Publications
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Victoriano, Margarida; Pavlovic, Milena; Sandve, Geir Kjetil Ferkingstad; Oliveira, Hélder P.; Rocha, Artur & Greiff, Victor
(2026).
From virtual experiments to biomedical insight with synthetic data.
Nature Machine Intelligence.
8,
p. 866–879.
doi:
10.1038/s42256-026-01244-6.
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Gopalakrishnan, Ramakrishna Prabhu; Ward, Jerrold M.; Greiff, Victor; Braathen, Ranveig; Hu, Xian & Bajelan, Livia
[Show all 10 contributors for this article]
(2026).
Idiotype-specific CD4+ T cells chronically stimulate autoreactive B cells to develop into B lymphomas in mice.
Nature Communications.
doi:
10.1038/s41467-026-69916-w.
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Abstract Autoimmunity increases the risk of developing B lymphoma in humans; however, the molecular mechanism(s) underlying this link remain(s) unexplained. Here, we develop a mouse model to dissect the contribution of Id-driven T-B collaboration and show that chronic interaction between B cells and CD4 + T cells first leads to autoimmunity and later to the development of B (and T) cell lymphomas. We find that serum autoantibodies and lymphoma B cell receptor (BCR) are related and have the same specificity for ubiquitous self-antigens (histone and nucleosome) (Signal 1). Self-reactive B lymphoma cells are helped by CD4 + T cells that recognize a lymphoma neoantigen, an MHC class II-presented Idiotypic (Id) peptide (Signal 2). The mechanism, called Id-driven T-B collaboration, results in relentless mutual stimulation of B and T cells with germinal center markers, autoimmunity, and finally, malignant transformation of either B or T cells. Our results thus indicate Id-driven T-B collaboration as a potential mechanism linking autoimmunity and the development of lymphomas.
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Bielska, Weronika; Jaszczyszyn, Igor; Dudzic, Pawel; Janusz, Bartosz; Chomicz, Dawid & Wrobel, Sonia
[Show all 10 contributors for this article]
(2025).
Applying computational protein design to therapeutic antibody discovery - current state and perspectives.
Frontiers in Immunology.
16.
doi:
10.3389/fimmu.2025.1571371.
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Machine learning applications in protein sciences have ushered in a new era for designing molecules in silico. Antibodies, which currently form the largest group of biologics in clinical use, stand to benefit greatly from this shift. Despite the proliferation of these protein design tools, their direct application to antibodies is often limited by the unique structural biology of these molecules. We note that multiple methods attempting antibody design focus on the discovery of an antigen-specific antibody. Here, we review the current computational methods for antibody design, focusing on binder discovery, contextualizing their role in the drug discovery process
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Abrantes, Rafaela; Forcados, Christopher; Warren, David; Santos-Ferreira, Liliana; Fleten, Karianne Giller & Senra, Emanuel
[Show all 23 contributors for this article]
(2025).
Pan-carcinoma sialyl-Tn-targeting expands CAR therapy to solid tumors.
Cell Reports Medicine.
6(9).
doi:
10.1016/j.xcrm.2025.102350.
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Accurate identification of tumor-specific markers is vital for developing chimeric antigen receptor (CAR)-based therapies. While cell surface antigens are seldom cancer-restricted, their post-translational modifications (PTMs), particularly aberrant carbohydrate structures, offer attractive alternatives. Among these, the sialyl-Tn (STn) antigen stands out for its prevalent presence in various epithelial tumors. Although monoclonal antibodies (mAbs) against STn have been developed, their clinical application has been hindered by concerns regarding specificity. Herein, we describe AM52.1, a mAb with unprecedented specificity for STn and lack of reactivity with healthy tissues. The single-chain variable fragment (scFv) of AM52.1 was assembled into a second-generation CAR scaffold. AM52.1CAR T cells efficiently targeted STn-expressing cancer cell lines and patient-derived organoids (PDOs), while sparing STn-negative cells. In further preclinical models, AM52.1CAR T cells robustly controlled gastric and tubo-ovarian tumors, as well as colorectal cancer mucinous peritoneal metastases, highlighting their strong therapeutic potential for targeting and managing complex solid tumors.
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Ursu, Eugen; Minnegalieva, Aygul; Rawat, Puneet; Chernigovskaya, Maria; Tacutu, Robi & Sandve, Geir Kjetil Ferkingstad
[Show all 8 contributors for this article]
(2025).
Training data composition determines machine learning generalization and biological rule discovery.
Nature Machine Intelligence.
7(8),
p. 1206–1219.
doi:
10.1038/s42256-025-01089-5.
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Supervised machine learning models depend on training datasets containing positive and negative examples: dataset composition directly impacts model performance and bias. Given the importance of machine learning for immunotherapeutic design, we examined how different negative class definitions affect model generalization and rule discovery for antibody–antigen binding. Using synthetic-structure-based binding data, we evaluated models trained with various definitions of negative sets. Our findings reveal that high out-of-distribution performance can be achieved when the negative dataset contains more similar samples to the positive dataset, despite lower in-distribution performance. Furthermore, by leveraging ground-truth information, we show that binding rules associated with positive data change based on the negative data used. Validation on experimental data supported simulation-based observations. This work underscores the role of dataset composition in creating robust, generalizable and biology-aware sequence-based ML models
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Chernigovskaya, Maria; Quy, Khang Le; Stensland, Maria; Singh, Sachin; Nelson, Rowan & Yilmaz, Melih
[Show all 19 contributors for this article]
(2025).
