WO2025027127A1 - New prognostic method of kidney failure - Google Patents

Abstract

The present invention relates to the prediction of kidney injury. In this study, the inventors studied the transcriptomic landscape in AAV-GN to find biomarkers for refining diagnosis and/or prognosis, which would help stratify patient risk and tailor therapeutic management. The objectives of this study were to investigate the potential prognostic value and/or pathogenic role of the most relevant genes associated with kidney survival. They identified a 4-gene signature (XRCC6, PRKCD, TEK, and CLU) that predicted kidney survival better than histological-based classifications. Thus, the invention relates to a method for predicting the development of a kidney failure in a patient suffering from an ANCA-associated vasculitis-associated GN (AAV-GN).

Description

NEW PROGNOSTIC METHOD OF KIDNEY FAILURE

FIELD OF THE INVENTION:

The present invention relates to a method for predicting the development of a kidney failure in a patient suffering from an ANCA-associated vasculitis-associated GN (AAV-GN).

BACKGROUND OF THE INVENTION:

Patients with ANCA-associated vasculitides (AAV) have kidney involvement in up to 80% of cases in granulomatosis with polyangiitis (GPA) and 95% of cases in micropolyangeitis (MPA) (1-3). Ranging from a severe and rapidly progressive crescentic glomerulonephritis (GN) to a more smoldering chronic presentation, AAV-associated GN (AAV-GN) has a major impact on renal (2,4) and global (5,6) survival. The prediction of end-stage kidney disease (ESKD) at diagnosis remains inaccurate with the currently available tools (clinical, biological, or histological) (2). A better prediction of ESKD appears as a key step towards individualization of immunosuppressive therapy that would help to adequately balance between vasculitis control, immunosuppression-related side effects and kidney damage.

Major progress has been made in the transcriptomic field allowing identification of key actors in pathogenesis or biomarkers for diagnosis and/or prognosis refinement. Despite these advancements, few transcriptomic studies have been carried out in AAV-GN. Those analysing peripheral blood mononuclear cells (PBMC) or blood neutrophils highlighted associations between some gene expression and disease activity (7-9), responses to therapy (7,10), sometimes enabling relapse risk stratification (11,12). However, one major limitation of blood cell transcriptomic analysis is that it may not adequately capture the molecular mechanisms taking place within the tissues. Tissue transcriptomic analyses in AAV-GN are even more scarce, highlighting the role of the CCL18/CCR8 axis in the local recruitment of phagocytes (13), alterations in cellular metabolism (14) and the importance of matrix dysregulation (15). Other studies focused on non-kidney tissue, such as orbital (16), nasal (17) or subglottic (18) but without significant clinical implications.

To contribute to filling this knowledge gap, the inventors conducted a retrospective and exploratory study in severe AAV-GN patients to investigate the potential prognostic value of kidney transcripts mostly associated with kidney survival. SUMMARY OF THE INVENTION:

In this study, the inventors studied the transcriptomic landscape in AAV-GN to find biomarkers for refining diagnosis and/or prognosis, which would help stratify patient risk and tailor therapeutic management. The objectives of this study were to investigate the potential prognostic value and/or pathogenic role of the most relevant genes associated with kidney survival. They identified a 4-gene signature (XRCC6, PRKCD, TEK, and CLU) that predicted kidney survival better than histological-based classifications. Thus, the invention relates to a method for predicting the development of a kidney failure in a patient suffering from an ANCA- associated vasculitis-associated GN (AAV-GN).

Particularly, the invention is defined by its claims.

DETAILED DESCRIPTION OF THE INVENTION:

The inventors showed in the results section that the expression level of the biomarkers of the invention (XRCC6, PRKCD, TEK and CLU) correlate with the development or the severity of a kidney failure in a patient suffering from an ANCA-associated vasculitis- associated GN (AAV-GN).

Thus, a first aspect of the invention relates to a method for predicting the development of a kidney failure in a patient suffering from an ANCA-associated vasculitis-associated GN (AAV-GN) comprising i) determining in a sample obtained from the patient the expression level of at least one biomarker selected from the group consisting of XRCC6, PRKCD, TEK and CLU ii) comparing the expression level determined at step i) with its predetermined reference value and iii) providing that the patient will develop a kidney failure depending of the variation of the expression level determined at step i) compared to its predetermined reference value.

In a particular embodiment, the invention relates to a method for predicting the survival time of the kidney in a patient suffering from an ANCA-associated vasculitis-associated GN (AAV-GN) comprising i) determining in a sample obtained from the patient the expression level of at least one biomarker selected from the group consisting of XRCC6, PRKCD, TEK and CLU ii) comparing the expression level determined at step i) with its predetermined reference value and ii) providing that the patient will have a bad prognosis depending of the variation of the expression level determined at step i) compared to its predetermined reference value. According to the invention, the method is also useful to improve the kidney survival prediction of a patient suffering from an ANCA-associated vasculitis-associated GN (AAV- GN).

According to the invention, a kidney failure can be a kidney injury.

According to the invention, the term “biomarkers of the invention” denotes the biomarkers XRCC6, PRKCD, TEK and CLU.

According to the invention, “the expression level of the biomarkers of the invention” denotes the expression level of the genes, mRNA or proteins of the biomarkers.

In a particular embodiment, the expression level of the biomarker CLU is determined with at least one biomarker selected from the group consisting of XRCC6, PRKCD, TEK.

In a particular embodiment, the expression level of the biomarkers CLU and XRCC6, or CLU and PRKCD or CLU and TEK or CLU, XRCC6 and PRKCD or CLU, XRCC6, TEK or CLU, PRKCD and TEK or CLU, XRCC6, PRKCD and TEK are determined.

In a particular embodiment and according to the invention, the expression level of the genes of the 4 biomarkers of the invention (XRCC6, PRKCD, TEK and CLU) is determined.

In others words, the expression levels of the genes of the 4 biomarkers are determined in the same time to predict the development of a kidney failure in a patient suffering from an ANCA-associated vasculitis-associated GN (AAV-GN).

In a particular invention, the expression levels of the genes of the 4 biomarkers of the invention are determined in combination with the CKD-EPI to predict the development of a kidney failure in a patient suffering from an ANCA-associated vasculitis-associated GN (AAV- GN).

In a particular embodiment, the expression level of the ARNs of the biomarkers of the invention (XRCC6, PRKCD, TEK and CLU) will be useful for the prediction according to the invention at the time of the renal biopsy at the diagnostic.

In a particular embodiment, the expression level of the protein CLU will be useful for the prediction according to the invention when the sample is blood or serum at the time of the renal biopsy (at AAV-GN diagnosis) at the diagnostic or 6 months later.

According to the methods of the invention, to provide that the patient will develop or not a kidney failure, variation of the level of the biomarkers of the invention will have to be determined. Particularly, an over-expression (or higher expression) of XRCC6 and CLU and down-expression (or lower expression) of PRKCD or TEK compared to their predetermined reference values mean that the patient have an increased risk to develop a kidney failure.

According to the invention, the kidney failure is an acute kidney failure.

According to the invention, the patient suffering from an ANCA-associated vasculitis- associated GN (AAV-GN) has a granulomatosis with polyangiitis (GPA) and/or a micropolyangeitis (MPA) as phenotype.

In a particular embodiment, the determination of the expression level of the biomarker is done at the diagnostic of the ANCA-associated vasculitis-associated GN (AAV-GN) (after the renal biopsy) or 6 months after the diagnostic after an immunosuppressive therapy.

As used herein and according to all aspects of the invention, the term “sample” denotes, blood, peripheral-blood, PBMC, serum, plasma, urine or renal biopsy (or kidney sample).

As used herein, the term “XRCC6” (also known as Ku70) has is general meaning in the art and forms the Ku heterodimer, with Ku80 The Ki heterodimer binds to DNA double-strand break ends and is required for the non-homologous end joining (NHEJ) pathway of DNA repair. It is also required for V(D)J recombination, which utilizes the NHEJ pathway to promote antigen diversity in the mammalian immune system. Its Entrez gene number is: 2547 and its UniProt protein number is: P12956.

As used herein, the term “PRKCD” for “Protein kinase C delta type” has is general meaning in the art and denotes an enzyme that in humans is encoded by the PRKCD gene. Protein kinase C (PKC) is a family of serine- and threonine-specific protein kinases that can be activated by the second messenger diacylglycerol. PKC family members phosphorylate a wide variety of protein targets and are known to be involved in diverse cellular signaling pathways. Its Entrez gene number is: 5580 and its UniProt protein number is: Q05655.

As used herein, the term “TEK” (also known as angiopoietin-1 receptor or CD202B) has is general meaning in the art and denotes a protein that in humans is encoded by the TEK gene. The TEK receptor tyrosine kinase is expressed almost exclusively in endothelial cells in mice, rats, and humans. (TEK is closely related to the TIE receptor tyrosine kinase.). Its Entrez gene number is: 7010 and its UniProt protein number is: Q02763.

As used herein, the term “CLU” for Clusterin has is general meaning in the art and denotes a 75-80 kDa disulfide-linked heterodimeric protein associated with the clearance of cellular debris and apoptosis. In humans, clusterin is encoded by the CLU gene on chromosome 8. CLU is a molecular chaperone responsible for aiding protein folding of secreted proteins, and its three isoforms have been differentially implicated in pro- or antiapoptotic processes. Through this function, CLU is involved in many diseases related to oxidative stress, including neurodegenerative diseases, cancers, inflammatory diseases, and aging. Its Entrez gene number is: 1191 and its UniProt protein number is: P10909.

Measuring the expression level of the biomarkers of the invention can be done by measuring the gene expression level of the biomarkers or by measuring the level of the protein of the biomarkers and can be performed by a variety of techniques well known in the art.

