Novel predictive biomarkers for secondary autoimmunity after lymphocyte depletion therapy

Abstract

The present invention provides a method for assessing the risk of secondary autoimmunity in patients with a primary autoimmune disease (e.g., MS) after lymphocyte depletion therapy (e.g., anti-CD52 antibody therapy) based on the proportion of a novel cell type called platelet lineage cells (PLC) among total cells and / or the immature platelet fraction (IPF) value in a biological sample from the patient.

Description

[Technical field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims priority from U.S. Provisional Patent Application No. 63 / 188,302, filed May 13, 2021. The disclosure of that priority application is incorporated herein by reference in its entirety. [Background technology]

[0002] Multiple sclerosis (MS) is an immune-mediated, chronic inflammatory neurodegenerative disease affecting the central nervous system. It is characterized by loss of motor and sensory function due to inflammation, demyelination, and axonal damage and loss (Non-Patent Document 1, Non-Patent Document 2). MS patients exhibit a wide range of severe clinical symptoms with increasing physical disability, fatigue, pain, and cognitive impairment as the disease progresses. MS affects more than 2 million people worldwide, with at least 2-3 times more women than men. MS significantly impacts patients' quality of life, shortening their life expectancy by an average of 5 to 10 years.

[0003] Alemtuzumab, a humanized anti-CD52 monoclonal antibody, has been approved for the treatment of relapsing forms of MS (RMS). Although its efficacy has been proven in clinical trials, its use is associated with unpredictable non-MS secondary autoimmunity that can appear months or years after treatment. Approximately 40% of alemtuzumab-treated patients present with autoimmune thyroid events, 2% with platelet deficiency (immune thrombocytopenia; ITP) (Non-Patent Document 3), and 0.34% with autoimmune nephropathy (Non-Patent Document 4). In the absence of predictive biomarkers for secondary autoimmunity, clinical practice requires the implementation of a drug risk management plan or risk evaluation and risk mitigation strategy (RMP / REMS) and careful monitoring to detect these autoimmune events early.

[0004] Thus, there is an unmet need to identify novel biomarkers to predict the risk of developing secondary autoimmunity in patients considering treatment with lymphocyte-depleting therapies such as alemtuzumab. [Prior art documents] [Non-patent literature]

[0005] [Non-Patent Document 1] Friese et al., Nat Rev Neurol. (2014) 10(4):225-38 [Non-Patent Document 2] Trapp and Nave, Ann Rev Neurosci. (2008) 231:247-69 [Non-Patent Document 3] Cuker et al., Mult Scler Houndmills Basingstoke Engl. (2020) 26:48-56 [Non-Patent Document 4] Phelps et al., Mult Scler Houndmills Basingstoke Engl. (2019) 25:1273-88 Summary of the Invention [Problem to be solved by the invention]

[0006] The present disclosure provides a novel and useful method for improving risk management in the treatment of autoimmune diseases such as MS. The method reduces treatment side effects such as secondary autoimmunity and assists healthcare providers and patients in selecting treatment regimens for autoimmune disease treatment and in post-treatment monitoring. The method of the present disclosure is based on the discovery that in MS patients, low content of platelet lineage cells (PLC) and / or high immature platelet fraction (IPF), detected even before lymphocyte depletion therapy (e.g., alemtuzumab therapy), correlates with an increased risk of developing secondary autoimmunity after therapy. [Means for solving the problem]

[0007] In one aspect, the disclosure provides a method for assessing the risk of developing secondary autoimmunity in a patient having a primary autoimmune disease following lymphocyte depletion therapy, comprising: a) obtaining a blood sample from a patient; b) (i) a ratio of platelet lineage cells (PLC) (e.g., mature PLC) among total cells of a blood sample, where a decrease in the ratio of PLC (e.g., mature PLC) compared to the first baseline indicates an increased risk of the patient developing secondary autoimmunity following treatment; and / or (ii) an immature platelet fraction (IPF) in a blood sample, where an increase in IPF compared to a second baseline indicates an increased risk of the patient developing secondary autoimmunity following treatment; determining a In certain embodiments, the method includes determining both (i) and (ii).

[0008] In another aspect, the disclosure provides a method for treating a patient having a primary autoimmune disease, comprising: a) Selecting patients who have been diagnosed as not being at increased risk of developing secondary autoimmunity after lymphocyte depletion therapy, the risk being: (i) a ratio of platelet lineage cells (PLC) (e.g., mature PLC) among total cells of a blood sample, where a decrease in the ratio of PLC (e.g., mature PLC) compared to a first baseline indicates an increased risk of the patient developing secondary autoimmunity following treatment; and / or (ii) an immature platelet fraction (IPF) in a blood sample, where an increase in IPF compared to a second baseline indicates an increased risk of the patient developing secondary autoimmunity following treatment; The diagnosis was made by determining b) administering to the patient a therapeutically effective amount of lymphocyte depletion therapy. The present invention provides a method comprising: In certain embodiments, the method includes determining both (i) and (ii).

[0009] In some embodiments, the primary autoimmune disease is multiple sclerosis (MS). In certain embodiments, the primary autoimmune disease is relapsing MS, relapsing-remitting MS (RR-MS), or secondary progressive MS (SPMS).

[0010] In some embodiments, the lymphocyte depletion therapy is a lymphocyte depletion antibody therapy, such as an anti-CD52 antibody or an antigen-binding portion thereof. In certain embodiments, the anti-CD52 antibody has the six CDRs of alemtuzumab. In certain embodiments, the anti-CD52 antibody has the heavy chain variable domain and the light chain variable domain of alemtuzumab. In certain embodiments, the anti-CD52 antibody is alemtuzumab.

[0011] In some embodiments of the methods described herein, the first and second standards are obtained from patients with the primary autoimmune disease in question who do not develop secondary autoimmunity following lymphocyte depletion therapy or from healthy subjects.

[0012] In some embodiments, the blood sample is a blood sample in which red blood cells have been lysed, hi some embodiments, the blood sample is a peripheral blood mononuclear cell (PBMC) sample (e.g., the sample has been depleted of neutrophils).

[0013] In some embodiments, the PLC ratio is decreased by more than 2 standard deviations compared to the ratio in control subjects, hi some embodiments, the IPF value is decreased by more than 2 standard deviations compared to the value in a control subject.

[0014] In some embodiments, the PLC is CD41 + CD61 + SPARC + TREML1 + It is characterized in that:

[0015] The disclosed methods can further include determining the proportion of immature PLCs among the total PLC population in a biological sample from the patient, where an increase in the proportion of immature PLCs compared to a third standard indicates an increased risk of the patient developing secondary autoimmunity following treatment. In some embodiments, immature PLCs are CD41 低 CD61 低 PDGFA 高 PDCD10 高 and optionally further DAB2 高 RGS10 高 RGS18高 TSC22D1 高 In some embodiments, the third standard is obtained from patients with the primary autoimmune disease who do not develop secondary autoimmunity after lymphocyte depletion therapy or from healthy subjects.

[0016] In some embodiments, the secondary autoimmunity is selected from the group consisting of immune thrombocytopenic purpura (ITP), Graves' disease, Hashimoto's disease, Goodpasture's disease (anti-glomerular basement membrane (GBM) disease), membranous glomerulonephritis (membranous nephropathy), red blood cell aplasia, autoimmune thyroid disease, transient thyroiditis, autoimmune hemolytic anemia, type 1 diabetes, alopecia areata / alopecia universalis, vitiligo, myalgia, sarcoidosis, autoimmune neutropenia, autoimmune hepatitis, and autoimmune lymphopenia.

