Methods for stratifying subjects with type 1 diabetes for treatment with an Anti-CD3 antibody and for predicting progression to type 1 diabetes

Abstract

The present disclosure provides anti-commensal antibody (ACAb)-based profiles and methods of selecting a subject at risk of developing type 1 diabetes (T1D) for treatment with an anti-CD3 antibody. Also provided is a method of treating a subject at risk of developing T1D with an anti-CD3 antibody when the subject has an ACAb-based profile that predicts the subject is a responder the treatment. Further provided are ACAb-based profiles and methods of predicting that a subject at risk of developing T1D will progress to T1D.

Description

METHODS FOR STRATIFYING SUBJECTS WITH TYPE 1 DIABETES FOR TREATMENT WITH AN ANTI-CD3 ANTIBODY AND FOR PREDICTING PROGRESSION TO TYPE 1 DIABETESRelated Applications

[0001] This application claims the benefit of U.S. provisional patent application 63 / 592,751 filed on October 24, 2023, herein incorporated by reference.Incorporation of Sequence Listing

[0002] A computer readable form of the Sequence Listing “9962- P73569PC00_SequenceListing” (11 ,603 bytes), filed herewith by electronic submission and created on October 17, 2024, is herein incorporated by reference.Field

[0003] The present disclosure relates to methods for predicting whether a subject will progress to type I diabetes, methods for selecting a subject at risk of developing type I diabetes (T1 D) for treatment with an anti-CD3 antibody, and methods for treating said subject with an anti-CD3 antibody.Background

[0004] Advances in understanding the pathologic mechanisms of autoimmune and inflammatory diseases have facilitated the clinical advancement of immunomodulatory drugs (1-10). Antibodies and fusion proteins targeting CD20 on B lymphocytes and pro- inflammatory cytokines such as TNF, IL-17, and IL-23, have shown efficacy in the treatment of relapse-remitting multiple sclerosis, plaque psoriasis, and Crohn’s Disease (1-5). A recurrent observation in these settings is variation in treatment responses. Given the resource demands of clinical trials of immune-modulatory drugs, heterogeneity in patient responses propels efforts to identify biomarkers reflective of either the pathophysiology targeted by the treatment or a subset of patients most likely to respond within the trial period (2,3,7,9,11).

[0005] Type 1 Diabetes (T 1 D) is mediated by T cells that destroy insulin-secreting p cells in the pancreatic islets (12-14). Decades of research have identified HLA-DR haplotypes and the appearance of islet autoantibodies (IAB) in the blood as biomarkers of disease risk (15-17). However, the mechanism underlying heterogeneity in T1 D progression, including the contributions of genetic risk loci, the number and antigen specificity of IAB, the age of IAB appearance, and time to T1 D onset have not been fullyelucidated. The search for biomarkers predictive of treatment response and disease onset is particularly relevant in T1 D, because the intervention window for preserving p cell function is limited and intensive glucose control initiated early after disease onset does not preserve p cell function (78).

[0006] The T cell-specific anti-CD3 monoclonal antibody teplizumab was tested in patients with recent onset T1 D where it preserved residual insulin production (79).Summary

[0007] Here, the inventor determined that lgG2 responses to commensal bacteria of the gut were predictive of responders and non-responders to treatment with an anti- CD3 antibody in individuals at risk of developing type 1 diabetes. The inventor further determined that lgG2 and IgA responses to three species of commensal bacteria of the gut were predictive of progression to type 1 diabetes in individuals at risk of type 1 diabetes, which provides a means to select subjects for participation in type 1 diabetes clinical trials.

[0008] Accordingly, provided herein is a method of selecting a subject at risk of developing type 1 diabetes for treatment with an anti-CD3 antibody or antibody fragment thereof comprising: a) determining a test profile comprising an immunoglobulin G2 (lgG2) response to at least one species of commensal bacteria of the gut, from a sample from the subject; b) comparing the test profile with a control profile comprising the lgG2 response to the at least one species of commensal bacteria of the gut, from known responders (KRs) or known non-responders (KNRs) to the anti-CD3 antibody treatment; c) determining the level of similarity of a) and b); and d) selecting the subject for the treatment if there is a high level of similarity of the test profile to the control profile of KRs; and / or a low similarity of the test profile to the control profile of KNRs, and not selecting the subject for the treatment if there is a low level of similarity of the test profile to the control profile of KRs; and / or a high level of similarity of the test profile to the control profile of KNRs.

[0009] Also provided herein is a method of treating a subject at risk of developing type 1 diabetes with an anti-CD3 antibody or antibody fragment thereof comprisinga) determining a test profile comprising an immunoglobulin G2 (lgG2) response to at least one species of commensal bacteria of the gut, from a sample from the subject; b) comparing the values of the test profile with a control profile comprising the lgG2 response to the at least one species of commensal bacteria of the gut, from known responders (KRs) or known non-responders (KNRs) to the anti-CD3 antibody treatment; c) determining the level of similarity of a) and b); and d) administering the anti-CD3 antibody or antibody fragment thereof if there is a high level of similarity of the test profile to the control profile of KRs; and / or a low level of similarity of the test profile to the control profile of KNRs, and not administering the anti-CD3 antibody or antibody fragment thereof if there is a low level of similarity of the test profile to the control profile of KRs; and / or a high level of similarity of the test profile to the control profile of KNRs.

[0010] In an embodiment, the test and control profiles are analyzed using combinatorial or multivariate dimension reduction statistical or mathematical methods.

[0011] In another embodiment, the combinatorial or multivariate dimension reduction statistical or mathematical methods comprise clustering analyses or desirability analysis to enable comparisons between multivariate profiles with dimension-reduced empirical values.

[0012] In another embodiment, determining the high level of similarity is indicated by a high correlation value computed between the test profile and the control profile, optionally wherein the correlation value is a correlation coefficient.

[0013] In yet another embodiment, determining the low level of similarity is indicated by a low correlation value computed between the test profile and the control profile, optionally wherein the correlation value is a correlation coefficient.

[0014] In a further embodiment, the correlation coefficient is a linear coefficient, optionally a Pearson correlation coefficient, or a monotonic coefficient, optionally a Spearman’s correlation coefficient.

[0015] Tin some embodiments, the high level of similarity is indicated by a Pearson correlation coefficient or a Spearman’s correlation coefficient between the test profile and the control profile having an absolute value between 0.5 to 1 , optionally between 0.75 to 1 , and wherein the low level of similarity is indicated by a Pearson correlation coefficientor a Spearman’s correlation coefficient between the test profile and the control profile having an absolute value between 0 to 0.5, optionally between 0 to 0.25.

[0016] Also provided herein is a method of treating a subject at risk of developing type 1 diabetes comprising administering an anti-CD3 antibody or antibody fragment thereof to a subject that has been previously identified as having a responder profile by a method described herein.

[0017] Further provided herein is a use of an anti-CD3 antibody or antibody fragment thereof, for treating a subject at risk of developing type 1 diabetes, wherein the subject has been identified as having a responder profile by the methods disclosed herein. Also provided herein is an anti-CD3 antibody or antibody fragment thereof for use in treating a subject at risk of developing type 1 diabetes, wherein the subject has been identified as having a responder profile by the methods disclosed herein. Further provided herein is a use of an anti-CD3 antibody or antibody fragment thereof in the preparation of a medicament for treating a subject at risk of developing type 1 diabetes, wherein the subject has been identified as having a responder profile by the methods disclosed herein.

[0018] In an embodiment, the at least one species of commensal bacteria of the gut comprises an actinobacteria and / or a firmicute.

[0019] In another embodiment, the at least one species of commensal bacteria of the gut comprises Bifidobacterium longum (B. longum), Enterococcus faecalis (E. faecalis), Dialisterinvisus (D. invisus), or a combination thereof.

[0020] In yet another embodiment, the at least one species of commensal bacteria of the gut comprises B. longum and / or E. faecalis.

[0021] In a further embodiment, the at least one species of commensal bacteria of the gut comprises B. longum.

[0022] In some embodiments, the test profile and the control profile further comprise an HLA class II haplotype of the subject, and wherein the HLA class II haplotype is HLA-DR4.

[0023] In another embodiment, the at least one species of commensal bacteria of the gut comprises D. invisus.

[0024] In some embodiments, the anti-CD3 antibody or antibody fragment thereof comprises a heavy chain variable region comprising complementarity determining regionsCDR-H1 , CDR-H2, and CDR-H3, and / or a light chain variable region comprising CDR-L1 , CDR-L2, and CDR-L3; wherein the amino acid sequences comprise:CDR-H1 : RSTMH SEQ ID NO: 1 ;CDR-H2: YINPSSAYTNYNQKFKD SEQ ID NO: 2;CDR-H3: PQVHYDYNGFPY SEQ ID NO: 3;CDR-L1 : SASSSVSYMN SEQ ID NO: 4CDR-L2: DSSKLAS SEQ ID NO: 5; andCDR-L3: QQWSRNPPT SEQ ID NO: 6.

[0025] In an embodiment, the anti-CD3 antibody or antibody fragment thereof comprises a heavy chain variable region comprising an amino acid sequence as set forth in SEQ ID NO: 7 or 8 and / or a light chain variable region comprising an amino acid sequence as set forth in any one of SEQ ID NOs: 9-11 .

[0026] In another embodiment, the anti-CD3 antibody is teplizumab.

[0027] In yet another embodiment, the treatment comprises a single dose of the anti-CD3 antibody or antibody fragment thereof.

[0028] In a further embodiment, the treatment comprises multiple doses of the anti- CD3 antibody or antibody fragment thereof.

[0029] Also provided herein is a method for predicting progression to type 1 diabetes (T 1 D) in a subject at risk of developing T 1 D comprising: a) determining a test profile from a sample from the subject comprising:- an lgG2 response to Bifidobacterium longum (B. longumy- an lgG2 response to Enterococcus faecalis (E. faecalisy- an lgG2 response to Dialister invisus (D. invisusy and / or- an IgA response to Dialister invisus (D. invisusy b) comparing the test profile with a control profile from known progressors(KPs) or known non-progressors (KNPs) to T1 D comprising:- the lgG2 response to B. longum',- the lgG2 response to E. faecalis,- the lgG2 response to D. invisus’, and / or- the IgA response to D. invisus, c) determining the level of similarity of a) and b); andd) classifying the subject as likely to progress to T1 D if there is a high level of similarity of the test profile to the control profile of KPs; and or a low similarity of the test profile to the control profile of KNPs, and not classifying the subject as likely to progress to T1 D if there is a low level of similarity of the test profile to the control profile of KPs; and / or a high level of similarity of the test profile to the control profile of KNPs.

[0030] In some embodiments, the method is used to select the subject for participation in a clinical trial for an anti-CD3 antibody or antibody fragment thereof.

[0031] In an embodiment, the test and control profiles are analyzed using combinatorial or multivariate dimension reduction statistical or mathematical methods.

[0032] In another embodiment, the combinatorial or multivariate dimension reduction statistical or mathematical methods comprise clustering analyses or desirability analysis to enable comparisons between multivariate profiles with dimension-reduced empirical values.

[0033] In another embodiment, determining the high level of similarity is indicated by a high correlation value computed between the test profile and the control profile, optionally wherein the correlation value is a correlation coefficient.

[0034] In yet another embodiment, determining the low level of similarity is indicated by a low correlation value computed between the test profile and the control profile, optionally wherein the correlation value is a correlation coefficient.

[0035] In a further embodiment, the correlation coefficient is a linear coefficient, optionally a Pearson correlation coefficient, or a monotonic coefficient, optionally a Spearman’s correlation coefficient.

[0036] In some embodiments, the high level of similarity is indicated by a Pearson correlation coefficient or a Spearman’s correlation coefficient between the test profile and the control profile having an absolute value between 0.5 to 1 , optionally between 0.75 to 1 , and wherein the low level of similarity is indicated by a Pearson correlation coefficient or a Spearman’s correlation coefficient between the test profile and the control profile having an absolute value between 0 to 0.5, optionally between 0 to 0.25.

[0037] In an embodiment, the test profile and the control profile comprise the lgG2 response to B. longum and / or the lgG2 response to E. faecalis.

[0038] In another embodiment, the test profile and the control profile comprise the lgG2 response to B. longum.

[0039] In yet another embodiment, the test profile and the control profile further comprise an HLA class II haplotype of the subject, and wherein the HLA class II haplotype is HLA-DR4.

[0040] In a further embodiment, the test profile and the control profile comprise the lgG2 response to D. invisus and / or the IgA response to D. Invisus.

[0041] In an embodiment, the sample is a serum sample or a plasma sample.

[0042] In another embodiment, the subject is a human subject.

[0043] In a further embodiment, the subject is a juvenile or an adult.

[0044] These and other features and advantages of the present disclosure will become apparent from the following detailed description taken together with the accompanying drawings. It should be understood, however, that the detailed description and specific examples, while indicating preferred implementations of the present disclosure, are given by way of illustration only, since various changes and modifications within the spirit and scope of the disclosure will become apparent to those of skill in the art from this detailed description.Brief Description of the Drawings

[0045] For a better understanding of the various embodiments described herein, and to show more clearly how these various embodiments may be carried into effect, reference will be made, by way of example, to the accompanying drawings which show at least one example embodiment, and which are now described. The drawings are not intended to limit the scope of the teachings described herein.

