WO2004107240A1 - Method for assessing the response behavior of an individual to antirheumatics - Google Patents

Method for assessing the response behavior of an individual to antirheumatics Download PDF

Info

Publication number
WO2004107240A1
WO2004107240A1 PCT/EP2003/005701 EP0305701W WO2004107240A1 WO 2004107240 A1 WO2004107240 A1 WO 2004107240A1 EP 0305701 W EP0305701 W EP 0305701W WO 2004107240 A1 WO2004107240 A1 WO 2004107240A1
Authority
WO
WIPO (PCT)
Prior art keywords
protein
genes
marker genes
responders
gene
Prior art date
Application number
PCT/EP2003/005701
Other languages
German (de)
French (fr)
Inventor
Hans-Jürgen Thiesen
Jörn KEKOW
Reinhard Guthke
Dirk Koczan
Original Assignee
Thiesen Hans-Juergen
Kekow Joern
Reinhard Guthke
Dirk Koczan
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Thiesen Hans-Juergen, Kekow Joern, Reinhard Guthke, Dirk Koczan filed Critical Thiesen Hans-Juergen
Priority to EP03740163A priority Critical patent/EP1629411A1/en
Priority to AU2003304165A priority patent/AU2003304165A1/en
Priority to PCT/EP2003/005701 priority patent/WO2004107240A1/en
Publication of WO2004107240A1 publication Critical patent/WO2004107240A1/en

Links

Classifications

    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B25/00—ICT specially adapted for hybridisation; ICT specially adapted for gene or protein expression
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B25/00—ICT specially adapted for hybridisation; ICT specially adapted for gene or protein expression
    • G16B25/10—Gene or protein expression profiling; Expression-ratio estimation or normalisation
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • 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

Definitions

  • the invention relates to a method for assessing the response behavior of an individual to anti-rheumatic drugs on the basis of gene expression profiles.
  • the response behavior to medications differs from patient to patient.
  • Several clinical parameters are usually used to assess responsiveness.
  • the following clinical parameters are used to assess the response in addition to the general patient data such as age and gender and year of initial diagnosis of the rheumatic disease: number of pressure-painful joints (TIC) and swollen joints (SJC), the sedimentation rate (BSG), the concentration of C-reactive protein (CPR), the scaled subjective patient judgment (VAS), the function index according to Steinbrocker (SBI, from level I - without hindrance to everyday life - to Level IN - complete disability).
  • TIC pressure-painful joints
  • SJC swollen joints
  • BSG sedimentation rate
  • CPR concentration of C-reactive protein
  • VAS scaled subjective patient judgment
  • SBI Steinbrocker
  • DNA array techniques can be used to measure the expression of both the known and the functionally unknown DNA sections.
  • SNPs genetic differences of the individual
  • Known pharmacogenomic methods for assessing the response behavior are based on these differences in the genetic material. Different responses can also be acquired by the individual during their life history, so it does not have to be caused in the genetic material.
  • gene expression profiles have been used to assess individual disease events. These techniques are already established in tumor typing. So far, various methods for locating differentially expressed genes have been developed.
  • a lack of the methods mentioned for assessing an individual's response to an anti-rheumatic is that they are based on clinical data that are not sufficiently objective or that are collected with unacceptable effort (X-ray findings).
  • Previous approaches to assess the pathophysiological condition based on gene expression profiles are objective, but are currently not established in practice because the data are largely semi-quantitative in nature and the inaccuracy or fuzziness inherent in the data is not taken into account, which would lead to incorrect conclusions.
  • the object of the invention is to eliminate the shortcomings inherent in these prior art methods.
  • this object is achieved by a method having the features specified in claim 1.
  • the method according to the invention can be carried out quickly and easily and enables the early and reliable determination of whether a particular patient responds to an anti-rheumatic or not, ie is a so-called “responder” or “non-responder”.
  • non-responders can make up a high percentage and treatment with certain novel anti-inflammatory drugs such as ethanercept (Enbrel®, Wyeth, a tumor necrosis alpha antagonist), anakinra (Kineret®, Amgen, an interleukin-1 receptor antagonist) - nist) or infliximab (Remicade®, Essex Pharma) causes very high annual treatment costs, the method according to the invention can save considerable costs for unsuccessful treatment attempts.
  • ethanercept Engel®, Wyeth, a tumor necrosis alpha antagonist
  • anakinra Kineret®, Amgen, an interleukin-1 receptor antagonist
  • infliximab Remicade®, Essex Pharma
  • biosensor chip e.g. a DNA microarray
  • a medical or diagnostic device or kit as defined in claim 12 or claim 13, respectively.
  • ILl-beta is particularly well suited for distinguishing between “responders” or “non-responders” using the method according to the invention (as defined in claim 5).
  • ILL-beta is down-regulated for »responders « and up-regulated for »non-responders «.
  • the assessment of the response behavior of an individual to a specific anti-inflammatory agent is carried out by quantitatively determining the expression of one or more previously selected genes and, if appropriate, providing them with an error measure. The selection must be made specifically for each anti-rheumatic. Gene expression can take place either before the administration or after the administration of the relevant anti-inflammatory drug.
  • the gene expression data are available as a measured value and error measure (s). In the case of the measurement, these are usually available as SIGNAL and p-value before the antirheumatic is administered. When measured after administration, they are available as LOGRATIO and as limits LOW and HIGH of a confidence interval.
  • the term “expression data” is also used below in summary.
  • the numbers N and R should be 5 or more.
  • the assessment is based on the expression of individual genes.
  • the assessment is based on the expression of several genes in relation to one another.
  • J (j) (
  • the evaluation measure J calculated according to equation (1) evaluates the difference between the mean values of the signals of the gene expression (SIGNAL) or the logarithmic quotient with reference to a control (LOGRATIO) of responders (MR) and non-responders (MN) for the gene with the Index j.
  • the mean MD of the confidence interval widths is subtracted from the absolute amount.
  • the value obtained in this way is divided by a denominator which, as in Golub et al. is calculated from the sum of the standard deviations for responders (SR) and non-responders (SN). In contrast to Golub et al. the sum of the constituent interval widths MD is added in the denominator.
  • a suitable summand E can be added in the denominator, as is known to be useful when the standard deviations take extremely small values (Tusher VG, Tibshirani R, Chu G: Significance analysis of microarrays applied to the ionization radiation response. PNAS 2001, 98: 5116-5121).
  • the value E 0 is set. If necessary, it is increased to such an extent that the selection of genes to be made below does not contain any control genes.
  • genes are selected in which the SIGNAL values of all R responders are greater than the SIGNAL values of the N non-resonders, or - vice versa - the SIGNAL values of all R responders are smaller than the SIGNAL values of the N non-resonders and the p values are smaller than a threshold PMAX.
  • genes are selected in which the lower confidence interval limits LOW of all R responders are larger than the upper confidence interval limits HIGH of the N non-resonders or - conversely - the lower confidence interval limits LOW of all N non-responders are larger than the upper confidence interval limits HIGH are the R resonder.
  • invention C groups of multiple genes are selected based on the evaluation measure K.
  • the evaluation measure K is determined as the percentage of correctly predicted response behavior for the individuals LEARNING.
  • the first step there is a larger selection NC of individual genes as described in embodiment A. The number of NCs is typically 100.
  • all TC tuples of each KC gene are formed from this selection of NC genes.
  • the number KC is 2 or greater. The number KC should be chosen as small as possible and should only be chosen larger than 2 if the resulting evaluation measure K is less than 95 percent.
  • total data are formed as KC-dimensional vectors for each of the TC tuples DC.
  • GC learning and test data are formed from the total data.
  • GC LERN * DC.
  • the data set, ie KC-dimensional vector, of one individual is used as the test data and the data sets of the remaining (GC -1) individuals as the learning data set.
  • the determination of a separating surface using a vector support machine in the learning mode (SVM, www.kernel-machines.org; B. Schölkopf, Support Vector Learning., R. Oldenbourg Verlag, Kunststoff, 1997; N. Cristianini, J. Shawe-Taylor, An Introduction to Support Vector Machines, Cambridge UP, 2000).
  • the interface is typically a plane in the KC-dimensional space.
  • the vector support machine is used in test mode, the previously determined interface and the test data forming the input and the predicted response behavior (responder or non-responder) forming the binary output.
  • the response behavior predicted in this way is compared with the response behavior known from clinical parameters and is therefore correct (correct) or incorrect (incorrect).
  • the evaluation measure K is the quotient of the number of correct predictions divided by the total number GC of the tests carried out.
  • the evaluation measure K is determined for each of the TC tuples.
  • Genebank genes whose sequences, biological function or activity of the encoded protein and other data are stored in the so-called “gene bank”. Each gene is assigned a unique gene bank number or gene bank access number, via which it can be found in the database. Several Genbank numbers for a protein with the same biological function mean splice variants of the gene in question.
  • the genebank has the following URL:
  • TNF-alpha tumor necrosis factor
  • TNF-alpha tumor necrosis factor
  • X02596 bcr breakpoint cluster region gene in Philadelphia chromosome
  • HCK hemopoietic cell pro tein-tyro sine kinase
  • TNF-alpha tumor necrosis factor
  • CSPG3 AF026547 neurocan
  • the clinical parameters identified 7 patients with the identification numbers 5, 7, 13, 14, 15, 16 and 17 as responders and 5 patients with the identification numbers 1, 3, 6, 9 and 10 as non-responders.
  • the mean values MR of the SIGNAL values of the first 2 in the list of genes mentioned in claim 5 averaged over these 7 responders and the corresponding mean values MN averaged over the 5 non-responders and the mean MRN of MR and MN are listed in the following table .
  • the SIGNAL values S2 and S8 of two patients with the identification numbers 2 and 8, whose response behavior is to be assessed, are also listed in the table.
  • the p-values are sufficiently small for both genes and both patients ( ⁇ 0.04) so that all 4 SIGNAL values can be used to assess the response behavior B2 or B8.
  • MRN mean value from MR and MN
  • SIGNAL values S8 are closer to the value MN than the value MR and from this it was concluded that patient 8 is a non- Responder is.
  • SIGNAL values S2 come closer to the MN value in the case of the first gene and come closer to the MR values in the case of the second gene, so that no clear statement is possible for patient 2.
  • the assessment of the two patients already mentioned in exemplary embodiment 9 with the identification numbers 2 and 8 takes place on the basis of the LOGRATIO values L2 and L8, which for example for LD78-alpha precursor or IL-1 receptor antagonist (with the Genbank Nm. D90144 or X52015) coding genes for a time tl (eg 3 days) after the administration of etanercept (Enbrel®).
  • L2 and L8 as well as the mean values MR, MN and MRN of LOGRATIOS averaged over the named 7 responders or 5 non-responders and the mean MRN of both is shown in the following table.
  • the two patients with the identification numbers 2 and 8 already mentioned in the examples 9 and 10 are assessed on the basis of the LOGRATIO values L2 and L8 of the genes coding for IL-8 and GIF (with the gene bank numbers M28130 and S72043). for a time t2 (eg 6 days) after administration of etanercept (Enbrel®).
  • the values L2 and L8 as well as the mean values MR, MN or MRN of LOGRATIOS, averaged over the 7 responders or 5 non-responders mentioned in the exemplary embodiment 9 and the mean value MRN from both, are shown in the following table.
  • the dashed line was determined using a support vector machine algorithm and separates responders from non-responders.
  • the positions of the LOGRATIO values of the patients with the identification numbers 2 and 8 are marked with triangles. The position of these values with respect to the dashed dividing line leads to the conclusion that patient 2 is a responder and patient 8 is a non-responder.
  • the two patients with the identification numbers 2 and 8 already mentioned in the exemplary embodiments 9 to 12 are assessed on the basis of pairs of the LOGRATIO values of the genes coding for TNF-alpha or GTPase-activating protein (rap 1 GAP) (with the Genbank Nm. X02910 and M64788) for a time t2 (e.g. 6 days) after the administration of Etanercept (Enbrel®).
  • Figure 1 GAP TNF-alpha or GTPase-activating protein
  • t2 e.g. 6 days
  • Figure 2 shows the positions of the LOGRATIO values of the 7 responders as black-filled circles and that of the 5 non-responders as open circles.
  • the respective confidence intervals from LOW to HIGH are shown as crosses, the centers of which mark the aforementioned LOGRATIO values and whose leg lengths correspond to the confidence intervals.
  • the dashed line was determined using a support vector machine algorithm and separates responders from non-responders.
  • the positions of the LOGRATIO values of the patients with the identification numbers 2 and 8 are marked with triangles. The position of these values with respect to the dashed dividing line leads to the conclusion that patient 2 is a responder and patient 8 is a non-responder.

Abstract

The invention relates to a method for assessing an individual's response behavior to antirheumatics based on gene expression profiles.