[0017] Other features, objects, and advantages of the present invention will become apparent from the following detailed description. It should be understood, however, that the detailed description, while indicating embodiments and aspects of the present invention, is given by way of illustration only and not by way of limitation. Various changes and modifications within the scope of the present invention will become apparent to those skilled in the art from the detailed description. [Brief description of the drawings]

[0018] [Figure 1] Bar graphs showing the relative abundance of all major immune cell types (T cells, monocytes, B cells, and NK cells), as well as rare cell types (plasmacytoid dendritic cells (pDCs), and platelet-like cells), and scatter plots showing that platelet-like cells are significantly enriched in patients who do not develop secondary autoimmunity compared to patients who do develop secondary autoimmunity (mean ± SEM; Student's t-test **p<0.01). Solid circles represent samples taken from patients who developed secondary autoimmunity; solid squares represent samples taken from patients who did not develop secondary autoimmunity. [Diagram 2]Scatter plot showing PLC abundance in individual patients based on sc-RNAseq data before and after alemtuzumab treatment in patients who developed secondary autoimmunity (sAI) and those who did not develop secondary autoimmunity (non-sAI). Non-sAI patients, represented by large squares, showed a decrease in PLC counts at T24 (24 months after the first course) and developed sAI between 4 and 7 years of follow-up. [Diagram 3] Scatter plots showing enrichment of platelet lineage cells (PLC) in patients who did not develop secondary autoimmunity at both T0 (pretreatment) and T24 (24 months after the first course of alemtuzumab) (mean ± SEM; 2-way ANOVA, overall effect of AI status **p<0.01). Solid circles represent samples taken from patients who developed secondary autoimmunity; solid squares represent samples taken from patients who did not develop secondary autoimmunity. [Figure 4-1] Scatter plots and associated SPRING plots showing gene expression levels of SPARC, TREML1, GP9, ITGB3, ITGA2B, and GP1B (mean ± SEM; two-way ANOVA with Sidak's multiple comparison test *p<0.05, **p<0.001, and ***p<0.001). Solid circles represent samples taken from patients who developed secondary autoimmunity; solid squares represent samples taken from patients who did not develop secondary autoimmunity. [Figure 4-2] Continued from Figure 4-1. [Diagram 5] 1 is a bar graph showing the percentage of platelets in whole blood, fresh PBMCs, and frozen PBMCs by flow cytometry (mean±SEM). [Figure 6] 1 is a bar graph showing the percentage of PLC in whole blood, fresh PBMCs, and frozen PBMCs by flow cytometry (mean±SEM). [Figure 7] 13 is a bar graph quantifying the percentage of PLC in RBC lysed blood, fresh PBMCs, and frozen PBMCs among cells that are 1) TREML1 high SPARC+ or 2) TREML1 low SPARC+. [Figure 8]1 is a bar graph showing the percentage of cells within the sAI and non-sAI groups that belong to subset 1 (juvenile / resting transcriptome state) or subset 2 (mature / activated transcriptome state). [Figure 9A] Heatmap showing unsupervised clustering analysis of RNAseq data from 161 baseline samples derived from MS patients prior to alemtuzumab treatment. Figure 9A shows the relative expression levels of six mature PLC genes (GP1BA, PPBP, ITGA2B, ITGB3, SPARC, and TREML1) and five juvenile PLC genes (PDCD10, RGS10, DAB2, TSC22D1, and RGS18) in patients who developed secondary autoimmunity ("AI-enriched patients", left) and those who did not develop secondary autoimmunity ("non-AI-enriched patients", right). Figure 9B shows the same heatmap as Figure 9A, with additional information on patient characteristics such as thyroid activity, race, and sex at the bottom of the heatmap. [Figure 9B] Heatmap showing unsupervised clustering analysis of RNAseq data from 161 baseline samples derived from MS patients prior to alemtuzumab treatment. Figure 9A shows the relative expression levels of six mature PLC genes (GP1BA, PPBP, ITGA2B, ITGB3, SPARC, and TREML1) and five juvenile PLC genes (PDCD10, RGS10, DAB2, TSC22D1, and RGS18) in patients who developed secondary autoimmunity ("AI-enriched patients", left) and those who did not develop secondary autoimmunity ("non-AI-enriched patients", right). Figure 9B shows the same heatmap as Figure 9A, with additional information on patient characteristics such as thyroid activity, race, and sex at the bottom of the heatmap. [Figure 10A] Plots showing expression levels of selected genes shown in Figures 9A and 9B at different sampling times (0, 12, and 24 months), with mature PLC genes shown in Figure 10A and immature PLC in 10B. Data are shown for patients who developed secondary autoimmunity ("AI", top) and patients who did not develop secondary autoimmunity ("non-AI", bottom). [Figure 10B]Plots showing expression levels of selected genes shown in Figures 9A and 9B at different sampling times (0, 12, and 24 months), with mature PLC genes shown in Figure 10A and immature PLC in 10B. Data are shown for patients who developed secondary autoimmunity ("AI", top) and patients who did not develop secondary autoimmunity ("non-AI", bottom). [Figure 11] Heatmap showing unsupervised clustering analysis of RNAseq data from MS patient baseline samples, some of whom were treated with IFNβ-1a. Data is provided for the same mature and immature PLC genes as shown in Figures 9A and 9B. "AI": patients who developed secondary autoimmunity. "Non-AI": patients who did not develop secondary autoimmunity. [Figure 12A] Box-and-whisker plot of immature platelet fraction (IPF) clinical values ​​at T0 (baseline) in patients who developed secondary autoimmunity (sAI; solid circles) and in patients who did not develop secondary autoimmunity (non-sAI; solid squares); normal ranges are indicated in red brackets (error bars span the 10th–90th percentile range). [Figure 12B] Plots showing change over time (0–24 months) in IPF clinical values ​​in patients who develop secondary autoimmunity (sAI; solid circles) and those who do not develop secondary autoimmunity (non-sAI; solid squares) (mean ± SEM; two-way ANOVA, difference in overall sAI status ***p=0.0001). [Figure 13] Correlation graphs of IPF clinical values ​​and the percentage of PLC from single-cell data for patients who develop secondary autoimmunity (sAI; solid circles) and those who do not develop secondary autoimmunity (non-sAI; solid squares) at T0 (baseline). Left: Post-hoc analysis of true sAI and true non-sAI identification of patients in our cohort based on either IPF values ​​alone or the percentage of IPF and PLC values. Right: Tabular representation of the combined use of clinical IPF values ​​and single-cell PLC data in identifying pre-treatment AI status in a given patient cohort. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0019] The present disclosure is based on the discovery that the development of secondary autoimmunity in patients with primary autoimmune disease (e.g., MS) after lymphocyte depletion therapy is associated with a lower abundance of platelet lineage cells (PLC) and / or a higher value of immature platelet fraction (IPF) compared to control subjects, even before lymphocyte depletion. The control subjects can be, for example, healthy subjects or patients with primary autoimmune disease who do not develop secondary autoimmunity after lymphocyte depletion therapy. Thus, a decrease in the proportion of PLC and / or an increase in IPF are predictive biomarkers for assessing the risk of developing secondary autoimmunity after lymphocyte depletion.

[0020] Based on the above findings, the present disclosure provides a method for improving risk management for patients with a primary autoimmune disease (e.g., MS) when considering lymphocyte depleting therapy, such as treatment with an anti-CD52 antibody (e.g., alemtuzumab). For example, by enabling a healthcare provider to determine whether an MS patient is at high or low risk of developing secondary autoimmunity after therapy, the healthcare provider can determine whether the patient should receive therapy (e.g., if the patient is not at increased risk), or whether the patient should not receive therapy and should be monitored more closely for secondary autoimmunity after therapy (e.g., if the patient is at increased risk).

[0021] For example, the disclosure provides methods of assessing the risk of developing secondary autoimmunity in patients with a primary autoimmune disease (e.g., MS) that is at increased risk of developing a secondary autoimmune disease following lymphocyte depletion therapy. In some embodiments, patients assessed as not at increased risk are treated with lymphocyte depletion therapy. In some embodiments, patients assessed as at increased risk are not treated with lymphocyte depletion therapy. In some embodiments, patients assessed as at increased risk receive lymphocyte depletion therapy and are subsequently subjected to enhanced monitoring compared to patients assessed as not at increased risk.

[0022] The present disclosure also provides methods for treating patients with a primary autoimmune disease (eg, MS patients) who are not at increased risk of developing a secondary autoimmune disease following lymphocyte depletion therapy.

[0023] The present disclosure also provides a method of treating a patient (e.g., an MS patient) with a primary autoimmune disease at increased risk of developing a secondary autoimmune disease after lymphocyte depletion therapy, by intensifying monitoring for the development of secondary autoimmunity after therapy (compared to monitoring for patients without increased risk). Such intensified monitoring can follow an appropriate monitoring plan determined by a healthcare provider after lymphocyte depletion therapy. An appropriate monitoring plan for at-risk patients can include, but is not limited to, more frequent monitoring for secondary autoimmunity after lymphocyte depletion therapy, for example at intervals of 1 week, 2 weeks, 1 month, 2 months, 3 months, 6 months, or 1 year. Monitoring can continue for an extended period, for example, more than 1 year, more than 2 years, more than 3 years, more than 4 years, more than 5 years, or longer, since some patients may not develop secondary autoimmunity until more than 1 year after lymphocyte depletion therapy. Intensified monitoring may also be required, such as thorough medical testing (e.g., blood tests) by a specialist for signs of secondary autoimmunity. Additionally, pharmacists or clinical staff distributing lymphocyte-depleting drugs to patients for the treatment of MS may be required to counsel patients about the increased risk of developing secondary autoimmunity after drug use if the patient exhibits decreased PLC levels and / or increased IPF levels (possibly with increased levels of immature PLC), and may be required to obtain informed consent from the patient before distributing the drug to the patient.

