Method for evaluating and comparing immune network metrics

Abstract

The present disclosure relates to methods for testing the health of a subject's immune system and treating the subject based on the condition of the immune system. In particular, disclosed herein is a method comprising the steps of obtaining a sample of blood from the subject, reverse transcribing DNA from RNA in the sample; amplifying all seven chains of T and B cell receptors in the DNA sample using multiplex PCR to form amplicons; sequencing the amplicons to generate a sequencing library; identifying nodetypes, clonotypes, v genes, d genes, and j genes and corresponding read counts in the sequencing library; performing the foregoing steps using two or more healthy samples to form a consensus reference pool; identifying the number of nodetypes associated with each V gene sequence in the consensus reference pool and in the subject's sequencing library; comparing the number of nodetypes for each V gene in the consensus reference pool and the subject's sequencing library; and treating the subject if the number of nodetypes is significantly less than the number of nodetypes for V genes in the consensus reference pool. The disclosed method is able to identify gaps in an individual's immune system and treat that individual to improve the diversity of the individual's immune response.

Description

[0001] METHOD FOR EVALUATING AND COMPARING IMMUNE NETWORK METRICS

[0002] Background of the Invention

[0003]

[0001] The most influential theory in immunology is the clonal selection theory. First proposed over 70 years ago by Sir Frank Macfarlane Burnet to explain how our adaptive immune system could mounting an attack against the “enemy” antigens, it essentially proposes that, to recognize non-self disease antigens, the body’s immune system must be able to recognize self antigens first. The clonal selection theory claims therefore that, to produce an effective and efficient adaptive immunity, the body’s immune system must produce many clones, wherein each clone undergoes a strict selection process. As part of this selection process, so-called positive selection occurs in the thymus, specifically in the cortex. There, immature T lymphocytes (referred to herein as T cells) are tested for their ability to recognize self-major histocompatibility complex (MHC) molecules presented on epithelial cells. Only those T cells that can properly bind to self-MHC molecules receive survival signals and continue to develop. Following this positive selection, a negative selection occurs mainly in the thyme medulla. There, T cells that strongly bind to self-antigens presented by MHC molecules are induced to undergo apoptosis. This negative selection process eliminates T cells that could potentially cause autoimmunity by reacting against the body’s own tissues.

[0004]

[0002] Although the clonal selection theory has been used to guide the development of many therapies, vaccines, and other clinical practices, certain aspects of the theory remain in question, particularly with respect to the self-recognition process. Clonal selection theory would require the presence of a kind of cell in thyme medulla capable of expressing the entire genome of over 20,000 genes. Further, under the clonal selection theory, such a cell should also be capable to cleave each protein (average size is 430 amnio acid) into overlapping peptides of 10-12 amino acids and present each self-antigen peptide to each T cell to test whether the T cells “recognizes self’. To complete this self-recognition process, however, each naive T cell would be required to participate in least 20 million antigen-receptor interactions, wherein each reaction could lead to an apoptosis event if the binding of any one interaction were too tight.

[0005]

[0003] In addition to questions over the practicality of clonal selection theory’s negative selection process, the clonal selection theory also fail to explain some other key observations related to immune functions, including with respect to crossreactivity, polyclonality, self-tolerance, memory and rapid responses, regulatory T cells, and antigen processing and presentation. With respect to crossreactivity, the clonal selection theory assumes that each lymphocyte has specificity for a single antigen receptor. However, some T cells and B cells can recognize multiple antigens or epitopes. With respect to polyclonality, the immune system appears to generate a more diverse range of antigen receptors than the clonal selection theory may predict. With respect to tolerance and self-reactivity, while negative selection in the thymus eliminates self-reactive T cells, some self-reactive T cells can still be found in the periphery; the mechanisms behind self-tolerance and the persistence of some self- reactive lymphocytes are not fully elucidated by clonal selection theory. With respect to memory and rapid responses, the rapidity and efficiency of immune responses upon re-exposure to a pathogen, especially in the context of memory T and B cells, pose challenges to the classical clonal selection theory. With respect to regulatory T cells (Tregs), the role of regulatory Tregs in suppressing immune responses and maintaining self-tolerance adds complexity to the simple clonal selection model. Finally, with respect to antigen processing and presentation, clonal selection theory does not provide a detailed explanation for how antigen processing and presentation work in antigen-presenting cells (APCs), which is crucial for T cell activation.

