Detection and quantification of immune landscape changes

Abstract

A method comprising: accessing first and second cell sequence data sets, wherein each set represents immune cell receptor (ICR) types within a sample, the first and second sets being taken before and

Description

Technical Field

[0001] The present invention relates to a method and computer program for processing sequence data from immunological entities, such as T cell receptor (TCR) or B cell receptor (BCR) sequence data, in order to quantify and detect changes in the immune landscape. Background Art

[0002] The T cell receptor (TCR) is a protein complex found on the surface of T cells that is responsible for recognizing fragments of antigen peptides. The binding between TCR and antigen peptides is of relatively low affinity and is degenerate: many TCRs recognize the same antigen peptide and many antigen peptides are recognized by the same TCR. It is therefore desirable to be able to identify similar TCR sequences, for example based on their ability to react to the same or similar antigen peptides.

[0003] The TCR protein is composed of two chains, called the alpha chain and the beta chain, which are held together by disulphide bonds. Each chain contains variable (V), diversity (D), and joining (J) regions, which are responsible for the antigen recognition. The V regions of the alpha and beta chains come together to form the antigen-binding site or the complementarity-determining region (CDR), which is responsible for specifically recognizing the antigen.

[0004] The diversity of the TCR structure is made possible by the diversity of the amino acids that make up the V, D, and J regions of the alpha and beta chains. These regions contain a high variability of amino acid sequences, allowing for a wide range of antigen recognition. The amino acids in the CDRs of the TCR chains form a three-dimensional structure that enables the TCR to specifically recognize and bind to the antigen.

[0005] The sequence encoded by the V(D)J junction is called complementarity determining region 3 or CDR3. This sequence has the highest variability in both alpha and beta chains and determines the ability of a T cell to recognize an antigen peptide.

[0006] The different amino acids have distinct properties, such as size, charge, and polarity, which play a critical role in determining the structure and function of the TCR. For example, amino acids with large side chains, such as arginine or lysine, may be involved in forming salt bridges or hydrogen bonds, while amino acids with small side chains, such as glycine or alanine, may contribute to the flexibility of the TCR structure.

[0007] Next generation immunosequencing technology, such as the Omniscope (RTM) T Cell Sequencing Platform OS-T, are able to sequence up to 1 million T cells per sample, characterising immune response with a sensitivity that allows changes in repertories to be detected early and at high-resolution for advanced translational and clinical research. Aspects of this technology are described in WO 2024 / 170771 Al.

[0008] Improved techniques are required to process and analyse the resulting large quantity of sequence data. One such technique is described in WO2024 / 231510, in which sequence data is clustered based on similarity and a representative sequence is assigned to each cluster.

[0009] It would be desirable to monitor and evaluate immune responses, for example in clinical and research settings, to predict patient prognosis, evaluate treatment efficacy and / or guide treatment decisions in cancer, autoimmune, neurological and infectious diseases. Summary

[0010] Aspects of the present invention are defined by the accompanying claims.

[0011] In at least some embodiments of the invention, a T cell Activity Score (TAS) is determined so as to quantify T cell activation by detecting and measuring changes in the TCR repertoire, taking into account the expansion of T cell clonotypes, in response to treatment or challenge. The following steps may be performed in order to determine the TAS.

[0012] TCR sequencing is used to provide a system-level representation of the TCR repertoire by analysing very large numbers (e.g. over a million) of TCR clonotypes per sample. This allows for the detection of rare T cell clones and subtle changes in their frequencies, providing a level of detail unmatched by conventional methods that often overlook these critical aspects of the immune response.

[0013] A statistical noise model is used to accurately identify significant clonal expansions, filtering out background noise for precise measurement of treatment-related changes in the T cell repertoire. This ensures that only truly relevant changes are considered, increasing the accuracy of the analysis. Also, it may enable detection of treatment-related clones, which helps to identify key targets for the development of targeted therapies.

[0014] TCR clustering is used to group TCRs based on functional similarity, revealing clusters of T cells likely targeting the same antigen. This feature provides valuable insights into the specificity of the T cell response and also helps identify the key targets of the response.

