures or correlations. The applied analysis methods of present invention are the most adequate in terms of developing a clinically useful diagnostic test. The use of stepwise logistic regression and the LS-SVM to analyze endometriosis related biomarker values (as parameters) in the secretory phase provide an accurate, reliable and convenient diagnostic and research tool. These two methods -namely the stepwise logistic regression analysis and the LS- SVMs- either individually or in combined use have several features that make them more suitable, applicable and convenient than other methods.
These two methods require significantly fewer parameters than -for example- the artificial neural networks (also referred to as neural networks) where the number of hidden layers and hidden neurons has to be determined beforehand. Additionally, finding the optimal model is not guaranteed since the optimization problem is not convex. This means that model building can result in sub-optimal solutions due to the existence of local minima. For both logistic regression and LS-SVM there is always only one solution which is also optimal. Thus neural networks are more complex, require more data to perform model building and there is no guarantee that the solution will provide the best results. The models of present invention are also more convenient to use than the Bayesian networks because of the lower data requirement of the system. As an example, testing the six biomarkers in a Bayesian network would require a model search in a space of nearly 4 million models. Additionally, Bayesian networks can only provide non-linear model if the analyzed variables are discrete. Since the measured values of the endometriosis related biomarkers are continuous variable, this requirement can not be met and discretisation would cause a serious loss of information..
The models developed and described in present invention are also more efficient in solving the problem of non-invasively diagnosing and prognosing endometriosis than decision trees or the nearest neighbor(s) approaches. In case of decision tree (also referred to as classification tree or reduction tree) there is no interaction between the variables in the later decision levels whilst the nearest neighbor(s) method does not guarantee good generalization performance as it does not provide a model, new samples are classified based on there closeness to the already collected samples. Therefore the performance of nearest neighbor methods can be serioulsy degraded by the presence of noise.
The application of the system of present invention is a suitable research tool for at least five major innovative areas. Firstly, the analysis of large patient populations based not only on the stage of the disease but also on the actual menstrual cycle phase is an innovative feature. Previous studies have shown that many molecules in the peripheral circulation show a menstrual cycle phase dependent variability in their expression/plasma concentration. To present inventions is based on studies on studies aiming at identifying disease specific alterations in the different cycle phases. In contrast to the approach of present invention, in previous studies authors either arbitrarily chose a specific phase of the cycle or analyzed a study-population containing individuals from all phases of the menstrual cycle. However, both of these strategies assume -sometimes maybe involuntarily- that differences observed between diseased and non-diseased women in one phase of the cycle are going to be present to the same extent or at least in similar proportions in other phases of the cycle. Secondly, the system of present invention concerns a tool for developing novel medicaments and for prediction of the responsiveness to a drug. The statistical models used to analyze the experimental data are innovative in the development of a noninvasive diagnostic test for endometriosis. In our study several potential biomarkers (variables) are measured but obviously at the date of this invention a skilled man is thought that other sets of biomarker variables can be tested to further optimize the diagnostic system of present invention without undue experimentation but by following the guidelines instructed in this application. For instance a stepwise selection procedure - a combination of forward and backward selection- to select the optimal set of variables in the logistic regression model can be used. This approach makes no assumption about the distribution of the independent variables and the logistic regression parameters can be easily interpreted. Additionally, results can be analyzed by using Least Squares Support Vector Machines (LS-SVM). Given a training set of data with the corresponding class labels, LS-SVMs can be used for binary classification. By using LS-SVM non-linear relationships between the dependent and independent variables can be discovered and the number of variables that can be modeled is large.
Thirdly, present invention provides a tool for determining whether or not a known biomarker -which can be any known biomarker-, is relevant in the diagnosis and/or prognosis of endometriosis. One of the ways to perform such a determination is demonstrated in the following example . A body fluid for instance blood, plasma or serum samples from women with or without endometriosis in the secretory phase of the menstrual cycle. The levels of the diagnostically relevant biomarkers and the candidate biomarker(s) that may or may not be relevant for diagnosis and/or prognosis can be measured. These measurement values can be entered into an existing stepwise logistic regression model which will build a new diagnostic model including the new biomarker(s) indicating that the new marker is relevant for the diagnosis and/or prognosis of endometriosis or will keep the previous model indicating that the new marker is not relevant for diagnosis and/or prognosis of endometriosis Below two ways to determine whether or not a known biomarker is relevant in the diagnosis/prognosis of endometriosis are given.
1) Stepwise Logistic Regression analysis initially showed that simultaneous measurement and analysis of IL-6 and TNF-a in the secretory phase has better diagnostic/prognostic value than any of the six biomarkers (IL-6, IL-8, TNF-α, CA 125, CA 19-9 and CRP) alone or in any combination other than the combination of IL-6 and TNF-a. If someone wants to test whether VEGF is relevant in the diagnosis/prognosis of endometriosis he/or she needs to measure IL-6, TNF-a and VEGF concentrations in a set of secretory phase women without endometriosis or with stage l-ll endometriosis. Enter the biomarker concentration values of IL-6, TNF-a and VEGF into the Stepwise Logistic Regression model and analyze it. If the new model contains VEGF than VEGF is relevant in the diagnosis/prognosis of endometriosis if the model does not contain VEGF then VEGF is not relevant.
2) Similarly LS-SVM can be used to determine whether or not a known biomarker is relevant in the diagnosis/prognosis of endometriosis. If someone wants to test whether VEGF is relevant in the diagnosis/prognosis of endometriosis he or she needs to measure all biomarker that are already included in the LS-SVM model (for example biomarkers IL-6, IL-8, TNF-α, CA 125, CA 19-9 and CRP as in analysis 4) and additionally he or she has to measure the VEGF concentrations also in a set of secretory phase women without endometriosis or with stage l-ll endometriosis. Enter the concentration values of all biomarkers -including VEGF- into the LS-SVM model and analyze it. If the new model performs better than the previous one then VEGF is relevant in the diagnosis/prognosis of endometriosis if the model does not perform better then the model without VEGF then this biomarker is not relevant. Fourthly, by identifying new biomarkers relevant in endometriosis -as described above- the present invention can provide a better insight and understanding of the pathogenesis of the disease and will help to identify the so far poorly understood environmental, nutritional, professional and social risk factors that can promote the onset and development of endometriosis.