For measuring the expression level of the biomarkers of the invention techniques like ELISA (see below) allowing to measure the level of the soluble proteins are particularly suitable.

Typically, the expression level of a gene may be determined by determining the quantity of mRNA. Methods for determining the quantity of mRNA are well known in the art. For example, the nucleic acid contained in the samples (e.g., cell or tissue prepared from the patient) is first extracted according to standard methods, for example using lytic enzymes or chemical solutions or extracted by nucleic-acid-binding resins following the manufacturer's instructions. The extracted mRNA is then detected by hybridization (e. g., Northern blot analysis, in situ hybridization) and/or amplification (e g., RT-PCR).

Other methods of Amplification include ligase chain reaction (LCR), transcription- mediated amplification (TMA), strand displacement amplification (SDA) and nucleic acid sequence based amplification (NASBA).

Nucleic acids having at least 10 nucleotides and exhibiting sequence complementarity or homology to the mRNA of interest herein find utility as hybridization probes or amplification primers. It is understood that such nucleic acids need not be identical but are typically at least about 80% identical to the homologous region of comparable size, more preferably 85% identical and even more preferably 90-95% identical. In certain embodiments, it will be advantageous to use nucleic acids in combination with appropriate means, such as a detectable label, for detecting hybridization.

Typically, the nucleic acid probes include one or more labels, for example to permit detection of a target nucleic acid molecule using the disclosed probes. In various applications, such as in situ hybridization procedures, a nucleic acid probe includes a label (e.g., a detectable label). A “detectable label” is a molecule or material that can be used to produce a detectable signal that indicates the presence or concentration of the probe (particularly the bound or hybridized probe) in a sample. Thus, a labeled nucleic acid molecule provides an indicator of the presence or concentration of a target nucleic acid sequence (e.g., genomic target nucleic acid sequence) (to which the labeled uniquely specific nucleic acid molecule is bound or hybridized) in a sample. A label associated with one or more nucleic acid molecules (such as a probe generated by the disclosed methods) can be detected either directly or indirectly. A label can be detected by any known or yet to be discovered mechanism including absorption, emission and/ or scattering of a photon (including radio frequency, microwave frequency, infrared frequency, visible frequency and ultra-violet frequency photons). Detectable labels include colored, fluorescent, phosphorescent and luminescent molecules and materials, catalysts (such as enzymes) that convert one substance into another substance to provide a detectable difference (such as by converting a colorless substance into a colored substance or vice versa, or by producing a precipitate or increasing sample turbidity), haptens that can be detected by antibody binding interactions, and paramagnetic and magnetic molecules or materials.

Particular examples of detectable labels include fluorescent molecules (or fluorochromes). Numerous fluorochromes are known to those of skill in the art, and can be selected, for example from Life Technologies (formerly Invitrogen), e.g., see, The Handbook — A Guide to Fluorescent Probes and Labeling Technologies). Examples of particular fluorophores that can be attached (for example, chemically conjugated) to a nucleic acid molecule (such as a uniquely specific binding region) are provided in U.S. Pat. No. 5,866, 366 to Nazarenko et al., such as 4-acetamido-4'-isothiocyanatostilbene-2,2' disulfonic acid, acridine and derivatives such as acridine and acridine isothiocyanate, 5-(2'-aminoethyl) aminonaphthalene- 1 -sulfonic acid (EDANS), 4-amino -N- [3 vinylsulfonyl)phenyl]naphthalimide-3,5 disulfonate (Lucifer Yellow VS), N-(4-anilino-l- naphthyl)maleimide, antllranilamide, Brilliant Yellow, coumarin and derivatives such as coumarin, 7-amino-4-methylcoumarin (AMC, Coumarin 120), 7-amino-4- trifluoromethylcouluarin (Coumarin 151); cyanosine; 4',6-diaminidino-2-phenylindole (DAPI); 5',5"dibromopyrogallol-sulfonephthalein (Bromopyrogallol Red); 7 -diethylamino -3 (4'-isothiocyanatophenyl)-4-methylcoumarin; diethylenetriamine pentaacetate; 4,4'- diisothiocyanatodihydro-stilbene-2,2'-disulfonic acid; 4,4'-diisothiocyanatostilbene-2,2'- disulforlic acid; 5 -[dimethylamino] naphthalene-l-sulfonyl chloride (DNS, dansyl chloride); 4-(4'-dimethylaminophenylazo)benzoic acid (DABCYL); 4-dimethylaminophenylazophenyl- 4'-isothiocyanate (DABITC); eosin and derivatives such as eosin and eosin isothiocyanate; erythrosin and derivatives such as erythrosin B and erythrosin isothiocyanate; ethidium; fluorescein and derivatives such as 5-carboxyfluorescein (FAM), 5-(4,6diclllorotriazin-2- yDaminofluorescein (DTAF), 2'7'dimethoxy-4'5'-dichloro-6-carboxyfluorescein (JOE), fluorescein, fluorescein isothiocyanate (FITC), and QFITC Q(RITC); 2',7'-difluorofluorescein (OREGON GREEN®); fluorescamine; IR144; IR1446; Malachite Green isothiocyanate; 4- methylumbelliferone; ortho cresolphthalein; nitrotyrosine; pararosaniline; Phenol Red; B- phycoerythrin; o-phthaldialdehyde; pyrene and derivatives such as pyrene, pyrene butyrate and succinimidyl 1 -pyrene butyrate; Reactive Red 4 (Cibacron Brilliant Red 3B-A); rhodamine and derivatives such as 6-carboxy-X-rhodamine (ROX), 6-carboxyrhodamine (R6G), lissamine rhodamine B sulfonyl chloride, rhodamine (Rhod), rhodamine B, rhodamine 123, rhodamine X isothiocyanate, rhodamine green, sulforhodamine B, sulforhodamine 101 and sulfonyl chloride derivative of sulforhodamine 101 (Texas Red); N,N,N',N'-tetramethyl-6-carboxyrhodamine (TAMRA); tetramethyl rhodamine; tetramethyl rhodamine isothiocyanate (TRITC); riboflavin; rosolic acid and terbium chelate derivatives. Other suitable fluorophores include thiol-reactive europium chelates which emit at approximately 617 mn (Heyduk and Heyduk, Analyt. Biochem. 248:216-27, 1997; J. Biol. Chem. 274:3315-22, 1999), as well as GFP, LissamineTM, diethylaminocoumarin, fluorescein chlorotriazinyl, naphthofluorescein, 4,7-dichlororhodamine and xanthene (as described in U.S. Pat. No. 5,800,996 to Lee et al.) and derivatives thereof. Other fluorophores known to those skilled in the art can also be used, for example those available from Life Technologies (Invitrogen; Molecular Probes (Eugene, Oreg.)) and including the ALEXA FLUOR® series of dyes (for example, as described in U.S. Pat. Nos. 5,696,157, 6, 130, 101 and 6,716,979), the BODIPY series of dyes (dipyrrometheneboron difluoride dyes, for example as described in U.S. Pat. Nos. 4,774,339, 5,187,288, 5,248,782, 5,274,113, 5,338,854, 5,451,663 and 5,433,896), Cascade Blue (an amine reactive derivative of the sulfonated pyrene described in U.S. Pat. No. 5,132,432) and Marina Blue (U.S. Pat. No. 5,830,912).

In addition to the fluorochromes described above, a fluorescent label can be a fluorescent nanoparticle, such as a semiconductor nanocrystal, e g., a QUANTUM DOTTM (obtained, for example, from Life Technologies (QuantumDot Corp, Invitrogen Nanocrystal Technologies, Eugene, Oreg.); see also, U.S. Pat. Nos. 6,815,064; 6,682,596; and 6,649, 138). Semiconductor nanocrystals are microscopic particles having size-dependent optical and/or electrical properties. When semiconductor nanocrystals are illuminated with a primary energy source, a secondary emission of energy occurs of a frequency that corresponds to the handgap of the semiconductor material used in the semiconductor nanocrystal. This emission can he detected as colored light of a specific wavelength or fluorescence. Semiconductor nanocrystals with different spectral characteristics are described in e.g., U.S. Pat. No. 6,602,671. Semiconductor nanocrystals that can he coupled to a variety of biological molecules (including dNTPs and/or nucleic acids) or substrates by techniques described in, for example, Bruchez et al., Science 281 :20132016, 1998; Chan et al., Science 281:2016-2018, 1998; and U.S. Pat. No. 6,274,323. Formation of semiconductor nanocrystals of various compositions are disclosed in, e.g., U.S. Pat. Nos. 6,927, 069; 6,914,256; 6,855,202; 6,709,929; 6,689,338; 6,500,622; 6,306,736; 6,225,198; 6,207,392; 6,114,038; 6,048,616; 5,990,479; 5,690,807; 5,571,018; 5,505,928; 5,262,357 and in U.S. Patent Publication No. 2003/0165951 as well as PCT Publication No. 99/26299 (published May 27, 1999). Separate populations of semiconductor nanocrystals can he produced that are identifiable based on their different spectral characteristics. For example, semiconductor nanocrystals can he produced that emit light of different colors based on their composition, size or size and composition. For example, quantum dots that emit light at different wavelengths based on size (565 mn, 655 mn, 705 mn, or 800 mn emission wavelengths), which are suitable as fluorescent labels in the probes disclosed herein are available from Life Technologies (Carlshad, Calif).

Additional labels include, for example, radioisotopes (such as 3 H), metal chelates such as DOTA and DPTA chelates of radioactive or paramagnetic metal ions like Gd3+, and liposomes.

Detectable labels that can he used with nucleic acid molecules also include enzymes, for example horseradish peroxidase, alkaline phosphatase, acid phosphatase, glucose oxidase, beta-galactosidase, beta-glucuronidase, or beta-lactamase.