[0015] Longitudinal Tracking is used to monitor changes in TCR clonotype frequencies over time, capturing the dynamic evolution of the T cell response and enabling early assessment of treatment efficacy. This dynamic view allows for timely adjustments to therapy and better management of disease progression.

[0016] The above procedure may enable prediction of patient outcomes (for example response, relapse, etc.) soon after treatment initiation, facilitating timely intervention and potentially improving survival rates. This predictive power is important for personalised medicine and optimising treatment strategies.

[0017] The main area of application of TAS is monitoring and evaluating immune responses in various clinical and research settings, particularly in cancer and other diseases like autoimmune, neurological and infectious diseases. In cancer, TAS may be used for monitoring treatment response, predicting patient prognosis, evaluating treatment efficacy, and guiding treatment decisions. TAS may also be used to predict disease outcomes, monitor disease progression, and measure treatment efficacy in autoimmune, neurological and infectious diseases.

[0018] Advantages of TAS include that it is non-invasive, utilising peripheral blood samples and reducing the need for invasive biopsies. Prior art practices often require tumour tissue or repeated biopsies, which can be invasive and may not be feasible for all patients.

[0019] Another advantage is safety. TAS may rely on peripheral blood samples, minimising the use of toxic or hazardous materials. Prior art practices may involve radioactive tracers or harsh chemicals.

[0020] Another advantage is system-level representation. TAS may analyse high quantity and quality TCR clonotypes per sample, providing a highly detailed view of the T cell repertoire. Prior art practices are often limited to a few surface markers or a smaller subset of TCRs, potentially missing rare clones or subtle changes in the repertoire.

[0021] Another advantage is dynamic monitoring. TAS may track changes in TCR clonotype frequencies over time, enabling real-time monitoring of T cell activity in response to treatment. Prior art practices often provide static snapshots of the immune response, limiting the ability to assess dynamic changes.

[0022] Another advantage may be qualitative and / or quantitative improvements in the output. TAS may provide both quantitative measures of T cell activity (OS-TAS score) and qualitative insights into TCR sequences and clonal expansion patterns. Prior art practice often focus on either quantitative or qualitative aspects, but rarely provide a comprehensive view of both.

[0023] Another advantage may be predictive potential. TAS may identify TCR clonotypes associated with treatment response or disease progression, potentially enabling early prediction of patient outcomes. Prior art practices typically have limited predictive power, often relying on lagging indicators of response or subjective clinical assessments.

[0024] Another advantage may be applicability to many different treatment modalities. TAS may for example be applicable to many different cancer therapies, including immunotherapy, chemotherapy, cancer vaccines, and cellular therapies. Prior art strategies are typically focused on anti-PDLl / PDl immune checkpoint inhibitors.

[0025] Another advantage may be versatility. TAS may be applicable to various diseases and therapeutic settings, including cancer, autoimmune, neurological and infectious diseases. Prior art practices are often disease-specific or limited to certain therapeutic modalities. Brief Description of Drawings

[0026] Specific embodiments will now be described with reference to the accompanying drawings as identified below.

[0027] Figure 1 is a pipeline diagram of a method of determining a T cell Activity Score (TAS) in an embodiment.

[0028] Figure 2 is a flowchart of the method.

[0029] Figure 3A is a chart showing the TAS for each patient in a first study.

[0030] Figure 3B is a chart of the TAS across all patients in the first study.

[0031] Figure 4A is a chart showing the TAS for each patient in a second study.

[0032] Figure 4B is a chart of the TAS across all patients in the second study.

[0033] Figure 5 is a diagram of an example of a computer system on which the method of an embodiment may be implemented. Detailed Description

[0034] T cells become activated when they encounter their specific antigen. This activation triggers them to divide, creating many copies of themselves with the same TCR. This process is called clonal expansion. Each of these identical T cells is called a clonotype, and they all share the same CDR3 sequence, which is the part of the TCR that recognizes the antigen. In embodiments of the invention, a TAS is constructed to be able to detect and track changes in the expansion of a T cell clonotype in response to treatment or challenge, as detailed below and summarised in Figures 1 and 2.