[0046] FIG. 1 shows a schematic of the anti-commensal antibody (ACAb) assay for determining patient responders to teplizumab in an exemplary embodiment of the disclosure. Top panel: Treatment with the anti-T cell antigen receptor monoclonal Ab teplizumab delayed type 1 diabetes (T1 D) in at-risk individuals in TN10 trial, but participant response were heterogeneous with some displaying significant delay in T1 D diagnosis (“responders”) while others did not (“non-responders”). Middle panel: An approach was developed to identify and quantify antibodies in the blood that are reactive against (bind to) species of non-pathogenic “commensal” bacteria that comprise the human gut microbial community (microbiome). Anti-commensal antibody (ACAb) assay: serially diluted participant serum (fluid portion of the blood) was incubated with cultured strains of human commensal bacteria. The bacterially-bound antibodies (circled on the right) were detected and quantified. Bottom panel: ACAb response patterns against threeof these bacterial species displayed significant association with future T 1 D diagnosis (T 1 D risk) and with efficacy of teplizumab (distinguishing responders from non-responders). Specifically, Individuals with higher Ab (lgG2) responses to three specific gut bacteria were more likely to display an early T 1 D diagnosis and to have a better response to teplizumab.

[0047] FIG. 2 shows a phylogenetic tree of bacterial strains used in ACAb assays in an exemplary embodiment of the disclosure. Multiple strains for a species were used to better represent within-species heterogeneity and maximize antigenic diversity. Plot generated by Interactive tree of life (81).

[0048] FIGs. 3A-C show an overview of an anti-commensal antibody assay in an exemplary embodiment of the disclosure. FIG. 3A) Schematic representation of the anticommensal antibody assay. In a first step, bacteria targets of a specific strain pool were incubated with 4 serial dilutions of the serum sample allowing serum antibodies to bind bacteria surface antigens. Next fluorescently-labelled, secondary anti-isotype antibodies (anti-lgA, lgG1 , lgG2 and total Ig) were added allowing the visualization of the bacteria through flow cytometry. FIG. 3B) An example of the signal measured by flow cytometry for one bacteria pool and four serial dilutions of a serum sample. The top four panels show contour plots of the anti-isotype intensity vs forward scatter parameter. The bottom panel shows a histogram overlay of the anti-isotype signal intensities. FIG. 3C) Schematic representation of the Response Index calculation. Forevery serum sample, bacteria strain pool and isotype the dilution curve of the mean fluorescence intensity (MFI) vs dilution factor were generated on a log-log scale (left panel, grey lines). The mean dilution curve was generated using the average MFI values for all serum samples (left panel, black line). For every sample the difference between its dilution AUC and the mean dilution AUC was calculated (AAUC, right panel). The resulting Response Index was calculated as 2AAUC. Figure is reproduced from Paun et al., 2019 (28).

[0049] FIG. 4 shows the number of individuals with samples available at baseline and after the randomization in an exemplary embodiment of the disclosure. Of the 63 individuals providing serum samples for this study, all individuals had one serum sample before the randomization and at least one serum sample after the randomization. Due to loss of follow up after T1 D diagnosis, fewer serum samples were available at later time points after randomization. Association between 6-month ACAb responses and time to T1 D in addition to baseline ACAbs were examined because it was the timepoint closest to and after the randomization with most samples available.

[0050] FIG. 5 shows that teplizumab was associated with delayed T 1 D progression in the TN-10 subset in an exemplary embodiment of the disclosure. Kaplan-Meier survival analysis of T1 D progression in the TN10 subset used in this study. The hazard ratio for T 1 D diagnosis in teplizumab arm vs placebo arm was 0.45 (95% confidence interval [Cl], 0.31 to 0.65; two-sided P = 0.0298 by adjusted Cox proportional-hazards model). The median time to T1 D was 30.6 months in the placebo group, and 61.1 months in the treatment group.

[0051] FIGs. 6A-C show overviews of ACAb responses of the three isotypes to all 15 bacterial species in an exemplary embodiment of the disclosure. FIGs. 6A-C) The trajectories of IgG 1 (FIG. 6A), lgG2 (FIG. 6B), and IgA (FIG. 6C) ACAb responses before and after the randomization. Trajectories coded according to bacteria species and line types correspond to treatment arms. Vertical bars are standard deviations within treatment arms at a single timepoint.

[0052] FIGs. 7A-D show that teplizumab treatment did not change total antibody isotype titres or magnitude of antibody responses to four defined islet antigens in an exemplary embodiment of the disclosure. FIG. 7A) Titres of total lgG1 , I gG2, and IgA antibodies in teplizumab vs placebo groups aggregating across timepoints measured by ELISA (lgG1 ; lgG2; IgA; teplizumab, solid line; placebo, dashed line). FIG. 7B) Trajectories of total lgG1 , lgG2, and IgA antibodies over time. No significant difference across treatment arms or timepoints. FIG. 7C) Percentage of participants positive for one, two, three, or four lABs at single timepoints before or after randomization across treatment arms. No significant difference in distribution across treatment arms or timepoints. FIG. 7D) Trajectories of GADA, I A2A, mlAA, and ZnT8A overtime (GADA; IA2A; mlAA; ZnT8A; teplizumab, solid line; placebo, dashed line). Y-axis was log-10 transformed to display IAB titres with different scales. No significant difference in IAB abundance across treatment arms or timepoints. Vertical bars are standard deviations within treatment arms at a single timepoint. Color palette from ghibli (v 0.3.3) (82)

[0053] FIGs. 8A-H show that baseline and 6-month B. longum-\gG2 response indices were associated with time to T1 D diagnosis and teplizumab treatment effect in an exemplary embodiment of the disclosure. FIGs. 8A-C) Distribution of B. longum-\gG2 response indices across timepoints (FIG. 8A), at baseline (FIG. 8B), and at six months (FIG. 8C). The vertical dashed line (x=140) marks the trough between the two modes and the cutoff for high and low subgroups. FIGs. 8D-F) Proportion of participants remaining T1 D-free in each treatment arm (teplizumab; placebo) separately in low (FIG. 8D) andhigh (FIG. 8E) B. longum-\gG2 response subgroups at baseline (Wald test p = 0.011 , hazard ratio (HR) = 1 .007), and together (FIG. 8F) (low, solid line, n = 12 placebo, n = 28 teplizumab; high, dashed line, n = 10 placebo, n = 13 teplizumab; log rank p = 0.0011). FIGs. 8G-H) Proportion of participants remaining T1 D-free in each treatment arm in low (FIG. 8G) (n = 12 placebo, n = 28 teplizumab) and high (FIG. 8H) (n = 9 placebo, n = 11 teplizumab) B. longum-\gG2 response subgroups at 6 months (Wald test p = 0.007, HR=1 .008). HRs estimated by multivariate Cox PH adjusting for age, sex, BMI, treatment arm, and HLA-DR.

[0054] FIGs. 9A-G show that baseline and 6-month E. faecalis-\gG2 response indices were associated with time to T1 D diagnosis in an exemplary embodiment of the disclosure. FIGs. 9A-C) Distribution of E. faecalis-\gG2 response indices across timepoints (FIG. 9A), at baseline (FIG. 9B), and at six months (FIG. 9C). The vertical dashed line (x=159.61 ) marks the median E. faecalis-\gG2 response indices of all samples across timepoints and the cutoff for high and low subgroups. FIGs. 9D-E) Proportion of participants remaining T1 D-free in each treatment arm (teplizumab; placebo) in low (FIG. 9D) (n = 8 placebo, n = 22 teplizumab) and high (FIG. 9E) (n = 14 placebo, n = 19 teplizumab) baseline E. faecalis-\gG2 response subgroups (p = 0.002, HR = 1 .008). FIGs. 9F-G) Proportion of participants remaining T 1 D-free in each treatment arm in low (FIG. 9F) (n = 13 in placebo, n = 29 in teplizumab) and high (FIG. 9G) (n = 8 in placebo, n = 10 in teplizumab) 6-month E. faecalis-\gG2 response subgroups (p = 0.0009, HR = 1.009). HRs estimated by multivariate Cox PH adjusting for age, sex, BMI, treatment arm, and HLA-DR.

[0055] FIGs. 10A-G show that baseline and 6-month C-peptide were associated with time to T1 D in an exemplary embodiment of the disclosure. FIGs. 10A-C) Distribution of C-peptide values across all time points (FIG. 10A), at baseline (FIG. 10B), and at 6- month timepoint (FIG. 10C). The vertical dashed line (x = 1.86) marks the median C- peptide of all samples across timepoints and the cutoff for high and low C-peptide subgroups. FIGs. 10D-E) Proportion of participants remaining T 1 D-free in each treatment arm (teplizumab; placebo) in low (FIG. 10D) and high (FIG. 10E) C-peptide subgroups at baseline (p= 0.022, HR = 0.484 by multivariate Cox PH regression analysis). FIGs. 10F- G) Proportion of participants remaining T1 D-free in each treatment arm in low (FIG. 10F) and high (FIG. 10G) C-peptide subgroups at 6 months (p = 0.044, HR = 0.538 by multivariate Cox PH regression analysis). Low baseline C-peptide (FIG. 10D) and 6- month C-peptide (FIG. 10G) were associated with better treatment responses.

[0056] FIG. 11 shows that responses to teplizumab were associated with baseline lgG2 responses to selected gut bacteria in an exemplary embodiment of the disclosure. Log HRs and 95% confidence intervals for T1 D diagnosis in the teplizumab group compared to the placebo group for baseline-lgG2-response-stratified subgroups. Treatment effect was better in individuals with high baseline B. longum-\gG2 and E. faecalis-\gG2 responses, and, within the DR4-present group, D. invisus-\gG2 responses. Multivariate Cox PH models were adjusted for age, sex, BMI, and HLA-DR, but not for multiple testing. Plot generated using forester (v 0.2.0) (83).

[0057] FIGs. 12A-D show that 6-month D. invisus-\gG2 response indices interacted with HLA-DR4 in association with time to T1 D in an exemplary embodiment of the disclosure. FIGs. 12A-B) Normalized distribution of D. invisus-\gG2 response indices across all time points (FIG. 12A) and at 6-month timepoint (FIG 12B). The vertical dashed line (x = -0.009) marks the median D. invisus-\gG2 response indices of all samples across timepoints and the cutoff for high and low 6-month D. invisus-\gG2 response subgroups. FIGs. 12C-D) Proportion of participants remaining T1 D-free in each treatment arm (teplizumab; placebo) in low (solid line) and high (dashed line) D. invisus-\gG2 response subgroups at 6-month time point in HLA-DR4- (FIG. 12C) (n = 5 High, Placebo; n = 6 High, Teplizumab; n = 2 Low, Placebo; n = 7 Low, Teplizumab) and HLA-DR4+ (FIG. 12D) (n = 9 High, Placebo; n = 9 High, Teplizumab; n = 5 Low, Placebo; n = 17 Low, Teplizumab) subgroups. Interaction term between 6-month D. invisus-\gG2 and HLA- DR4, p = 0.001 < 0.0012, the adjusted cutoff based on effective number of independent tests (74), HR = 3.97 by multivariate Cox-PH regression adjusting for age, sex, BMI, treatment arm, HLA-DR, and baseline D. invisus-\gG2.

[0058] FIGs. 13A-D show that ACAb responses across timepoints and C-peptide values were associated with T1 D progression in an exemplary embodiment of the disclosure. HR of progressing to T1 D diagnosis as a function of B. longum-\gG2 responses (FIG. 13A) (p = 0.006, HR = 1.009), E. faecalis-\gG2 responses (FIG. 13B) (p = 0.015, HR = 1 .006), C-peptide value (FIG. 13C) (p = 0.0001 , HR = 0.32), and normalized D. invisus-\gG2 responses, stratified by HLA-DR4 (FIG. 13D) (HLA-DR4+’, HLA-DR4-, D. invisus-\gG2 p = 0.02, HR = 0.46; D. invisus-\gG2-HLA-DR4 interaction p = 0.005, HR = 3.12) aggregating across time points. HR estimated by time-dependent Cox model adjusting for age, sex, BMI, treatment arm, and HLA-DR. Horizontal dashed lines are placed at HR = 1 , indicating no change in risk of progressing to T1 D diagnosis. Vertical dashed lines are placed at the mean ACAb responses (B. longum-\gG2, x = 118.05; E.faecalis-\gG2, x = 158.02; normalized D. invisus-\gG2, x = 0) orC-peptide value (x = 2.09) of the cohort.

[0059] FIGs. 14A-D show that longitudinal ACAbs were associated with time to T1 D diagnosis in an exemplary embodiment of the disclosure. Representative plots for expected probability of four selected individuals based on baseline characteristics (age, sex, BMI, HLA-DR) and longitudinal B. longum-\gG2 (FIG. 14A) and E. faecalis- lgG2 (FIG. 14B) responses, and eight selected individuals based on baseline characteristics and longitudinal D. invisus-\gG2 responses when HLA-DR4- (FIG. 14C) or when HLA- DR4+ (FIG. 14D). Trajectories are coded according to bacteria species and line types correspond to high or low binarized ACAb responses. Shaded areas are 95% confidence interval for the probabilities.