[0006]

[0004] In view of the foregoing, a novel approach to assessing the immune systems development and functions may be needed, for which novel methods may be employed.

[0007] Summary of the Invention

[0008]

[0005] In some embodiments, the present disclosure describes a method for assessing the health of a subject’s immune system, comprising the steps of: obtaining a sample of blood from the subject, reverse transcribing DNA from RNA in the sample; amplifying all seven chains of T and B cell receptors in the DNA sample using multiplex PCR to form amplicons; sequencing the amplicons to generate a sequencing library; and identifying nodetypes, clonotypes, v genes, d genes, and j genes and corresponding read counts in the sequencing library. In some embodiments, the method comprises an additional step of using computational fdters to remove analytical noise prior to identifying the nodetype and clonotype sequences. In some embodiments, the blood is peripheral blood.

[0009]

[0006] In some embodiments, the method further comprises the step of performing the foregoing steps on two or more samples from healthy individuals and pooling the resulting sequencing libraries of the two or more individuals to form a consensus reference pool. In some embodiments, the two or more samples from healthy individuals comprise cord blood. In some embodiments, the healthy individuals are age-matched to the subject. In some embodiments, the method further comprises the steps of identifying the number of nodetypes associated with each V gene sequence in the consensus reference pool and in the subject’s sequencing library; comparing the number of nodetypes for each V gene in the consensus reference pool and the subject’s sequencing library; and treating the subject if the number of nodetypes is significantly less than the number of nodetypes for V genes in the consensus reference pool.

[0010]

[0007] In some embodiments, the treatment comprises using CRISPR to reduce the overrepresented clonotypes. In some embodiments, the treatment comprises using antibody therapy to reduce the overrepresented clonotypes. In some embodiments, the treatment comprises using stem cell therapy. In some embodiments, the treatment comprises using traditional Chinese medicine. In some embodiments, the treatment comprises using PD-1 / PD-L1 inhibitors. In some embodiments, the treatment comprises using anti- PD-1 / PD-L1 antibodies. In some embodiments, the treatment comprises targeting TIGIT or LAG-3. In some embodiments, the treatment comprises using Bcl-2 inhibitors. In some embodiments, the treatment comprises using CAR T cell depletion. In some embodiments, the treatment comprises using glucocorticoids. In some embodiments, the treatment comprises CD8+ T cell depletion.

[0011]

[0008] In some embodiments, the reverse transcription and multiplex PCR amplification steps of the method comprises the steps of reverse transcribing at least one first strand of cDNA from mRNA containing at least one target sequence, using a reverse primer mix, forming at least one first strand cDNA; wherein the reverse primer mix contains at least one reverse primer configured to bind to one target sequence and incorporate a reverse common primer binding site into each first strand of cDNA; selecting each first strand cDNA and removing unused reverse primer; synthesizing at least one second strand of cDNA from each of the at least one first strand of cDNA using a forward primer mix, forming at least one first strand:second strand complex; wherein the forward primer mix contains at least one forward primer, each forward primer configured to bind to a particular first strand of cDNA and to incorporate a forward common primer binding site into each second strand of cDNA; selecting each first strand: second strand complex and removing unused forward primer; amplifying the first and second cDNA strands using a reverse common primer which binds to the at least one reverse common primer binding site and using a forward common primer which binds to the at least one forward common primer binding site; and selecting the amplified cDNA strands.

[0012]

[0009] In some embodiments, the individual is diagnosed with, or suspected of having, cancer, an autoimmune disease, an acute infection, or leukemia. Brief Description of the Drawings

[0013]

[0010] The disclosure can be better understood with reference to the following drawings. The elements of the drawings are not necessarily to scale relative to each other, emphasis instead being placed upon clearly illustrating the principles of the disclosure. Furthermore, like reference numerals designate corresponding parts throughout the several views.

[0014] [Oi l] FIG. 1 depicts repertoire distribution in an individual. (A) Publicly shared

[0015] CDR3 TCRP (T cell receptor beta chain) clonotypes from 300,000 individuals are represented in the treemap, where each color coded dot represent a unique, shared CDR3 clonotype, and the size of the dot represent the sharing frequencies. (B) Publicly shared CDR3 clonotypes from one particular V gene are depicted, showing fractal, self-similarity.

