Methods for monitoring and evaluating the efficacy of Treg cell therapy

Abstract

The present invention relates to methods for carefully monitoring and evaluating the efficacy of cell therapy in patients treated with CD4+FoxP3+ regulatory T cells. Further subject matter relates to cell therapy methods using CD4+FoxP3+ regulatory T cells and the monitoring methods of the invention.

Description

[Technical Field]

[0001] The present invention relates to methods for carefully monitoring and evaluating the efficacy of cell therapy in patients treated with CD4+ FoxP3+ T regulatory cells (hereinafter "Treg cells" or "Tregs"). Further subject matter relates to cell therapy methods using CD4+ FoxP3+ regulatory T cells and the monitoring methods of the invention. [Background technology]

[0002] The importance of the immune system in the development of type 1 diabetes mellitus (DM1) has been demonstrated by both clinical and experimental data. While triggering factors can be viral infection or genetic susceptibility, the disease develops due to an imbalance between excessive autoaggressive T cell responses and impaired immune tolerance induction mechanisms (Non-Patent Documents 1, 2). Therefore, the most promising disease-modifying strategies have centered around immunotherapy (Non-Patent Documents 3, 4). Recently, teplizumab, the first drug of the century to be approved by the FDA for the treatment of presymptomatic DM1 patients, was announced. This monoclonal antibody, targeting autoreactive CD3+ T cells, can delay the onset of symptomatic DM1 for at least two years (5). Among the many ongoing attempts to halt or at least delay DM1, cell therapy using FoxP3+ regulatory T cells (Tregs) appears to be of particular interest. We have conducted several clinical trials using regulatory T cell preparations and obtained promising results (Non-Patent Documents 5-10).

[0003] A major challenge for all these treatments is finding a good immune marker to predict treatment response, which correlates with the beta-cell destruction that leads to DM1 (Non-Patent Document 11). The tissues that can be sampled are usually peripheral blood, which is very far from local tissue lesions. Therefore, beta-cell function remains the only acceptable endpoint of treatment efficacy to date (Non-Patent Document 4). Unfortunately, this type of monitoring can only detect the disease relatively late, when pancreatic islet destruction is progressing. It only indicates the progression of DM1 and cannot predict the effectiveness of treatment early, at the time of treatment administration. [Prior art documents] [Non-patent literature]

[0004] [Non-Patent Document 1] Budd MA, Monajemi M, Colpitts SJ, Crome SQ, Verchere CB, Levings MK. Interactions between islets and regulatory immune cells in health and type 1 diabetes. Diabetologia. 2021;64(11):2378-2388. doi:10.1007 / S00125-021-05565-6 [Non-patent document 2] Raugh A, Allard D, Bettini M. Nature vs. nurture: FOXP3, genetics, and tissue environment shape Treg function. Front Immunol. 2022;13:4199. doi:10.3389 / FIMMU.2022.911151 / BIBTEX [Non-patent document 3] Roep BO. The need and benefit of immune monitoring to define patient and disease heterogeneity, mechanisms of therapeutic action and efficacy of intervention therapy for precision medicine in type 1 diabetes. Front Immunol. 2023;1 doi:10.3389 / FILM.2023.1112858

Outdoor Tools 4

Direct Environment 5

Outdoor Configuration6

Direct Environment 7

Non-Patent Document 8

Non-Patent Document 9

Non-Patent Document 10

[0005] The technical problem underlying the present invention is the provision of a means for assessing the progress of cell therapy using Treg cells. [Means for solving the problem]

[0006] The solution to the above technical problem is provided by the embodiments of the present invention as characterized in the claims and disclosed in this specification and the accompanying drawings.

[0007] The present invention, as described herein and in further detail in the Examples below, is based on the discovery that certain biomarkers are useful for characterizing cell therapy regimens in patients requiring such cell therapy using Treg cells. Biomarkers identified as particularly useful for this purpose are PD-1 and CD73. Furthermore, the inventors have determined that certain levels of PD-1 and / or CD73 expression indicate efficient cell therapy treatment with Treg cells.

[0008] In particular, the present invention provides methods for monitoring cell therapy in a patient treated with CD4+FoxP3+ Treg cells, the method comprising determining in vitro the expression of at least one protein selected from the group consisting of PD-1 and CD73 on the patient's CD4+FoxP3+ T cells, preferably CD4+FoxP3+ Treg cells, and / or determining in vitro the expression of PD-1 on CD4+FoxP3- T cells, preferably CD4+FoxP3- T effector cells ("Teff cells" or "Teff").

[0009] Preferably, determining the expression of at least one protein on CD4+FoxP3+ T cells and / or determining the expression of PD-1 on CD4+FoxP3− cells has been performed on (and in other instances of this disclosure is used interchangeably with “performed using”) a patient sample.

[0010] In a preferred embodiment, the monitoring method of the present invention comprises isolating CD4+FoxP3+ T cells from a patient sample before determining the expression of at least one protein, and / or isolating CD4+FoxP3- cells from a patient sample before determining the expression of PCD1 on CD4+FoxP3- cells. Isolation of cells of interest according to the present invention as disclosed herein, particularly from peripheral blood, is typically carried out according to methods known in the art, which typically involve isolation of peripheral blood mononuclear cells (PBMCs), specific methods being described in more detail in the Examples below.

[0011] More preferably, the isolated CD4+FoxP3+ T cells and / or the isolated CD4+FoxP3- cells are expanded as outlined above before determining the expression of a biomarker according to the present invention. As used herein, the term "expanding the isolated cells" means that after the cells have been isolated from a patient sample, they are cultured under appropriate conditions and for an appropriate period of time, as known in the art and exemplified in the examples below, to allow for increasing the number of cells to be analyzed in a given culture or a test sample taken therefrom, in order to perform detection of the expression of a protein of interest on said cells.

[0012] Preferably, the patient sample is peripheral blood.

[0013] As outlined above, the isolated and optionally expanded CD4+FoxP3+ T cells are preferably CD4+FoxP3+ regulatory T cells.

[0014] Furthermore, the isolated and optionally expanded CD4+FoxP3- T cells are preferably CD4+FoxP3- effector T cells.

[0015] In a preferred embodiment of the present invention, the monitoring method comprises the following steps: (i) determining the expression of PD-1 on CD4+FoxP3+ regulatory T cells; and / or (ii) determining the expression of PD-1 on CD4+FoxP3- effector T cells; and / or (iii) To determine the expression of CD73 on CD4+FoxP3+ regulatory T cells.

[0016] In certain embodiments of the present invention, the monitoring method preferably includes step (i). In other embodiments of the present invention, the monitoring method preferably includes step (ii). In a further embodiment of the present invention, the monitoring method preferably includes step (iii). In yet other embodiments of the present invention, the monitoring method preferably includes steps (i) and (ii). Furthermore, in other embodiments of the present invention, the monitoring method preferably includes steps (i) and (iii). In a further embodiment of the present invention, the monitoring method preferably includes steps (ii) and (iii). In yet a further embodiment of the present invention, the monitoring method preferably includes steps (i), (ii), and (iii). When two or more of steps (i), (ii), and (iii) are performed, these steps can be performed simultaneously or sequentially.

[0017] In a preferred embodiment, the regulatory T cells used in the present invention are of the phenotype CD4+FoxP3+CD25highCD127- doublets-.

[0018] In a further preferred embodiment, the effector T cells used in the present invention are of the phenotype CD4+FoxP3-CD25lowCD127+doublets-.

[0019] 10. The method according to any one of the preceding claims, wherein the method is performed on the patient's cells at least once, preferably at least two weeks, preferably one month, more preferably two months, even more preferably three months after administration of the cell therapy.

[0020] In another embodiment of the invention, the method of the invention is performed on the cells (CD4+FoxP3, preferably CD4+FoxP3+CD25highCD127- doublet- and / or CD4+FoxP3-CD25lowCD127+ doublet-, T cells) two or more times. More preferably, the method of the invention is performed at intervals, which may be regular or irregular, preferably regular. It is particularly preferred that the monitoring method is performed continuously to continuously monitor the cell therapy of a patient; preferably, the monitoring method of the invention is performed as long as the patient's cell therapy treatment continues. The method can be carried out until a particular endpoint is reached, for example, a predetermined threshold of PD-1 and / or CD73 expression on CD4+FoxP3+, preferably CD4+FoxP3+CD25highCD127- doublet- (Treg) cells and / or a predetermined threshold of PD-1 expression on preferably CD4+FoxP3-CD25highCD127- doublet- (Teff) cells.

[0021] An "expression threshold" in the context of the present invention is preferably a certain percentage value of each cell expressing each biomarker for use in the present invention, preferably based on the total number of those cells present in a measurement in a test sample, more preferably in a test sample obtained from a patient, even more preferably in a test sample after isolation from a patient sample, and most preferably in a test sample after expansion of said isolated cells. Such a predetermined threshold may be a maximum or minimum value of the expression value of each biomarker for use in the present invention on each cell.

[0022] The maximum or minimum endpoint threshold may be a predetermined absolute maximum or minimum value over the entire duration of cell therapy. In other embodiments, the maximum or minimum value may be a maximum or minimum value within a predetermined time frame over the duration of cell therapy, such as about 1 to 6 months, e.g., about 1, about 2, about 3, about 4, about 5, or about 6 months, preferably about 1 year, about 1.5 years, or about 2 years.

[0023] One preferred threshold value according to the present invention is PD-1 expression in at least 16%, more preferably greater than 16%, of CD4+FoxP3+ T cells, preferably Treg cells, based on the total number of said cells in the test sample, e.g., the volume of the test sample containing a certain number of CD4+FoxP3+ T cells, preferably Treg cells. Another preferred threshold value according to the present invention is CD73 expression in at least 7%, more preferably greater than 7%, of CD4+FoxP3+ T cells, preferably Treg cells, based on the total number of said cells in the test sample, e.g., the volume of the test sample containing a certain number of CD4+FoxP3+ T cells, preferably Treg cells. A further preferred threshold value according to the present invention is PD-1 expression in at least 8%, more preferably greater than 8%, of CD4+FoxP3- T cells, preferably Teff cells, based on the total number of said cells in the test sample, e.g., the volume of the test sample containing a certain number of CD4+FoxP3- T cells, preferably Teff cells. In one embodiment of the present invention, at least one of the preferred threshold values ​​of the above biomarkers on each cell determines an endpoint. In other embodiments of the invention, at least two of the thresholds determine the endpoint. In one embodiment, the endpoint is determined by the above-mentioned preferred threshold for PD-1 on the CD4+FoxP3+ T cells, preferably Treg cells, and the above-mentioned threshold for CD73 on the CD4+FoxP3+ T cells, preferably Treg cells. In another embodiment, the endpoint is determined by the above-mentioned preferred threshold for PD-1 on the CD4+FoxP3+ T cells, preferably Treg cells, and the above-mentioned threshold for PD-1 on the CD4+FoxP3- T cells, preferably Teff cells. In a further embodiment, the endpoint is determined by the above-mentioned preferred threshold for CD73 on the CD4+FoxP3+ T cells, preferably Treg cells, and the above-mentioned threshold for PD-1 on the CD4+FoxP3- T cells, preferably Teff cells. In a further embodiment, the endpoint is determined by all three of the above-mentioned preferred thresholds.In a preferred embodiment, the preferred endpoints outlined above are reached when the expression of said at least one, or said at least two, or said three biomarkers falls below the value(s) indicated above, whereby it is understood that during the monitoring method, the expression value of said biomarker(s) reaches said threshold(s) at least once before the expression value reaches the respective endpoint value(s).