[0060] FIGs. 15A-D show that baseline ACAb responses improved performance of Cox models in predicting T1 D diagnosis in an exemplary embodiment of the disclosure. FIG. 15A) Time-dependent areas under the curve (AUCs) and FIG. 15B) receiver operating characteristic (ROC) curves of models using one or more of treatment arm, age, HLA-DR4, and baseline D. invisus-\gG2 responses as covariates for T1 D diagnosis prediction post randomization. FIG. 15C) Time-dependent AUCs and FIG. 15D) ROC curves of models using one or more of treatment arm, age, BMI, C-peptide value, baseline B. longum-\gG2 responses, and baseline E. faecalis-\gG2 responses as covariates for T1 D diagnosis prediction. Vertical dotted lines mark 550 days, the collection time of the last follow-up sample for which ROC curves were generated. Specification of variables included in each model are listed in Table 4. The covariates were incorporated into these models using a step-up procedure and included only if they improved the partial loglikelihood of the model at a = 0.1 level (two-sided).

[0061] FIGs. 16A-B show key differences in participant characteristics of discovery (TN-10) and validation (TN-01) cohorts in an exemplary embodiment of the disclosure. FIG. 16A) Distribution of participant age in the two cohorts, mean [± sd]: TN-10: 18.0[± 11.4] years; TN-01 : 11.5[± 4.8] years, p = 7.5e-05, two-sample t test. FIG. 16B) Distribution of time to T1 D / last follow-up in the two cohorts, median [IQR]: TN-10: 1101

[1123] days; TN-01 : 343.76 [176.87] days, p = 1.8e-14, Wilcoxon rank sum test.

[0062] FIGs. 17A-B show that B longum-\gG2 response interacted with HLA-DR4 in association with Time to T1 D in TN-01 cohort in an exemplary embodiment of the disclosure. Proportion of participants remaining T1 D-free in each treatment arm (HLA- DR4 present; HLA-DR4 absent) in low B.longum-\gG2 response (FIG. 17A) (n = 11 , HLA-DR4 absent; n = 20, HLA-DR4 present) and high B.longum-\gG2 response (FIG. 17B) (n = 20, HLA-DR4 absent; n = 10, HLA-DR4 present) subgroups. Interaction term between B.longum-\gG2 and HLA-DR4, p = 0.03 by multivariate Cox-PH regression adjusting for age, sex, and HLA-DR.

[0063] FIGs. 18A-C show that D. invisus-\gA response interacted with HLA-DR4 in association with Time to T1 D and correlated with D. invisus-\gG2 response in TN-01 cohort in an exemplary embodiment of the disclosure. Proportion of participants remaining T1 D-free in each treatment arm (HLA-DR4 present; HLA-DR4 absent) in low D. invisus- IgA response (FIG. 18A) (n = 19, HLA-DR4 absent; n = 17, HLA-DR4 present) and high D. invisus-\gA response (FIG. 18B) (n = 12, HLA-DR4 absent; n = 13, HLA-DR4 present) subgroups. Interaction term between D. invisus-\gA and HLA-DR4, p = 0.04 by multivariate Cox-PH regression adjusting for age, sex, and HLA-DR. FIG. 18C) Correlation between D. invisus-lgA response and D. invisus-lgG2 response in TN-01 cohort. (Pearson correlation = 0.43, p = 0.0006).

[0064] Further aspects and features of the example embodiments described herein will appearfrom the following description taken together with the accompanying drawings.Detailed Description of the Disclosure

[0065] Unless otherwise indicated, the definitions and embodiments described in this and other sections are intended to be applicable to all embodiments and aspects of the present application herein described for which they are suitable as would be understood by a person skilled in the art.

[0066] All features disclosed in the specification, including the claims, abstract, and drawings, and all the steps in any method or process disclosed, may be combined in any combination, except combinations where at least some of such features and / or steps are mutually exclusive. Each feature disclosed in the specification, including the claims, abstract, and drawings, can be replaced by alternative features serving the same, equivalent, or similar purpose, unless expressly stated otherwise.I. Definitions

[0067] In understanding the scope of the present disclosure, the term "comprising" and its derivatives, as used herein, are intended to be open ended terms that specify the presence of the stated features, elements, components, groups, integers, and / or steps, but do not exclude the presence of other unstated features, elements, components,groups, integers and / or steps. The foregoing also applies to words having similar meanings such as the terms, "including", "having" and their derivatives.

[0068] The term “consisting” and its derivatives, as used herein, are intended to be closed ended terms that specify the presence of stated features, elements, components, groups, integers, and / or steps, and also exclude the presence of other unstated features, elements, components, groups, integers and / or steps. The term “consisting essentially of”, as used herein, is intended to specify the presence of the stated features, elements, components, groups, integers, and / or steps as well as those that do not materially affect the basic and novel characteristic(s) of features, elements, components, groups, integers, and / or steps.

[0069] Further, terms of degree such as "substantially", "about" and "approximately" as used herein mean a reasonable amount of deviation of the modified term such that the end result is not significantly changed. These terms of degree should be construed as including a deviation of at least ±5% of the modified term if this deviation would not negate the meaning of the word it modifies.

[0070] More specifically, the term “about” means plus or minus 0.1 to 50%, 5-50%, or 10-40%, 10-20%, 10%-15%, preferably 5-10%, most preferably about 5% of the number to which reference is being made.

[0071] As used in this specification and the appended claims, the singular forms “a”, “an” and “the” include plural references unless the content clearly dictates otherwise. Thus, for example, a composition containing “a compound” includes a mixture of two or more compounds.

[0072] The phrase "and / or," as used herein in the specification and in the claims, should be understood to mean "either or both" of the elements so conjoined, i.e. , elements that are conjunctively present in some cases and disjunctively present in other cases. Multiple elements listed with "and / or" should be construed in the same fashion, i.e., "one or more" of the elements so conjoined. Other elements may optionally be present other than the elements specifically identified by the "and / or" clause, whether related or unrelated to those elements specifically identified.

[0073] As used herein, "or" should be understood to have the same meaning as "and / or" as defined above. For example, when separating items in a list, "or" or "and / or" shall be interpreted as being inclusive, i.e., the inclusion of at least one, but also including more than one, of a number or list of elements, and, optionally, additional unlisted items.Only terms clearly indicated to the contrary, such as "only one of or "exactly one of" or, when used in the claims, "consisting of" will refer to the inclusion of exactly one element of a number or list of elements. In general, the term "or" as used herein shall only be interpreted as indicating exclusive alternatives (i.e. , "one or the other but not both") when preceded by terms of exclusivity, such as "either," "one of," "only one of," or "exactly one of."

[0074] As used herein, the phrase "at least one," in reference to a list of one or more elements, should be understood to mean at least one element selected from anyone or more of the elements in the list of elements, but not necessarily including at least one of each and every element specifically listed within the list of elements and not excluding any combinations of elements in the list of elements. This definition also allows that elements may optionally be present other than the elements specifically identified within the list of elements to which the phrase "at least one" refers, whether related or unrelated to those elements specifically identified.

[0075] The recitation of numerical ranges by endpoints herein includes all numbers and fractions subsumed within that range (e.g. 1 to 5 includes 1 , 1.5, 2, 2.75, 3, 3.90, 4, and 5). It is also to be understood that all numbers and fractions thereof are presumed to be modified by the term "about."

[0076] It should also be understood that, in certain methods described herein that include more than one step or act, the order of the steps or acts of the method is not necessarily limited to the order in which the steps or acts of the method are recited unless the context indicates otherwise.

[0077] Further, the definitions and embodiments described in particular sections are intended to be applicable to other embodiments herein described for which they are suitable as would be understood by a person skilled in the art. For example, in the following passages, different aspects are defined in more detail. Each aspect so defined may be combined with any other aspect or aspects unless clearly indicated to the contrary. In particular, any feature indicated as being preferred or advantageous may be combined with any other feature or features indicated as being preferred or advantageous.

[0078] Although any methods and materials similar or equivalent to those described herein can also be used in the practice or testing of the present disclosure, examples of methods and materials are now described.II. Methods and Uses

[0079] Here, the inventor determined that lgG2 responses to commensal bacteria of the gut were predictive of responders and non-responders to treatment with an anti- CD3 antibody in individuals at risk of developing type 1 diabetes. The inventor further determined that lgG2 and IgA responses to three species of commensal bacteria of the gut were predictive of progression to type 1 diabetes in individuals at risk of type 1 diabetes, which provides a means to select subjects for type 1 diabetes clinical trials.

[0080] Accordingly, in one aspect, the present disclosure provides a method of selecting a subject at risk of developing type 1 diabetes for treatment with an anti-CD3 antibody or antibody fragment thereof comprising: a) determining a test profile comprising an immunoglobulin G2 (lgG2) response to at least one species of commensal bacteria of the gut, from a sample from the subject; b) comparing the values of the test profile with a control profile comprising the lgG2 response to the at least one species of commensal bacteria of the gut, from known responders (KRs) or known non-responders (KNRs) to the anti-CD3 antibody treatment; c) determining the level of similarity of a) and b); and d) selecting the subject for the treatment if there is a high level of similarity of the test profile to the control profile of KRs; and or a low similarity of the test profile to the control profile of KNRs, and not selecting the subject for the treatment if there is a low level of similarity of the test profile to the control profile of KRs; and / or a high level of similarity of the test profile to the control profile of KNRs.

[0081] Also provided herein is a method of treating a subject at risk of developing type 1 diabetes with an anti-CD3 antibody or antibody fragment thereof comprising: a) determining a test profile comprising an immunoglobulin G2 (lgG2) response to at least one species of commensal bacteria of the gut, from a sample from the subject; b) comparing the values of test profile with a control profile comprising the lgG2 response to the at least one species of commensal bacteria of the gut, from known responders (KRs) or known non-responders (KNRs) to the anti-CD3 antibody treatment; c) determining the level of similarity of a) and b); and d) administering the anti-CD3 antibody treatment if there is a high level of similarity of the test profile to the control profile of KRs; and / or a low level of similarity ofthe test profile to the control profile of KNRs, and not administering the anti-CD3 antibody treatment if there is a low level of similarity of the test profile to the control profile of KRs; and / or a high level of similarity of the test profile to the control profile of KNRs.

[0082] Further provided herein is a method of treating a subject at risk of developing type 1 diabetes comprising administering an anti-CD3 antibody or antibody fragment thereof to a subject that has been previously identified as having a responder profile by the method disclosed herein.

[0083] In an aspect, herein provided is a use of an anti-CD3 antibody or antibody fragment thereof, for treating a subject at risk of developing type 1 diabetes, wherein the subject has been identified as having a responder profile by the methods disclosed herein. In another aspect, herein provided is an anti-CD3 antibody or antibody fragment thereof for use in treating a subject at risk of developing type 1 diabetes, wherein the subject has been identified as having a responder profile by the methods disclosed herein. In yet another aspect, herein provided is a use of an anti-CD3 antibody or antibody fragment thereof in the preparation of a medicament for treating a subject at risk of developing type 1 diabetes, wherein the subject has been identified as having a responder profile by the methods disclosed herein.

[0084] In an aspect, herein provided is a use of an anti-CD3 antibody or antibody fragment thereof in treating a subject at risk of developing type 1 diabetes, wherein the subject has been previously identified as having a responder profile, wherein the responder profile has been identified by determining a similarity of a) a test profile comprising an immunoglobulin G2 (lgG2) response to at least one species of commensal bacteria of the gut, from a sample from the subject; and b) a control profile comprising the lgG2 response to the at least one species of commensal bacteria of the gut, from known responders (KRs) or known non-responders (KNRs) to the anti-CD3 antibody treatment.

[0085] In another aspect, herein provided is a use of an anti-CD3 antibody or antibody fragment thereof in the manufacture of a medicament for treating a subject at risk of developing type 1 diabetes, wherein the subject has been previously identified as having a responder profile, wherein the responder profile has been identified by determining a similarity ofa) a test profile comprising an immunoglobulin G2 (lgG2) response to at least one species of commensal bacteria of the gut, from a sample from the subject; and b) a control profile comprising the lgG2 response to the at least one species of commensal bacteria of the gut, from known responders (KRs) or known non-responders (KNRs) to the anti-CD3 antibody treatment.

[0086] In yet another aspect, herein provided is an anti-CD3 antibody or antibody fragment thereof for use in treating a subject at risk of developing type 1 diabetes, wherein the subject has been previously identified as having a responder profile, wherein the responder profile has been identified by determining a similarity of a) a test profile comprising an immunoglobulin G2 (lgG2) response to at least one species of commensal bacteria of the gut, from a sample from the subject; and b) a control profile comprising the lgG2 response to the at least one species of commensal bacteria of the gut, from known responders (KRs) or known non-responders (KNRs) to the anti-CD3 antibody treatment.

[0087] In some embodiments, the subject has been previously identified as having a responder profile by a method disclosed herein.

[0088] The term “type 1 diabetes” or “T 1 D” as used herein refers to an autoimmune disease that is characterized by the destruction of insulin-producing p cells of the pancreas by the immune system. Loss of insulin-producing p cells in a subject abrogates the subject’s ability to autoregulate blood glucose levels and causes lifelong dependence on exogenous insulin treatment to sustain life.

