Intestinal tissue-adherent microbial signatures predictive of response to Anti-TNF-alpha in crohn's disease

Abstract

The invention relates to methods of predicting a response of an individual suffering from an inflammatory bowel diseases, such as Crohn's disease (CD) to treatment with a Tumor Necrosis Factor alpha inhibitor (TNFα-i). The invention further relates to anti-TNFα therapy, for treating an individual who was predicted to positively respond to said therapy by the methods of the invention, and to an integrin α4β7 blocking agent, or interleukin (IL)-12 and IL-23 blocking agent, for treating an individual who was predicted not to respond to anti-TNFα therapy by the methods of the invention.

Description

[0001] P136534PC00 Title: Intestinal tissue-adherent microbial signatures predictive of response to anti- TNF-alpha in Crohn’s disease FIELD The invention relates to methods for predicting a response to therapy of an individual suffering from an inflammatory bowel diseases, such as Crohn’s disease, especially for treatment with anti-TNFα. BACKGROUND Inflammatory Bowel Disease (IBD) is an immune-mediated chronic inflammatory condition of the gastrointestinal tract and is divided into two clinical subtypes: Crohn’s disease (CD) and Ulcerative Colitis (UC) (Andoh and Nishida, 2023. Digestion 104: 16–23). IBD has a multifactorial disease etiology that is a result of an aberrant immune response towards the microbiome in a genetic susceptible host (Turpin et al., 2018. Inflamm Bowel Dis 24: 1133-48; Wirtz and Neurath, 2007. Adv Drug Deliv Rev 59: 1073-83). This notion is supported by the finding that diverting the fecal stream with surgical interventions in IBD patients, can decrease inflammation in inflamed bowel segments (Burke, 2019. Clin Colon Rectal Surg 32: 273-79). Furthermore, in experimental animal IBD models, intestinal inflammation is more difficult to generate in germ-free conditions (Hernández-Chirlaque et al., 2016. J Crohns Colitis 10: 1324-35). The microbiome of IBD patients is associated with an overall reduced microbial alpha diversity and species richness compared to the microbiome of healthy individuals (Becker et al., 2015. ILAR J 56: 192-204; Frank et al., 2007. Proc Natl Acad Sci U S A 104: 13780- 5). The observed dysbiosis is characterized by increased prevalence of a low cell count Bacteroides 2-like composition, with the bacterial load associating inversely with systemic and intestinal inflammation. Crohn’s disease patients are currently being treated with immunosuppressive medications and biological agents, such as vedolizumab (VDZ), ustekinumab (USTE) or anti-TNFα biologicals (adalimumab (ADA) and infliximab (IFX)) (Torres et al., 2020. J Crohns Colitis 14: 4-22). Unfortunately, on average 40% of patients treated with biological agents will fail to respond or lose their response over time (Peyrin-Biroulet et al., 2019. Clin Gastroenterol Hepatol 17: 838-846.e2; Sands et al., 2004. N Engl J Med 350: 876-85; Papamichael et al., 2015. Inflamm Bowel Dis 21: 182-97; Singh et al., 2016. Inflamm Bowel Dis 22: 2121-6). Currently, clinicians have to decide upon treatments with different modes of action, without a diagnostic test that can predict which therapy is suited for the individual patient. Understanding mechanisms behind treatment resistance and finding biomarkers that predict response to biological treatment would advance the current practice for CD patients (Torres et al., 2020. J Crohns Colitis 14: 4-22). The luminal microbial signature of fecal material have been investigated for their predictive potential in regards to patient response to anti-TNFα (Park et al., 2022. Sci Rep 12: 6359; Sanchis-Artero et al., 2021. Sci Rep 11: 10016; Ventin- Holmberg et al., 2021. J Crohns Colitis 15: 1019-31; Busquets et al., 2015. J Crohns Colitis 9: 899-906; Chen et al., 2022. Front Pharmacol 13: 913720; Kolho et al., 2015. Am J Gastroenterol 110: 921-30; Lewis et al., 2015. Cell Host Microbe 18: 489-500; Ribaldone et al., 2019. J Clin Med 8: 1646; Zhou et al., 2018. mSystems 3: e00188-17; Aden et al., 2019. Gastroenterology 157: 1279-920), VDZ Ananthakrishnan et al., 2017. Cell Host Microbe 21: 603-610) and USTE therapy (Doherty et al., 2018. mBio 9: 24). Nonetheless, these studies focused on microbial profiling of fecal samples and do not provide data on adherent microbiome retrieved from intestinal biopsies. The intestinal tissue-adherent microbiome consists of a bacterial community that directly adheres to the inner lining of the gut and is therefore in close contact with intestinal epithelial cells and the underlying immune cells (Juge, 2022. Biochem Soc Trans 50: 1225-36; Shi et al., 2017. Mil Med Res 4: 14). The adherent microbiome may potentially provide a more stable signature for biomarker purposes, as the fecal composition may be more strongly influenced by daily life style variations such as diet (Dinsmoor et al., 2021. Adv Nutr 12: 1734-50), and positively the stool consistency, a well-established confounder factor of the fecal microbiome. There is thus a need to provide reliable markers that are able to predict response to biological treatment such as VDZ, USTE or anti-TNFα biologicals, for patients suffering from an inflammatory bowel diseases, such as Crohn’s disease and ulcerative colitis, irrespective of daily life style variations and stool consistency. BRIEF DESCRIPTION OF THE INVENTION Here, the ileal and colonic tissue-adherent microbiome in patients suffering from an inflammatory bowel diseases such as Crohn’s disease, before the start of anti-TNFα, VDZ or USTE, was investigated. 16S rRNA gene sequencing was performed on ileal and colonic biopsies taken during endoscopy at baseline in order to predict therapy response employing a machine learning tool. The invention provides a method of predicting a response of an individual suffering from an inflammatory bowel diseases, such as Crohn’s disease (CD) to treatment with a Tumor Necrosis Factor alpha inhibitor (TNF^-i), the method comprising the steps of a) providing a sample of the gastrointestinal microbiome from the individual; b) determining presence or abundance of at least one of Ruminococcus gnavus and Agathobacter in said gastrointestinal sample; c) whereby the presence or higher abundance of R. gnavus, relative to a control, indicates that the individual may not respond to treatment with TNF^-i, while the presence or higher abundance of Agathobacter, relative to a control, indicates that the individual may respond to treatment with TNF^-i. A method of the invention may further comprise determining presence or abundance of Escherichia / Shigella in said gastrointestinal sample, whereby the presence or higher abundance of Escherichia / Shigella, relative to a control, indicates that the individual may not respond to treatment with TNF^-i. A method of the invention may further comprise determining presence or abundance of one or more of Lachnoclostridium, Erysipelotrichaceae UCG-003, Lachnospira pectinoschiza, Subdoligranulum, Lachnoclostridium, Lachnospieraceae UCG-004, Sutterella wadsworhensis, Fusicatenibacter saccharivorans, Erysipelotrichaceae UCG-003, Blautia, Clostridium scindens, Anaerostipes, Agathobacter and Subdoligranulum in said gastrointestinal sample, whereby the presence or higher abundance of Lachnoclostridium, Erysipelotrichaceae UCG-003, Lachnospira pectinoschiza, Subdoligranulum, Lachnoclostridium, Lachnospieraceae UCG-004, Sutterella wadsworhensis and Fusicatenibacter saccharivorans, relative to a control, indicates that the individual may not respond to treatment with TNF^-I, and whereby presence or higher abundance of Erysipelotrichaceae UCG-003, Blautia, Clostridium scindens, Anaerostipes, Agathobacter and Subdoligranulum, relative to a control, indicates that the individual may respond to treatment with TNF^-i. In methods of the invention, presence or abundance of R. gnavus may be determined by one or more of amplified sequence variants (ASV) ASV_3972, ASV_3965, ASV_3724, and ASV_3849, as depicted in Figures 5 and 6, preferably by ASV_3965. In methods of the invention, presence or abundance of Agathobacter may be determined by ASV_2436, as depicted in Figures 5 and 6. In methods of the invention, presence or abundance Escherichia / Shigella may be determined by one or more of ASV_2698, ASV_2690 and ASV_2694, as depicted in Figures 5 and 6, preferably by ASV_2698. In methods of the invention, presence or abundance of one or more of ASV 3972, ASV_3965, ASV_3724, ASV_3849, ASV_2202, ASV_2698, ASV_6014, ASV_4167, ASV_3373, ASV_3375, ASV_3385, ASV_3330, ASV_3577, ASV_3296, ASV_2436, ASV_2690, ASV_2694, ASV_4298, ASV_0479, ASV_5867, ASV_5794, ASV_3793, ASV_3709, ASV_0490, ASV_3387, ASV_3379, and ASV_6033 may be determined, whereby presence of, or higher abundance of, ASV 3972, ASV_3965, ASV_3724, ASV_3849, ASV_2202, ASV_2698, ASV_6014, ASV_4167, ASV_2690, ASV_2694, ASV_4298, ASV_0479, ASV_5867, ASV_5794, and ASV_3793, relative to a control, indicates that the individual may not-respond to treatment with TNF^-i, and whereby presence of, or higher abundance of, ASV_3373, ASV_3375, ASV_3385, ASV_3330, ASV_3577, ASV_3296, ASV_2436, ASV_3709, ASV_0490, ASV_3387, ASV_3379, and ASV_6033, relative to a control, indicates that the individual may respond to treatment with TNF^-i. A preferred method of the invention, comprises determining presence or abundance of ASV_3965, ASV_2698, ASV_3793, ASV_2436, ASV_3330, and ASV_3709, whereby presence of, or higher abundance of, ASV_3965, ASV_2698, and ASV_3793, relative to a control, indicates that the individual may not-respond to treatment with TNF^-i, and whereby presence of, or higher abundance of, ASV_2436, ASV_3330, and ASV_3709, relative to a control, indicates that the individual may respond to treatment with TNF^-i. In methods of the invention, the presence or abundance of said ASVs in said gastrointestinal sample is determined by sequencing, preferably next generation sequencing. In methods of the invention, the sample of the gastrointestinal microbiome from the individual comprises stable intestinal tissue-adherent microbial biomarkers. A sample of the gastrointestinal microbiome from the individual is preferably obtained by ileal and / or colonic biopsy. In methods of the invention, presence or abundance in a colonic biopsy of one or more of ASV 3972, ASV_3965, ASV_3724, and ASV_3849, ASV_2202, ASV_2698, ASV_6014, ASV_4167, ASV_3373, ASV_3375, ASV_3385, ASV_3330, ASV_3577, ASV_3296, ASV_2436, relative to a control, are indicative for a response to TNF^-i, while presence or abundance in an ileal biopsy of ASV_2698, ASV_2690, ASV_2694, ASV_3965, ASV_4298, ASV_0479, ASV_5867, ASV_5794, ASV_3793, ASV_3709, ASV_2436, ASV_0490, ASV_3387, ASV_3379, and ASV_6033, relative to a control, are indicative for a response to TNF^-i. The invention further provides anti-TNFα therapy, for use in a method of treating an individual suffering from an inflammatory bowel diseases, such as Crohn’s disease (CD), whereby said individual has been predicted to positively respond to said therapy by the methods of the invention. Said anti-TNFα therapy preferably comprises infliximab and / or adalimumab. The invention further provides an integrin α₄β₇ blocking agent, or interleukin (IL)-12 and IL-23 blocking agent, for use in a method of treating an individual suffering from an inflammatory bowel diseases, such as Crohn’s disease (CD), whereby said individual has been predicted not to respond to anti-TNFα therapy by the methods of the invention. Said integrin α₄β₇ blocking agent preferably comprises vedolizumab, Said interleukin (IL)-12 and IL-23 blocking agent preferably comprises ustekinumab. LEGENDS TO THE FIGURES Figure 1. Baseline alpha and beta diversity-related indexes of colonic and ileal biopsies from anti-TNFα treated patients. Colonic and ileal biopsies are presented at panels A and B, respectively. Alpha diversity is evaluated with Shannon Index (left side of the panels) and beta diversity with Principal coordinate axis 1 (PCoA1) of the dimensionality reduction approach using Bray-Curtis at ASV level (right side of the panels). NR; non-responder; R: responder. Figure 2. Intestinal tissue-adherent microbiome distinguishes responders from non-responders before the start of treatment with anti-TNFα. Receiver operatic characteristic (ROC) curves illustrate the performance of the XGB-derived models to predict therapy response based on colonic (A) and ileal (C) biopsies. The 15 most informative ASVs of the colonic (B) and ileal (D) biomarker panels ranked based on their relative feature importance. Taxa in red associated with non- response to treatment with anti-TNFα, while taxa in green with therapy response. Figure 3. Normalized relative abundance of the three shared between colonic and ileal biomarker panels to predict response to treatment with anti-TNFα. ASV_3965 Ruminococcus gnavus (A, D) and ASV_2698 Escherichia / Shigella (B, E) were more abundant in NR, while ASV_2436 Agathobacter (C, F) was more abundant in responders to anti-TNFα. NR; non-responder; R: responder. Figure 4. Significant associations between Principal coordinate axis 1 (PCoA1) and the relative abundance of all ASVs annotated to (A, D) the genus Escherichia / Shigella, and (B, E) the species Ruminococcus gnavus, as well as the Shannon Index (C, F). Values of the x axis are log-normalized, but the Spearman’s associations are calculated based on the non-normalized data. Results of the colonic biopsies are shown in panels A, B and C, while the ileal counterparts in D, E and F. NR; non-responder; R: responder. Figure 5. Representative sequences of the Top15 most informative ASVs of the colonic biopsies to distinguish between responders and non-responders to therapy with anti-TNFα (see also Table 2). Figure 6. Representative sequences of the Top15 most informative ASVs of the ileal biopsies to distinguish between responders and non-responders to therapy with anti-TNFα (see also Table 3). DETAILED DESCRIPTION OF THE INVENTION Definitions As is used herein, the term “inflammatory bowel disease (IBD)” refers to a chronic immune‑mediated intestinal inflammatory disorder associated with microbial dysbiosis at multiple sites, particularly the gut. Types of Types of IBD include ulcerative colitis and Crohn's disease. As is used herein, the term “ulcerative colitis” refers to a condition involving inflammation and sores (ulcers) along the lining of the large intestine (colon) and rectum. As is used herein, the term “Crohn’s disease (CD)”, refers to a condition involving inflammation of the lining of the digestive tract, often involving the deeper layers of the digestive tract. Crohn's disease most commonly affects the small intestine. However, it can also affect the large intestine and uncommonly, the upper gastrointestinal tract. As is used herein, the term “Tumor Necrosis Factor-alpha (TNFα)”, refers to a cytokine that has pleiotropic effects on various cell types. TNFα is major regulator of inflammatory responses and is known to be involved in the pathogenesis of some inflammatory and autoimmune diseases. TNFα is initially synthesized as a transmembrane protein, which is processed by a TNF-α-converting