[0024] Preferably, the method is performed on the patient's cells at an interval of at least about 2 weeks, preferably at least about 1 month, more preferably at least about 2 months, and even more preferably at least about 3 months after the initiation of cell therapy, i.e., after administration of CD4+FoxP3+Treg cells. The "initiation" of cell therapy is defined herein as day 0, i.e., the first administration of CD4+FoxP3+Treg cells (also referred to elsewhere in this disclosure as the "first administration of cell therapy").

[0025] In a preferred embodiment of the invention, the monitoring method is performed on the patient's cells for at least about 6 months, more preferably at least about 1 year, more preferably at least about 2 years after administration of the cell therapy, preferably the first administration, in particular administration of CD4+FoxP3+ Treg cells, preferably CD4+FoxP3+, preferably CD4+FoxP3+CD25highCD127- doublet- (Treg) cells, and the monitoring method is preferably performed at the intervals described above.

[0026] It is further preferred that the monitoring method is performed on said cells of a patient at least once before administration of cell therapy, in particular at least once before administration of CD4+FoxP3+ Treg cells, preferably CD4+FoxP3+, preferably CD4+FoxP3+CD25highCD127-doublet-(Treg) cells. That is, the monitoring method of the present invention comprises determining a baseline value(s) of expression on each of the above-mentioned cells before initiation of cell therapy.

[0027] As outlined above, determining the expression value(s) of the biomarkers for use in the present invention on each cell can be carried out by various methods known in the art, such as Western blotting, detecting mRNA of the protein of interest, cell sorting, etc. A preferred methodology for the determination(s), such as Western blotting, cell sorting, etc., is treating a suitable test sample with a compound, preferably an antibody, more preferably a monoclonal antibody, including suitable antibody fragments specific to each biomarker(s). It is further preferred to use cell sorting, more preferably fluorescence-activated cell sorting (FACS), to determine the proportion of each cell expressing each biomarker. Specific determination steps that can be used to implement the present invention are outlined in more detail in the following examples.

[0028] The monitoring method of the present invention is particularly useful for monitoring cell therapy using Treg cells in patients with or developing an autoimmune disease, respectively. The autoimmune disease in the context of the present invention may be one or more of the following:

[0029] acromegaly; Acquired aplastic anemia; acquired hemophilia; agammaglobulinemia, primary; Alopecia areata; Ankylosing spondylitis (AS); anti-NMDA receptor encephalitis; Antiphospholipid syndrome (APS) | Fulminant antiphospholipid syndrome (CAPS) / Asherson syndrome; arteriosclerosis; Autoimmune Addison's disease (AAD); Autoimmune autonomic ganglionopathy (AAG) / autoimmune autonomic dysregulation | autoimmune gastrointestinal motility disorder (AGID); Autoimmune encephalitis|acute disseminated encephalomyelitis (ADEM); autoimmune gastritis; Autoimmune hemolytic anemia (AIHA); autoimmune hepatitis (AIH); autoimmune hyperlipidemia; autoimmune hypophysitis; Autoimmune inner ear disease (AIED); Autoimmune lymphoproliferative syndrome (ALPS); autoimmune myelofibrosis; Autoimmune myocarditis; Autoimmune oophoritis; autoimmune pancreatitis (AIP); Autoimmune polyglandular syndromes, types I, II, and III (APS1, APS2, APS3, APECED); Autoimmune progesterone dermatitis; autoimmune retinopathy (AIR); Autoimmune sudden sensorineural hearing loss (SNHL); Barrow's disease; Behçet's disease; Birdshot chorioretinopathy / birdshot uveitis; Bullous pemphigoid; Castleman's disease; Celiac disease; Chagas disease; chronic inflammatory demyelinating polyneuropathy (CIDP); Chronic autoimmune urticaria; Churg-Strauss syndrome / eosinophilic granulomatosis with polyangiitis (EGPA); Cogan's syndrome; cold agglutinin disease; CREST syndrome │ localized cutaneous sclerosis; Crohn's disease (CD); Cronkite-Canada syndrome (CSS); Cryptogenic organizing pneumonia (COP); Dermatitis herpetiformis; dermatomyositis; Type 1 diabetes (DM1); discoid lupus; Dressler syndrome / post-myocardial infarction / post-pericardiotomy syndrome; Eczema / atopic dermatitis; endometriosis; Eosinophilic esophagitis; Eosinophilic fasciitis; erythema nodosum; essential mixed cryoglobulinemia; Evans syndrome; fibrosing alveolitis / idiopathic pulmonary fibrosis (IPF); Giant cell arteritis / temporal arteritis / Horton's disease Giant cell myocarditis; glomerulonephritis; Goodpasture syndrome / anti-GBM / anti-TBM disease; Granulomatosis with polyangiitis (GPA) / Wegener's granulomatosis; Graves disease / thyroid eye disease; Guillain-Barré syndrome (GBS); Hashimoto's thyroiditis / chronic lymphocytic thyroiditis / autoimmune thyroiditis; Henoch-Schönlein purpura / IgA vasculitis; Hidradenitis suppurativa; Hurst disease / acute hemorrhagic leukoencephalitis (AHLE); hypogammaglobulinemia; IgA nephropathy / Berger's disease; Immune-mediated necrotizing myopathy (IMNM); Immune thrombocytopenia (ITP) / autoimmune thrombocytopenic purpura / autoimmune thrombocytopenia; inclusion body myositis; IgG4-related sclerosing disease (ISD); interstitial cystitis; Juvenile idiopathic arthritis / adult-onset Still's disease; Juvenile polymyositis|Juvenile dermatomyositis|Juvenile myositis; Kawasaki disease; Lambert-Eaton myasthenic syndrome (LEMS); Leukocytoclastic vasculitis; lichen planus; lichen sclerosus; woody conjunctivitis; Linear IgA Disease (LAD) | Linear IgA Bullous Skin Disease (LABD); lupus nephritis; Lyme disease / chronic Lyme disease / post-treatment Lyme disease syndrome (PTLDS); lymphocytic colitis / microscopic colitis; lymphocytic hypophysitis / autoimmune hypophysitis; Meniere's disease; microscopic polyangiitis (MPA) / ANCA-associated vasculitis; mixed connective tissue disease (MCTD); Mooren's ulcer; Mukka-Habermann disease;

[0030] multifocal motor neuropathy; Multiple sclerosis (MS); Myalgic Encephalomyelitis (ME) / Chronic Fatigue Syndrome (CFS); myasthenia gravis (MG); Narcolepsy; Neuromyelitis optica / Devic's disease; Ocular cicatricial pemphigoid; Opsoclonus-myoclonus syndrome (OMS); relapsing rheumatism; paraneoplastic cerebellar degeneration; Paraneoplastic pemphigus; Parry-Romberg syndrome (PRS) / hemifacial atrophy (HFA) / progressive hemifacial atrophy; Paroxysmal nocturnal hemoglobinuria (PNH); Peripheral uveitis / peripheral uveitis; PANS / PANDAS; Parsonage-Turner syndrome; Pemphigus gestationis / herpes gestationis; Pemphigus foliaceus; Pemphigus vulgaris; pernicious anemia; POEMS syndrome; Polyarteritis nodosa; Polymyalgia rheumatica; Polymyositis; Postural orthostatic tachycardia syndrome (POTS); primary biliary cirrhosis (PBC) / primary biliary cholangitis; Primary sclerosing cholangitis (PSC); psoriasis; Palmoplantar pustulosis; psoriatic arthritis; idiopathic pulmonary fibrosis (IPF);

[0031] Aplasia erythroblastoma (PRCA); pyoderma gangrenosum; Rasmussen's encephalitis; Raynaud's syndrome / phenomenon; reactive arthritis / Reiter's syndrome; Reflex Sympathetic Dystrophy Syndrome (RSD) / Complex Regional Pain Syndrome (CRPS); Relapsing polychondritis; Restless Legs Syndrome (RLS) / : Willis-Ekbom Disease rheumatic fever; rheumatoid arthritis; sarcoidosis; Schmidt syndrome / polyglandular autoimmune disease syndrome type II; scleritis; scleroderma; Sclerosing mesenteritis / mesenteric panniculitis; creeping choroidopathy; Sjögren's syndrome; stiff-person syndrome (SPS); small fiber sensory neuropathy; Systemic lupus erythematosus (SLE); Subacute infective endocarditis (SBE); Subacute cutaneous lupus; Susac syndrome; Sydenham chorea; sympathetic ophthalmia; Takayasu arteritis (vasculitis); testicular autoimmunity (vasculitis, orchitis); Tolosa-Hunt syndrome; Transverse myelitis (TM); tubulointerstitial nephritis uveitis syndrome (TINU); Ulcerative colitis (UC); Undifferentiated Connective Tissue Disease (UCTD); Uveitis|Anterior / Intermediate / Posterior; vasculitis; VEXAS syndrome; Vitiligo and Vogt-Koyanagi-Harada syndrome (VKH)

[0032] Preferably, the autoimmune disease is type 1 diabetes mellitus (DM1).

[0033] In a preferred embodiment of the present invention, the patient is a child or adolescent, particularly a child or adolescent with or developing DM1, respectively. Preferably, the patient with or developing an autoimmune disease, preferably DM1, is between about 7 and about 18 years of age, more preferably between about 8 and about 16 years of age, respectively. In a further preferred embodiment of the present invention, with respect to all of the monitoring, diagnosis, evaluation, and treatment methods defined and disclosed herein, particularly with respect to autoimmune diseases, particularly DM-1, the patient, preferably a child or adolescent, preferably a child or adolescent of the ages outlined above, is in the early stage or early onset of the autoimmune disease, preferably DM1. In a preferred embodiment of the present invention, "early stage" or "early onset" of the disease, respectively, particularly in the context of DM-1, means that the patient has a fasting plasma C-peptide level greater than about 0.7 ng / ml and / or preferably exhibits at least a 100% increase in plasma C-peptide levels in the GST (glucagon-stimulated C-peptide test (42); 1 mg glucagon administered intravenously within 10 seconds) compared to plasma C-peptide levels in the fasting state. In other preferred embodiments of the present invention, "early stage" or "early onset" of the disease, particularly in the context of DM-1, means that the patient has a fasting plasma C-peptide level greater than about 0.7 ng / ml and / or preferably an increase in plasma C-peptide levels in a mixed meal tolerance test (MMT), preferably at least 100% compared to plasma C-peptide levels in the fasting state, as disclosed in (41).

[0034] A further aspect of the present invention is a method for assessing the efficacy of cell therapy in a patient treated with CD4+FoxP3+ regulatory T cells, comprising carrying out a monitoring method of the present invention, wherein expression of PD-1 in at least 16%, more preferably more than 16%, of CD4+FoxP3+ regulatory T cells and / or expression of CD73 in at least 7%, more preferably more than 7%, of CD4+FoxP3+ regulatory T cells and / or expression of PD-1 in at least 8%, more preferably more than 8%, of CD4+FoxP3- T cells indicates efficient cell therapy. It is understood that the percentage of said cells is based on the total number of said cells in the test sample, as outlined above with regard to the preferred thresholds or endpoints, respectively, in the context of the monitoring method of the present invention.

[0035] The cell therapy may also include the administration of additional medications useful in the treatment of the respective disease, preferably an autoimmune disease as outlined above, most preferably DM1.