[0016]

[0012] FIG. 2 depicts treemaps of nodetypes from normal individuals as well as individuals with acute infection, autoimmunity, cancer, or leukemia. Each color represents a different nodetype and the size represents its frequency.

[0017]

[0013] FIG. 3 depicts TRA, TRB, TRD, and TRG T clonotypes in an individual with so-called “greedy clones” (clonotypes) of expanded, long-lived CD8+ T cells, representing a lack of clonotype diversity.

[0018]

[0014] FIG. 4 depicts TCR clonotype treemaps in an individual post-fasting, two- week post-fasting, and post-administration of Chinese traditional medicine.

[0015] FIG. 5 depicts TCR Beta clonotype treemaps in an individual without treatment, two weeks post-treatment with stem cells, and twelve weeks post -treatment with stem cells.

[0019]

[0016] FIG. 6 depicts a chart showing the clonotype ( / ., CDR3) amino acid sequence associated with each identified nodetype nucleotide sequence and read counts.

[0020]

[0017] FIG. 7 depicts a chart showing the clonotype amino acid sequence associated with each identified nodetype nucleotide sequence and read counts from a consensus reference pool and the number of libraries within the consensus cohort that the nodetype appeared.

[0021]

[0018] FIG. 8 depicts a chart showing the same clonotype amino acid sequence associated with multiple nodetypes.

[0022]

[0019] FIG. 9 depicts a chart showing a V gene distribution analysis (partial TRA V gene distribution shown) displaying the number of nodetypes associated with each V gene.

[0023]

[0020] FIG. 10 shows a treemap comparison of V genes (each V gene is within a separate box and each color represents a nodetype) in: (A) a consensus reference pool from cord blood samples; and (B) an individual with “greed clone syndrome” wherein certain V genes have significantly fewer nodetypes in the individual compared with the consensus reference pool.

[0021] FIG. 11 depicts an immune network based on peptide sequence similarity wherein each link is created when two clonotype nodes have one amino acid different in their sequences in the CDR3 region.

[0024]

[0022] FIG. 12 depicts the immune network of FIG. 11, wherein the clonotype nodes are further clustered based on their V and J gene distributions.

[0025]

[0023] FIG. 13 depicts embedded linklets frequencies into high dimensions, clustered them, and projected the result onto a t-SNE graph.

[0026]

[0024] FIG. 14 depicts shows nodetypes from one query sample (light) could not fully cover the consensus nodetypes (dark) and therefore expose the “holes” in a query repertoire.

[0027] Detailed Description

[0028]

[0025] The present disclosure relates to methods for testing the health of a subject’s immune system and treating the subject based on the condition of the immune system. In particular, disclosed herein is a method comprising the steps of obtaining a sample of blood from the subject, reverse transcribing DNA from RNA in the sample; amplifying all seven chains of T and B cell receptors in the DNA sample using multiplex PCR to form amplicons; sequencing the amplicons to generate a sequencing library; identifying nodetypes, clonotypes, v genes, d genes, and j genes and corresponding read counts in the sequencing library; performing the foregoing steps using two or more healthy samples to form a consensus reference pool; identifying the number of nodetypes associated with each V gene sequence in the consensus reference pool and in the subject’s sequencing library; comparing the number of nodetypes for each V gene in the consensus reference pool and the subject’s sequencing library; and treating the subject if the number of nodetypes is significantly less than the number of nodetypes for V genes in the consensus reference pool. The disclosed method is able to identify gaps in an individual’s immune system and treat that individual to improve the diversity of the individual’s immune response.

[0029]

[0026] The examples, applications, descriptions and content disclosed herein are exemplary and explanatory, and are non-limiting and non-restrictive in any way.

[0030]

[0027] All scientific terms used herein have the same meaning as commonly used and understood by one of ordinary skill in the art. Examples, materials, methods, figures and tables are illustrative only and not intended to be limiting.

[0031]

[0028] In previous patent disclosures (US Patent No. 12,060,610 and US Patent Application No. 17 / 300,937), Applicants detailed methods for evaluating and comparing immune repertoires. Additionally, Applicants initiated an international collaborative project for biomarker discovery known as R10K, which aimed to sequence 10,000 samples across 100 diseases to identify disease-specific sequences (Han et al. J Immunol (2012) 188 (I Supplement): 58.7). The fundamental hypothesis of the R10K project, grounded in the clonal selection theory, posited that patients with the same disease would be exposed to the same group of antigens, leading to a shared set of corresponding T cell receptors. However, after investing millions of dollars and studying over 30 different diseases with various cohorts, Applicants were unable to identify disease-specific signals; specifically, the study did not find disease- specific sharing of particular CDR3 Clonotypes. This outcome raises doubts about the clonal selection theory.