[0024] I. Risk assessment methodology The risk of a patient with an autoimmune disease (e.g., MS) to develop secondary autoimmunity following lymphocyte depletion can be assessed by determining: i) the proportion of platelet lineage cells (PLC) (e.g., mature PLC) among all cells in a biological (e.g., blood) sample from a patient, where a decrease in the proportion of PLC (e.g., mature PLC) compared to a control subject indicates an increased risk; and / or ii) Immature platelet fraction (IPF) in a biological sample, where an increase in IPF compared to control subjects indicates an increased risk.

[0025] In some embodiments, risk is assessed by determining i) (and optionally ii)) and further iii) the proportion of immature PLCs in the total PLC population in the biological sample, where an increased proportion of immature PLCs compared to control subjects indicates increased risk. In some embodiments, risk is assessed by determining iii), or ii) and iii).

[0026] In some embodiments, risk is assessed by determining i), ii), iii), or any combination thereof, and further by iv) testing for the presence of antibodies to mature or activated platelets in the biological sample, where an increase in anti-(mature / activated) platelet antibodies compared to control subjects indicates increased risk. In some embodiments, risk is assessed by determining iv).

[0027] In certain embodiments, the biological sample obtained from the patient is a bodily fluid sample such as blood (e.g., whole blood, freshly isolated peripheral blood mononuclear cells (PBMCs), or frozen PBMCs), serum, plasma, urine, saliva, lymph, or cerebrospinal fluid. In certain embodiments, the biological sample is blood with lysed red blood cells (RNA).

[0028] In certain embodiments, the relative PLC abundance, IPF value, and / or immature PLC ratio in patients who develop secondary autoimmunity after treatment are based on a control subject, such as a healthy subject. A healthy subject in this context refers to a person who does not have a known inflammatory condition, including a person who does not have an autoimmune disease (e.g., a person who does not have detectable symptoms of an autoimmune disease). In some embodiments, the healthy subject is not lymphopenic. In some embodiments, the control subject is an autoimmune disease patient who does not develop secondary autoimmunity after lymphocyte depletion.

[0029] Obtaining information on relative PLC abundance, IPF values, and / or immature PLC ratios in biological samples from patients with autoimmune diseases (e.g., MS) is useful in selecting treatment and post-treatment monitoring plans for those patients. If information can be obtained before lymphocyte-depleting therapy, patients can be informed of their relative risk of developing secondary autoimmunity after therapy and treatment decisions can be made accordingly. Patients classified as "at risk" can also be informed of the need for increased post-treatment monitoring, e.g., more frequent and more thorough testing by specialists. This information therefore improves risk management (by physicians, pharmacists, and patients) in the treatment of autoimmune diseases. Obtaining information during or after lymphocyte-depleting therapy may also be useful in monitoring the development of secondary autoimmunity and making treatment decisions.

[0030] A. Platelet cells Platelet lineage cells (PLCs) are a novel and rare platelet-like cell type that strongly resemble platelets but differ from them in their larger size, granularity, and transcript content. PLCs express classical platelet markers such as CD41 and CD61, as well as additional surface markers that are not universally associated with high levels in platelets, such as SPARC (Secreted Protein Acidic and Rich in Cysteine) and TREML1 (Triggering Receptor Expressed on Myeloid Cells Like 1).

[0031] As detailed in the following examples, PLC was found to contain two distinct subsets with different expression of some markers. The first subset (subset 1) is characterized by lower expression of platelet markers and higher expression of platelet-derived growth factor subunit A (PDGFA), inhibitory markers (e.g., programmed cell death 10 (PDCD10)), and nuclear proteins (e.g., DAB2, RGS10, RGS18, and TSC22D1). The second subset (subset 2) has relatively higher expression of actin genes ACTB and ACTG1, and platelet-derived growth factor PPBP, a potent chemoattractant and activator of neutrophils. The second subset is also enriched in expression of SPARC and TREML1 genes. Subset 1 contains most PLCs from patients who develop secondary autoimmunity after treatment. We further discovered that there are differences in maturity and activation state between the two subsets, with subset 1 representing juvenile or dormant PLCs (enriched in patients exhibiting secondary autoimmunity following treatment) and subset 2 representing mature or activated PLCs.

[0032] Thus, PLCs can be identified based on the expression of certain cell surface markers. In some embodiments, PLCs are identified based on the co-expression of CD41, CD61, SPARC, and / or TREML1 (e.g., CD41 + CD61 + SPARC + TREML1 +In certain embodiments, mature or activated PLC can be identified based on co-expression of MYL9, CLU, PPBP, SPARC, TREML1, ACTB, NCOA4, TMSB4X, AP001189.4, F13A1, PARVB, ALOX12, RBPMS2, PVALB, PF4V1, ARPC1B, SH3BGRL3, PKM, TAGLN2, TGFB1I1, HLA.E, FERMT3, LTBP1, GSN, CD9, C6orf25, ITGA2B, SERF2, and C19orf33. In certain embodiments, mature or activated PLC can be identified based on co-expression of GP1BA, ITGA2B, ITGB3, ACTB, ACTG1, PPBP, SPARC, and / or TREML1, e.g., any combination of any two, three, four, five, six, seven, or all eight of the markers (e.g., ACTB, 高 ACTG1 高 PPBP 高 SPARC 高 TREML1 高 In certain embodiments, juvenile or dormant PLCs can be identified based on co-expression of any combination of RGS18, ACRBP, PTCRA, TSC22D1, HIST1H3H, HIST1H2AC, MYL4, HIST1H2BJ, TMEM40, SLC40A1, SMIM5, TAL1, PEGFA, FAM110A, THEM5, ARHGAP6, NFE2, MMD, NEXN, SCGB1C1, DNM3, GP6, GFI1B, LIMS1, GSTO1, DAB2, ERV3.1, ELOVL7, and LCN2. In certain embodiments, juvenile or dormant PLCs exhibit co-expression of PDGFA, PDCD10, DAB2, RGS10, RGS18, and / or TSC22D1, e.g., a combination of any two, three, four, five, or all six of such markers (e.g., DAB2, RGS10, RGS18, and / or TSC22D1). 高 RGS10 高 RGS18 高 TSC22D1 高 For example, immature PLCs can be identified based on the co-expression of CD41 低 CD61低 PDGFA 高 PDCD10 高 It can be characterized as being.

[0033] In the disclosed method, the relative PLC abundance (the ratio of PLC, e.g., mature PLC, in whole cells) can be measured by many techniques well known to those skilled in the art. In some embodiments, a biological sample is obtained from a subject, and the relative PLC abundance in the sample is measured by any method or assay suitable for detecting RNA-containing cells. In certain embodiments, the relative PLC abundance is measured using flow cytometry analysis (e.g., high-dimensional flow cytometry analysis), such as a fluorescence-activated cell sorting (FACS) assay, or nCounter®. In certain other embodiments, the relative PLC abundance is measured using single-cell RNA sequencing (scRNA-seq). In certain embodiments, the scRNA-seq is droplet-based parallel scRNA-seq.

[0034] In some embodiments, patients with an autoimmune disease (e.g., MS) who are at increased risk of developing secondary autoimmunity following lymphocyte depletion therapy (e.g., anti-CD52 antibody therapy such as alemtuzumab) have a decreased ratio of PLCs among all cells in a biological sample (e.g., blood) compared to control subjects, and the ratio of PLCs is decreased by >1.5, >2, >3, >4, or >5 (e.g., >2) standard deviations compared to the ratio in control subjects.

[0035] In some embodiments, patients with an autoimmune disease (e.g., MS) who are at increased risk of developing secondary autoimmunity following lymphocyte depletion therapy (e.g., anti-CD52 antibody therapy such as alemtuzumab) have an increased proportion of immature PLCs among total PLCs in a biological sample (e.g., blood) compared to control subjects, and the proportion of immature PLCs is increased by >1.5, >2, >3, >4, or >5 (e.g., >2) standard deviations compared to the proportion in control subjects.