Fifthly, by identifying new biomarkers relevant in endometriosis -as described above- the present invention can provide new therapeutic targets for developing novel, more efficient medical (non-surgical) treatment modalities for endometriosis with less adverse effects. Additionally - by monitoring the biomarkers in the patients using the system of present invention- the patients' response to medical (non-surgical) or surgical treatment can be followed and evaluated.
Present invention allows simultaneous measurement of 6 or more potential plasma markers in precisely defined, large populations followed by sophisticated statistical analysis. This is a powerful diagnostic and research tool.
The system of present invention provides a first time simultaneous measurement of 6 or more potential plasma markers for. endometriosis in precisely defined, large populations followed by a model of sophisticated statistical analysis of the data and provides a tool for non-invasively diagnosing endometriosis at different stages. The diagnostic method and tool of present invention can be used for development of new treatment strategies to prevent progression of the disease and furthermore it can be used to monitor the disease and/or its symptoms and determine response to certain treatment modalities which in turn can provide deeper understanding of the pathophysiology of endometriosis also.
A panel of biomarkers has been identified and at the date of the invention instructions have been provided for the man skilled in the art to enlarge that panel, which panel can be used to significantly reduce the time from the onset of pain symptoms to diagnosis of endometriosis. Such earlier diagnosis and treatment will improve health related quality of life and the disease's natural progression may also be impeded. The system is particularly suitable for distinguishing patients being either negative - having no endometriosis- or positive -having stage l-ll or stage IH-IV endometriosis
EXAMPLES B
Examples 1 - 8 Methods.
Examples 1 Patients and tissue Selection A total of 29 samples of secretory phase eutopic endometrium collected previously from women undergoing laparoscopies for infertility and pain and frozen at -80 0C were used in the study. All patients had signed a written informed consent before surgery and had agreed on the collection of tissues for research. The study protocol had been approved by the institutional ethical and review board of University Hospital Gasthuisberg for the protection of human subjects. The tissues included secretory phase endometrium from women with (n = 19) and without (n =10) endometriosis. All women with endometriosis had minimal to mild (l-ll, n= 9) and moderate to severe (III- IV, n= 10) endometriosis, according to the classification system of the American Society of Reproductive Medicine (ASRM, 1997).
All endometrial samples were dated between days 16 - 26 of a 28-day menstrual cycle according to the Noyes criteria (Noyes et al., 1950). A blind approach for a wide screening experiment was applied on these samples using Surface Enhanced Laser Desorption Ionization Time-of-Flight Mass spectrometry (SELDI-TOF -MS) to search for potential biomarkers.
Example 2 Preparation of protein lysate from endometrial samples Homogenisation of tissue
Frozen tissues biopsies were weighed (100mg/ml lysis buffer) and immediately thawed in phosphate buffered saline (PBS) while on ice. Tissues were washed five times in PBS to rinse off any adhering haemoglobin (Hb). The tissue homogenisation was realised by addition of 500μl (100μl/10mg) of U9 lysis buffer ( 9M Urea, 2% CHAPS, 50 mM Tris-HCI pH 9.0) (Ciphergen Biosystems, Fremont, CA, USA). Tissues were homogenised using tissue sonicator while on ice, until they were completely dissolved in the lysis buffer. The protein lysates were incubated on ice for 1 hour to allow optimal extraction from the membrane fraction. The protein lysate was centrifuged at 13,000 rpm for 10 minutes at 40C to remove cell membranes and other undissolved components. Samples were subjected to resin treatment.
Example 3 Preparation of Spin-Column IMAC-ZnSO4 Resin for Haemoglobin depletion
Resin of 100μl was added to Spin-column and spun down at high speed. The IMAC resin (Biopsera, Cergy, France) was saturated with 200μl of 10OmM ZnSO4 and incubated for 15 minutes at room temperature (RT), with frequent shaking. The 100m ZnSO4 composition was centrifuged at high speed for approximately 30 seconds and discarded. The spin column were washed twice with 20OuI of MiIIi-Q H2O. Further saturation was done by additional 200μl of 10OmM ZnSO4 to 100μl of IMAC-resin and incubated for 15 minutes at room temperature (RT). The spin columns were washed twice with 200μl MiIIi-Q H2O. Further washing was done by using 200μl of 10% Lysis buffer (10μl lysis to 90μl PBS) two times. Protein lysate of 300μl was run through the spin-column resin (IMAC-ZnSO4) and rotated in cold room (40C) for 30 minutes and centrifuged then the elute collected in an eppendorf tube. The column was washed with 200μl of 10OmM NH4OAC, then rotated for 30 minutes in the cold room (40C) and spun down to collect the elution supernatant. The two elutes were pooled. The protein concentration was determined using spectrophotometer. The supernatant was aliquoted in 50μl volume in eppendorff tubes and used immediately or stored at -80 C. Pooled samples of controls and endometriosis were used as reference to control variation between experiments run at different times. Example 4 Profiling of cell lysates on ProteinChip Arrays
To increase the scale of detectable proteins, four different chip surfaces with distinct chromatographic properties and differential binding affinities were utilised for proteins with specific biochemical characteristics (Table IX). Briefly, ProteinChip array spots were equilibrated with 150μl of respective binding buffer (Ciphergen, Fremont, CA, USA) with shaking for five minutes at room temperature to pre-activate binding surfaces. Then 20μl of sample lysates (10μg per spot) with surface-type dependent binding buffer (Table IX) was loaded onto each spot in duplicate, and incubated for 60 minutes at room temperature (RT) while shaking. The proteins not retained on the ProteinChip array surfaces were washed away twice with appropriate buffer for five minutes, rinsed in 150μl of distilled milli-Q water and air-dried. Spectra of the retained proteins was obtained by ionising the proteins to gaseous phase using two types of energy absorbing molecules (EAM): alpha-cyano-4-hydroxy cinnamic acid (CHCA), recommended for small molecules (<15 kDa), and Sinnapinic acid (SPA) (both from Ciphergen, Fremont, CA, USA), recommended for all larger molecules. The CHCA (5mg CHCA dissolved in 75μl acetonitrile plus 75μl, 1% TFA) was diluted fives times in the respective solvent and 1μl was applied twice onto the retained proteins on the spots. The SPA (5mg SPA dissolved in 200μl acetonitrile and 200μl of 1% TFA) was applied twice in volume of 1μl per spot.