[0036] In the context of the cell therapy outlined above, it is preferred that the cell therapy is carried out under the further administration of at least one anti-CD20 antibody or fragment thereof having anti-CD20 affinity, i.e., a fragment of said antibody that retains specific binding to CD20. Preferably, the anti-CD20 antibody is a monoclonal antibody, more preferably, the anti-CD20 monoclonal antibody is a humanized antibody. Most preferably, the anti-CD20 antibody is rituximab (hereinafter also referred to as "RTX"). The administration of Treg cells and the anti-CD20 antibody or fragment thereof, most preferably RTX, can be carried out simultaneously or non-simultaneously. Preferably, the administration of the anti-CD20 antibody or fragment thereof is most preferably at a concentration of about 200 mg / m of the patient's body surface area ("BSA") 2 ~Approximately 400 mg / m2 of body surface 2 more preferably about 330 mg / m 2 BSA ~ approx. 390mg / m 2 BSA, most preferably about 375 mg / m 2BSA is required. Preferably, the administration of the unit dose of the anti-CD20 antibody or fragment thereof, most preferably RTX, is performed more than once, wherein the administration is preferably by intravenous administration. The administration of the anti-CD20 antibody or fragment thereof, preferably RTX, is preferably performed at intervals, which may be regular or irregular. A preferred regimen is the administration of a unit dose, preferably as outlined above, at intervals of about 1 to about 2 weeks, for example, at intervals of about 6 to 15 days, for example, 7, 8, 9, 10, 11, 12, 13, or 14 days.

[0037] The BSA value of a patient can be calculated from the patient's weight and height, and optionally from age.In a preferred embodiment of the present invention, BSA is calculated according to formulas known in the art, such as Boyd formula, Dubois formula, Gehan-George formula, Haycock formula, Mosteller formula or Takahira formula.In the context of treating children or adolescents according to the present invention, BSA is preferably calculated according to Gehan-George formula and / or Haycock formula.

[0038] Antibodies for use in the present invention can be polyclonal or monoclonal. Preferred antibodies for use in the present invention are monoclonal antibodies.

[0039] According to the present invention, "antibody fragments having affinity" and / or "fragments retaining specific binding" to the respective antigens (such as CD20, PD-1, and / or CD73) refer to one or more fragments of an antibody that retain the ability to specifically bind to the respective antigen. Examples of such antibody fragments include Fab fragments, Fab' fragments, F(ab')2 fragments, heavy chain antibodies, single domain antibodies (sdAbs), single chain variable fragments (scFvs), variable fragments (Fvs), VH domains, VL domains, single domain antibodies, nanobodies, IgNARs (immunoglobulin novel antigen receptors), dimeric scFvs (di-scFvs), bispecific T cell engagers (BiTEs), dual affinity retargeting (DART) molecules, trimers, diabodies, single chain diabodies, alternative scaffold proteins, and fusion proteins thereof.

[0040] The term "specific binding" as used herein preferably refers to the binding of a respective entity, such as an antibody or fragment thereof as outlined above, to a target, typically an antigen of an antibody, such as the antibodies and fragments thereof disclosed herein for use in the present invention, with a specific binding activity of about 10 -6 M or less, preferably about 10 -7 M or less, more preferably about 10 -8 M or less, and even more preferably about 10 -9 M or less, most preferably about 10 -10 The dissociation constant (K D ) is meant to indicate

[0041] A further subject of the present invention is a cell therapy of a patient in need thereof, preferably a patient such as a child or adolescent, who has or develops an autoimmune disease, such as an autoimmune disease disclosed above, most preferably DM1, respectively, comprising the administration of Treg cells as outlined above, carrying out the monitoring and / or evaluation method of the present invention, and preferably further comprising the administration of rituximab. Preferably, the cell therapy is carried out until at least one threshold and / or endpoint, as detailed above, is reached.

[0042] The present invention is also directed to the use of Treg cells as described herein for the treatment of a patient, preferably a child or adolescent, each having or developing an autoimmune disease, such as an autoimmune disease as disclosed above, most preferably DM1, by cell therapy comprising the administration of said Treg cells and, optionally, an anti-CD20 antibody or fragment thereof as defined herein, most preferably rituximab, and further comprising carrying out the evaluation and / or monitoring method of the present invention. Preferably, the cell therapy of said patient is carried out until at least one threshold and / or endpoint is reached, as detailed above. In a preferred embodiment, the dose of Treg cells, particularly CD4+FoxP3+Treg cells, most preferably CD4+FoxP3+CD25highCD127-doublet-Treg cells, for use in the treatment as defined herein is preferably as outlined below.

[0043] A preferred dosage of Treg cells in the context of the present invention is about 10×10 6 Cells ~ approx. 90×10 6 and preferably about 20×10 6 ~Approx. 70×10 6 , more preferably about 30×10 6 ~Approx. 60×10 6 , for example, about 30 × 10 6 , about 40×10 6 , about 50×10 6 , or approximately 60 × 10 6 , and most preferably about 30 x 10 per kg of patient body weight 6 of Treg cells, which are typically administered in one dose per administration, preferably intravenously (iv).

[0044] A unit dose of Treg cells for use in the present invention, preferably the unit dose described above, preferably comprises Treg cells in a volume of about 100 to about 500 ml, preferably about 200 to about 300 ml, preferably in about 250 ml of a suitable medium adapted for iv administration such as a physiological NaCl solution, for example 0.9% (w / v) NaCl in water for injection.

[0045] The administration of Treg cells is carried out at least once. Preferably, the administration is carried out more than once, where the intervals between administrations may be equal or different. Preferred intervals in the context of treatment according to the present invention are about 1 to about 6 months, preferably about 2 to 4 months, and most preferably about 3 months. In a preferred embodiment of the present invention, the administration of Treg cells is carried out using the unit doses outlined above, preferably at least twice within the intervals outlined above, most preferably two or more times at intervals of 3 months.

[0046] According to the present invention, the term "treatment of a patient with a disease," such as an autoimmune disease, preferably an autoimmune disease as outlined above, most preferably DM1, is understood to involve treating said disease in said patient, preferably a child or adolescent. The term "treatment," as used herein, includes delaying the onset of a disease as outlined herein, most preferably DM-1, for example, by at least about 6 months, preferably at least about 1 year, more preferably at least about 1.5 years, even more preferably at least about 2 years or more, for example, at least about 3 years, at least about 4 years, at least about 5 years, or at least about 6 years or more. As used herein, "delaying the onset" of an autoimmune disease, such as an autoimmune disease as disclosed herein, preferably DM-1, preferably means that the patient's condition is such that, according to common knowledge and accepted clinical criteria by those skilled in the art, particularly physicians, the time until the patient develops a clinical stage of the disease is prolonged, preferably by the period outlined above. In the context of DM1, delayed progression or onset, respectively, preferably means that the time until a patient develops an accepted clinical stage at which insulin therapy is required according to accepted clinical criteria is extended, preferably over the period outlined above.

[0047] A further subject of the present invention is a method for diagnosing an efficient cell therapy in a patient treated with CD4+FoxP3+ regulatory T cells, comprising the steps of obtaining a patient sample, isolating CD4+FoxP3+ T cells and / or CD4+FoxP3− cells from the patient, and subjecting the isolated CD4+FoxP3+ T cells and / or isolated CD4+FoxP3− cells to a marker that has affinity for (i.e. specifically binds to) PD-1 and / or CD73, preferably according to the methods outlined above. Dand detecting binding of said compound to CD4+FoxP3+ T cells that express PD-1 and / or CD73 and / or CD4+FoxP3- cells that express PD-1, respectively; and preferably determining the percentage of CD4+FoxP3+ T cells that express PD-1 and / or CD73 and / or the percentage of CD4+FoxP3- cells that express PD-1 in the test sample.

[0048] The present invention also relates to the use of said detectable compound in a diagnostic method according to the invention as defined in the previous paragraph.

[0049] Preferably, the isolated CD4+FoxP3+ T cells and / or the isolated CD4+FoxP3- cells are expanded before contacting said cells with said detectable compound.

[0050] Preferably, the detectable compound is an anti-PD1 antibody or fragment thereof that retains specific binding to PD-1, or an anti-CD73 antibody or fragment thereof that retains specific binding to CD73.

[0051] It is further preferred that the detectable compound, such as an anti-PD-1 antibody or an anti-CD73 antibody (or a fragment of such an antibody as defined herein), is conjugated to a detectable label, preferably a fluorescent label. Preferred fluorescent labels according to the present invention include, but are not limited to, the fluorescent dyes set forth in Table 3A.

[0052] In the diagnostic method defined above, which comprises obtaining a patient sample, the sample is preferably peripheral blood.For preferred embodiments and techniques for isolating cells, growing cells, and detecting the bound detectable compound, and determining the proportion of cells bound to the detectable compound, reference is made to the in vitro monitoring method described above.Preferably, the proportion of cells bound to each detectable compound, which indicates effective cell therapy, is as outlined above. [Brief explanation of the drawings]