enzyme to be released as the soluble TNF-α. Soluble TNF-α may bind type 1 receptors and type 2 receptors to transmit molecular signals for biological functions such as inflammation and cell death. A gene encoding TNFα can be characterized by HUGO Gene Nomenclature Committee (HGNC) accession number 11892; by NCBI gene accession number 7124, and by Ensembl accession number ENSG00000232810. The TNFα can be characterized by UniProtKB / Swiss-Prot accession number P01375. As is used herein, the term “sample” refers to a specimen taken for analysis or testing. As used herein, a sample comprises biomarkers such as microbial biomarkers. Said microbial biomarkers may include a collection of microbes, such as bacteria, fungi, viruses, and their genes, or may include remnants that are derived of said collection of microbes. Said remnants include nucleic acid molecules, such as DNA and RNA, and / or proteins or peptides. As is used herein, the term “control” refers to a sample from one or more individuals, preferably at least 5, more preferably at least 10 individuals, such as between 10-100 individuals that are known to suffer from IBD or, preferably, are known not to suffer from IBD. Said control is representative of the gastrointestinal microbiome from individual not suffering from IBD such as CD. Preferably said individuals are generally healthy and not suffering from any illness. The individuals that are included in the control may be of the same age and sex as the individual of which the level of biomarkers is to be determined. The term control thus refers to the average level of biomarkers in a sufficiently large reference group, such as between 10-100 individuals that are not suffering from IBD. As is used herein, the term “amplified sequence variants (ASV)” refers to DNA sequences recovered from high-throughput analyses of marker genes, following the removal of erroneous sequences generated during amplification and sequencing. ASVs allow to distinguish sequence variation by a single nucleotide change. The use of ASVs includes classifying species, or groups of species, based on DNA sequences, especially on 16S rDNA sequences (Eren et al., 2013. Methods Ecol Evol 4: 1111–1119). As is used herein, the term “sequencing” refers to a sequencing technique, such as a high-throughput sequencing technique, preferably using next-generation sequencing (NGS), to characterize the quantity and / or sequence of a nucleic acid molecule such as DNA in a sample, with or without prior amplification of the nucleic acid molecule. As is used herein, the term “gastrointestinal microbiome”, refers to the microorganisms, including bacteria, archaea, fungi, and viruses, that live in the digestive tracts of an individual. Alternative names include gut microbiota, gut microbiome, and gut flora. Over 99% of the bacteria in the gut are anaerobes, but in the cecum, aerobic bacteria are more prominent. The four dominant bacterial phyla in the human gut are Bacillota (Firmicutes), Bacteroidota, Actinomycetota, and Pseudomonadota (Khanna et al., 2014. Mayo Clinic Proceedings 89: 107–14). Species from the genus Bacteroides constitute about 30% of all bacteria in the gut, suggesting that this genus is especially important in the functioning of the host. As is used herein, the term “a sample of the gastrointestinal microbiome” refers to a specimen comprising biomarkers that are indicative of the gastrointestinal microbiome. Said sample of the gastrointestinal microbiome includes a fecal sample (stool) and a sample comprising intestinal tissue-adherent microbial biomarkers. As is used herein, the term “intestinal tissue-adherent microbial biomarkers” refers to biomarkers retrieved from intestinal biopsies. The intestinal tissue- adherent microbiome consists of a bacterial community that directly adheres to the inner lining of the gut and is therefore in close contact with intestinal epithelial cells and the underlying immune cells. As is used herein, the term “anti-TNFα therapy” refers to anti-TNFα antibodies, such as infliximab (e.g. Remicade, Remsima, Flixabi), adalimumab (e.g. Humira, Imraldi, Amgevita), certolizumab pegol (e.g. Cimzia), and golimumab (e.g. Simponi). The term anti-TNFα therapy is used interchangeably herein with the terms anti-TNFα, anti-TNFα agent, and anti-TNFα biologicals. As is used herein, the term “ integrin α₄β₇ blocking agent” refers to antibodies against integrin α₄β₇, such as vedolizumab (e.g. Entyvio), natalizumab (e.g. Tysabri), and etrolizumab. As is used herein, the term “interleukin (IL)-12 and IL-23 blocking agent” refers to antibodies targeting IL-12 and IL-23, such as ustekinumab (e.g. Stelara), brazikumab, risankizumab, mirikizumab, and guselkumab. Furthermore, the term “interleukin (IL)-12 and IL-23 blocking agent” may include selective IL-23 blocking agents, such as anti-IL-23 antibodies risankizumab (e.g. Skyrizi) and mirikizumab. As used herein, the term “biosimilar of infliximab” refers to a biological product that is highly similar to the reference product infliximab (e.g., commercially available under the name Remicade), notwithstanding minor differences in clinically inactive components. A biosimilar of infliximab demonstrates no clinically meaningful differences in terms of safety, purity, or potency when compared to infliximab. Biosimilars of infliximab are approved following rigorous analytical, non-clinical, and clinical testing as required by regulatory agencies such as the European Medicines Agency (EMA) or the United States Food and Drug Administration (FDA). Examples of infliximab biosimilars include, but are not limited to: CT-P13 (marketed as Inflectra or Remsima, developed by Pfizer / Celltrion), SB2 (marketed as Renflexis, developed by Samsung Bioepis), PF-06438179 (marketed as Ixifi, developed by Pfizer), and ABP 710 (marketed as Avsola, developed by Amgen). For the purposes of this application, the term also encompasses biosimilars approved in other jurisdictions that meet comparable regulatory standards to those of the EMA or FDA. As used herein, the term “biosimilar of adalimumab” refers to a biological product that is highly similar to the reference product adalimumab (e.g., commercially available under the name Humira), with no clinically meaningful differences in terms of safety, purity, or potency. Minor differences in clinically inactive components are permitted, provided they do not impact the therapeutic equivalence to adalimumab. Biosimilars of adalimumab are approved based on extensive analytical, non-clinical, and clinical testing to demonstrate biosimilarity, as required by regulatory agencies such as the European Medicines Agency (EMA) or the United States Food and Drug Administration (FDA). Examples of adalimumab biosimilars include, but are not limited to: ABP 501 (marketed as Amgevita, developed by Amgen), BI 695501 (marketed as Cyltezo, developed by Boehringer Ingelheim), FKB327 (marketed as Hulio, developed by Mylan / Fujifilm Kyowa Kirin Biologics), GP2017 (marketed as Hyrimoz, developed by Sandoz), MSB11022 (marketed as Idacio, developed by Fresenius Kabi), and CT-P17 (marketed as Yuflyma, developed by Celltrion). For the purposes of this application, the term also encompasses biosimilars approved in other jurisdictions that meet comparable regulatory standards to those of the EMA or FDA. Sampling methods A sample from an individual suffering from a IBD such as CD, can be a stool sample or, preferably, a biopsy. In an embodiment, said sample is a sample that is obtained from stool by contacting a stool surface, for example with a stick or a brush, and providing a part of the obtained sample in a test tube or on an absorbent surface, for example a test card. Said test tube preferably comprises a buffer, for example a stool stabilization buffer such as a buffer comprising phosphate-buffered saline and sodium azide. A sample comprising nucleic acid molecules can be freshly prepared at the moment of isolation of the specimen, or it can be prepared from specimen that have been stored, for example at -20°C, until processing for sample preparation. Said sample preferably is a biopsy. Said biopsy can be obtained in numerous ways, as is known to a skilled person, such as by esophagogastroduodenoscopy, colonoscopy, or sigmoidoscopy. Said biopsy preferably is an ileal and / or colonic intestinal biopsy from patients before the start of treatment, for example obtained upon baseline endoscopy. The sample may be collected in any clinically acceptable manner, but is preferably collected and conserved upon isolation such as to preserve at least nucleic acid material, especially DNA. Nucleic acid material can be obtained from a sample immediately upon harvesting, or from a conserved sample. A sample can be conserved by fixation e.g. in formalin and / or by treating the tissue sample with an RNase inhibitor. Preferred conservation methods of a sample include fresh frozen (FF) conservation, for example in dry ice or in liquid nitrogen, and formalin-fixed paraffin-embedded (FFPE) conservation. DNA can be extracted and / or isolated from a sample to purify DNA by using physical and / or chemical methods from a sample separating DNA from cell membranes, proteins, and other cellular components. Methods known in the art include (1) organic extraction (phenol–chloroform method); (2) nonorganic method (salting out and proteinase K treatment); and (3) adsorption method (silica–gel membrane). The use of a DNA isolation technique preferably results in efficient extraction with good quantity and quality of DNA, which is pure and is devoid of contaminants, such as RNA and proteins. Manual methods as well as commercially available kits are available for DNA extraction, including QIAamp DNA Microbiome Kit (Qiagen), PureLink™ Microbiome DNA Purification Kit (Thermo Fisher Scientific), Maxwell 16 tissue Low Elution Volume total DNA purification kit (Promega) and NEBNext® Microbiome DNA Enrichment Kit (New England Biolabs). In addition, optimized bacterial DNA isolation method for microbiome analysis of human tissues have been described (Bruggeling et al., 2021. Microbiol Open 10: e1191). A preferred method of extracting and isolating DNA is provided by the repeated bead beating (method 5; Costea et al., 2017. Nat Biotechnol 35:1069-1076) and DNA isolation by affinity chromatography. For this, mechanical lysis with STAR buffer (Roche, Basel, Switzerland) may be performed using FastPrep beads (BioSPX, Abcoude, The Nether-lands) with three repetitive rounds of 30 s at 6.5 m / s, and with cooling for 30 s on ice in between. Finally, DNA may be obtained with the Maxwell 16 tissue Low Elution Volume total DNA purification kit (Promega, Madison, WI, USA). Following DNA isolation, the isolated nucleic acid by be amplified. Amplification may be performed by any suitable amplification system including, for example, ligase chain reaction (LCR); isothermal ribonucleic acid amplification systems such as nucleic acid sequence-based amplification (NASBA), loop-mediated isothermal amplification (LAMP), helicase-dependent amplification (HDA), recombinase polymerase amplification (RPA) reaction, and nicking enzyme amplification reaction (NEAR); and polymerase chain reaction (PCR). A preferred amplification method is PCR. Suitable commercial PCR kits include QIAamp DNA Microbiome Kit (Qagen), Direct PCR—Phire and Phusion Kits & Master Mixes (ThermoFisher) and Q5® High-Fidelity 2X Master Mix (New England Biolabs). A DNA-dependent DNA polymerase that is used in the amplification reaction preferably is a high fidelity DNA polymerase, such as Q5, Deep Vent, or Phusion (Potapov and Ong, 2017. PlosOne 12: e0181128). Primers that may be used for amplification include primers that amplify a conserved sequences, including universally conserved genes such as non-coding RNAs, and genes encoding proteins required for transcription and translation, GTP-binding elongation factors, methionine aminopeptidase 2, serine hydroxymethyltransferase, and ATP transporters. Said genes encoding proteins required for transcription and translation include RNA polymerase and helicase, ribosomal RNAs, tRNAs and ribosomal proteins. Said primers preferably amplify genomic DNA that encodes ribosomal RNA, including large (23S) ribosomal RNA, 16S ribosomal RNA and 5S rRNA of bacteria, archaebacteria and chloroplasts. Especially preferred are sequences of the gene or gene encoding the 16S rRNA. Said primers preferably are "universal" primers, such as the forward (F) and reverse (R) primer of Table 4. Preferred primers pairs are 341F and 805R. Amplification may be performed in the presence of indexing / barcoding primers by including adapter sequences and said indexing / barcoding primers. Amplification may be performed by a routine amplification reaction, starting with an initial denaturation step at 98°C for 30 s; 25 cycles of denaturation at 98°C for 10 s, annealing at 55°C for 20 s, and elongation at 72° C for 90 s; and an extension step at 72°C for 10 min (Kozich et al., 2013. Appl Environ Microbiol 79: 5112–5120). Methods of analyzing The determination of an expression level of one or more microbial biomarkers may be accomplished by any means known in the art such as quantitative PCR (qPCR), microarray analysis or DNA sequencing (DNA-seq). Preferably, the expression levels of multiple marker genes are assessed simultaneously, for example by multiplex qPCR, microarray analysis or DNA-seq. Microarray analysis involves the use of selected probes that are immobilized on a solid surface, an array. Said probes are able to hybridize to isolated DNA comprising microbial biomarkers. The probes are exposed to labeled DNA comprising microbial biomarkers, or labelled derivates thereof, hybridized, washed, where after the abundance of specific microbial biomarkers or derivates thereof in the sample that are complementary to a probe is determined by determining the amount of label that remains associated to a probe. The probes on a microarray may comprise DNA sequences, RNA sequences, or copolymer sequences of DNA and RNA. The probes may also comprise DNA and / or RNA analogues such as, for example, nucleotide analogues or peptide nucleic acid molecules (PNA), or combinations thereof. The sequences of the probes may be full or partial fragments of genomic DNA. The sequences may also be in vitro synthesized nucleotide sequences, such as synthetic oligonucleotide sequences. Methods for microarray- based analyses are known to a person skilled in the art. High throughput sequencing techniques for sequencing DNA, or DNA-seq, have been developed, including next generation sequencing (NGS) platforms. Said NGS platforms, including Illumina® sequencing, Roche 454 pyrosequencing®, ion torrent and ion proton sequencing, and ABI SOLiD® sequencing, allow sequencing of fragments of DNA in parallel. Bioinformatics analyses are used to piece together these fragments by mapping the individual reads. Each base is sequenced multiple times, providing high depth to deliver accurate data and an insight into unexpected DNA variation. NGS can be used to sequence a complete exome including all or small numbers of individual genes. Pyrosequencing detects the release of inorganic