Systematic benchmarking of mass spectrometry-based antibody sequencing reveals methodological biases.
Cell Systems.
ISSN 2405-4712.
16(11).
doi:
10.1016/j.cels.2025.101449.
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The circulating antibody (Ab) repertoire is crucial for immune protection, holding significant immunological and biotechnological value. While bottom-up mass spectrometry (MS) is widely used for profiling the sequence diversity of circulating Abs (Ab repertoire sequencing [Ab-seq]), it has not been thoroughly benchmarked. We quantified the replicability and robustness of Ab-seq using six monoclonal Ab spike-ins in 70 combinations of concentration and oligoclonality, with and without polyclonal serum immunoglobulin G (IgG) background. Each combination underwent four protease treatments and was analyzed across four experimental and three technical replicates, totaling 3,360 liquid chromatography-tandem MS (LC-MS/MS) runs. We quantified the dependence of Ab-seq identification on Ab sequence, concentration, protease, presence of background IgGs, and bioinformatics methods. Integrating the data from experimental replicates, proteases, and bioinformatics tools enhanced Ab identification. De novo sequencing performed similarly to database-dependent methods at higher Ab concentrations, but de novo Ab reconstruction remains challenging. Our work provides a foundational resource for the field of MS-based Ab profiling. A record of this paper’s transparent peer review process is included in the supplemental information
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Kunze-Schumacher, Heike; Verheyden, Nikita A.; Grewers, Zoe; Meyer-Hermann, Michael; Greiff, Victor & Robert, Philippe
[Show all 7 contributors for this article]
(2025).
High-resolution mapping of cell cycle dynamics during steady-state T cell development and regeneration in vivo.
Cell reports.
44(1).
doi:
10.1016/j.celrep.2024.115132.
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Control of cell proliferation is critical for the lymphocyte life cycle. However, little is known about how stage-specific alterations in cell cycle behavior drive proliferation dynamics during T cell development. Here, we employed in vivo dual-nucleoside pulse labeling combined with the determination of DNA replication over time as well as fluorescent ubiquitination-based cell cycle indicator mice to establish a quantitative high-resolution map of cell cycle kinetics of thymocytes. We developed an agent-based mathematical model of T cell developmental dynamics. To generate the capacity for proliferative bursts, cell cycle acceleration followed a "stretch model" characterized by the simultaneous and proportional contraction of both G1 and S phases. Analysis of cell cycle phase dynamics during regeneration showed tailored adjustments of cell cycle phase dynamics. Taken together, our results highlight intrathymic cell cycle regulation as an adjustable system to maintain physiologic tissue homeostasis and foster our understanding of dysregulation of the T cell developmental program.
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Rawat, Puneet; Crescioli, Silvia; Prabakaran, R.; Sharma, Divya; Greiff, Victor & Reichert, Janice M.
(2025).
YAbS: The Antibody Society's antibody therapeutics database.
mAbs.
ISSN 1942-0862.
17(1).
doi:
10.1080/19420862.2025.2468845.
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Therapeutic antibodies have gained prominence in recent years due to their precision in targeting specific diseases. As these molecules become increasingly essential in modern medicine, comprehensive data tracking and analysis are critical for advancing research and ensuring successful clinical outcomes. YAbS, The Antibody Society’s Antibody Therapeutics Database, serves as a vital resource for monitoring the development and clinical progress of therapeutic antibodies. The database catalogs detailed information on over 2,900 commercially sponsored investigational antibody candidates that have entered clinical study since 2000, as well as all approved antibody therapeutics. Data for the late-stage clinical pipeline and antibody therapeutics in regulatory review or approved (over 450 molecules) are openly accessible (https://db.antibodysociety.org). Antibody-related information includes molecular format, targeted antigen, current development status, indications studied, and the clinical development timeline of the antibodies, as well as the geographical region of company sponsors. Furthermore, the database supports in-depth industry trends analysis, facilitating the identification of innovative developments and the assessment of success rates within the field. This resource is continually updated and refined, providing invaluable insights to researchers, clinicians, and industry professionals engaged in antibody therapeutics development.
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Chernigovskaia, Maria; Pavlović, Milena; Kanduri, Chakravarthi; Gielis, Sofie; Robert, Philippe & Scheffer, Lonneke
[Show all 12 contributors for this article]
(2025).
Simulation of adaptive immune receptors and repertoires with complex immune information to guide the development and benchmarking of AIRR machine learning.
Nucleic Acids Research (NAR).
ISSN 0305-1048.
53(3).
doi:
10.1093/nar/gkaf025.
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Machine learning (ML) has shown great potential in the adaptive immune receptor repertoire (AIRR) field. However, there is a lack of large-scale ground-truth experimental AIRR data suitable for AIRR-ML-based disease diagnostics and therapeutics discovery. Simulated ground-truth AIRR data are required to complement the development and benchmarking of robust and interpretable AIRR-ML methods where experimental data is currently inaccessible or insufficient. The challenge for simulated data to be useful is incorporating key features observed in experimental repertoires. These features, such as antigen or disease-associated immune information, cause AIRR-ML problems to be challenging. Here, we introduce LIgO, a software suite, which simulates AIRR data for the development and benchmarking of AIRR-ML methods. LIgO incorporates different types of immune information both on the receptor and the repertoire level and preserves native-like generation probability distribution. Additionally, LIgO assists users in determining the computational feasibility of their simulations. We show two examples where LIgO supports the development and validation of AIRR-ML methods: (i) how individuals carrying out-of-distribution immune information impacts receptor-level prediction performance and (ii) how immune information co-occurring in the same AIRs impacts the performance of conventional receptor-level encoding and repertoire-level classification approaches. LIgO guides the advancement and assessment of interpretable AIRR-ML methods.