Alternatively, an enzyme can he used in a metallographic detection scheme. For example, silver in situ hybridization (SISH) procedures involve metallographic detection schemes for identification and localization of a hybridized genomic target nucleic acid sequence. Metallographic detection methods include using an enzyme, such as alkaline phosphatase, in combination with a water-soluble metal ion and a redox-inactive substrate of the enzyme. The substrate is converted to a redox-active agent by the enzyme, and the redoxactive agent reduces the metal ion, causing it to form a detectable precipitate. (See, for example, U.S. Patent Application Publication No. 2005/0100976, PCT Publication No. 2005/ 003777 and U.S. Patent Application Publication No. 2004/ 0265922). Metallographic detection methods also include using an oxido-reductase enzyme (such as horseradish peroxidase) along with a water soluble metal ion, an oxidizing agent and a reducing agent, again to form a detectable precipitate. (See, for example, U.S. Pat. No. 6,670,113). Probes made using the disclosed methods can be used for nucleic acid detection, such as ISH procedures (for example, fluorescence in situ hybridization (FISH), chromogenic in situ hybridization (CISH) and silver in situ hybridization (SISH)) or comparative genomic hybridization (CGH).

In situ hybridization (ISH) involves contacting a sample containing target nucleic acid sequence (e.g., genomic target nucleic acid sequence) in the context of a metaphase or interphase chromosome preparation (such as a cell or tissue sample mounted on a slide) with a labeled probe specifically hybridizable or specific for the target nucleic acid sequence (e.g., genomic target nucleic acid sequence). The slides are optionally pretreated, e.g., to remove paraffin or other materials that can interfere with uniform hybridization. The sample and the probe are both treated, for example by heating to denature the double stranded nucleic acids. The probe (formulated in a suitable hybridization buffer) and the sample are combined, under conditions and for sufficient time to permit hybridization to occur (typically to reach equilibrium). The chromosome preparation is washed to remove excess probe, and detection of specific labeling of the chromosome target is performed using standard techniques.

For example, a biotinylated probe can be detected using fluorescein-labeled avidin or avidin-alkaline phosphatase. For fluorochrome detection, the fluorochrome can be detected directly, or the samples can be incubated, for example, with fluorescein isothiocyanate (FITC)- conjugated avidin. Amplification of the FITC signal can be effected, if necessary, by incubation with biotin-conjugated goat antiavidin antibodies, washing and a second incubation with FITC- conjugated avidin. For detection by enzyme activity, samples can be incubated, for example, with streptavidin, washed, incubated with biotin-conjugated alkaline phosphatase, washed again and pre-equilibrated (e.g., in alkaline phosphatase (AP) buffer). For a general description of in situ hybridization procedures, see, e.g., U.S. Pat. No. 4,888,278.

Numerous procedures for FISH, CISH, and SISH are known in the art. For example, procedures for performing FISH are described in U.S. Pat. Nos. 5,447,841; 5,472,842; and 5,427,932; and for example, in Pirlkel et al., Proc. Natl. Acad. Sci. 83:2934-2938, 1986; Pinkel et al., Proc. Natl. Acad. Sci. 85:9138-9142, 1988; and Lichter et al., Proc. Natl. Acad. Sci. 85:9664-9668, 1988. CISH is described in, e g., Tanner et al., Am. .1. Pathol. 157:1467-1472, 2000 and U.S. Pat. No. 6,942,970. Additional detection methods are provided in U.S. Pat. No. 6,280,929.

Numerous reagents and detection schemes can be employed in conjunction with FISH, CISH, and SISH procedures to improve sensitivity, resolution, or other desirable properties. As discussed above probes labeled with fluorophores (including fluorescent dyes and QUANTUM DOTS®) can be directly optically detected when performing FISH. Alternatively, the probe can be labeled with a nonfluorescent molecule, such as a hapten (such as the following nonlimiting examples: biotin, digoxigenin, DNP, and various oxazoles, pyrrazoles, thiazoles, nitroaryls, benzofurazans, triterpenes, ureas, thioureas, rotenones, coumarin, courmarin-based compounds, Podophyllotoxin, Podophyllotoxin-based compounds, and combinations thereof), ligand or other indirectly detectable moiety. Probes labeled with such non-fluorescent molecules (and the target nucleic acid sequences to which they bind) can then be detected by contacting the sample (e.g., the cell or tissue sample to which the probe is bound) with a labeled detection reagent, such as an antibody (or receptor, or other specific binding partner) specific for the chosen hapten or ligand. The detection reagent can be labeled with a fluorophore (e.g., QUANTUM DOT®) or with another indirectly detectable moiety, or can be contacted with one or more additional specific binding agents (e.g., secondary or specific antibodies), which can be labeled with a fluorophore.

In other examples, the probe, or specific binding agent (such as an antibody, e.g., a primary antibody, receptor or other binding agent) is labeled with an enzyme that is capable of converting a Anorogenic or chromogenic composition into a detectable Auorescent, colored or otherwise detectable signal (e.g., as in deposition of detectable metal particles in SISH). As indicated above, the enzyme can be attached directly or indirectly via a linker to the relevant probe or detection reagent. Examples of suitable reagents (e g., binding reagents) and chemistries (e.g., linker and attachment chemistries) are described in U.S. Patent Application Publication Nos. 2006/0246524; 2006/0246523, and 2007/ 01 17153.

It will be appreciated by those of skill in the art that by appropriately selecting labelled probe-specific binding agent pairs, multiplex detection schemes can he produced to facilitate detection of multiple target nucleic acid sequences (e.g., genomic target nucleic acid sequences) in a single assay (e.g., on a single cell or tissue sample or on more than one cell or tissue sample). For example, a first probe that corresponds to a first target sequence can he labelled with a first hapten, such as biotin, while a second probe that corresponds to a second target sequence can be labelled with a second hapten, such as DNP. Following exposure of the sample to the probes, the bound probes can he detected by contacting the sample with a first specific binding agent (in this case avidin labelled with a first Auorophore, for example, a first spectrally distinct QUANTUM DOT®, e.g., that emits at 585 mn) and a second specific binding agent (in this case an anti-DNP antibody, or antibody fragment, labelled with a second Auorophore (for example, a second spectrally distinct QUANTUM DOT®, e.g., that emits at 705 mn). Additional probes/binding agent pairs can he added to the multiplex detection scheme using other spectrally distinct fluorophores. Numerous variations of direct, and indirect (one step, two step or more) can he envisioned, all of which are suitable in the context of the disclosed probes and assays.

Probes typically comprise single-stranded nucleic acids of between 10 to 1000 nucleotides in length, for instance of between 10 and 800, more preferably of between 15 and 700, typically of between 20 and 500. Primers typically are shorter single-stranded nucleic acids, of between 10 to 25 nucleotides in length, designed to perfectly or almost perfectly match a nucleic acid of interest, to be amplified. The probes and primers are “specific” to the nucleic acids they hybridize to, i.e. they preferably hybridize under high stringency hybridization conditions (corresponding to the highest melting temperature Tm, e.g., 50 % formamide, 5x or 6x SCC. SCC is a 0.15 M NaCl, 0.015 M Na-citrate).

The nucleic acid primers or probes used in the above amplification and detection method may be assembled as a kit. Such a kit includes consensus primers and molecular probes. A preferred kit also includes the components necessary to determine if amplification has occurred. The kit may also include, for example, PCR buffers and enzymes; positive control sequences, reaction control primers; and instructions for amplifying and detecting the specific sequences.

In a particular embodiment, the methods of the invention comprise the steps of providing total RNAs extracted from cumulus cells and subjecting the RNAs to amplification and hybridization to specific probes, more particularly by means of a quantitative or semi- quantitative RT-PCR (or q RT-PCR).

In another preferred embodiment, the expression level is determined by DNA chip analysis. Such DNA chip or nucleic acid microarray consists of different nucleic acid probes that are chemically attached to a substrate, which can be a microchip, a glass slide or a microsphere-sized bead. A microchip may be constituted of polymers, plastics, resins, polysaccharides, silica or silica-based materials, carbon, metals, inorganic glasses, or nitrocellulose. Probes comprise nucleic acids such as cDNAs or oligonucleotides that may be about 10 to about 60 base pairs. To determine the expression level, a sample from a test subject, optionally first subjected to a reverse transcription, is labelled and contacted with the microarray in hybridization conditions, leading to the formation of complexes between target nucleic acids that are complementary to probe sequences attached to the microarray surface. The labelled hybridized complexes are then detected and can be quantified or semi-quantified. Labelling may be achieved by various methods, e.g. by using radioactive or fluorescent labelling. Many variants of the microarray hybridization technology are available to the man skilled in the art (see e.g. the review by Hoheisel, Nature Reviews, Genetics, 2006, 7:200-210). Expression level of a gene may be expressed as absolute expression level or normalized expression level. Typically, expression levels are normalized by correcting the absolute expression level of a gene by comparing its expression to the expression of a gene that is not a relevant for determining the cancer stage of the patient, e.g., a housekeeping gene that is constitutively expressed. Suitable genes for normalization include housekeeping genes such as the actin gene ACTB, ribosomal 18S gene, GUSB, PGK1, TFRC, GAPDH, GUSB, TBP and ABLE This normalization allows the comparison of the expression level in one sample, e.g., a patient sample, to another sample, or between samples from different sources.

According to the invention, the level of the proteins of the biomarkers of the invention may also be measured and can be performed by a variety of techniques well known in the art.