pyrophosphate (PPi) as particular nucleotides are incorporated into the nascent strand (Ronaghi et al., 1996. Analytical Biochemistry 242: 84-9; Ronaghi, 2001. Genome Res 11: 3-11; Ronaghi et al., 1998. Science 281: 363; U.S. Patent No.6,210,891 ; U.S. Patent No. 6,258,568 ; and U.S. Patent No.6,274,320, which are all incorporated herein by reference. In pyrosequencing, released PPi can be detected by being immediately conversion to adenosine triphosphate (ATP) by ATP sulfurylase, and the level of ATP generated is detected via luciferase-produced photons. NGS also includes so called third generation sequencing platforms, for example nanopore sequencing on an Oxford Nanopore Technologies platform, and single-molecule real-time sequencing (SMRT sequencing) on a PacBio platform, with or without prior amplification of the RNA expression products. Further high throughput sequencing techniques include, for example, sequencing-by-synthesis. Sequencing-by-synthesis or cycle sequencing can be accomplished by stepwise addition of nucleotides containing, for example, a cleavable or photobleachable dye label as described, for example, in U.S. Patent No. 7,427,673; U.S. Patent No. 7,414,116; WO 04 / 018497; WO 91 / 06678; WO 07 / 123744; and U.S. Patent No.7,057,026, all of which are incorporated herein by reference. Sequencing techniques also include sequencing by ligation techniques. Such techniques use DNA ligase to incorporate oligonucleotides and identify the incorporation of such oligonucleotides and are inter alia described in U.S. Patent No 6,969,488 ; U.S. Patent No. 6,172,218; and U.S. Patent No.6,306,597. Other sequencing techniques include, for example, fluorescent in situ sequencing (FISSEQ), and Massively Parallel Signature Sequencing (MPSS). The resulting sequencing can be used for classification and identification of microbes. Type strains of 16S rRNA gene sequences for most bacteria and archaea are available on public databases, such as NCBI, EzBioCloud, the Ribosomal Database Project (RDP), SILVA 16S ribosomal database V132 (Quast et al., 2013. Nucleic Acids Res 41: D590-6), IDTaxa (Murali et al., 2018. Microbiome 6: 140) and GreenGenes. In addition, the microbiome analysis package Quantitative Insights Into Microbial Ecology (QIIME) is a microbiome bioinformatics platform. QIIME 2 facilitates comprehensive and fully reproducible microbiome data science, improving accessibility to diverse users by adding multiple user interfaces (available at / / curr-protoc-bioinformatics.qiime2.org / ). Subsequently, eukaryotic and reagent-associated ASVs need to be removed as contaminating ASVs. Reagent associated ASVs may be identified by (a) clear batch effect, between the biological agent cohorts; (b) not reproducible; (c) known contaminant and / or ecologically impossible to be found in tissue-adherent microbiome; and / or (d) found only, or in high relative abundance, in the negative controls. Analysis of the resulting true ASVs for discriminating potential responders to therapy from potential non-responders, may be performed using machine learning models such as logistic regression, linear discriminant analysis, k-nearest neighbors analyses, support vector machines, and extreme gradient boosting (XGB; or XGBoost) algorithm (Reeskamp et al., 2020. EBioMedicine 61: 103079; de Krijger et al., 2022. Front Immunol 13: 840935). XGBoost is an optimized distributed gradient boosting library designed for efficient and scalable training of machine learning models. It is an ensemble learning method that combines the predictions of multiple weak models to produce a stronger prediction. XGBoost is able to handle large datasets and to achieve state-of-the-art performance in many machine learning tasks such as classification and regression. Treatment methods The methods of the invention allow to determine whether an individual suffering from an inflammatory bowel diseases, such as Crohn’s disease (CD), may benefit from anti-TNFα therapy, or not. An individual that has been predicted not to respond to anti-TNFα therapy by the methods according to the invention may be administered any of the standard therapies, including corticosteroids, 5-aminosalicylic compounds (5-ASAs) and combinations thereof. Examples of corticosteroids include prednisolone, methylprednisolone, beclomethasone dipropionate, budesonide, hydrocortisone. Examples of 5-ASAs include sulfasalazine, mesalazine, olsalazine, balsazide. Furthermore, an individual that has been predicted not to respond to anti- TNFα therapy by the methods according to the invention may be administered broad-spectrum antibiotics (e.g. metronidazole, ciprofloxacin), thiopurines (e.g. azathioprine, 6-mercaptopurine), immune-system suppressant (e.g. methotrexate). Furthermore, an individual that has been predicted not to respond to anti- TNFα therapy by the methods according to the invention may be administered Janus kinase 1 (JAK1) selective inhibitors, sphingosine-1-phosphate (S1P) receptor modulators (e.g. ozanimod such as Zeposia). Example of anti- MAdCAM-1 antibody is ontamalimab. Ex-amples of JAK selective inhibitors include filgotinib (e.g. Jyseleca), upadacitinib (e.g. Rinvoq), tofacitinib (e.g. Xeljanz). Furthermore, an individual that has been predicted not to respond to anti- TNFα therapy by the methods according to the invention may undergo faecal microbial transplant (FMT) that aims for microbial restoration. Furthermore, an individual that has been predicted not to respond to anti- TNFα therapy by the methods according to the invention may be administered antibodies against mucosal addressing cell adhesion molecule-1 (MAdCAM-1), In addition, an individual that has been predicted not to respond to anti- TNFα therapy by the methods according to the invention may be administered an integrin α₄β₇ blocking agent, or interleukin (IL)-12 and IL-23 blocking agent, such as vedolizumab or ustekinumab. Vedolizumab may be administered intravenously (e.g. infusion) or subcutaneously. Alternatively, vedolizumab may be administered intravenously during initial stage of treatment, followed by subcutaneous administration. Vedolizumab may be administered at a dose of 80, 100, 200, 300 or 350 mg. Preferably, vedolizumab is administered at a dose of 80 to 120 mg or 280 to 320 mg. More preferably, vedolizumab is administered at a dose of 108 mg or 300 mg. In particular, the dose of 108 mg is recommended for subcutaneous administration, while the dose of 300 mg is recommended for intravenous administration. Vedolizumab may be administered at intervals of 1 to 10 weeks, preferably at intervals of 4 or 8 weeks. During initial stage of treatment (e.g. up to week 6 or 10) vedolizumab may be administered at gradually increasing intervals, for example at weeks 0, 2 and 6. Thereafter vedolizumab may be administered at intervals of 4 or 8 weeks. Administration of vedolizumab in a form of intravenous injection is preferably at interval of every 2 weeks. Ustekinumab may be administered intravenously or subcutaneously. Ustekinumab may be administered at a dose of 70, 80, 90, 100, 110, 120, 130, 140 or 150 mg. Preferably, ustekinumab is administered at a dose of 80 to 100 mg or 120 to 140 mg, more preferably at a dose of 90 mg. Preferably, the first dose of ustekinumab depends on the individual’s weight. For example, for individuals weighing 55 kg or less ustekinumab may be administered at a first dose of 260 mg; for individuals weighing between 56 kg and 85 kg ustekinumab may be administered at a first dose of 390 mg; and for individuals weighing more than 85 kg ustekinumab may be administered at a first dose of 520 mg. Ustekinumab may be administered at intervals of 1 to 10 weeks, preferably at intervals of 6 to 9 weeks, more preferably at intervals of 8 weeks. An individual that has been predicted to respond to anti-TNFα therapy by the methods according to the invention may be administered an anti-TNFα agent, such as infliximab or adalimumab. Infliximab may be administered intravenously, for example as an infusion, injection or combination thereof. Infliximab may be administered at a dose of 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 or 15 mg / kg. Infliximab may be administered at a dose of 2 to 7 mg / kg, or 8 to 12 mg / kg. Preferably, infliximab is administered at a dose of 5 mg / kg or 10 mg / kg. Infliximab in a form of injection may be administered at a dose of 80 to 160 mg, preferably at a dose of 100 to 140 mg, most preferably at a dose of 120 mg. Infliximab may be administered at intervals of 1 to 10 weeks, preferably at intervals of 5 to 9 weeks, more preferably at intervals of 8 weeks. Individuals not previously treated with infliximab may require initial loading, where infliximab is administered at gradually increasing intervals, for example at weeks 0, 2 and 6. Thereafter the maintenance treatment with infliximab may be administered at intervals of 8 weeks. Alternatively, infliximab is first administered as an intravenous infusion followed by intravenous injection. For example, infliximab may be administered via infusion at weeks 0 and 2, followed by intravenous injection of infliximab at week 6 and continued at a set interval such as 2 weeks. Adalimumab may be administered at a dose of 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190 or 200 mg. Adalimumab may be administered at a dose of 10 to 25 mg, 30 to 50 mg, 70 to 90 mg, 150 to 170 mg. Preferably, adalimumab is administered at a dose of 20, 40, 80 or 160 mg. Adalimumab may be administered at intervals of 1 to 8 weeks, preferably at intervals of 4 or 2 weeks, more preferably at intervals of 2 weeks. Adalimumab may be initially administered at a higher dose and then gradually reduced to a lower dose. For example, the starting dose may be 160 mg, 80 mg or 40 mg and then reduced to 80 mg, 40 mg or 20 mg, respectively after 2 weeks. Alternatively, adalimumab may be administered at a starting dose of 160 mg, then at a dose of 80 mg after 2 weeks and later continually at a dose of 40 mg every 2 weeks. Preferably, adalimumab is administered at a starting dose of 80 mg, followed by a dose of 40 mg every 2 weeks. EXAMPLES Example 1 MATERIAL AND METHODS Human clinical samples We collected ileal and / or colonic intestinal biopsies from CD patients, upon baseline endoscopy, who were scheduled to start treatment with anti-TNFα, VDZ or USTE, according to standard medical protocol8. Anti-TNFα treatment included IFX and ADA. Interval intensification was allowed, if needed, at the discretion of the treating physicians. To ensure assessment of mechanistic failures to the biological agents, only patients with measurable drug concentrations at response assessment were selected. Upon baseline endoscopy, mucosal biopsies of either ileal and / or colonic locations were taken using standard biopsy forceps. If possible, paired ileal and colonic biopsies were taken from each individual patient. All patients used bowel preparation before endoscopy consisting of macrogol and electrolytes (Klean- Prep, Norgine BV, Amsterdam, The Netherlands). The assembly of this cohort was approved by the medical ethics committee of the Academic Medical Hospital (METC NL57944.018.16 and NL53989.018.15), and written informed consent was obtained from all subjects prior to sampling. Biopsies were stored in 1.10mL micronic tubes and snap-frozen immediately after sampling in liquid nitrogen. Defining therapy response Response assessment occurred after 26 to 52 weeks of treatment. At response assessment, patients were distinguished as responders (R) or non-responders (NR) based on endoscopic- (≥50% reduction in simple endoscopic disease activity score (SES-CD)), combined with either corticosteroid-free clinical- (≥3 point drop in Harvey Bradshaw Index (HBI) or HBI ≤4 and no systemic steroids), and / or biochemical response (C-reactive protein (CRP) and fecal calprotectin reduction ≥50% or ≤5 g / mL and fecal calprotectin ≤250 µg / g). When endoscopic assessment was not possible, imaging such as ultrasound or magnetic resonance imaging of the abdomen or was used8, 28. 16S rRNA gene sequencing DNA was extracted from the intestinal biopsies. In brief, we used a combination of repeated bead beating (method 5; Costea et al., 2017. Nat Biotechnol 35:1069-1076) and DNA isolation by affinity chromatography. Mechanical lysis with STAR buffer (Roche, Basel, Switzerland) was performed using FastPrep beads (BioSPX, Abcoude, The Netherlands) with three repetitive rounds of 30 s at 6.5 m / s, and with cooling for 30 s on ice in between. Finally, the DNA was obtained with the Maxwell 16 tissue Low Elution Volume total DNA purification kit (Promega, Madison, WI, USA), and DNA concentrations were measured with a Nanodrop spectrophotometer (Thermo Fisher Scientific, Bleiswijk, The Netherlands), and a Qubit fluorometric DNA quantitation method (Thermo Fisher Diagnostics, Nieuwegein, The Netherlands). The DNA was used for the amplification of the bacterial 16S rRNA gene. The 16S rRNA gene amplicons were produced using a PCR procedure targeting the V3–V4 region, and this was carried out at the Microbiota Center Amsterdam (MiCA), The Netherlands. This protocol and amplification program has been published earlier (Haak et al., 2021. mSystems 6: 10.1128 / msystems.01148-20). Microbiota profiling Amplified sequence variants (ASVs) were extracted for each biological sample with a minimum of 4 reads (Callahan et al., 2016. Nat Methods 13: 581-3). Unfiltered reads were mapped against the collective ASV set to establish the relative abundances. Taxonomy was assigned using the IDTaxa (Murali et al., 2018. Microbiome 6: 140) and SILVA 16S ribosomal database V132 (Quast et al., 2013. Nucleic Acids Res 41: D590-6). The representative sequences of each ASV were reference library independent and their identity was double-checked, using blast (Altschul et al., 1990. J Mol Biol 215: 403-10), if they were found to be of interest. Decontamination of low biomass samples Mucosal biopsies are of low bacterial biomass, which allows for potential contamination in various steps throughout the samples collection, storing, and DNA extraction. Therefore, a detailed and careful decontamination approach was employed after the bioinformatics metabarcoding to identify and remove host- (i.e. Eukaryotes), and reagent-associated ASVs. Reagent-associated ASVs were identified based on four distinct independent screening steps; (a) clear batch effect, between the biological agent cohorts; (b) not reproducible, between the colonic and ileal biopsies of the same patient, signal; (c) known contaminant and / or ecologically impossible to be found in tissue-adherent microbiome; and (d) found only, or in high relative abundance, in the negative controls. The potential ASV-candidates were cross-referenced in the 4 distinct screening steps, and respectively removed from the count table. Stability selection of ASVs and extreme gradient boosting The extreme gradient boosting (XGB) algorithm (Reeskamp et al., 2020. EBioMedicine 61: 103079; de Krijger et al., 2022. Front Immunol 13: 840935) was used to distinguish R from NR. Models were deployed to compare R versus NR of the colonic and ileal sub-cohorts separately. ASVs