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WO2004107240A1 - Procede pour estimer le comportement reactionnel d'un individu a des antirhumatismaux - Google Patents

Procede pour estimer le comportement reactionnel d'un individu a des antirhumatismaux Download PDF

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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
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WO
WIPO (PCT)
Prior art keywords
protein
genes
marker genes
responders
gene
Prior art date
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Ceased
Application number
PCT/EP2003/005701
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German (de)
English (en)
Inventor
Hans-Jürgen Thiesen
Jörn KEKOW
Reinhard Guthke
Dirk Koczan
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Individual
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Individual
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Priority to PCT/EP2003/005701 priority Critical patent/WO2004107240A1/fr
Priority to AU2003304165A priority patent/AU2003304165A1/en
Priority to EP03740163A priority patent/EP1629411A1/fr
Publication of WO2004107240A1 publication Critical patent/WO2004107240A1/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • 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.

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Abstract

L'invention concerne un procédé servant à estimer le comportement réactionnel d'un individu à des antirhumatismaux sur la base de profils d'expression génétique.
PCT/EP2003/005701 2003-05-30 2003-05-30 Procede pour estimer le comportement reactionnel d'un individu a des antirhumatismaux Ceased WO2004107240A1 (fr)

Priority Applications (3)

Application Number Priority Date Filing Date Title
PCT/EP2003/005701 WO2004107240A1 (fr) 2003-05-30 2003-05-30 Procede pour estimer le comportement reactionnel d'un individu a des antirhumatismaux
AU2003304165A AU2003304165A1 (en) 2003-05-30 2003-05-30 Method for assessing the response behavior of an individual to antirheumatics
EP03740163A EP1629411A1 (fr) 2003-05-30 2003-05-30 Procede pour estimer le comportement reactionnel d'un individu a des antirhumatismaux

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
PCT/EP2003/005701 WO2004107240A1 (fr) 2003-05-30 2003-05-30 Procede pour estimer le comportement reactionnel d'un individu a des antirhumatismaux

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WO2004107240A1 true WO2004107240A1 (fr) 2004-12-09

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AU (1) AU2003304165A1 (fr)
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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2009150216A1 (fr) * 2008-06-12 2009-12-17 INSERM (Institut National de la Santé et de la Recherche Médicale) Procédé pour prévoir la réponse à un traitement à l'anakinra
WO2009117791A3 (fr) * 2008-03-28 2011-04-14 Katholieke Universiteit Leuven Signatures génétiques des muqueuses
CN101105841B (zh) * 2007-02-12 2011-06-15 浙江大学 由大规模基因芯片表达谱数据构建基因调控亚网络的方法

Citations (4)

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Publication number Priority date Publication date Assignee Title
WO2002012440A2 (fr) * 2000-08-07 2002-02-14 Gene Logic, Inc. Identification de medicaments contre l'hyperplasia prostatique benine et diagnostic de ladite hyperplasia au moyen de profils d'expression genique
WO2003021261A2 (fr) * 2001-09-06 2003-03-13 Decode Genetics Ehf. Procedes pour prevoir la sensibilite a un medicament de patients atteints d'une maladie inflammatoire
WO2003027633A2 (fr) * 2001-09-24 2003-04-03 Gene Logic, Inc. Identification de medicaments et etablissement de diagnostic pour l'hyperplasie prostatique benigne, a base de profils d'expression genique
WO2003033744A1 (fr) * 2001-10-18 2003-04-24 Trustees Of Princeton University Methodes permettant de determiner les multiples effets de medicaments qui modulent la fonction des proteines de regulation de la transcription

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2002012440A2 (fr) * 2000-08-07 2002-02-14 Gene Logic, Inc. Identification de medicaments contre l'hyperplasia prostatique benine et diagnostic de ladite hyperplasia au moyen de profils d'expression genique
WO2003021261A2 (fr) * 2001-09-06 2003-03-13 Decode Genetics Ehf. Procedes pour prevoir la sensibilite a un medicament de patients atteints d'une maladie inflammatoire
WO2003027633A2 (fr) * 2001-09-24 2003-04-03 Gene Logic, Inc. Identification de medicaments et etablissement de diagnostic pour l'hyperplasie prostatique benigne, a base de profils d'expression genique
WO2003033744A1 (fr) * 2001-10-18 2003-04-24 Trustees Of Princeton University Methodes permettant de determiner les multiples effets de medicaments qui modulent la fonction des proteines de regulation de la transcription

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 (zh) * 2007-02-12 2011-06-15 浙江大学 由大规模基因芯片表达谱数据构建基因调控亚网络的方法
WO2009117791A3 (fr) * 2008-03-28 2011-04-14 Katholieke Universiteit Leuven Signatures génétiques des muqueuses
WO2009150216A1 (fr) * 2008-06-12 2009-12-17 INSERM (Institut National de la Santé et de la Recherche Médicale) Procédé pour prévoir la réponse à un traitement à l'anakinra

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

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