Detection of protein concentration in the sample may also be performed by measuring the level of proteins of the biomarkers of the invention. In the present application, the “level of protein” or the “protein level expression” or the “protein concentration” means the quantity or concentration of said protein. In another embodiment, the “level of protein” means the level of the proteins fragments of the biomarkers of the invention.

According to the invention, the protein level of the biomarkers of the invention may be measured a in blood, serum or plasma).

Typically protein concentration may be measured for example by capillary electrophoresis-mass spectroscopy technique (CE-MS) or ELISA performed on the sample

Such methods comprise contacting a sample with a binding partner capable of selectively interacting with proteins present in the sample. The binding partner is generally an antibody that may be polyclonal or monoclonal, preferably monoclonal.

The presence of the protein can be detected using standard electrophoretic and immunodiagnostic techniques, including immunoassays such as competition, direct reaction, or sandwich type assays. Such assays include, but are not limited to, Western blots; agglutination tests; enzyme-labeled and mediated immunoassays, such as ELISAs; biotin/avidin type assays; radioimmunoassays; immunoelectrophoresis; immunoprecipitation, capillary electrophoresismass spectroscopy technique (CE-MS). The reactions generally include revealing labels such as fluorescent, chemioluminescent, radioactive, enzymatic labels or dye molecules, or other methods for detecting the formation of a complex between the antigen and the antibody or antibodies reacted therewith.

The aforementioned assays generally involve separation of unbound protein in a liquid phase from a solid phase support to which antigen-antibody complexes are bound. Solid supports which can be used in the practice of the invention include substrates such as nitrocellulose (e. g., in membrane or microtiter well form); polyvinylchloride (e. g., sheets or microtiter wells); polystyrene latex (e.g., beads or microtiter plates); polyvinylidine fluoride; diazotized paper; nylon membranes; activated beads, magnetically responsive beads, and the like.

More particularly, an ELISA method can be used, wherein the wells of a microtiter plate are coated with a set of antibodies against the proteins to be tested. A sample containing or suspected of containing the marker protein is then added to the coated wells. After a period of incubation sufficient to allow the formation of antibody-antigen complexes, the plate(s) can be washed to remove unbound moieties and a detectably labeled secondary binding molecule is added. The secondary binding molecule is allowed to react with any captured sample marker protein, the plate is washed and the presence of the secondary binding molecule is detected using methods well known in the art.

Methods of the invention may comprise a step consisting of comparing the proteins and fragments concentration in circulating cells with a control value. As used herein, "concentration of protein" refers to an amount or a concentration of a transcription product, for instance the proteins of the biomarkers of the invention. Typically, a level of a protein can be expressed as nanograms per microgram of tissue or nanograms per millilitre of a culture medium, for example. Alternatively, relative units can be employed to describe a concentration. In a particular embodiment, "concentration of proteins" may refer to fragments of the protein of the biomarkers of the invention.

Predetermined reference values used for comparison of the expression levels may comprise “cut-off’ or “threshold” values that may be determined as described herein. Each reference (“cut-off’) value for the biomarkers level of the invention may be predetermined by carrying out a method comprising the steps of a) providing a collection of samples from patients suffering of an AAV-GN; b) determining the level of the biomarkers of the invention for each sample contained in the collection provided at step a); c) ranking the samples according to said level d) classifying said samples in pairs of subsets of increasing, respectively decreasing, number of members ranked according to their expression level, e) providing, for each sample provided at step a), information relating to the actual clinical outcome and/or the development or not of a of a kidney failure for the corresponding patient; f) for each pair of subsets of samples, obtaining a Kaplan Meier percentage of survival curve; g) for each pair of subsets of samples calculating the statistical significance (p value) between both subsets h) selecting as reference value for the level, the value of level for which the p value is the smallest.

For example the expression level of the biomarkers of the invention has been assessed for 100 AAV-GN samples of 100 patients. The 100 samples are ranked according to their expression level. Sample 1 has the best expression level and sample 100 has the worst expression level. A first grouping provides two subsets: on one side sample Nr 1 and on the other side the 99 other samples. The next grouping provides on one side samples 1 and 2 and on the other side the 98 remaining samples etc., until the last grouping: on one side samples 1 to 99 and on the other side sample Nr 100. According to the information relating to the actual clinical outcome for the corresponding pancreatic cancer patient, Kaplan Meier curves are prepared for each of the 99 groups of two subsets. Also for each of the 99 groups, the p value between both subsets was calculated.

The reference value is selected such as the discrimination based on the criterion of the minimum p value is the strongest. In other terms, the expression level corresponding to the boundary between both subsets for which the p value is minimum is considered as the reference value. It should be noted that the reference value is not necessarily the median value of expression levels.

In routine work, the reference value (cut-off value) may be used in the present method to discriminate pancreatic cancer samples and therefore the corresponding patients.

Kaplan-Meier curves of percentage of survival as a function of time are commonly used to measure the fraction of patients living for a certain amount of time after treatment and are well known by the man skilled in the art.

The man skilled in the art also understands that the same technique of assessment of the expression level of a protein should of course be used for obtaining the reference value and thereafter for assessment of the expression level of a protein of a patient subjected to the method of the invention.

Such predetermined reference values of expression level may be determined for any protein defined above.

According to the invention, the reference values for CLU may be 284 ug/mL in the serum 6 months after renal biopsy at diagnosis of AAV-GN. According to the invention, the reference values for CLU may be 216 ug/mL in the serum after renal biopsy at diagnosis of AAV-GN.

A further object of the invention relates to kits for performing the methods of the invention, wherein said kits comprise means for measuring the expression level of of the biomarkers of the invention in the sample obtained from the patient.

The kits may include probes, primers macroarrays or microarrays as above described. For example, the kit may comprise a set of probes as above defined, usually made of DNA, and that may be pre-labelled. Alternatively, probes may be unlabelled and the ingredients for labelling may be included in the kit in separate containers. The kit may further comprise hybridization reagents or other suitably packaged reagents and materials needed for the particular hybridization protocol, including solid-phase matrices, if applicable, and standards. Alternatively the kit of the invention may comprise amplification primers that may be prelabelled or may contain an affinity purification or attachment moiety. The kit may further comprise amplification reagents and also other suitably packaged reagents and materials needed for the particular amplification protocol.

The present invention also relates to XRCC6, PRKCD, TEK and CLU 1 as a biomarkers for outcome of kidney failure.

The patient which will develop a kidney failure, or which will have a bad prognosis according to the invention will benefit of adaptation of their treatment. For example immunosuppressive medication (including steroids, rituximab, cyclophosphamide) or the use of plasma exchange could be adapted

Thus, the invention also relates to a method for treatment of a kidney failure in a patient suffering from an ANCA-associated vasculitis-associated GN (AAV-GN) which will develop a kidney failure or which will have a bad prognosis according to the methods of the invention comprising administering a therapeutically effective amount of an immunosuppressive medication.

The invention will be further illustrated by the following figures and examples. However, these examples and figures should not be interpreted in any way as limiting the scope of the present invention. FIGURES:

Figure 1. Expression of genes associated with kidney survival according to eGFR and pathological phenotype. Expression of PRKCD, TEK, XRCC6, and CLU in AAV-GN patients according to eGFR categorized (A) or continuous (B), and according to Berden classification (C) or Renal Risk Score (D). Y-axis (“value”) refers to the log2 expression of each transcript. In (B), correlation coefficients (p) were computed using the Pearson method. *p < 0.05, **p < 0.01, ***p < 0.001, and ****p < 0.0001.

Figure 2. Kidney survival according to the 4 selected genes. Kidney survival according to the tertiles of gene expression: PRKCD (A), TEK (B), XRCC6 (C), and CLU (D).

Figure 3. Circulating CLU levels. A. CLU (pg/mL) was quantified in serums from 46 healthy controls (HC), 58 AAV-GN patients at diagnosis, and, among the latter, 45 AAV-GN patients at 6 months from diagnosis. Left panel shows the comparison between the 3 groups. Right panel shows individual evolution for the 45 patients with serum available at diagnosis and 6 months later. P-values comparing AAV-GN patients at diagnosis and M6 are computed using paired Mann-Whitney test. B. Relative CLU mRNA expression (normalized on GAPDH and ACTB mRNA expression) in PBMC from 10 healthy controls (HC) and 9 AAV-GN patients at diagnosis. C. Correlation between serum CLU levels and proteinuria (g/g), both at diagnosis. D. Western blot analysis of serum CLU protein. The 9 lowest values of CLU were selected for 9 healthy controls (HC) and 9 AAV-GN patients at diagnosis. First column of each blot is recombinant human CLU (positive control), second column of each blot is the molecular weight ladder (“lad”, molecular weight ladder). *p < 0.05, and ****p < 0.0001.

Figure 4. Association between serum CLU levels, clinical presentation and outcomes. A. Kidney survival according to serum CLU levels at diagnosis (Cluhigh or Clulow refer to CLU level above or under = 216 pg/mL, respectively). B. Kidney survival according to serum CLU levels at M6 (Cluhigh or Clulow refer to CLU level above or under = 284 pg/mL, respectively).

Table 1: Clinical, biological, and pathological presentation at AAV-GN diagnosis.

AAV-GN, ANCA-associated Vasculitis with Glomerulonephritis; BMI, Body Mass

Index; BVAS, Birmingham Vasculitis Activity Score, ENT, Ear Nose Throat; ICU, Intensive

Care Unit; MPO, Myeloperoxidase; PR3, Proteinase 3.

Table 2. Therapeutic management and main outcomes of each cohort.

ESKD, End Stage Kidney Disease; KRT, Kidney Replacement Therapy.

Table 3. C-index values for predicting ESKD, by combining gene expression and clinical variables.