were filtered prior to dimensionality reduction. Per model, the top 250 most abundant ASVs were used as input to the LASSO regression that was employed to define 50 “stability- selected” ASVs, namely hereafter Top50 LASSO-selected, which were further used in XGB stimulations. The same stability selection procedure was used in the colonic and ileal cohorts separately to prevent overfitting (Meinshausen and Bühlmann, 2010. J Royal Statist Soc Series B: Statist Meth 72: 417-473). The XGB methodology was published earlier (Reeskamp et al., 2020. EBioMedicine 61: 103079; de Krijger et al., 2022. Front Immunol 13: 840935; van der Vossen et al., 2023. Microbiome 11: 99). The performance of the different models was estimated via an area under the curve (AUC) of the test dataset to distinguish R from NR. Biomarker discovery analysis was performed using permutation tests in the Top50 LASSO-selected ASVs. All feature importance values were normalized to the highest, per cohort, receiving a relative feature importance value that was used to rank and identify the ASVs with the highest predictive ability. This machine learning pipeline was implemented in python (v3.7.7), using the scikit-learn (v0.23.1) package. Statistical analysis Baseline characteristics of all included patients were summarized using descriptive statistics, with clinical data archived in Castor EDC. Categorical variables are presented as percentages and continuous variables as medians annotated with the interquartile range (IQR). Differences in the distribution between R and NR in the different cohorts were assessed using a chi-square test (categorical variables) or Mann-Whitney U-test (continuous variables). Two-tailed probabilities were used with a P-value of ≤ .05 being considered as statistically significant. Analyses of clinical data were performed in IBM SPSS statistics version (Shi et al., 2017. Mil Med Res 4: 14). Alpha diversity was evaluated using Shannon index in R (v4.2.3) with the means of the packages phyloseq (v1.40.0), and microViz (v0.9.3). Differences in diversity indices between R and NR were calculated using Kruskal- Wallis test in R, with the means of the package statistics (v4.2.3). RESULTS Characteristics of the anti-TNFα cohort patients The anti-TNFα cohort consisted of 39 patients that started treatment with IFX or ADA. We collected colonic biopsies from 21 R and 17 NR, and ileal biopsies from 20 R and 16 NR, with similar clinical characteristics, such as (P corresponding to colonic and ileal sub-cohorts, respectively) age (P = .60 and .60), sex (P = .85 and .55), CRP (P = .84 and .64), fecal calprotectin (P = .28 and .27), HBI score (P = .43 and .73), SES-CD score (P = .65 and .53), and earlier exposure to biological agents (P = .39 and .34, Table 1). The only difference observed was in the ileal sub-cohort, where more NR (37.5%) than R (10.0%) were active smokers (P = .04, Table 1). We obtained paired ileal and colonic biopsies from 20 R and 15 NR. Fecal microbiota profiling prior to start of treatment with anti-TNFα Alpha diversity, as described by Shannon index, was significantly different between the baseline colon-related microbiome of patients who started treatment with anti-TNFα agents and were assessed as R or NR (P = .031, Figure 1A, left panel). Importantly, patients who responded to anti-TNFα were characterized by significantly higher alpha diversity already before treatment, compared to patients who did not respond. This difference was not seen at the ileal biopsies of R and NR (P = .395, Figure 1B, left panel). Beta diversity mirrored the afore-described differences. The first principal coordinate axis (PCoA1) of the dimensionality reduction approach using Bray- Curtis at ASV level was discriminative between R and NR; in the colonic sub- cohort, R had significantly lower PCoA1 scores than NR (P = .013, Figure 1A, right panel), and that tended to be the case in the ileal sub-cohort (P = .065, Figure 1B, right panel). Adherent microbial signature discriminates response to anti-TNFα Feature stability selection was performed with LASSO models using the top250 most abundant ASVs. Per sub-cohort, 50 ASVs were LASSO-selected and introduced to the XGB approach in order to distinguish R from NR. In the colonic sub-cohort of patients who were treated with anti-TNFα, the baseline tissue- adherent microbial signatures could differentiate successfully between R and NR with a very good predictive value of AUC = 0.90 (Figure 2A). Respectively, in the ileal sub-cohort, the baseline tissue-adherent microbial signatures could differentiate between R and NR with a good predictive value of AUC = 0.75 (Figure 2C). Predictive to therapy with anti-TNFα microbial signatures regardless of the samples location Cross-reference of the Top50 LASSO-selected ASVs between the colonic and ileal sub-cohorts revealed that 17 ASVs were shared in both colonic- and ileal-panels, thereby indicating their potential in predicting response to therapy with anti- TNFα, regardless of the location of the sample used to evaluate the adherent microbiome. Ten of these 17 LASSO-selected ASVs shared between colonic and ileal panels were associated with non-response to therapy with anti-TNFα, or were more abundant in NR patients, while the remaining seven with response to therapy with anti-TNFα, or were more abundant in R. Follow-up permutation analysis of the Top50 LASSO-selected ASVs unraveled that actually seven of the 17 afore described ASVs were not “important” in the XGB- derived model to predict therapy response based on the microbial signatures of the colonic biopsies. Two of these seven ASVs were “unimportant” in the XGB-derived model to predict therapy response based on the microbial signatures of the ileal biopsies, as well, and that was the case for four more LASSO-selected ASVs, which turned out “unimportant” in the XGB-derived model of the ileal biopsies, albeit important in the colonic sub-cohort. Overall, cross-evaluation of the Top50 LASSO-selected ASVs and the permutation test-derived results identified six ASVs that were “important” for both models to predict therapy response based on the microbial signature coming from colonic and ileal biopsies namely ASV_3965, ASV_2698, ASV_3793, ASV_2436, ASV_3330, and ASV_3709. The former three ASVs were associated with non-response to treatment with anti-TNFα, while the latter three with response. Biomarker discovery analysis for therapy with anti-TNFα Evaluating the ASVs as potential features to predict response to anti-TNFα agents allowed us to find the bacterial biomarkers with the highest feature importance in colonic and ileal biopsies, separately. In the acquisition of our model, feature importance scores are used to determine the relative importance of each ASV when building an XGB-derived predictive model. In the colonic sub-cohort, the feature importance values of the 15 most informative ASVs in predicting response to anti-TNFα ranged from 0.009 to 0.068 (Table 2). Visualization of the feature importance values relative to the highest value (here, 0.068) are shown in Figure 2B. Eight out of the 15 most informative ASVs were associated with non-response to anti-TNF therapy, or were more abundant in the NR patients, including four ASVs identified as Ruminococcus gnavus (ASV_3972, ASV_3965, ASV_3724, and ASV_3849), one ASV each of Lachnospira pectinoschiza (ASV_2202), Escherichia / Shigella (ASV_2698, 16S does not allow us to differentiate these two taxa), Subdoligranulum (ASV_6014) and Lachnoclostridium (ASV_4167) (all shown in red in Figure 2B). The remaining seven ASVs were associated with anti-TNF therapy response, including four ASVs of the Blautia genus (ASV_3373, ASV_3375, ASV_3385, ASV_3330), and one ASV each of Clostridium scindens (ASV_3577), Anaerostipes (ASV_3296) and Agathobacter (ASV_2436) (all shown in green in Figure 2B). Representative sequences of the 15 most informative ASVs in the colonic sub-cohort can be found in Figure 5. In the ileal sub-cohort, the feature importance values of the 15 most informative ASVs in predicting response to anti-TNFα ranged from 0.005 to 0.023 (Table 3). Visualization of the feature importance values relative to the highest value (here, 0.023) are shown in Figure 2D. Nine out of the 15 most informative ASVs were associated with non-response to anti-TNF therapy; three belonging to Escherichia / Shigella (ASV_2698, ASV_2690 and ASV_2694), and one ASV each to R. gnavus (ASV_3695), Lachnoclostridium (ASV_4298), Erysipelotrichaceae UCG- 003 (ASV_0479), Lachnospieraceae UCG-004 (ASV_5867), Sutterella wadsworhensis (ASV_5794) and Fusicatenibacter saccharivorans (ASV_3793) (all shown in red in Figure 2D). The remaining six ASVs were associated with therapy response and identified as R. lactaris (ASV_3709), Agathobacter (ASV_2436), Erysipelotrichaceae UCG-003 (ASV_0490), Blautia (ASV_3387 and ASV_3379), and Subdoligranulum (ASV_6033) (all shown in green in Figure 2D). Representative sequences of the 15 most informative ASVs in the ileal sub-cohort can be found in Figure 6. Predictive biomarkers for therapy with anti-TNFα in both GI locations Exploratory analysis of both colonic- and ileal-specific biomarkers highlighted the importance of three distinct ASVs that contributed significantly in the XGB- derived model to predict therapy response. Specifically, ASV_3965 R. gnavus, ASV_2698 Escherichia / Shigella and ASV_2436 Agathobacter were among the Top15 most predictive ASVs in both ileal- and colonic-specific biomarker panels (highlighted in bold in Figure 2B and 2D). The former two ASVs were associated with non-response to treatment with anti-TNFα, or were more abundant in NR patients (Figure 3A, B, D and E), while the latter ASV was associated with therapy response, or was more abundant in R patients (Figure 3C and F). Dysbiotic-associated predictive signatures for non-response to anti-TNFα Conventional microbiome analyses suggested already that baseline colonic tissue adherent microbiome of R patients is less diverse than NR (P = .031, Figure 1). Colonic tissue-adherent microbial signatures of R were also distinctly clustered to NR on PCoA1 (P = .013, Figure 1). The XGB-derived predictive value of the colonic tissue-adherent microbiome was very good with an AUC of 0.90 distinguishing R from NR patients (Figure 2A), and most ASVs associating with non-response belonged to R. gnavus and Escherichia / Shigella (ASV_3972, ASV_3965, ASV_3724, ASV_3849 and ASV_2698, Figure 2B). Combining the afore described results of conventional and machine learning-based approaches, the significance of R. gnavus and Escherichia / Shigella to predict non- response to therapy with anti-TNFα was confirmed. PCoA1 was significantly positively associated with the sum of all ASVs belonging to the genus of Escherichia / Shigella (rho = 0.709, P = 6.23E-07, Figure 4A), and the sum of all ASVs belonging to R. gnavus (rho = 0.365, P = .024, Figure 4B). PCoA1 was significantly negatively associated with alpha diversity (measured by Shannon Index, rho = -0.631, P =3.32E-05, Figure 4C), as well, demonstrating the dysbiotic tissue-adherent microbiome composition already at baseline, of the NR patients. These associations were significant in ileal biopsies as well (Figure 4D-F). Anti-TNFα-specific microbiome-based prediction of therapy response Similar conventional and machine learning-based approaches evaluated the potential of the colonic and ileal tissue-adherent microbiome to predict response to therapy with the biological agents VDZ and USTE. The results suggested that the predictive value of the adherent microbiome to predict therapy response is specific to anti-TNFα. In both colonic and ileal biopsies, alpha and beta diversity did not differ between R and NR to VDZ, nor USTE (P ≥ .05 for all comparisons, data not shown). In VDZ, the XGB-derived model failed to distinguish between R and NR based on the colonic tissue-adherent microbiome (AUC = 0.59, Supplementary Figure 1A). The predictive value of the ileal tissue-adherent microbiome was fairly better (AUC = 0.75, data not shown), yet the informative for the model ASVs were belonging to taxa mostly abundant in healthy Dutch populations, rather than CD patients (data not shown), specifically Segatella copri (earlier classified as Prevotella copri). Moreover, they were mostly abundant in few patients (data not shown), pooling the model towards a mistaken direction based on outlier values. The inability of the tissue adherent microbiome to predict response to therapy with VDZ may be explained by the baseline clinical characteristics of the patients, CRP was significantly higher in NR than R (P = .03 and .02, for colonic and ileal sub-cohorts, respectively, data not shown), and the earlier exposure to other biological agents, more NR than R were previously exposed to USTE (46.7% vs 10.7%, P = .008, and 40.0% vs 7.7%, P = .01, for colonic and ileal sub-cohorts, respectively). In USTE, the XGB-derived model failed to distinguish between R and NR based on the ileal tissue-adherent microbiome (AUC = 0.68, data not shown), and that may be, as for VDZ, explained by the baseline clinical characteristics of the patients; CRP was significantly higher in NR than R of the ileal sub-cohort (P = .004). The colonic tissue-adherent microbiome demonstrated good predictive value in distinguishing between R and NR to USTE (AUC = 0.81, data not shown), yet the informative for the model ASVs were, as for VDZ, more abundant in few patients and that pooled the model towards a mistaken direction based on outlier values. Table 1. Baseline characteristics of anti-TNFα treated patients. Values are median (standard deviation) unless otherwise defined. The number of missing data is shown in square brackets. Percentages have been calculated in the available data. Anti-TNFα: infliximab & adalimumab; R: responder; NR: non-responder; IQR: interquartile range; HBI: Harvey Bradshaw Index; SES-CD: simple endoscopic disease activity score; Immunomodulator: azathioprine, mercaptopurine, thioguanine, methotrexate. COLON ILEUM R NR P R NR P N=21 N=17 N=20 N=16 Biological agent, n (antiTNF .85 .82 %) 8 (38.1) 7 (41.2) 8 (40.0) 7 (43.8) - Infliximab Sex, female, n (%) 13 (61.9) 10 (58.8) .85 12 (60.0) 8 (50.0) .55Age (years) 32.0 (22.8-33.8) 26.0 (22.8-33.8) .60 32.0 (22.8-33.8) 26.0 (22.9-33.8) .60Ethnic background, n (%) .26 .24 - Caucasian 18 (85.7) 12 (70.6) 17 (85.0) 11 (68.8) Diet, no restrictions, n (%) 18 (85.7) 13 (76.5) .47 17 (85.0) 12 (80.0) [1] .70Family history of IBD, n (%) 2 (10.0) [1] 1 (6.3) [1] .69 2 (10.5) [1] 2 (7.1) [2] .74C-reactive protein (mg / L) 3.0 (1.1-19.6) 7.8 (1.3-13.6) .84 2.7 (1.1-14.6) 8.0 (1.1-12.9) .64Fecal calprotectin (ug / g) 502 (61.8-1,777)914 (362-1,800).28 398 (56.5-1,806)894 (402.5-1,278) .27 [3] [2] [3] [2] Total HBI 4.0 (3.0-6.8) [1] 6.0 (2.8-9.0) [3] .43 4.0 (3.0-7.0) [1] 6.0 (2.0-9.0) [3] .73Total SES-CD 7.0 (4.0-12.5) 9.0 (6.0-12.0) [2] .65 7.0 (4.0-12.8) 9.0 (7.0-12) [1] .53Disease location, n (%) .33 .48 - Ileal disease (L1) 4 (19.0) 6 (35.3) 4 (20.0) 5 (31.3) - Colonic disease (L2) 6 (28.6) 2 (11.8) 5 (25.0) 2 (12.5) - Ileocolonic disease (L3) 11 (52.4) 9 (52.9) 11 (55.0) 8 (50.0) - Upper GI involvement (L4) 0 0 0 0