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Rawat, Puneet; Prabakaran, R.; Sharma, Divya; Mandala, Vasanth; Greiff, Victor & Kumar, Sandeep
[Show all 7 contributors for this article]
(2025).
Investigating Local Sequence-Structural Attributes of Amyloidogenic Light Chain Variable Domains.
Proteins: Structure, Function, and Bioinformatics.
ISSN 0887-3585.
93(9),
p. 1451–1464.
doi:
10.1002/prot.26815.
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Light chain amyloidosis is a medical condition characterized by the aggregation of misfolded antibody light chains into insoluble amyloid fibrils in the target organs, causing organ dysfunction, organ failure, and death. Despite extensive research to understand the factors contributing to amyloidogenesis, accurately predicting whether a given protein will form amyloids under specific conditions remains a formidable challenge. In this study, we have conducted a comprehensive analysis to understand the amyloidogenic tendencies within a dataset containing 1828 (348 amyloidogenic and 1480 non-amyloidogenic) antibody light chain variable region (VL) sequences obtained from the AL-Base database. Physicochemical and structural features often associated with protein aggregation, such as net charge, isoelectric point (pI), and solvent-exposed hydrophobic regions did not reveal a consistent association with the aggregation capability of the antibody light chains. However, the solvent-exposed aggregation-prone regions (APRs) occur with higher frequencies among the amyloidogenic light chains when compared with the non-amyloidogenic ones, with the difference ranging from 2% to 15% at various relative solvent-accessible surface area (rASA) cutoffs. We have, for the first time, identified structural gatekeeping residues around the APRs and assessed their impact on the amyloidogenicity of the antibody light chains. The non-amyloidogenic light chains contain these structural gatekeeper residues vicinal to their APRs more often than the amyloidogenic ones. We observed that the rASA cutoff of 35% is optimal for identifying the surface-exposed APRs, and a 4 Å distance cutoff from the APR motif(s) is optimal for identifying the structural gatekeeper residues. Moreover, lambda light chains were found to contain solvent-exposed APRs more often and surrounded by fewer gatekeepers, rendering them more susceptible to aggregation. The insights gained from this report have significant implications for understanding the molecular origins of light-chain amyloidosis in humans and the design of aggregation-resistant therapeutic antibodies.
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Hajj, Ghadi al; Hubin, Aliaksandr; Kanduri, Chakravarthi; Pavlovic, Milena; Rand, Knut Dagestad & Widrich, Michael
[Show all 11 contributors for this article]
(2024).
Incorporating probabilistic domain knowledge into deep multiple instance learning.
Proceedings of Machine Learning Research (PMLR).
235,
p. 17279–17297.
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Deep learning methods, including deep multiple instance learning methods, have been criticized for their limited ability to incorporate domain knowledge. A reason that knowledge incorporation is challenging in deep learning is that the models usually lack a mapping between their model components and the entities of the domain, making it a non-trivial task to incorporate probabilistic prior information. In this work, we show that such a mapping between domain entities and model components can be defined for a multiple instance learning setting and propose a framework DeeMILIP that encompasses multiple strategies to exploit this mapping for prior knowledge incorporation. We motivate and formalize these strategies from a probabilistic perspective. Experiments on an immune-based diagnostics case show that our proposed strategies allow to learn generalizable models even in settings with weak signals, limited dataset size, and limited compute.
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Le, Quy Khang; Chernigovskaia, Mariia; Stensland, Maria; Singh, Sachin; Leem, Jinwoo & Revale, Santiago
[Show all 14 contributors for this article]
(2024).
Benchmarking and integrating human B-cell receptor genomic and antibody proteomic profiling.
npj Systems Biology and Applications.
10.
doi:
10.1038/s41540-024-00402-z.
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Immunoglobulins (Ig), which exist either as B-cell receptors (BCR) on the surface of B cells or as antibodies when secreted, play a key role in the recognition and response to antigenic threats. The capability to jointly characterize the BCR and antibody repertoire is crucial for understanding human adaptive immunity. From peripheral blood, bulk BCR sequencing (bulkBCR-seq) currently provides the highest sampling depth, single-cell BCR sequencing (scBCR-seq) allows for paired chain characterization, and antibody peptide sequencing by tandem mass spectrometry (Ab-seq) provides information on the composition of secreted antibodies in the serum. Yet, it has not been benchmarked to what extent the datasets generated by these three technologies overlap and complement each other. To address this question, we isolated peripheral blood B cells from healthy human donors and sequenced BCRs at bulk and single-cell levels, in addition to utilizing publicly available sequencing data. Integrated analysis was performed on these datasets, resolved by replicates and across individuals. Simultaneously, serum antibodies were isolated, digested with multiple proteases, and analyzed with Ab-seq. Systems immunology analysis showed high concordance in repertoire features between bulk and scBCR-seq within individuals, especially when replicates were utilized. In addition, Ab-seq identified clonotype-specific peptides using both bulk and scBCR-seq library references, demonstrating the feasibility of combining scBCR-seq and Ab-seq for reconstructing paired-chain Ig sequences from the serum antibody repertoire. Collectively, our work serves as a proof-of-principle for combining bulk sequencing, single-cell sequencing, and mass spectrometry as complementary methods towards capturing humoral immunity in its entirety.