[0089] The term “at risk of developing type 1 diabetes” as used herein refers to a subject with one or more baseline T 1 D risk factor. Baseline T 1 D risk factors can include, for example having a first degree relative with T1 D. A first degree relative is a relative with whom the subject shares about half of their genetic information, such as the subject’s parent, full sibling, or offspring. Other baseline T 1 D risk factors can include having an HLA class II haplotype that is high risk for T1 D, optionally a high-risk HLA-DR / DQ genetic variant. Several of these high-risk HLA-DR / DQ genetic variants are known in the art. For example, the highest risk for T1 D is imparted by the heterozygous HLA-DR3 / DR4 genotype, and other high risk genetic variants can include the homozygous HLA- DR3 / DR3 and HLA-DR4 / DR4 genotypes. Yet other baseline T 1 D risk factors can includepresence of an islet autoantibody (IAB) in a subject’s serum. Islet antibodies are serum biomarkers of autoimmune response against pancreatic p cells. Multiple islet antibodies are known in the art, and can include, for example, glutamic acid decarboxylase 65 antibodies (GADA), insulinoma associated protein 2 antibodies (IA2A), micro-insulin autoantibodies (mlAA), and / or zinc transporter 8 antibodies (ZnT8A). Further baseline T1 D risk factors can include signs of dysglycemia, which can be observed as abnormal glucose levels in the blood and / or abnormal responses to a glucose challenge. Abnormal fasting glucose levels include, for example, values above 100mg / dL (5.6mmol / L). Several glucose challenge tests are known in the art, and can include, for example an oral glucose tolerance test. The oral glucose tolerance test can include measuring C-peptide. C- peptide is a component of (cleaved from) the pro-insulin protein and a C-peptide response can be measured in a blood sample as a proxy for insulin production. An impaired response can include glucose levels of 140-199mg / dL two hours after exposure to the oral glucose challenge.

[0090] In an embodiment of a method or use disclosed herein, at risk of developing T 1 D comprises having at least one baseline T 1 D risk factor. In another embodiment of a method or use disclosed herein, at risk of developing T1 D comprises having at least two baseline T 1 D risk factors. In yet another embodiment of a method or use disclosed herein, at risk of developing T 1 D comprises having at least three baseline T 1 D risk factors. In a further embodiment of a method or use disclosed herein, at risk of developing T1 D comprises having at least four baseline T 1 D risk factors.

[0091] In an embodiment of a method or use disclosed herein, at risk of developing T1 D comprises having a first degree relative with T1 D. In another embodiment of a method or use disclosed herein, at risk of developing T1 D comprises having an HLA- DR / DQ genetic variant that is high risk for T1 D. In another embodiment of a method or use disclosed herein, at risk of developing T 1 D comprises having an HLA-DR / DQ genetic variant that is high risk for T1 D and having islet autoantibodies in the serum.

[0092] In yet another embodiment of a method or use disclosed herein, at risk of developing T1 D comprises:- having an HLA-DR / DQ genetic variant that is high risk for T 1 D;- having islet autoantibodies in the serum; and- having abnormal glucose levels and / or an abnormal response to a glucose challenge.

[0093] In a further embodiment of a method or use disclosed herein, at risk of developing T1 D comprises having a first degree relative with T1 D, having at least two lABs in the serum, and having signs of dysglycemia, optionally with the high-risk HLA- DR4 genetic variant.

[0094] The term “commensal bacteria of the gut” as used herein refers to bacterial species that commonly reside in the gut of healthy human subjects and are not associated with pathology under these conditions. Commensal bacteria can comprise species of the phyla Firmicutes, Actinobacteria, Bacteroidetes, and Proteobacteria.

[0095] In an embodiment of a method or use disclosed herein, the at least one species of commensal bacteria of the gut comprises an actinobacteria and / or a firmicute. In another embodiment of a method or use disclosed herein, the at least one species of commensal bacteria of the gut comprises Bifidobacterium longum (B. longum), Enterococcus faecalis (E. faecalis), Dialisterinvisus (D. invisus), ora combination thereof. In yet another embodiment of a method or use disclosed herein, the at least one species of commensal bacteria of the gut comprises B. longum and / or E. faecalis. In a further embodiment, the at least one species of commensal bacteria of the gut comprises B. longum.

[0096] In an embodiment of a method or use disclosed herein, the test profile and the control profile further comprise an HLA class II haplotype of the subject, optionally HLA-DR4.

[0097] The test profile and the control profile can further comprise an IAB response, a blood glucose level, or a response to a glucose challenge.

[0098] In an embodiment of a method or use disclosed herein, the anti-CD3 antibody or antibody fragment thereof comprises a heavy chain variable region comprising complementarity determining regions CDR-H1 , CDR-H2, CDR-H3, and / or a light chain variable region comprising CDR-L1 , CDR-L2, and CDR-L3; wherein the amino acid sequences comprise:CDR-H1 : RSTMH SEQ ID NO: 1 ;CDR-H2: YINPSSAYTNYNQKFKD SEQ ID NO: 2;CDR-H3: PQVHYDYNGFPY SEQ ID NO: 3;CDR-L1 : SASSSVSYMN SEQ ID NO: 4CDR-L2: DSSKLAS SEQ ID NO: 5; andCDR-L3: QQWSRNPPT SEQ ID NO: 6.

[0099] The term “amino acid” includes all of the naturally occurring amino acids as well as modified L-amino acids as well as D-amino acids. The atoms of the amino acid can for example include different isotopes. For example, the amino acids can comprise deuterium substituted for hydrogen, nitrogen-15 substituted for nitrogen-14, and carbon- 13 substituted for carbon-12 and other similar changes.

[0100] The term “antibody” as used herein refers to monoclonal antibodies, polyclonal antibodies, chimeric antibodies, and humanized antibodies. The antibody can be from recombinant sources and / or produced in transgenic animals. The term “antigen binding fragment” or "antibody fragment" as used herein is intended to include without limitations Fv (a molecule comprising the VL and VH), single-chain variable fragment (scFv; a molecule comprising the VL and VH connected by a peptide linker), Fab, Fab', F(ab')2, dsFv, ds-scFv, single-domain antibodies (nanobodies), and multivalent presentations of these. Also included are dimers, minibodies, diabodies, and multimers thereof, bi-specific and multi-specific antibody fragments, and domain antibodies. Antibodies can be fragmented using conventional techniques. For example, F(ab')2 fragments can be generated by treating the antibody with pepsin. The resulting F(ab')2 fragment can be treated to reduce disulfide bridges to produce Fab' fragments. Papain digestion can lead to the formation of Fab fragments. Fab, Fab' and F(ab')2, scFv, dsFv, ds-scFv, sdAB, dimers, minibodies, diabodies, nanobodies, bispecific antibody fragments and other fragments can also be synthesized by recombinant techniques.

[0101] The term “complementarity determining region” or “CDR” as used herein refers to particular hypervariable regions of antibodies that are commonly understood to define epitope binding. Computational methods for identifying CDR sequences include Kabat, Chothia, and IMGT. A person skilled in the art having regard to the sequences comprised herein would also be able to identify CDR sequences based on Kabat and Chothia etc.

[0102] The term “CD3” or “cluster of differentiation 3” as used herein refers to a protein complex that is part of the T cell antigen receptor complex that is involved in activation of T cells following antigen recognition. Accordingly, an anti-CD3 antibody or antibody fragment thereof is an antibody or a fragment thereof that recognizes and selectively binds an epitope on CD3, optionally wherein the epitope is in an extracellular domain of CD3. The term “selectively binds an epitope on CD3” as used herein refers to an antibody or antibody fragment thereof that binds to an epitope on CD3 with a greater affinity than to epitopes on other proteins or protein complexes.

[0103] In an embodiment of a method or use disclosed herein, the anti-CD3 antibody binds to the £ chain of CD3. The £ chain of CD3 can be from any organism and optionally according to GenBank accession number NM_000733.4. In another embodiment of a method or use disclosed herein, the anti-CD3 antibody inhibits T cell activation, optionally through steric inhibition of antigen recognition. Several anti-CD3 antibodies are known in the art, for example those disclosed in US patent 10,150,812 Huang et al., which is herein incorporated in its entirety.

[0104] In an embodiment of a method or use disclosed herein, the anti-CD3 antibody or antibody fragment thereof comprises a heavy chain variable region (VH) comprising an amino acid sequence as set forth in SEQ ID NO: 7 or 8 and / or a light chain variable region (VL) comprising an amino acid sequence as set forth in any one of SEQ ID NOs: 9-11. The sequences for SEQ ID NOs:7-11 are provided in Table 1.

[0105] In a further embodiment of a method or use disclosed herein, the anti-CD3 antibody is teplizumab (Prevention Bio, a Sanofi company).

[0106] The term “administering” or “administration” as used herein refers to the placement of an anti-CD3 antibody or antibody fragment thereof as disclosed herein into a subject by a method or route which results in at least partial delivery to a desired site. The anti-CD3 antibody treatment disclosed herein can be administered by any appropriate route which results in an effective treatment in the subject, and can be by intravenous injection or intra-muscularly.

[0107] In an embodiment of a method or use disclosed herein, the CD3 antibody or antibody fragment thereof is administered or for use by intravenous injection.

[0108] The term “treating”, “treatment”, and the like, as used herein, and as is well understood in the art, refers to an approach for obtaining beneficial or desired results, including clinical results. Beneficial or desired clinical results include, but are not limited to alleviation or amelioration of one or more symptoms or conditions, arresting development of disease, diminishment of extent of disease, stabilized (i.e. not worsening) state of disease, preventing spread of disease, delay or slowing of disease progression, amelioration or palliation of the disease state, including regression of the disease, diminishment of the reoccurrence of disease, and remission (whether partial or total), whether detectable or undetectable. “Treating” and “treatment” may also refer to prolonging survival as compared to expected survival if not receiving treatment.

[0109] The anti-CD3 antibody treatment can comprise a composition comprising the anti-CD3 antibody or antibody fragment thereof and a pharmaceutically acceptable carrier or diluent.

[0110] Suitable diluents and carriers are described, for example, in Remington's Pharmaceutical Sciences, 22ndEdition (Pharmaceutical Press and Philadelphia College of Pharmacy at the University of the Sciences, 2012). On this basis, the compositions include, albeit not exclusively, solutions of the anti-CD3 antibody or antibody fragment thereof in association with one or more pharmaceutically acceptable vehicles or diluents, and contained in buffered solutions with a suitable pH and iso-osmotic with the physiological fluids.

[0111] The anti-CD3 antibody treatment can comprise a single dose of the anti- CD3 antibody or antibody fragment thereof or multiple doses of the anti-CD3 antibody or antibody fragment thereof. The treatment can also comprise one or more course of treatment, which can be followed by an inter-course interval. A person of skill in the art can readily determine suitable dosing regimens, including for example number of doses, number of courses of treatment, duration of courses of treatment, frequency of courses of treatment, duration of inter-course intervals, and the like.

[0112] In an embodiment of a method or use disclosed herein, the treatment comprises a single dose of the anti-CD3 antibody or antibody fragment thereof. In another embodiment of a method or use disclosed herein, the treatment comprises multiple doses of the anti-CD3 antibody or antibody fragment thereof. In yet another embodiment of a method or use disclosed herein, the treatment comprises at least one course of treatment comprising 10 doses administered over 12-14 days.

[0113] The term “known responder” or “KR” as used herein refers to a subject at risk of developing T 1 D that exhibited a beneficial or desired clinical result post-treatment with the anti-CD3 antibody or antibody fragment thereof compared to pre-treatment with the anti-CD3 antibody or antibody fragment thereof. The term “known non-responder” or “KNR” as used herein refers to a subject at risk of developing T 1 D that did not exhibit a beneficial or desired clinical result post-treatment with the anti-CD3 antibody or antibody fragment thereof compared to pre-treatment with the anti-CD3 antibody or antibody fragment thereof.

[0114] The control profile may be a reference value and / or may be derived from one or more samples, optionally from historical type 1 diabetes data from a pool of samples with known annotation of KR or KNR.

[0115] In some embodiments of a method or use disclosed herein, the test profile from the subject at risk of developing T1 D is compared to a control profile from a KR and / or a KNR with at least one baseline T 1 D risk factor in common with the subject at risk of developing T1 D. In other embodiments of a method or use disclosed herein, the test profile from the subject at risk of developing T1 D is compared to a control profile from a KR and / or a KNR with the same baseline T1 D risk factors as the subject at risk of developing T1 D.

[0116] In another aspect, the present disclosure provides a method for predicting progression to type 1 diabetes (T 1 D) in a subject at risk of developing T 1 D comprising: a) determining a test profile from a sample from the subject comprising:- an lgG2 response to Bifidobacterium longum (B. longumy- an lgG2 response to Enterococcus faecalis (E. faecalisy- an lgG2 response to Dialister invisus (D. invisusy and / or- an IgA response to Dialister invisus (D. invisusy b) comparing the test profile with a control profile from known progressors (KPs) or known non-progressors (KNPs) to T1 D comprising:- the lgG2 response to B. longum',- the lgG2 response to E. faecalis,- the lgG2 response to D. invisus’, and / or- the IgA response to D. invisus, c) determining the level of similarity of a) and b); and d) classifying the subject as likely to progress to T1 D if there is a high level of similarity of the test profile to the control profile of KPs; and or a low similarity of the test profile to the control profile of KNPs, and not classifying the subject as likely to progress to T1 D if there is a low level of similarity of the test profile to the control profile of KPs; and / or a high level of similarity of the test profile to the control profile of KNPs.

[0117] In an embodiment, the method is used to select a subject for participation in a clinical trial for an anti-CD3 antibody or antibody fragment thereof herein disclosed.

[0118] In an embodiment, the test profile and the control profile comprise the lgG2 response to B. longum and / or the lgG2 response to E. faecalis. In another embodiment, the test profile and the control profile comprise the lgG2 response to B. longum.