C-index values have been computed for predicting ESKD by combining gene expression signatures (whether alone or the 4 combined). In some instances, these values have been computed in addition to eGFR, Berden histopathological classification, or Renal Risk Score (RRS). Best C-index of each column are in bold.

EXAMPLES:

Material & Methods Selection of patients and controls.

The Maine-Anjou AAV registry (19-21) gathers all successive AAV-GN patients diagnosed between 2000 and 2021 in nephrology departments from 4 hospitals in central western France (Angers University Hospital, Le Mans, Cholet, and Laval General Hospitals). AAV diagnosis was based on the revised 2012 Chapel Hill Consensus Conference (57). AAV- GN diagnosis was assessed on active renal involvement (active urinary sediment with hematuria, proteinuria, and/or impaired renal function). All patients included in the present study underwent a kidney biopsy showing pauci-immune glomerulonephritis confirming AAV- GN diagnosis.

Two types of controls were included in the study: controls for pathological analyses and controls for serological analyses. Control subjects (n = 23) for pathological analyses were adult patients that underwent a kidney biopsy in the University Hospital of Angers between 2011 and 2021. Kidney biopsies were selected as controls if eGFR was > 15 ml/min, and if the diagnosis was minimal change disease or benign nephroangiosclerosis (22-24). Control subjects (n = 46) for blood analyses were healthy blood donor volunteers (Blood collection center, Angers, France; Agreement PLER ANG 2017-01). According to French regulations, blood samples from healthy subjects were anonymized and only limited information on the donors is available (aged 18 to 70; weight >50 kg; no active infectious, neoplastic, hemostatic, or vascular disease).

Ethical issues.

The Maine-Anjou Registry has been declared and authorized by the French Data Protection Authority (CNIL) (agreement number 2018-MR03-02). Participants gave their written informed consent for the use of their biological material through our “Biological Resources Center” (authorization AC-2017-2993). Transcriptomic analyses were performed on kidney biopsy tissue samples obtained during routine diagnostic/therapeutic procedures that provided enough remaining material for further research analyses. In addition, remaining tissue is available for any future additional analysis.

This study was approved by the local ethics committee of the University Hospital of Angers (CE 2021-210).

Data collection and definitions.

Collected data included baseline characteristics of patients, relevant medical history, and organ involvement at AAV diagnosis. Therapeutic management data was also gathered. All kidney biopsies from the four centers were routinely analyzed by a centralized team. Berden’s histopathological classification (58) and Brix’s Renal Risk Score (25) were assessed where applicable. Interstitial fibrosis and tubular atrophy (IF/TA) was quantified by the same pathologist (MCC) on each biopsy.

GPA or MPA diagnosis was determined according to the recently updated ACR/EULAR classification (59,60). End-stage kidney disease (ESKD) was defined as the need for kidney replacement therapy (KRT) for more than 3 months. The primary outcome was kidney survival, i.e., the time to reach ESKD. Estimated glomerular filtration rate (eGFR) was calculated using the Chronic Kidney Disease Epidemiology Collaboration research group equation (CKD-EPI) (61). Relapse was defined as the recurrence or renewed appearance of organ involvement attributable to AAV activity with BVAS increase (>1), and requiring an increase in steroid doses or introduction of a novel immunosuppressive drug (62).

RNA extraction from kidney biopsies.

Kidney biopsies containing at least 7 glomeruli and sufficient tissue after clinical use were selected for subsequent mRNA extraction. Ten-micrometer sections were cut from the formalin-fixed paraffin-embedded (FFPE) blocks and placed in 2 ml microcentrifuge tubes after discarding the first two sections (to avoid collecting RNA that would have been damaged by contact with air). Whole sections, including glomeruli and tubulo-interstitium were used. The number of sections per sample was dependent on the size of the FFPE tissue. Preliminary studies allowed us to determine that 2 to 5 sections were necessary to obtain a minimum of 100 of total RNA.

Extraction of total RNA from FFPE specimens was performed using the High Pure FFPE RNA Isolation Kit (Roche Molecular Diagnostics, Rotkreuz, Switzerland) according to the manufacturer’s instructions. Extraction of RNA was either performed immediately after FFPE sectioning or sections were frozen at -20°C until the extraction was performed.

RNA purity and quantification was assessed by spectrophotometry (NanoDrop 2000; Thermo Fisher Scientific, Waltham, MA): RNA with a 260/280 ratio of 1.7-2.0 as well as a 260/230 ratio in the range of 1.8-2.3 were considered to be of acceptable quality for downstream assays). RNA quality analysis (fragmentation analysis) was performed to assess the integrity of RNA by measuring the proportion of RNA fragments greater than 200 base pairs (DV200) (TapeStation; Agilent, Santa Clara, CA). The resulting data were used to calculate the corrected RNA input for each NanoString hybridization reaction following the manufacturer’s instructions. Only samples with DV200 > 45% were considered for subsequent analyses. RNA was stored at -80 °C prior to NanoString assays. RNA was diluted in nuclease free water at a concentration of 20 ng/pL immediately before the assay, and 100 ng (5 pL) of total RNA per sample were used for quantification. RNA quantification from kidney biopsies.

RNA expression in samples was quantified using NanoString (nCounter Analysis System; NanoString Technologies, Seattle, WA). This method was selected over other targeted multiplex approaches because i) it requires minimal sample preparation, ii) it does not require cDNA conversion or target amplification - both of which are major sources of variation in conventional RT-qPCR approaches, especially for FFPE samples, iii) it has robust user software for gene analysis, and iv) it has repeatedly been shown to correlate well with other microarray platforms (63-65). This technique uses digital color-coded molecular barcode technology to measure gene expression based on the target RNA counts. It enables measurement of selected genes in a single reaction with high sensitivity and linearity across a broad dynamic range (24,64). Samples were run according to the manufacturer’ s protocol on the NanoString Human Autoimmune Profiling panel with a Gene Expression Code Set profiling 770 genes (750 immunology-related human genes + 20 housekeeping genes + 14 internal controls).

Raw gene expression data were analyzed using nSolver Analysis Software v4.0 (NanoString Technologies) with the Advanced Analysis Module v2.0. The software first identifies and checks standard quality controls such as imaging, binding density, and positive controls to ensure the samples were read and reported properly. No samples were flagged for poor RNA quality.

For gene expression data, raw counts were normalized to the positive controls and then log2 transformed. To reduce technical bias, background thresholding was performed using the eight negative controls included within the panel. Genes with counts below a threshold of three standard deviations above the mean background signal were excluded from subsequent analysis. Data normalization was performed on background-thresholded samples using internal positive controls and selected housekeeping genes that were identified using the geNorm algorithm (66). Data were normalized for hybridization and counting efficiency and kept within the range of 0.3-3 (Nanostring recommended). The positive control also normalizes all platform associated sources of variation and avoid any batch effect.

RNA data analysis.

Differential gene expression analyses were performed using nSolver, which employs multiple multivariate linear regression models to identify significantly regulated genes (mixture negative binomial, simplified negative binomial, or log-linear model). Resulting p-values were adjusted using the Benjamini-Hochberg (BH) (q-value) method to control the false discovery rate. Statistically significant, differentially expressed genes were defined as those with expression levels corresponding to a log2 fold change > 1 or < -1 (i.e., a linear fold change > 2 or < -2) and q-value < 0.05 for AAV-GN patients compared to the control group.

Kidney CLU mRNA expression.

To confirm the differentially expressed gene expression observed in Nanostring nCounter analyses for CLU, clusterin mRNA expression on kidney tissue was analyzed by RT- qPCR and was assessed in publicly available microarrays repositories.

First, clusterin mRNA expression (normalized on RPS18 mRNA expression) was assessed in the kidney of 34 patients at AAV-GN diagnosis (and 14 controls) by RT-qPCR. Primer sequences are available upon request.

Second, the NCBI GEO and ArrayExpress databases were used to identify microarray datasets of interest. The terms “ANCA” or “rapidly progressive glomerulonephritis”, and “kidney biopsy” were used after selecting studies assessing expression profiling by array in humans. To align with our method, we discarded datasets with manually microdissected glomerular and tubulointerstitial compartments to consider only whole FFPE section transcriptomic analyses. Finally, only one dataset was selected (E-MTAB-1944) (13). It contained RNA profiles from the FFPE kidney biopsies of 30 patients with ANCA-associated crescentic glomerulonephritis and 12 controls (preimplantation kidney biopsies of living or deceased kidney donors) obtained on the Affymetrix GeneChip Human Genome U133 Plus 2.0 platform.

Quantification of CLU and of other soluble inflammatory molecules.

Clusterin levels were assessed in sera from AAV-GN patients gathered in our biobank (at diagnosis, n = 58, and 6 months after diagnosis, n = 45) and from 46 healthy controls (HC) by ELISA assay (Quantikine Elisa Human Clusterin; R&D Systems, Minneapolis, MN). C5a and IL-6 levels were assessed using magnetic Luminex assay kit (R&D Systems) with a Luminex 200 analyzer and Bio-Plex Manager software v6, as described by the manufacturer.

Western blotting analysis.

The clusterin status in the plasma of patients and HC was evaluated using western blotting. Proteins (75 pg/lane) were electophoretically separated on a 4-20% SDS-PAGE gel in non-reducing conditions and transferred to a nitrocellulose membrane 0.2 pm (BioRad, Hercules, CA). After blocking in TBST buffer (20 mM Tris, 50 mM NaCl, 0.1% Tween 20, pH 7.4) containing 5% non-fat dry milk, membranes were incubated for 16 h at 4°C with 0.1 pg/mL of biotinylated goat polyclonal anti-human CLU antibody (R&D Systems). After washing, membranes were incubated with HRP-conjugated anti-goat IgG Ab (Diaclone, Besan^on, France). Bounds antibodies were detected using enhanced chemiluminescence (Thermo Fisher Scientific).