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O'Donnell, Timothy J.; Kanduri, Chakravarthi; Isacchini, Giulio; Limenitakis, Julien P.; Brachman, Rebecca A. & Alvarez, Raymond A.
[Show all 9 contributors for this article]
(2024).
Reading the repertoire: Progress in adaptive immune receptor analysis using machine learning.
Cell Systems.
ISSN 2405-4712.
15(12),
p. 1168–1189.
doi:
10.1016/j.cels.2024.11.006.
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The adaptive immune system holds invaluable information on past and present immune responses in the form of B and T cell receptor sequences, but we are limited in our ability to decode this information. Machine learning approaches are under active investigation for a range of tasks relevant to understanding and manipulating the adaptive immune receptor repertoire, including matching receptors to the antigens they bind, generating antibodies or T cell receptors for use as therapeutics, and diagnosing disease based on patient repertoires. Progress on these tasks has the potential to substantially improve the development of vaccines, therapeutics, and diagnostics, as well as advance our understanding of fundamental immunological principles. We outline key challenges for the field, highlighting the need for software benchmarking, targeted large-scale data generation, and coordinated research efforts.
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Balashova, Daria; Schaik, Barbera D C van; Stratigopoulou, Maria; Guikema, Jeroen E J; Caniels, Tom G. & Claireaux, Mathieu
[Show all 12 contributors for this article]
(2024).
Systematic evaluation of B-cell clonal family inference approaches.
BMC Immunology.
25:13,
p. 1–22.
doi:
10.1186/s12865-024-00600-8.
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The reconstruction of clonal families (CFs) in B-cell receptor (BCR) repertoire analysis is a crucial step to understand the adaptive immune system and how it responds to antigens. The BCR repertoire of an individual is formed throughout life and is diverse due to several factors such as gene recombination and somatic hypermutation. The use of Adaptive Immune Receptor Repertoire sequencing (AIRR-seq) using next generation sequencing enabled the generation of full BCR repertoires that also include rare CFs. The reconstruction of CFs from AIRR-seq data is challenging and several approaches have been developed to solve this problem. Currently, most methods use the heavy chain (HC) only, as it is more variable than the light chain (LC). CF reconstruction options include the definition of appropriate sequence similarity measures, the use of shared mutations among sequences, and the possibility of reconstruction without preliminary clustering based on V- and J-gene annotation. In this study, we aimed to systematically evaluate different approaches for CF reconstruction and to determine their impact on various outcome measures such as the number of CFs derived, the size of the CFs, and the accuracy of the reconstruction. The methods were compared to each other and to a method that groups sequences based on identical junction sequences and another method that only determines subclones. We found that after accounting for data set variability, in particular sequencing depth and mutation load, the reconstruction approach has an impact on part of the outcome measures, including the number of CFs. Simulations indicate that unique junctions and subclones should not be used as substitutes for CF and that more complex methods do not outperform simpler methods. Also, we conclude that different approaches differ in their ability to correctly reconstruct CFs when not considering the LC and to identify shared CFs. The results showed the effect of different approaches on the reconstruction of CFs and highlighted the importance of choosing an appropriate method.
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Bashour, Habib; Smorodina, Eva; Pariset, Matteo; Zhong, Jahn; Akbar, Rahmad & Chernigovskaia, Maria
[Show all 15 contributors for this article]
(2024).
Biophysical cartography of the native and human-engineered antibody landscapes quantifies the plasticity of antibody developability.
Communications Biology.
7(1),
p. 1–25.
doi:
10.1038/s42003-024-06561-3.
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Designing effective monoclonal antibody (mAb) therapeutics faces a multi-parameter optimization challenge known as “developability”, which reflects an antibody’s ability to progress through development stages based on its physicochemical properties. While natural antibodies may provide valuable guidance for mAb selection, we lack a comprehensive understanding of natural developability parameter (DP) plasticity (redundancy, predictability, sensitivity) and how the DP landscapes of human-engineered and natural antibodies relate to one another. These gaps hinder fundamental developability profile cartography. To chart natural and engineered DP landscapes, we computed 40 sequence- and 46 structure-based DPs of over two million native and human-engineered single-chain antibody sequences. We find lower redundancy among structure-based compared to sequence-based DPs. Sequence DP sensitivity to single amino acid substitutions varied by antibody region and DP, and structure DP values varied across the conformational ensemble of antibody structures. We show that sequence DPs are more predictable than structure-based ones across different machine-learning tasks and embeddings, indicating a constrained sequence-based design space. Human-engineered antibodies localize within the developability and sequence landscapes of natural antibodies, suggesting that human-engineered antibodies explore mere subspaces of the natural one. Our work quantifies the plasticity of antibody developability, providing a fundamental resource for multi-parameter therapeutic mAb design.
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Sørensen, Christoffer V.; Hofmann, Nils; Rawat, Puneet; Sørensen, Frederik V.; Ljungars, Anne & Greiff, Victor
[Show all 8 contributors for this article]
(2024).
ExpoSeq: simplified analysis of high-throughput sequencing data from antibody discovery campaigns.
Bioinformatics Advances.
4:vbae020(1),
p. 1–5.
doi:
10.1093/bioadv/vbae020.