Description

Verfahren zur Beurteilung des Ansprechverhaltens eines Individuums auf Antirheumatika Procedure for assessing an individual's response to anti-inflammatory drugs
Die Erfindung betrifft ein Verfahren zur Beurteilung des Ansprechverhaltens eines Indivi- duums auf Antirheumatika auf der Basis von Genexpressionsprofilen.The invention relates to a method for assessing the response behavior of an individual to anti-rheumatic drugs on the basis of gene expression profiles.
Bekanntermaßen ist das Ansprechverhalten auf Medikationen von Patient zu Patient verschieden. Zur Beurteilung des Ansprechverhaltens werden gewöhnlich mehrere klinische Parameter herangezogen. Im Falle der Beurteilung des Ansprechverhaltens auf Antirheu- matika werden außer den allgemein im Rahmen der Anamnese erhobenen Patientendaten wie Alter und Geschlecht sowie Jahr der Erstdiagnose der rheumatischen Erkrankung vor allem folgende klinische Parameter zur Beurteilung des Ansprechverhaltens genutzt: Anzahl der druckschmerzhaften Gelenke (TIC) sowie der geschwollenen Gelenke (SJC), die Blutsenkungsgeschwindigkeit (BSG), die Konzentration an C-reaktivem Protein (CPR), das skalierte subjektive Patientenurteil (VAS), der Funktionsindex nach Steinbrocker (SBI, von Stufe I - ohne Behinderung des alltäglichen Lebens - bis Stufe IN - vollständige Behinderung). Es ist üblich, die Parameter TIC, SJC, VAS und BSG oder CRP zu einer globalen Maßzahl DAS (Disease Activity Score) zusammenzufassen (www.das-score.nl). Neben dem DAS und dem Funktionsindex nach Steinbrocker ist ein Röntgenbefund sehr aussagekräftig und für die derzeitige klinische Praxis maßgebend. Schließlich ist es üblich, dass diese klinischen Parameter durch weitere Laborbefunde ergänzt werden, wie beispielsweise die Konzentrationen an Tumornekrosisfaktor (TNF alpha) und Interleukin-6 (IL-6).As is known, the response behavior to medications differs from patient to patient. Several clinical parameters are usually used to assess responsiveness. In the case of an assessment of the response to anti-rheumatic drugs, the following clinical parameters are used to assess the response in addition to the general patient data such as age and gender and year of initial diagnosis of the rheumatic disease: number of pressure-painful joints (TIC) and swollen joints (SJC), the sedimentation rate (BSG), the concentration of C-reactive protein (CPR), the scaled subjective patient judgment (VAS), the function index according to Steinbrocker (SBI, from level I - without hindrance to everyday life - to Level IN - complete disability). It is common to combine the parameters TIC, SJC, VAS and BSG or CRP into a global measure DAS (Disease Activity Score) (www.das-score.nl). In addition to the DAS and the Steinbrocker function index, an X-ray finding is very meaningful and decisive for current clinical practice. Finally, it is common for these clinical parameters to be supplemented by other laboratory findings, such as the concentrations of tumor necrosis factor (TNF alpha) and interleukin-6 (IL-6).
Diese üblichen klinischen Parameter haben eine Reihe von Nachteilen. Sie haben zum Teil subjektiven Charakter (VAS, SBI, TIC) oder sind nicht spezifisch für die rheumatische Erkrankung, sondern werden auch durch andere entzündliche Erkrankungen beeinflusst (BSG) oder sind für eine regelmäßig Anwendung auszuschließen (Röntenbefund) oder basieren auf vorhandenem, aber sehr unvollkommenem immunologischem Wissen, dass nicht für die gesamte Population der Patienten in ihrer individuellen Vielfalt zutreffend ist (TNF, IL-6). Jede Auswahl von klinischen Parametern reflektiert den aktuellen Stand des Wissens und ist damit ebenfalls subjektiv, wenn auch weitgehend im Konsens der Fachwelt.These common clinical parameters have a number of disadvantages. Some of them have a subjective character (VAS, SBI, TIC) or are not specific for the rheumatic disease, but are also influenced by other inflammatory diseases (BSG) or can be excluded for regular use (X-ray findings) or are based on existing but very imperfect immunological knowledge that is not applicable to the entire population of patients in their individual diversity (TNF, IL-6). Each selection of clinical parameters reflects the current state of knowledge and is therefore also subjective, albeit largely in the consensus of experts.
Ein neuer, ganzheitlicher und vorurteilsfreier, weil rein Daten-getriebener Ansatz zur Beurteilung des Ansprechverhaltens von Therapeutika verwendet Genexpressionsprofile. Dabei wird die Expression der Gesamtheit aller Gene, d.h. die Produkte der Transkription (mRNA) der Gene in ihrer Häufigkeit gemessen. Von den geschätzten über 30000 Genen sind 10000 bis 20000 Gene in ihrer Funktion bekannt. Die Mehrzahl der Gene ist funktio- nell noch aufzuklären und bisher wenig charakterisiert (sogenannte ESTs). Mittels DNA- Array-Techniken kann die Expression sowohl der bekannten als auch der noch funktioneil unbekannten DNA- Abschnitte messend verfolgt werden.A new, holistic and unprejudiced, because purely data-driven approach to assessing the response behavior of therapeutic agents uses gene expression profiles. The expression of the entirety of all genes, i.e. the products of transcription (mRNA) of the genes measured in their frequency. Of the estimated over 30,000 genes, 10,000 to 20,000 genes are known in their function. The majority of the genes can still be functionally elucidated and have so far been poorly characterized (ESTs). DNA array techniques can be used to measure the expression of both the known and the functionally unknown DNA sections.
Das Ansprechverhalten eines Individuums auf Antirheumatika und andere Medikamente kann von genetischen Unterschieden der Individuen abhängen (SNPs). Auf diesen Unterschieden im Erbmaterial beruhen bekannte pharmakogenomische Verfahren zur Beurtei- lung des Ansprechverhaltens. Unterschiedliches Ansprechverhalten kann aber auch vom Individuum während seiner Lebensgeschichte erworben sein, muss also nicht im Erbmaterial verursacht sein. Seit wenigen Jahren werden deshalb Genexpressionsprofile zur Beurteilung des individuellen Krankheitsgeschehens herangezogen. Diese Techniken sind bereits etabliert in der Tumortypisierung. Es wurden bisher verschiedene Methoden zum Auf- finden differentiell exprimierter Gene entwickelt. Die meisten etablierten Methoden basieren auf dem Quotienten der logarithmierten Expresssionshöhe der Probe im Vergleich zu dem entsprechenden Wert einer Kontrolle (Fold-Change-Analyse; Chen Y, Dougherty ER, Bittner ML: Ratio based decisions and the quantitative analysis of cDNA microarray images, J. of Biomedical Optics 1997, 2: 364-374) und der t-Statistik (Mutch DM, Berger A, Mansourian R, Rytz A, Roberts MA: Microarray data analysis: a practical approach for selecting differentially expressed genes. Genome Biology 2001, 2: preprint 9.1-9.31; Baldi P, Long AD: A Bayesian framework for the analysis of microarray expression data: regu- larized t -test and statistical inferences of gene changes. Bioinformatics 2001, 17: 509-519; Golub T, Slonim D, Tamayo P, Huard C, Gaasenbeek M, Mesirov J, Coller H, Loh M, Downing J, Caligiuri M, Bloomfield C, Lander E: Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 1999, 286: 531- 537; Tusher VG, Tibshirani R, Chu G: Significance analysis of microarrays applied to the ionization radiation response. PNAS 2001, 98: 5116-5121).An individual's response to anti-inflammatory drugs and other drugs may depend on the genetic differences of the individual (SNPs). Known pharmacogenomic methods for assessing the response behavior are based on these differences in the genetic material. Different responses can also be acquired by the individual during their life history, so it does not have to be caused in the genetic material. For a few years now, gene expression profiles have been used to assess individual disease events. These techniques are already established in tumor typing. So far, various methods for locating differentially expressed genes have been developed. Most established methods are based on the quotient of the logarithmic expression level of the sample compared to the corresponding value of a control (fold-change analysis; Chen Y, Dougherty ER, Bittner ML: Ratio based decisions and the quantitative analysis of cDNA microarray images, J . of Biomedical Optics 1997, 2: 364-374) and the t-statistics (Mutch DM, Berger A, Mansourian R, Rytz A, Roberts MA: Microarray data analysis: a practical approach for selecting differentially expressed genes. Genome Biology 2001, 2: preprint 9.1-9.31; Baldi P, Long AD: A Bayesian framework for the analysis of microarray expression data: regularized t -test and statistical inferences of gene changes. Bioinformatics 2001, 17: 509-519; Golub T, Slonim D, Tamayo P, Huard C, Gaasenbeek M, Mesirov J, Coller H, Loh M, Downing J, Caligiuri M, Bloomfield C, Lander E: Molecular classification of cancer: class discovery and class prediction by gene expression monitoring , Science 1999, 286: 531-537; Tusher VG, Tibshirani R, Chu G: Significance analysis of microarrays applied to the ionization radiation response. PNAS 2001, 98: 5116-5121).
Es sind auch Verfahren der Clusteranalyse bekannt, mit denen die Genexpressionsprofile verschiedenen Zuständen zugeordnet werden können. Beispielsweise sind in der weit verbreiteten Software GeneSpring (SiliconGenetics; www.silicongenetics.com) sowohl Methoden der statistischen Datenanalyse enthalten wie die Principal Component Analysis (PCA) und Analysis of Variance (ANOVA) als auch Methoden der Clusteranalyse (hierarchische Clusteranalyse; k-Means, Self-Organizing Maps).Methods of cluster analysis are also known with which the gene expression profiles can be assigned to different states. For example, the widespread software GeneSpring (SiliconGenetics; www.silicongenetics.com) contains methods of statistical data analysis as well as Principal Component Analysis (PCA) and Analysis of Variance (ANOVA) as well as methods of cluster analysis (hierarchical cluster analysis; k-Means , Self-Organizing Maps).
Ein Mangel der genannten Verfahren zur Beurteilung des Ansprechverhaltens eines Individuums auf ein Antirheumatikum ist, dass diese auf klinischen Daten basieren, die nicht hinreichend objektiv sind oder mit unvertretbarem Aufwand (Röntgenbefund) erhoben werden. Bisherige Ansätze zur Beurteilung des pathophysiologischen Zustandes auf der Basis von Genexpressionsprofilen sind zwar objektiv, aber derzeit nicht in der Praxis etabliert, weil die Daten weitgehend semiquantitativer Natur sind und die den Daten immanente Ungenauigkeit oder Unscharfe unberücksichtigt bleibt, womit es zu Fehlschlüssen käme.A lack of the methods mentioned for assessing an individual's response to an anti-rheumatic is that they are based on clinical data that are not sufficiently objective or that are collected with unacceptable effort (X-ray findings). Previous approaches to assess the pathophysiological condition based on gene expression profiles are objective, but are currently not established in practice because the data are largely semi-quantitative in nature and the inaccuracy or fuzziness inherent in the data is not taken into account, which would lead to incorrect conclusions.
Aufgabe der Erfindung ist die Beseitigung der diesen Verfahren des Standes der Technik anhaftenden Mängel.The object of the invention is to eliminate the shortcomings inherent in these prior art methods.
Erfindungsgemäß wird diese Aufgabe durch ein Verfahren mit den in Anspruch 1 angegebenen Merkmalen gelöst. Das erfindungsgemäße Verfahren ist schnell und einfach durchzuführen und gestattet die frühzeitige und zuverlässige Feststellung, ob ein bestimmter Patient auf ein Antirheumati- kum anspricht oder nicht, also ein sog. »Responder« oder »Non-Responder« ist. Da die »Non-Responder« einen hohen Prozentsatz ausmachen können und die Behandlung mit bestimmten neuartigen Antirheumatika wie Ethanercept (Enbrel®, Wyeth, ein Tumorne- krosis-alpha-Antagonist), Anakinra (Kineret®, Amgen, ein Interleukin-1-Rezeptorantago- nist) oder Infliximab (Remicade®, Essex Pharma) sehr hohe jährliche Behandlungskosten verursacht, lassen sich durch das erfindungsgemäße Verfahren erhebliche Kosten für er- folglose Behandlungsversuche einsparen.According to the invention, this object is achieved by a method having the features specified in claim 1. The method according to the invention can be carried out quickly and easily and enables the early and reliable determination of whether a particular patient responds to an anti-rheumatic or not, ie is a so-called "responder" or "non-responder". As the »non-responders« can make up a high percentage and treatment with certain novel anti-inflammatory drugs such as ethanercept (Enbrel®, Wyeth, a tumor necrosis alpha antagonist), anakinra (Kineret®, Amgen, an interleukin-1 receptor antagonist) - nist) or infliximab (Remicade®, Essex Pharma) causes very high annual treatment costs, the method according to the invention can save considerable costs for unsuccessful treatment attempts.
Ein weiterer Aspekt der Erfindung betrifft einen Biosensorchip, z.B. einen DNA-Micro- array, und ein medizinisches oder diagnostisches Gerät oder einen Kit wie in Anspruch 12 bzw. Anspruch 13 definiert.Another aspect of the invention relates to a biosensor chip, e.g. a DNA microarray, and a medical or diagnostic device or kit as defined in claim 12 or claim 13, respectively.
Weitere vorteilhafte und/oder bevorzugte Ausführungsformen der Erfindung sind Gegenstand der Unteransprüche.Further advantageous and / or preferred embodiments of the invention are the subject of the dependent claims.
Im Falle des Antirheumatikums Ethanercept (Enbrel®, Wyeth) eignet sich ILl-beta beson- ders gut zur Unterscheidung zwischen »Respondern« oder »Non-Respondem« nach dem erfindungsgemäßen Verfahren (wie in Anspruch 5 definiert). Bei »Respondern« ist ILl- beta herunterreguliert und bei »Non-Respondern« hochreguliert.In the case of the anti-rheumatic agent Ethanercept (Enbrel®, Wyeth), ILl-beta is particularly well suited for distinguishing between "responders" or "non-responders" using the method according to the invention (as defined in claim 5). ILL-beta is down-regulated for »responders« and up-regulated for »non-responders«.