CLU mRNA expression in PBMC by qPCR.

After isolation of peripheral blood mononuclear cells (PBMC) using standard densitygradient centrifugation on lymphocyte separation medium (Eurobio, Courtaboeuf, France), clusterin mRNA expression (normalized on GAPDH and ACTB mRNA expression) was assessed in the PBMC of 9 patients at AAV-GN diagnosis by RT-qPCR. Primer sequences are available upon request.

Statistical analysis.

Continuous variables were described using median values [1st - 3rd quartile]; categorical variables were described using effective and percentage values. Data were compared using the Mann-Whitney U test for continuous variables (with paired test when applicable) or the %² test (or Fisher’s exact test if necessary) for categorical variables. Correlations between continuous variables were assessed using Pearson’s correlation coefficient. Kaplan Meyer analysis was performed for estimating kidney survival, and survival curves were compared with a log-rank test.

Transcriptomic data were analyzed using the pheatmap Bioconductor package using default parameters for unsupervised hierarchical cluster analyses (dendrograms built with Ward.D2 method (67)) with log2 transcript count data centered and scaled by genes. We employed two strategies to identify genes associated with kidney failure (prognostic signature). First, we performed standard Cox proportional hazards regression analysis. Genes associated with kidney survival and with an adjusted p-value (q-value, Benjamini -Hochberg method of estimating false discovery rate) < 0.05 were selected and included in a multivariate model. Second, we performed a least absolute shrinkage and selection operator (LASSO) Ll-penalized Cox proportional hazards regression analysis (68). This penalization was optimized using leave one-out cross-validation and bootstrap sampling (1000 repetitions) to avoid overfitting and to account for sampling variability. The final step of selecting variables was then performed by minimizing the Akaike information criterion (AIC). The variance inflation factor was computed to check the absence of collinearity against dependent variables. Corresponding hazard ratios (HR) with 95% confidence intervals (CI) are reported.

Several performance indices were used for the assessment of the predictive value of each model: i) Harell’s C statistic or global C-index (69), ii) time-dependent area under the curve (AUC) (70), and iii) time-dependent Brier score (71). AUC and C-index measures the discrimination of a model; it ranges from 0 (no discriminatory power) to 1 (perfect discriminatory power). The Brier score assesses prediction error; it ranges from 0 (no prediction error) to 1 (high prediction error).

No imputation of missing data was performed. Statistical analyses were performed using R software v4.0 with the following packages: survival, surminer, pec, timeROC, pheatmap, glmnet, FactoMineR, factoextra, stepAIC, and performance. All tests were two-sided, and a p- value below 0.05 was considered statistically significant.

Results

Baseline characteristics of the AAV-GN cohort and controls.

Among the 197 patients from the Maine-Anjou Registry, 97 patients with biopsy-proven AAV-GN had available kidney tissue for transcriptomic analyses. There was a majority of MPA patients (66%) and the median estimated glomerular filtration rate (eGFR) at diagnosis was 18 [10-38] ml/min. Table 1 and Table 2 present the baseline characteristics of patients, their therapeutic management and main outcomes. After a median follow-up of 55 [28-95] months, 28 (29%) patients reached ESKD.

Identification of a clustering of patients associated with kidney survival.

When considering all analyzed RNA, hierarchical clustering of patients identified three groups, with one tending to have a better kidney survival than the 2 others (p = 0.066, data not shown). We carried the same analyses considering only DEG (n = 123, data not shown) between AAV-GN and controls. Again, we identified a specific cluster within AAV-GN patients that exhibited significantly better kidney survival (p = 0.034, data not shown).

Identification of a molecular signature associated with kidney survival.

Based on these findings, we then focused on the identification of a molecular signature (a gene set) associated with kidney survival and sought to identify which genes carried the most weight in renal prognosis. First, a univariate Cox regression analysis identified 20 RNA associated with kidney survival (q-value < 0.05, data not shown). These 20 genes were then considered in a multivariate Cox regression analysis, in addition to eGFR at diagnosis to correct for the effect of the kidney function on gene selection. After step-by-step backward analysis, four genes were selected: XRCC6, PRKCD, TEK, and CLU (data not shown). Second, we performed LAS SO-penalized Cox regression analysis with robust internal validation to select the genes mostly associated with kidney survival. eGFR at diagnosis was added to the 750 genes before starting the LASSO procedure. Third, six genes exceeded eGFR selection frequency for ESKD prediction and were selected (data not shown). After step-by-step backward selection, the same four genes described above were selected (data not shown). In multivariate analysis, these four genes remained associated with renal survival when keeping eGFR in the models (data not shown).

Hierarchical clustering of AAV-GN patients based on these four genes identified three clusters (data not shown) strongly associated with renal survival (p < 0.01, data not shown). Whether adjusted with eGFR alone or in combination with pathological analyses (Renal Risk Score or Berden classification), these clusters remained strongly associated with renal survival (p < 0.05, data not shown).

Performance of the molecular signature for kidney survival prediction.

To evaluate the prognosis performance of this molecular signature, alone or combined with known predictors, we evaluated the global C-index, time dependent AUC, and time dependent Brier score. When considered alone, CLU had the best C-index (0.73) compared to the three other genes of interest. CLU was the gene that added most to the prognosis performance of eGFR at diagnosis. The 4-gene signature, whether considered alone (C-index = 0.87) or combined (C-index ranging from 0.87 to 0.88), performed better than known predictors alone (C-index ranging from 0.66 to 0.84) for kidney survival prediction (Table 3). We found similar results when evaluating time dependent AUC and Brier scores: whether considered alone or combined with known predictors, the signature had the best prognosis performance over time, especially beyond 12 months alone (data not shown). Lastly, over time, 4-gene, 3- gene, or 2-gene combinations had better kidney survival prediction performance with CLU than without (data not shown).

Globally, these results demonstrated moderate performance of pathological classification for predicting kidney survival, especially when compared to eGFR at diagnosis alone, as previously reported [personnal data, submitted]. Importantly, we identified a set of 4 genes that improved performance for the prognosis evaluation, with sustained performance over time. In contrast, performance decreased for eGFR and pathological classification for late occurring outcomes.

Genes included in the kidney prognostic signature (PKRCD, TEK. XRCC6. and CLU).

In comparison to controls, AAV-GN patients had significantly lower expression of TEK and higher expression of CLU. XRCC6 was only slightly upregulated in AAV-GN patients, while PKRCD was not significantly differentially expressed (data not shown). None of the gene was differentially expressed between GPA and MPA subgroups (data not shown).

Interestingly, among the 4 genes, CLU was the most strongly associated with eGFR at diagnosis (p < 0.0001, Figure 1A p = -0.5, p < 0.0001, Figure IB). It was also the most associated with glomerular involvement (p = 0.0088, Figure 1C), especially with the percentage of normal glomeruli (higher expression of CLU in patients with lower percentage of normal glomeruli; p = -0.50, p < 0.0001, Data not shown). CLU and XRCC6 were the most strongly associated with the Renal Risk Score (p = 0.0012 and 0.00013, respectively Figure ID XRCC6 was the only transcript associated with IF/TA (p = 0.0049, data not shown).

The expression of the four genes was gradually associated with kidney survival, with higher expression of PKRCD and TEK, and lower expression of XRCC6 and CLU being associated with better survival (Figures 2A-2D). After adjustment for eGFR at diagnosis, PKRCD, TEK, XRCC6, and CLU were still associated with kidney survival: HR = 0.12 [0.03- 0.53], p = 0.005; HR 0.28 [0.11-0.71], p = 0.007; HR = 76.1 [3.31-1748], p = 0.007; HR = 3.42 [1.20-9.77], p = 0.022, respectively.

Collectively, these results confirmed the value of the 4 genes expression for risk stratification in AAV-GN. Lastly, the CLU gene’s expression appears to be the most correlated with glomerular involvement, renal function at diagnosis, and renal survival. We could confirm the overexpression of CLU in AAV-GN tissue compared to control subjects: i) in a subset of our samples, by RT-qPCR (p = 0.0036, data not shown), ii) in a publicly available repository (13), which also confirmed an upregulated expression of CLU, in microarrays analysis (p = 0.0078, data not shown).

Circulating clusterin levels in AAV-GN patients.

We next quantified CLU in the serums of AAV-GN patients at diagnosis (n = 58), at 6 months (n = 45) and in 46 healthy controls (HC). At AAV-GN diagnosis, CLU levels were lower compared to HC (203 [186-261] vs. 247 [227-276] pg/mL, p < 0.0001). After six months, CLU levels were increased (279 [249-293] pg/mL) whether compared to levels at diagnosis (p < 0.0001), or when compared to HC (p = 0.014) (Figure 3A).

Lastly, we investigated the causes of this reduced serum expression of CLU. We found that PBMC isolated from ANCA-GN patients expressed higher levels of CLU mRNA than PBMC isolated from HC (p < 0.0001, Figure 3B). CLU levels did not correlate with the proteinuria (p = 0.11, p = 0.40, Figure 3C). Western blotting analysis of serum CLU in patients and controls did not reveal differences in molecular mass of CLU, making it unlikely it was degraded during the course of AAV-GN (Figure 3D .

Serum CLU levels did not correlate with clinical, biological (eGFR, proteinuria), or histological parameters. CLU inversely correlated with C5a with higher serum CLU levels being associated with lower C5a levels (at diagnosis, p = -0.25, p = 0.061; at 6 months, -0.30, p = 0.046) (data not shown).