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High-throughput sequencing (HTS) offers a modern, fast, and explorative solution to unveil the full potential of display techniques, like antibody phage display, in molecular biology. However, a significant challenge lies in the processing and analysis of such data. Furthermore, there is a notable absence of open-access user-friendly software tools that can be utilized by scientists lacking programming expertise. Here, we present ExpoSeq as an easy-to-use tool to explore, process, and visualize HTS data from antibody discovery campaigns like an expert while only requiring a beginner’s knowledge.
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ULVMOEN, AINA; Greiff, Victor; Bechensteen, Anne Grete & Inngjerdingen, Marit
(2024).
NKG2A discriminates natural killer cells with a suppressed phenotype in pediatric acute leukemia.
Journal of Leukocyte Biology.
ISSN 0741-5400.
115(2),
p. 334–343.
doi:
10.1093/jleuko/qiad112.
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Natural killer (NK) cells are important for early tumor immune surveillance. In patients with hematological cancers, NK cells are generally functional deficient and display dysregulations in their receptor repertoires. Acute leukemia is the most common cancer in children, and we here performed a comparative phenotypic profiling of NK cells from B-cell precursor acute lymphoblastic leukemia (BCP-ALL) patients to identify aberrant NK cell phenotypes. NK cell phenotypes, maturation, and function were analyzed in matched bone marrow and blood NK cells from BCP-ALL patients at diagnosis, during treatment, and at end of treatment and compared with age-matched pediatric control subjects. Expression of several markers were skewed in patients, but with large interindividual variations. Undertaking a multiparameter approach, we found that high expression levels of NKG2A was the single predominant marker distinguishing NK cells in BCP-ALL patients compared with healthy control subjects. Moreover, naïve CD57−NKG2A NK cells dominated in BCP-ALL patients at diagnosis. Further, we found dysregulated expression of the activating receptor DNAM-1 in resident bone marrow CXCR6+ NK cells. CXCR6+ NK cells lacking DNAM-1 expressed NKG2A and had a tendency for lower degranulation activity. In conclusion, high expression of NKG2A dominates NK cell phenotypes from pediatric BCP-ALL patients, indicating that NKG2A could be targeted in therapies for this patient group.
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Natali, Eriberto Noel; Horst, Alexander; Meier, Patrick; Greiff, Victor; Nuvolone, Mario & Babrak, Lmar Marie
[Show all 8 contributors for this article]
(2024).
The dengue-specific immune response and antibody identification with machine learning.
npj Vaccines.
9:16(1),
p. 1–15.
doi:
10.1038/s41541-023-00788-7.
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Dengue virus poses a serious threat to global health and there is no specific therapeutic for it. Broadly neutralizing antibodies recognizing all serotypes may be an effective treatment. High-throughput adaptive immune receptor repertoire sequencing (AIRR-seq) and bioinformatic analysis enable in-depth understanding of the B-cell immune response. Here, we investigate the dengue antibody response with these technologies and apply machine learning to identify rare and underrepresented broadly neutralizing antibody sequences. Dengue immunization elicited the following signatures on the antibody repertoire: (i) an increase of CDR3 and germline gene diversity; (ii) a change in the antibody repertoire architecture by eliciting power-law network distributions and CDR3 enrichment in polar amino acids; (iii) an increase in the expression of JNK/Fos transcription factors and ribosomal proteins. Furthermore, we demonstrate the applicability of computational methods and machine learning to AIRR-seq datasets for neutralizing antibody candidate sequence identification. Antibody expression and functional assays have validated the obtained results.
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Scheffer, Lonneke; Reber, Eric Emanuel; Mehta, Brij Bhushan; Pavlović, Milena; Chernigovskaia, Maria & Richardson, Eve
[Show all 11 contributors for this article]
(2024).
Predictability of antigen binding based on short motifs in the antibody CDRH3.
Briefings in Bioinformatics.
ISSN 1467-5463.
25(6),
p. 1–10.
doi:
10.1093/bib/bbae537.
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Adaptive immune receptors, such as antibodies and T-cell receptors, recognize foreign threats with exquisite specificity. A major challenge in adaptive immunology is discovering the rules governing immune receptor–antigen binding in order to predict the antigen binding status of previously unseen immune receptors. Many studies assume that the antigen binding status of an immune receptor may be determined by the presence of a short motif in the complementarity determining region 3 (CDR3), disregarding other amino acids. To test this assumption, we present a method to discover short motifs which show high precision in predicting antigen binding and generalize well to unseen simulated and experimental data. Our analysis of a mutagenesis-based antibody dataset reveals 11 336 position-specific, mostly gapped motifs of 3–5 amino acids that retain high precision on independently generated experimental data. Using a subset of only 178 motifs, a simple classifier was made that on the independently generated dataset outperformed a deep learning model proposed specifically for such datasets. In conclusion, our findings support the notion that for some antibodies, antigen binding may be largely determined by a short CDR3 motif. As more experimental data emerge, our methodology could serve as a foundation for in-depth investigations into antigen binding signals.
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Iacobescu, Maria; Pop, Cristina; Uifălean, Alina; Mogoşan, Cristina; Cenariu, Diana & Zdrenghea, Mihnea
[Show all 13 contributors for this article]
(2024).
Unlocking protein-based biomarker potential for graft-versus-host disease following allogenic hematopoietic stem cell transplants.
Frontiers in Immunology.
15:1327035,
p. 1–13.
doi:
10.3389/fimmu.2024.1327035.
