WO2012066536A2 - Transcriptome signatures discriminate etanercept-treated rheumatoid arthritis (ra) patients according to their response or refractory status - Google Patents

Transcriptome signatures discriminate etanercept-treated rheumatoid arthritis (ra) patients according to their response or refractory status Download PDF

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Publication number
WO2012066536A2
WO2012066536A2 PCT/IL2011/000878 IL2011000878W WO2012066536A2 WO 2012066536 A2 WO2012066536 A2 WO 2012066536A2 IL 2011000878 W IL2011000878 W IL 2011000878W WO 2012066536 A2 WO2012066536 A2 WO 2012066536A2
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etanercept
tnf
responder
genes
treated
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PCT/IL2011/000878
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French (fr)
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WO2012066536A3 (en
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Philippe Benech
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Prediguard
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    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C12Q1/6883—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/158—Expression markers

Definitions

  • the present Invention relates to the field of medical diagnostics, and more particularly to the field of prognosis of rheumatoid arthritis in patients following an etanercept treatment or more generally an anti-Tumor Necrosis Factor (TNF) therapy.
  • TNF anti-Tumor Necrosis Factor
  • an object of the present invention is to provide a method to discriminate responders from non responders among RA patients following an etanercept treatment or an anti-TNF therapy.
  • the present invention relates to a method for determining whether an etanercept- or anti-TNF-treated rheumatoid arthritis (RA) patient is an etanercept/anti-TNF responder (R) or an etanercept/anti-TNF non- responder (NR), said method comprising:
  • RNAs from a blood sample obtained from an etanercept- or anti-TNF-treated RA patient;
  • RNA at least one of the following analysis: 1) determining whether a specific gene or set of genes is upregulated when compared to RNAs extracted from an untreated patient;
  • the specific gene in (ii.l) is PTGDS.
  • the specific gene in (ii.2) is PTGDS.
  • the specific set of genes in (ii.2) comprises interferon-stimulated genes (ISGs).
  • ISGs are selected from the group consisting of IFIT1, IFI44, IFI44L, HERC5, and FAM20A.
  • the threshold value in (ii.2) is equal or greater than 2.5.
  • RNA issued from etanercept ETA treated RA patients presenting different response status (complete failure or remission). 12 RA patients were selected. 6 RA were in remission after ETA (according to EULAR criteria) and 6 RA were refractory to etanercept (no change in DAS28 month 3). Blood samples were collected and RNAs were extracted using the PAXgene technology (QIAGEN®).
  • Transcriptomic data analyses were made through different approaches using hierarchical clustering (HCL). 2 patients were removed from the study, since it appeared that they presented either an autoimmune disorder or a Lupus.

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  • Chemical & Material Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Proteomics, Peptides & Aminoacids (AREA)
  • Health & Medical Sciences (AREA)
  • Organic Chemistry (AREA)
  • Wood Science & Technology (AREA)
  • Analytical Chemistry (AREA)
  • Zoology (AREA)
  • Genetics & Genomics (AREA)
  • Engineering & Computer Science (AREA)
  • Pathology (AREA)
  • Immunology (AREA)
  • Microbiology (AREA)
  • Molecular Biology (AREA)
  • Biotechnology (AREA)
  • Biophysics (AREA)
  • Physics & Mathematics (AREA)
  • Biochemistry (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • General Engineering & Computer Science (AREA)
  • General Health & Medical Sciences (AREA)
  • Peptides Or Proteins (AREA)
  • Measuring Or Testing Involving Enzymes Or Micro-Organisms (AREA)

Abstract

A method for determining whether an etanercept- or anti-TNF-treated rheumatoid arthritis (RA) patient is an etanercept/anti-TNF responder (R) or an etanercept/anti-TNF non-responder (NR).