[0036] Certain statistical analyses can be applied to determine whether the relative PLC abundance or juvenile PLC ratio in a test sample is significantly different from a reference level (e.g., from a control subject). Such statistical analyses are well known to those of skill in the art and include, but are not limited to, parametric (e.g., two-tailed Student's t-test) or non-parametric (e.g., Wilcoxon-Mann-Whitney U test) tests.

[0037] B. Immature platelet ratio (IPF) Patients with autoimmune diseases who develop secondary autoimmunity after lymphocyte depletion therapy may have different clinical indicators of platelet maturity than those who do not develop secondary autoimmunity after treatment. Specifically, the inventors found that patients who develop thyroid autoimmunity after alemtuzumab treatment had significantly higher immature platelet fraction (IPF) values ​​before treatment compared to patients who do not develop thyroid autoimmunity.

[0038] IPF reflects the proportion of circulating platelets that still retain RNA. IPF is a parameter that measures the number of young reticulated platelets in peripheral blood. In situations where rapid platelet destruction is observed, IPF is usually elevated.

[0039] In the disclosed method, IPF can be measured by many techniques well known to those skilled in the art. IPF is usually measured by flow cytometry (such as high-dimensional flow cytometry) or hematological analysis. For example, the residual RNA content of immature platelets can be easily stained with dyes such as thiazole orange (TO), and IPF can be measured by flow cytometry. Alternatively, IPF can be quantified using optical fluorescence methods performed in the reticulocyte / optical platelet channel of an automated hematology system. In this method, polymethine fluorescent dyes are used to stain the RNA / DNA of reticulocytes, platelet membranes, and granules. In this method, reticulocytes, red blood cells, and fluorescent platelets can be counted simultaneously. Other methods of quantifying IPF include, for example, looking for specific transcriptomic signatures of IPF in biological (e.g. peripheral blood) samples, and / or IPF-enriched cell populations in biological (e.g. whole blood) samples; or nCounter®.

[0040] Thus, in some embodiments, any of the methods described herein for assessing the risk of an autoimmune disease (e.g., MS) patient to develop secondary autoimmunity after lymphocyte depletion therapy can include determining an IPF value in a biological sample from the patient. In some embodiments, patients at increased risk of developing secondary autoimmunity after lymphocyte depletion therapy (e.g., anti-CD52 antibody therapy such as alemtuzumab) have an increased IPF value in a biological sample (e.g., blood) that is increased by >1.5, >2, >3, >4, or >5 (e.g., >2) standard deviations compared to the IPF value of a control subject.

[0041] In some embodiments, increased risk correlates with increased IPF values ​​and decreased PLC abundance compared to control subjects.

[0042] II. Lymphocyte depletion therapy As used herein, "lymphocyte depletion therapy" refers to a type of immunosuppression by therapeutic depletion of circulating lymphocytes, e.g., T cells and / or B cells, resulting in lymphopenia. For example, autologous bone marrow transplantation (BMT) or total lymphocyte irradiation are used to treat multiple sclerosis, resulting in prolonged lymphocyte depletion. See, e.g., Cox et al., Eur J Immunol. (2005) 35:3332-42. For example, lymphocyte depletion can be achieved by combining thymoglobulin, cyclophosphamide, and total body irradiation. Lymphocyte depletion in MS patients can also be achieved by a number of drug treatments. For example, the humanized anti-CD52 monoclonal antibody, alemtuzumab (CAMPATH-1H), is used in lymphocyte depletion therapy to treat MS patients. Alemtuzumab-induced lymphopenia has been shown to effectively reduce central nervous system inflammation both clinically and radiologically (Coles et al., Ann. Neurol. (1999) 46:296-304; Coles et al., N. Engl. J. Med. (2008) 359:1786-1801).

[0043] Thus, in some embodiments, the lymphocyte depletion therapy described herein is an agent that targets CD52-expressing cells. In certain embodiments, the lymphocyte depletion therapy is an anti-CD52 antibody or an antigen-binding portion thereof. The antibody can be, for example, monoclonal, polyclonal, oligoclonal, or bifunctional. In certain embodiments, the anti-CD52 antibody or antigen-binding portion binds to the same epitope as alemtuzumab. The antibody or antigen-binding portion can include the six CDR amino acid sequences or the amino acid sequences of the heavy chain variable domain and the light chain variable domain of alemtuzumab. In certain embodiments, the anti-CD52 antibody is alemtuzumab.

[0044] The term "antigen-binding portion" as used herein refers to one or more fragments of an antibody that retain the ability to specifically bind to the same antigen as the whole antibody from which it is derived. Examples of "antigen-binding portion" include, but are not limited to, Fab fragments, F(ab')2 fragments, Fd fragments, Fv fragments, dAb fragments, isolated complementarity determining regions (CDRs), scFvs, and diabodies. The antibodies and antigen-binding portions thereof described herein can be produced by any method known in the art.

[0045] Other drugs can also be used in lymphocyte-targeted therapy to treat patients with autoimmune diseases (e.g., MS). These drugs can cause lymphocyte cell death or inhibit lymphocyte function. (1) Drugs biologically similar to alemtuzumab, i.e., drugs that target cells bearing CD52, such as other anti-CD52 antibodies (e.g., chimeric, humanized, or human antibodies) that bind to the same or different epitopes as alemtuzumab or compete with alemtuzumab for binding to CD52; (2) Antibodies such as anti-CD2 antibodies, anti-CD3 antibodies, anti-CD4 antibodies, anti-CD20 antibodies (e.g., rituximab), anti-CD38 antibodies, anti-TCR antibodies, and anti-integrin antibodies (e.g., natalizumab). These include, but are not limited to, biomolecules such as peptides, proteins, and antibodies (e.g., chimeric, humanized, and human antibodies) that target cell surface molecules on lymphocytes; (3) cytotoxins (e.g., apoptosis inducers, cyclophosphamide, alkylating agents, and DNA intercalators) that are delivered specifically or nonspecifically to lymphocytes; (4) antigen-binding portions of the aforementioned antibodies, (5) IMiDs (e.g., teriflunomide), and (6) BTK inhibitors (e.g., trebrutinib).

[0046] III. Patient Population The disclosed methods can be used in the context of patients with an autoimmune disease ("primary" autoimmune disease, to distinguish from secondary autoimmunity). The primary autoimmune disease can be, for example, multiple sclerosis (MS), N-methyl-D-aspartate receptor (NMDAR) encephalitis, scleroderma, myasthenia gravis, systemic lupus erythematosus (SLE), rheumatoid arthritis (RA), myelin oligodendrocyte glycoprotein (MOG) spectrum disorder (MOGSD), or neuromyelitis optica spectrum disorder (NMOSD).

[0047] In some embodiments, the primary autoimmune disease is MS, such as relapsing-remitting MS, primary progressive MS, or secondary progressive MS. In the context of this disclosure, an MS patient is a patient who has been diagnosed with a form of MS by, for example, a medical history of symptoms and a neurological examination using tests such as magnetic resonance imaging (MRI), spinal tap, evoked potential testing, and laboratory analysis of blood samples.

[0048] MS, also known as diffuse sclerosis, is a complex disease characterized by considerable heterogeneity in its clinical, pathological, and radiological manifestations. MS is an autoimmune condition in which the immune system attacks the central nervous system, causing demyelination (Compston and Coles, Lancet (2008) 372(9648):1502-17). MS destroys the fatty layer called the myelin sheath that surrounds and electrically insulates nerve fibers. The disease can cause most neurological symptoms and often progresses to physical and cognitive impairment (Compston and Coles, 2008). New symptoms may occur in discrete attacks (relapsing) or may accumulate slowly over time (progressive) (Lublin et al., Neurology (1996) 46(4):907-11). Between attacks, symptoms may disappear completely (remission), but permanent neurological problems often result, especially as the disease progresses (Lublin et al., 1996). Several subtypes, or patterns of progression, have been described that are important for prognosis and treatment decisions. In 1996, the National Multiple Sclerosis Society standardized the definition of four subtypes: relapsing-remitting, secondary progressive, primary progressive, and progressive relapsing (Lublin et al., 1996).

[0049] Relapsing-remitting subtype (RRMS) is characterized by unpredictable acute attacks, called exacerbations or relapses, followed by relatively quiet periods (remissions) lasting months to years without any signs of new disease activity. This represents the initial course of most MS patients. RRMS is the most heterogeneous and complex phenotype of the disease, characterized by different levels of disease activity and severity, especially in the early stages. Inflammation predominates, but neurodegeneration is also present. Demyelination occurs during acute relapses lasting days to months, followed by partial or complete recovery during periods of remission without disease activity. RRMS affects approximately 65-70% of the MS population and tends to progress to secondary progressive MS.