[0032]

[0029] Herein, Applicants disclose new theory called the Adaptive Immune

[0033] Defensive Network (AIDeN).

[0034]

[0030] In nature, there are numerous decentralized, self-organized networks, such as swarms of bees, ants, birds, and fish, among others. In complex adaptive systems, individual members following some simple and basic rules to be part of the network. Emergence is a key feature of complex systems. Emergence is a wonder made by a system without the conscious effort of its components. Applicants propose that each T and B cell, with a specific receptor sequence, is part of the network.

[0035]

[0031] There are different kinds of networks including: (1) scale-free networks, wherein nodes have varying degrees of connections, following a power-law distribution; (2) random network, wherein connections between nodes are established randomly; (3) regular networks, wherein nodes are connected in a regular, predictable pattern; (4) small-world networks, which exhibit short average path lengths with some randomness in connections; (5) hierarchical networks, which are organized in a hierarchical structure with different levels; (6) mesh networks, wherein all nodes are connected to every other node in the network; (7) tree networks, wherein nodes are arranged in a hierarchical tree structure; (8) bus networks, wherein all nodes share a common communication line; and (9) ring networks, wherein nodes are connected in a circular or ring-like arrangement. Applicants have developed evidence to suggest that the adaptive immune system forms scale-free networks.

[0032] A scale-free network is a type of network where the distribution of connections (or degrees) among nodes follows a long-tailed, power-law distribution. This means that most nodes have few connections, while a small number of nodes

[0036] (known as hubs) have a large number of connections. Scale-free networks often exhibit self-similarity, or fractal, meaning that their structure looks similar at different scales. Whether you examine a small or large portion of the network, the overall pattern of connectivity remains consistent. Applicants were the first to report that immune repertoires are in long-tailed distributions (Han and Lotze, “Adaptive Immunity and the Tumor Microenvironment” Tumor Microenvironment, edited by Peter P. Lee and Francesco M. Marincolar, Springer, 2020, pp. 111-148.).

[0037]

[0033] Applicants disclose herein data to show fractal distribution of the clonotypes. In FIG. 1A, publicly shared CDR3 TCR[3 (T cell receptor beta chain) clonotypes from 300,000 individuals are represented in the treemap, where each color coded dot represent a unique, shared CDR3 clonotype, and the size of the dot represents the sharing frequencies. FIG. IB depicts publicly shared CDR3 clonotypes from one particular V gene, showing fractal, self-similarity. Peripheral blood was drawn from the left arm (L) in the morning and the right arm (R) in the afternoon in the same individual. Each sample is divided into 20 aliquots and amplified with the arm-PCR method to form a sequencing library. On average, each aliquot includes about 1 million T cells, each library given 5 million reads, resulting in about 350,000 unique clonotypes. Clonotype sharing between two samples is about 50%. Morning samples have better diversity and therefore more sharing among the aliquots (55%). Of the 40 sequencing runs, 5,068,928 unique CDR3 clonotypes (sequences) were identified. Of these, 2,349,962 unique clonotypes appeared only once in the 40 aliquots. Another 1,122,362 appeared only twice. Of the over 5 million clonotypes discovered in one individual, only 12,220 can be found in all 40 aliquots.

[0038]

[0034] The Adaptive Immune Defensive Network (AIDeN) theory can be better understood as follows: (1) the adaptive immune system is inherently defensive, and its functions are primarily carried out at the network level, rather than at the individual clonotype level as suggested by the clonal selection theory; (2) the health of the host is closely tied to the integrity of the immune network; gaps or deficiencies in this network can increase vulnerability to diseases; (3) specific gaps within the network may be associated with particular diseases; and (4) effective treatment of diseases should not only focus on eliminating causative antigens but also on addressing and repairing the gaps in the defensive network.

[0039]

[0035] To better describe AIDeN, Applicants disclose a new concept, known as

[0040] “nodetype,” which contrasts with the traditional “clonotype” in conventional clonal selection theory, as shown in Tables 1 and 2.