Patients with serum CLU levels below 216 pg/mL (determined as the best threshold for ESKD prediction according to area under ROC curve, hereafter referred to as Clulow) had similar eGFR at diagnosis compared to patients above this threshold (hereafter referred to as Cluhigh group) (14 [8-33] vs. 21 [10-38], p = 0.5, data not shown). Twelve (35%) patients in the Clulow group vs. 3 (12%) in the Cluhigh group reached ESKD over a median follow-up duration of 55 [31-77] months (p = 0.051, data not shown). Because of the low number of patients, we were unable to build a multivariate analysis. However, serum CLU levels, whether at diagnosis or at 6 months, were not correlated with eGFR at diagnosis or at 6 months. As such, we hereafter assessed the association between serum CLU levels and kidney survival as exploratory analyses. At diagnosis, patients in the Clulow group tended to have lower kidney survival compared to patients in the Cluhigh group (p = 0.10, Figure 4A). At 6 months from diagnosis, after exclusion of patients who already reached ESKD, patients with CLU levels below 284 pg/mL (new threshold determined as the best threshold for ESKD prediction beyond 6 months) had worse kidney survival (p = 0.023, Figure 4B). Interestingly, at this time point, CLU levels were gradually associated with renal survival, with higher levels being better, as each quartile was associated with a decreased risk of ESKD (HR= 0.39 [0.16-0.98], p = 0.044).

Conclusion:

In conclusion, transcriptomic analysis of kidney biopsies of AAV-GN identified a restricted set of transcripts that enabled a better kidney survival prediction than histopathological-based classifications. This signature may facilitate designing more effective induction remission therapies for AAV-GN and guide therapeutic approaches in nonresponders.

REFERENCES:

Throughout this application, various references describe the state of the art to which this invention pertains. The disclosures of these references are hereby incorporated by reference into the present disclosure. 1. Jennette JC, Nachman PH. ANCA Glomerulonephritis and Vasculitis. Clin J Am Soc Nephrol 2017; 12( 10) : 1680-91.

2. Moiseev S, Novikov P, Jayne D, Mukhin N. End-stage renal disease in ANCA- associated vasculitis. Nephrol Dial Transplant 2017;32(2):248-53.

3. Sinico RA, Di Toma L, Radice A. Renal involvement in anti -neutrophil cytoplasmic autoantibody associated vasculitis. Autoimmunity Reviews 2013;12(4):477-82.

4. de Joode AAE, Sanders JSF, Stegeman CA. Renal Survival in Proteinase 3 and Myeloperoxidase ANCA-Associated Systemic Vasculitis. CJASN 2013;8(10): 1709-17.

5. Flossmann O, Berden A, de Groot K, et al. Long-term patient survival in ANCA- associated vasculitis. Ann Rheum Dis 2011;70(3):488-94.

6. Berti A, Cornec-Le Gall E, Comec D, et al. Incidence, prevalence, mortality and chronic renal damage of anti-neutrophil cytoplasmic antibody-associated glomerulonephritis in a 20-year population-based cohort. Nephrol Dial Transplant 2019;34(9):1508-17.

7. Grayson PC, Carmona-Rivera C, Xu L, et al. Neutrophil-Related Gene Expression and Low-Density Granulocytes Associated With Disease Activity and Response to Treatment in Antineutrophil Cytoplasmic Antibody -Associated Vasculitis. Arthritis Rheumatol 2015;67(7): 1922-32.

8. Alcorta DA, Barnes DA, Dooley MA, et al. Leukocyte gene expression signatures in antineutrophil cytoplasmic autoantibody and lupus glomerulonephritis. Kidney International 2007;72(7):853-64.

9. Yanaoka H, Nagafuchi Y, Hanata N, et al. Identifying the most influential gene expression profile in distinguishing ANCA-associated vasculitis from healthy controls. Journal of Autoimmunity 2021; 119: 102617.

10. Ishizu A, Tomaru U, Masuda S, et al. Prediction of response to remission induction therapy by gene expression profiling of peripheral blood in Japanese patients with microscopic polyangiitis. Arthritis Res Ther 2017;19(l): 117.

11. McKinney EF, Lyons PA, Carr EJ, et al. A CD8+ T cell transcription signature predicts prognosis in autoimmune disease. Nat Med 2010; 16(5):586- 91.

12. McKinney EF, Lee JC, Jayne DRW, Lyons PA, Smith KGC. T-cell exhaustion, co-stimulation and clinical outcome in autoimmunity and infection. Nature 2015;523(7562):612-6.

13. Brix SR, Stege G, Disteldorf E, et al. CC Chemokine Ligand 18 in ANCA- Associated Crescentic GN. JASN 2015;26(9):2105-17. 14. Grayson PC, Eddy S, Taroni JN, et al. Metabolic pathways and immunometabolism in rare kidney diseases. Ann Rheum Dis 2018;annrheumdis-2017-212935.

15. Faypal CA, Oszwald A, Feilen T, et al. An adapted passive model of anti-MPO dependent crescentic glomerulonephritis reveals matrix dysregulation and is amenable to modulation by CXCR4 inhibition. Matrix Biology 2022;106:12-33.

16. Rosenbaum JT, Choi D, Wilson DJ, et al. Orbital pseudotumor can be a localized form of granulomatosis with polyangiitis as revealed by gene expression profiling. Experimental and Molecular Pathology 2015;99(2):271-8.

17. Grayson PC, Steiling K, Platt M, et al. Brief Report: Defining the Nasal Transcriptome in Granulomatosis With Polyangiitis (Wegener’s): NASAL TRANSCRIPTOME IN GPA. Arthritis & Rheumatology 2015;67(8):2233-9.

18. Zhang C, Wang S, Moura MC, et al. RNA Sequencing of Idiopathic Subglottic Stenosis Tissues Uncovers Putative Profibrotic Mechanisms and Identifies a Prognostic Biomarker. The American loumal of Pathology 2022;S0002944022002139.

19. Wacrenier S, Riou J, Jourdain P, et al. Lymphopenia at diagnosis of ANCA- vasculitis with renal involvement is correlated with severity and renal prognosis. Nephrology Dialysis Transplantation 2021;gfabl58.

20. Jourdain P, Brilland B, Medhioub O, et al. Incidence and Temporal Trend in Risk Factors of Severe Infections in ANCA-Glomerulonephritis Patients. Kidney International Reports 2021;6(4): 1161-5.

21. Samoreau C, Piccoli GB, Martin C, et al. Association between kinetic of AntiNeutrophil Cytoplasmic Antibody (ANCA), renal survival and relapse risk in ANCA- glomerulonephritis. Nephrology Dialysis Transplantation 2022;gfac240.

22. Hodgin JB, Borczuk AC, Nasr SH, et al. A Molecular Profile of Focal Segmental Glomerulosclerosis from Formalin-Fixed, Paraffin-Embedded Tissue. The American Journal of Pathology 2010; 177(4): 1674-86.

23. Berthier CC, Zhang H, Sehin M, et al. Enhanced Expression of Janus Kinase- Signal Transducer and Activator of Transcription Pathway Members in Human Diabetic Nephropathy. Diabetes 2009;58(2):469-77.

24. Mejia- Vilet JM, Parikh SV, Song H, et al. Immune gene expression in kidney biopsies of lupus nephritis patients at diagnosis and at renal flare. Nephrology Dialysis Transplantation 2019 ; 34(7) : 1197-206.

25. Brix SR, Noriega M, Tennstedt P, et al. Development and validation of a renal risk score in ANCA-associated glomerulonephritis. Kidney International 2018;94(6): 1177- 88. 26. Salzer E, Santos-Valente E, Keller B, Warnatz K, Boztug K. Protein Kinase C 5: a Gatekeeper of Immune Homeostasis. J Clin Immunol 2016;36(7):631-40.

27. Kuehn HS, Niemela JE, Rangel-Santos A, et al. Loss-of-function of the protein kinase C 8 (PKC6) causes a B-cell lymphoproliferative syndrome in humans. Blood 2013;121(16):3117-25.

28. Miyamoto A, Nakayama K, Imaki H, et al. Increased proliferation of B cells and auto-immunity in mice lacking protein kinase Cdelta. Nature 2002;416(6883):865-9.

29. Belot A, Kasher PR, Trotter EW, et al. Protein kinase c8 deficiency causes mendelian systemic lupus erythematosus with B cell-defective apoptosis and hyperproliferation. Arthritis Rheum 2013;65(8):2161-71.

30. Walker JR, Corpina RA, Goldberg J. Structure of the Ku heterodimer bound to DNA and its implications for double-strand break repair. Nature 2001;412(6847):607-14.

31. Sui H, Hao M, Chang W, Imamichi T. The Role of Ku70 as a Cytosolic DNA Sensor in Innate Immunity and Beyond. Frontiers in Cellular and Infection Microbiology [Internet] 2021 [cited 2022 Oct 15]; 11. Available from: https://www.frontiersin.org/articles/10.3389/fcimb.2021.761983

32. Parikh SM. Angiopoietins and Tie2 in vascular inflammation. Curr Opin Hematol 2017;24(5):432-8.

33 Wilson MR, Easterbrook- Smith SB. Clusterin is a secreted mammalian chaperone. Trends Biochem Sci 2000;25(3):95-8.

34. Rizzi F, Coletta M, Bettuzzi S. Chapter 2: Clusterin (CLU): From one gene and two transcripts to many proteins. Adv Cancer Res 2009;104:9-23.

35. Murphy BF, Kirszbaum L, Walker ID, d’ Apice AJ. SP-40,40, a newly identified normal human serum protein found in the SC5b-9 complex of complement and in the immune deposits in glomerulonephritis. J Clin Invest 1988;81(6): 1858-64.

36. Wilson MR, Easterbrook- Smith SB. Clusterin binds by a multivalent mechanism to the Fc and Fab regions of IgG. Biochimica et Biophy sica Acta (BBA) - Protein Structure and Molecular Enzymology 1992; 1159(3) : 319-26.