[0035] In the following description, a treatment is described as an example of an event that occurs between pre and post time point samples which can be any combination of two samples from a longitudinal study. However, this event can be any challenge that causes an activation of the immune system or a change in the immune landscape, such as vaccination, cancer therapy, infection, disease onset, etc.

[0036] At steps SI and S2, a set of TCR sequences are obtained from a sample, respectively pre- and post-event. Each set of TCR sequences identifies the immune landscape of the sample, represented for example by a count of the number or frequency of each clonotype in the sample.

[0037] In one specific example of the method, longitudinal PBMC (Peripheral Blood Mononuclear Cell) samples are used, from before and after treatment. The PBMC samples are subjected to T cell receptor (TCR) sequencing as described for example in WO 2024 / 170771, the contents of which are incorporated by reference. The output, and subsequent input for TAS, are quantifiable CDR3 sequences, determining each clonotype and expansion. The accurate construction of TAS relies on a system-level representation of the immune repertoire, capturing the diversity and relative abundance of clonotypes within the sampled population. This necessitates sufficient sequencing depth to detect both dominant and rare clonotypes, ensuring a comprehensive view of the immune response and augmenting the possibility to correctly assess the response of each patient to the undergoing treatment.

[0038] While TCR sequencing is a currently preferred method, the present invention is not limited to any specific sequencing technology. Any method capable of providing a quantitative and representative snapshot of the immune repertoire, including other emerging technologies, can be utilised to generate the input data for TAS analysis.

[0039] To construct the TAS, at step S3 specific significant T cell clonotypes are identified that expand in number or frequency in response to treatment, allowing the method to pinpoint the T cells that are likely reacting to the treatment. To achieve this, a statistical noise model may be used to determine which clonotypes have expanded significantly beyond what would be expected by chance.

[0040] A Null Model is constructed under the hypothesis that there were no significant differences on the variable of interest (ex. response, treatment) in order to assess the potential influences of batch effects. To establish a Null Model only non-significantly expanded clonotypes after treatment are selected by the statistical noise model, instead of the differentially expanded ones. This Null Model allows the specificity of TAS to be assessed by demonstrating that the observed results are driven by significantly expanded clonotypes and functionally similar clonotypes in their clusters, rather than arising from any arbitrary set of clonotypes. This approach ensures that the identified clonotypes are truly reflective of the driving forces behind the immune response.

[0041] For each clonotype count, a clonotype count probability distribution is generated based on the Poisson model, with the lambda parameter as the count at the pre-event time point, as a null distribution. The probability distribution serves to generate a p-value of the likelihood of finding the post-event count under the null distribution previously generated. Ranking of the differentially expanded clonotypes is based on a decreasing sorting of the p-values for each clonotype. A sliding parameter is included to select the amount of clonotypes that will be identified as differentially expanded i.e. most likely involved in the treatment response.

[0042] Preferably, the number of differentially expanded clonotypes selected is the same in every patient, meaning that the analysis maintains a consistent level of stringency across all individuals despite variations in their initial clonal repertoire. This approach ensures that the comparison of differentially expanded clonotypes between patients is not biased by the initial number of clonotypes present, only focusing on the top-ranked clonotypes based on p-values.

[0043] While a specific statistical noise model may be used to identify differentially expanded clonotypes, the invention is not limited to this particular algorithm. Alternative statistical methods or computational models capable of detecting significant changes in clonotype frequencies between time points can be employed. These may include, but are not limited to, models based on different varieties of the Poisson distribution such as Gamma-Poisson or Mixed-Poisson, Bayesian models, or machine learning algorithms trained to identify patterns of clonal expansion associated with immune responses.

[0044] While detecting the expansion of individual T cell clonotypes is informative, it may not capture the full spectrum of the treatment response. To address this, at step S4 TCR clustering analysis of the TCR sequences of the post-event sample may be used to group together TCRs with similar amino acid sequences, which may indicate that they recognize similar antigens, even if their individual clonal expansion is subtle. This step may identify a broader range of T cell clonotypes potentially involved in tumour targeting, including those that may not have expanded sufficiently to be detected by the statistical noise model alone. By incorporating these clustered clonotypes, TAS may be more sensitive and provide a more complete picture of the T cell response.