Im folgenden wird die Erfindung anhand von Ausführungsbeispielen und unter Bezug- nähme auf die Figur detaillierter erläutert. Es ist klar, dass damit keinerlei Beschränkung der Erfindung verbunden oder beabsichtigt ist. Die Beurteilung des Ansprechverhaltens eines Individuums auf ein bestimmtes Antirheu- matikum erfolgt, indem die Expression eines oder mehrerer zuvor ausgewählter Gene quantitativ und gegebenenfalls versehen mit einem Fehlermaß bestimmt wird. Die Auswahl ist für jedes Antirheumatikum spezifisch vorzunehmen. Die Genexpression kann entweder vor der Verabreichung oder nach der Verabreichung des betreffenden Antirheu- matikums erfolgen. Die Daten der Genexpression liegen als Messwert und Fehlermaß(e) vor. Üblicherweise liegen diese im Falle der Messung vor der Verabreichung des An- tirheumatikums als SIGNAL und p-Wert vor. Bei Messung nach der Verabreichung liegen sie als LOGRATIO und als Grenzen LOW und HIGH eines Konfidenzintervalles vor. Im folgenden wird der Einfachheit halber zusammenfassend auch der Begriff "Expressionsda- ten verwendet.The invention is explained in more detail below on the basis of exemplary embodiments and with reference to the figure. It is clear that this is not intended to limit or limit the invention. The assessment of the response behavior of an individual to a specific anti-inflammatory agent is carried out by quantitatively determining the expression of one or more previously selected genes and, if appropriate, providing them with an error measure. The selection must be made specifically for each anti-rheumatic. Gene expression can take place either before the administration or after the administration of the relevant anti-inflammatory drug. The gene expression data are available as a measured value and error measure (s). In the case of the measurement, these are usually available as SIGNAL and p-value before the antirheumatic is administered. When measured after administration, they are available as LOGRATIO and as limits LOW and HIGH of a confidence interval. For the sake of simplicity, the term “expression data” is also used below in summary.
Für andere Antirheumatika erfolgt die Auswahl in einer Lernphase anhand einer hinreichend großen Anzahl LERN von Individuen, für die das Ansprechverhalten mittels be- kannter klinischer Parameter zweifelsfrei beurteilt werden kann, die also als Responder (R) oder Non- bzw. Nicht-Responder (N) bezeichnet werden können (LERN=N + R). Die Anzahlen Zahlen N und R sollten 5 oder größer sein. Für die Auswahl geeigneter Gene werden Verfahren in einer oder mehreren von 4 Ausführungsformen A, Bl, B2 und C angewandt.For other anti-rheumatic drugs, the selection is made in a learning phase on the basis of a sufficiently large number of LEARNING individuals for whom the response behavior can be assessed beyond doubt using known clinical parameters, i.e. who are responders (R) or non- or non-responders (N ) can be designated (LEARN = N + R). The numbers N and R should be 5 or more. Methods in one or more of 4 embodiments A, B1, B2 and C are used for the selection of suitable genes.
In den Ausführungsform A, Bl und B2 erfolgt die Beurteilung anhand der Expression einzelner Gene. In der Ausführungsform C erfolgt die Beurteilung anhand der Expression mehrerer Gene in ihrer Relation zueinander.In embodiments A, B1 and B2, the assessment is based on the expression of individual genes. In embodiment C, the assessment is based on the expression of several genes in relation to one another.
Die Auswahl der einzelnen Gene in der Ausführungsform A erfolgt mittels folgenden BewertungsmaßesThe individual genes in embodiment A are selected using the following evaluation measure
J(j) = (|MR(j) - MN(j)| - MD(j)) * P(j) / (SRC) + SN(j) + MD(j) + E) (1) Das nach Gleichung (1) berechnete Bewertungsmaß J bewertet die Differenz der Mittelwerte der Signale der Genexpression (SIGNAL) oder des logarithmierten Quotienten mit Bezug auf eine Kontrolle (LOGRATIO) von Respondern (MR) und Non-Respondern (MN) für das Gen mit dem Index j. Im Unterschied zum Verfahren von Golub (Golub T, Slonim D, Tamayo P, Huard C, Gaasenbeek M, Mesirov J, Coller H, Loh M, Downing J, Caligiuri M, Bloomfield C, Lander E: Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 1999, 286: 531-537) wird vom Absolutbetrag der Mittelwert MD der Konfidenzintervallbreiten subtrahiert. Derartige Konfidenzintervallbreiten als Differenz der Werte HIGH und LOW werden bei DNA- Mikroarrays, insbesondere denen der Firma Affymetrix, neben den Werten für die LOGRATIO üblicherweise mit bereitgestellt. Die Faktoren P werden in diesem Falle für alle Gene einheitlich mit P = 1 belegt. Für die Werte SIGNAL liegen jedoch in der Regel keine Konfidenzintervall- Werte vor, sondern sogenannte p-Werte. In diesem Falle wird der Faktor P in Formel (1) mit P=l belegt, falls der p-Werte kleiner als ein geeignet zu wählender Schwellenwert ist, im typischen Falle für p kleiner als 0.04. Anderenfalls, d.h. wenn der p- Wert größer als der genannte Schwellenwert ist, wird P =0 gesetzt. Der so erhaltene Wert wird durch einen Nenner dividiert, der wie bei Golub et al. aus der Summe der Standardabweichungen für Responder (SR) und Non-Responder (SN) gebildet wird. Im Unterschied zu Golub et al. wird im Nenner die Summe der Konfinzendintervallbreiten MD ad- diert. Optional kann im Nenner auch ein geeignet zu wählender Summand E hinzugefügt werden, wie dies bekanntermaßen sinnvoll ist, wenn die Standardabweichungen extrem kleine Werte annehmen (Tusher VG, Tibshirani R, Chu G: Significance analysis of micro- arrays applied to the ionization radiation response. PNAS 2001, 98: 5116-5121). In der Regel wird der Wert E=0 gesetzt. Er wird erforderlichenfalls soweit erhöht, dass in der nachfolgend zu treffenden Auswahl von Genen keine Kontrollgene enthalten sind. Nach der Berechnung der Bewertungsmaße J gemäss Formel (1) für alle Gene werden diese Gene sortiert nach fallendem Wert J. Eine bestimmte Anzahl NA von mit J höchstbewerteten Genen wird auswählt. Die Anzahl NA sollte zwischen 2 und 10 Genen liegen.J (j) = (| MR (j) - MN (j) | - MD (j)) * P (j) / (SRC) + SN (j) + MD (j) + E) (1) The evaluation measure J calculated according to equation (1) evaluates the difference between the mean values of the signals of the gene expression (SIGNAL) or the logarithmic quotient with reference to a control (LOGRATIO) of responders (MR) and non-responders (MN) for the gene with the Index j. In contrast to the Golub method (Golub T, Slonim D, Tamayo P, Huard C, Gaasenbeek M, Mesirov J, Coller H, Loh M, Downing J, Caligiuri M, Bloomfield C, Lander E: Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 1999, 286: 531-537), the mean MD of the confidence interval widths is subtracted from the absolute amount. Such confidence interval widths as the difference between the values HIGH and LOW are usually provided in addition to the values for the LOGRATIO in DNA microarrays, in particular those from the Affymetrix company. In this case, the factors P are uniformly assigned P = 1 for all genes. However, there are usually no confidence interval values for the SIGNAL values, but rather so-called p-values. In this case, the factor P in formula (1) is assigned P = 1 if the p-value is less than an appropriately selected threshold value, typically for p less than 0.04. Otherwise, ie if the p-value is greater than the specified threshold value, P = 0 is set. The value obtained in this way is divided by a denominator which, as in Golub et al. is calculated from the sum of the standard deviations for responders (SR) and non-responders (SN). In contrast to Golub et al. the sum of the constituent interval widths MD is added in the denominator. Optionally, a suitable summand E can be added in the denominator, as is known to be useful when the standard deviations take extremely small values (Tusher VG, Tibshirani R, Chu G: Significance analysis of microarrays applied to the ionization radiation response. PNAS 2001, 98: 5116-5121). As a rule, the value E = 0 is set. If necessary, it is increased to such an extent that the selection of genes to be made below does not contain any control genes. After calculating the evaluation measures J according to formula (1) for all genes, these genes are sorted according to the falling value J. A certain number NA of genes with the highest J rating is selected. The number of NA should be between 2 and 10 genes.
In der Ausführungsform Bl werden solche Gene ausgewählt, bei denen die SIGNAL- Werte aller R Responder größer als die SIGNAL- Werte der N Non-Resonder sind oder - umgekehrt - die SIGNAL- Werte aller R Responder kleiner als die SIGNAL- Werte der N Non-Resonder und die p-Werte kleiner als eine Schwelle PMAX sind.In the embodiment B1, genes are selected in which the SIGNAL values of all R responders are greater than the SIGNAL values of the N non-resonders, or - vice versa - the SIGNAL values of all R responders are smaller than the SIGNAL values of the N non-resonders and the p values are smaller than a threshold PMAX.
In der Ausführungsform B2 werden solche Gene ausgewählt, bei denen die unteren Konfi- denzintervallgrenzen LOW aller R Responder größer als die oberen Konfϊdenzintervall- grenzen HIGH der N Non-Resonder sind oder - umgekehrt - die unteren Kofidenzinter- vallgrenzen LOW aller N Non-Responder größer als die oberen Konfidenzintervallgrenzen HIGH der R Resonder sind.In embodiment B2, genes are selected in which the lower confidence interval limits LOW of all R responders are larger than the upper confidence interval limits HIGH of the N non-resonders or - conversely - the lower confidence interval limits LOW of all N non-responders are larger than the upper confidence interval limits HIGH are the R resonder.
In der Ausführungsform C werden Gruppen mehrerer Gene auf Grundlage des Bewertungsmaßes K ausgewählt. Das Bewertungsmaß K wird ermittelt als die Prozentzahl korrekt vorhergesagten Ansprechverhaltens für die Individuen LERN. Im ersten Schritt erfolgt eine größere Auswahl NC von einzelnen Genen wie in der Ausführungsform A beschrieben. Die Anzahl NC ist typischerweise 100. Im zweiten Schritt werden aus dieser Auswahl von NC Genen alle TC Tupel von je KC Genen gebildet. Die Zahl KC ist 2 oder größer. Die Zahl KC soll möglichst klein gewählt und nur größer 2 gewählt werden, wenn das resultierenden Bewertungsmaß K kleiner 95 Prozent ist. Für NC=100 und KC=2 ist die Anzahl der zu bildenden Tupel TC = 4950, d.h. TC = NC*(NC-l)/2. Im dritten Schritt werden für jedes der TC Tupel DC Gesamtdaten als KC-dimensionale Vektoren gebildet. Dieser Schritt ist verschieden für die Verwendung von Daten SIGNAL einerseits und Daten LOGRATIO mit den Konfidenzintervallgrenzen LOW und HIGH andererseits, wobei letztere zu bevorzugen ist. Im Falle der Verwendung von Expressionswerten SIGNAL sind die Gesamtdaten identisch mit den Werten SIGNAL (DC =1). Im Falle der Verwendung von LOGRATIO ist die Zahl DC = KC*2 +1. Die Gesamtdaten werden in diesem Falle gebil- det aus den KC-dimensionalen Vektoren LOGRATIO und den Vektoren LOGRATIO, bei denen jeweils für eine der KC Komponenten, d.h. Gene, der LOGRATIO durch die untere (LOW) beziehungsweise obere (HIGH) Grenze der Konfidenzintervalle ersetzt wird. In einem vierten Schritt werden aus den Gesamtdaten GC Lern- und Testdaten gebildet. Im typischen Fall der Crossvalidierung ist GC = LERN * DC. Hierbei wird der Datensatz, d.h. KC-dimensionaler Vektor, jeweils eines Individuums als Testdaten verwendet und die Datensätze der verbleibenden (GC -1) Individuen als Lerndatensatz. Im fünften Schritt erfolgt für jeden der Lerndaten die Ermittlung einer Trennfläche mittels einer Vektor-Support- Maschine im Lernmodus (SVM, www.kernel-machines.org; B. Schölkopf, Support Vector Learning., R. Oldenbourg Verlag, München, 1997; N. Cristianini, J. Shawe-Taylor, An Introduction to Support Vector Machines, Cambridge UP, 2000). Die Trennfläche ist in typischer Weise eine Ebene im KC-dimensionalen Raum. Im sechsten Schritt erfolgt die Anwendung der Vektor-Support-Maschine im Testmodus, wobei die zuvor ermittelte Trennfläche und die Testdaten den Eingang und das vorhergesagte Ansprechverhalten (Responder oder Non-Responder) den binären Ausgang bilden. Das so vorhergesagte Ansprechverhalten wird mit dem über klinische Parameter bekannten Ansprechverhalten ver- glichen und ist somit zutreffend (richtig) oder unzutreffend (falsch). Das Bewertungsmaß K ist der Quotient aus der Anzahl der richtigen Vorhersagen dividiert durch die Gesamtanzahl GC der durchgeführten Tests. Das Bewertungsmaß K wird für jedes der TC Tupel ermittelt. Im siebenten Schritt werden die TC Tupel sortiert nach fallendem K. Es werden alle Tupel ausgewählt, für die K nicht kleiner als eine bestimmte Schwelle KS ist. Typi- scherweise wird KS=0.98 gewählt.In embodiment C, groups of multiple genes are selected based on the evaluation measure K. The evaluation measure K is determined as the percentage of correctly predicted response behavior for the individuals LEARNING. In the first step, there is a larger selection NC of individual genes as described in embodiment A. The number of NCs is typically 100. In the second step, all TC tuples of each KC gene are formed from this selection of NC genes. The number KC is 2 or greater. The number KC should be chosen as small as possible and should only be chosen larger than 2 if the resulting evaluation measure K is less than 95 percent. For NC = 100 and KC = 2, the number of tuples to be formed is TC = 4950, ie TC = NC * (NC-1) / 2. In the third step, total data are formed as KC-dimensional vectors for each of the TC tuples DC. This step is different for the use of data SIGNAL on the one hand and data LOGRATIO with the confidence interval limits LOW and HIGH on the other hand, the latter being preferred. If expression values SIGNAL are used, the total data are identical to the values SIGNAL (DC = 1). If LOGRATIO is used, the number DC = KC * 2 +1. In this case, the total data is formed from the KC-dimensional vectors LOGRATIO and the vectors LOGRATIO, in which the LOGRATIO is replaced by the lower (LOW) or upper (HIGH) limit of the confidence intervals for one of the KC components, ie genes becomes. In a fourth step, GC learning and test data are formed from the total data. In the typical case of cross-validation, GC = LERN * DC. The data set, ie KC-dimensional vector, of one individual is used as the test data and the data sets of the remaining (GC -1) individuals as the learning data set. In the fifth step for each of the learning data, the determination of a separating surface using a vector support machine in the learning mode (SVM, www.kernel-machines.org; B. Schölkopf, Support Vector Learning., R. Oldenbourg Verlag, Munich, 1997; N. Cristianini, J. Shawe-Taylor, An Introduction to Support Vector Machines, Cambridge UP, 2000). The interface is typically a plane in the KC-dimensional space. In the sixth step, the vector support machine is used in test mode, the previously determined interface and the test data forming the input and the predicted response behavior (responder or non-responder) forming the binary output. The response behavior predicted in this way is compared with the response behavior known from clinical parameters and is therefore correct (correct) or incorrect (incorrect). The evaluation measure K is the quotient of the number of correct predictions divided by the total number GC of the tests carried out. The evaluation measure K is determined for each of the TC tuples. In the seventh step, the TC tuples are sorted according to falling K. All tuples for which K is not less than a certain threshold KS are selected. Typically KS = 0.98 is selected.