Example 5 Statistical analysis:
Univariate analyses were carried out using using Ciphergen's ProteinChip Software v3.1.1. A differentially expressed mass peak with P- value <0.05 was considered to be statistically significant (Ciphergen, Fremont, CA, USA). Multivariate analysis was applied to the data set to evaluate and identify biomarkers with diagnostic value. Diagnostic models were developed and validated using a Leave-One-Out- Support Vector Machine (LOO-SVM) algorithm and logistic regression classification models with Leave-One-Out -Cross Validation (LOO - CV) towards ranking the significant mass peaks according to their classification power
Example 6 Data pre-processing We filtered background noise through baseline correction. Normalisation of variation in signal intensity between spectra was done through rescaling each spectrum by total ion current. Peak detection was done First S/N ratio 10, 7, 5 and 3 and minimum peak threshold were 30%, 25% and 20%. Peak cluster were completed using second pass peak selection (S/N ratio 3), within mass window of 0.3% mass error. All these were performed using ProteinChip Software 3.1 (Ciphergen, Fremont, CA, USA).
Example 7 -Support Vector Machine (SVM) Peak Ranking and classification
SVM is a new machine learning approach that is powerful tool for ranking mass peaks according to a support vector machines algorithm. This algorithm gives the average ranking of each feature when doing leave-one-out cross validation (LOO-CV). We constructed a SVM classification to discriminate the different groups. Feature selection by cross-validation approach was applied to estimate the accuracy of the classification.
This approach randomly selected the (n-1) of all the samples to be the blinded training set, and the remaining 1/(n-1) samples to be the test set and repeated the procedure n times.
Example 8 Feature selection and Building Diagnostic models
The power of each peak in discriminating different groups was estimated by logistic regression classification models with leave-one-out cross validation (LOO-CV). To further select the set of candidate biomarkers, two models based on logistic regression were built (i.e LOO-Logistic regression model) and LOO-logistic ridge regression Odds ratio >2 were used.
Example 9 - 11 Results
After filtrating noise using Ciphergen ProteinChip Software 3.1, atotal of 134 qualified mass peaks were upregulated or downregulated in endometrium from women with endometriosis (stages I-IV, ASRM classification 1997) when compared to endometrium from controls. The peaks were between 1.923 kDa - 133 kDa. Peaks with m/z<1.6 kDa were considered to be mainly ion noise from the matrix and therefore excluded. In order to determine whether certain biomarker combinations can provide greater diagnostic power for diagnosing endometriosis we ran several analysis on the experimental results or subset of it.
Example 9 - Mass peak expression in women with endometriosis compared with controls
Women with endometriosis had 73 mass peaks detected with significant differences when compared to controls. To select the set of candidate biomarkers, two models based on logistic regression were built. The first model (LOO-Logistic Model) selected 14 peaks with a LOO-CV performance of 76%, while the second logistic regression (LOO-logistic ridge regression Odds ratio >2) selected 16 peaks with the same performance of 79% (LOO-CV). We had 8 peaks which were identical between these two models and out of 8 peaks, 6 were highly ranked among the top 15 of the SVM ranking list. Using LOO-SVM algorithm ranking and logistic regression classification models (LOO - CV), 5 downregulated mass peaks ( 8.65OkDa, 8.659kDa, 13.91kDa, 5.183kDa and 1.949kDa) were selected as endometrial biomarkers for the diagnosis of endometriosis with a high sensitivity (89.5%) and specificity (90 %) (Table IXI). This corresponds to only 3 misclassifications.
Example 10 - Mass peak expression in women with minimal - mild endometriosis compared with controls
Women with Stage I - Il endometriosis, when compared to controls, generated 30 differentially expressed mass peaks. To select the set of candidate biomarkers, two models based on logistic regression were built. The first model (LOO-Logistic Model) selected 3 mass peaks with a LOO-CV performance of %, while the second logistic regression (LOO-logistic ridge regression Odds ratio >2) selected 4 mass peaks with the same performance of % (LOO-CV). We had 1 peak which overlapped between these two models and was highly ranked in the SVM ranking list. However there was some overlap between each model separately and the SVM ranking. Therefore Using LOO-SVM algorithm ranking and logistic regression classification models (LOO - CV), 4 mass peaks (2 upregulated: 90.675kDa and 35.956kDa) and 2 downregulated: 1.924kDa and 2.504kDa) were selected as biomarkers for the diagnosis of Stage I - Il endometriosis with maximal sensitivity of 100% and specificity of 100% (Table IXI).