[0053] The drawings show: [Figure 1] Fig. 1 Research flow diagram of clinical trials and in vitro model experiments according to embodiments of the present invention. [Figure 2A-1] Fig. 2 Regulatory T cell phenotype during patient follow-up compared with in vitro models. [Figure 2A-2] Fig. 2 Regulatory T cell phenotype during patient follow-up compared with in vitro models. [Figure 2A-3] Fig. 2 Regulatory T cell phenotype during patient follow-up compared with in vitro models. [Figure 2A-4] Fig. 2 Regulatory T cell phenotype during patient follow-up compared with in vitro models. [Figure 2B] Fig. 2 Regulatory T cell phenotype during patient follow-up compared with in vitro models. [Figure 2C] Fig. 2 Regulatory T cell phenotype during patient follow-up compared with in vitro models. [Figure 2D-1] Fig. 2 Regulatory T cell phenotype during patient follow-up compared with in vitro models. [Figure 2D-2] Fig. 2 Regulatory T cell phenotype during patient follow-up compared with in vitro models. [Figure 2E] Fig. 2 Regulatory T cell phenotype during patient follow-up compared with in vitro models. [Figure 2F] Fig. 2 Regulatory T cell phenotype during patient follow-up compared with in vitro models. [Figure 2G] Fig. 2 Regulatory T cell phenotype during patient follow-up compared with in vitro models. [Figure 2H] Fig. 2 Regulatory T cell phenotype during patient follow-up compared with in vitro models. [Figure 3A] Fig. 3 PD-1 expression on regulatory and effector T cells during patient follow-up compared with in vitro models. [Figure 3B] Fig. 3 PD-1 expression on regulatory and effector T cells during patient follow-up compared with in vitro models. [Figure 3C] Fig. 3 PD-1 expression on regulatory and effector T cells during patient follow-up compared with in vitro models. [Figure 3D] Fig. 3 PD-1 expression on regulatory and effector T cells during patient follow-up compared with in vitro models. [Figure 3E] Fig. 3 PD-1 expression on regulatory and effector T cells during patient follow-up compared with in vitro models. [Figure 3F] Fig. 3 PD-1 expression on regulatory and effector T cells during patient follow-up compared with in vitro models. [Figure 3G] Fig. 3 PD-1 expression on regulatory and effector T cells during patient follow-up compared with in vitro models. [Figure 3H] Fig. 3 PD-1 expression on regulatory and effector T cells during patient follow-up compared with in vitro models. [Figure 3I] Fig. 3 PD-1 expression on regulatory and effector T cells during patient follow-up compared with in vitro models. [Figure 4] Fig. 4 Surface expression of CD73 on regulatory T cells (Treg FoxP3+) and effector CD4+ T cells at follow-up up to 2 years from administration. ROC curves for the percentage of polyclonal regulatory T cells and CD73+ Tregs in patients treated with rituximab. [Figure 5A] Fig. 5 Evolution of the patient's humoral immune response up to 2 years after treatment. [Figure 5B] Fig. 5 Evolution of the patient's humoral immune response up to 2 years after treatment. [Figure 5C] Fig. 5 Evolution of the patient's humoral immune response up to 2 years after treatment. [Figure 5D]Fig. 5 Evolution of the patient's humoral immune response up to 2 years after treatment. [Figure 5E] Fig. 5 Evolution of the patient's humoral immune response up to 2 years after treatment. [Figure 5F] Fig. 5 Evolution of the patient's humoral immune response up to 2 years after treatment. [Figure 5G] Fig. 5 Evolution of the patient's humoral immune response up to 2 years after treatment. [Figure 5H] Fig. 5 Evolution of the patient's humoral immune response up to 2 years after treatment. [Figure 5I] Fig. 5 Evolution of the patient's humoral immune response up to 2 years after treatment. [Figure 5J] Fig. 5 Evolution of the patient's humoral immune response up to 2 years after treatment. [Figure 6-1] Fig. 6 Clinical correlation of regulatory and effector T cells during patient follow-up. [Figure 6-2] Fig. 6 Clinical correlation of regulatory and effector T cells during patient follow-up. [Figure 7A-1] Fig. 7 Serum cytokine network during patient follow-up. [Figure 7A-2] Fig. 7 Serum cytokine network during patient follow-up. [Figure 7A-3] Fig. 7 Serum cytokine network during patient follow-up. [Figure 7A-4] Fig. 7 Serum cytokine network during patient follow-up. [Figure 7B] Fig. 7 Serum cytokine network during patient follow-up. [Figure 7C] Fig. 7 Serum cytokine network during patient follow-up. [Figure 7D] Fig. 7 Serum cytokine network during patient follow-up. [Figure 7E] Fig. 7 Serum cytokine network during patient follow-up. [Figure 7F] Fig. 7 Serum cytokine network during patient follow-up. [Figure 7G] Fig. 7 Serum cytokine network during patient follow-up. [Figure 8A-1] Fig. 8 Flow cytometry gating strategy for antigen expression on single cells. (A) Flow cytometry gating strategy for antigen expression on single cells. Representative examples of surface expression of CD279 (PD-1) antigen on regulatory T cells (Treg FoxP3+), effector CD4+ T cells, and CD8+ T cells are shown. [Figure 8A-2] Fig. 8 Flow cytometry gating strategy for antigen expression on single cells. (A) Flow cytometry gating strategy for antigen expression on single cells. Representative examples of surface expression of CD279 (PD-1) antigen on regulatory T cells (Treg FoxP3+), effector CD4+ T cells, and CD8+ T cells are shown. [Figure 8A-3] Fig. 8 Flow cytometry gating strategy for antigen expression on single cells. (A) Flow cytometry gating strategy for antigen expression on single cells. Representative examples of surface expression of CD279 (PD-1) antigen on regulatory T cells (Treg FoxP3+), effector CD4+ T cells, and CD8+ T cells are shown. [Figure 8B-1] Fig. 8 Flow cytometry gating strategy for antigen expression on single cells. (B) Flow cytometry gating strategy for antigen expression on single cells. Representative example of lymphocyte B subset. [Figure 8B-2] Fig. 8 Flow cytometry gating strategy for antigen expression on single cells. (B) Flow cytometry gating strategy for antigen expression on single cells. Representative example of lymphocyte B subset. [Figure 8C-1] Fig. 8 Flow cytometry gating strategy for antigen expression on single cells. (C) Flow cytometry gating strategy for antigen expression on single cells. Representative example of surface expression of CD279 (PD-1) antigen on regulatory T cells in in vitro culture. [Figure 8C-2]Fig. 8 Flow cytometry gating strategy for antigen expression on single cells. (C) Flow cytometry gating strategy for antigen expression on single cells. Representative example of surface expression of CD279 (PD-1) antigen on regulatory T cells in in vitro culture. [Figure 8C-3] Fig. 8 Flow cytometry gating strategy for antigen expression on single cells. (C) Flow cytometry gating strategy for antigen expression on single cells. Representative example of surface expression of CD279 (PD-1) antigen on regulatory T cells in in vitro culture. [Figure 8D-1] Fig. 8 Flow cytometry gating strategy for antigen expression in single cells. (D) Representative examples of flow cytometry FCS gating strategies and proliferation modeling in FlowJo software. [Figure 8D-2] Fig. 8 Flow cytometry gating strategy for antigen expression in single cells. (D) Representative examples of flow cytometry FCS gating strategies and proliferation modeling in FlowJo software. [Figure 8D-3] Fig. 8 Flow cytometry gating strategy for antigen expression in single cells. (D) Representative examples of flow cytometry FCS gating strategies and proliferation modeling in FlowJo software. [Figure 8D-4] Fig. 8 Flow cytometry gating strategy for antigen expression in single cells. (D) Representative examples of flow cytometry FCS gating strategies and proliferation modeling in FlowJo software. [Figure 9A-1] Fig. 9 Scheme for basic flow cytometry FCS gating strategy and dimension reduction algorithm analysis in FlowJo software. (A) Basic flow cytometry FCS gating strategy in FlowJo software for dimension reduction algorithm analysis. [Figure 9A-2]Fig. 9 Scheme for basic flow cytometry FCS gating strategy and dimension reduction algorithm analysis in FlowJo software. (A) Basic flow cytometry FCS gating strategy in FlowJo software for dimension reduction algorithm analysis. [Figure 9B] Fig. 9 Scheme for basic flow cytometry FCS gating strategy and dimension reduction algorithm analysis in FlowJo software. (B) Scheme for dimension reduction algorithm analysis in FlowJo software. [Figure 10] Fig. 10 Surface expression of CD39 on regulatory T cells (Treg FoxP3+) and effector CD4+ T cells at up to 2-year follow-up after administration. [Figure 11] Fig. 11 Surface expression of CD304 (NRP-1) on regulatory T cells (Treg Helios+FoxP3+) at up to 2-year follow-up after administration. [Figure 12] Fig. 12 Surface expression of CD134 (OX-40) on regulatory T cells (Treg Helios+FoxP3+) at up to 2-year follow-up after administration. [Figure 13] Fig. 13 Serum IgA concentrations at follow-up for up to 2 years after administration. [Figure 14] Fig. 14 Correlation between IL-1Ra serum concentration and daily insulin dose per body weight (DDI / kg bw) at 12 months after treatment and glycated hemoglobin at 24 months after treatment. [Figure 15] Fig. 15 Correlation between IL-17 serum concentrations and surface expression of CD73 on effector CD4+ T cells 3 months after treatment. [Figure 16] Fig. 16 Serum IL-8 / CXCL8 concentrations at follow-up for up to 2 years after treatment, and correlation between IL-8 / CXCL8 serum concentrations and surface expression of CD39 on regulatory T cells (Treg FoxP3+) 6 months after treatment. [Figure 17]Fig. 17 Serum IL-4 and IL-5 concentrations at follow-up for up to 2 years after administration. [Figure 18] Fig. 18 Serum sCD40L concentrations at follow-up for up to 2 years after treatment, and correlation between sCD40L serum concentrations and daily insulin dose per body weight (DDI / kg bm) at 24 months after treatment. [Figure 19] Fig. 19 Lymphocyte B at follow-up for up to 2 years after administration. [Example]

[0054] Introduction

[0055] This study, according to the present example, aimed to identify and validate biomarker candidates for immune intervention in DM1 in the clinical trial TregVAC2.0 (clinical trial registration ISRCTN37116985). We evaluated cellular and humoral immunity in newly diagnosed DM1 patients treated with a combination of autologous polyclonal CD3+CD4+CD25highCD127- regulatory T cells and anti-CD20 antibodies (Treg+RTX group) compared with patients treated with polyclonal Treg administration alone (Treg group) or standard treatment with insulin (control group). As a final result, we hoped to find a marker(s) in the immune system that correlates with β-cell function and can be used to predict response to the immunotherapy used in the trial (Fig. 1).

[0056] material and method

[0057] research design

[0058] We conducted a phase 1 / 2, prospective, multicenter clinical trial to investigate the efficacy and impact on selected immune parameters of autologous Treg administration combined with anti-CD20 monoclonal antibodies in children and adolescents recently diagnosed with DM1. The trial, registered as ISRCTN37116985 and EudraCT2014-004319-35, was a prospective, open-label, randomized, 24-month clinical trial. This three-arm study included a standard-of-care control group (control; 11 patients), a group treated with autologous polyclonal Tregs alone (Treg; 12 patients), and a group treated with autologous polyclonal Tregs in combination with anti-CD20 antibodies (Treg+RTX; 13 patients). The intervention group, consisting of Treg+RTX and Treg patients, received 30 × 10 mAbs per dose at 3-month intervals starting on day 0. 6 Patients received two doses of Tregs at 100 cells / kg body weight in an open-label fashion. Administration of the anti-CD20 antibody was blinded and placebo-controlled. Patients were randomly assigned to either the anti-CD20 antibody or placebo by chance (coin) to receive four doses of rituximab (Treg + RTX group) or placebo (Treg group) on days +14, +22, +29, and +36 of the study. All participants were followed for two years after treatment and evaluated at 3, 6, 12, and 24 months after administration, as shown in the study flow diagram. Reports on the efficacy and safety of the combination therapy administered in this clinical trial have been published. We demonstrated that the combination of autologous Treg administration and anti-CD20 antibody therapy was superior to Treg administration alone in DM1, as assessed by the area under the curve of the C-peptide mixed meal challenge test and the percentage of patients in clinical remission at 24 months of the study. Despite an 80% adverse event rate in Treg+RTX patients, the treatment was safe, as no adverse events led to discontinuation of the intervention or patient death ( 7 ).

[0059] This study aimed to evaluate immune imprinting in DM1 individuals. Over a 2-year follow-up period, we performed multicolor immunophenotyping of CD4+ Treg cells, CD4+ Teff cells, and CD8+ T cells and attempted to correlate these with clinical and laboratory results. Similarly, immune correlates were analyzed in the humoral immune and cytokine environments. The results were then matched with in vitro models of Treg and CD4 Teff cell cultures from DM1 patients and healthy volunteers, as shown in the study flow diagram (Fig. 1). Finally, the experimental results were correlated with clinical outcomes. The study was approved by an independent institutional review board (NKBBN / 374 / 2012-NKBBN / 374-7 / 2014 for clinical trials and NKBBN / 414 / 2018 for in vitro studies), and all participants signed informed consent forms.

[0060] patient

[0061] All participants were recruited based on detailed inclusion and exclusion criteria for both the clinical trial and the in vitro model (Tables 1A, 1B, and 2). A power analysis of sample size determined that 13 patients in the randomized treatment group were sufficient to detect a 20% difference in the geometric mean ratio of the AUC (0–240 min) of C-peptide (α5%), a significant outcome for the clinical trial. For the in vitro model, 12 DM1 patients and 12 healthy controls were enrolled. DM1 patients were selected based on inclusion and exclusion criteria equivalent to those for the clinical trial, and healthy controls were blood donors from the Regional Blood Bank of Gdańsk whose buffy coats remained after blood product preparation. Detailed baseline demographics for the clinical trial and in vitro model are shown in Tables 1A and 1B.

[0062] method

[0063] Cell isolation

[0064] PBMC isolation from EDTA whole blood of DM1 patients or buffy coats of blood products prepared by healthy volunteers was performed using a density gradient isolation device, Ficoll® Paque Plus (GE Healthcare, Chicago, IL). Isolated cells were then counted and tested for viability using an automated trypan blue cell viability analyzer, Bio-Rad TC20 (Hercules, CA). The minimum viability cutoff used for the test was 90%.