Im folgenden wird auf Gene Bezug genommen, deren Sequenzen, biologische Funktion oder Aktivität des kodierten Proteins und sonstigen Daten in der sog. »Genbank« gespeichert sind. Jedem Gen ist eine eindeutige Genbank-Nummer bzw. Genbank-Zugangs- nummer zugeordnet, über die es in der Datenbank aufgefunden werden kann. Mehrere Genbank-Nummern für ein Protein mit der gleichen biologischen Funktion bedeuten Spli- cevarianten des betreffenden Gens. Die Genbank hat folgenden URL:In the following, reference is made to genes whose sequences, biological function or activity of the encoded protein and other data are stored in the so-called “gene bank”. Each gene is assigned a unique gene bank number or gene bank access number, via which it can be found in the database. Several Genbank numbers for a protein with the same biological function mean splice variants of the gene in question. The genebank has the following URL:
http://www.ncbi.nlm.gov/Genbank/GenbankSearch.htmlhttp://www.ncbi.nlm.gov/Genbank/GenbankSearch.html
Im folgenden wird das erfindungsgemäße Verfahren zwar speziell zur Beurteilung des Ansprechverhaltens auf Etanercept (Enbrel®) angewendet, jedoch ist klar, dass dies lediglich beispielhaft ist und damit keine Beschränkung auf dieses konkrete Antirheumatikurn beabsichtigt ist. Ausführungsbeispiel 1In the following, the method according to the invention is used specifically for assessing the response behavior to etanercept (Enbrel®), but it is clear that this is only exemplary and is therefore not intended to be restricted to this specific anti-rheumatic. Embodiment 1
Im Ergebnis der Genexpressionsanalyse vor Verabreichung des Antirheumatikums Etanercept (Enbrel®) wurden in der Ausführungsform A folgende Gene als differentiell expri- miert gefunden (LERN-12; E=0; NA=9, Verwendung von SIGNAL- Werten):As a result of the gene expression analysis before administration of the anti-rheumatic agent etanercept (Enbrel®), the following genes were found to be differentially expressed in embodiment A (LERN-12; E = 0; NA = 9, use of SIGNAL values):
Genbank-Nr. FunktionGenbank no. function
AF050640 NADH-ubiquinone oxidoreductase NDUFS2 subunitAF050640 NADH ubiquinone oxidoreductase NDUFS2 subunit
X78817 partial ClX78817 partial Cl
U90916 clone 23815U90916 clone 23815
AL050144 DKFZp586C1620AL050144 DKFZp586C1620
X06617 for ribosomal protein S 11X06617 for ribosomal protein S 11
U70321 he esvirus entry mediatorU70321 he esvirus entry mediator
AI557497 Pt2.1 16 A04.rAI557497 Pt2.1 16 A04.r
AL049397 DKFZp586C1019AL049397 DKFZp586C1019
L76703 protein phosphatase 2A B56-epsilon (PP2A)L76703 protein phosphatase 2A B56-epsilon (PP2A)
Ausführungsbeispiel 2Embodiment 2
Im Ergebnis der Genexpressionsanalyse 3 Tage nach Verabreichung des Antirheumatikums Etanercept (Enbrel®) werden in der Ausführungsform A folgende 9 Gene als differentiell exprimiert gefunden (LERN=12; E=0; NA=9, Verwendung von LOGRATIO- Werten):As a result of the gene expression analysis 3 days after administration of the antirheumatic agent etanercept (Enbrel®), the following 9 genes were found as differentially expressed in embodiment A (LERN = 12; E = 0; NA = 9, use of LOGRATIO values):
Genbank-Nr. FunktionGenbank no. function
D90144 LD78 alpha precursorD90144 LD78 alpha precursor
X52015 interleukin-1 receptor antagonistX52015 interleukin-1 receptor antagonist
M24283 rhinovirus receptor (HRV)M24283 rhinovirus receptor (HRV)
AI535946 vicpro2.D07.rAI535946 vicpro2.D07.r
X04500 prointerleukin 1 betaX04500 prointerleukin 1 beta
M15330 interleukin 1-beta (IL1B)M15330 interleukin 1-beta (IL1B)
U52112 neural cell adhesion molecule LlU52112 neural cell adhesion molecule Ll
X02910 tumor necrosis factor (TNF-alpha)X02910 tumor necrosis factor (TNF-alpha)
X66893 complementation group C (FA(C)) Ausführungsbeispiel 3X66893 complementation group C (FA (C)) Embodiment 3
Im Ergebnis der Genexpressionsanalyse 6 Tage nach Verabreichung des Antirheumatikums Etanercept (Enbrel®) werden in der Ausführungsform A folgende Gene als differen- tiell exprimiert gefunden (LERN=12; E=0; NA=9, Verwendung von LOGRATIO- Werten):In the result of the gene expression analysis 6 days after administration of the anti-rheumatic agent etanercept (Enbrel®), the following genes were found to be differentially expressed in embodiment A (LERN = 12; E = 0; NA = 9, use of LOGRATIO values):
Genbank-Nr. FunktionGenbank no. function
M28130 interleukin 8 (IL8)M28130 interleukin 8 (IL8)
S72043 GIF=growth inhibitory factorS72043 GIF = growth inhibitory factor
X02910 tumor necrosis factor (TNF-alpha)X02910 tumor necrosis factor (TNF-alpha)
M60502 profilaggrinM60502 profilaggrin
D90144 LD78 alpha precursorD90144 LD78 alpha precursor
M15330 interleukin 1-beta (IL1B)M15330 interleukin 1-beta (IL1B)
X04500 prointerleukin 1 betaX04500 prointerleukin 1 beta
M64788 Human GTPase activating protein (raplGAP)M64788 Human GTPase activating protein (raplGAP)
AF150241 CBFAZB10AF150241 CBFAZB10
Ausführungsbeispiel 4Embodiment 4
Im Ergebnis der Genexpressionsanalyse vor Verabreichung des Antirheumatikums Etanercept (Enbrel®) werden in der Ausführungsform Bl folgende Gene als differentiell exprimiert gefunden (LERN=12; PMAX = 0.04):As a result of the gene expression analysis before administration of the anti-rheumatic agent etanercept (Enbrel®), the following genes were found to be differentially expressed in embodiment B1 (LERN = 12; PMAX = 0.04):
Genbank-Nr. FunktionGenbank no. function
AB002405 LAK-4pAB002405 LAK-4p
AF036927 adenylyl cyclase type IXAF036927 adenylyl cyclase type IX
X02596 bcr (breakpoint cluster region) gene in Philadelphia chromosomeX02596 bcr (breakpoint cluster region) gene in Philadelphia chromosome
AL031670 dJ681N20.2 (ferritin, light polypeptide-like 1)AL031670 dJ681N20.2 (ferritin, light polypeptide-like 1)
U09510 glycyl-tRNA synthetaseU09510 glycyl tRNA synthetase
X70476 subunit of coatomer complexX70476 subunit of coatomer complex
M57567 ADP-ribosylation factor (hARF5)M57567 ADP ribosylation factor (hARF5)
X63717 APO-1 cell surface antigenX63717 APO-1 cell surface antigen
X52560 nuclear factor NF-IL6X52560 nuclear factor NF-IL6
M37033 CD53 glycoproteinM37033 CD53 glycoprotein
D87448 KIAA0259D87448 KIAA0259
Z78368 HSZ78368Z78368 HSZ78368
D31883 for KIAA0059 U68723 Checkpoint suppressor 1D31883 for KIAA0059 U68723 Checkpoint suppressor 1
AB023208 KIAA0991AB023208 KIAA0991
Ausführungsbeispiel 5Embodiment 5
Im Ergebnis der Genexpressionsanalyse vor Verabreichung des Antirheumatikums Etaner- cept (Enbrel®) werden in der Ausführungsform C folgende Gene als differentiell expri- miert gefunden (LERN=12; KC=2; KS=1.0, Verwendung von SIGNAL- Werten):As a result of the gene expression analysis before administration of the anti-rheumatic drug Etanercept (Enbrel®), the following genes were found to be differentially expressed in embodiment C (LERN = 12; KC = 2; KS = 1.0, use of SIGNAL values):
Genbank-Nr. FunktionGenbank no. function
X55954 HL23 ribosomal protein homologueX55954 HL23 ribosomal protein homologue
U14970 Human ribosomal protein S5U14970 Human ribosomal protein S5
D00760 proteasome subunit HC3D00760 proteasome subunit HC3
X87949 BiP proteinX87949 BiP protein
U90916 clone 23815U90916 clone 23815
AI803447 :tc39g04.xlAI803447: tc39g04.xl
L76703 protein phosphatase 2A B56-epsilon (PP2A)L76703 protein phosphatase 2A B56-epsilon (PP2A)
M57567 ADP-ribosylation factor (hARF5)M57567 ADP ribosylation factor (hARF5)
AL049397 DKFZp586C1019AL049397 DKFZp586C1019
AF016507 C-terminal binding protein 2AF016507 C-terminal binding protein 2
M74524 HHR6A (yeast RAD 6 homologue)M74524 HHR6A (yeast RAD 6 homologue)
AW044624 wy78c04.xlAW044624 wy78c04.xl
U70321 herpesvirus entry mediatorU70321 herpesvirus entry mediator
U34252 gamma-aminobutyraldehyde dehydrogenaseU34252 gamma-aminobutyraldehyde dehydrogenase
AB006679 ATP binding proteinAB006679 ATP binding protein
AB023209 KIAA0992AB023209 KIAA0992
S80071 brain-specific L-proline transporterS80071 brain-specific L-proline transporter
Ausführungsbeispiel 6 Im Ergebnis der Genexpressionsanalyse 3 Tage nach Verabreichung des Antirheumatikums Etanercept (Enbrel®) werden in der Ausführungsform C folgende Gene als differentiell exprimiert gefunden (LERN=12; KC=2; KS=0.98, Verwendung von LOGRATIO- Werten):Embodiment 6 As a result of the gene expression analysis 3 days after administration of the anti-rheumatic agent etanercept (Enbrel®), the following genes were found as differentially expressed in embodiment C (LERN = 12; KC = 2; KS = 0.98, use of LOGRATIO values):
Genbank-Nr. FunktionGenbank no. function
W29040 55d6W29040 55d6
AF028840 Krüppel-associated box proteinAF028840 cripple-associated box protein
U22029 cytochrome P450 (CYP2A7) W26700 1 lh4U22029 cytochrome P450 (CYP2A7) W26700 1 lh4
D29963 CD151D29963 CD151
X74039 urokinase plasminogen activator receptorX74039 urokinase plasminogen activator receptor
X66893 complementation group C (FA(C))X66893 complementation group C (FA (C))
X66362 PCTAIRE-3 for serine/threonine protein kinaseX66362 PCTAIRE-3 for serine / threonine protein kinase
U52112 neural cell adhesion molecule LlU52112 neural cell adhesion molecule Ll
M63835 IgG Fc receptor I geneM63835 IgG Fc receptor I gene
AL080188 DKFZp434A132AL080188 DKFZp434A132
AI435954 th80d08.xlAI435954 th80d08.xl
M24283 major group rhinovirus receptor (HRV)M24283 major group rhinovirus receptor (HRV)
Ml 1567 angiogenin geneMl 1567 angiogenin gene
U90313 glutathione-S-transferaseU90313 glutathione-S-transferase
M63193 platelet-derived endothelial cell growth factorM63193 platelet-derived endothelial cell growth factor
X93093 LW geneX93093 LW gene
AFO 13570 smooth muscle myosin heavy chain SM2AFO 13570 smooth muscle myosin heavy chain SM2
AI825798 tdl8e08.xlAI825798 tdl8e08.xl
U76702 follistatin-related protein FLRG (FLRG)U76702 follistatin-related protein FLRG (FLRG)
X89059 unknown protein expressed in macrophagesX89059 unknown protein expressed in macrophages
U91616 I kappa B epsilon (DcBe)U91616 I kappa B epsilon (DcBe)
AI525665 PT1.3_04_D06.rAI525665 PT1.3_04_D06.r
M29874 cytochrome P450-IJ-B (hll-B 1)M29874 cytochrome P450-IJ-B (hll-B 1)
M69043 MAD-3M69043 MAD-3
AF055989 Shaw type potassium Channel Kv3.3 (KCNC3)AF055989 Shaw type potassium Channel Kv3.3 (KCNC3)
M14564 cytochrome P450cl7 (steroid 17-alpha-hydroxylaseM14564 cytochrome P450cl7 (steroid 17-alpha-hydroxylase
Y16280 G protein-coupled receptor ETBR-LP-2Y16280 G protein-coupled receptor ETBR-LP-2
Ml 6591 hemopoietic cell pro tein-tyro sine kinase (HCK)Ml 6591 hemopoietic cell pro tein-tyro sine kinase (HCK)
AI535946 vicpro2.D07.rAI535946 vicpro2.D07.r
AI148772 qc69h01.xlAI148772 qc69h01.xl
AI832082 tdl2c04.xlAI832082 tdl2c04.xl
D83664 CAAF1 (calcium-binding protein in amniotic fluid 1)D83664 CAAF1 (calcium-binding protein in amniotic fluid 1)
U22398 Cdk-inhibitor p57KIP2 (KIP2)U22398 Cdk inhibitor p57KIP2 (KIP2)
U95090 chromosome 19 cosmid F19541U95090 chromosome 19 cosmid F19541
U37143 cytochrome P450 monooxygenase CYP2J2U37143 cytochrome P450 monooxygenase CYP2J2
Ausführungsbeispiel 7Embodiment 7
Im Ergebnis der Genexpressionsanalyse 6 Tage nach Verabreichung des Antirheumatikums Etanercept (Enbrel®) werden in der Ausführungsform C folgende Gene als differentiell exprimiert gefunden (LERN=12; KC=2; KS=1.0, Verwendung von LOGRATIO- Werten): Genbank-Nr. FunktionAs a result of the gene expression analysis 6 days after administration of the anti-rheumatic agent etanercept (Enbrel®), the following genes were found as differentially expressed in embodiment C (LERN = 12; KC = 2; KS = 1.0, use of LOGRATIO values): Genbank no. function
AB018563 TML1AB018563 TML1
M16938 homeo box c8 proteinM16938 homeo box c8 protein
AF150241 CBFAZB10AF150241 CBFAZB10
M 16441 tumor necrosis factor and lymphotoxinM 16441 tumor necrosis factor and lymphotoxin
M92843 zinc finger transcriptional regulatorM92843 zinc finger transcriptional regulator
M57703 melanin concentrating hormone (MCH)M57703 melanin concentrating hormone (MCH)
AF010310 p53 induced proteinAF010310 p53 induced protein
Ml 7254 erg2 gene encoding erg2 proteinMl 7254 erg2 gene encoding erg2 protein
M64788 GTPase activating protein (rap 1 GAP)M64788 GTPase activating protein (rap 1 GAP)
Lim-Domain Transcription Factor Lim-1Lim-Domain Transcription Factor Lim-1
X02910 tumor necrosis factor (TNF-alpha)X02910 tumor necrosis factor (TNF-alpha)
S72043 growth inhibitory factorS72043 growth inhibitory factor
Ausführungsbeispiel 8Embodiment 8