[0119] The test profile and the control profile can further comprise other known predictors of progression to T1 D, for example an HLA class II haplotype or a C-peptide response. In an embodiment, the test profile and the control profile further comprise an HLA class II haplotype of the subject. In another embodiment, the HLA class II haplotype is HLA-DR4. In yet another embodiment, the test profile and the control profile comprise the lgG2 response to D. invisus and / or the IgA response to D. Invisus and the HLA class II haplotype is HLA-DR4.

[0120] The term “known progressor” or“KP” as used herein means a subject at risk of developing T 1 D that progressed to T1 D. The term “known non-progressor” or “KNP” as used herein means a subject at risk of developing T1 D that did not progress to T1 D. In some embodiments, the test profile from the subject at risk of developing T1 D is compared to a control profile from KPs and / or KNPs with at least one baseline T1 D risk factor in common with the subject at risk of developing T1 D. In other embodiments, the test profile from the subject at risk of developing T1 D is compared to a control profile from KPs and / or KNPs with the same baseline T 1 D risk factors as the subject at risk of developing T 1 D.

[0121] Likewise, the control profile may be a reference value and / ormay be derived from one or more samples, optionally from historical type 1 diabetes data from a pool of samples with known annotation of KP or KNP.

[0122] In an embodiment, the control profile disclosed herein is a value that is continually updated as further samples are collected and lgG2 / lgA response, HLA class II haplotype values, and / or C-peptide response are measured and correlated. It will be understood that the control profile represents an average of the lgG2 / lgA response, HLA class II haplotype values, and / or C-peptide response described herein. Average values may, for example, be the mean values or median values. In an embodiment, the control profile may be obtained experimentally.

[0123] In an embodiment, a higher level of similarity to the control profile is indicated by a higher correlation value computed between the test profile and the control profile, optionally wherein the correlation value is a correlation coefficient. In another embodiment, a lower level of similarity to the control profile is indicated by a lower correlation value computed between the sample test profile and control profile, optionallywherein the correlation value is a correlation coefficient. In one embodiment, the correlation coefficient is a linear coefficient, optionally a Pearson correlation coefficient, or a monotonic coefficient, optionally a Spearman’s correlation coefficient.

[0124] In another embodiment, a high level of similarity is indicated by a Pearson correlation coefficient or a Spearman’s between the test profile and the control profile having an absolute value between 0.5 to 1 , optionally between 0.75 to 1 , and a low level of similarity to the control profile is indicated by a Pearson correlation coefficient or a Spearman’s correlation coefficient between the test profile and the control profile having an absolute value between 0 to 0.5, optionally between 0 to 0.25.

[0125] Methods of determining the similarity between profiles are well known in the art. Methods of determining similarity may in some embodiments provide a non- quantitative measure of similarity, for example, using visual clustering. In other embodiments, similarity may be determined using methods which provide a quantitative measure of similarity.

[0126] In an embodiment, similarity may be measured by computing a “correlation coefficient”, which is a measure of the interdependence of random variables that ranges in value from -1 to +1 , indicating perfect negative correlation at -1 , absence of correlation at zero, and perfect positive correlation at +1 . In an embodiment, the correlation coefficient may be a linear correlation coefficient, for example, a Pearson product-moment correlation coefficient.

[0127] A Pearson correlation coefficient (r) is calculated using the following formula:

[0128] In an embodiment, the correlation coefficient may be a monotonic correlation coefficient, for example, a Spearman’s rank correlation coefficient.

[0129] A Spearman’s rank correlation coefficient (p) is calculated using the following formula:where cov(R(X),R(Y)) is the covariance of rank variables and OR(X) and ORCO are their standard deviations.

[0130] In one embodiment, X and Y are the lgG2 / lgA response, HLA class II haplotype values, and / or C-peptide response in a test profile and a control profile, respectively.

[0131] In an embodiment, a correlation coefficient calculated between a test profile and a control profile indicates a high level of similarity to the control profile when the correlation coefficient has an absolute value between 0.5 to 1 , optionally between 0.75 to 1 , and a low level of similarity to the control profile when the correlation coefficient has an absolute value between 0 to 0.5, optionally between 0 to 0.25.

[0132] It will be appreciated that any “correlation value” which provides a quantitative scaling measure of similarity between profiles may be used to measure similarity.

[0133] In an embodiment, the lgG2 / lgA response, HLA class II haplotype value, and / or C-peptide response is analyzed using combinatorial or multivariate dimension reduction statistical or mathematical methods. Combinatorial or multivariate dimension reduction statistical or mathematical methods may be clustering analyses or desirability analyses to enable comparisons between gene profiles with dimension-reduced empirical values.

[0134] In an embodiment, the clustering analysis is principal component analysis. Principal component analysis is applied as an unbiased tool to visualize similarity between the lgG2 / lgA response, HLA class II haplotype value, and / or C-peptide response of different groups (e.g., a KR specific control profile, a KNR specific control profile, and a test profile; or a KP specific profile, a KNP specific profile, and a test profile). In another embodiment, the clustering analysis is canonical correlation analysis. Canonical correlation analysis is applied as a supervised technique to test whether multivariate gene profiles of different groups are statistically distinct, and to provide information on their level of similarity.

[0135] The lgG2 and / or IgA response to the commensal bacteria of the gut, for example Bifidobacterium longum (B. longum), Enterococcus faecalis (E. faecalis), and / or Dialisterinvisus (D. invisus), can be measured in any sample obtained from a subject that contains antibodies. This sample can include fluid and / or tissue, for example serum or plasma fractions of the blood, breast milk, fecal fluid, cerebrospinal fluid, and / or intestinal or lung lavage. In an embodiment, the sample comprises a serum sample, a plasma sample, a breast milk sample, a fecal fluid sample, a cerebrospinal fluid sample, an intestinal lavage sample and / or a lung lavage sample. In another embodiment, the sample comprises a serum or a plasma sample. In another embodiment, the sample comprises a serum sample. In another embodiment, the sample comprises a plasma sample. In another embodiment, the sample comprises a breast milk sample. In another embodiment, the sample comprises a fecal fluid sample. In another embodiment, the sample comprises a cerebrospinal fluid sample. In yet another embodiment, the sample comprises an intestinal lavage sample. In a further embodiment, the sample comprises a lung lavage sample.

[0136] The lgG2 and / or IgA response to the commensal bacteria of the gut, for example Bifidobacterium longum (B. longum), Enterococcus faecalis (E. faecalis), and / or Dialister invisus (D. invisus), can be measured by any method known in the art for measuring response of a sample’s lgG2 and / or IgA to the commensal bacteria. For example, the lgG2 and / or IgA response to the commensal bacteria of the gut can be measured using an anti-commensal antibody (ACAb) assay. The ACAb essay was described in detail in Paun et al., 2019, the content of which is herein incorporated in its entirety.

[0137] The term “subject” as used herein refers to a mammal, for example a human, a domesticated animal, or livestock. A domesticated animal can, for example, be a canine, an equine, or a feline. In an embodiment of a method or use disclosed herein, the subject is a human subject. In another embodiment of a method or use disclosed herein, the subject is canine, equine, or feline. In an embodiment of a method or use disclosed herein, the subject is a juvenile or an adult. In another embodiment of a method or use disclosed herein, the subject is a juvenile. In a further embodiment of a method or use disclosed herein, the subject is an adult.Examples

[0138] The following non-limiting Examples are illustrative of the present disclosure:Example 1

[0139] The search for biomarkers predictive of treatment response and disease onset is particularly relevant in T1 D, because the intervention window for preserving p cell function is limited and intensive glucose control initiated early after disease onset does not preserve p cell function (78).

[0140] The T cell-specific anti-CD3 monoclonal antibody teplizumab was tested in patients with recent onset T1 D where it preserved residual insulin production (79). Although the mechanism of effect is not fully elucidated, teplizumab treatment promotes CD8+T cells to adopt an exhausted state that may reduce activity against cells (20-23). In a humanized mouse model, teplizumab resulted in T cell migration to the intestine resulting in increased expression of immunomodulatory cytokines and suggesting that exposure to the gut mucosal environment contributed to improved cell tolerance (24). A recent T1 D prevention trial (TrialNet; TN-10) enrolled pre-diabetic individuals with a family history of T1 D, presence of > 2 IAB, and dysglycemia (70) who were randomized to a 14- day course of treatment with teplizumab or a placebo. TN-10 met the primary outcome, delay in diabetes diagnosis by a median of 32.5 months (10,25) and improved p cell function (25,26). Increased frequencies of T cells with an exhausted phenotype were associated with these clinical effects (20-23). These results prompted the recent approval of teplizumab to delay T 1 D onset by the U.S. Food and Drug Administration (27).

[0141] The TN-10 trial provided the first demonstration that a T cell-directed therapy could delay onset of an autoimmune disease, however, participant responses were heterogeneous. Better treatment outcomes were associated with HLA class II genotype, presence of HLA-DR4 and absence of HLA-DR3, and presence of residual insulin production (C-peptide responses) (70). Remaining gaps in knowledge include understanding the basis of heterogeneity in patient teplizumab responses and expanding the suite of biomarkers to better identify pre-diabetic as well as new-onset participants most likely to benefit from treatment.

[0142] Identification of serum antibody responses to live, cultured gut commensal bacteria (anti-commensal antibodies; ACAbs) was previously reported in an observational study of relatives of persons with T1 D (28). Several of the identified ACAb reactivities displayed / 7L4-DR-dependence and were associated with the presence of IAB before diabetes development.

[0143] Whether the patterns of ACAbs might further reveal associations with beneficial teplizumab responses in the TN-10 participants was investigated. Extensivestudies have sought to identify changes in composition of the gut microbiome in young children at risk for T1 D, after islet antibody seroconversion, and at onset of clinical diabetes (29), but significant gaps remain. ACAb responses were characterized in TN-10 participants before and after treatment with teplizumab or placebo and the results were analyzed together with available clinical data. Antibody responses to specific intestinal bacteria were associated with time to T1 D diagnosis and with teplizumab effects in prediabetic TN-10 trial participants, thus linking immune detection of intestinal microbes with progression and prevention of T1 D.Methods

[0144] Study Design

[0145] Non-diabetic relatives of T1 D patients (n=63) between 8 and 49 years of age comprised a subgroup of the entire cohort (n=76) enrolled in the TN10 trial (NCT01030861 ), and were studied for ACAb responses. This subgroup was selected based upon the availability of serial serum samples for analysis. The TN-10 trial was a phase 2, randomized, placebo-controlled, double-blind trial to test the efficacy of a single course of teplizumab treatment on time to T1 D in at-risk individuals with evidence of early dysglycemia and at least two types of IAB. The details and clinical outcomes of the TN10 trial have been reported (70). The use of samples for this study was approved by the Research Ethics Boards at the Hospital for Sick Children (REB # 1000057146). The data presented here consist of ACAb responses generated from samples collected at baseline and at 6, 12, 18 months after randomization, C peptide values obtained from oral glucose tolerance test at matching time points, and participant characteristics at baseline. C- peptide area under the curve (AUC) was calculated using the trapezoidal rule and divided by 120, the duration of the oral glucose tolerance test in minute.

[0146] IAB Assays

[0147] All IAB essays to define GADA, IA2A, mlAA, and ZnT8A positivity used well- established methods (75,76) and were performed at the Barbara Davis Diabetes Center (Denver, CO). IAB positivity was defined using threshold indexes of GADA > 20, IA2A > 5, mlAA > 0.01 , and ZnT8A > 0.02.

[0148] Anti-commensal antibody assays

[0149] The ACAb assay platform was used to define the response index for each serum sample, bacterial target, and antibody isotype combination (FIG. 1). The ACAb platform was replicated once on the TN10 samples. The technical replicates reproducedthe results shown herein. A collection of well-characterized human bacterial strains isolated from fecal samples representative of the gut microbiota in healthy humans were employed (Table 2, FIG. 2). The E. coli strain was from American Type Culture Collection (ATCC). This bacterial collection included 31 strains from 15 bacterial species representative of the four most abundant bacteria phyla in the human gut microbiota, Firmicutes, Actinobacteria, Bacteroidetes, and Proteobacteria. With the exception of E. coli, multiple strains of each species were used to capture potential strain-specific antigen complexity. For each species, the strains were grown individually and pooled in equal abundance before labelling for the ACAb assay. Details of the ACAb assay (FIGs. 3A-C) have been previously reported (28). Briefly, each serum sample was serially diluted four times at dilutions of 1 / 50, 1 / 150, 1 / 450, and 1 / 1350, and incubated with freshly cultured bacteria species at equal concentration based upon optical density measurements, stained with one of the amine-reactive dyes CellTrace CFSE or Violet (Thermofisher), respectively (FIG. 3A). Incubation was at 4 °C for 1-2 h, and samples were then washed twice with wash buffer (13,000 RPM, 4 °C, 10 min). After washing, fluorochrome-labelled anti-lgG1(SouthernBiotech, Cat# 9052-09, RRID: AB_2796621 ), lgG2 (SouthernBiotech, Cat# 9070-31 , RRID: AB_2796641), and IgA (Jackson ImmunoResearch Laboratories, Cat# 109-585-011 , RRID: AB_2337859) secondary antibodies were added to the serum- incubated bacteria to qualify of the percentage of bacteria bound by serum antibodies using flow cytometry (FIG. 3B). For each ACAb assay, serum samples from the 63 TN- 10 participants were incubated with a specific pair of labelled bacteria species, fixed by addition of paraformaldehyde to a final concentration of 2% on the same day and the data acquired by flow cytometry within two days. The staining efficiencies of CellTrace CFSE and Violet were tested for each bacterial culture and were required to be > 75% for all strains on the day of the experiment to ensure bacteria viability. Samples were acquired on an LSRFortessa (BD Biosciences) with FACSDiva software. The instrument was standardized daily by adjusting the voltages for photomultiplier tube using rainbow beads (Spherotech). The flow cytometry standard (fcs) files were analyzed in Flowjo (v 10.8.1).