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Despite the numerous advantages of allogeneic hematopoietic stem cell transplants (allo-HSCT), there exists a notable association with risks, particularly during the preconditioning period and predominantly post-intervention, exemplified by the occurrence of graft-versus-host disease (GVHD). Risk stratification prior to symptom manifestation, along with precise diagnosis and prognosis, relies heavily on clinical features. A critical imperative is the development of tools capable of early identification and effective management of patients undergoing allo-HSCT. A promising avenue in this pursuit is the utilization of proteomics-based biomarkers obtained from non-invasive biospecimens. This review comprehensively outlines the application of proteomics and proteomics-based biomarkers in GVHD patients. It delves into both single protein markers and protein panels, offering insights into their relevance in acute and chronic GVHD. Furthermore, the review provides a detailed examination of the site-specific involvement of GVHD. In summary, this article explores the potential of proteomics as a tool for timely and accurate intervention in the context of GVHD following allo-HSCT.
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Wossnig, Leonard; Furtmann, Norbert; Buchanan, Andrew; Kumar, Sandeep & Greiff, Victor
(2024).
Best practices for machine learning in antibody discovery and development.
Drug Discovery Today.
ISSN 1359-6446.
29:104025(7),
p. 1–17.
doi:
10.1016/j.drudis.2024.104025.
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In the past 40 years, therapeutic antibody discovery and development have advanced considerably, with machine learning (ML) offering a promising way to speed up the process by reducing costs and the number of experiments required. Recent progress in ML-guided antibody design and development (D&D) has been hindered by the diversity of data sets and evaluation methods, which makes it difficult to conduct comparisons and assess utility. Establishing standards and guidelines will be crucial for the wider adoption of ML and the advancement of the field. This perspective critically reviews current practices, highlights common pitfalls and proposes method development and evaluation guidelines for various ML-based techniques in therapeutic antibody D&D. Addressing challenges across the ML process, best practices are recommended for each stage to enhance reproducibility and progress.
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Erasmus, M. Frank; Ferrara, Fortunato; D’Angelo, Sara; Spector, Laura; Leal-Lopes, Camila & Teixeira, André A.
[Show all 17 contributors for this article]
(2023).
Insights into next generation sequencing guided antibody selection strategies.
Scientific Reports.
13:18370(1),
p. 1–16.
doi:
10.1038/s41598-023-45538-w.
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Abstract Therapeutic antibody discovery often relies on in-vitro display methods to identify lead candidates. Assessing selected output diversity traditionally involves random colony picking and Sanger sequencing, which has limitations. Next-generation sequencing (NGS) offers a cost-effective solution with increased read depth, allowing a comprehensive understanding of diversity. Our study establishes NGS guidelines for antibody drug discovery, demonstrating its advantages in expanding the number of unique HCDR3 clusters, broadening the number of high affinity antibodies, expanding the total number of antibodies recognizing different epitopes, and improving lead prioritization. Surprisingly, our investigation into the correlation between NGS-derived frequencies of CDRs and affinity revealed a lack of association, although this limitation could be moderately mitigated by leveraging NGS clustering, enrichment and/or relative abundance across different regions to enhance lead prioritization. This study highlights NGS benefits, offering insights, recommendations, and the most effective approach to leverage NGS in therapeutic antibody discovery.
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Timis, Tanase; Bergthorsson, Jon Thor; Greiff, Victor; Cenariu, Mihai & Cenariu, Diana
(2023).
Pathology and Molecular Biology of Melanoma.
Current Issues in Molecular Biology.
ISSN 1467-3037.
45(7),
p. 5575–5597.
doi:
10.3390/cimb45070352.
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Almost every death in young patients with an advanced skin tumor is caused by melanoma. Today, with the help of modern treatments, these patients survive longer or can even achieve a cure. Advanced stage melanoma is frequently related with poor prognosis and physicians still find this disease difficult to manage due to the absence of a lasting response to initial treatment regimens and the lack of randomized clinical trials in post immunotherapy/targeted molecular therapy settings. New therapeutic targets are emerging from preclinical data on the genetic profile of melanocytes and from the identification of molecular factors involved in the pathogenesis of malignant transformation. In the current paper, we present the diagnostic challenges, molecular biology and genetics of malignant melanoma, as well as the current therapeutic options for patients with this diagnosis.
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Kanduri, Chakravarthi; Scheffer, Lonneke; Pavlović, Milena; Rand, Knut Dagestad; Chernigovskaia, Maria & Pirvandy, Oz
[Show all 9 contributors for this article]
(2023).
simAIRR: simulation of adaptive immune repertoires with realistic receptor sequence sharing for benchmarking of immune state prediction methods.
GigaScience.
12,
p. 1–16.
doi:
10.1093/gigascience/giad074.