Example 11 - Mass peak expression in women with moderate-severe endometriosis compared with controls
Women with Stage III -IV endometriosis, when compared to controls, generated 131 differentially expressed mass peaks. To select the set of candidate biomarkers, two models based on logistic regression were built. The first model (LOO-Logistic Model) selected 5, while the second logistic regression (LOO-logistic ridge regression Odds ratio >2) was excluded because performance was poor. However there was some overlap between the model and the SVM ranking. Using LOO-SVM algorithm ranking and logistic regression classification models (LOO - CV), 5 mass peaks (2 upregulated: 10.1094kDa and 10.045kDa) and (3 downregulated: 5.828kDa, 12.172 kDa and 4.279kDa) were selected as endometrial biomarkers for the diagnosis of endometriosis with a high sensitivity (70%) and specificity (80 %) (Table IXI).
Table HX.: Different proteinchip surfaces with their respective binding buffer that were used in the study
ProteinChip surfaces Binding buffers
low stringency binding buffer
Weak cation exchange surface (CMlO) (5OmM NaOAC, pH 4.0)
Immobilized metallic affinity capture surface (IMAC-30-Cu ) 0. IM phosphate, 0.5M NaCl3 pH7.0 loaded with CuSO4,
10% acetonitrile, 0.1% triflouroacetic
Hydrophobic surface (H50) acid (TFA) Strong anion exchange surface (QlO) 50mM Tris-HCl pH8.0.
Table X: Summary of sensitivity and specificity values of the best selected biomarker combination in women with endometriosis compared with controls during luteal phase.
Figure imgf000079_0001
Discussion
Endometriosis clinically may manifest as peritoneal disease, ranging from sparse peritoneal implants to diffuse peritoneal disease, endometriotic ovarian cysts and/or deeply infiltrating deeply endometriosis affecting the rectum, sigmoid and other parts of the bowel, as well as the bladder and rectovaginal septum (Nisolle and Donnez 1997). Currently, the diagnosis of endometriosis is through a laparoscopy with subsequent histological confirmation of endometrial stroma and glands of a biopsy. Compared to laparoscopy, transvaginal ultrasound (TVS) is only useful to diagnose an ovarian endometrioma cyst (Moore et a/. ,2002, Kennedy et al. 2005). Other noninvasive diagnostic approaches such as MRI or blood tests for CA-125 lack sufficient diagnostic power (Kennedy et al. 2005), although some markers such as the ovarian cancer antigen CA 125 or the pancreatic tumor marker CA 19-9 have been suggested to have a diagnostic potential in endometriosis [Chen et al. 1998, Harada et al. 2002, Somigliana et al. 2004]. The early detection of endometriosis is crucial for its ultimate control and prevention. A non- invasive diagnostic test in serum or endometrium would be beneficial to both physicians and patients. Lack of non-invasive test for diagnosis of endometriosis has weighed down efforts to study the aetiopathogenesis of the disease among women of reproductive age. In many cases endometriosis is not diagnosed and treated until the disease has established itself and caused pathological symptoms. A published poll reveals that women have to wait an average of 11.7 years in USA and 8.0 years in the UK to get a correct diagnosis after the initial onset of symptoms for endometriosis. Alterations in protein expression act as useful indicators of pathological abnormalities prior to development of clinical symptoms of a disease. The multifactorial nature of endometriosis necessitates the use of biomarkers for early detection. Translational research is needed to transform discoveries arising from laboratory studies into clinical applications by providing biomarkers that may be indicators of either disease susceptibility, onset, progression or suggest endometriosis biomarkers that might be used in diagnosis and/or as novel therapeutic targets. SELDI-MS coupled with bioinformatics tools for complex data analysis will find the "fingerprints" of endometriosis and build the diagnosis model.
In this study we developed the integrated approach of bioinformatics tools to analyze a data set of spectra. The LOO-CV was applied to rank and select the peaks according to their discriminating power between women without endometriosis and those with early
(minimal-mild) and advanced (moderate-severe) stages of the disease. The findings of the present study suggest that selecting mass peaks with stepwise logistic regression combined with SVM may provide a clinically relevant panel of biomarkers that demonstrate high sensitivity and specificity especially minimal to mild disease. Thus a noninvasive diagnostic test can be developed for endometriosis with improved diagnostic power in the discrimination between women with and without endometriosis.
Since plasma proteome contains all traces of leaked molecules encountered by blood during circulation (Anderson and Anderson, 2002), the biomarkers, may be identified and validated for non-invasive diagnostic test of endometriosis. In conclusion, the approach applying SELDI-TOF -MS ProteinChip technology combined with
19 bioinformatics analysis tools may help develop a diagnostic model test with a high sensitivity especially for minimal to mild endometriosis.
Other embodiments of the invention will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein.
Drawing Description
BRIEF DESCRIPTION OF THE DRAWINGS
The present invention will become more fully understood from the detailed description given herein below and the accompanying drawings which are given by way of illustration only, and thus are not limitative of the present invention, and wherein:
FIG. 1 is schematic view showing the ProteinChip array technology for biomarker discovery.
FIG. 2 s schematic view showing the expression difference mapping using chromatographic MS. FIG. 3 demonstrates the ProteinChip Technology
FIG. 4 s a schematic diagram showing the experimental design
FIG. 5 is a schematic diagram providing a summary of results
FIG. 6 is a schematic diagram showing LOO-CV and SVM
FIG. 7 is a schematic diagram providing a summary of results
REFERENCES TO THIS APPLICATION
Agic A, Xu H, Rehbein M, Wolfler MM, Ebert AD, Hornung D. Cognate chemokine receptor 1 messenger ribonucleic acid expression in peripheral blood as a diagnostic test for endometriosis. Fertil Steril. 2007 Jan 26; [Epub ahead of print]. Anderson NL, Anderson NG. The human plasma proteome: history, character, and diagnostic prospects. MoI Cell Proteomics. 2002; 1:845-67.