[0045] TCR clustering may use a multi-step approach as described in WO2024 / 231510, the contents of which are incorporated by reference. First, TCR sequences are encoded into numerical vectors using for example the Bidirectional Encoder Representations from Transformers (BERT) language model. These vectors may then be clustered into initial groups (superclusters) using the Facebook Al Similarity Search (FAISS) K-means clustering approach. Next, a density-based clustering algorithm, HDBSCAN, may be applied within each supercluster, using Levenshtein distance as a metric to account for potential variations in TCR sequence length. This allows for the identification ofclusters of TCRs with similar amino acid sequences and structural motifs, even if their CDR3B lengths differ. Finally, a refinement process may be applied to ensure accurate cluster assignments and minimise noise. This clustering method may enhance the ability of TAS to identify and quantify the diverse repertoire of T cells contributing to the treatment response.

[0046] Alternative clustering methods may be used, capable of grouping TCRs with similar binding properties. This may include, but is not limited to, k-means clustering, hierarchical clustering, or graph-based clustering approaches. Furthermore, advanced techniques such as deep learning models trained on TCR sequence and antigen-binding data may be employed to refine the clustering process and improve the identification of functionally related clonotypes. Also, other clustering algorithms that do not take the amino acid sequence into account but use other TCR features, such as cell dynamics or structural information could also be used. It may not be necessary to cluster all of the TCR sequences of the post-event sample; instead, only clusters including significantly expanded clonotypes may be identified.

[0047] The final step in defining the TAS involves calculating a score that represents the overall magnitude of the T cell response for each individual. This score reflects the degree of clonal expansion observed in the T cell repertoire after treatment.

[0048] To calculate the TAS, clonotypes are identified as potentially relevant to the immune response. This includes both the individually expanded clonotypes detected by the statistical noise model and those grouped together through TCR clustering analysis (step S5).

[0049] To ensure consistent comparisons across individuals, the number of differentially expanded clonotypes identified by the statistical noise model may be the same, or similar, for all patients. This allows assessment of the degree of expansion of an equivalent number of initial clonotypes across individuals, providing a standardised baseline for comparison. While the total number of clonotypes considered (including those added through clustering) may vary between patients, this variation reflects the inherent biological diversity of individual immune responses and the nature of the clustering algorithm itself. The clustering process, by design, groups together TCRs with similar sequences and potential antigen specificities, regardless of their initial expansion levels. This allows a more complete picture of the T cell response to be captured, acknowledging that T cells with similar TCRs, even if not individually meeting the expansion threshold of the statistical model, may still be contributing to the overall response. Therefore, while the initial number of differentially expanded clonotypes may be standardised, the final number of clonotypes contributing to the TAS may differ between patients, reflecting the inherent variability in the composition and dynamics of their T cell repertoires.

[0050] Next, the expansion of these clonotypes in the post-treatment sample is quantified (step S6). This involves summing the clonotype counts within each cluster. However, raw cluster sizes, represented by the total counts of clonotypes within them, can be influenced by variations in sequencing depth between samples. To account for this and ensure accurate comparisons across individuals, each cluster size is normalised. This may be achieved by dividing the total clonotype counts within each cluster by the total number of clonotypes in the corresponding sample. This normalisation step may effectively remove the bias introduced by differences in sequencing depth, allowing for a more accurate assessment of clonal expansion.

[0051] Following normalisation, the median value of these normalised cluster sizes is determined for each patient. The median may be chosen over the mean as it provides a more robust measure of central tendency, less susceptible to distortion by extreme values or outliers that may arise due to technical artefacts or rare, highly expanded clonotypes. Alternatively the mean or the maximum may be used, or other mathematical function that achieves a similar result. To further enhance the robustness of the TAS and account for the typically low median values observed, a square transformation can be applied to the normalised cluster sizes before calculating the median.

[0052] The final median value represents the TAS for each patient. This score captures the overall level of clonal expansion associated with the treatment response, effectively quantifying the degree to which T cells have been activated and mobilised. By providing a single, standardised metric, the TAS enables effective assessment and comparison of T cell activity across different individuals, facilitating a deeper understanding of the immune response and its relationship to clinical outcomes.