37. Cunin P, Beauvillain C, Miot C, et al. Clusterin facilitates apoptotic cell clearance and prevents apoptotic cell-induced autoimmune responses. Cell Death Dis 2016;7:e2215.

38. Yerbury JJ, Poon S, Meehan S, et al. The extracellular chaperone clusterin influences amyloid formation and toxicity by interacting with prefibrillar structures. FASEB J 2007;21(10):2312-22. 39. Wehrli P, Charnay Y, Vallet P, et al. Inhibition of post-ischemic brain injury by clusterin overexpression. Nat Med 2001; 7(9): 977-9.

40. Zhou W, Guan Q, Kwan CCH, et al. Loss of clusterin expression worsens renal ischemia-reperfusion injury. Am J Physiol Renal Physiol 2010;298(3):F568-578.

41. Nguan CYC, Guan Q, Gleave ME, Du C. Promotion of cell proliferation by clusterin in the renal tissue repair phase after ischemia-reperfusion injury. Am J Physiol Renal Physiol 2014;306(7):F724-733.

42. Li S, Guan Q, Chen Z, Gleave ME, Nguan CYC, Du C. Reduction of cold ischemia-reperfusion injury by graft-expressing clusterin in heart transplantation. J Heart Lung Transplant 2011;30(7):819-26.

43. De Miguel Z, Khoury N, Betley MJ, et al. Exercise plasma boosts memory and dampens brain inflammation via clusterin. Nature 2021;600(7889):494-9.

44. McLaughlin L, Zhu G, Mistry M, et al. Apolipoprotein J/clusterin limits the severity of murine autoimmune myocarditis. J Clin Invest 2000;106(9):l 105-13.

45. Hogasen K, Mollnes TE, Brandtzaeg P. Low levels of vitronectin and clusterin in acute meningococcal disease are closely associated with formation of the terminalcomplement complex and the vitronectin-thrombin-antithrombin complex. Infect Immun 1994;62(l l):4874-80.

46 DeCoux A, Tian Y, DeLeon-Pennell KY, et al. Plasma Glycoproteomics Reveals Sepsis Outcomes Linked to Distinct Proteins in Common Pathways. Crit Care Med 2015;43(10):2049-58.

47. Garcia-Obregon S, Azkargorta M, Seijas I, et al. Identification of a panel of serum protein markers in early stage of sepsis and its validation in a cohort of patients. J Microbiol Immunol Infect 2018;51 (4):465- 72.

48. Newkirk MM, Apostolakos P, Neville C, Fortin PR. Systemic lupus erythematosus, a disease associated with low levels of clusterin/apoJ, an antiinflammatory protein. J Rheumatol 1999;26(3):597-603.

49. Monach PA, Warner RL, Tomasson G, et al. Serum proteins reflecting inflammation, injury and repair as biomarkers of disease activity in ANCA-associated vasculitis. Ann Rheum Dis 2013 ;72(8): 1342-50.

50. Yoon T, Ahn SS, Pyo JY, Song JJ, Park Y-B, Lee S-W. Serum Clusterin Level Could Reflect the Current Activity of Antineutrophil Cytoplasmic Antibody-Associated Vasculitis. Yonsei Med J 202I;62(ll):1016. 51. Correa-Rotter R, Ibarra-Rubio ME, Schwochau G, et al. Induction of clusterin in tubules of nephrotic rats. J Am Soc Nephrol 1998;9(l):33-7.

52. Sansanwal P, Li L, Sarwal MM. Inhibition of intracellular clusterin attenuates cell death in nephropathic cystinosis. J Am Soc Nephrol 2015;26(3):612-25.

53. Moll S, Menoud PA, French L, et al. Tubular up-regulation of clusterin mRNA in murine lupus-like nephritis. Am J Pathol 1998;152(4):953-62.

54. He J, Dijkstra KL, Bakker K, et al. Glomerular clusterin expression is increased in diabetic nephropathy and protects against oxidative stress-induced apoptosis in podocytes. Sci Rep 2020; 10(1): 14888.

55. Jr S, A A, JI M, Ka O, Wr A, Bf M. Clusterin depletion enhances immune glomerular injury in the isolated perfused kidney. Kidney international [Internet] 1994 [cited 2022 Oct 15];45(3). Available from: https://pubmed.ncbi.nlm.nih.gov/8196284/

56. Brilland B, Garnier A-S, Chevailler A, Jeannin P, Subra J-F, Augusto J-F. Complement alternative pathway in ANCA-associated vasculitis: Two decades from bench to bedside. Autoimmunity Reviews 2020;19(l):102424.

57 Jennette JC, Falk RJ, Bacon PA, et al. 2012 Revised International Chapel Hill Consensus Conference Nomenclature of Vasculitides. Arthritis & Rheumatism 2013;65(l): 1— 11.

58 Berden AE, Ferrario F, Hagen EC, et al. Histopathologic Classification of ANCA-Associated Glomerulonephritis. JASN 2010;21(10): 1628-36.

59. Robson JC, Grayson PC, Ponte C, et al. 2022 American College of Rheumatology /European Alliance of Associations for Rheumatology Classification Criteria for Granulomatosis With Polyangiitis. Arthritis & Rheumatology 2022;74(3):393-9.

60. Suppiah R, Robson JC, Grayson PC, et al. 2022 American College of Rheumatology /European Alliance of Associations for Rheumatology Classification Criteria for Microscopic Polyangiitis. Arthritis & Rheumatology 2022;74(3):400-6.

61. Levey AS, Stevens LA, Schmid CH, et al. A new equation to estimate glomerular filtration rate. Ann Intern Med 2009;150(9):604-12.

62. Monti S, Quinn KA, Christensen R, et al. Use and reporting of outcome measures in randomized trials for anti-neutrophil cytoplasmic antibody-associated vasculitis: a systematic literature review. Seminars in Arthritis and Rheumatism 2020;50(6): 1314-25.

63. Reis PP, Waldron L, Goswami RS, et al. mRNA transcript quantification in archival samples using multiplexed, color-coded probes. BMC Biotechnol 2011; 11:46. 64. Tsang H-F, Xue VW, Koh S-P, Chiu Y-M, Ng LP-W, Wong S-CC. NanoString, a novel digital color-coded barcode technology: current and future applications in molecular diagnostics. Expert Review of Molecular Diagnostics 2017; 17(1):95— 103.

65. Prokopec SD, Watson JD, Waggott DM, et al. Systematic evaluation of mediumthroughput mRNA abundance platforms. RNA 2013 ; 19(1):51—62.

66. Vandesompele J, De Preter K, Pattyn F, et al. Accurate normalization of realtime quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biology 2002;3(7):research0034.1.

67. Murtagh F, Legendre P. Ward’ s Hierarchical Agglomerative Clustering Method: Which Algorithms Implement Ward’s Criterion? J Classif 2014;31(3):274-95.

68. Tibshirani R. The Lasso Method for Variable Selection in the Cox Model. Statistics in Medicine 1997;16(4):385-95.

69 Harrell FE, Lee KL, Mark DB MULTIVARIABLE PROGNOSTIC MODELS: ISSUES IN DEVELOPING MODELS, EVALUATING ASSUMPTIONS AND ADEQUACY, AND MEASURING AND REDUCING ERRORS. Statist Med 1996;15(4):361-87.

70. Blanche P, Dartigues J-F, Jacqmin-Gadda H. Estimating and comparing timedependent areas under receiver operating characteristic curves for censored event times with competing risks Statist Med 2013;32(30):5381-97.

71. van de Wiel MA, Berkhof J, van Wieringen WN. Testing the prediction error difference between 2 predictors. Biostatistics 2009;10(3):550-60.

Claims

CLAIMS;

1. A method for predicting the development of a kidney failure in a patient suffering from an ANCA-associated vasculitis-associated GN (AAV-GN) comprising i) determining in a sample obtained from the patient the expression level of at least one biomarker selected from the group consisting of XRCC6, PRKCD, TEK and CLU ii) comparing the expression level determined at step i) with its predetermined reference value and iii) providing that the patient will develop a kidney failure depending of the variation of the expression level determined at step i) compared to its predetermined reference value.

2. A method for predicting the survival time of the kidney in a patient suffering from an ANCA-associated vasculitis-associated GN (AAV-GN) comprising i) determining in a sample obtained from the patient the expression level of at least one biomarker selected from the group consisting of XRCC6, PRKCD, TEK and CLU ii) comparing the expression level determined at step i) with its predetermined reference value and ii) providing that the patient will have a bad prognosis depending of the variation of the expression level determined at step i) compared to its predetermined reference value

3. The methods according to claims 1 or 2 wherein the expression level of the biomarker CLU is determined with at least one biomarker selected from the group consisting of XRCC6, PRKCD, TEK.

4. The methods according to claims 1 or 2 wherein the expression level of the biomarkers CLU and XRCC6, or CLU and PRKCD or CLU and TEK or CLU, XRCC6 and PRKCD or CLU, XRCC6, TEK or CLU, PRKCD and TEK or CLU, XRCC6, PRKCD and TEK are determined.

5. The methods according to claims 1 or 2 wherein the expression level of the biomarkers XRCC6, PRKCD, TEK and CLU are determined.

6. The methods according to claims 1 to 5 wherein the sample is blood, peripheral-blood, PBMC, serum, plasma, urine or renal biopsy.

7. A method for treatment of a kidney failure in a patient suffering from an ANCA- associated vasculitis-associated GN (AAV-GN) which will develop a kidney failure or which will have a bad prognosis according to the methods of the invention comprising administering a therapeutically effective amount of an immunosuppressive medication.