Berkkanoglu M and Arid A. Immunology and endometriosis. Am J Reprod Immunol,
50,48-59. Brosens, J., et al. Noninvasive diagnosis of endometriosis: the role of imaging and biomarkers Obstet Gynecol Clin North Am 2003;30(1):95-114.
Carlson K, Nichols D, Schiff I. Indications for hysterectomy. N Engl J Med 1993; 328:
856 - 60.
Carlson, K., Miller B. and Fowler F.J. The main women's health study. Outcomes of hysterectomy. Obstet. Gynecol 1994; 83: 556 - 565.
Chen, F.-P., et al. The use of serum CA-125 as a marker for endometriosis in patients with dysmenorrhea for monitoring therapy and for recurrence of endometriosis. Acta
Obstet Gynecol Scand;1998;77(6):665-70.
D'Hooghe TM, Hill JA. Endometriosis. In: Novak's Gynecology, Williams and Wilkins, J.S. Berek, editors, 13th edition, Philadelphia, USA, 2002: 931 - 972pp.
Gagne, D., et al. Development of a nonsurgical diagnostic tool for endometriosis based on the detection of endometrial leukocyte subsets and serum CA-125 levels. Fertility and Sterility;2003.80(4):876-85.
Gao X, Outley J, Botteman M, Spalding J, Simon JA, Pashos CL. Economic burden of endometriosis. Fertil Steril. 2006 ;86:1561-72.
Gelbaya TA, El-Halwagy HE. Focus on primary care: chronic pelvic pain in women.
Obstet Gynecol Surv. 2001 ; 56: 757-64.
Guo SW, Wang Y. The prevalence of endometriosis in women with chronic pelvic pain.
Gynecol Obstet Invest. 2006;62:121-30. Hadfield R, Mardon H, Barlow D, Kennedy S. Delay in the diagnosis of endometriosis:
A survey of women from USA and the UK. Hum Reprod. 1996; 11 : 878 - 80. Harada T, Kubota T, Aso T. Usefulness of CA19-9 versus CA125 for the diagnosis of endometriosis. Fertil Steril. 2002;78:733-9.
Hu J, Coombes KR, Morris JS, Baggerly KA. The importance of experimental design in proteomic mass spectrometry experiments: some cautionary tales. Brief Funct Genomic Proteomic. 2005; 4:322-331.
Kafali, H., H. Artuc, and N. Demir. Use of CA125 fluctuation during the menstrual cycle as a tool in the clinical diagnosis of endometriosis; a preliminary report. European
Journal of Obstetrics & Gynecology and Reproductive Biology; 2004.116(1):85-8.
Kennedy S, Bergqvist A, Chapron C, D'Hooghe T, Dunselman G, Greb R, Hummelshoj L, Prentice A, Saridogan E. ESHRE guideline for the diagnosis and treatment of endometriosis. Hum Reprod. 2005 ;20:2698-704.
Martinez S, Garrido N, Coperias JL, Pardo F, Desco J, Garcia-Velasco JA, Simon C,
Pellicer A. Serum interleukin-6 levels are elevated in women with minimal-mild endometriosis. Hum Reprod. 2006 Oct 24; [Epub ahead of print]. Moore J, Copley S, Morris J, Lindsell D, Golding S and Kennedy S. A systematic review of the accuracy of ultrasound in the diagnosis of endometriosis. Ultrasound
Obstet Gynecol 2002; 20,630-634.
Moore, J., et al. A systematic review of the accuracy of ultrasound in the diagnosis of endometriosis.Ultrasound Obstet Gynecol 2002;20(6):630-4. Nisolle M & J Donnez. Peritoneal endometriosis, ovarian endometriosis, and adenomyotic nodules of the rectovaginal septum are three different entities, Fertil Steril
1997; 68: 585-596.
Noyes RW, Hertig AT, Rock J Dating the endometrial biopsy. Fertil Steril, 1950;1 : 3-25.
Revised American Society for reproductive Medicine classification of endometriosis: 1996. Fertil Steril 1997; 67: 817-821.
Rosa E Silva AC, Rosa E Silva JC, Ferriani RA. Serum CA-125 in the diagnosis of endometriosis, lnt J Gynaecol Obstet. 2007 Feb 6; [Epub ahead of print]
Simoens S, Hummelshoj L, D'Hooghe T. Endometriosis: cost estimates and methodological perspective. Hum Reprod Update. 2007 ;13:395-404. Somigliana, E., et al. Use of the concomitant serum dosage of CA 125, CA 19-9 and interleukin-6 to detect the presence of endometriosis. Results from a series of reproductive age women undergoing laparoscopic surgery for benign gynaecological conditions. Hum. Reprod.;2004.19(8):1871-6
Weaver F, Hynes D, Goldberg JM, Khuri S, Daley J, Henderson W. Hysterectomy in Veterans Affairs Medical Centers. Obstet Gynecol. 2001 ;97:880-4. Yang, W.-C.V., et al. Serum and endometrial markers. Best Practice & Research Clinical Obstetrics & Gynaecology 2004;18(2):305-18. Zhao SZ, Wong JM, Davis MB, Gersh GE, Johnson KE. The cost of inpatient endometriosis treatment: an analysis based on the Healthcare cost and utilization project Nationwide Inpatient sample. Am J Manag Care 1998; 8: 1127 - 34.