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Abstract Background Machine learning (ML) has gained significant attention for classifying immune states in adaptive immune receptor repertoires (AIRRs) to support the advancement of immunodiagnostics and therapeutics. Simulated data are crucial for the rigorous benchmarking of AIRR-ML methods. Existing approaches to generating synthetic benchmarking datasets result in the generation of naive repertoires missing the key feature of many shared receptor sequences (selected for common antigens) found in antigen-experienced repertoires. Results We demonstrate that a common approach to generating simulated AIRR benchmark datasets can introduce biases, which may be exploited for undesired shortcut learning by certain ML methods. To mitigate undesirable access to true signals in simulated AIRR datasets, we devised a simulation strategy (simAIRR) that constructs antigen-experienced-like repertoires with a realistic overlap of receptor sequences. simAIRR can be used for constructing AIRR-level benchmarks based on a range of assumptions (or experimental data sources) for what constitutes receptor-level immune signals. This includes the possibility of making or not making any prior assumptions regarding the similarity or commonality of immune state–associated sequences that will be used as true signals. We demonstrate the real-world realism of our proposed simulation approach by showing that basic ML strategies perform similarly on simAIRR-generated and real-world experimental AIRR datasets. Conclusions This study sheds light on the potential shortcut learning opportunities for ML methods that can arise with the state-of-the-art way of simulating AIRR datasets. simAIRR is available as a Python package: https://github.com/KanduriC/simAIRR.
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Tigu, Adrian Bogdan; Hotea, Ionut; Drula, Rares; Zimta, Alina-Andreea; Dirzu, Noemi & Santa, Maria
[Show all 15 contributors for this article]
(2023).
RNA sequencing suggests that non-coding RNAs play a role in the development of acquired haemophilia.
Journal of Cellular and Molecular Medicine.
ISSN 1582-1838.
27(13),
p. 1790–1796.
doi:
10.1111/jcmm.17741.
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Abstract Acquired haemophilia (AH) is a rare disorder characterized by bleeding in patients with no personal or family history of coagulation/clotting‐related diseases. This disease occurs when the immune system, by mistake, generates autoantibodies that target FVIII, causing bleeding. Small RNAs from plasma collected from AH patients ( n = 2), mild classical haemophilia ( n = 3), severe classical haemophilia ( n = 3) and healthy donors ( n = 2), for sequencing by Illumina, NextSeq500. Based on bioinformatic analysis, AH patients were compared to all experimental groups and a significant number of altered transcripts were identified with one transcript being modified compared to all groups at fold change level. The Venn diagram shows that haemoglobin subunit alpha 1 was highlighted to be the common upregulated transcript in AH compared to classical haemophilia and healthy patients. Non‐coding RNAs might play a role in AH pathogenesis; however, due to the rarity of HA, the current study needs to be translated on a larger number of AH samples and classical haemophilia samples to generate more solid data that can confirm our findings.
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Heimli, Marte; Flåm, Siri Tennebø; Hjorthaug, Hanne Sagsveen; Bjørnstad, Pål Marius; Chernigovskaia, Maria & Le, Quy Khang
[Show all 9 contributors for this article]
(2023).
Human thymic putative CD8αα precursors exhibit a biased TCR repertoire in single cell AIRR-seq.
Scientific Reports.
13:17714(1),
p. 1–13.
doi:
10.1038/s41598-023-44693-4.
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Thymic T cell development comprises T cell receptor (TCR) recombination and assessment of TCR avidity towards self-peptide-MHC complexes presented by antigen-presenting cells. Self-reactivity may lead to negative selection, or to agonist selection and differentiation into unconventional lineages such as regulatory T cells and CD8 T cells. To explore the effect of the adaptive immune receptor repertoire on thymocyte developmental decisions, we performed single cell adaptive immune receptor repertoire sequencing (scAIRR-seq) of thymocytes from human young paediatric thymi and blood. Thymic PDCD1+ cells, a putative CD8 T cell precursor population, exhibited several TCR features previously associated with thymic and peripheral ZNF683+ CD8 T cells, including enrichment of large and positively charged complementarity-determining region 3 (CDR3) amino acids. Thus, the TCR repertoire may partially explain the decision between conventional vs. agonist selected thymocyte differentiation, an aspect of importance for the development of therapies for patients with immune-mediated diseases.
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Elster, Christin; Ommer-Bläsius, Miriam; Lang, Alexander; Vajen, Tanja; Pfeiler, Susanne & Feige, Milena
[Show all 16 contributors for this article]
(2023).
Application and challenges of TCR and BCR sequencing to investigate T- and B-cell clonality in elastase-induced experimental murine abdominal aortic aneurysm.
Frontiers in Cardiovascular Medicine.
10:1221620,
p. 1–15.
doi:
10.3389/fcvm.2023.1221620.
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Background An abdominal aortic aneurysm (AAA) is a life-threatening cardiovascular disease. Although its pathogenesis is still poorly understood, recent evidence suggests that AAA displays autoimmune disease characteristics. Particularly, T cells responding to AAA-related antigens in the aortic wall may contribute to an initial immune response. Single-cell RNA (scRNA) T cell receptor (TCR) and B cell receptor (BCR) sequencing is a powerful tool for investigating clonality. However, difficulties such as limited numbers of isolated cells must be considered during implementation and data analysis, making biological interpretation challenging. Here, we perform a representative single-cell immune repertoire analysis in experimental murine AAA and show a reliable bioinformatic processing pipeline highlighting opportunities and limitations of this approach. Methods We performed scRNA TCR and BCR sequencing of isolated lymphocytes from the infrarenal aorta of male C57BL/6J mice 3, 7, 14, and 28 days after AAA induction via elastase perfusion of the aorta. Sham-operated mice at days 3 and 28 and non-operated mice served as controls. Results Comparison of complementarity-determining region (CDR3) length distribution of 179 B cells and 796 T cells revealed neither differences between AAA and control nor between the disease stages. We found no clonal expansion of B cells in AAA. For T cells, we identified several clones in 11 of 16 AAA samples and one of eight control samples. Immune receptor repertoire comparison indicated that only a few clones were shared between the individual AAA samples. The most frequently used V-genes in the TCR beta chain in AAA were TRBV3, TRBV19, and the splicing variant TRBV12-2 + TRBV13-2. Conclusion We found no clonal expansion of B cells but evidence for clonal expansion of T cells in elastase-induced AAA in mice. Our findings imply that a more precise characterization of TCR and BCR distribution requires a more extensive number of lymphocytes to prevent undersampling and potentially detect rare clones. Thus, further experiments are necessary to confirm our findings. In summary, this paper examines TCR and BCR sequencing results, identifies limitations and pitfalls, and offers guidance for future studies.