[0002] Disease behavior, n (%) .51 .27 - Non structuring non- penetrating (B1) 14 (66.7) 10 (58.8) 13 (65.0) 10 (62.5) - Stricturing (B2) 2 (9.5) 3 (17.6) 2 (10.0) 2 (12.5) - Penetrating (B3) 5 (23.8) 4 (23.5) 5 (25.0) 3 (18.8) - Perianal disease (p) 7 (33.3) 6 (35.3) 7 (35.0) 5(31.3) Previous IBD-related surgery,12 (57.1) 9 (56.3) [1] .50 12 (60.0) 9 (60.0) [1] .49n (%) Concomitant medication, n (%) .49 .45 - Immunomodulators 11 (52.4) 7 (41.2) 10 (50) 6 (37.5) Previous biological treatment 12 (57.1) 12 (70.6) .39 12 (60.0) 12 (75.0) .34 exposure, n (%) - Anti TNFa 9 (42.9) 10 (58.8) .33 9 (45.0) 10 (62.5) .30 - Vedolizumab 3 (14.3) 2 (11.8) .82 3 (15.0) 3 (18.8) .76 - Ustekinumab 2 (9.5) 1 (5.9) .68 2 (10.0) 2 (12.5) .81 Smoking, active, n (%) 2 (9.5) 6 (35.3) .13 2 (10.0) 6 (37.5) .04NOTE. Values are median (standard deviation) unless otherwise defined. The number of missing data is shown in square brackets. Percentages have been calculated in the available data. Anti-TNFα: infliximab & adalimumab; R: responder; NR: non-responder; IQR: interquartile range; HBI: Harvey Bradshaw Index; SES-CD: simple endoscopic disease activity score; Immunomodulator: azathioprine, mercaptopurine, thioguanine, methotrexate.