Im Ergebnis der Genexpressionsanalyse 6 Tage nach Verabreichung des Antirheumatikums Etanercept (Enbrel®) werden in der Ausführungsform C mit KS=0.98 über die im Ausführungsbeispiel 7 genannten Gene hinaus folgende Gene als differentiell exprimiert gefunden (LERN=12; KC=2; KS=0.98, Verwendung von LOGRATIO- Werten):As a result of the gene expression analysis 6 days after administration of the antirheumatic agent etanercept (Enbrel®), the following genes were found to be differentially expressed in embodiment C with KS = 0.98 beyond the genes mentioned in embodiment example 7 (LEARN = 12; KC = 2; KS = 0.98 , Use of LOGRATIO values):
Genbank-Nr. FunktionGenbank no. function
AF026547 neurocan (CSPG3)AF026547 neurocan (CSPG3)
M60047 heparin binding protein (HBp 17)M60047 heparin binding protein (HBp 17)
Ml 7017 beta-thromboglobulin-like proteinMl 7017 beta-thromboglobulin-like protein
X52015 for interleukin- 1 receptor antagonistX52015 for interleukin-1 receptor antagonist
Z11697 HB15Z11697 HB15
M64788 GTPase activating protein (rap 1 GAP)M64788 GTPase activating protein (rap 1 GAP)
AB004904 for STAT induced STAT inhibitor-3AB004904 for STAT induced STAT inhibitor-3
X04500 prointerleukin 1 betaX04500 prointerleukin 1 beta
D90144 LD78 alpha precursorD90144 LD78 alpha precursor
M60502 profilaggrinM60502 profilaggrin
M28130 interleukin 8 (IL8) Ausführungsbeispiel 9M28130 interleukin 8 (IL8) Embodiment 9
Von 18 mit Etanercept (Enbrel®) behandelten Patienten wiesen die klinischen Parameter, insbesondere der DAS, 7 Patienten mit den Kennnummem 5, 7, 13, 14, 15, 16 und 17 als Responder aus und 5 Patienten mit den Kennnummem 1, 3, 6, 9 und 10 als Non-Responder aus. Die Mittelwerte MR der SIGNAL- Werte der ersten 2 in der Aufstellung von im Anspruch 5 genannten Gene gemittelt über diese 7 Responder sowie die entsprechenden Mittelwerte MN gemittelt über die 5 Non-Responder sowie das Mittel MRN von MR und MN sind in der folgender Tabelle aufgeführt. Die SIGNAL- Werte S2 und S8 zweier Patienten mit der Kennnummer 2 und 8, deren Ansprechverhalten beurteilt werden soll, sind in der Tabelle ebenfalls aufgeführt. Die p-Werte sind für beide Gene und beide Patienten hinreichen klein (<0.04), so dass alle 4 SIGNAL- Werte zur Beurteilung des Ansprechverhalten B2 bzw. B8 herangezogen werden können. Durch Vergleich mit dem Mittelwert MRN aus MR und MN, insbesondere wegen der Relation S8 < MRN < MR wird festgestellt, dass die SIGNAL- Werte S8 dem Wert MN näher sind als dem Wert MR und daraus der Schluss gezogen, dass Patient 8 ein Non-Responder ist. Für Patient 2 wird dagegen festgestellt, dass dessen SIGNAL- Werte S2 im Falle des ersten Gens den MN-Wert näher kommen und im Falle des zweiten Gens den MR- Werten näher kommen, so dass für Patient 2 keine klare Aussage möglich ist.Of 18 patients treated with Etanercept (Enbrel®), the clinical parameters, in particular the DAS, identified 7 patients with the identification numbers 5, 7, 13, 14, 15, 16 and 17 as responders and 5 patients with the identification numbers 1, 3, 6, 9 and 10 as non-responders. The mean values MR of the SIGNAL values of the first 2 in the list of genes mentioned in claim 5 averaged over these 7 responders and the corresponding mean values MN averaged over the 5 non-responders and the mean MRN of MR and MN are listed in the following table , The SIGNAL values S2 and S8 of two patients with the identification numbers 2 and 8, whose response behavior is to be assessed, are also listed in the table. The p-values are sufficiently small for both genes and both patients (<0.04) so that all 4 SIGNAL values can be used to assess the response behavior B2 or B8. By comparison with the mean value MRN from MR and MN, in particular because of the relation S8 <MRN <MR, it is found that the SIGNAL values S8 are closer to the value MN than the value MR and from this it was concluded that patient 8 is a non- Responder is. For patient 2, on the other hand, it is found that its SIGNAL values S2 come closer to the MN value in the case of the first gene and come closer to the MR values in the case of the second gene, so that no clear statement is possible for patient 2.
Genbank-Nr MR MN MRN S2 B2 S8 B8Genbank number MR MN MRN S2 B2 S8 B8
AF050640 920 615 768 609 N 538 NAF050640 920 615 768 609 N 538 N
X78817 4576 3148 3862 5772 R 2871 NX78817 4576 3148 3862 5772 R 2871 N
Ausführungsbeispiel 10Embodiment 10
Die Beurteilung der zwei bereits im Ausführungsbeispiel 9 genannten Patienten mit den Kennnummem 2 und 8 erfolgt anhand der LOGRATIO- Werte L2 und L8 der beispielsweise für LD78-alpha-precursor bzw. IL-1-Rezeptorantagonist (mit den Genbank-Nm. D90144 bzw. X52015) kodierenden Gene für einen Zeitpunkt tl (z.B. 3 Tage) nach der Verabreichung von Etanercept (Enbrel®). Die Werte L2 und L8 sowie die Mittelwerte MR, MN bzw. MRN der LOGRATIOS gemittelt über die im Ausführungsbeispiel 9 ge- nannten 7 Responder bzw. 5 Non-Responder und der Mittelwert MRN aus beiden, ist in der folgenden Tabelle gezeigt. Aus dem Vergleich der L2 und L8 mit den genannten Mittelwerten MR, MN und MRN, insbesondere aufgrund der Relationen L2 < MRN< MN und L8 > MRN > MR, folgt die Beurteilung, dass Patient 2 ein Responder und Patient 8 ein Non-Responder ist.The assessment of the two patients already mentioned in exemplary embodiment 9 with the identification numbers 2 and 8 takes place on the basis of the LOGRATIO values L2 and L8, which for example for LD78-alpha precursor or IL-1 receptor antagonist (with the Genbank Nm. D90144 or X52015) coding genes for a time tl (eg 3 days) after the administration of etanercept (Enbrel®). The values L2 and L8 as well as the mean values MR, MN and MRN of LOGRATIOS averaged over the named 7 responders or 5 non-responders and the mean MRN of both is shown in the following table. From the comparison of L2 and L8 with the above-mentioned mean values MR, MN and MRN, in particular on the basis of the relations L2 <MRN <MN and L8>MRN> MR, the assessment follows that patient 2 is a responder and patient 8 is a non-responder ,
Genbank-Nr MR MN MRN L2 B2 L8 B8Genbank number MR MN MRN L2 B2 L8 B8
D90144 -0.8775 1.1714 -0.0238 -3.4287 R 0.0469 ND90144 -0.8775 1.1714 -0.0238 -3.4287 R 0.0469 N
X52015 -1.1046 0.0054 -0.6421 -1.4687 R 0.0569 NX52015 -1.1046 0.0054 -0.6421 -1.4687 R 0.0569 N
Diese Beurteilungen der Patienten 2 und 8 als Responder bzw. Non-Responder gelten weitgehend auch in statistisch qualifizierter Weise, da die Relationen L2HIGH < MRN< MN (nur für das erste Gen) und L8LOW > MRN > MR für die obere Konfidenzintervall- grenze L2HIGH bzw. die untere Konfidenzintervallgrenze L8LOW gelten.These assessments of patients 2 and 8 as responders or non-responders are largely also valid in a statistically qualified manner, since the relations L2HIGH <MRN <MN (only for the first gene) and L8LOW> MRN> MR for the upper confidence interval limit L2HIGH or the lower confidence interval limit L8LOW apply.
Genbank-Nr MR MN MRN L2HIGH B2 L8LOW B8Genbank number MR MN MRN L2HIGH B2 L8LOW B8
M28130 -0.8775 1.1714 -0.0238 -1.6187 R 0.1269 NM28130 -0.8775 1.1714 -0.0238 -1.6187 R 0.1269 N
S72043 -1.1046 0.0054 -0.6421 -0.0987 - 0.4969 NS72043 -1.1046 0.0054 -0.6421 -0.0987 - 0.4969 N
Ausführungsbeispiel 11Embodiment 11
Die Beurteilung der zwei bereits in den Ausfülirungsbeispielen 9 und 10 genannten Patienten mit den Kennnummem 2 und 8 erfolgt anhand der LOGRATIO- Werte L2 und L8 der für IL-8 bzw. GIF kodierenden Gene (mit den Genbank-Nrn. M28130 bzw. S72043) für einen Zeitpunkt t2 (z.B. 6 Tage) nach der Verabreichung von Etanercept (Enbrel®). Die Werte L2 und L8 sowie die Mittelwerte MR, MN bzw. MRN der LOGRATIOS, gemittelt über die im Ausführungsbeispiel 9 genannten 7 Responder bzw. 5 Non-Responder und der Mittelwert MRN aus beiden, ist in der folgenden Tabelle gezeigt. Aus dem Vergleich der L2 und L8 mit den genannten Mittelwerten MR, MN und MRN, insbesondere aufgrund der Relationen L2 < MRN< MN und L8 > MRN > MR, folgt die Beurteilung, dass Patient 2 ein Responder und Patient 8 ein Non-Responder ist. Genbank-Nr MR MN MRN L2 Ϊ2 L8 B8The two patients with the identification numbers 2 and 8 already mentioned in the examples 9 and 10 are assessed on the basis of the LOGRATIO values L2 and L8 of the genes coding for IL-8 and GIF (with the gene bank numbers M28130 and S72043). for a time t2 (eg 6 days) after administration of etanercept (Enbrel®). The values L2 and L8 as well as the mean values MR, MN or MRN of LOGRATIOS, averaged over the 7 responders or 5 non-responders mentioned in the exemplary embodiment 9 and the mean value MRN from both, are shown in the following table. From the comparison of L2 and L8 with the above-mentioned mean values MR, MN and MRN, in particular on the basis of the relations L2 <MRN <MN and L8>MRN> MR, the assessment follows that patient 2 is a responder and patient 8 is a non-responder , Genbank number MR MN MRN L2 Ϊ2 L8 B8
M28130 -1.1781 2.7388 0.7803 -2.7176 R 1.5004 NM28130 -1.1781 2.7388 0.7803 -2.7176 R 1.5004 N
S72043 -1.0124 1.6368 0.6244 -0.5376 R 1.3904 NS72043 -1.0124 1.6368 0.6244 -0.5376 R 1.3904 N
Diese Beurteilung der Patienten 2 und 8 als Responder bzw. Non-Responder gilt auch in statistisch qualifizierter Weise, da die Relationen L2HIGH < MRN< MN und L8LOW > MRN > MR für die obere Konfidenzintervallgrenze L2HIGH bzw. die untere Konfidenzin- tervallgrenze L8LOW gelten.This assessment of patients 2 and 8 as responders or non-responders also applies in a statistically qualified manner, since the relationships L2HIGH <MRN <MN and L8LOW> MRN> MR apply to the upper confidence interval limit L2HIGH and the lower confidence interval limit L8LOW.
Genbank-Nr MR MN MRN L2HIGH B2 L8LOW B8Genbank number MR MN MRN L2HIGH B2 L8LOW B8
M28130 -1.1781 2.7388 0.7803 -1.9176 R 1.2104 NM28130 -1.1781 2.7388 0.7803 -1.9176 R 1.2104 N
S72043 -1.0124 1.6368 0.6244 -0.0676 R 1.1704 NS72043 -1.0124 1.6368 0.6244 -0.0676 R 1.1704 N
Ausführungsbeispiel 12Embodiment 12
Die Beurteilung der zwei bereits in den Ausführungsbeispielen 9 bis 11 genannten Patienten mit den Kennnummem 2 und 8 erfolgt anhand von Paaren der LOGRATIO- Werte der Gene für CD 151 bzw. Urokinase-Plasminogen- Activator-Rezeptor kodierenden Gene (mit den Genbank-Nm. D29963 und X74039) für den Zeitpunkt tl (z.B. 3 Tage) nach der Verabreichung von Etanercept (Enbrel®). In Figur 1 zeigt die Abbildung 1 die Positionen der LOGRATIO- Werte der 7 Responder als schwarz gefüllte Kreise und die der 5 Non- Responder als offene Kreise. Die jeweiligen Konfidenzintervalle von LOW bis HIGH sind als Kreuze dargestellt, deren Mittelpunkte die vorgenannten LOGRATIO-Werte markieren und deren Schenkellängen den Konfidenzintervalle entsprechen. Die gestrichelte Gerade wurde mit einem Support- Vector-Machine-Algorithmus ermittelt und trennt Responder von Non-Respondem. Die Positionen der LOGRATIO-Werte der Patienten mit den Kennnummem 2 und 8 sind mit Dreiecken markiert. Die Lage dieser Werte bezüglich der ge- strichtelten Trennlinie führt zu der Schlussfolgerung, dass Patient 2 ein Responder und Patient 8 ein Non-Responder ist. Ausführungsbeispiel 13The assessment of the two patients with the identification numbers 2 and 8 already mentioned in the exemplary embodiments 9 to 11 takes place on the basis of pairs of the LOGRATIO values of the genes for CD 151 or urokinase plasminogen activator receptor coding genes (with the Genbank Nm. D29963 and X74039) for the time tl (eg 3 days) after the administration of Etanercept (Enbrel®). In Figure 1, Figure 1 shows the positions of the LOGRATIO values of the 7 responders as black-filled circles and those of the 5 non-responders as open circles. The respective confidence intervals from LOW to HIGH are shown as crosses, the centers of which mark the aforementioned LOGRATIO values and whose leg lengths correspond to the confidence intervals. The dashed line was determined using a support vector machine algorithm and separates responders from non-responders. The positions of the LOGRATIO values of the patients with the identification numbers 2 and 8 are marked with triangles. The position of these values with respect to the dashed dividing line leads to the conclusion that patient 2 is a responder and patient 8 is a non-responder. Embodiment 13
Die Beurteilung der zwei bereits in den Ausführungsbeispielen 9 bis 12 genannten Patienten mit den Kennnummem 2 und 8 erfolgt anhand von Paaren der LOGRATIO-Werte der für TNF-alpha bzw. GTPase-aktivierendes Protein (rap 1 GAP) kodierenden Gene (mit den Genbank-Nm. X02910 und M64788) für einen Zeitpunkt t2 (z.B. 6 Tage) nach der Verabreichung von Etanercept (Enbrel®). In Figur 1 zeigt die Abbildung 2 zeigt die Positionen der LOGRATIO-Werte der 7 Responder als schwarz gefüllte Kreise und die der 5 Non- Responder als offene Kreise. Die jeweiligen Konfidenzintervalle von LOW bis HIGH sind als Kreuze dargestellt, deren Mittelpunkte die vorgenannten LOGRATIO-Werte markieren und deren Schenkellängen den Konfidenzintervallen entsprechen. Die gestrichelte Gerade wurde mit einem Support- Vector-Machine-Algorithmus ermittelt und trennt Responder von Non-Respondem. Die Positionen der LOGRATIO-Werte der Patienten mit den Kennnummem 2 und 8 sind mit Dreiecken markiert. Die Lage dieser Werte bezüglich der ge- strichtelten Trennlinie führt zu der Schlussfolgemng, dass Patient 2 ein Responder und Patient 8 ein Non-Responder ist. The two patients with the identification numbers 2 and 8 already mentioned in the exemplary embodiments 9 to 12 are assessed on the basis of pairs of the LOGRATIO values of the genes coding for TNF-alpha or GTPase-activating protein (rap 1 GAP) (with the Genbank Nm. X02910 and M64788) for a time t2 (e.g. 6 days) after the administration of Etanercept (Enbrel®). In Figure 1, Figure 2 shows the positions of the LOGRATIO values of the 7 responders as black-filled circles and that of the 5 non-responders as open circles. The respective confidence intervals from LOW to HIGH are shown as crosses, the centers of which mark the aforementioned LOGRATIO values and whose leg lengths correspond to the confidence intervals. The dashed line was determined using a support vector machine algorithm and separates responders from non-responders. The positions of the LOGRATIO values of the patients with the identification numbers 2 and 8 are marked with triangles. The position of these values with respect to the dashed dividing line leads to the conclusion that patient 2 is a responder and patient 8 is a non-responder.