[0150] Response index calculation

[0151] The percentage of anti-isotype antibody-coated bacteria (Ab+%) was calculated for each serum dilution, bacterial target, and isotype. Their corresponding mean fluorescence intensity (MFI) values were Iog2 transformed and linearly regressed using Prism (GraphPad Prism, GraphPad Software, La Jolla California USA) to produce 1 regression line for each sample / isotype / bacteria target combination. The Ab+% wasplotted as y-axis against log-transformed serum dilutions as x-axis. A spline model was used to generate a dilution curve of the ACAb response for each bacterial target and isotype, and the area under the curve was used as the response index (FIG. 3C). Briefly, the area under the curve (AUC) was calculated for each regression line. These AUCs were directly proportional to serum ACAb responses and were compared to the average AUC of all samples for each bacterial target and antibody isotype combination. The differences between sample AUC and the average AUC were expressed as AAUC. Response indices are the 2AAUCvalues, such that the average of all AUCs becomes 1 and sample responses are positive values and either greater than or less than 1 .

[0152] Serum Antibody Quantification

[0153] The total lgG1 , lgG2, and IgA antibodies in TN10 samples were quantified using the enzyme-linked immunosorbent assay (ELISA; lgG1 , IgA: MyBioSource; lgG2: Thermofisher). The mean and standard deviation of lgG1 , lgG2, and IgA serum concentrations were 15.808±6.74, 1 ,46±1 .12, and 4.1 1 ±3.82 mg / mL, respectively.

[0154] Statistical Analyses

[0155] All analyses were completed in R (v 3.6.2) (77). Duplicated samples (n=2) were removed from analysis. The difference in baseline characteristics (Table 3) was tested by Fisher’s exact test for categorical variables, and Welch Two Sample t-test assuming unequal variance for continuous variables using stats. The baseline ACAbs, unsealed or scaled by caret (v 6.0-85), were selected by cox models with boosting (mboost v 2.9-5; CoxBoost v 1.4) with baseline characteristics (Age, Sex, BMI, HLA-DR, Treatment Arm, C-peptide AUC) as mandatory variables (36). The time to event distribution for each stratum (for example high vs low B. longum-\gG2 response) was estimated by Kaplan-Meier analysis (30). The effects of baseline, 6-month, and longitudinal covariates on time to T1 D, as well as the treatment effects in subgroups were estimated by multivariate linear Cox Proportional-Hazards (PH) regression adjusting for age, sex, BMI, treatment arms, and HLA-DR (34). The adjusted p-value cutoff based on effective number of independent tests for estimating effect of interaction between 6-month ACAbs and HLA-DR on time to T1 D was calculated by Matrix Spectral Decomposition (78). All single timepoint survival analyses had modified time to event based on landmark time and model assumptions were validated using survival (v 3.3-1 ) (79, 80). Where the assumptions of proportional hazards did not hold, an additive Cox model with smoothing function was used to estimate the effect of the covariate. The linear mixed models were fitted with nlme (v 3.1-155) (49). The joint models for longitudinal and time-to-event datawere fitted using JM (v 1.5-2) (48). The incident / dynamic time-dependent AUC for Cox models were evaluated using risksetROC (v 1.0.4) (57). The partial likelihood ratio test and AIC for comparison between Cox models are completed using nonnestcox (v 0.0.0.9) with a nested option (54). P<0.05 was considered significant unless stated otherwise in text.Results

[0157] Teplizumab binds to a conserved epitope on the CD3s component of the T cell antigen receptor. Because T cells provide signals important to generating many B cell responses, the stability of antibody responses in the serum of available TN-10 trial participants (n=63) was first tested. All individuals in the current study had one serum sample taken before and at least one serum sample taken after randomization into teplizumab or placebo treatment arms (FIG. 4). The median age was 14 (8 to 49), and age (p=0.73), sex (p = 0.80), and HLA-DR haplotypes (HLA-DR3 p = 1 , HLA-DR4 p = 0.78) were not significantly different across treatment arms (Table 3). Previous studies demonstrated that teplizumab delays T1 D diagnosis by a median of 32.5 months in the entire study cohort (10,25). In the subset of participants whose samples were available for this study, the effect of teplizumab on delaying T 1 D diagnosis remained significant (p = 0.046), with a median time-to-T 1 D of 30.6 months in the placebo versus 61 .1 months in the teplizumab group (FIG. 5) (30).

[0158] The study was designed to characterize anti-commensal responses of isotypes lgG1 , lgG2, and IgA in serum samples (n=228) from trial participants either before or up to 18 months after randomization into treatment arms (FIG. 2A). The abundance of ACAb isotypes was measured against a panel of 31 bacterial strains representing 15 common species, distributed across the four major phyla found in healthy human gut microbiota (FIG. 2B; Table 2). First, all ACAb responses in serum samples isolated at baseline and up to 18 months post-randomization were examined. Responses to these bacterial targets were stable across time, in both treatment groups for each of the three isotypes (FIGs. 6A-C). Total serum lgG1 , lgG2, and IgA antibody concentrations were also quantified in participants samples displaying the well-known higher serum abundance of lgG1 (FIG. 7A) and observed no significant changes across time (12 months p = 0.13, 24 months p = 0.05, 36 months p = 0.16) or treatment arms (p=0.16) compared to baseline (FIG. 7B).

[0159] The TN-10 participants were assessed for islet autoantibodies (IAB) against four antigens (glutamic acid decarboxylase 65 antibodies, GADA; insulinoma associated protein 2 antibodies, IA2A; micro-insulin auto-antibodies, mlAA; and zinc transporter 8 antibodies, ZnT8A). Consistent positivity for >2 of these IAB was an inclusion criterion for the trial (10). IAB titers were collected from randomization until T1 D diagnosis. These IAB specificities did not differ between participants in the two treatment groups at baseline (Table 3) or at any time after randomization (FIG. 7C). Quantitatively, IAB displayed stable abundances and specificities across the observation period (FIG. 7D) and there were no significant differences in the trajectories of ACAb responses over time in either treatment arm (FIGs. 6A-C). Thus, the temporal stability of both IAB and ACAb profiles indicated that antibody responses were not altered by teplizumab treatment.

[0160] Association of ACAb responses with time to T1D diagnosis and treatment responses

[0161] Given the overall stability of both ACAb and IAB measures in both teplizumab- and placebo-treated groups, whether any of the participants’ ACAb responses were associated with the primary clinical endpoint, diabetes diagnosis, was investigated. Landmark analysis was performed to eliminate any potential immortal time bias resulting from use of response (T1 D diagnosis) as a predictor (32-35). Forty-five days, after which the transient effects of teplizumab treatment on lymphocyte counts returned to baseline, was selected as the landmark (10). Because the earliest time to T1 D diagnosis was 95 days, landmark analysis did not require removal of any of the participants. Boosted Cox models (36) were then used to identify baseline variables and ACAb responses associated with T1 D diagnosis, and linear Cox models (37) were used to estimate their effects on time to diagnosis. lgG2 responses to three bacterial species at baseline and at 6 months were identified that were associated with time to T1 D diagnosis. ACAb profiles and statistical significance of these associations are presented below for the actinobacteria Bifidobacterium longum (B. longum), as well as the firmicutes Enterococcus faecalis (E. faecalis) and Dialister invisus (D. invisus).

[0162] The B. longum-\gG2 response was bimodal when aggregated across all time points in this cohort (FIG. 8A). When segregated by time point, the B. longum-\gG2 response remained bimodal at baseline (FIG. 8B) and at 6 months (FIG. 8C), the closest timepoint for which samples were available after randomization. The probability of progressing to T1 D diagnosis positively correlated with B. longum-\gG2 responses at baseline (Cox model, B. longum-\gG2 p = 0.011 , treatment arm p = 0.031 ). The troughbetween the two response modes was selected to dichotomize the participants into high and low B. longum-\gG2 responders for Kaplan-Meier (KM) analysis (FIGs. 8D-F). The teplizumab treatment effect in the anti-B. longum-\gG2 subgroup was greater in those with high baseline titers (FIG. 8E) compared to those with low titers. (FIG. 8D). When comparing all four strata (placebo, treatment, high and low baseline titres), the highest risk of T1 D onset was seen in the placebo-treated, high baseline B. longum-\gG2 response subgroup (FIG. 8F). In contrast, the teplizumab-treated, high baseline B. longum-\gG2 response subgroup displayed reduced risk (pairwise log-rank p = 0.0221 , Benjamini-Hochberg adjusted), indicating that B. longum-\gG2 responses at baseline were predictive of responders to teplizumab treatment. Because ACAb responses were stable over time (FIGs. 6A-E), regression analyses were performed on the 6-month B. longum-\gG2 responses. Six months post-randomization, B. longum-\gG2 response remained associated with progression to T1 D diagnosis (Cox model, B.longum-\gG2 p = 0.007, treatment arm p = 0.031) in the entire cohort. Like the observations at baseline, a less evident teplizumab treatment response was seen for participants in the lower B. longum-\gG2 titre subgroup (FIG. 8G) compared to the higher B. longum-\gG2 titre subgroup at 6-months (FIG. 8H). Thus, higher B. longum-\gG2 responses present at baseline and at 6 months post-randomization were associated with greater teplizumab treatment effect.

[0163] In contrast to B. longum-\gG2, responses to E. faecalis-\gG2 displayed a roughly normal distribution across all time points (FIG. 9A) as well as at single timepoints (baseline, FIG. 9B; 6-month, FIG. 9C). Regression analyses was performed on E. faecalis-\gG2 responses at baseline (FIGs. 9D, 9E) and at 6 months (FIGs. 9F, 9G) using the median (all samples across all time points) to define high vs. low responses. E. faecalis-\gG2 responses at both time points were associated with time to T1 D diagnosis (Cox model, baseline E. faecalis-\gG2 p = 0.003, treatment arm p = 0.023; 6-month E. faecalis-\gG2 p = 0.0009, treatment arm p = 0.033). At both time points, the teplizumab treatment effect was more evident in the high E. faecalis-\gG2 response (baseline, FIG. 9E; 6 months FIG. 9G) compared to the low response subgroup (baseline, FIG. 9D; 6 months FIG. 9F). Thus, as observed for B. longum-\gG2 responses, greater responses to E. faecalis were associated with greater treatment effect.

[0164] C-peptide, a moiety cleaved from proinsulin prior to its co-secretion, is a well-established clinical marker for p cell function where its decline represents T1 D progression (38,39). The performance of the E. faecalis and B. longum lgG2 responseswas compared to C-peptide as predictors of time to T1 D diagnosis and for their association with teplizumab treatment effect. When aggregated across time (FIG. 10A), as well as at single timepoints (baseline, FIG. 10B; 6-month FIG. 10C), the distributions of C-peptide values were asymmetrical. The analysis of reported C-peptide values for these participants (70) showed that they were associated with time to T1 D diagnosis at baseline (FIGs. 10D, 10E) and at 6-month (FIGs. 10F, 10G) time points (Cox model, baseline C-peptide p = 0.022, treatment arm p = 0.0094; 6-month C-peptide p = 0.044, treatment arm p = 0.0456). The subgroup with lower than median baseline C-peptide values (FIG. 10D) displayed a greater treatment response compared to the subgroup with higher than median baseline C-peptide (FIG. 10E). E. faecalis- and B. longum- lgG2 responses displayed equally robust associations with time to T1 D diagnosis compared with C-peptide. These results indicated that these ACAb responses help to identify individuals at greater risk for T1 D diagnosis.

[0165] The analyses revealed associations of ACAb responses with time to T1 D diagnosis in the TN10 cohort adjusted for treatment, age, sex, body mass index (BMI) and HLA-DR haplotype. Next, whether ACAb responses were associated with the impact of treatment effect separable from association to time to T1 D diagnosis was investigated, given that this outcome was modified by the treatment. To achieve this, the effect of teplizumab treatment in subgroups stratified by baseline B. Iongum-ar\d E. faecalis-\gG2 responses using the cutoffs chosen for the Kaplan Meier analyses (FIG. 11) was examined. A high B. longum-\gG2 response at baseline was associated with a more robust teplizumab response (hazard ratio [95% Cl]: high 0.18 [0.10, 0.33]; low 0.83 [0.47, 1.44]). In addition, there was a tendency toward better teplizumab responses in the high baseline E. faecalis-\gG2 response subgroup (hazard ratio [95% Cl]: high 0.43 [0.25, 0.73]; low 0.78 [0.40, 1.52]). These results indicate that baseline ACAb responses may help to identify individuals likely to respond to teplizumab.