[0065] Cell sorting and culture for an in vitro model of Treg stimulation

[0066] CD4+ T cells were isolated from PBMCs using a negative immunomagnetic selection kit (EasySep Human CD4 Negative Selection Kit, StemCell Technologies; Vancouver, British Columbia, Canada) and then stained with fluorescently labeled monoclonal antibodies (BD Biosciences, Poland): anti-CD3 (clone UCHT1), anti-CD4 (clone SK3), anti-CD25 (clone M-A251), and anti-CD127 (clone HIL-7R-M21). Finally, cells were sorted into regulatory T cells (Tregs) with the phenotype (CD4+ / CD25high / CD127- / doublet-) and effector T cells (Teffs) with the phenotype (CD4+ / CD25low / CD127+ / doublet-) using a FACS Aria II sorter or FACS Influx sorter (BD Bioscience, Franklin Lakes, NJ). The sorted cells were then cultured according to previously published protocols. Briefly, cells were cultured in heat-inactivated autologous serum (DM1 patients) or AB human male serum (healthy control) and 1 × 10 4Tregs and Teffs were activated for proliferation with beads coated with anti-CD3 and anti-CD28 antibodies (MACS GMP ExpAct Treg Kit, Miltenyi Biotec, Germany) on days 0 and 5 at a bead-to-cell ratio of 1:1.

[0067] Flow cytometry

[0068] As shown in Table 3, we applied extended flow cytometry profiling of peripheral lymphocytes to study several immune parameters in the clinical trial cohort. We analyzed 22 markers using 16 three-color combinations of fluorescent dyes. We used minimum backbone markers for gating purposes, which refer to CD3, CD4, FoxP3, Helios, CD45RA, and CD62L markers. This allowed us to gate on Treg, CD4+ Teff, and CD8+ T cells, as shown in Figure 8A.

[0069] Peripheral blood lymphocytes B were gated as CD19 / CD20 double-positive lymphocytes (backbone markers) and further analyzed for antigens related to cell maturation, memory development, and class switching, as shown in Table 3 (Fig. 8B).

[0070] Appropriate isotype controls and a fluorescence minus one (FMO) approach were used to gate populations of interest for all analyses. The minimum number of cells per flow cytometry tube was 200,000 ± 20,000 viable cells, of which a minimum of 80,000 were collected during flow cytometry analysis using a BD LSRFortessa Cell Analyzer (BD Bioscience, Franklin Lakes, NJ, USA).

[0071] Suppression assay

[0072] Eight DM1 and five healthy control cultures were randomly selected for suppression assays on days 7 and 12 of culture. The suppressive potential of Tregs was assessed as the inhibition of Teff proliferation in the presence of Tregs. 1 × 10 cells per culture were used. 4 Teffs were stained with 1 μM / ml CFSE proliferation dye (BD Bioscience, Franklin Lakes, NJ) and cocultured with Tregs at titrated concentrations of 1:2, 1:1, 1:0.5, 1:0.25, and 1:0.125 (Teff:Treg). The cells were then activated with magnetic beads coated with anti-CD3 and anti-CD28 antibodies (MACS GMP ExpAct Treg Kit, Miltenyi Biotec, Germany) at a bead-to-Teff ratio of 1:1 and cultured for 72 hours. Bead-stimulated cultures of Teffs without Tregs served as positive controls, and unstimulated cultures of Teffs and Tregs served as negative controls. CFSE fluorescence in the samples was acquired using a BD LSR Fortessa Cell Analyzer (BD Bioscience, Franklin Lakes, NJ). Results were analyzed using the proliferation modeling tool in FlowJo (Ashland, OR) and presented as a proliferation index (PI), with a representative gating example shown in Fig. 8D.

[0073] Flow cytometry data analysis

[0074] Several immune parameters were analyzed for Tregs, CD4+ Teffs, CD8+ T cells, and B lymphocytes. First, we screened the data using a heatmap approach for each cohort with follow-up up to 2 years (Fig. 2A). Next, probabilistic analysis of cell phenotypes was used for further evaluation to find the best-fit parameters that could distinguish treated and control patients. Finally, ANOVA statistics were calculated for selected parameters to determine statistical significance.

[0075] Flow cytometry data were analyzed using Kaluza Software (Beckman Coulter, Brea, CA) and FlowJo Software (V10, Ashland, OR, USA). Backbone markers were used to identify significant T and B cell subsets, and differential antigen expression was recorded as a percentage. Next, FlowJo's software dimensionality reduction algorithm was used to analyze the data and identify significant cell subpopulations among the cohorts tested. We used t-SNE and the TriMap algorithm, along with the PhenoGraph algorithm, a clustering method to identify phenotypically distinct subpopulations. (37, 38, 39)

[0076] Starting with the raw FCS files, we downsampled to reduce the number of events and normalize the population size in FlowJo software, then gated on Treg, CD4+ Teff, and CD8+ T cells (Fig. 9A). We used 30,000 events, downsampling regularly across the time sequence of collected events. Normalized Treg, CD4+ Teff, and CD8+ T cell subpopulation events from a single sample were then aggregated into a single FCS file (specific criteria) for further dimension reduction algorithm analysis. tSNE was performed using opt-SNE settings: iteration = 1000, perplexity = 30, vantage point tree as the k-nearest neighbor algorithm, and Barnes-Hut as the gradient algorithm (37). TriMap analysis was performed using the Euclidean distance function, nearest neighbor = 10, and number of outliers = 5. (38) The PhenoGraph algorithm was then run using the k number (the number of nearest neighbors used in the nearest neighbor graph) suggested by the plugin on the FCS file structure. (39) Finally, the population of interest was selected using the Cluster Explorer plugin. The detailed analysis protocol is shown in Fig. 9B.

[0077] Serum immunoglobulins and autoantibodies

[0078] Serum concentrations of IgA, IgM immunoglobulins, and IgG subclasses: IgG1, IgG2, IgG3, and IgG4 were measured using a Bio-Plex Pro Human Isotyping Panel kit (Bio-Rad, Hercules, CA) according to the manufacturer's protocol and read on a Luminex MAGPIX analyzer (Merck Millipore; Burlington, MA, USA).

[0079] autoantibodies

[0080] Anti-GAD65 (glutamic acid decarboxylase antibody) and anti-IAA (insulin autoantibody) antibodies were tested by ELISA (Euroimmun, Germany). Anti-ICA ​​(anti-islet cell antibody) was tested by indirect immunofluorescence assay (IIF) using primate pancreas as the antigenic substrate (Euroimmun, Germany).

[0081] Serum cytokines

[0082] Serum concentrations of 38 cytokines were measured using a bead-based multiplex assay—Human Cytokine / Chemokine Magnetic Bead Panel, Milliplex® (Merck Millipore; Burlington, MA) according to the manufacturer's protocol and read on a Luminex MAGPIX analyzer (Merck Millipore; Burlington, MA).

[0083] Statistical Data Analysis

[0084] Data were expressed as medians with standard deviations. Only data cleaned by the Grubbs test were used for all statistical tests. All intergroup comparisons were performed using the nonparametric Mann-Whitney U test, while Kruskal-Wallace or Welch ANOVA was applied to multiple datasets. If the data were skewed rather than Gaussian, the Brown-Forsythe ANOVA test was used. Relationships between datasets were tested using Spearman's rank correlation, and frequencies were assessed using the chi-square test. Visualization of correlations calculated for several datasets was performed using color-coded correlation matrix graphs and XY data plots with 95% confidence bands for the best-fit lines. Monte Carlo simulations were performed using principal component analysis (PCA). Receiver operating characteristic curves (ROCs) were calculated using the Wilson-Brown method and 95% confidence intervals (95% CI). Data were presented as medians with interquartile ranges and visualized as bar graphs or individual values. The top of each bar indicates the mean, while the line represents the standard deviation. Significance was set at p<0.05. Significance codes for p-values ​​were as follows: "***" (0.000-0.001), "**" (0.001-0.010), and "*" (0.010-0.050). All analyses were performed in Prism9 (GraphPad Software; Boston, MA). Heatmaps were generated using Heatmapper (http: / / www.heatmapper.ca / ), using average linkage as the clustering method and Euclidean as the distance measure. (40)

[0085] result

[0086] Treg numbers, FoxP3+ and Helios transcription factor expression

[0087] No significant differences were observed between groups throughout follow-up (Fig. 2A). The proportion of FoxP3+ or FoxP3+Helios+ Tregs did not change significantly in control or treated patients. No differences were observed between groups throughout 2 years of continuous monitoring (Fig. 2B, 2C). Principal component analysis of FoxP3+ or FoxP3+Helios+ double-positive Tregs revealed only 11–20% of the difference between the treatment and control groups from recruitment to 2 years of monitoring (Fig. 2D).

[0088] In contrast, compared to stable proportions in healthy controls (HCs), we found that the proportions of FoxP3+ and FoxP3+Helios+ Tregs decreased between days 7 and 12 in cultures of cells from DM1 individuals in our in vitro model of Treg stimulation (Fig. 2E, G). Similarly, FoxP3 expression (measured as MFI) decreased throughout the culture, whereas Helios expression decreased only in the DM1 group (Fig. 2F, H).

[0089] PD-1, immune checkpoint antigen

[0090] Closer analysis revealed that the proportion of PD-1+ T cells changed significantly throughout the study (Fig. 3A–C). In the control group, the proportion of PD-1+ effector T cells (Teff) gradually decreased from +6 to +24 months of follow-up (p = 0.02). Conversely, there was an increase in the proportion of PD-1+ CD4+ Teff and PD-1+ CD8+ T cells in the treatment group throughout the study, but this was significant only in the combination treatment Treg + RTX group (Fig. 3B, 3C). Accordingly, there were substantial differences between the control and combination treatment groups in the proportion of PD-1+ Teff at +24 months (p = 0.009) (Fig. 3B, black arrow) and PD-1+ CD8+ T cells at +12 months (p = 0.04) (Fig. 3C, brown arrow). There was a similar trend in the level of PD-1+ Tregs over time, but neither the decrease in the control group nor the increase in the treatment group reached statistical significance. The only difference between the groups for Tregs was between controls and combination-treated patients at month +24 (p = 0.001) (Fig. 3A, black arrow). In vitro, higher percentages of PD-1+ Tregs and PD-1+ Teffs were observed in the DM1 group compared with healthy controls on day 0 (p = 0.032 and p = 0.006, respectively), with PD-1+ Tregs subsequently increasing equally in both groups of cultures on day +7 (Fig. 3D, F). Interestingly, on day +12, the percentage of PD-1+ Tregs in cultures dramatically decreased in cultures from DM1 patients and was significantly lower than in the healthy control group (Fig. 3D). Concurrently, PD-1 expression per cell, measured as MFI, was significantly higher in cultures from DM1 patients than in cultures from healthy controls on days 0, +7, and +12 (Fig. 3E). No such differences were observed in CD4 + Teff cultures (Fig. 3G).

[0091] Treg suppressive activity correlated with the proportion of PD-1+ Tregs in the DM1 group.

[0092] The suppressive activity of Tregs was examined as the inhibition of Teff proliferation in cocultures of stimulated Teffs and autologous Tregs. Interestingly, there was a correlation between the percentage of PD-1+ Tregs and suppression only in the DM1 group on day +7 (r = -0.552; p = 0.005) (Fig. 3H). This effect disappeared on day 12, when the percentage of PD-1+ Tregs significantly decreased in the DM1 group (Fig. 3D, I). No correlation was observed in healthy controls on either day +7 or day +12 (Fig. 3H, I).

[0093] Expression of other potential biomarkers during follow-up

[0094] A gradual decrease in the percentage of CD73+ Tregs and Teffs was found in control DM1 individuals (p = 0.003 and p = 0.007, respectively). Patients receiving Tregs or combination therapy did not show such a decrease (p > 0.05) (Fig. 4). No differences were observed in the percentage of CD39+ Tregs or Teffs (another enzyme involved in nucleotide metabolism) throughout the study (Fig. 10). Similar to CD73+ Tregs, the percentage of CD304+ (NRP-1 antigen) Helios+ Tregs decreased only in the control group during follow-up (p = 0.02). Accordingly, expression of this biomarker on Tregs was low, rarely exceeding 2% of total Tregs (Fig. 11). Finally, a significant increase in the percentage of CD134+ (OX-40 antigen) Helios+ Tregs was observed in both the control and intervention groups during follow-up, reaching the highest level in the control group (Fig. 12).