[0050] Secondary progressive MS (SPMS) begins with a relapsing-remitting course followed by occasional relapses, mild remissions, or plateaus without clear remission periods, evolving into progressive neurological decline between acute attacks. Prior to the availability of approved disease-modifying therapies, data from MS natural history studies demonstrated that half of RRMS patients would transition to SPMS within 10 years, and 90% would transition to SPMS within 25 years. SPMS affects approximately 20-25% of all MS patients.

[0051] Primary progressive subtype (PPMS) is characterized by a gradual but steady progression of disability after the initial symptoms of MS, with no apparent remissions (Miller et al., Lancet Neurol. (2007) 6(10):903-12). It is characterized by a progression of disability from onset, with occasional periods of mild improvement or plateauing. A small percentage of people with PPMS may experience relapses. Approximately 10% of all people with MS have PPMS. The age of onset of the primary progressive subtype is usually later than the other subtypes (Miller et al., 2007). Men and women are affected equally.

[0052] Progressive relapsing MS (PRMS) is characterized by steady neurological decline with acute attacks followed by some or no recovery. It is the least common of all the subtypes described herein above.

[0053] Cases with atypical behavior have also been described and are sometimes called borderline forms of MS (Fontaine, Rev Neurol. (Paris) (2001) 157(8-9 Pt2):929-34). These forms include Devic's disease, Barrow concentric sclerosis, Schilder diffuse sclerosis, and Marburg multiple sclerosis (Capello et al., Neurol Sci. 25 Suppl (2004) 4:S361-3; Hainfellner et al., J Neurol Neurosurg Psychiatr. (1992) 55(12):1194-6).

[0054] In general, the regulatory expression "relapsing MS" (RMS) includes both RRMS and SPMS with relapses. This phrase generally refers to three different patient subtypes: RRMS, SPMS with relapses, and clinically isolated demyelinating events with temporal and spatial dissemination of lesions on MRI (see, for example, the European Medicines Agency, Committee for Medicinal Products for Medical Use, Guideline on Clinical Trials of Medicinal Products for the Treatment of Multiple Sclerosis, revised 2nd edition, 2015).

[0055] IV.Secondary autoimmunity Autoimmunity is referred to herein as "secondary autoimmunity" (sAI) when it occurs after the onset of an initial ("primary") disease, e.g., a "primary" autoimmune disease, e.g., MS. Secondary autoimmunity may occur in MS patients who have or have had lymphopenia, e.g., following lymphocyte-depleting therapy. In some patients, secondary autoimmunity occurs soon after lymphocyte-depleting therapy (e.g., treatment with alemtuzumab). In other patients, secondary autoimmunity may not occur until months or years after lymphocyte-depleting therapy; some such patients may have substantial lymphocyte (total lymphocyte count) recovery and are no longer lymphopenic by the time secondary immunity occurs. Thus, in some cases, patients who have undergone lymphocyte-depleting therapy, such as anti-CD52 antibodies, should be closely monitored for signs of secondary autoimmunity and treated in a timely manner.

[0056] Secondary autoimmunity includes, but is not limited to, autoimmune thyroid disease (Graves' disease, hyperthyroidism, hypothyroidism, goiter, Hashimoto's disease, and thyroiditis (e.g., transient thyroiditis)), autoimmune cytopenias (including idiopathic thrombocytopenic purpura (ITP), autoimmune neutropenia, autoimmune hemolytic anemia, autoimmune lymphopenia, and red blood cell aplasia), type 1 diabetes, alopecia areata (e.g., alopecia universalis), vitiligo, myalgia, sarcoidosis, autoimmune hepatitis, and nephropathy, including glomerulonephritis (e.g., membranous glomerulonephritis) and anti-glomerular basement membrane (GBM) disease (Goodpasture's syndrome). Techniques for diagnosing and monitoring such secondary autoimmune diseases are well known to those skilled in the art, including medical tests such as symptom evaluation and blood analysis. The present invention contemplates the use of any known method. For example, autoantibody levels can be measured in a patient's body fluids (e.g., blood) as a means of detecting signs of secondary autoimmunity. Specifically, antinuclear antibodies, antismooth muscle antibodies, and antimitochondrial antibodies can be measured. If antinuclear antibodies are detected, additional assays can be performed to measure antidouble-stranded DNA antibodies, antiribonucleoprotein antibodies, and anti-La antibodies. To detect autoimmune thyroid disease, antithyroid peroxidase (TPO) antibodies and antithyroid stimulating hormone (TSH) receptor antibodies can be measured; if anti-TPO antibodies or anti-TSH receptor antibodies are detected, free T3, free T4, and TSH levels can be measured to determine whether thyroid function is affected. To detect autoimmune thrombocytopenia, antiplatelet antibodies can be measured; measuring platelet levels can help determine whether the presence of antiplatelet antibodies is causing a decrease in platelet count.

[0057] V. Treatment and testing kits for patients with autoimmune diseases The present invention provides a kit for treating primary autoimmune disease, such as multiple sclerosis.The kit of the present invention can include, for example, a lymphocyte depleting drug (e.g., alemtuzumab) and instructions to inform patients or medical providers of the contraindications of this drug, such as the possibility of increased risk of developing secondary autoimmune disease after treatment with this drug.The increased risk is associated with or indicated by: (i) a decrease in the proportion of platelet lineage cells (PLC) in total cells, (ii) an increase in IPF value, and / or (iii) an increase in the proportion of immature PLC in total PLC population, any combination thereof, and optionally (iv) an increase in antibodies against mature platelets or activated platelets in biological (e.g., blood) samples taken from the patient compared to control subjects.

[0058] In other embodiments, the present invention provides a kit for detecting the ratio of PLC in total blood cells, IPF value, and / or the ratio of immature PLC in total PLC in a biological (e.g., blood) sample from an autoimmune disease patient, and / or a kit for identifying a patient with an increased risk of developing secondary autoimmune disease after lymphocyte depletion. Such a kit can include PLC markers such as CD41, CD61, SPARC, and / or TREML1 (and / or any other PLC / mature PLC markers described herein); immature PLC markers such as PDGFA, PDCD10, DAB2, RGS10, RGS18, and / or TSC22D1 (and / or any other immature PLC markers described herein); and / or a reagent for detecting IPF value; and optionally an instruction that instructs the user to collect a biological sample (e.g., a blood sample) from a patient. Such a kit is validated or approved by appropriate regulatory authorities for performing medical diagnosis of patients, such as MS patients.

[0059] Unless otherwise defined herein, scientific and technical terms used in connection with this disclosure shall have the meanings commonly understood by those skilled in the art. Exemplary methods and materials are described below, although methods and materials similar or equivalent to those described herein may also be used in practicing or testing this disclosure. In case of conflict, the present specification, including definitions, shall control. In general, the terms and techniques used in connection with immunology, medicine, medicinal and pharmaceutical chemistry, and cell biology described herein are those well known and commonly used in the art. Furthermore, unless otherwise required by context, singular terms shall include plurals and plural terms shall include singulars. Throughout this specification and embodiments, the words "have" and "comprise", or variations such as "has", "having", "comprises" or "comprising" are understood to mean the inclusion of a stated integer or group of integers, but not the exclusion of any other integer or group of integers. As used herein, the term "approximately" or "about" as applied to one or more values ​​of interest refers to a value similar to a stated reference value. In certain embodiments, the term refers to a range of values ​​that falls within 10%, 9%, 8%, 7%, 6%, 5%, 4%, 3%, 2%, 1%, or less in any direction (greater or smaller) of the stated reference value, unless otherwise stated or otherwise clear from the context.

[0060] All publications and other references mentioned herein are incorporated by reference in their entirety. Although a number of documents are cited herein, this citation is not an admission that any of these documents form part of the common general knowledge in the art.