Claims

Patentansprüche claims
1. Verfahren zur Beurteilung des Ansprechverhaltens (Responsiveness) eines Individuums auf ein Antirheumatikum unter Verwendung von Genexpressionspro filen, bei dem1. A method of assessing the responsiveness of an individual to an anti-rheumatic using gene expression profiles, in which
(a) in Probenmaterial eines Individuums, dessen Ansprechverhalten auf ein Antirheumatikum beurteilt werden soll, quantitativ die Expression einer geeigneten Anzahl von Markergenen unter Erhalt eines Markergenexpressionsprofils bestimmt wird,(a) the expression of a suitable number of marker genes is determined quantitatively in sample material of an individual whose response to an anti-rheumatic agent is to be assessed, while maintaining a marker gene expression profile,
(b) das in (a) erhaltene Markergenexpressionsprofil mit dem/den Markergenexpressi- onsprofil(en) von Vergleichsindividuen verglichen wird, die bekanntermaßen auf das Antirheumatikum ansprechen (Responder) oder nicht ansprechen (Non-Responder) , und(b) the marker gene expression profile obtained in (a) is compared with the marker gene expression profile (s) of comparative individuals who are known to respond to the antirheumatic agent (responder) or do not respond (non-responder), and
(c) die Beurteilung des Ansprechverhaltens aufgrund des in (b) vorgenommenen Ver- gleichs erfolgt, wobei Markergene identifiziert werden können, indem(c) the response is assessed on the basis of the comparison made in (b), marker genes being able to be identified by
(d) in Probenmaterial einer geeigneten Anzahl von Respondern oder Non-Respondem quantitativ die differentielle Expression einer geeigneten Anzahl von Genen unter Erhalt eines Genexpressionsprofils bestimmt wird,(d) the differential expression of a suitable number of genes is determined quantitatively in sample material of a suitable number of responders or non-responders, while maintaining a gene expression profile,
(e) für jedes der Gene nach der folgenden Gleichung ein Bewertungsmaß J:(e) an evaluation measure J for each of the genes according to the following equation:
JÖ) = (|MRÖ) - N(j)| - MD(j)) * P(j) / (SRC) + SN(j) + MD(j) + E),JÖ) = (| MRÖ) - N (j) | - MD (j)) * P (j) / (SRC) + SN (j) + MD (j) + E),
berechnet wird, wobei bedeuten:is calculated, meaning:
MR(j), MN(j) = Mittelwerte der Expressiondaten von Respondern (MR) bzw. Non- Respondem (MN) für das Gen mit dem Index jMR (j), MN (j) = mean values of the expression data of responders (MR) or non-responders (MN) for the gene with the index j
MD(j) = Mittelwert der Konfidenzintervallbreiten für das Gen mit dem Index j, P(j) = 0 oder 1MD (j) = mean of the confidence interval widths for the gene with the index j, P (j) = 0 or 1
SR(j), SN(j) = Standardabweichungen für Responder (SR) bzw. Non-Responder (SN) für das Gen mit dem Index j,SR (j), SN (j) = standard deviations for responders (SR) or non-responders (SN) for the gene with index j,
E = 0 oder positive Zahl,E = 0 or positive number,
und eine geeignete Anzahl von mit J höchstbewerteten differentiell exprimierten Genen als Markergene auswählt wird,and a suitable number of genes with the highest possible differential expression with J is selected as marker genes,
oderor
(e1) Paare, Tripel, ... n-Tupel von Genen von Respondern und Non-Respondem in beliebigen Kombinationen gebildet werden und mit einem Support-Vector-Machine-Algo- rithmus diejenigen Paare, Tripel, ... n-Tupel von Genen als Markergene ausgewählt werden, für die sich eine optimale Trennlinie, Trennfläche, ... Trennhyperfläche bestimmen läßt, die eine Zuordnung der Expressionsdaten der Paare, Tripel, ... n-Tupel von Genen zu Respondern bzw. Non-Respondem ermöglicht.(e 1 ) Pairs, triples, ... n-tuples of genes of responders and non-responders are formed in any combination and those pairs, triples, ... n-tuples are formed using a support vector machine algorithm genes are selected as marker genes for which an optimal dividing line, dividing area, ... dividing hyper area can be determined, which enables the expression data of the pairs, triples, ... n-tuples of genes to be assigned to responders or non-responders.
2. Verfahren nach Anspruch 1, wobei im Fall von nach Alternative (e) identifizierten Markergenen in (b) die Expressionsdaten der Markergene des Individuums mit MR, MN und dem Mittelwert aus MR und MN (MRN) der Markergene der Vergleichsindividuen und im Fall von nach Alternative (e') identifizierten Markergenen in (b) die Expressionsdaten der Markergene des Individuums durch einen Support- Vector-Machine-Algorithmus einer Seite der in (e1) ermittelten optimalen Trennlinie, Trennfläche, ... Trennhyperfläche zugeordnet werden.2. The method according to claim 1, wherein in the case of marker genes identified according to alternative (e) in (b) the expression data of the marker genes of the individual with MR, MN and the mean of MR and MN (MRN) of the marker genes of the comparison individuals and in the case of according to alternative (e ') identified marker genes in (b) the expression data of the marker genes of the individual are assigned to a side of the optimal dividing line, dividing surface, ... dividing hyper surface determined by (e 1 ) using a support vector machine algorithm.
3. Verfahren nach Anspruch 1 oder 2, wobei das Antirheumatikum unter Etanercept, Anakinra, Diclofenac, Methotrexat, Infliximab und Adalimumab ausgewählt wird. 3. The method according to claim 1 or 2, wherein the anti-inflammatory agent is selected from etanercept, anakinra, diclofenac, methotrexate, infliximab and adalimumab.
4. Verfahren nach einem der vorstehenden Ansprüche, wobei in der ersten Woche der Behandlung mit einem Antirheumatikum mehrfach Proben entnommen und das Ansprechverhalten beurteilt wird.4. The method according to any one of the preceding claims, wherein several samples are taken in the first week of treatment with an anti-rheumatic and the response behavior is assessed.
5. Verfahren nach einem der vorstehenden Ansprüche zur Beurteilung des Ansprechverhaltens eines Individuums auf Etanercept, bei dem in dem in Anspmch 1 definierten Schritt (a) entweder vor der Verabreichung von Etanercept quantitativ die Expression mindestens eines der aus der folgenden Aufstellung ausgewählten Markergene, oder alleler oder mutierter Formen davon, bestimmt wird:5. The method according to any one of the preceding claims for assessing the response of an individual to etanercept, in the step (a) defined in Claim 1 either quantitatively expressing at least one of the marker genes selected from the list below, or alleler, before the administration of etanercept or mutated forms thereof, is determined:
Genbank-Nr. FunktionGenbank no. function
AF050640 NADH-ubiquinone oxidoreductase NDUFS2 subunitAF050640 NADH ubiquinone oxidoreductase NDUFS2 subunit
X78817 partial ClX78817 partial Cl
U90916 clone 23815U90916 clone 23815
AL050144 DKFZp586C1620AL050144 DKFZp586C1620
X06617 for ribosomal protein S 11X06617 for ribosomal protein S 11
U70321 herpesvirus entry mediatorU70321 herpesvirus entry mediator
AI557497 Pt2.1_16_A04.rAI557497 Pt2.1_16_A04.r
AL049397 DKFZp586C1019AL049397 DKFZp586C1019
L76703 protein phosphatase 2A B56-epsilon (PP2A)L76703 protein phosphatase 2A B56-epsilon (PP2A)
Genbank-Nr. FunktionGenbank no. function
AB002405 LAK-4pAB002405 LAK-4p
AF036927 adenylyl cyclase type IXAF036927 adenylyl cyclase type IX
X02596 bcr (breakpoint cluster region) gene in Philadelphia chromosomeX02596 bcr (breakpoint cluster region) gene in Philadelphia chromosome
AL031670 dJ681N20.2 (ferritin, light polypeptide-like 1)AL031670 dJ681N20.2 (ferritin, light polypeptide-like 1)
U09510 glycyl-tRNA synthetaseU09510 glycyl tRNA synthetase
X70476 subunit of coatomer complexX70476 subunit of coatomer complex
M57567 ADP-ribosylation factor (hARF5)M57567 ADP ribosylation factor (hARF5)
X63717 APO-1 cell surface antigen X52560 nuclear factor NF-IL6X63717 APO-1 cell surface antigen X52560 nuclear factor NF-IL6
M37033 CD53 glycoproteinM37033 CD53 glycoprotein
D87448 KIAA0259D87448 KIAA0259
Z78368 HSZ78368Z78368 HSZ78368
D31883 for KIAA0059D31883 for KIAA0059
U68723 Checkpoint suppressor 1U68723 Checkpoint suppressor 1
AB023208 KIAA0991AB023208 KIAA0991
GenBank-Nr. FunktionGenBank no. function
X55954 HL23 ribosomal protein homologueX55954 HL23 ribosomal protein homologue
U14970 Human ribosomal protein S5U14970 Human ribosomal protein S5
D00760 proteasome subunit HC3D00760 proteasome subunit HC3
X87949 BiP proteinX87949 BiP protein
U90916 clone 23815U90916 clone 23815
AI803447 :tc39g04.xlAI803447: tc39g04.xl
L76703 protein phosphatase 2A B56-epsilon (PP2A)L76703 protein phosphatase 2A B56-epsilon (PP2A)
M57567 ADP-ribosylation factor (hARF5)M57567 ADP ribosylation factor (hARF5)
AL049397 DKFZp586C1019AL049397 DKFZp586C1019
AF016507 C-terminal binding protein 2AF016507 C-terminal binding protein 2
M74524 HHR6A (yeast RAD 6 homologue)M74524 HHR6A (yeast RAD 6 homologue)
AW044624 wy78c04.xlAW044624 wy78c04.xl
U70321 herpesvirus entry mediatorU70321 herpesvirus entry mediator
U34252 gamma-aminobutyraldehyde dehydrogenaseU34252 gamma-aminobutyraldehyde dehydrogenase
AB006679 ATP binding proteinAB006679 ATP binding protein
AB023209 KIAA0992AB023209 KIAA0992
S80071 brain-specific L-proline transporterS80071 brain-specific L-proline transporter
AF054825 VAMP5AF054825 VAMP5
NM_006634.1 vesicle-associated membrane protein i 5 (myobrevin)NM_006634.1 vesicle-associated membrane protein i 5 (myobrevin)
(VAMP5),(VAMP5)
U28014 cysteine protease (ICErel-II)U28014 cysteine protease (ICErel-II)
U25804.1 Ich-2 cysteine protease X63717 APO-1 cell surface antigen NM 000043.1 tumor necrosis factor receptor superfamily, member 6U25804.1 I-2 cysteine protease X63717 APO-1 cell surface antigen NM 000043.1 tumor necrosis factor receptor superfamily, member 6
(TNFRSF6)(TNFRSF6)
und/oderand or
zu einem definierten Zeitpunkt tl nach der Verabreichung von Etanercept quantitativ die Expression mindestens eines der aus der folgenden Aufstellung ausgewählten Markergene, oder alleler oder mutierter Formen davon, bestimmt wird:at a defined time t1 after the administration of etanercept, the expression of at least one of the marker genes selected from the following list, or allelic or mutated forms thereof, is determined quantitatively:
Genbank-Nr. FunktionGenbank no. function
D90144 LD78 alpha precursorD90144 LD78 alpha precursor
X52015 interleukin- 1 receptor antagonistX52015 interleukin-1 receptor antagonist
U65590 IL-1 receptor antagonist IL-IRa (IL-IRN)U65590 IL-1 receptor antagonist IL-IRa (IL-IRN)
M24283 rhinovims receptor (HRV)M24283 rhinovims receptor (HRV)
AI535946 vicpro2.D07.rAI535946 vicpro2.D07.r
X04500 prointerleukin 1 betaX04500 prointerleukin 1 beta
M15330, NM_000576.1 interleukin 1-beta (IL1B)M15330, NM_000576.1 interleukin 1-beta (IL1B)
U52112 neural cell adhesion molecule LlU52112 neural cell adhesion molecule Ll
X02910 tumor necrosis factor (TNF-alpha)X02910 tumor necrosis factor (TNF-alpha)
X66893 complementation group C (FA(C))X66893 complementation group C (FA (C))
Genbank-Nr. FunktionGenbank no. function
W29040 55d6W29040 55d6
AF028840 Krüppel-associated box proteinAF028840 cripple-associated box protein
U22029 cytochrome P450 (CYP2A7)U22029 cytochrome P450 (CYP2A7)