[0166] HLA-dependent association of ACAb responses to time to T1D and treatment responses

[0167] HLA class II haplotypes, particularly the DR3 and DR4 variants in populations of European origin, account for the majority of T1 D genetic risk, and together with family history of T1 D are effective screening criteria to identify at-risk individuals (40- 43). In the TN-10 trial, treatment response heterogeneity was reported, with HLA-DR4 carriage associated with better treatment effects (10). An observational study of high-risk, pre-diabetic participants was examined and observed ACAb responses associated withlater T 1 D diagnosis that displayed H / _A-DR-dependence (27). Given the importance of HLA class II molecules in the formation of T cell-dependent antibodies (31), whether / - / LA- DR haplotypes influenced immune responses to gut bacteria in the TN-10 participants was examined. Specifically, ACAb responses 6 months after randomization that displayed HLA-DR4 associations with time to T1 D diagnosis were investigated. D. invisus is a common gut microbe that has been previously evaluated in studies of T1 D risk using fecal samples from pediatric cohorts (44, 45). D. invisus-\gG2 responses interacted with HLA- DR4 in association with time to T1 D onset (Cox model, D. invisus-\gG2- HLA-DR4 interaction p = 0.001 , treatment arm p = 0.021). D. invisus-\gG2 responses were transformed across all time points to a standard normal distribution for regression analyses (FIGs. 12A, 12B). The median D. invisus-\gG2 response was used as the cutoff to define high vs low 6-month response subgroups. When segregating participants by HLA-DR4 genotype, associations of D. invisus-\gG2 responses either with time to T1 D diagnosis or with treatment effect were absent in the HLA-DR4- subgroup (FIG. 12C). In contrast, HLA-DR4+individuals (FIG. 12D) with higher6-month D. invisus-\gG2 responses (dashed line) were at greater risk for progressing to T1 D than the subgroup with lower 6- month D. invisus-\gG2 responses (solid line). Moreover, teplizumab treatment of HLA- DR4+individuals independently delayed T1 D onset in both high and low D. invisus-\gG2 response subgroups compared to placebo treatment (FIG. 12D).

[0168] To examine the predictive value of D. invisus-\gG2 responses for response to teplizumab, treatment effect was compared in subgroups stratified by HLA-DR4 genotype and baseline D. invisus-\gG2 responses. Building on evidence that HLA-DR4 presence is associated with better teplizumab response, it was observed that in HLA- DR4-absent participants, higher baseline D. invisus-\gG2 response was associated with greater probability of progression to T1 D diagnosis despite teplizumab treatment (hazard ratio [95% Cl]: high 3.51 [0.57,21.67]; low 0.43 [0.12, 1.55]). In contrast, higher baseline D. invisus-\gG2 responses in / 7LA-DR4-present participants were associated with better outcome (hazard ratio [95% Cl]: high 0.05 [0.02,0.16]; low 0.21 [0.06, 0.70]). Collectively, these results indicated that D. invisus-\gG2 responses displayed an interaction with HLA- DR4 haplotype in their association with T1 D progression as well as with teplizumab treatment response. Specifically, HLA-DR4+individuals with high D. invisus-\gG2 responses had greater risk for progression to T1 D diagnosis and were more likely to benefit from teplizumab treatment, whereas the converse trend was observed in HLA- DR4~ individuals.

[0169] AC Ab responses associated with T1D progression contribute to disease risk prediction

[0170] The longitudinal design of TN-10 trial permitted the use of time-dependent models to explore associations between ACAb responses and time to T1 D diagnosis (43). Because ACAb responses were stable over time in both treatment arms (FIGs. 6A-E), inclusion of later time points increased power to detect ACAb response associations with time to T1 D diagnosis. Such time-dependent Cox models also provide a complementary approach to landmark analysis for addressing immortal time bias (34, 46). Indeed, as observed in single timepoint analysis, controlling for age, sex, BMI, treatment arm, and HLA-DR, higher B. longum- and E. faecalis- lgG2 responses evident at any time before or after treatment arm randomization were associated with increased risk of T1 D diagnosis (FIG. 13A, p = 0.006 and FIG. 13B, p = 0.015; respectively). Given the association of time-dependent ACAb responses with risk of T1 D diagnosis, these two ACAb responses were compared with C-peptide values for their performance in predicting time to T1 D diagnosis. An increase in C-peptide before or after the randomization, controlling for prespecified variables, was associated with decreased risk of T1 D diagnosis (FIG. 13C), consistent with prior evidence that higher C-peptide values reflect preservation of p cell function.

[0171] In contrast to B. longum- and E. faecalis-\gG2 responses, whose associations with time to T1 D diagnosis were significant in the entire TN10 cohort, D.invisus-\gG2 responses were differentially associated with risk of progressing to T1 D diagnosis in HLA-DR4+vs. HLA-DR4" participants (FIG. 13D). In HLA-DR4- participants, increase in D. invisus-\gG2 was associated with decreased risk of T1 D diagnosis (D. invisus-\gG2 p = 0.02). For HLA-DR4+but not HLA-DR4- individuals, D. invisus-\gG2 responses were positively associated with a future T 1 D diagnosis (D. invisus-\gG2- HLA- DR4 interaction p = 0.005). These results reveal the influence of HLA-DR4 on the association between D. invisus-\gG2 responses and time to T1 D diagnosis, in agreement with the observations in the single-timepoint analysis.

[0172] The previous analysis approach estimated the average hazards over time associated with repeated ACAb measures. To evaluate the association between longitudinal ACAb and time to T1 D diagnosis, joint models were used for longitudinal and time-to-event data (47, 48). For each ACAb response, measurements of all participants from all timepoints were normalized into a standard normal distribution and used linear mixed models to estimate time effects (49). Survival data was modelled to account fortreatment arms, age, sex, BMI, and HLA-DR genotype. Longitudinal B. longum- (HR = 1.99, p = 0.002) and E. faecalis- (HR = 1.76, p = 0.008) lgG2 responses, as well as the interaction between D. invisus-\gG2 responses and HLA-DR4 (HR = 2.79, p = 0.015) were associated with risk of T1 D diagnosis.

[0173] The joint models were used to calculate the expected probability of remaining T1 D-free for selected individuals combining baseline characteristics and longitudinal B. longum- (FIG. 14A), E. faecalis- (FIG. 14B), or D. invisus- lgG2 (FIGs. 14C, 14D) responses. These trends were consistent with single timepoint analyses. Longitudinal analyses indicated that ACAb responses were stable over time and consistently associated with risk of future T 1 D diagnoses. Whether ACAb responses at baseline, in addition to HLA-DR4 haplotype and baseline C-peptide values, provided predictive value for a future T1 D diagnosis was then assessed. Incident / dynamic timedependent area under the receiver operating characteristic curve (AUC)s, Akaike information criterion, and partial likelihood ratio test were used to compare performance of models incorporating baseline characteristics, treatment arm, and ACAb responses on predicting T 1 D diagnosis (50-54) (Table 4). These models demonstrate that an interaction between HLA-DR4 genotype and D. invisus-\gG2 responses improved prediction of T1 D diagnosis compared to HLA-DR4 genotype alone (FIGs. 15A, 15B). Moreover, B. longum- and E. faecalis-\gG2 responses independently improved T1 D diagnosis prediction compared to only C-peptide value (FIGs. 15C, 15D). The improved AUC by incorporation of ACAb responses indicated that their inclusion as markers will aid participant selection in future T 1 D prevention trials.

[0174] Validation in and independent cohort

[0175] Last, the validation of associations observed in TN-10 between ACAb responses and time to diabetes diagnosis was sought in an independent study cohort. Because TN-10 is the only trial in which teplizumab has been tested for TI D prevention, independent assessment of associations between the specific ACAb responses, HLA- DR4, a predictor of teplizumab response in TN-10, and time to T1 D diagnosis were prioritized. Pathways to Prevention (TN-01) is an observational study of non-diabetic relatives of persons with T 1 D who receive frequent screening for IAB (55). Serum samples from pre-diabetic TN-01 participants (n=61) with two lABs who later progressed to diabetes diagnosis were accessed. The time to diagnosis / last follow-up in this validation cohort (median [IQR]: 343.76 [176.87] days) was significantly shorter than in TN-10 trial participants (median [IQR]: 1101

[1123] days; p = 1.8e-14, Wilcoxon rank sum test; FIGs.16A-B). Despite this difference and sample size, an interaction between ACAb responses and carriage of HLA-DR4 was observed. In the validation cohort, the risk of progression to T1 D diagnosis in HLA-DR4+vs. HLA-DR4- participants differed based on the magnitude of B. longum-\gG2 responses (FIGs. 17A-B, p = 0.03). The low B. longum-\gG2 response subgroup (FIG. 17A) showed no significant difference between HLA-DR4+vs. HLA-DR4- participants in time to T1 D diagnosis. In contrast, in the high B. longum-\gG2 response subgroup, HLA-DR4+participants displayed significantly longer time to T1 D diagnosis (p = 0.03) (FIG. 17B). A similar association for IgA responses to D. invisus was observed, which correlated with lgG2 responses to D. invisus in both cohorts (FIG. 18C, TN-01 : p = 0.0006; TN-10: p = 2.6e-07).

[0176] The differential distribution of time to T1 D diagnosis in HLA-DR4+participants was evident in the high D. invisus-\gA response subgroup (FIG. 18B; p = 0.04) but not the low D. invisus-\gA response subgroup (FIG. 18A). The lack of significant association between D. invisus-\gG2 response and HLA-DR4 that was observed in the TN-10 samples may reflect the narrow range of time to T1 D diagnosis in the TN-01 validation cohort. In the TN-10 trial, HLA-DR4 was associated with response to teplizumab which in turn delayed time to diabetes diagnosis. The far more restricted time interval to diagnosis in TN-01 validation compared to TN-10 participants limited direct comparisons of the HLA-DR4 effect between the two cohorts. With this caveat, these TN-01 validation data concur with the previously reported / 7L4-D ?-dependence of ACAb responses associated with T1 D risk (28). The TN-01 cohort results substantiate the observed associations of B.longum and D. invisus ACAb responses with time to diabetes diagnosis and support the value of these responses for prediction of teplizumab response in the TN- 10 participants.Discussion

[0177] Teplizumab delayed T1 D diagnosis in the TN-10 trial of relatives of patients who displayed islet autoantibodies and dysglycemia at baseline (10,25,26). Based upon these outcomes, the U.S. FDA recently approved teplizumab for treatment of at-risk prediabetic patients (27). However, like many immunotherapies (2,3,7,9, 11,56), participant responses to teplizumab were heterogeneous. Participant age, HLA-DR genotype, and islet autoantibody specificities were associated with treatment response (10). In a prior trial in new-onset diabetics, the activity of teplizumab was associated with greater frequencies of CD8+T cells that co-express inhibitory receptors suggestive of partial “exhaustion” (21). In a humanized mouse model reconstituted with human hematopoieticcells, teplizumab treatment promoted xenograft tolerance accompanied by production of immunoregulatory interleukin-10 (IL-10) in gut lymphoid tissues. Induction of graft tolerance was compromised when these mice were pre-conditioned with an antibiotic cocktail that altered gut microbiome composition (57). Moreover, in this model, teplizumab treatment elicited human gut-tropic T cell migration to the intestine and production of IL- 10 (24). Collectively, findings in human trials and in humanized mouse models implicate a relationship between teplizumab’s activity and gut-associated immune responses where bacterial antigens are abundant. Thus, ACAb in the TN-10 cohort from randomization and up to 18 months before T 1 D diagnosis were here examined to evaluate their associations with disease progression and teplizumab effect.

[0178] Three ACAb responses were identified before and after randomization that were associated with progression to T1 D in the TN-10 cohort. First, higher lgG2 responses to B. longum- and E. faecalis were associated with higher risk of progression to T1 D when all participants were considered together. Without being bound by theory, a higher ACAb response observed before T1 D diagnosis may reflect enhanced intestinal permeability enabling immune exposure to bacterial antigens consistent with reports in cohorts of pre-diabetic and recently diagnosed individuals (58-60). Second, higher lgG2 antibody responses to B. longum were associated with time to T1 D diagnosis in the TN- 10 cohort. These data support previous suggestions that T1 D is associated with gut microbial responses that produce a non-inflammatory, homeostatic state (45,61-63). A prominent member of early life microbiota, B. longum can metabolize human milk oligosaccharides and persists in adult gut microbiota (64-67). A previous longitudinal study of fecal microbiome composition in infants and young children at risk for T1 D suggested that insufficient early immune exposure to this species is associated with increased T 1 D risk (67). In contrast to B. longum, E. faecalis can serve both as a probiotic that regulates infant inflammatory immune responses (68, 69) and as a pathogen that exacerbates inflammatory responses and impairs barrier integrity in settings of inflammatory bowel and graft-versus-host diseases (70-72). These variable impacts of E. faecalis underscore the dependence of immune responses to the gut bacteria on both microbial community composition and the tone of immune responses in the intestinal mucosa. The dynamic relationships within the intestinal microbial community and between which and the mucosal immune system may be uniquely affected by immunomodulatory treatment, such as teplizumab, whose effect on the microbial community is difficult to predict ex vivo and require clinical investigations to understand. The third association identified was an / 7L4-DR4-dependent association between D. invisus-\gG2 responsesand progression to T1 D diagnosis. The HLA-DR4 effect on the magnitude of the D. / nv / sus-reactive antibodies in the TN-10 participants suggests a role for T cell help in their formation. D. invisus abundance in the gut has been reported to be associated with risk for both T 1 D and celiac disease (44, 45, 73). Based upon the present observations of D. invisus-\gG2 responses, the association of this organism with both diseases likely reflects their shared contribution of HLA-DR4. Both the B. longum and / 7L4-DR4-dependent associations with time to T1 D diagnosis were validated in the independent TN-01 study cohort.