[0095] B-cell subsets and immunoglobulins

[0096] Significant changes were observed only in the Treg+RTX group (Fig. 5A). Near-complete depletion of B cells was evident during the first 6 months after anti-CD20 antibody injection. At +12 months, B cell recovery was observed, with a significant decrease in the proportion of CD27+ memory B cells compared with baseline (p<0.001) (Fig. 5B). Concurrently, the proportions of Breg-like (CD38++CD24++) and transitional (CD38+CD24+) B cells nearly doubled (p<0.001). At +24 months, the former subset declined to baseline, while the latter remained doubled (Fig. 5C, D).

[0097] Furthermore, characteristic changes in the IgG1 / IG2 index were observed in the Treg+RTX group. A significant decrease in serum IgG1 concentrations (p = 0.003) was observed, accompanied by an increase in IgG2 concentrations throughout the follow-up period (Fig. 5E, F). IgM levels significantly decreased throughout the study period up to +24 months (p = 0.001), with the lowest levels observed at +6 months (Fig. 5G). There was no change in serum IgA concentrations (Fig. 13). For IgA (<0.42 mg / ml) and IgG (IgG1 <3.16 mg / ml; IgG2 <0.86 mg / ml; IgG3 <0.14 mg / ml; IgG4 <0.01 mg / ml) subclasses, no late-onset hypogammaglobulinemia was observed 1 to 2 years after rituximab treatment. (12)

[0098] Serum autoantibodies, such as anti-GAD-65 autoantibodies, also partially decreased, but significantly only in Treg+RTX patients (p<0.001). In contrast, anti-IAA autoantibodies decreased in all patients at +24 months, whereas anti-ICA ​​autoantibodies remained unchanged throughout 24 months of continuous monitoring (Fig. 5H, I, J).

[0099] Correlation of immune markers with disease progression

[0100] The presented immune parameters were then correlated with clinical outcomes, including plasma C-peptide concentration (AUC), glycated hemoglobin (HbA1C), serum c-peptide, and the mixed meal tolerance test (MMTT) assessed by area under the curve of daily insulin dose per kg body weight (DDI / kg body weight). Throughout the 2-year follow-up, there was a common correlation between better clinical outcomes and the proportion of PD-1+ Tregs and PD-1+ Teffs in both control and treated patients (Fig. 6).

[0101] Less commonly, clinical parameters correlated with the percentage of CD73+ Tregs and CD73+ Teffs throughout the 2-year follow-up. Interestingly, there was also a correlation between CD304+ Tregs and better clinical outcomes (Fig. 6).

[0102] We calculated the area under the receiver operating characteristic (ROC) curve (AUC) and identified significant markers for Treg+ RTX patients only: PD-1+ Tregs, PD-1+ Teffs, and CD73+ Tregs (Table 4). For cutoff values ​​of >16% for PD-1+ Tregs and >8% for PD-1+ Teffs, the sensitivity was 73% (43.44%–90.25%; 95% CI) and 72% (45.25%–89.50%; 95% CI), respectively, and the specificity was 92% (64.61%–99.57%; 95% CI) and 90% (70.54%–96.28%; 95% CI), respectively (Fig. 3A–C). For the percentage of CD73+ Tregs with a cutoff value of >7%, the sensitivity was 82% (52.30%–96.77%; 95% CI) and the specificity was 83% (55.20%–97.04%; 95% CI) (Fig. 4).

[0103] Serum cytokine environment

[0104] Serum cytokines were screened using a heatmap approach, revealing no consistent pattern across the groups throughout the 2-year follow-up (Fig. 7A). Notably, IL-10 serum concentrations were two-fold and three-fold higher in Treg and Treg+RTX patients, respectively, at 3 months of follow-up compared with control patients (p<0.001) (Fig. 7B, dark gray arrows). This phenomenon persisted up to 6 months after recruitment (Fig. 6B, brown arrows). Furthermore, IL-10 concentrations positively correlated with the proportion of FoxP3+Helios+ double-positive Tregs in the Treg+RTX group at 6 months after recruitment (r=0.772; p=0.013) (Fig. 7C). IL-1 receptor antagonist (IL-1Ra) was another anti-inflammatory cytokine that increased in the serum of Treg (p<0.001) and Treg+RTX (p<0.001) patients compared with control patients (p=0.040) throughout follow-up (Fig. 7D). Notably, 6 months after recruitment, IL-1Ra serum concentrations were 14-fold higher in Treg patients and 12-fold higher in Treg+RTX patients than in the control group (p<0.001). This phenomenon persisted until the end of follow-up in Treg+RTX patients but nearly disappeared in the Treg group at 24 months of follow-up (Fig. 6D, black arrows). Next, we found a weak positive correlation between the concentrations of IL-1Ra and DDI / kg body weight at +12 months (r=0.671; p=0.028) and glycated hemoglobin at +24 months (r=0.834; p=0.009) in the Treg+RTX group (Fig. 14).

[0105] In the case of pro-inflammatory cytokines, we found a significant decrease in IL-17 concentrations throughout the study. IL-17 levels remained stable in control samples throughout the 2-year follow-up (p = 0.970), but gradually decreased in the Treg (p = 0.003) and Treg+RTX (p = 0.006) groups (Fig. 7E). In addition, there was a negative correlation between CD73+CD4+ Teff and IL-17 concentrations (r = -0.694, p = 0.016) in Treg+RTX patients at 3 months (Fig. 15).

[0106] Another cytokine that was increased in Treg and Treg+RTX was the chemotactic factor IL-8 / CXCL8. In control samples, IL-8 / CXCL8 remained comparable across the two follow-up visits (p=0.590), whereas serum concentrations increased in Treg (p=0.040) and Treg+RTX (p=0.010) (Fig. 16). A positive correlation between CD39+FoxP3+ Treg and IL-8 / CXCL8 serum concentrations (r=0.615, p=0.038) was demonstrated in the Treg group at +6 months (Fig. 16).

[0107] The B cell class switching process was accompanied by increases in serum concentrations of IL-4, IL-5, and soluble CD40 ligand (sCD40L) in the Treg and Treg+RTX groups. IL-4 was significantly elevated in the Treg group from +3 months to 2 years of follow-up. For IL-5, the peak serum concentration was observed at +6 months in Treg+RTX patients (p<0.001) (Fig. 17). IL-4 was positively correlated with serum IgG2 in the Treg+RTX group at +12 months of follow-up (r=0.767, p=0.014). Similarly, IL-5 was positively associated with serum IgG1 in Treg+RTX at +12 months after recruitment (r=0.753, p=0.019) (Fig. 7F, G).

[0108] Unlike the control or Treg groups, serum concentrations of sCD40L were increased in the Treg+RTX group (p=0.040), with peak concentrations at +12 months (Fig. 18). Furthermore, sCD40L was positively correlated with DDI / kg body weight at the end of the study (r=0.812, p=0.021) (Fig. 18).

[0109] Consideration

[0110] This study sought to identify the most accurate immune biomarkers of the efficacy of Treg-based treatment in DM1. In the TregVAC2.0 study (clinical trial ISRCTN37116985), we tracked immune parameters of cellular and humoral immunity and cytokine networks in newly diagnosed DM1 patients treated with a combination of autologous polyclonal Tregs and anti-CD20 antibodies. Data were compared between polyclonal Treg monotherapy and standard-of-care control patients treated with insulin alone. We found that increased percentages of PD-1+ cells among CD4+ Tregs, CD4+ Teffs, and CD8+ T cells from peripheral blood were associated with favorable treatment outcomes. In vitro, a higher percentage of PD-1+ Tregs correlated with better suppressive activity in functional suppression assays after 7 days of stimulation. The effect disappeared by day 12, when the percentage of PD-1+ CD4+ Tregs in culture significantly decreased. These correlations were not observed in cultures of healthy controls. Furthermore, the B cell compartment was remodeled toward a higher proportion of regulatory B cells at the expense of reduced memory B cells, and serum IgG2 increased at the expense of reduced serum IgG1 in the combination-treated group. These changes were accompanied by a decreased pro-inflammatory potential in treated subjects compared with controls, measured as increased serum IL-10 and IL-1Ra concentrations and decreased IL-17 concentrations.

[0111] A very important finding of this study was that administration of polyclonal Tregs maintained PD-1 expression, and combination therapy further improved it, which may be a substantial therapeutic effect of this treatment. The proportion of PD-1 on T cells was higher in CD4+ Tregs, CD4+ Teffs, and CD8+ T cells in DM1-treated patients compared with the DM1 control group at the end of the study. Compared to baseline, PD-1 expression decreased in the DM1 control group, was unaffected in the Treg group, and increased in the Treg + RTX group. To monitor therapy outcomes, PD-1+ cutoff values ​​could be calculated for both regulatory and effector T cells. A cutoff of >16% for PD-1+ Tregs and >8% for PD-1+ Teffs indicated remission in the Treg + RTX group. Similarly, a cutoff of >7% for CD73+ Tregs was also associated with better treatment outcomes. These calculations demonstrate the applicability of Treg and Teff phenotypes to individual therapy response monitoring. We then verified this observation in an in vitro model, comparing people with DM1 with healthy controls. This part of the study highlighted the paramount importance of PD-1 expression on Tregs in DM1. We found that in cultures from DM1 patients, expression of this receptor on Tregs and the proportion of PD-1+ Tregs significantly increased on day +7, followed by a significant decline on day +12 (Fig. 3D-G). Interestingly, in cultures from DM1 patients, the suppressive capacity of Tregs in suppression assays correlated primarily with the proportion of PD-1+ Tregs, which increased on day +7 and significantly decreased on day +12. No significant differences in stimulatory expression of the PD-1 receptor were observed in cultures of CD4+ Teffs, nor in cultures from healthy controls. This may suggest that expression of the PD-1 receptor on T cells protects against autoimmunity in DM1. This disease is a stimuli that increases PD-1 expression, similar to in vitro stimulation.Unfortunately, Tregs from DM1 patients can only transiently upregulate PD-1, and thus the suppressive effect rapidly wears off, potentially leading to disease progression (Fig. 3H–I). This is an intriguing observation in DM1, as expression of the PD-1 antigen on activated T cells and B cells is known to regulate T cell function and proliferation. (13, 14) This study demonstrates the link between this mechanism and protection from autoimmunity, such as DM1. Reports have shown that approximately one-third of cancer patients treated with PD-1 / PD-L1-blocking antibodies developed immune-related adverse effects similar to autoimmune syndromes, e.g., autoimmune insulin-dependent diabetes mellitus. (15, 16) Furthermore, in non-obese diabetic mice, a model of DM1, blockade of the PD-1 pathway resulted in rapid diabetes progression. (17) Furthermore, the PD-1 / PD-L1 axis was shown to only regulate early diabetogenic effector T cells in the pancreas, providing indirect evidence that only early intervention can slow disease progression. (18) Similar observations were made in patients with systemic lupus erythematosus (SLE) treated with rituximab, and PD-1. 高 The proportion of CD4+ T cells decreased over time, which correlated with disease progression. (19) Finally, PD-1 expression in our study most commonly correlated with clinical markers of β-cell function, and we found several correlations for both treated and control patients. Furthermore, PD-1 association was widespread across cell types (Tregs, CD4+ Teffs, and CD8+ T cells) and follow-up time points (Fig. 6). Increased PD-1 expression favored β-cell function, and its higher expression correlated with better suppressive function of Tregs, as evidenced by in vitro models.