[0003] Table 2. Top15 most predictive ASVs of the colonic biopsies to distinguish between R and NR to therapy with anti-TNFα. ASV_ID FI SILVA 132 Taxonomy (Family | GenusHigherBLASTN QueryPercent | Species) RA cover (%) identity (%) ASV_3972 0.068 Lachnospiraceae|na|na NR Ruminococcus 100 99.75gnavus ASV_3965 0.060 Lachnospiraceae|na|na NR Ruminococcus 100 100gnavus ASV_3373 0.033 Lachnospiraceae|Blautia|na R NA NA NAASV_3577 0.018 Lachnospiraceae|Lachnoclostridium|na R Clostridium 100 100scindens ASV_3724 0.017 Lachnospiraceae|na|na NR Ruminococcus 100 99.75gnavus ASV_2202 0.017 Lachnospiraceae|Lachnospira|pectinosc NR NA NA NAhiza ASV_3849 0.016 Lachnospiraceae|na|na NR Ruminococcus 100 99.75gnavus ASV_3375 0.016 Lachnospiraceae|Blautia|na R NA NA NAASV_3385 0.014 Lachnospiraceae|Blautia|na R NA NA NAASV_2698 0.014 Enterobacteriaceae|Escherichia / Shigell NR NA NA NAa|coli / fergusonii ASV_6014 0.014 Ruminococcaceae|Subdoligranulum|na NR NA NA NAASV_4167 0.010 Lachnospiraceae|Lachnoclostridium|na NR Non- NA NAinformative ASV_3296 0.010 Lachnospiraceae|Anaerostipes|na R NA NA NAASV_2436 0.010 Lachnospiraceae|Agathobacter|na R NA NA NAASV_3330 0.009 Lachnospiraceae|Blautia|na R NA NA NANOTE. The representative sequences of the ASV_ID can be found Figure 5. Sequences were further blasted using Blast (see material and methods), if genus level annotation was not available, or further exploration was needed. FI: feature importance; RA: relative abundance.