Claims

HIGH DISCRIMINATING POWER BIOMARKER DIAGNOSING
What is claimed is:
1) A method of in vitro diagnosing of a disorder which is associated with the female's menstrual cycle and is a disorder selected from the group consisting of premenstrual syndrome, migraine headache, endometriosis, psoriasis, acne, dysmenorrhoea, neurosia, asthma and premenstrual cramps, characterized in that the method comprises processing of a plurality of biomarker variables obtainable from in vitro assaying of at least one biological sample of a patient in a determined phase of the menstrual cycle, whereby the assay system produces input signals corresponding to concentration, activity or presence of each biomarker in said sample and whereby the input signals are processed in a signal processor comprising a mathematical model that produces output signals that determine the presence or absence of the disorder, the seriousness of the disorder or the progress of the disorder in the patient, and wherein said mathematical model is a Least Squares Support Vector Machine model (LS-SVM model) which is trained on in vitro assaying presence, level or activity of1 several specific biomarkers values which are presumed relevant for the disorder in at least one biological sample from a plurality of patients comprising patient groups without the disorder and patient groups with the disorder or with a known stage of the disorder and producing input signals.
2) The method of in vitro diagnosing of a disorder of claim 1 , wherein said mathematical model is a LS-SVM model which is constructed according to the following method a) in vitro assaying presence, level or activity of several specific biomarkers values which are presumed relevant for the disorder in at least one biological sample from a plurality of patients comprising patient groups without the disorder and patient groups with the disorder or with a known stage of the disorder and producing input signals thereof; b) performing a stepwise logistic regression analysis on the input signals to test the discriminating power of the specific biomarkers for indicating the presence or absence of the disorder or the severity of the disorder; c) training the LS- SVM using only those biomarkers that show discriminating power.
3) The method of any of the claims 1 to 2, wherein the LS-SVM model is further construed by testing the discriminating power for indicating presence or absence of the disorder of the specific biomarkers by performing a stepwise logistic regression analysis on said biomarker values and training the LS-SVM using only those biomarkers that show discriminating power.
4) The method of any of the claims 1 to 3, wherein analysis is done by the Least Squares Support Vector Machine model in combination with a Stepwise Logistic Regression model.
5) The method of in vitro diagnosing of claim 1 to test a biomarker for its relevance characterised in that it further comprises in vitro assaying at least one biological sample per patient from a plurality of patients comprising patient groups without the disorder and patient groups with the disorder or with a known stage of the disorder and in that the method comprises the steps of 1) producing a first input signal on the level or activity of at least one relevant biomarker for that disorder and a second input signal on the level or activity of a biomarker to be tested for its relevance to that selected disorder whereby the first and the second mathematical model are selected of the group consisting of Stepwise Logistic Regression Model and LS-SVM model 2) processing the first input signal in the signal processor to construct a first mathematical model that produces output signals which are indicative for the disorder, the seriousness of disorder or the progress of disorder in the affected patient, 3) processing both the first input signal and the second input signal in the signal processor to construct a second mathematical model that produces output signals which are indicative for the disorder, the seriousness of disorder or the progress of disorder in the affected patient, 4) selecting the test biomarker as relevant in the diagnosis or prognosis of that selected disorder if the second mathematical model reveals a better performance than the first mathematical model. 6) The method of in vitro diagnosing of any of the claims 1 to 5, wherein biological samples of the same patient but in different phases selected of the menstruation phase, the follicular phase, the ovulation phase or the proliferation phase of the menstrual cycle are assayed in vitro for biomarkers.
7) The method of any of the claims 1 to 6, wherein biomarker variables for diagnosing are obtained from in vitro assaying of at least one biological sample of a patient in a specific phase of the menstrual cycle.
8) The method of any of the claims 1 to 7, wherein biomarker variables for diagnosing are obtained from in vitro assaying of at least one biological sample of a patient in the secretory phase of the menstrual cycle.
9) The method of in vitro diagnosing of any of the claims 1 to 8, characterised in that the biomarker variables for diagnosing are obtained from in vitro assaying of at least one biological sample of patients when they were identified to be in the proliferation phase of the menstrual cycle.
10) A method for optimising the discriminating power of the in vitro diagnosing method of any of the claims 1 to 9, characterised in that the method comprises processing of a biomarker variable which has been validated to be relevant for endometriosis diagnosis or prognosis and is obtainable from in vitro assaying of at least one biological sample of a patient in a determined phase of the menstrual cycle, whereby the assay system produces input signals corresponding to concentration, activity or presence of each biomarker in said sample and whereby the input signals are processed in a signal processor comprising a mathematical model that produces output signals that determine the presence of the endometriosis, the seriousness of the disorder or the progress of the disorder in the patient and wherein said mathematical model is a LS- SVM model which is constructed according to the following method:
• in vitro assaying the presence, the level or the activity of a plurality specific biomarkers values which are presumed relevant for the disorder in at least one biological sample from a plurality of patients, wherein said plurality of patients comprising patients with a known stage of the disorder and patients without the selected disorder whereby the assay system produces input signals;
• testing the discriminating power for indicating presence or absence of the disorder of the specific biomarkers by performing a stepwise logistic regression analysis on said input signals;
• training the LS-SVM using only those biomarkers that show discriminating power.
11) The method of in vitro diagnosing of any of the claims 1 to 10, whereby the disorder is endometriosis and the mathematical model is described on the relationship of biomarker variables and disorder variables from in vitro assaying of biological samples of plurality of patients with no endometriosis, affected with endometriosis, affected with a defined seriousness or with defined progress of endometriosis.
12) The method of any of the claims 1 to 11 , whereby the endometriosis extent or endometriosis progress is classified in stages of endometriosis free, stage l-ll endometriosis, stage MI-IV endometriosis.
13) The method of any of the claims 1 to 12, whereby the endometriosis extent or endometriosis progress is classified in stages of endometriosis free, minimal endometriosis, mild endometriosis, moderate endometriosis and severe endometriosis
14) The method of any of the claims 1 to 13, wherein the output signals are compared to reference signals.
15) The method of any of the claims 1 to 14, characterized in that the in vitro assaying of the samples is carried out by an immunoprecipitation, a radioimmunoassay, an enzyme immunoassay, a fluorescent immunoassay, a chemiluminescent immunoassay, a competitive binding assay, an ELISA or a homogeneous immunoassay. 16) The method of any of the claims 1 to 15, characterized in that the samples are analysed by homogenous binding assays or multi phase assays.