[0112] Another important finding in this study was that PD-1 expression was highest in the Treg+ RTX cohort, which is thought to be due to anti-CD20 treatment. In autoimmunity, as in idiopathic thrombocytopenic purpura (ITP), rituximab has been shown to increase the number of Tregs, increase Fas ligand expression, and increase mRNA levels of the apoptosis-related proteins BAX and BCL2, as well as restore the Th1 / Th2 ratio and TCR Vβ clonality. (29, 21) From a functional perspective, it has been hypothesized that depletion of CD20+ B cells alters T cell activation in several pathways, of which reduced antigen presentation may be important. (22, 23) As in rheumatoid arthritis (RA), depletion of CD20+ B cells reduced the antigen-presenting cell (APC) pool and delayed autoimmunity, but only to a certain extent. When B cells returned, antibody production and T cell activation were restored, leading to disease relapse. (24, 25) B cell depletion inhibited antigen-specific CD4+ T cell proliferation in mouse models of arthritis and autoimmune diabetes, providing another example that B cells are essential for T cell responses. (26) We noted that B cell depletion resulted in an increase in the proportion of naive, transitional, and regulatory-like B cells in the Treg+RTX group. Furthermore, when disease-specific autoantibodies were measured, anti-GAD65 levels persisted in the control and Treg groups but significantly decreased in the Treg+RTX group (Fig. 5H). Furthermore, the Treg+RTX group was characterized by increased levels of anti-inflammatory cytokines, particularly IL-10 and IL-1Ra, and decreased serum concentrations of IL-17. This was also true for the Treg group, but only with a sustained decrease in IL-17, and the time-dependent increase in IL-1Ra and IL-10 was not maintained (Fig. 7B–D). This is consistent with other reports that reconstitution of the B cell compartment with rituximab resulted in fewer autoreactive clones and more B cell subsets, primarily transitional B cells, capable of IL-10 production ( 22 ).

[0113] From this perspective, the therapeutic strategy presented here, combining rituximab with the administration of regulatory T cells, is well-founded, as B cell depletion reduced antigen presentation and induced a tolerogenic B cell phenotype and an anti-inflammatory cytokine environment. At the same time, Tregs suppressed T cell proliferation and promoted anti-inflammatory responses. PD-1 expression can be employed as a biomarker for this immunomodulatory therapy, as its increase predicts therapeutic efficacy (Fig. 3A, B).

[0114] In a previous clinical trial (TN-05), four doses of rituximab were shown to maintain β-cell function for 1 year, but no significant improvement was observed when treatment was extended to 30 months (27, 29). Surprisingly, however, this study demonstrates that the combination of rituximab and polyclonal Tregs resulted in improved control of DM1 in terms of MMTT and fasting C-peptide levels, DDI / kg body weight, HbA1c, and better outcomes in remission and insulin independence at 2-year follow-up. The timing of B cell repopulation between 6 and 12 months post-depletion (Fig. 19) and persistently reduced serum IgM levels were similar between TN-05 and TregVAC2.0 (Fig. 5G). Previously, conflicting data have been reported regarding the effect of B cell depletion on serum immunoglobulin levels: In the TN-05 trial, DM1 patients treated with rituximab were characterized by comparable or increased serum total IgG concentrations over a longer follow-up period compared with control patients (27, 28). However, some reports have shown no change in serum IgG1, IgG2, IgG3, or IgG4 concentrations after rituximab (29) or a selective decrease in serum IgG4 subclasses only (30). However, in our study, B cell repopulation in Treg+RTX patients resulted in an increase in IgG2 at the expense of IgG1 (Fig. 5E, F). This is consistent with the concentration of cytokines responsible for class switching, as IgG1 serum concentrations positively correlated with IgG2 concentrations along with peripheral IL-4 and IL-5 levels at approximately 12 months after rituximab (the time of B cell repopulation) (31). This switch may directly affect IgG function, as IgG1 binds more effectively to complement and FcR receptors on monocytes and neutrophils than IgG2. (32) Therefore, together with higher levels of IgG2 and a higher proportion of regulatory-like B cells, this may contribute to the better clinical outcomes in the Treg+RTX group.Interestingly, serum IgG2 levels have been reported in healthy individuals to be negatively correlated with whole-body and muscle insulin sensitivity and insulin-stimulated glucose disposal, especially when factors known to alter insulin sensitivity, such as age, sex, and BMI, are taken into account (33). Changes in antibody levels after B cell depletion with rituximab also affect immunity to infection. For example, after influenza vaccination in RA patients, humoral responses were reduced in IgM, IgG1, and IgG3 levels. This phenomenon was time-dependent and was observed only in individuals depleted approximately 1 month before vaccination, whereas it disappeared in individuals treated with rituximab 6–10 months before vaccination. (34) In this study, approximately 60% of the Treg+RTX cohort was affected, but this was comparable to the Treg and control groups. (7)

[0115] In summary, the efficacy of the combination therapy may be due to several factors. It was primarily associated with an increase in the proportion of PD-1+ T cells (Teff and CD8+ T cells in vivo and Tregs in vivo and in vitro) and reconstitution of the B cell compartment toward a tolerogenic phenotype. PD-1 expression on T cells may be a promising biomarker for the efficacy of this therapy. Our data provide a solid background for immune monitoring in future clinical trials and clarify the immunopathogenesis of DM1.

[0116] References

[0117] (1) Budd MA, Monajemi M, Colpitts SJ, Crome SQ, Verchere CB, Levings MK. Interactions between islets and regulatory immune cells in health and type 1 diabetes. Diabetologia. 2021;64(11):2378-2388. doi:10.1007 / S00125-021-05565-6 (2) Raugh A, Allard D, Bettini M. Nature vs. nurture: FOXP3, genetics, and tissue environment shape Treg function. Front Immunol. 2022;13:4199. doi:10.3389 / FIMMU.2022.911151 / BIBTEX (3) Roep BO. The need and benefit of immune monitoring to define patient and disease heterogeneity, mechanisms of therapeutic action and efficacy of intervention therapy for precision medicine in type 1 diabetes. Front Immunol. 2023;14. doi:10.3389 / FIMMU.2023.1112858 (4) Roep BO, Montero E, van Tienhoven R, Atkinson MA, Schatz DA, Mathieu C. Defining a cure for type 1 diabetes: a call to action. lancet Diabetes Endocrinol. 2021;9(9):553-555. doi:10.1016 / S2213-8587(21)00181-9 (5) Herold KC, Bundy BN, Long SA, et al. An Anti-CD3 Antibody, Teplizumab, in Relatives at Risk for Type 1 Diabetes. N Engl J Med. 2019;381(7):603-613. doi:10.1056 / NEJMOA1902226 (6) Dong S, Hiam-Galvez KJ, Mowery CT, et al. The effect of low-dose IL-2 and Treg adoptive cell therapy in patients with type 1 diabetes. JCI insight. 2021;6(18). doi:10.1172 / JCI.INSIGHT.147474 (7) Zielinski M, Zalinska M, Iwaszkiewicz-Grzes D, et al. Combined therapy with CD4+CD25highCD127- T regulatory cells and anti-CD20 antibody in recent-onset type 1 diabetes is superior to monotherapy: Randomised phase I / II trial. Diabetes, Obes Metab. 2022;24(8):1534-1543. doi:10.1111 / DOM.14723 (8) Bluestone JA, Buckner JH, Fitch M, et al. Type 1 diabetes immunotherapy using polyclonal regulatory T cells. Sci Transl Med. 2015;7(315):315ra189. doi:10.1126 / scitranslmed.aad4134 (9) Marek-Trzonkowska N, Mysliwiec M, Dobyszuk A, et al. Therapy of type 1 diabetes with CD4(+)CD25(high)CD127-regulatory T cells prolongs survival of pancreatic islets - results of one year follow-up. Clin Immunol. 2014;153(1):23-30. doi:10.1016 / j.clim.2014.03.016 (10) Marek-Trzonkowska N, Wujtewicz MA, Mysliwiec M, et al. Administration of CD4+CD25highCD127- regulatory T cells preserves β-cell function in type 1 diabetes in children. Diabetes Care. 2012;35(9):1817-1820. doi:10.2337 / DC12-0038 (11) Trzonkowski P, Bacchetta R, Battaglia M, et al. Hurdles in therapy with regulatory T cells. Sci Transl Med. 2015;7(304). doi:10.1126 / SCITRANSLMED.AAA7721 (12) Primary Immunodeficiency Diseases - Immunoglobulin Disorders | Choose the Right Test. https: / / arupconsult.com / content / immunoglobulin-disorders?_ga=2.163356788.958945285.1684150934-1231057015.1684150933&_gl=1*tlimzr*_ga*MTIzMTA1NzAxNS4xNjg0MTUwOTMz*_ga_Z8H49DQE4D*MTY4NDE1MDkzMy4xLjEuMTY4NDE1MTI0My4wLjAuMA.. Accessed May 15, 2023. (13) Vecchione A, Di Fonte R, Gerosa J, et al. Reduced PD-1 expression on circulating follicular and conventional FOXP3+ Treg cells in children with new onset type 1 diabetes and autoantibody-positive at-risk children. Clin Immunol. 2020;211. doi:10.1016 / J.CLIM.2019.108319 (14) Tucker CG, Dwyer AJ, Fife BT, Martinov T. The role of programmed death-1 in type 1 diabetes. Curr Diab Rep. 2021;21(6):20. doi:10.1007 / S11892-021-01384-6 (15) Bluestone JA, Anderson M, Herold KC, et al. Collateral Damage: Insulin-Dependent Diabetes Induced With Checkpoint Inhibitors. Diabetes. 2018;67(8):1471-1480. doi:10.2337 / DBI18-0002 (16) Postow MA, Sidlow R, Hellmann MD. Immune-Related Adverse Events Associated with Immune Checkpoint Blockade. N Engl J Med. 2018;378(2):158-168. doi:10.1056 / NEJMRA1703481 (17) Keir ME, Liang SC, Guleria I, et al. Tissue expression of PD-L1 mediates peripheral T cell tolerance. J Exp Med. 2006;203(4):883-895. doi:10.1084 / JEM.20051776 (18) Guleria I, Gubbels Bupp M, Dada S, et al. Mechanisms of PDL1-mediated regulation of autoimmune diabetes. Clin Immunol. 2007;125(1):16-25. doi:10.1016 / J.CLIM.2007.05.013 (19) Faustini F, Sippl N, Stalesen R, et al. Rituximab in Systemic Lupus Erythematosus: Transient Effects on Autoimmunity Associated Lymphocyte Phenotypes and Implications for Immunogenicity. Front Immunol. 2022;13:1494. doi:10.3389 / FIMMU.2022.826152 / BIBTEX (20) Stasi R, Del Poeta G, Stipa E, et al. Response to B-cell-depleting therapy with rituximab reverts the abnormalities of T-cell subsets in patients with idiopathic thrombocytopenic purpura. Blood. 2007;110(8):2924-2930. doi:10.1182 / BLOOD-2007-02-068999 (21) Stasi R, Cooper N, Poeta G Del, et al. Analysis of regulatory T-cell changes in patients with idiopathic thrombocytopenic purpura receiving B cell-depleting therapy with rituximab. Blood. 2008;112(4):1147-1150. doi:10.1182 / BLOOD-2007-12-129262 (22) Cooper N, Arnold DM. The effect of rituximab on humoral and cell mediated immunity and infection in the treatment of autoimmune diseases. Br J Haematol. 2010;149(1):3-13. doi:10.1111 / J.1365-2141.2010.08076.X (23) Cooper N, Stasi R, Cunningham-Rundles S, et al. The efficacy and safety of B-cell depletion with anti-CD20 monoclonal antibody in adults with chronic immune thrombocytopenic purpura. Br J Haematol. 2004;125(2):232-239. doi:10.1111 / J.1365-2141.2004.04889.X (24) Edwards JCW, Szczepanski L, Szechinski J, et al. Efficacy of B-cell-targeted therapy with rituximab in patients with rheumatoid arthritis. N Engl J Med. 2004;350(25):2572-2581. doi:10.1056 / NEJMOA032534 (25) Wehr P, Purvis H, Law SC, Thomas R. Dendritic cells, T cells and their interaction in rheumatoid arthritis. Clin Exp Immunol. 2019;196(1):12-27. doi:10.1111 / CEI.13256 (26) Bouaziz JD, Yanaba K, Venturi GM, et al. Therapeutic B cell depletion impairs adaptive and autoreactive CD4+ T cell activation in mice. Proc Natl Acad Sci U S A. 2007;104(52):20878-20883. doi:10.1073 / PNAS.0709205105 (27) Pescovitz MD, Greenbaum CJ, Krause-Steinrauf H, et al. Rituximab, B-Lymphocyte Depletion, and Preservation of Beta-Cell Function. N Engl J Med. 2009;361(22):2143-2152. doi:10.1056 / NEJMOA0904452 / SUPPL_FILE / NEJM_PESCOVITZ_2143SA1.PDF (28) Pescovitz MD, Greenbaum CJ, Bundy B, et al. B-Lymphocyte Depletion With Rituximab and β-Cell Function: Two-Year Results. Diabetes Care. 2014;37(2):453-459. doi:10.2337 / DC13-0626 (29) Kavuru MS, Malur A, Marshall I, et al. An open-label trial of rituximab therapy in pulmonary alveolar proteinosis. Eur Respir J. 2011;38(6):1361-1367. doi:10.1183 / 09031936.00197710 (30) Khosroshahi A, Carruthers MN, Deshpande V, Unizony S, Bloch DB, Stone JH. Rituximab for the treatment of IgG4-related disease: lessons from 10 consecutive patients. Medicine (Baltimore). 2012;91(1):57-66. doi:10.1097 / MD.0B013E3182431EF6 (31) Avery DT, Bryant VL, Ma CS, de Waal Malefyt R, Tangye SG. IL-21-induced isotype switching to IgG and IgA by human naive B cells is differentially regulated by IL-4. J Immunol. 2008;181(3):1767-1779. http: / / www.ncbi.nlm.nih.gov / pubmed / 18641314. Accessed November 15, 2018. (32) Hamilton R, Abmli D, Mohan C. The Human IgG Subclasses. Hum IgG Subclasses. 2001. doi:10.1016 / C2009-0-00373-0 (33) Fiorentino TV, Succurro E, Arturi F, et al. Serum IgG2 levels are specifically associated with whole-body insulin-mediated glucose disposal in non-diabetic offspring of type 2 diabetic individuals: a cross-sectional study. Sci Rep. 2018;8(1). doi:10.1038 / S41598-018-32108-8 (34) Westra J, Van Assen S, Wilting KR, et al. Rituximab impairs immunoglobulin (Ig)M and IgG (subclass) responses after influenza vaccination in rheumatoid arthritis patients. Clin Exp Immunol. 2014;178(1):40-47. doi:10.1111 / IEC.12390 (35) Marek N, Bieniaszewska M, Krzystyniak A, et al. The timing is crucial for ex vivo expansion of T regulatory cells for therapy. Cell Transpl. 2011;20(11-12):1747-1758. doi:10.3727 / 096368911X566217 (36) Marek-Trzonkowska N, Piekarska K, Filipowicz N, et al. Mild hypothermia provides Treg stability. Sci Rep. 2017;7(1):11915. doi:10.1038 / s41598-017-10151-1 (37) Belkina AC, Ciccolella CO, Anno R, Halpert R, Spidlen J, Snyder-Cappione JE. Automated optimised parameters for T-distributed stochastic neighbor embedding improve visualisation and analysis of large datasets. Nat Commun. 2019;10(1). doi:10.1038 / S41467-019-13055-Y (38) Amid E, Warmuth MK. TriMap: Large-scale Dimensionality Reduction Using Triplets. December 2019 (39) Levine JH, Simonds EF, Bendall SC, et al. Data-Driven Phenotypic Dissection of AML Reveals Progenitor-like Cells that Correlate with Prognosis. Cell. 2015;162(1):184-197. doi:10.1016 / j.cell.2015.05.047 (40) Babicki S, Arndt D, Marcu A, et al. Heatmapper: web-enabled heat mapping for all. Nucleic Acids Res. 2016;44(W1):W147-53. doi:10.1093 / nar / gkw419 (41) Zielinski M, Zalinska M, Iwaszkiewicz-Grzes D, et al. Combined therapy with CD4+CD25highCD127- T regulatory cells and anti-CD20 antibody in recent-onset type 1 diabetes is superior to monotherapy: Randomized phase I / II trial. Diabetes, Obes Metab. 2022;24(8):1534-1543. doi:10.1111 / DOM.14723 (42) Faber OK, Binder C. C-peptide response to glucagon. A test for the residual beta-cell function in diabetes mellitus. Diabetes. 1977 (26): 605-610. doi:10.2337 / diab.26.7.605.