[0004] Table 3. Top15 most predictive ASVs of the ileal biopsies to distinguish between R and NR to therapy with anti-TNFα. ASV_ID FI SILVA 132 Taxonomy (Family | Genus | Species) Higher BLASTN QueryPercent RA cover identity (%) (%) ASV_2698 0.023 Enterobacteriaceae|Escherichia / Shigella|coli / fer NR NA NA NAgusonii ASV_3965 0.021 Lachnospiraceae|na|na NR Ruminococc 100 100us gnavus ASV_4298 0.017 Lachnospiraceae|Lachnoclostridium|na NR Non- NA NAinformative ASV_2690 0.015 Enterobacteriaceae|Escherichia / Shigella| NR NA NA NAalbertii / boydii / coli / dysenteriae / fergusonii / flexne ri / sonnei / vulneris ASV_0479 0.013 Erysipelotrichaceae|Erysipelotrichaceae_UCG- NR Non- NA NA003|na informative ASV_3709 0.011 Lachnospiraceae|na|na R Ruminococc 100 100us lactaris ASV_2694 0.010 Enterobacteriaceae|Escherichia / Shigella|na NR NA NA NAASV_2436 0.010 Lachnospiraceae|Agathobacter|na R NA NA NAASV_5867 0.010 Lachnospiraceae|Lachnospiraceae_UCG-004|na NR Non- NA NAinformative ASV_5794 0.010 Burkholderiaceae|Sutterella|wadsworthensis NR NA NA NAASV_0490 0.008 Erysipelotrichaceae|Erysipelotrichaceae_UCG- R Non- NA NA003|na informative ASV_3387 0.007 Lachnospiraceae|Blautia|na R NA NA NAASV_6033 0.007 Ruminococcaceae|Subdoligranulum|na R NA NA NAASV_3379 0.007 Lachnospiraceae|Blautia|obeum / wexlerae R NA NA NAASV_3793 0.005 Lachnospiraceae|Fusicatenibacter|saccharivorans NR NA NA NA