17) The method of any of the claims 1 to 16, characterized in that biomarker variables are corresponding to substances of the group consisting of immunological factors, inflammatory factors, Intercellular adhesion molecules and cystine-knot growth factors of the PDGF/VEGF growth factor family.
18) The method of any of the claims 1 to 17, characterized in that biomarker variables are corresponding to substances of the group consisting of interleukin (IL)-6, interleukin IL-8, tumor necrosis factor (TNF)-α, CA-125, CA-19-9, C-reactive protein (CRP), intercellular adhesion molecule-1 (slCAM-1) and vascular endothelial growth factor (VEGF), Placental Growth Factor (PIGF), a gene expression product in the 8.648 to 8.652 kDa molecular weight range as detectable by SELDI -TOF MS, a gene expression product in the 8.658 to 8.661 kDa molecular weight range as detectable by SELDI -TOF MS, a gene expression product in the 13.89 to 13.93 kDa molecular weight range as detectable by SELDI -TOF MS, a gene expression product in the 5.181 to 5.185 kDa molecular weight range as detectable by SELDI -TOF MS and a gene expression product in the 1.947 to1.951 kDa molecular weight range as detectable by SELDI -TOF MS.
19) The method of any of the claims 1 to 18, for predicting responsiveness to a medicament.
20) The method of any of the claims 1 to 19, for predicting responsiveness to a surgery.
21) The method of any of the claims 1 to 20, whereby the output signal identifies endometriosis with a sensitivity of > 60 % and a specificity of > 40 % as compared to a non endometriosis or control condition
22) The method of any of the claims 1 to 21 , whereby the output signal identifies endometriosis with a sensitivity of > 70 % and a specificity of > 50 % as compared to a non endometriosis or control condition 23) The method of any of the claims 1 to 22, whereby the output signal identifies endometriosis with a sensitivity of > 70 % and a specificity of >60 % as compared to a non endometriosis or control condition
24) The method of any of the claims 1 to 24, wherein the biological sample is chosen from serum, blood, and plasma
25) A method for detecting or testing a endometriosis modulating or preventing agent in a non human mammalian endometriosis model, the method comprising: (a) administration of the modulation agent to said endometriosis model; (b) providing a biological sample from an endometriosis model (c) carrying out the diagnostic method of any of the claims 1 to 24; and (e) comparing output signals form samples if the endometriosis model treated with said agent, with output signals form samples of the non treated endometriosis and/or with output signals form control biological samples.
26) An operating system for operating the methods of any of the previous claims 1 to 25 which controls the allocation of an in vitro essay system to generate biomarker values of a patient and which feeds the input signals from the in vitro essay system into signal processor comprising a mathematical model that is described on the relationship of a plurality of biomarker variables and a plurality of disorder variables from in vitro assaying of biological samples of plurality of patients with no disorder, affected with disorder, affected with a defined seriousness or with defined progress of disorder.
27) The operating system of claim 26 for determining the presence or absence of disorder, the seriousness of disorder or the progress of disorder in the patient of any of the previous claims.
28) The operating system of claim 27 whereby the operating system also controls usage of the in vitro essay system.
29) The operating system of any of the claims 25 to 28, whereby the operating system includes a user interface that to enable the user to interact with the functionality of the computer. 30) The operating system of any of the claims 25 to 29, whereby the operating system includes a graphical user interface whereby the operating system controls the ability to generate graphics on the computer's display device that can be displayed in a variety of manners representative for or associated with the condition of disorder in a selected patient or a group of patients to allow a user to distinguish between the absence of disorder, the seriousness of disorder or the progress of disorder in identified patients or patient groups.
31) A computer-executable code, stored in a computer-readable medium, the computer executable code adapted, when running on a computer system to run the operating system of any of the claims 26 to 30 or to execute the mathematical model described in any of the claims 1 to 4, and to direct a processing means to produce out put signals that are representative for a condition of disorder or a modifying condition of disorder.
32) A computer system for operating the operating system of any of the claims 26 to 31 comprising a file system for storing files and a processor for analysing the content of biomarker value files stored in said file system to wherein said processor clusters said files in accordance with the mathematical model described in any of the claims claims 1 to 4, to establish plural levels of clusters that represent the presence or absence of disorder, the seriousness of disorder or the progress of disorder in the patient.
33) An apparatus comprising an in vitro diagnosis system for generating the biomarker values for identifying a condition of disorder or any modification of such condition, wherby the apparatus comprises or is interrelating with the computers system of claim 32.