W26700 l lh4W26700 l lh4
D29963 CD151D29963 CD151
X74039 urokinase plasminogen activator receptorX74039 urokinase plasminogen activator receptor
X66893 complementation group C (FA(C))X66893 complementation group C (FA (C))
X66362 PCTAIRE-3 for serine/threonine protein kinase U52112 neural cell adhesion molecule LlX66362 PCTAIRE-3 for serine / threonine protein kinase U52112 neural cell adhesion molecule Ll
M63835 IgG Fc receptor I geneM63835 IgG Fc receptor I gene
AL080188 DKFZp434A132AL080188 DKFZp434A132
AI435954 th80d08.xlAI435954 th80d08.xl
M24283 major group rhinovims receptor (HRV)M24283 major group rhinovims receptor (HRV)
M11567 angiogenin geneM11567 angiogenic gene
U90313 glutathione-S-transferaseU90313 glutathione-S-transferase
M63193 platelet-derived endothelial cell growth factorM63193 platelet-derived endothelial cell growth factor
X93093 LW geneX93093 LW gene
AF013570 smooth muscle myosin heavy chain SM2AF013570 smooth muscle myosin heavy chain SM2
AI825798 tdl8e08.xlAI825798 tdl8e08.xl
U76702 follistatin-related protein FLRG (FLRG)U76702 follistatin-related protein FLRG (FLRG)
X89059 unknown protein expressed in macrophagesX89059 unknown protein expressed in macrophages
U91616 I kappa B epsilon (IkBe)U91616 I kappa B epsilon (IkBe)
AI525665 PT1.3_04_D06.rAI525665 PT1.3_04_D06.r
M29874 cytochrome P450-IIB (hlTBl)M29874 cytochrome P450-IIB (hlTBl)
M69043 MAD-3M69043 MAD-3
AF055989 Shaw type potassium Channel Kv3.3 (KCNC3)AF055989 Shaw type potassium Channel Kv3.3 (KCNC3)
M14564 cytochrome P450cl7 (steroid 17-alpha-hydroxylaseM14564 cytochrome P450cl7 (steroid 17-alpha-hydroxylase
Y16280 G protein-coupled receptor ETBR-LP-2Y16280 G protein-coupled receptor ETBR-LP-2
M16591 hemopoietic cell protein-tyrosine kinase (HCK)M16591 hemopoietic cell protein-tyrosine kinase (HCK)
AI535946 vicpro2.D07.rAI535946 vicpro2.D07.r
AI148772 qc69h01.xlAI148772 qc69h01.xl
AI832082 tdl2c04.xlAI832082 tdl2c04.xl
D83664 CAAFl (calcium-binding protein in amniotic fluid 1)D83664 CAAFl (calcium-binding protein in amniotic fluid 1)
U22398 Cdk-inhibitor p57KIP2 (KJJP2)U22398 Cdk inhibitor p57KIP2 (KJJP2)
U95090 chromosome 19 cosmid F 19541U95090 chromosome 19 cosmid F 19541
U37143 cytochrome P450 monooxygenase CYP2J2U37143 cytochrome P450 monooxygenase CYP2J2
S81914 radiation-inducible immediate-early geneS81914 radiation-inducible immediate-early gene
NM_003897.1 immediate early response 3 (IER3)NM_003897.1 immediate early response 3 (IER3)
J04130 activation (Act-2) NM_002984.1 small inducible cytokine A4 (homologous to mouse Mip lb)J04130 activation (Act-2) NM_002984.1 small inducible cytokine A4 (homologous to mouse Mip lb)
(SCYA4)(SCYA4)
M59465 tumor necrosis factor alpha inducible protein A20M59465 tumor necrosis factor alpha inducible protein A20
NM_006290.1 tumor necrosis factor, alpha-induced protein 3 (TNFAJP3),NM_006290.1 tumor necrosis factor, alpha-induced protein 3 (TNFAJP3),
L11329 protein tyrosine phosphatase (PAC-1)L11329 protein tyrosine phosphatase (PAC-1)
NM_004418.2 dual specificity phosphatase 2 (DUSP2)NM_004418.2 dual specificity phosphatase 2 (DUSP2)
U09937 urokinase-type plasminogen receptorU09937 urokinase-type plasminogen receptor
U08839.1 urokinase-type plasminogen activator receptorU08839.1 urokinase-type plasminogen activator receptor
L20971 pho sphodiesteraseL20971 phophosphodiesterase
NM_002600.1 phosphodiesterase 4BNM_002600.1 phosphodiesterase 4B
U83981 apoptosis associated protein (GADD34)U83981 apoptosis associated protein (GADD34)
NM_014330.2 growth arrest and DNA-damage-inducible 34 (GADD34),NM_014330.2 growth arrest and DNA-damage-inducible 34 (GADD34),
NM_002983.1 small inducible cytokine A3 (homologous to mouse Mip- la) (SCYA3),NM_002983.1 small inducible cytokine A3 (homologous to mouse Mipla) (SCYA3),
U64197 chemokine exodus-1 NM_004591.1 small inducible cytokine subfamily A (Cys-Cys), memberU64197 chemokine exodus-1 NM_004591.1 small inducible cytokine subfamily A (Cys-Cys), member
20 (SCYA20)20 (SCYA20)
M63835 IgG Fc receptor I L03419.1 Fc-gamma receptor I Bl AL080188 DKFZp434A132 AI825832 ESTM63835 IgG Fc receptor I L03419.1 Fc-gamma receptor I Bl AL080188 DKFZp434A132 AI825832 EST
und/oderand or
zu einem definierten Zeitpunkt t2 nach der ersten Verabreichung von Etanercept quantitativ die Expression mindestens eines der aus der folgenden Aufstellung ausgewählten Markergene, oder alleler oder mutierter Formen davon, bestimmt wird: Genbank-Nr. Funktionat a defined time t2 after the first administration of etanercept, the expression of at least one of the marker genes selected from the following list, or allelic or mutated forms thereof, is determined quantitatively: Genbank no. function
M28130 interleukin 8 (IL8)M28130 interleukin 8 (IL8)
S72043 GEF=growth inhibitory factorS72043 GEF = growth inhibitory factor
X02910 tumor necrosis factor (TNF-alpha)X02910 tumor necrosis factor (TNF-alpha)
M60502 profilaggrinM60502 profilaggrin
D90144 LD78 alpha precursorD90144 LD78 alpha precursor
M15330 interleukin 1-beta (IL1B)M15330 interleukin 1-beta (IL1B)
X04500 prointerleukin 1 betaX04500 prointerleukin 1 beta
M64788 Human GTPase activating protein (rap 1 GAP)M64788 Human GTPase activating protein (rap 1 GAP)
AF150241 CBFAZB10AF150241 CBFAZB10
Genbank-Nr. FunktionGenbank no. function
AB018563 TML1AB018563 TML1
M16938 homeo box c8 proteinM16938 homeo box c8 protein
AF150241 CBFAZB10AF150241 CBFAZB10
M16441 tumor necrosis factor and lymphotoxinM16441 tumor necrosis factor and lymphotoxin
M92843 zinc finger transcriptional regulatorM92843 zinc finger transcriptional regulator
M57703 melanin concentrating hormone (MCH)M57703 melanin concentrating hormone (MCH)
AF010310 p53 induced proteinAF010310 p53 induced protein
M17254 erg2 gene encoding erg2 proteinM17254 erg2 gene encoding erg2 protein
M64788 GTPase activating protein (rap 1 GAP)M64788 GTPase activating protein (rap 1 GAP)
U14755 Lim-Domain Transcription Factor Lim-1U14755 Lim-Domain Transcription Factor Lim-1
X02910 tumor necrosis factor (TNF-alpha)X02910 tumor necrosis factor (TNF-alpha)
S72043 growth inhibitory factorS72043 growth inhibitory factor
Genbank-Nr. FunktionGenbank no. function
AF026547 neurocan (CSPG3)AF026547 neurocan (CSPG3)
M60047 heparin binding protein (HBpl7)M60047 heparin binding protein (HBpl7)
M17017 beta-thromboglobulin-like proteinM17017 beta-thromboglobulin-like protein
X52015 for interleukin- 1 receptor antagonist ZI 1697 HB15X52015 for interleukin-1 receptor antagonist ZI 1697 HB15
M64788 GTPase activating protein (rap 1 GAP)M64788 GTPase activating protein (rap 1 GAP)
AB004904 for STAT induced STAT inhibitor-3AB004904 for STAT induced STAT inhibitor-3
X04500 prointerleukin 1 betaX04500 prointerleukin 1 beta
D90144 LD78 alpha precursorD90144 LD78 alpha precursor
M60502 profilaggrinM60502 profilaggrin
M28130 interleukin 8 (IL8)M28130 interleukin 8 (IL8)
U72649 BTG2U72649 BTG2
NM_006763.1 BTG family, member 2 (BTG2)NM_006763.1 BTG family, member 2 (BTG2)
J04617 elongation factor EF-1 -alphaJ04617 elongation factor EF-1 -alpha
NM_001402.1 eukaryotic translation elongation factor 1 alpha 1 (EEF1A1)NM_001402.1 eukaryotic translation elongation factor 1 alpha 1 (EEF1A1)
M29039 transactivator (jun-B)M29039 transactivator (jun-B)
NM_002229.1 jun B proto-oncogene (JUNB)NM_002229.1 jun B proto-oncogene (JUNB)
NM 005306.1 G protein-coupled receptor 43 (GPR43)NM 005306.1 G protein-coupled receptor 43 (GPR43)
6. Verfahren nach Anspmch 5, wobei der Zeitpunkt tl 3 Tage und der Zeitpunkt t2 6 Tage beträgt.6. Method according to Claim 5, the time t1 being 3 days and the time t2 being 6 days.
7. Verfahren nach einem der vorstehenden Ansprüche, wobei in dem in Anspruch 1 definierten Schritt (a) das/die Expressionsprofιl(e) des betreffenden Markergens/der betreffenden Markergene unter Verwendung von Nukleinsäuresonden, die spezifisch an die betreffenden Markergene hybridisieren, und/oder unter Verwendung von Antiköφem, die spezifisch an die von den betreffenden Markergenen kodierten Proteine binden, bestimmt wird/werden.7. The method according to any one of the preceding claims, wherein in step (a) defined in claim 1, the expression profile (s) of the marker gene / marker genes in question using nucleic acid probes which hybridize specifically to the marker genes in question, and / or is determined using antibodies which specifically bind to the proteins encoded by the marker genes in question.
8. Verfahren nach Anspmch 7, wobei die Nukleinsäuren unter DNA oder RNA ausgewählt werden.8. The method according to claim 7, wherein the nucleic acids are selected from DNA or RNA.
Verfahren nach Anspmch 8, wobei die DNA oder RNA ein Oligonukleotid ist. Method according to Claim 8, wherein the DNA or RNA is an oligonucleotide.
10. Verfahren nach Anspmch 7, wobei der/die Antiköφer unter polyklonalen, mo- noklonalen, chimären oder »Single-chain«-Antiköφem oder funktionellen Fragmenten oder Derivaten derartiger Antiköφer ausgewählt wird/werden.10. The method according to claim 7, wherein the antibody (s) is / are selected from polyclonal, monoclonal, chimeric or “single-chain” antibodies or functional fragments or derivatives of such antibodies.
11. Verfahren nach einem der Ansprüche 7 bis 10, wobei die Nukleinsäuresonde(n) und/oder der/die Antiköφer mit einer nachweisbaren Markierung versehen ist/sind, die ausgewählt wird unter radioaktiven, farbigen, fluoreszierenden, biolumineszierenden, chemilumineszierenden oder phosphoreszierenden Markierungen oder die auf einem Enzym/Substrat-System, einem auf Antiköφem oder funktionellen Fragmenten oder Deriva- ten davon basierenden System oder auf einem Protein- A/Gold-, Protein-G/Gold- oder Avi- din/Streptavidin/Biotin-System beruht.11. The method according to any one of claims 7 to 10, wherein the nucleic acid probe (s) and / or the antibody (s) is / are provided with a detectable label which is selected from radioactive, colored, fluorescent, bioluminescent, chemiluminescent or phosphorescent labels or which is based on an enzyme / substrate system, a system based on antibodies or functional fragments or derivatives thereof or on a protein A / gold, protein G / gold or avidin / streptavidin / biotin system.
12. Biosensorchip, der auf seiner Oberfläche ein adressierbares Muster aus immobilisierten Nukleinsäuren, die spezifisch an die nach Anspmch 1 (d), (e), (e') identifizierten oder an die in Anspmch 5 definierten Markergene hybridisieren, oder ein adressierbares Muster aus immobilisierten Antiköφem, die spezifisch an die von diesen Markergenen kodierten Proteine binden, aufweist.12. A biosensor chip which has an addressable pattern of immobilized nucleic acids on its surface which hybridize specifically to the marker genes identified according to Claim 1 (d), (e), (e ') or to the marker genes defined in Claim 5, or an addressable pattern immobilized antibodies that specifically bind to the proteins encoded by these marker genes.
13. Medizinisches, diagnostisches Gerät oder Kit, das/der einen Biosensorchip nach Anspmch 12 aufweist/umfasst. 13. Medical, diagnostic device or kit that has / comprises a biosensor chip according to Anspmch 12.
PCT/EP2003/005701 2003-05-30 2003-05-30 Method for assessing the response behavior of an individual to antirheumatics WO2004107240A1 (en)