[0053] The effectiveness of the TAS stems from its ability to capture the fundamental hallmarks of an adaptive immune response. By focusing on the frequencies of specific T cell clonotypes, the TAS directly quantifies the cellular response to a specific immune challenge. The statistical noise model distinguishes true clonal expansion from random fluctuations, ensuring the identification of relevant T cell populations. Furthermore, TCR clustering analysis accounts for convergent evolution of TCRs recognizing similar antigens, enhancing the sensitivity and comprehensiveness of the metric. This combination of rigorous statistical analysis and biologically informed clustering allows TAS to effectively quantify the dynamic changes within the T cell repertoire that are indicative of an active and targeted immune response. Experimental Results

[0054] To illustrate the practical applications and benefits of the TAS, the following study reports demonstrate its successful implementation and highlight key findings.

[0055] In a study of muscle-invasive bladder cancer (MIBC) patients, TAS was successfully employed to evaluate the efficacy of a novel immunotherapy and radiotherapy combination (ICI+RT) compared to standard-of-care (SOC) chemotherapy.

[0056] The study revealed that patients receiving ICI+RT exhibited significantly elevated TAS scores compared to those undergoing SOC chemotherapy (Figures 3A and 3B). This finding suggests that ICI+RT elicits a more robust T cell response, aligning with previous observations of improved survival and response rates associated with this treatment approach.

[0057] Importantly, the enhanced T cell activity observed was specifically associated with the ICI+RT treatment and not attributable to confounding factors or batch effects. This was confirmed by analysing a null model utilising non-treatment-related clonotypes, which did not show significant differences in TAS between the two treatment groups.

[0058] Figure 3A displays the TAS for each patient, represented as box plots. TAS is calculated by identifying treatment-related clonotypes, grouping them based on sequence similarity, and then summing and normalising sequence counts within each cluster. The median of these normalised cluster sizes, after square transformation, represents the TAS for each patient. To account for differences in sample size between the treatment groups, a bootstrapping approach with random undersampling was employed. The ICI+RT group is shown in green, and the SOC group is shown in orange.

[0059] Figure 3B provides a detailed visualisation of the TAS across all patients. Each row represents a patient, and the colour intensity indicates the square-transformed, normalised count of clonotypes within each cluster. The black line denotes the TAS value for each patient. Patients are arranged along the x-axis according to their TAS, providing a clear visual representation of the distribution of T cell activity across the cohort.

[0060] In another study, TAS was applied to analyse T cell responses in patients with microsatellite stable colorectal cancer (MSS CRC) treated with immune checkpoint inhibitors (ICIs). The study demonstrated the ability of TAS to effectively stratify responders and non-responders to ICI therapy from a sample collected in the first cycle of immunotherapy (between 1 and 6 weeks after treatment).

[0061] Analysis of pooled data from multiple MSS CRC cohorts receiving various ICI treatments revealed a clear and statistically significant difference in TAS between responders and non-responders. Responders consistently exhibited higher TAS scores, indicating a more robust T cell response associated with improved treatment outcomes (Figures 4A and 4B). Importantly, this association was not attributable to batch effects or sample size variations, as confirmed by a null model analysis. The consistent stratification of responders and non-responders based on TAS was observed even when individual cohorts were analysed separately, further supporting the robustness and generalizability of this metric.

[0062] Furthermore, a strong concordance was observed between TAS and the Response Evaluation Criteria in Solid Tumors (RECIST) 1.1 criteria, a widely used standard for evaluating tumour response. Patients with higher TAS scores were more likely to exhibit a partial response according to RECIST 1.1 and also experienced a higher incidence of immune-related adverse events (irAEs). This finding suggests a potential link between heightened T cell activity, as captured by TAS, and both treatment efficacy and the development of irAEs.

[0063] This study highlights the potential of TAS as a valuable tool for monitoring T cell responses in ICI-treated MSS CRC patients. Its ability to stratify responders and non-responders, predict treatment outcomes, and potentially identify patients at risk for irAEs underscores its clinical utility in guiding personalised treatment decisions and improving patient care.