[0179] It was here found that the same ACAb responses that were associated with progression to T1 D also predict treatment responses to the anti-CD3 antibody teplizumab prior to randomization. The difference in treatment effect between subgroups stratified by baseline ACAb indicates that immune responses to the gut microbiota are connected to the mechanism of action of teplizumab in the context of T1 D prevention. Teplizumab displayed a more beneficial effect on participants who were further progressed towards T1 D onset, since treatment effect was more evident in participant subgroups defined by high baseline B. longum- or E. faecalis-\gG2 responses. Moreover, an interaction is here reported between HLA-DR4, an established disease risk marker and predictor of treatment response, and D. invisus-\gG2 responses, which provided additional features to stratify at-risk individuals for treatment. The clinical relationship between ACAb responses and progression to islet autoimmunity or T1 D are unknown, and as single measures displayed a modest clinical impact (as assessed by hazard ratios) in the TN-10 analysis. However, without being bound by theory, the robust association of ACAb responses, independently (B. longum and E. faecalis) or in interaction with HLA-DR (D. invisus) to treatment response and time to T1 D may reflect T cell involvement in mediating response to the gut microbiota and reactivity to the islet tissue. Notably, titers of both antiislet autoantibodies and ACAb antibodies remained stable overtime and were not altered by the single course of teplizumab. Thus, these antibody responses provide reliable markers that have predictive value, and that add predictive value to defined risk markers HLA-DR4 and C-peptide for early identification of individuals at risk of developing T1 D or who have recently been diagnosed with T1 D, and who will benefit from an anti-CD3 antibody treatment.

[0180] In summary, it is here shown that immune responses to common intestinal microbes are predictive of progression to T1 D diagnosis in the context of an immunotherapy that delayed disease onset. ACAb responses identified at recruitment ofthe TN-10 trial were predictive of teplizumab responders. Thus, ACAb responses are useful to guide the recruitment of individuals for T1 D trials and to select subjects for treatment with an anti-CD3 antibody. At a mechanistic level, ACAb responses defined here provide past records of exposure to the gut microbial community and provide independent evidence that T1 D risk is impacted by early life factors that modify the gut microbiota and especially, their interaction with the host immune system.

[0181] While the present disclosure has been described with reference to examples, it is to be understood that the scope of the claims should not be limited by the embodiments set forth in the examples, but should be given the broadest interpretation consistent with the description as a whole.

[0182] All publications, patents and patent applications are herein incorporated by reference in their entirety to the same extent as if each individual publication, patent or patent application was specifically and individually indicated to be incorporated by reference in its entirety. Where a term in the present description is found to be defined differently in a document incorporated herein by reference, the definition provided herein is to serve as the definition for the term.Table 1. Amino acid sequences comprised in heavy chain variable regions and light chain variable regions.Table 2. Bacterial species and strains used in the study.Table 3. TN-10 Participants were randomized across treatment arms in terms of baseline characteristics. Difference in distribution of continuous variables (such as age, BMI, and C-peptide) across the two treatment arms were tested by Welch Two Sample t- test. Difference in distribution of discrete variables across treatment arms were tested by Fisher's Exact Test. No significant difference across treatment arms was found.TeplizumabBaseline Characteristics Placebo (n=22)(n=41)AgeMedian (IQR) 14 (11-21) 14 (11-15.8)Range 8 - 49 8 - 45Male (n (%)) 22 (54) 11 (50)BMI (median (IQR)) 20.0 (17.3- 21.7 (18.4-25.5) 24.8)C-peptide (median (IQR)) 1.8 (1.5-2.1) 2.1 (1.6-2.7)HLA-DR Carriage (n (%))HLA-DR3 18 (45) 10 (42)HLA-DR4 27 (66) 14 (64)Islet Autoantibody Positivity (n (%))Glutamic acid decarboxylase 65 antibodies (GADA) 5 (12) 3 (14)Micro-insulin auto-antibodies (mlAA) 17 (41 ) 11 (50)Insulinoma associated protein 2 antibodies (IA2A) 15 (36) 4 (18)Zinc transporter 8 antibodies (ZnT8A) 11 (27) 3 (14)Table 4. Baseline ACAb responses improve performance of Cox models in predicting T1D diagnosis. 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Claims

Claims:1 . A method of selecting a subject at risk of developing type 1 diabetes for treatment with an anti-CD3 antibody or antibody fragment thereof comprising: a) determining a test profile comprising an immunoglobulin G2 (lgG2) response to at least one species of commensal bacteria of the gut, from a sample from the subject; b) comparing the test profile with a control profile comprising the lgG2 response to the at least one species of commensal bacteria of the gut, from known responders (KRs) or known non-responders (KNRs) to the anti-CD3 antibody treatment; c) determining the level of similarity of a) and b); and d) selecting the subject for the treatment if there is a high level of similarity of the test profile to the control profile of KRs; and / or a low similarity of the test profile to the control profile of KNRs, and not selecting the subject for the treatment if there is a low level of similarity of the test profile to the control profile of KRs; and / or a high level of similarity of the test profile to the control profile of KNRs.

2. The method of claim 1 , wherein the test and control profiles are analyzed using combinatorial or multivariate dimension reduction statistical or mathematical methods.

3. The method of claim 2, wherein the combinatorial or multivariate dimension reduction statistical or mathematical methods comprise clustering analyses or desirability analysis to enable comparisons between multivariate profiles with dimension-reduced empirical values.

4. The method of claim 1 , wherein determining the high level of similarity is indicated by a high correlation value computed between the test profile and the control profile, optionally wherein the correlation value is a correlation coefficient.

5. The method of claim 1 , wherein determining the low level of similarity is indicated by a low correlation value computed between the test profile and the control profile, optionally wherein the correlation value is a correlation coefficient.

6. The method of claim 4 or 5, wherein the correlation coefficient is a linear coefficient, optionally a Pearson correlation coefficient, or a monotonic coefficient, optionally a Spearman’s correlation coefficient.

7. The method of claim 6, wherein the high level of similarity is indicated by a Pearson correlation coefficient or a Spearman’s correlation coefficient between the test profile and the control profile having an absolute value between 0.5 to 1 , optionally between 0.75 to 1 , and wherein the low level of similarity is indicated by a Pearson correlation coefficient or a Spearman’s correlation coefficient between the test profile and the control profile having an absolute value between 0 to 0.5, optionally between 0 to 0.25.

8. Use of an anti-CD3 antibody or antibody fragment thereof in treating a subject at risk of developing type 1 diabetes, wherein the subject has been previously identified as having a responder profile, wherein the responder profile has been identified by determining a similarity of a) a test profile comprising an immunoglobulin G2 (lgG2) response to at least one species of commensal bacteria of the gut, from a sample from the subject; and b) a control profile comprising the lgG2 response to the at least one species of commensal bacteria of the gut, from known responders (KRs) or known non-responders (KNRs) to the anti-CD3 antibody treatment.

9. The method of any one of claims 1 to 7 or the use of claim 8, wherein the at least one species of commensal bacteria of the gut comprises an actinobacteria and / or a firmicute.

10. The method of any one of claims 1 to 7 and 9 or the use of claim 8 or 9, wherein the at least one species of commensal bacteria of the gut comprises Bifidobacterium longum (B. longum), Enterococcus faecalis (E. faecalis), Dialister invisus (D. invisus), or a combination thereof.11 . The method of any one of claims 1 to 7, 9, and 10 or the use of any one of claims 8 to 10, wherein the at least one species of commensal bacteria of the gut comprises B. longum and / or E. faecalis.

12. The method of claim any one of claims 1 to 7 and 9 to 11 or the use of any one of claims 8 to 11 , wherein the at least one species of commensal bacteria of the gut comprises B. longum.

13. The method of any one of claims 1 to 7, 9, and 10 or the use of any one of claims 8 to 10, wherein the test profile and the control profile further comprise an HLA class II haplotype of the subject, and wherein the HLA class II haplotype is HLA-DR4.

14. The method or the use of claim 13, wherein the at least one species of commensal bacteria of the gut comprises D. invisus.

15. The method of any one of claims 1 to 7 and 9 to 14 or the use of any one of claims 8 to 14, wherein the anti-CD3 antibody or antibody fragment thereof comprises a heavy chain variable region comprising complementarity determining regions CDR-H1 , CDR-H2, and CDR-H3, and / or a light chain variable region comprising CDR-L1 , CDR- L2, and CDR-L3; wherein the amino acid sequences comprise:CDR-H1 : RSTMH SEQ ID NO: 1 ;CDR-H2: YINPSSAYTNYNQKFKD SEQ ID NO: 2;CDR-H3: PQVHYDYNGFPY SEQ ID NO: 3;CDR-L1 : SASSSVSYMN SEQ ID NO: 4CDR-L2: DSSKLAS SEQ ID NO: 5; andCDR-L3: QQWSRNPPT SEQ ID NO: 6.

16. The method of any one of claims 1 to 7 and 9 to 14 or the use of any one of claims 8 to 14, wherein the anti-CD3 antibody or antibody fragment thereof comprises a heavy chain variable region comprising an amino acid sequence as set forth in SEQ ID NO: 7 or 8 and / or a light chain variable region comprising an amino acid sequence as set forth in any one of SEQ ID NOs: 9-11 .

17. The method of any one of claims 1 to 7 and 9 to 14 or the use of any one of claims 8 to 14, wherein the anti-CD3 antibody is teplizumab.

18. The method of any one of claims 1 to 7 and 9 to 17 or the use of any one of claims 8 to 17, wherein the treatment comprises a single dose of the anti-CD3 antibody or antibody fragment thereof.

19. The method of any one of claims 1 to 7 and 9 to 17 or the use of any one of claims 8 to 17, wherein the treatment comprises multiple doses of the anti-CD3 antibody or antibody fragment thereof.

20. A method for predicting progression to type 1 diabetes (T 1 D) in a subject at risk of developing T 1 D comprising: a) determining a test profile from a sample from the subject comprising:- an lgG2 response to Bifidobacterium longum (B. longumy- an lgG2 response to Enterococcus faecalis (E. faecalisy- an lgG2 response to Dialister invisus (D. invisusy and / or- an IgA response to Dialister invisus (D. invisusy b) comparing the test profile with a control profile from known progressors (KPs) or known non-progressors (KNPs) to T1 D comprising:- the lgG2 response to B. longum',- the lgG2 response to E. faecalis,- the lgG2 response to D. invisus’, and / or- the IgA response to D. invisus, c) determining the level of similarity of a) and b); and d) classifying the subject as likely to progress to T1 D if there is a high level of similarity of the test profile to the control profile of KPs; and or a low similarity of the test profile to the control profile of KNPs, and not classifying the subject as likely to progress to T1 D if there is a low level of similarity of the test profile to the control profile of KPs; and / or a high level of similarity of the test profile to the control profile of KNPs.21 . The method of claim 20, wherein the method is used to select the subject for participation in a clinical trial for an anti-CD3 antibody or antibody fragment thereof.

22. The method of claim 20 or 21 , wherein the test and control profiles are analyzed using combinatorial or multivariate dimension reduction statistical or mathematical methods.

23. The method of claim 22, wherein the combinatorial or multivariate dimension reduction statistical or mathematical methods comprise clustering analyses or desirability analysis to enable comparisons between multivariate profiles with dimension-reduced empirical values.

24. The method of claim 20 or 21 , wherein determining the high level of similarity is indicated by a high correlation value computed between the test profile and the control profile, optionally wherein the correlation value is a correlation coefficient.

25. The method of claim 20 or 21 , wherein determining the low level of similarity is indicated by a low correlation value computed between the test profile and the control profile, optionally wherein the correlation value is a correlation coefficient.

26. The method of claim 24 or 25, wherein the correlation coefficient is a linear coefficient, optionally a Pearson correlation coefficient, or a monotonic coefficient, optionally a Spearman’s correlation coefficient.

27. The method of claim 26, wherein the high level of similarity is indicated by a Pearson correlation coefficient or a Spearman’s correlation coefficient between the test profile and the control profile having an absolute value between 0.5 to 1 , optionally between 0.75 to 1 , and wherein the low level of similarity is indicated by a Pearson correlation coefficient or a Spearman’s correlation coefficient between the test profile and the control profile having an absolute value between 0 to 0.5, optionally between 0 to 0.25.

28. The method of any one of claims 20 to 27, wherein the test profile and the control profile comprise the lgG2 response to B. longum and / or the lgG2 response to E. faecalis.

29. The method of claim any one of claims 20 to 28, wherein the test profile and the control profile comprise the lgG2 response to B. longum.

30. The method of any one of claims 20 to 27, wherein the test profile and the control profile further comprise an HLA class II haplotype of the subject, and wherein the HLA class II haplotype is HLA-DR4.31 . The method of claim 30, wherein the test profile and the control profile comprise the lgG2 response to D. invisus and / or the IgA response to D. Invisus.

32. The method of any one of claims 1 to 7 and 9 to 31 or the use of any one of claims 8 to 19, wherein the sample is a serum sample or a plasma sample.

33. The method of any one of claims 1 to 7 and 9 to 32 or the use of any one of claims 8 to 19, wherein the subject is a human subject.

34. The method of any one of claims 1 to 7 and 9 to 33 or the use of any one of claims 8 to 19, wherein the subject is a juvenile or an adult.

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