[0041] TABLE 1 - CLONOTYPE CHARACTERISTICS TABLE 2 - NODETYPE CHARACTERISTICS

[0042]

[0036] Unlike neurons in the brain, which form well-known and extensively studied networks with observable physical connections like synapses, adaptive immune cells lack such visible physical links connecting them. Consequently, the concepts of AIDeN and nodetypes are not immediately apparent or intuitive. It was only through careful observation of the long-tailed distribution in immune repertoires, the shortcomings of the R10K projects, and critical analysis of self-antigen presentation during negative selection that Applicants were able to conceive of this alternative theory. This approach represents a significant departure from conventional understanding, offering a novel perspective on how the adaptive immune system operates and opening up novel, useful methods of analysis.

[0037] Applicants previously described a method for producing an immune status profile for humans or animals. This method leveraged multiplex PCR technology to specifically target T and B cell receptor sequences, combined with NGS to sequence the entire repertoire, allowing Applicants to exclude non-receptor sequences (noise) and enhance the analyte signal strength. However, Applicants encountered challenges in converting the clonotype analyte (a sample-derived observable structure) into actionable analytics (data-derived signatures).

[0043]

[0038] Building on the novel concepts of AIDeN and nodetypes, Applicants shifted focus from merely identifying “disease-specific TCR / BCR” to evaluating “the integrity of the immune network.” This approach involves identifying diseasespecific gaps within the network as diagnostic signals and developing targeted strategies to repair these often personalized gaps in AIDeN. The therapeutic tools disclosed herein offer an effective and personalized approach to immune system analysis and treatment previously unavailable.

[0044]

[0039] An example of the disclosed method comprises the steps of: taking peripheral blood; extracting RNA; performing dam-PCR (as disclosed in US Patent Application 17 / 300,937 incorporated by reference herein) to amplify all 7 chains of T and B cell receptors from the sample to make up the NGS sequencing library; applying computational filters to remove analytical noise and collecting the nodetype sequence (including the full V(D)J and C gene sequences), clonotype, v, d, j gene sequences as well as read counts for the nodetypes. An example of the resulting output is shown in FIG. 6. Each clonotype can have multiple nodetypes associated with it, as shown in

[0045] FIG. 8.

[0046]

[0040] As another example, the method of the preceding paragraph can be applied to two or more cord blood samples (although preferably at least ten samples) to compose a consensus reference that represents the “default setting” of human adaptive immune repertoires. Once all individual sequencing libraries are made and sequences obtained, Applicants merged all the data (from 83 samples) into a “synthetic repertoire pool” by combining all the nodetypes, each with annotations of clonotype (CDR3), V gene used, and J gene used. If a nodetype appeared in multiple libraries, the synthetic repertoire only lists it once but the read counts are cumulative (see FIG. 7). From the 83 samples, more than 120 million reads were made which comprised 1,458,775 CDR3 clonotypes and 2,328,984 nodetypes. From the “synthetic repertoire pool”, for each chain (TRA, TRB, TRD, TRG, BRH, BRK, and BRL) a V gene distribution analysis was performed (see FIG. 9).

[0047]

[0041] The V gene nodetype distribution data shown in FIG. 9 was used to calculate and establish a consensus reference network. For example, by randomly selecting 100,000 nodetypes (which may add weight depending on nodetype frequency, so that high frequency nodetypes have higher chance to be selected) from the synthetic repertoire pool and recording the nodetype V gene distributions, repeating this analysis 100 iterations and obtainomg the average (and standard deviation), a consensus reference for cord blood, also called “Default Consensus Reference”, was obtained. The consensus reference represents the determination that “given a certain sampling size (100,000 nodetypes), each V gene should have certain number of nodetypes to participate in immune network functions”.

[0048]

[0042] In addition to cord blood, age group specific consensus references can be used. The consensus reference can also be “dynamic” in size, depending on the quantity and quality of the query sequence, such that the sampling scale can be increased or decreased (100,000 nodetype from consensus, compared to 100,000 molecules from the query).

[0049]

[0043] As an example application of the consensus reference, a query sample from an individual was sequenced as mentioned above and the sample’s nodetype V gene distribution was obtained. The same number (100,000) of reads (each read labeled with unique molecular identifier, or UMI) was selected, nodetype V gene distributions were determined, and network metrics between consensus and the query were compared. As shown in FIG. 10, a particular V gene defensive zone in the query sample had a “greedy clone”, demonstrating that the nodetypes associated with that particular V gene were significantly less than the consensus reference, making a virtual “hole” in the individual’s immune network.