[0064] Figure 4A displays the TAS for each patient, represented as box plots. TAS is calculated by identifying treatment-related clonotypes, grouping them based on sequence similarity, and then summing and normalising sequence counts within each cluster. The median of these normalised cluster sizes, after square transformation, represents the TAS for each patient. To account for differences in sample size between the treatment groups, a bootstrapping approach with random undersampling was employed. The "Responder" group is shown in green, and the "Nonresponder" group is shown in orange.

[0065] Figure 4B provides a detailed visualisation of the TAS across all patients. Each row represents a patient, and the colour intensity indicates the square-transformed, normalised count of clonotypes within each cluster. The black line denotes the TAS value for each patient. Patients are arranged along the x-axis according to their TAS, providing a clear visual representation of the distribution of T cell activity across the cohort. Applications

[0066] Some potential applications of the TAS are listed below. These are not intended to be limiting, but are illustrative of the technical applications of the TAS.

[0067] The following applications relate to, but may not be limited to cancer, autoimmune, neurological and infectious diseases: Biomarkers for prediction and tracking • Quantitative: Measuring the expansion levels of T cells before, during, and after treatment to track changes in the immune response. • Qualitative: Analysing the sequences of TCRs to identify those associated with treatment response or disease progression, even before treatment initiation. Treatment monitoring and tracking • Monitoring patient response to therapy in real-time. • Stratifying responders from non-responders for early intervention. • Predicting patient prognosis and treatment response based on early changes in T cell activity. Treatment efficacy measure • Evaluating the efficacy of different cancer therapies, including immunotherapy, chemotherapy, cancer vaccines, and cellular therapies. • Assessing the impact of treatment on lymphocyte activation and immune response. Clinical trial evaluation • Comparing the efficacy of different treatments in clinical trials. • Guiding treatment selection and optimization. • Informing therapy switching decisions based on individual patient responses. Adverse event detection • Identifying immune-related adverse events and toxicities associated with cancer therapies. • Guiding dose escalation strategies to minimise toxicity. Relapse / recurrence prediction • Predicting the likelihood of relapse or recurrence based on T cell activity after treatment. Cellular therapy discovery • Identifying and prioritising target sequences for targeted therapies and immunotherapies such as, but not limited to TCR-T, CAR-T, and T cell engager therapies, as well as B-cell based therapies such as antibodies, antibody drug conjugates (ADCs) and Bi-specific T-cell engager (BiTE). • Measuring the affinity of TCRs to cancer antigens for improved therapy design.

[0068] The following applications relate to diseases other than cancer, although they may also be applicable to cancer. Autoimmune, neurological and infectious diseases: • Predicting disease outcomes and patient prognosis. • Monitoring and tracking disease progression and treatment response. • Measuring treatment efficacy and guiding treatment decisions. • Identifying and prioritising target sequences for targeted therapies and immunotherapies such as, but not limited to such as CAR-T, T cell engager, adoptive T cell transfers and therapeutic vaccines, as well as B-cell based therapies such as antibodies, antibody drug conjugates (ADCs) and Bi-specific T-cell engager (BiTE).

[0069] Other applications of TAS include: In vitro and in vivo data analysis: • Analysing T cell activity in both in vitro and in vivo experimental settings. Analysis of other immunological entities • Assessing B cell activity for a comprehensive view of the immune response. • Informing antibody development by identifying relevant B cell populations and antibody sequences. • Application on other immune cells with diverse receptors, such as NK cells or MAIT cells. GenAI models: • As an additional input variable to train TCR associated GenAI models in the context of therapeutics.

[0070] The following applications relate to TAS and covariates: • Developing TAS ranking scores based on target HLA types. • Analysing correlations between TAS and HLA to understand immune response variations. • Stratifying TAS scores by age groups to account for age-related immune changes. Computer System

[0071] Figure 5 illustrates an example computer system 900 in which the above method, or portions thereof, can be implemented as computer-readable code to program processing components of the computer system 900. Various embodiments of the invention are described in terms of this example computer system 900. For example, the steps of the method of Fig. 2 can each be implemented in system 900. After reading this description, it will become apparent to a person skilled in the relevant art how to implement the invention using other computer systems and / or computer architectures.