[0061] In order that the present invention may be better understood, the following examples are presented, which are for illustrative purposes only and are not to be construed as limiting the scope of the invention in any manner. EXAMPLES

[0062] Determining immune cell type composition in alemtuzumab-treated patients method: Clinical Trials and Sample Collection Cryopreserved PBMC samples were obtained from the CAMM323 trial (CARE-MS I, Clinicaltrials.gov identifier NCT00530348). In this study, patients diagnosed with relapsing-remitting multiple sclerosis (RR-MS) and not previously treated with MS disease-modifying therapies were treated with alemtuzumab for 5 consecutive days at baseline (T0) and 3 consecutive days after 12 months (T12) or with interferon beta-1a subcutaneous injections (44 μg, 3 times per week). Whole blood (6–8 mL) was collected into CPT™ tubes containing sodium citrate at T0, T12 (12 months after the first course), and T24 (12 months after the second course). In this study, T0 and T24 samples were analyzed from a total of 32 patients, of whom 11 developed secondary autoimmunity (sAI, defined by the occurrence of a thyroid event) after alemtuzumab treatment, 18 did not (non-sAI), and 3 patients had laboratory abnormalities (LA) defined by the presence of autoantibodies (Jones et al., J Clin Invest. (2009) 119:2052-61). For adjudication, thyroid events were defined as having either laboratory findings (e.g., TSH abnormality) or clinical adverse events (AEs). If diagnosed, clinical AEs were classified as Graves' disease (i.e., hyperthyroidism), Hashimoto's disease (i.e., hypothyroidism), transient thyroiditis, transition from Graves' disease to hypothyroidism, transition from Hashimoto's disease to hyperthyroidism, or unknown. Demographic covariates such as age, sex, or BMI showed no association with sAI (data not shown). The timeline of autoimmune onset has been described in previous studies (Berger et al., CNS Drugs (2017) 31:33-50; Havrdova et al., Neurology (2017) 89:1107-16).

[0063] PBMC processing and storage After blood collection, the CPT™ tubes were centrifuged at the clinical site, trapping red blood cells (RBCs) within the gel barrier. The plasma layer and white buffy coat of the PBMCs were mixed before shipping. The CPT™ tubes were sent to the laboratory at room temperature and processed within 60 hours after collection. Collection of PBMCs was performed in a class II biological safety cabinet. After gently inverting the cells 5-10 times in the plasma, the CPT™ tube was opened and the entire suspension above the gel was transferred to a sterile 15 mL conical tube. The volume of the solution was recorded. The samples were centrifuged at 300 g for 10-15 minutes, after which the plasma was removed and discarded without disturbing the cell pellet. The pellet was resuspended by gentle pipetting and Dulbecco's PBS (1X) was added to bring the volume to 10-13 mL. The samples were then centrifuged at 300 g for 10-15 minutes. The supernatant was aspirated without disturbing the pellet, and Dulbecco's PBS (1X) was added to bring the volume to 10 mL. Samples were mixed gently by inversion. White blood cell counts, and % lymphocytes and % monocytes were determined using a Gen-S hematology analyzer. Cell viability was determined by staining with propidium iodide and processing on a FACSCalibur™. Samples were then centrifuged at 300g for 10-15 minutes and the supernatant was aspirated. The pellet was gently resuspended by pipetting. 2 mL of freezing solution Cryostor® CS10 (Stemcell Technologies, Cat. No. 07930) was added and mixed using a 1 mL pipette. 1 mL of cell solution was dispensed into 2 cryovials and stored at -80°C for a minimum of 24 hours and a maximum of 72 hours, followed by long-term storage in liquid nitrogen.

[0064] Thawing and Pretreatment of PBMCs for Single Cell Workflow Cryopreserved PBMCs were thawed in a 37 °C water bath for 1–2 min (2 vials at a time) until small crystals remained, as previously described in Hanamsagar et al., Sci Rep. (2020) 10:2219. The cryovials were removed from the water bath and the cell solution was transferred to a sterile 2 mL Eppendorf® tube using a wide-bore pipette tip. The cryovials were washed with 1 mL of 0.04% BSA / PBS and the solution was transferred to an Eppendorf® tube. The samples were centrifuged at 150 g for 5 min at room temperature (RT). The supernatant was carefully removed and the samples were washed with 1 mL of 0.04% BSA / PBS using a wide-bore pipette tip. The samples were washed two more times under the same conditions as above, for a total of three washes. After the final wash, cells were resuspended in 1 mL of 0.04% BSA / PBS and counted using a hemocytometer (C-Chip™, SKC, Cat. No. DHCF015) with Trypan Blue (0.4%, GIBCO™ Cat. No. 15250061) as stain. If viability was found to be less than 75%, samples were subjected to a "clean-up" step using a Dead Cell Removal kit (Miltenyi Biotec, Cat. No. 130-090-101). Cells were washed again, resuspended in 500 μL of 0.04% BSA / PBS and counted. The volume was adjusted to 1 × 10 6 Adjusted to cells / mL. Cells were encapsulated in a 10X Genomics Chromium apparatus.

[0065] Single Cell Transcriptomics Cell volume 1×10 6After adjusting to cells / mL, the 10X Genomics 5' Gene Expression Library Preparation protocol was used. 8000 cells per sample were targeted. The quality of the uniquely indexed libraries was determined using a High Sensitivity DNA kit (Agilent, Cat. No. 5067-4626) using a 2100 Bioanalyzer instrument (Agilent) and quantified using a Kapa™ Library Quantification Kit (Kapa Biosystems, Cat. No. KK4824-07960140001) on a QuantStudio™ 7 Flex Real-Time PCR System. Libraries were diluted in 10 mM Tris-HCl buffer (pH 8.0) and pooled at equimolar concentrations (2 nM) for sequencing.

[0066] Sequencing was performed on a NOVAseq™ 6000 System (Illumina) using the NOVAseq™ 6000 S2 Reagent Kit (300 cycles, Illumina, Cat. No. 20028314). Sequencing depth and cycle numbers followed the 10X Genomics recommendations: Read1 = 26 cycles, i7 index = 8 cycles, Read2 = 98 cycles, aiming for a sequencing depth of 35,000 reads per cell.

[0067] Single-cell pipeline, pre-processing, and QC Single-cell analysis was performed as previously described (Hanamsagar et al., Sci Rep. (2020) 10:2219). Briefly, after BCL conversion, FASTq were processed with CellRanger™ version v2.1.1 for demultiplexing, alignment, filtering, barcode counting, UMI counting, and creation of gene x barcode matrix. An in-house built preprocessing pipeline was subsequently run for background removal of surrounding RNA and filtering of empty barcodes, as well as stress and mitochondrial genes. The resulting hdf5 files were then input into an internal single-cell visualization tool called SPRING (Weinreb, Wolock, and Klein, Bioinforma Oxf Engl. (2018) 34:1246-8). SPRING can also compress / reduce the size of large single-cell datasets for fast visualization. Then, an in-house developed tool was used to automatically annotate cell types and cell subtypes, and also to identify novel cell types. Samples with poor quality were excluded from the analysis after scrutiny. Briefly, the CellRanger™ web summary of each sample was evaluated. If the "cliff graph" was determined to pass QC, the sequencing depth was evaluated. For samples with low sequencing depth, libraries were repooled and resequenced, taking into account low-read samples. Data from all sequencing runs were merged and re-run in CellRanger™. Web summaries were evaluated again, and samples with low cell numbers and poor "cliff graphs" were flagged. All samples were processed in an internal pipeline for decontamination and filtering (as described above) and visualized in the SPRING portal. Samples with poor clustering on the SPRING layout or with less than 100 cells were flagged. Thus, samples that failed CellRanger™ QC and SPRING clustering were excluded from the analysis. Seven such samples were identified and excluded. The resulting new dataset was labeled Lemtrada_SC (sample cleanup).As these samples belonged to different patients at different time points, we created another dataset by excluding paired samples from patients in the Lemtrada_SC dataset, and called this new dataset Lemtrada_PC (patient cleaned).

[0068] result: Changes in relative abundance of cell types before and after alemtuzumab treatment As previously observed (Baker et al., JAMA Neurol. (2017) 74:961), an increase in B cells and a decrease in T cells were detected at the post-treatment time point (12 months after the second course; T24) (data not shown).

[0069] Furthermore, the individual proportions of these cell types in each sample, determined using single-cell RNA sequencing (scRNA-seq), correlated strongly with clinically recorded lymphocyte counts. This indicates that the scRNA-seq relative abundance measurements measured using the developed pipeline provide a reliable view of cell numbers. Furthermore, they provided deeper resolution in terms of cell types than the standard fluorescence-activated cell sorting (FACS) routinely used. No differences were observed between total monocyte and NK cell counts in either group (data not shown). NK cell abundance from single-cell data also correlated strongly with clinical values ​​(data not shown), further strengthening the feasibility of using scRNA-seq data as a surrogate for clinical values.