[0118] table

[0119] [Table 1A]

[0120] P values ​​in Table 1A are based on sequential one-way ANOVA F statistics and Kruskal-Wallis statistics for multilevel categorical data; adopted from (7).

[0121] [Table 1B]

[0122] p-values ​​are based on Fisher's exact test for categorical data and t-test for continuous data. Healthy controls were anonymous blood donors and all characteristics were assumed to be within normal ranges. n / a - not applicable

[0123] [Table 2] TIFF2025534931000005.tif252162TIFF2025534931000006.tif50168

[0124] Adapted from (41).

[0125] [Table 3A] TIFF2025534931000008.tif106170

[0126] For surface / intracellular staining, we used the Foxp3 / transcription factor staining buffer set from eBioscience, Thermo Fisher Scientific (Waltham, MA, USA) following the protocol exactly.

[0127] [Table 3B]

[0128] [Table 3C]

[0129] For surface / intracellular staining, we used the Foxp3 / transcription factor staining buffer set from eBioscience, Thermo Fisher Scientific (Waltham, MA, USA) following the protocol exactly.

[0130] [Table 4]

Claims

1. 1. A method for monitoring cell therapy in a patient treated with CD4+FoxP3+ regulatory T cells, the method comprising determining in vitro the expression of at least one protein selected from the group consisting of PD-1 and CD73 on the patient's CD4+FoxP3+ T cells and / or determining the expression of PD-1 on CD4+FoxP3- cells.

2. 2. The method of claim 1, wherein determining the expression of the at least one protein on CD4+FoxP3+ T cells and / or determining the expression of PD-1 on CD4+FoxP3- cells is performed on a sample from the patient.

3. 3. The method of claim 1 or 2, comprising isolating CD4+FoxP3+ T cells from the patient sample before determining the expression of the at least one protein and / or isolating CD4+FoxP3- cells from the patient sample before determining the expression of PCD1 on CD4+FoxP3- cells.

4. The method of claim 2 or 3, further comprising expanding the isolated CD4+FoxP3+ T cells and / or the isolated CD4+FoxP3- cells.

5. The method according to any one of claims 1 to 4, wherein the sample is peripheral blood.

6. The method of any one of claims 1 to 5, wherein the isolated and optionally expanded CD4+FoxP3+ T cells are CD4+FoxP3+ regulatory T cells.

7. The method of any one of claims 1 to 6, wherein the isolated and optionally expanded CD4+FoxP3- T cells are CD4+FoxP3- effector T cells.

8. Steps below: (i) determining the expression of PD-1 on said CD4+FoxP3+ regulatory T cells; and / or (ii) determining the expression of PD-1 on said CD4+FoxP3− effector T cells; and / or (iii) determining the expression of CD73 on the CD4+FoxP3+ regulatory T cells. The method according to any one of claims 1 to 7, comprising:

9. The method of any one of claims 1 to 8, wherein the regulatory T cells are of the phenotype CD4+FoxP3+CD25highCD127-doublet-.

10. The method of any one of claims 1 to 9, wherein the effector T cells are of the phenotype CD4+FoxP3-CD25lowCD127+doublet-.

11. 11. The method of any one of claims 1 to 10, wherein the method is performed on the patient's cells one or more times at least two weeks, preferably one month, more preferably two months, and even more preferably three months after administration of the CD4+FoxP3+ regulatory T cells.

12. 12. The method of claim 11, wherein the method is performed on the patient's cells at an interval of at least two weeks, preferably at least one month, more preferably at least two months, and even more preferably at least three months after administration of the CD4+FoxP3+ regulatory T cells.

13. 13. The method of claim 11 or 12, wherein the method is performed on the patient's cells for at least six months, more preferably at least one year, more preferably at least two years after administration of the CD4+FoxP3+ regulatory T cells.

14. The method of any one of claims 11 to 13, wherein the method is performed at least once on the patient's cells prior to administration of the CD4+FoxP3+ regulatory T cells.

15. The method of any one of claims 1 to 14, wherein the patient has an autoimmune disease.

16. 16. The method of claim 15, wherein the autoimmune disease is type 1 diabetes.

17. 17. The method of claim 15 or 16, wherein the patient is a child or adolescent.

18. The method of any one of claims 1 to 17, wherein the patient is further treated with an anti-CD20 antibody or a fragment thereof that retains specific binding to CD20.

19. 19. The method of claim 18, wherein the anti-CD20 antibody is rituximab.

20. 20. A method for evaluation of the efficacy of cell therapy in a patient treated with CD4+FoxP3+ regulatory T cells, comprising carrying out the method of any one of claims 1 to 19, wherein expression of PD-1 on at least 16% of said CD4+FoxP3+ regulatory T cells and / or expression of CD73 on at least 7% of said CD4+FoxP3+ regulatory T cells and / or expression of PD-1 on at least 8% of said CD4+FoxP3- T cells indicates efficient cell therapy.

21. 15. CD4+FoxP3+ regulatory T cells for use in treating an autoimmune disease in a patient having or developing an autoimmune disease, respectively, by cell therapy comprising carrying out a method according to any one of claims 1 to 14 and comprising administering said regulatory T cells.

22. The CD4+FoxP3+ regulatory T cells for use according to claim 21, further comprising the administration of an anti-CD20 antibody or a fragment thereof that retains specific binding to CD20.

23. The CD4+FoxP3+ regulatory T cells for use according to claim 21, wherein the anti-CD20 antibody is rituximab.

24. 22. The CD4+FoxP3+ regulatory T cells for use according to claim 21, wherein the autoimmune disease is as defined in claim 15 or 16.

25. 1. A detectable compound having a high affinity for a protein selected from the group consisting of PD-1 and CD73 for use in a method of diagnosing efficient cell therapy in a patient treated with CD4+FoxP3+ regulatory T cells, the method comprising the steps of obtaining a patient sample, isolating CD4+FoxP3+ T cells and / or CD4+FoxP3- cells from the patient, contacting the isolated CD4+FoxP3+ T cells and / or isolated CD4+FoxP3- cells with a detectable label, detecting the label bound to CD4+FoxP3+ T cells that express PD-1 and / or CD73 and / or to CD4+FoxP3- cells that express PD-1, and determining the percentage of CD4+FoxP3+ T cells that express PD-1 and / or CD73 and / or the percentage of CD4+FoxP3- cells that express PD-1.

26. 26. The detectable compound for use according to claim 25, wherein the isolated CD4+FoxP3+ T cells and / or isolated CD4+FoxP3- cells are expanded prior to contacting the cells with the detectable label.

27. 27. The detectable compound for use according to claim 25 or 26, wherein the detectable label is an anti-PD-1 antibody or a fragment thereof that retains specific binding to PD-1, or an anti-CD73 antibody or a fragment thereof that retains specific binding to CD73.

28. A detectable compound for use according to any one of claims 25 to 27, wherein said compound is linked to a detectable label.

29. 29. The detectable compound for use according to claim 28, wherein the label is a fluorescent label.

Images