[0072] Computer system 900 includes one or more processors, such as processor 904. Processor 904 can be a special purpose or a general-purpose processor. Processor 904 is connected to a communication infrastructure 906 (for example, a bus, or network). Computer system 900 also includes a user input interface 903 connected to one or more input device(s) 905 and a display interface 907 connected to one or more output devices 909, such as a display, which may be integrated input and display components. Input devices 905 may include, for example, a connected device such as a mouse or touchpad, a keyboard, a touchscreen such as a resistive or capacitive touchscreen, etc.

[0073] Computer system 900 also includes a main memory 908, preferably random-access memory (RAM), and may also include a secondary memory 910. Secondary memory 910 may include, for example, a hard disk drive 912, a removable storage drive 914, flash memory, a memory stick, and / or any similar non-volatile storage mechanism. Removable storage drive 914 may comprise a floppy disk drive, a magnetic tape drive, an optical disk drive, a flash memory, or the like. The removable storage drive 914 reads from and / or writes to a removable storage unit 918 in a well-known manner. Removable storage unit 918 may comprise a floppy disk, magnetic tape, optical disk, etc. which is read by and written to by removable storage drive 914. As will be appreciated by persons skilled in the relevant art(s), removable storage unit 918 includes a non-transitory computer usable storage medium having stored therein computer software and / or data.

[0074] In alternative implementations, secondary memory 910 may include other similar means for allowing computer programs or other instructions to be loaded into computer system 900. Such means may include, for example, a removable storage unit 922 and an interface 920. Examples of such means may include a program cartridge and cartridge interface (such as that found in video game devices), a removable memory chip (such as an EPROM, or PROM) and associated socket, and other removable storage units 922 and interfaces 920 which allow software and data to be transferred from the removable storage unit 922 to computer system 900.

[0075] Computer system 900 may also include a communications interface 924 implemented for example at the operating system level to allow data to be transferred between computer system 900 and external devices, for example as signals 928 over a communication channel 926. Communications interface 924 may include a modem, a network interface (such as an Ethernet card), a communications port, a PCMCIA slot and card, or the like.

[0076] Various aspects of the present invention, such as one or more of the method steps described above, can be implemented by software and / or firmware (also called computer programs, instructions or computer control logic) to program programmable hardware, or hardware including special-purpose hardwired circuits such as application-specific integrated circuits (ASICs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), etc. of the computer system 900, or a combination thereof. Computer programs for use in implementing the techniques introduced here may be stored on a machine-readable storage medium and may be executed by one or more general-purpose or special-purpose programmable microprocessors. The terms "computer program medium", "non-transitory computer readable medium" and "computer usable medium" introduced herein can generally refer to media such as removable storage unit 918, removable storage unit 922, and a hard disk installed in hard disk drive 912. Computer program medium, computer readable storage medium, and computer usable medium can also refer to memories, such as main memory 908 and secondary memory 910, which can be memory semiconductors (e.g. DRAMs, etc.). These computer program products are means for providing software to computer system 900.

[0077] Computer programs are stored in main memory 908 and / or secondary memory 910. Computer programs may also be received via communications interface 924. Such computer programs, when executed, enable computer system 900 to implement the present invention as described herein. In particular, the computer programs, when executed, enable processor 904 to implement the processes of embodiments of the present invention as described above. Accordingly, such computer programs represent controllers of the computer system 900. Where the invention is implemented using software, the software may be stored in a computer program product and loaded into computer system 900 using removable storage drive 914, interface 920, hard drive 912, or communications interface 924. Alternative Embodiments

[0078] The descriptions of the various embodiments have been presented for purposes of illustration, but are not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments.

[0079] While TAS is described above with a focus on T cell receptor repertoires, the underlying principles and methodology may be readily extended to other immunological entities characterised by diverse receptor sequences. For example, B cell receptor (BCR) repertoires could be similarly analysed to quantify B cell activation and clonal expansion in response to antigenic challenges or vaccination. Hence similar methods may be applied to BCR sequencing data. Furthermore, the TAS framework may be adapted to analyse the repertoires of other immune cells with diverse receptors, such as NK cells or MAIT cells, providing a versatile tool for quantification and detection of immune landscape changes across a broader range of cell types.