[0070] Differences in B and T cells in sAI vs. non-sAI patients before and after alemtuzumab treatment Consistent with previous findings (Baker et al., 2017; Evan et al., Expert Opin Biol Ther. (2018) 18:323-34), we found that naive B cell numbers increased significantly, whereas CD4 and CD8 T cell numbers decreased after treatment (data not shown). No differences in B and T cell subtypes were observed between sAI vs. non-sAI patients (data not shown).

[0071] Classification of unknown cell types Because we found no differences in the relative abundance of known cell types or subtypes between those with and without thyroid events, we analyzed the abundance of cell types classified as unknown by the automated algorithm. Surprisingly, we found that rare platelet-like cell types (PLCs) were significantly lower in those who had experienced a thyroid event compared to those who had not (Figure 1, 0.07% sAI; 0.52% non-sAI). This effect was not driven by any specific patient (Figure 2). Although the rate of PLCs was low (<0.1%), the difference between sAI and non-sAI was highly significant and was maintained at both pre- and post-treatment time points (Figure 3). Based on marker expression (Figure 4), PLCs strongly resembled platelets but expressed high levels of two additional surface markers not universally associated with platelets: SPARC and TREML. Platelets are a common contaminant in PBMC preparations (McFarland et al., Cytom Part J Int Soc Anal Cytol. (2006) 69:86-94), however, they are small and not expected to contain RNA. Therefore, it was hypothesized that this cell type constitutes a platelet lineage cell (PLC) with special physical characteristics (e.g., larger size, high transcript content) that make it suitable for sc-RNAseq capture. EXAMPLES

[0072] Identification of Platelet Lineage Cells (PLC) using FACS To confirm the identity of PLC, FACS experiments were performed using samples from two healthy donors to estimate the relative abundance of PLC throughout the sample isolation and processing steps.

[0073] method: Flow cytometry: Cell pretreatment Whole blood samples were collected from two healthy subjects utilizing Sanofi's in-house donor research program in Framingham, MA. 75–100 mL of blood was collected per donor in CPT™ tubes (BD Biosciences, catalog number 362761) containing sodium citrate. In the first experiment, PBMCs were isolated from the blood as described above. After this, PBMCs were washed twice with 1x PBS and counted using propidium iodide / acridine orange staining (Nexcelom, catalog number CS2-0106-5ML) in a Cellaca™ MX (Nexcelom, model MX-SYS1). Half of the freshly collected PBMCs were stored in a Cryostor™ CS10 (Stemcell technologies, catalog number 07930) and frozen at -80°C for 24 hours before being stored in liquid nitrogen. After one week, cells were thawed, counted, and subjected to flow cytometry. The remaining half of fresh PBMCs were processed for antibody staining and flow analysis. In a second set of experiments, healthy donors were recalled and blood was collected as described above. Red blood cell lysis was performed using ACK lysis buffer (Gibco™, A10492-01, lot no. 2048611) according to the manufacturer's instructions. Briefly, two 50 mL Falcon™ conical tubes were used for each donor. After an initial centrifugation to remove plasma, approximately 10-12 mL of whole blood was poured into each 50 mL tube. 45 mL of ACK buffer was added and incubated for 10 min on a VWR variable speed rocker. After the third 10 min incubation, the cell pellet was mostly white, indicating that the red blood cells had been lysed and removed by washing. After this, the cells were processed for staining and flow cytometry.

[0074] Compensation, staining, and flow analysis: Before running samples on the BD Influx™ (Becton Dickinson Influx Configurable, model 646500, s / n X64650000137), auto-compensation was performed according to the Influx protocol. Briefly, two drops of compensation beads (see below) were mixed with one test stain and incubated on ice in the dark for 20 minutes before being resuspended in 350 μL of stain buffer in a 5 mL Falcon tube.

[0075] All cells were centrifuged at 300g for 5 min at 4°C. The pellet was resuspended in 3 mL of BD staining buffer and transferred to a 5 mL Falcon polypropylene tube. The cells were centrifuged again and the pellet was then suspended in 100 μL of staining panel and incubated on ice for 30 min in the dark. The staining panel consisted of: CD61-BV510 (5 μL / test), CD41A-APC (20 μL / test), TREML / TLT-1-FITC (5 μL / test), and SPARC-(350) (5 μL / test). After incubation, 3 mL of staining buffer was added and centrifuged. The supernatant was removed and the pellet was resuspended in 2 mL of staining buffer and analyzed on a BD Influx according to the manufacturer's instructions. BD Influx™ information: Amplitude was 4.91, drop frequency was 44.70, flux restriction was 15, drop position was 200, max drop was 101, drop delay was 28.43, flux bend for tube was set at -84, -33, 33, 86.

[0076] [Table 1]

[0077] Gating strategy: Live cells were gated from the logarithmic scale of FSC and SSC, excluding dead cells. Among live cells, "small cells" were gated using the logarithmic scale of FSC and SSC due to their small size (2-3 μm). Singlets were gated from the FSC and SSC gates using the trigger pulse width. CD41A + and CD61+ CD41A was considered as a marker for platelets and was gated using the singlet gate. + CD61 + From the gates, PLCs were identified by double positivity for SPARC and TREML1.

[0078] statistical analysis FlowJo™ (version 10) was used to analyze flow data. Graphs were created and statistical analysis was performed using GraphPad Prism (version 8). Significance levels were: *** p<0.001, ** p<0.01, * Indicated by p<0.05.

[0079] The PLC abundance results were examined to confirm robustness to the presence of anti-PLGY antibodies by excluding one sAI patient (10553163) who had this laboratory abnormality, with a significance level of p<0.015, and this patient was excluded.

[0080] result: PLC Identification CD41 + CD61 + The percentage of PLC cells was observed to be 35% in whole blood, increasing to 55% in fresh PBMCs (after neutrophil depletion) and decreasing to 30% in frozen PBMCs (Figure 5). However, PLC (CD41 + CD61 + SPARC + TREML1 + PLCs (defined as PLC-peptides) comprised only 0.55% of whole blood and 0.1–0.2% of fresh and frozen PBMCs (Figures 6 and 7). To better understand the physical characteristics of size and granularity of PLCs, they were analyzed using SSC / FSC gating, revealing that SPARC - TREML1 - (double negative) platelets were shown to be larger and more granular (data not shown).

[0081] Differences in PLC between sAI and non-sAI patients prior to alemtuzumab treatment After uncovering the identity of this new cell type, we performed clustering of the PLC transcriptome to determine whether there were qualitative differences in PLCs between patients with thyroid events before alemtuzumab treatment. We found that there were distinct subsets of PLCs that differed in the expression of several markers and their relative proportions (data not shown). Subset 1 ("juvenile / dormant PLCs") contained most PLCs from sAI patients and was represented by five patients. It was characterized by lower expression of platelet markers and higher expression of PDGFA, inhibitory markers (PDCD10), and nuclear proteins (DAB2, RGS10, RGS18, and TSC22D1). The second subset (subset 2, "mature / activated" PLCs) was relatively enriched in the actin genes ACTB and ACTG1, growth factors, potent chemoattractants, and activators of neutrophil PPBP. This subset was also enriched in the expression of the SPARC and TREML1 genes. These results suggest differences in maturity and activation states between the two subsets, with the subset exhibiting a juvenile / quiescent state of PLCs being enriched in those with thyroid events ( Fig. 8 ).

[0082] Indeed, unsupervised clustering analysis of scRNA-seq data from 161 samples from baseline patients revealed that patients who did not develop secondary autoimmunity had higher relative expression levels of six genes (GP1BA, PPBP, ITGA2B, ITGB3, SPARC, and TREML1) that may be markers of mature PLC (Figure 9A). This finding was independent of patient characteristics such as thyroid activity, race, and gender (Figure 9B). Expression levels of six mature PLC genes (Figure 10A) and five immature PLC genes (PDCD10, RGS10, DAB2, TSC22D1, and RGS18) (Figure 10B) were assessed at 0, 12, and 24 months after alemtuzumab treatment. In patients who developed secondary autoimmunity, the expression levels of mature PLC genes were significantly decreased. In patients who developed secondary autoimmunity, the expression levels of immature PLC genes were similar or slightly decreased.

[0083] Similar unsupervised clustering analysis was performed on scRNA-seq data from samples from MS patients treated with IFNβ-1a, revealing distinct clustering patterns for the same mature and juvenile PLC genes, suggesting that these are specific markers of risk for secondary autoimmune development after alemtuzumab treatment (Figure ​(Figure11). 11).