[0050]

[0044] It may be understood that each unique CDR3 (junction) nucleotide or peptide sequence (nodetype or clonotype, respectively) can be defined as a node in the immune network structure. It is not obvious, however, what should link such nodes. In FIGS. 11 and 12, applicants used peptide sequence similarity based on Levenshtein distance. Peptide sequence similarity can be determined by String analysis, Hamming distance or Levenshtein distance. In FIG. 11, a link is created when two clonotype nodes have one amino acids different in their sequences in the CDR3 region. In FIG. 12, the clonotype nodes are further clustered based on their V and J gene distributions. Applicant selected such links between two nodes, because if the two nodes are structurally related or similar, they must be functionally related. For example, they may bind to the same antigen; no matter if the receptor is observed in one person, or in different individuals in the population, their function (neutralizing antigens) will be the same. If, however, as applicant’s data indicate, when facing the same group of antigens, such as COVID, different individuals will have different pools of activated clones, and there are no strong structural similarities among the responding clones. If there is no strong correlation between antigen and receptors, a structural based link between nodes will not be informative.

[0051]

[0045] For FIGS. 13 and 14, applicants did not perform string analysis on peptide or nucleotide sequences, but instead used “co-occurrence” as the link between two nodetypes. In US patent application 17 / 531,364, applicant introduced the concept of “linklets”, i.e., two receptor sequences co-occurring in one sample. Here, applicants believe co-occurrence is more informative in terms of describing network structures. In FIGS. 13 and 14, applicants embedded linklets frequencies into high dimensions, clustered them, and projected the result onto a t-SNE graph. FIG. 14 shows nodetypes from one query sample (light) could not fully cover the consensus nodetypes (dark) and therefore expose the “holes” in a query repertoire.

[0052]

[0046] Network evaluation metrics can be applied to the nodetypes identified using the disclosed method to ascertain the strength of a subject’s immune network. For example, connectivity metrics (degree centrality, clustering coefficient, network density), robustness metrics (network resilience, average path length), centrality metrics (betweenness centrality, eigenvector centrality), Cohesion Metrics (modularity, Assortativity), Efficiency Metrics (Network efficiency, flow centrality), Redundancy and fault tolerance (redundancy, fault tolerance), Dynamical Properties (synchronization, diffusion rate), structural properties (bridge strength, edge and node strength) and community detection (community strength) can be employed.

[0053]

[0047] As an example of a condition in which nodetype is informative, “greedy clone syndrome” is condition in which a significant portion of the adaptive immune response is preoccupied by a relatively small number of nodetypes and clonotypes, in effect consuming resources that otherwise could be distributed to a wider immune repertoire and thereby disrupting the overall immune network. Some define this condition as occurring where the top five ranked clonotypes for TRA and TRB comprising at least twenty percent of all reads in an individual. While approximately 5% of individuals under 30 years of age and approximately 10% of individuals under 60 years of age have this condition, the incidence in individuals diagnosed with autoimmune disease or cancer can approach 50%.

[0054]

[0048] Treatment of greedy clone syndrome can comprise: (1) targeting the overrepresented CDR3 receptor sequences with CRISPR or antibody therapy (e.g., using V or J gene-specific monoclonal antibodies such that the antibodies may remove particular clonotypes if used in an affinity column); or (2) targeting the phenotype ( / .< ., effector phenotype, not memory) of the overrepresented CDR3 receptor sequences. Applicants have performed single cell phenotype analysis and discovered that the “greedy” (overrepresented) clonotypes are often effector cells (not memory) and exhausted. Therefore, therapies targeting exhausted T cells maybe used to treat the diseases. In the context of immunotherapy or disease treatment, specifically targeting exhausted T cells can be challenging because exhaustion is a functional state rather than a cell type. However, some approaches and drugs can target exhausted T cells preferentially or modulate their function. Relevant strategies comprise:

[0055]

[0049] Immune checkpoint blockade using PD-1 / PD-L1 inhibitors. Exhausted T cells often express high levels of immune checkpoint molecules like PD-1 (programmed death- 1) and CTLA-4, which inhibit their activity. Drugs such as nivolumab and pembrolizumab (anti -PD-1 antibodies) and atezolizumab (anti-PD-Ll) can restore function to exhausted T cells by blocking these inhibitory signals. While these drugs do not kill T cells, they aim to “rejuvenate” them.