[0080] The methods described above may be applied to samples taken in vivo, such as blood sample, before or after the event. Alternatively or additionally, the methods may be applied to samples of ex-vivo cell cultures before or after an event applied to the cell culture, such as drug testing.

[0081] Aspects of the present embodiments may be embodied as a system, method or computer program product. Accordingly, aspects of the present disclosure may take the form of an entirely hardware embodiment, an entirely software embodiment (including firmware, resident software, micro-code, etc.) or an embodiment combining software and hardware aspects that may all generally be referred to herein as an "engine", a "module," a "system," or a "computer." In addition, any hardware and / or software technique, process, function, component, engine, module, or system described in the present disclosure may be implemented as a circuit or set of circuits. Furthermore, aspects of the present disclosure may take the form of a computer program product embodied in one or more computer readable medium(s) having computer readable program code embodied thereon.

[0082] Aspects of the present disclosure are described above with reference to flowchart and / or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the disclosure. It will be understood that each block of the flowchart and / or block diagrams, and combinations of blocks in the flowchart and / or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general-purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine. The instructions, when executed via the processor of the computer or other programmable data processing apparatus, enable the implementation of the functions / acts specified in the flowchart and / or block diagram block or blocks. Such processors may be, without limitation, general purpose processors, specialpurpose processors, application-specific processors, or field-programmable gate arrays.

[0083] The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the blocks of the flowchart diagrams may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the flowchart diagrams, and combinations of blocks in the flowchart diagrams, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

[0084] While the preceding is directed to embodiments of the present disclosure, other and further embodiments of the disclosure may be devised without departing from the basic scope thereof, and the scope thereof is determined by the claims that follow.

Claims

1. A method of detection and quantification of immune landscape changes due to an event, comprising:a) accessing at least first and second sets of cell sequence data, wherein each set represents a number or frequency of different immune cell receptor (ICR) types within a sample, the first and second sets being derived from respective first and second samples of an immune cell repertoire taken before and after the event;b) comparing said first and second sets to identify, as significant ICR types, those ICR types showing a significant change in number or frequency between the first and second sets;c) clustering the cell sequence data of the second set, such that ICR types having functional similarity are clustered together;d) selecting the clusters which include at least one of the significant ICR types; ande) quantifying immune landscape changes due to the event based on the selected clusters.

2. The method of claim 1, wherein significant ICR types are identified using a statistical noise model.

3. The method of claim 2, wherein a probability distribution is generated as a null distribution based on the first set.

4. The method of claim 3, wherein the probability distribution is based on a Poisson model, with a lambda parameter as a count of each of the ICR types of the first set, and a p-value is generated of the likelihood of finding the count of the number of each of the ICR types of the second set.

5. The method of claim 4, wherein the ICR types of the second set are ranked by p-value and a predetermined number of said ICRs are selected as significant based on said ranking.

6. The method of any preceding claim, wherein clustering the ICR types comprises encoding the ICR types as numerical vectors and clustering the numerical vectors into initial groups.

7. The method of claim 6, wherein a density-based clustering algorithm is applied to the initial groups so as to generate said clustered groups.

8. The method of any preceding claim, wherein the immune landscape changes are quantified as an immune response score calculated by summing the counts of ICR types of each of the selected clusters, the counts being normalised based on the total number of ICR types in the second set.

9. The method of claim 8, wherein the immune response score is determined from a median of the count of ICR types across all the selected clusters.

10. The method of any preceding claim, wherein the first and second sets of cell sequence data are obtained by sequencing respective samples taken from a patient before or after the event.

11. The method of any preceding claim, wherein the event comprises a treatment, therapy, vaccination, infection or the like.

12. The method of any one of claims 1 to 9 wherein the first and second sets of cell sequence data are obtained by sequencing respective samples of an ex-vivo cell culture before and after the event.

13. A system configured to carry out the steps of the method of any preceding claim.

14. A computer-readable storage medium comprising instructions which, when executed by a computer, cause the computer to carry out the method of any of the above method claims.

15. A method of therapy or diagnosis using the method of any one of claims 1 to 12.

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