Priority Applications (3)

Application Number Priority Date Filing Date Title
EP03740163A EP1629411A1 (en) 2003-05-30 2003-05-30 Method for assessing the response behavior of an individual to antirheumatics
AU2003304165A AU2003304165A1 (en) 2003-05-30 2003-05-30 Method for assessing the response behavior of an individual to antirheumatics
PCT/EP2003/005701 WO2004107240A1 (en) 2003-05-30 2003-05-30 Method for assessing the response behavior of an individual to antirheumatics

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
PCT/EP2003/005701 WO2004107240A1 (en) 2003-05-30 2003-05-30 Method for assessing the response behavior of an individual to antirheumatics

Publications (1)

Publication Number Publication Date
WO2004107240A1 true WO2004107240A1 (en) 2004-12-09

Family

ID=33483765

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/EP2003/005701 WO2004107240A1 (en) 2003-05-30 2003-05-30 Method for assessing the response behavior of an individual to antirheumatics

Country Status (3)

Country Link
EP (1) EP1629411A1 (en)
AU (1) AU2003304165A1 (en)
WO (1) WO2004107240A1 (en)

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2009150216A1 (en) * 2008-06-12 2009-12-17 INSERM (Institut National de la Santé et de la Recherche Médicale) A method for predicting the response to a treatment with anakinra
WO2009117791A3 (en) * 2008-03-28 2011-04-14 Katholieke Universiteit Leuven Mucosal gene signatures
CN101105841B (en) * 2007-02-12 2011-06-15 浙江大学 Method for constructing gene controlled subnetwork by large scale gene chip expression profile data

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2002012440A2 (en) * 2000-08-07 2002-02-14 Gene Logic, Inc. Identifying drugs for and diagnosis of benign prostatic hyperplasia using gene expression profiles
WO2003021261A2 (en) * 2001-09-06 2003-03-13 Decode Genetics Ehf. Methods for predicting drug sensitivity in patients afflicted with an inflammatory disease
WO2003027633A2 (en) * 2001-09-24 2003-04-03 Gene Logic, Inc. Identifying drugs for and diagnosis of benign prostatic hyperplasia using gene expression profiles
WO2003033744A1 (en) * 2001-10-18 2003-04-24 Trustees Of Princeton University Methods for determining multiple effects of drugs that modulate function of transcription regulatory proteins

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2002012440A2 (en) * 2000-08-07 2002-02-14 Gene Logic, Inc. Identifying drugs for and diagnosis of benign prostatic hyperplasia using gene expression profiles
WO2003021261A2 (en) * 2001-09-06 2003-03-13 Decode Genetics Ehf. Methods for predicting drug sensitivity in patients afflicted with an inflammatory disease
WO2003027633A2 (en) * 2001-09-24 2003-04-03 Gene Logic, Inc. Identifying drugs for and diagnosis of benign prostatic hyperplasia using gene expression profiles
WO2003033744A1 (en) * 2001-10-18 2003-04-24 Trustees Of Princeton University Methods for determining multiple effects of drugs that modulate function of transcription regulatory proteins

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
BAO L ET AL: "Identifying genes related to drug anticancer mechanisms using support vector machine", FEBS LETTERS, ELSEVIER SCIENCE PUBLISHERS, AMSTERDAM, NL, vol. 521, no. 1-3, 19 June 2002 (2002-06-19), pages 109 - 114, XP004362149, ISSN: 0014-5793 *

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101105841B (en) * 2007-02-12 2011-06-15 浙江大学 Method for constructing gene controlled subnetwork by large scale gene chip expression profile data
WO2009117791A3 (en) * 2008-03-28 2011-04-14 Katholieke Universiteit Leuven Mucosal gene signatures
WO2009150216A1 (en) * 2008-06-12 2009-12-17 INSERM (Institut National de la Santé et de la Recherche Médicale) A method for predicting the response to a treatment with anakinra

Also Published As

Publication number Publication date
AU2003304165A1 (en) 2005-01-21
EP1629411A1 (en) 2006-03-01

Similar Documents

Publication Publication Date Title
JP5684724B2 (en) Serum markers to predict clinical response to anti-TNFα antibodies in patients with ankylosing spondylitis
US20200400682A1 (en) Biomarkers and Methods for Measuring and Monitoring Inflammatory Disease Activity
Park et al. Assessment and diagnostic relevance of novel serum biomarkers for early decision of ST-elevation myocardial infarction
US9122777B2 (en) Method for determining coronary artery disease risk
JP7177868B2 (en) Biomarkers and methods for assessing disease activity in psoriatic arthritis
CN101796197A (en) The biomarker of prediction anti-TNF responsiveness or non-responsiveness
US20210208139A1 (en) Biomarkers and methods for assessing response to inflammatory disease therapy withdrawal
WO2015191423A1 (en) Biomarkers and methods for assessing response to inflammatory disease therapy
Mariani et al. Precision nephrology identified tumor necrosis factor activation variability in minimal change disease and focal segmental glomerulosclerosis
CA2943821A1 (en) Biomarkers and methods for measuring and monitoring juvenile idiopathic arthritis activity
JP2019511924A (en) Predicting the therapeutic response of inflammatory conditions
CA3071597A1 (en) Adjusted multi-biomarker disease activity score for inflammatory disease assessment
Gu et al. Identification of RGS1 as a candidate biomarker for undifferentiated spondylarthritis by genome‐wide expression profiling and real‐time polymerase chain reaction
WO2004107240A1 (en) Method for assessing the response behavior of an individual to antirheumatics
US20060115826A1 (en) Gene expression profiling for identification monitoring and treatment of multiple sclerosis
EP2069533A2 (en) Gene expression profiling for identification, monitoring and treatment of multiple sclerosis
US20080070243A1 (en) Gene expression profiling for identification, monitoring and treatment of multiple sclerosis
WO2006138561A2 (en) Gene expression profiling for identification and monitoring of multiple sclerosis
AU2007254206A1 (en) Assessment of effect of an agent on a human biological condition using rodent gene expression panels
US20190302112A1 (en) Methods and systems using c4 gene copy number and cell-bound complement activation products for identification of lupus and pre-lupus
BE1030423B1 (en) Application of biomarkers for the diagnosis and treatment of pulmonary hypertension (PH)
Odia Longitudinal transcriptomic profiling of whole blood during tuberculosis treatment
EP3446127A2 (en) Biomarkers and methods for assessing response to inflammatory disease therapy
Czarnewski et al. Conserved transcriptomic profile between mouse and human colitis allows temporal dynamic visualization of IBD-risk genes and unsupervised patient stratification
WO2022212890A1 (en) Companion diagnostic and therapies for dysregulated host response

Legal Events

Date Code Title Description
AK Designated states

Kind code of ref document: A1

Designated state(s): AE AG AL AM AT AU AZ BA BB BG BR BY BZ CA CH CN CO CR CU CZ DE DK DM DZ EC EE ES FI GB GD GE GH GM HR HU ID IL IN IS JP KE KG KP KR KZ LC LK LR LS LT LU LV MA MD MG MK MN MW MX MZ NI NO NZ OM PH PL PT RO RU SC SD SE SG SK SL TJ TM TN TR TT TZ UA UG US UZ VC VN YU ZA ZM ZW

AL Designated countries for regional patents

Kind code of ref document: A1

Designated state(s): GH GM KE LS MW MZ SD SL SZ TZ UG ZM ZW AM AZ BY KG KZ MD RU TJ TM AT BE BG CH CY CZ DE DK EE ES FI FR GB GR HU IE IT LU MC NL PT RO SE SI SK TR BF BJ CF CG CI CM GA GN GQ GW ML MR NE SN TD TG

DFPE Request for preliminary examination filed prior to expiration of 19th month from priority date (pct application filed before 20040101)
121 Ep: the epo has been informed by wipo that ep was designated in this application
WWE Wipo information: entry into national phase

Ref document number: 2003740163

Country of ref document: EP

WWP Wipo information: published in national office

Ref document number: 2003740163

Country of ref document: EP

NENP Non-entry into the national phase

Ref country code: JP