Description

TRANS CRIPTOME SIGNATURES DISCRIMINATE ETANERCEFT-TREATED RHEUMATOID ARTHRITIS (RA) PATIENTS ACCORDING TO THEIR RESPONSE OR
REFRACTORY STATUS
Field of the Invention
The present Invention relates to the field of medical diagnostics, and more particularly to the field of prognosis of rheumatoid arthritis in patients following an etanercept treatment or more generally an anti-Tumor Necrosis Factor (TNF) therapy.
Background of the Invention
Rheumatoid arthritis (RA) is a heterologous disorder leading to disability and serious loss of quality of life. Since 30-50% of RA patients do not respond efficiently to current therapy, identification of predictive biomarkers will arm clinicians with tools that will potentially increase the effectiveness of RA treatment. Therefore, an object of the present invention is to provide a method to discriminate responders from non responders among RA patients following an etanercept treatment or an anti-TNF therapy.
Summary of the Invention
The present invention relates to a method for determining whether an etanercept- or anti-TNF-treated rheumatoid arthritis (RA) patient is an etanercept/anti-TNF responder (R) or an etanercept/anti-TNF non- responder (NR), said method comprising:
(i) extracting RNAs from a blood sample obtained from an etanercept- or anti-TNF-treated RA patient;
(ii) performing on said RNA at least one of the following analysis: 1) determining whether a specific gene or set of genes is upregulated when compared to RNAs extracted from an untreated patient;
2) calculating a ratio between the expression values of a specific gene and a specific set of genes, and determining whether said ratio is above a threshold value; and
(iii) classifying said etanercept- or /anti-TNF-treated RA patient as a responder if the result in either (ii.l), (ii.2) or both is positive, and as a non responder (NR) if the results in both (ii.l) and (ii.2) are negative; and
(iv) treating responders with etanercept or an anti-TNF.
In an embodiment of the method of the Invention, the specific gene in (ii.l) is PTGDS.
In a further embodiment of the method of the Invention, the specific gene in (ii.2) is PTGDS.
In another embodiment of the method of the Invention, the specific set of genes in (ii.2) comprises interferon-stimulated genes (ISGs). In a specific embodiment, these ISGs are selected from the group consisting of IFIT1, IFI44, IFI44L, HERC5, and FAM20A.
In yet another embodiment of the method of the Invention, the threshold value in (ii.2) is equal or greater than 2.5.
Detailed description of the Invention
In an attempt to identify useful markers, gene expression arrays were performed using RNA issued from etanercept (ETA) treated RA patients presenting different response status (complete failure or remission). 12 RA patients were selected. 6 RA were in remission after ETA (according to EULAR criteria) and 6 RA were refractory to etanercept (no change in DAS28 month 3). Blood samples were collected and RNAs were extracted using the PAXgene technology (QIAGEN®). Gene expression profiling was performed using Agilent pangenomic 4x44K DNA arrays on the 12 samples issued from RA patients as well as on RNAs extracted from 4 patients with chronic bacterial infection (Mean C-reactive protein=128 mg/dl) to distinguish genes specifically involved in RA inflammation. Transcriptomic data analyses were made through different approaches using hierarchical clustering (HCL). 2 patients were removed from the study, since it appeared that they presented either an autoimmune disorder or a Lupus.
Among genes eliciting a differential modulation in responder (R), non- responder (NR) and control patients, expression of the PTGDS gene was up- regulated in all etanercept responders (6/6) while it was down-re ulated in 3 out of 4 non responders. The protein encoded by this gene is a gluthatione- independent D synthase responsible for the biosynthesis of prostaglandin D (PGD) and J series, which have been shown to exhibit anti-inflammatory and anti-catabolic effects.
Interestingly, while all patients presented a similar expression pattern for TNF-target genes, it was found that expression of several interferon- stimulated genes (ISG) were up-regulated in non responders (3/4) and down- regulated only in 3 out of 6 responders. However, calculating a ratio between the expression values of a specific set of genes for each patient allowed classifying accurately all the responders in the same group. The classification was based on the ratio deduced from the relative gene expression value of PTGDS versus the value found for each of the following ISG: IFIT1; IFI44; IFI44L; HERC5, FAM20A. It was observed that a ratio (PTGDS/ISG) > or equal to 2.5, allows to identify accurately all the responders.