[0084] These studies suggest differences in clinical measurements of platelet maturity in patients with or without thyroid events following alemtuzumab treatment. To test this hypothesis, we performed an analysis of the immature platelet fraction (IPF) data available in the cohort and found that patients with thyroid events had significantly increased IPF at T0 compared to those without (Figure 12A). Furthermore, patients with thyroid events had a higher follow-up rate of IPF at monthly measurements throughout the 2-year period (Figure 12B). High IPF at T0 is not specific for sAI. However, low relative PLC and high IPF together appear to be more specific indicators than high IPF alone (Figure 13).

[0085] To examine how PLC deficiency is associated with subclinical expression of sAI, we obtained data from three additional patients in the cohort who did not have clinical sAI events but in whom autoantibodies were detected: two had anti-PLGLY antibodies and one had anti-TPO antibodies. PLC was absent in all three samples (data not shown).

[0086] We next investigated whether the PLC values ​​at T0 indicate the outcome of AI after 4 years. Seven-year follow-up data were obtained for the same patients. The seven-year data were almost consistent with the four-year data, except for one patient who developed AI after four years of follow-up. This patient was observed to have a dramatic decrease in PLC at T24 compared with T0 (Figure 2).

Claims

1. 1. A method for assessing the risk of developing secondary autoimmunity in a patient with a primary autoimmune disease following lymphocyte depletion therapy, comprising: a) obtaining a blood sample from a patient; b) i) the ratio of platelet lineage cells (PLCs) among the total cells of the blood sample, wherein a decrease in the ratio of PLCs compared to the first baseline indicates an increased risk of the patient developing secondary autoimmunity after treatment; and / or ii) Immature platelet fraction (IPF) in the blood sample, where an increase in IPF compared to a second baseline indicates an increased risk of the patient developing secondary autoimmunity after treatment. determining

2. The method of claim 1 , wherein the determining step includes determining both (i) and (ii).

3. 10. The method of claim 1, wherein the primary autoimmune disease is multiple sclerosis (MS).

4. 10. The method of claim 1, wherein the lymphocyte depletion therapy is lymphocyte depletion antibody therapy.

5. The antibody is an anti-CD52 antibody or an antigen-binding portion thereof, and optionally the anti-CD52 antibody comprises: a) has the six CDRs of alemtuzumab, or b) the heavy chain variable domain and the light chain variable domain of alemtuzumab.

6. The method of claim 5 , wherein the anti-CD52 antibody is alemtuzumab.

7. 2. The method of claim 1, wherein the first and second criteria are obtained from patients with said primary autoimmune disease who do not develop secondary autoimmunity after lymphocyte-depleting therapy, or from healthy subjects.

8. The blood sample is a) the blood sample is one in which the red blood cells have been lysed, or 2. The method of claim 1, wherein b) the sample is a peripheral blood mononuclear cell (PBMC) sample, optionally with neutrophils removed from the sample.

9. a) the PLC ratio is reduced by more than two standard deviations compared to the ratio in control subjects; and / or 2. The method of claim 1, wherein b) the IPF value is increased by more than two standard deviations compared to the value of the control subject.

10. PLC is CD41 + CD61 + SPARC + TREML1 + The method of claim 1, wherein

11. 2. The method of claim 1, further comprising determining the proportion of immature PLCs among the total PLC population in a biological sample from the patient, wherein an increased proportion of immature PLCs compared to a third standard indicates an increased risk of the patient developing secondary autoimmunity after treatment.

12. The juvenile PLC is a) CD41 低 CD61 低 PDGFA 高 PDCD10 高 ;or b)CD41 低 CD61 低 PDGFA 高 PDCD10 高 DAB2 高 RGS10 高 RGS18 高 TSC22D1 高 The method according to claim 11, characterized in that:

13. 12. The method of claim 11, wherein the third standard is obtained from patients with the primary autoimmune disease who do not develop secondary autoimmunity after lymphocyte-depleting therapy, or from healthy subjects.

14. 4. The method of claim 3, wherein the primary autoimmune disease is relapsing MS, relapsing-remitting multiple sclerosis (RR-MS), or secondary progressive MS (SPMS).

15. 15. The method of any one of claims 1 to 14, wherein the secondary autoimmunity is selected from the group consisting of immune thrombocytopenic purpura (ITP), Graves' disease, Hashimoto's disease, Goodpasture's disease, membranous glomerulonephritis, red blood cell aplasia, autoimmune thyroid disease, transient thyroiditis, autoimmune hemolytic anemia, type 1 diabetes, alopecia areata / alopecia universalis, vitiligo, myalgia, sarcoidosis, autoimmune neutropenia, autoimmune hepatitis, and autoimmune lymphopenia.

16. 1. A pharmaceutical composition for treating a patient with a primary autoimmune disease, comprising: the pharmaceutical composition comprises lymphocyte depletion therapy; The patient is selected so that he or she is not at increased risk of developing secondary autoimmunity after lymphocyte depletion therapy, and the risk is i) the ratio of platelet lineage cells (PLCs) among all cells in a blood sample from the patient, wherein a decrease in the ratio of PLCs compared to the first baseline indicates an increased risk of the patient developing secondary autoimmunity after treatment; and / or ii) the pharmaceutical composition, wherein the composition is assessed by determining the immature platelet fraction (IPF) in a blood sample, wherein an increase in IPF compared to a second baseline indicates an increased risk of the patient developing secondary autoimmunity after treatment.

17. 17. The pharmaceutical composition of claim 16, wherein the determining step comprises determining both (i) and (ii).

18. 17. The pharmaceutical composition of claim 16, wherein the primary autoimmune disease is multiple sclerosis (MS).

19. The pharmaceutical composition according to claim 16, wherein the lymphocyte depletion therapy is lymphocyte depletion antibody therapy.

20. The antibody is an anti-CD52 antibody or an antigen-binding portion thereof, and optionally the anti-CD52 antibody comprises: a) has the six CDRs of alemtuzumab, or 20. The pharmaceutical composition of claim 19, wherein b) the pharmaceutical composition has a heavy chain variable domain and a light chain variable domain of alemtuzumab.

21. The pharmaceutical composition of claim 20, wherein the anti-CD52 antibody is alemtuzumab.

22. 17. The pharmaceutical composition of claim 16, wherein the first and second standards are obtained from patients with said primary autoimmune disease who do not develop secondary autoimmunity after lymphocyte depletion therapy, or from healthy subjects.

23. The blood sample is a) the blood sample is one in which the red blood cells have been lysed, or 17. The pharmaceutical composition of claim 16, wherein b) the sample is a peripheral blood mononuclear cell (PBMC) sample, optionally with neutrophils removed from the sample.

24. a) the PLC ratio is reduced by more than two standard deviations compared to the ratio in control subjects; and / or 17. The pharmaceutical composition of claim 16, wherein b) the IPF value is increased by more than two standard deviations compared to the value of the control subject.

25. PLC is CD41 + CD61 + SPARC + TREML1 + The pharmaceutical composition according to claim 16, characterized in that it is

26. 17. The pharmaceutical composition of claim 16, wherein assessing whether the patient is at increased risk of developing secondary autoimmunity after lymphocyte depletion therapy further comprises determining the proportion of immature PLCs among the total PLC population in a biological sample from the patient, wherein an increase in the proportion of immature PLCs compared to a third standard indicates an increased risk of the patient developing secondary autoimmunity after treatment.

27. The juvenile PLC is a) CD41 低 CD61 低 PDGFA 高 PDCD10 高 ;or b)CD41 低 CD61 低 PDGFA 高 PDCD10 高 DAB2 高 RGS10 高 RGS18 高 TSC22D1 高 27. The pharmaceutical composition according to claim 26, characterized in that it is

28. 27. The pharmaceutical composition of claim 26, wherein the third standard is obtained from patients with said primary autoimmune disease who do not develop secondary autoimmunity after lymphocyte depletion therapy, or from healthy subjects.

29. The pharmaceutical composition according to claim 18, wherein the primary autoimmune disease is relapsing MS, relapsing-remitting multiple sclerosis (RR-MS), or secondary progressive MS (SPMS).

30. Secondary autoimmunity includes immune thrombocytopenic purpura (ITP), Graves' disease, Hashimoto's disease, Goodpasture's disease, membranous glomerulonephritis, red blood cell aplasia, autoimmune thyroid disease, transient thyroiditis, autoimmune hemolytic anemia, type 1 diabetes, alopecia areata / alopecia universalis, vitiligo, myalgia, sarcoidosis, autoimmune neutropenia, autoimmune hepatitis, and autoimmune lymphopenia. The pharmaceutical composition according to any one of claims 16 to 29, wherein the composition is selected from the group consisting of:

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