[0056]

[0050] Depleting T cells expressing checkpoints using anti-PD-l / PD-Ll with ADCC (Antibody-Dependent Cellular Cytotoxicity). Some therapeutic antibodies are engineered to not only block checkpoint pathways but also promote the depletion of cells expressing PD-1 via ADCC. This could preferentially target exhausted T cells. For instance, engineered versions of PD-1 -targeting antibodies could have this dual function.

[0057]

[0051] Anti-TIGIT or LAG-3 therapies. Exhausted T cells can also express markers like

[0058] TIGIT and LAG-3. Experimental drugs that target these molecules are being studied. Like PD-1 / PD-L1 inhibitors, these drugs are primarily aimed at reversing exhaustion rather than killing the T cells, but they can modulate or deplete the most dysfunctional populations.

[0059]

[0052] Bcl-2 Inhibitors (e.g., Venetoclax). Venetoclax is a Bcl-2 inhibitor used to treat certain cancers, and it has shown potential in targeting dysfunctional or exhausted T cells, especially in the context of chronic viral infections or cancers. By inducing apoptosis in cells dependent on Bcl-2 for survival, venetoclax may preferentially eliminate exhausted T cells, which tend to have altered survival signaling.

[0060]

[0053] CAR T Cell Depletion. In the context of CAR-T cell therapy, drugs like cyclophosphamide and fludarabine are used to deplete existing T cells, including exhausted ones, before introducing engineered CAR T cells. This approach helps ensure the effectiveness of the CAR-T therapy by reducing the competition from nonfunctional T cells.

[0061]

[0054] Glucocorticoids (Corticosteroids). Corticosteroids (e.g., dexamethasone) can suppress the immune response by inducing apoptosis in T cells, including exhausted T cells. However, they are not specific to exhausted T cells and can affect a broader range of immune cells, so their use can have widespread immunosuppressive effects.

[0062]

[0055] CD8+ T cell depletion. In some cases, drugs or antibodies targeting CD8+ T cells could be used to deplete exhausted T cells, which are typically CD8+ in chronic infections and cancer. However, this approach would also remove healthy, functional CD8+ T cells, so it may not be practical for all situations.

[0056] Non-specific treatment for individuals with greedy clone syndrome can include, but are not limited to, stem cell therapy and traditional Chinese medicine.

Claims

CLAIMSNow, therefore, the following is claimed:

1. A method for assessing the health of an individual’s immune system comprising the steps of:(a) obtaining a sample of blood from the subject;(b) reverse transcribing DNA from RNA in the sample;(c) amplifying all seven chains of T and B cell receptors in the DNA sample using multiplex PCR to form amplicons;(d) sequencing the amplicons to generate a sequencing library; and(e) identifying nodetypes, clonotypes, v genes, d genes, and j genes and corresponding read counts in the sequencing library.

2. The method of claim 1, wherein the method further comprises the steps of: repeating steps (a) - (e) using two or more blood samples from healthy individuals; and pooling the resulting sequencing libraries of the two or more individuals to form a consensus reference pool.

3. The method of claim 2, wherein the samples comprise cord blood.

4. The method of claim 2, wherein the two or more individuals are age-matched to the subject.

5. The method of claim 2, wherein the method further comprises the steps of:identifying the number of nodetypes associated with each V gene sequence in the consensus reference pool and in the subject’s sequencing library; comparing the number of nodetypes for each V gene in the consensus reference pool and the subject’s sequencing library; and treating the subject if the number of nodetypes is significantly less than the number of nodetypes for V genes in the consensus reference pool.

6. The method of claim 5, wherein the treatment comprises stem cell therapy.

7. The method of claim 5, wherein the treatment comprises traditional Chinese medicine.

8. The method of claim 5, wherein the treatment comprises antibodies.

9. The method of claim 5, wherein the treatment comprises the use of CRISPR.

10. The method of claim 5, wherein the treatment comprises CD8+ T cell depletion.

11. The method of claim 1, wherein the individual is diagnosed with, or suspected of having, cancer, an autoimmune disease, an acute infection, or leukemia.

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