[0005] NOTE. The representative sequences of the ASV_ID can be found Figure 6. Sequences were further blasted using Blast (see material and methods), if genus level annotation was not available, or further exploration was needed. FI: feature importance; RA: relative abundance. Table 4. Primers used for amplifying the 16S RNA gene, or part thereof.Primer name Sequence (5′–3′)8F AGA GTT TGA TCC TGG CTC AG27F AGA GTT TGA TCM TGG CTC AG336R ACT GCT GCS YCC CGT AGG AGT CT337F GAC TCC TAC GGG AGG CWG CAG341F CCT ACG GGN GGC WGC AG518R GTA TTA CCG CGG CTG CTG G533F GTG CCA GCM GCC GCG GTA A785F GGA TTA GAT ACC CTG GTA805R GAC TAC HVG GGT ATC TAAT CC806R GGA CTA CVS GGG TAT CTA AT907R CCG TCA ATT CCT TTR AGT TT928F TAA AAC TYA AAK GAA TTG ACG GG1100F YAA CGA GCG CAA CCC1100R GGG TTG CGC TCG TTGU1492R GGT TAC CTT GTT ACG ACT T1492R CGG TTA CCT TGT TAC GAC TT

Claims

Claims 1. A method of predicting a response of an individual suffering from an inflammatory bowel diseases, such as Crohn’s disease (CD) to treatment with a Tumor Necrosis Factor alpha inhibitor (TNF^-i), the method comprising the steps of: a) providing a sample of the gastrointestinal microbiome from the individual, whereby the sample is obtained by ileal and / or colonic biopsy; b) determining presence or abundance of at least one of Ruminococcus gnavus and Agathobacter in said gastrointestinal sample; c) whereby the presence or higher abundance of R. gnavus, relative to acontrol, indicates that the individual may not respond to treatment with TNF^-i,while the presence or higher abundance of Agathobacter, relative to a control, indicates that the individual may respond to treatment with TNF^-i.

2. The method of claim 1, further comprising determining presence or abundance of Escherichia / Shigella in said gastrointestinal sample, whereby the presence or higher abundance of Escherichia / Shigella, relative to a control, indicates that the individual may not respond to treatment with TNF^-i.

3. The method of claim 1 or 2, further comprising determining presence or abundance of one or more of Lachnoclostridium, Erysipelotrichaceae UCG-003, Lachnospira pectinoschiza, Subdoligranulum, Lachnoclostridium,Lachnospieraceae UCG-004, Sutterella wadsworhensis, Fusicatenibactersaccharivorans, Erysipelotrichaceae UCG-003, Blautia, Clostridium scindens, Anaerostipes, Agathobacter and Subdoligranulum in said gastrointestinal sample, whereby the presence or higher abundance of Lachnoclostridium, Erysipelotrichaceae UCG-003, Lachnospira pectinoschiza, Subdoligranulum, Lachnoclostridium, Lachnospieraceae UCG-004, Sutterella wadsworhensis and Fusicatenibacter saccharivorans, relative to a control, indicates that the individual may not respond to treatment with TNF^-i, and whereby presence or higher abundance of Erysipelotrichaceae UCG-003, Blautia, Clostridium scindens, Anaerostipes, Agathobacter and Subdoligranulum, relative to a control, indicates that the individual may respond to treatment with TNF^-i.

4. The method according to any one of claims 1-3, wherein the presence or abundance of Ruminococcus gnavus, Agathobacter and Blautia in said gastrointestinal sample is determined, whereby the presence or higher abundance of R. gnavus, relative to a control, indicates that the individual may not respond to treatment with TNF^-i, while the presence or higher abundance of Agathobacter and of Blautia, relative to a control, indicates that the individual may respond to treatment with TNF^-i.

5. The method according to any one of claims 1-4, wherein the presence or abundance of Ruminococcus gnavus, Agathobacter, Escherichia / Shigella and Blautia in said gastrointestinal sample is determined, whereby the presence or higher abundance of R. gnavus and of Escherichia / Shigella, relative to a control, indicates that the individual may not respond to treatment with TNF^-i, while the presence or higher abundance of Agathobacter and of Blautia, relative to a control, indicates that the individual may respond to treatment with TNF^-i.

6. The method of any one of claims 1-5, wherein presence or abundance of R. gnavus is determined by one or more of amplified sequence variants (ASV) ASV_3965, ASV_3972, ASV_3724, and ASV_3849, as depicted in Figures 5 and 6, preferably by ASV_3965.

7. The method of any one of claims 1-6, wherein presence or abundance Agathobacter is determined by ASV_2436, as depicted in Figures 5 and 6.

8. The method of any one of claims 1-7, wherein presence or abundance of Escherichia / Shigella is determined by one or more of ASV_2698, ASV_2690 and ASV_2694, as depicted in Figures 5 and 6, preferably by ASV_2698.

9. The method of any one of claims 1-8, comprising determining presence or abundance of one or more of ASV 3972, ASV_3965, ASV_3724, ASV_3849, ASV_2202, ASV_2698, ASV_6014, ASV_4167, ASV_3373, ASV_3375, ASV_3385, ASV_3330, ASV_3577, ASV_3296, ASV_2436, ASV_2690, ASV_2694, ASV_4298, ASV_0479, ASV_5867, ASV_5794, ASV_3793, ASV_3709, ASV_0490, ASV_3387,ASV_3379, and ASV_6033, whereby presence of, or higher abundance of, ASV 3972, ASV_3965, ASV_3724, ASV_3849, ASV_2202, ASV_2698, ASV_6014, ASV_4167, ASV_2690, ASV_2694, ASV_4298, ASV_0479, ASV_5867, ASV_5794, and ASV_3793, relative to a control, indicates that the individual may not-respond to treatment with TNF^-i, and whereby presence of, or higher abundance of, ASV_3373, ASV_3375, ASV_3385, ASV_3330, ASV_3577, ASV_3296, ASV_2436, ASV_3709, ASV_0490, ASV_3387, ASV_3379, and ASV_6033, relative to a control, indicates that the individual may respond to treatment with TNF^-i.

10. The method of any one of claims 1-9, comprising determining presence or abundance of ASV_3965, ASV_2698, ASV_3793, ASV_2436, ASV_3330, and ASV_3709, whereby presence of, or higher abundance of, ASV_3965, ASV_2698, and ASV_3793, relative to a control, indicates that the individual may not-respond to treatment with TNF^-i, and whereby presence of, or higher abundance of, ASV_2436, ASV_3330, and ASV_3709, relative to a control, indicates that the individual may respond to treatment with TNF^-i.

11. The method according to any one of claims 6-10, wherein the presence or abundance of said ASVs in said gastrointestinal sample is determined by sequencing, preferably next generation sequencing.

12. The method according to any one of claims 1-11, wherein the sample of the gastrointestinal microbiome from the individual comprises stable intestinal tissue- adherent microbial biomarkers.

13. The method of claim 9, whereby presence or abundance in a colonic biopsy of one or more of ASV 3972, ASV_3965, ASV_3724, and ASV_3849, ASV_2202, ASV_2698, ASV_6014, ASV_4167, ASV_3373, ASV_3375, ASV_3385, ASV_3330, ASV_3577, ASV_3296, ASV_2436, relative to a control, are indicative for a response to TNF^-i, while presence or abundance in an ileal biopsy of ASV_2698, ASV_2690, ASV_2694, ASV_3965, ASV_4298, ASV_0479, ASV_5867, ASV_5794, ASV_3793, ASV_3709, ASV_2436, ASV_0490, ASV_3387, ASV_3379, and ASV_6033, relative to a control, are indicative for a response to TNF^-i.

14. Anti-TNFα therapy, for use in a method of treating an individual suffering from an inflammatory bowel diseases, such as Crohn’s disease (CD), whereby said individual has been predicted to positively respond to said therapy by the methods of any one of claims 1-13.

15. Anti-TNFα therapy, for use according to claim 14, whereby said anti-TNFα therapy comprises infliximab, adalimumab, or a biosimilar thereof 16. An integrin α₄β₇ blocking agent, or interleukin (IL)-12 and IL-23 blocking agent, for use in a method of treating an individual suffering from an inflammatory bowel diseases, such as Crohn’s disease (CD), whereby said individual has been predicted not to respond to anti-TNFα therapy by the methods of any one of claims 1-13.

17. An integrin α₄β₇ blocking agent, or interleukin (IL)-12 and IL-23 blocking agent, for use according to claim 16, whereby said integrin α₄β₇ blocking agent comprises vedolizumab, and whereby said interleukin (IL)-12 and IL-23 blocking agent comprises ustekinumab.

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