PCT/BE2007/000115 2006-10-24 2007-10-24 High discriminating power biomarker diagnosing WO2008049175A1 (en)

Applications Claiming Priority (12)

Application Number Priority Date Filing Date Title
GB0621086.8 2006-10-24
GB0621086A GB0621086D0 (en) 2006-10-24 2006-10-24 Diagnostic model for endometriosis
US87965607P 2007-01-10 2007-01-10
GB0700424A GB0700424D0 (en) 2007-01-10 2007-01-10 Diagnostic model for endometriosis
GB0700425.2 2007-01-10
GB0700423.5 2007-01-10
GB0700425A GB0700425D0 (en) 2007-01-10 2007-01-10 High discriminating power biomarker diagnosing
GB0700424.5 2007-01-10
US60/879,656 2007-01-10
GB0700423A GB0700423D0 (en) 2007-01-10 2007-01-10 Determining the biomarker relevance of a bioactive protein of a patient for a specific disorder
GB0712747A GB0712747D0 (en) 2007-07-02 2007-07-02 Endometriosis
GB0712747.5 2007-07-02

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Cited By (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2012013955A1 (en) 2010-07-28 2012-02-02 Singapore Volition Pte. Limited Method for detecting the presence of a gynaecological growth
CN102497343A (en) * 2011-11-25 2012-06-13 南京邮电大学 Combined modulation recognition method based on clustering and support vector machine
CN102661759A (en) * 2012-05-11 2012-09-12 西安科技大学 Method for identifying and predicting nonlinear multivariable key parameters of circulating fluidized bed boiler
CN102799895A (en) * 2012-06-26 2012-11-28 福建鸿博印刷股份有限公司 Offset printing ink color matching method based on least square support vector machine
CN102869064A (en) * 2012-07-27 2013-01-09 南京邮电大学 Cluster modulation identification method based on feature level and strategy level combined fusion
CN103728879A (en) * 2014-01-20 2014-04-16 华北电力大学 Power station boiler emission soft measuring method based on least squares support vector machine and on-line updating
US8932993B1 (en) 2007-06-11 2015-01-13 Juneau Biosciences, LLC. Method of testing for endometriosis and treatment therefor
US9434991B2 (en) 2013-03-07 2016-09-06 Juneau Biosciences, LLC. Method of testing for endometriosis and treatment therefor
CN106770007A (en) * 2016-11-29 2017-05-31 中国石油大学(华东) A kind of characteristic wavelength of near-infrared spectrum system of selection for least square method supporting vector machine model
CN108388113A (en) * 2018-02-07 2018-08-10 浙江大学 Least square method supporting vector machine soft-measuring modeling method based on distribution estimation local optimum
US11287425B2 (en) 2009-04-22 2022-03-29 Juneau Biosciences, Llc Genetic markers associated with endometriosis and use thereof

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2005008247A2 (en) * 2003-07-11 2005-01-27 Science & Technology Corporation @ Unm. Detection of endometrial pathology
WO2006099126A2 (en) * 2005-03-11 2006-09-21 Ciphergen Biosystems, Inc. Biomarkers for ovarian cancer and endometrial cancer: hepcidin

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2005008247A2 (en) * 2003-07-11 2005-01-27 Science & Technology Corporation @ Unm. Detection of endometrial pathology
WO2006099126A2 (en) * 2005-03-11 2006-09-21 Ciphergen Biosystems, Inc. Biomarkers for ovarian cancer and endometrial cancer: hepcidin

Non-Patent Citations (4)

* Cited by examiner, † Cited by third party
Title
BEDAIWY M A ET AL: "Laboratory testing for indometriosis", CLINICA CHIMICA ACTA, AMSTERDAM, NL, vol. 340, 2003, pages 41 - 56, XP003009905, ISSN: 0009-8981 *
KYAMA ET AL: "Increased peritoneal and endometrial gene expression of biologically relevant cytokines and growth factors during the menstrual phase in women with endometriosis", FERTILITY AND STERILITY, ELSEVIER SCIENCE INC, NEW YORK, NY, US, vol. 85, no. 6, June 2006 (2006-06-01), pages 1667 - 1675, XP005482144, ISSN: 0015-0282 *
KYAMA ET AL: "ProteinChip technology is a useful method in the pathogenesis and diagnosis of endometriosis: a preliminary study", FERTILITY AND STERILITY, ELSEVIER SCIENCE INC, NEW YORK, NY, US, vol. 86, no. 1, July 2006 (2006-07-01), pages 203 - 209, XP005527702, ISSN: 0015-0282 *
TIMMERMAN DIRK: "The use of mathematical models to evaluate pelvic masses; can they beat an expert operator?", BEST PRACTICE & RESEARCH. CLINICAL OBSTETRICS & GYNAECOLOGY FEB 2004, vol. 18, no. 1, February 2004 (2004-02-01), pages 91 - 104, XP002466644, ISSN: 1521-6934 *

Cited By (13)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8932993B1 (en) 2007-06-11 2015-01-13 Juneau Biosciences, LLC. Method of testing for endometriosis and treatment therefor
US9840738B2 (en) 2007-06-11 2017-12-12 Juneau Biosciences, Llc Method of testing for endometriosis and treatment therefor
US11287425B2 (en) 2009-04-22 2022-03-29 Juneau Biosciences, Llc Genetic markers associated with endometriosis and use thereof
WO2012013955A1 (en) 2010-07-28 2012-02-02 Singapore Volition Pte. Limited Method for detecting the presence of a gynaecological growth
CN102497343A (en) * 2011-11-25 2012-06-13 南京邮电大学 Combined modulation recognition method based on clustering and support vector machine
CN102661759A (en) * 2012-05-11 2012-09-12 西安科技大学 Method for identifying and predicting nonlinear multivariable key parameters of circulating fluidized bed boiler
CN102799895A (en) * 2012-06-26 2012-11-28 福建鸿博印刷股份有限公司 Offset printing ink color matching method based on least square support vector machine
CN102799895B (en) * 2012-06-26 2015-09-30 鸿博股份有限公司 Based on the offset ink color matching method of least square method supporting vector machine
CN102869064A (en) * 2012-07-27 2013-01-09 南京邮电大学 Cluster modulation identification method based on feature level and strategy level combined fusion
US9434991B2 (en) 2013-03-07 2016-09-06 Juneau Biosciences, LLC. Method of testing for endometriosis and treatment therefor
CN103728879A (en) * 2014-01-20 2014-04-16 华北电力大学 Power station boiler emission soft measuring method based on least squares support vector machine and on-line updating
CN106770007A (en) * 2016-11-29 2017-05-31 中国石油大学(华东) A kind of characteristic wavelength of near-infrared spectrum system of selection for least square method supporting vector machine model
CN108388113A (en) * 2018-02-07 2018-08-10 浙江大学 Least square method supporting vector machine soft-measuring modeling method based on distribution estimation local optimum

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