Claims

1. A method for determining whether an etanercept- or anti-TNF-treated rheumatoid arthritis (RA) patient is an etanercept/anti-TNF responder (R) or an etanercept/anti-TNF non-responder (NR), said method comprising:
(i) extracting RNAs from a blood sample obtained from an etanercept- /anti-TNF-treated RA patient;
(ii) performing on said RNA at least one of the following analysis:
1) determining whether a specific gene or set of genes is upregulated when compared to RNAs extracted from an untreated patient ;
2) calculating a ratio between the expression values of a specific gene and a specific set of genes, and determining whether said ratio is above a threshold value;
(iii) classifying said etanercept- or anti-TNF-treated RA patient as a responder if the result in either (ii.l), (ii.2) or both is positive, and as a non responder (NR) if the results in both (ii.l) and (ii.2) are negative; and
(iv) treating responders with etanercept or an anti-TNF.
2. A method according to claim 1, wherein the specific gene in (ii.l) is PTGDS.
3. A method according to claim 1, wherein the specific gene in (ii.2) is PTGDS.
4. A method according to claim 1, wherein the specific set of genes in (ii.2) comprises interferon-stimulated genes (ISGs).
5. A method according to claim 4, wherein the ISGs are selected from the group consisting of IFIT1, IFI44, IFI44L, HERC5, and FAM20A.
6. A method according to claim 1, wherein the threshold value in (ii.2) is equal or greater than 2.5.
PCT/IL2011/000878 2010-11-16 2011-11-15 Transcriptome signatures discriminate etanercept-treated rheumatoid arthritis (ra) patients according to their response or refractory status WO2012066536A2 (en)

Applications Claiming Priority (2)

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US41405310P 2010-11-16 2010-11-16
US61/414,053 2010-11-16

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WO2012066536A2 true WO2012066536A2 (en) 2012-05-24
WO2012066536A3 WO2012066536A3 (en) 2012-07-19

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2021518432A (en) * 2018-03-16 2021-08-02 サイファー メディシン コーポレイション Methods and systems for predicting responsiveness to anti-TNF therapy
US11783913B2 (en) 2019-06-27 2023-10-10 Scipher Medicine Corporation Methods of treating a subject suffering from rheumatoid arthritis with alternative to anti-TNF therapy based in part on a trained machine learning classifier

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20090258848A1 (en) * 2005-12-06 2009-10-15 The Johns Hopkins University Biomarkers for inflammatory bowel disease
US20100279296A1 (en) * 2006-04-07 2010-11-04 Hitachi Chemical Co., Ltd. Enhanced fc receptor-mediated tumor necrosis factor superfamily mrna expression in peripheral blood leukocytes in patients with rheumatoid arthritis

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20090258848A1 (en) * 2005-12-06 2009-10-15 The Johns Hopkins University Biomarkers for inflammatory bowel disease
US20100279296A1 (en) * 2006-04-07 2010-11-04 Hitachi Chemical Co., Ltd. Enhanced fc receptor-mediated tumor necrosis factor superfamily mrna expression in peripheral blood leukocytes in patients with rheumatoid arthritis

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
GALLIGAN ET AL.: 'Distinctive gene expression signatures in rheumatoid arthritis synovial tissue fibroblast cells: correlates with disease activity' GENES ADN IMMUNITY vol. 8, 09 January 2007, pages 480 - 497 *

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2021518432A (en) * 2018-03-16 2021-08-02 サイファー メディシン コーポレイション Methods and systems for predicting responsiveness to anti-TNF therapy
EP3765634A4 (en) * 2018-03-16 2021-12-01 Scipher Medicine Corporation Methods and systems for predicting response to anti-tnf therapies
JP7496324B2 (en) 2018-03-16 2024-06-06 サイファー メディシン コーポレイション Method and system for predicting responsiveness to anti-TNF therapy - Patent Application 20070123333
US11783913B2 (en) 2019-06-27 2023-10-10 Scipher Medicine Corporation Methods of treating a subject suffering from rheumatoid arthritis with alternative to anti-TNF therapy based in part on a trained machine learning classifier
US12062415B2 (en) 2019-06-27 2024-08-13 Scipher Medicine Corporation Methods of treating a subject suffering from rheumatoid arthritis with anti-TNF therapy based in part on a trained machine learning classifier

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