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Federico, Lorenzo; Malone, Brandon; Tennøe, Simen; Chaban, Viktoriia; Osen, Julie Røkke & Gainullin, Murat
[Show all 13 contributors for this article]
(2023).
Experimental validation of immunogenic SARS-CoV-2 T cell epitopes identified by artificial intelligence.
Frontiers in Immunology.
14:1265044,
p. 1–14.
doi:
10.3389/fimmu.2023.1265044.
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During the COVID-19 pandemic we utilized an AI-driven T cell epitope prediction tool, the NEC Immune Profiler (NIP) to scrutinize and predict regions of T cell immunogenicity (hotspots) from the entire SARS-CoV-2 viral proteome. These immunogenic regions offer potential for the development of universally protective T cell vaccine candidates. Here, we validated and characterized T cell responses to a set of minimal epitopes from these AI-identified universal hotspots. Utilizing a flow cytometry-based T cell activation-induced marker (AIM) assay, we identified 59 validated screening hits, of which 56% (33 peptides) have not been previously reported. Notably, we found that most of these novel epitopes were derived from the non-spike regions of SARS-CoV-2 (Orf1ab, Orf3a, and E). In addition, ex vivo stimulation with NIP-predicted peptides from the spike protein elicited CD8 + T cell response in PBMC isolated from most vaccinated donors. Our data confirm the predictive accuracy of AI platforms modelling bona fide immunogenicity and provide a novel framework for the evaluation of vaccine-induced T cell responses.
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Tigu, Adrian Bogdan; Constantinescu, Catalin Sorin; Teodorescu, Patric; Kegyes, David; Munteanu, Raluca & Feder, Richard
[Show all 23 contributors for this article]
(2023).
Design and preclinical testing of an anti-CD41 CAR T cell for the treatment of acute megakaryoblastic leukaemia.
Journal of Cellular and Molecular Medicine.
ISSN 1582-1838.
27(19),
p. 2864–2875.
doi:
10.1111/jcmm.17810.
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Abstract Acute megakaryoblastic leukaemia (AMkL) is a rare subtype of acute myeloid leukaemia (AML) representing 5% of all reported cases, and frequently diagnosed in children with Down syndrome. Patients diagnosed with AMkL have low overall survival and have poor outcome to treatment, thus novel therapies such as CAR T cell therapy could represent an alternative in treating AMkL. We investigated the effect of a new CAR T cell which targets CD41, a specific surface antigen for M7‐AMkL, against an in vitro model for AMkL, DAMI Luc2 cell line. The performed flow cytometry evaluation highlighted a percentage of 93.8% CAR T cells eGFP‐positive and a limited acute effect on lowering the target cell population. However, the interaction between effector and target (E:T) cells, at a low ratio, lowered the cell membrane integrity, and reduced the M7‐AMkL cell population after 24 h of co‐culture, while the cytotoxic effect was not significant in groups with higher E:T ratio. Our findings suggest that the anti‐CD41 CAR T cells are efficient for a limited time spawn and the cytotoxic effect is visible in all experimental groups with low E:T ratio.
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Khan, Asif; Cowen-Rivers, Alexander I.; Grosnit, Antoine; Deik, Derrick-Goh-Xin; Robert, Philippe A. & Greiff, Victor
[Show all 15 contributors for this article]
(2023).
Toward real-world automated antibody design with combinatorial Bayesian optimization.
Cell Reports Methods.
3:100374(1),
p. 1–21.
doi:
10.1016/j.crmeth.2022.100374.
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Antibodies are multimeric proteins capable of highly specific molecular recognition. The complementarity determining region 3 of the antibody variable heavy chain (CDRH3) often dominates antigen-binding specificity. Hence, it is a priority to design optimal antigen-specific CDRH3 to develop therapeutic antibodies. The combinatorial structure of CDRH3 sequences makes it impossible to query binding-affinity oracles exhaustively. Moreover, antibodies are expected to have high target specificity and developability. Here, we present AntBO, a combinatorial Bayesian optimization framework utilizing a CDRH3 trust region for an in silico design of antibodies with favorable developability scores. The in silico experiments on 159 antigens demonstrate that AntBO is a step toward practically viable in vitro antibody design. In under 200 calls to the oracle, AntBO suggests antibodies outperforming the best binding sequence from 6.9 million experimentally obtained CDRH3s. Additionally, AntBO finds very-high-affinity CDRH3 in only 38 protein designs while requiring no domain knowledge.
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Sharma, Divya; Rawat, Puneet; Greiff, Victor; Janakiraman, Vani & Gromiha, M. Michael
(2023).
Predicting the immune escape of SARS-CoV-2 neutralizing antibodies upon mutation.
Biochimica et Biophysica Acta - Molecular Basis of Disease.
ISSN 0925-4439.
1870(2).
doi:
10.1016/j.bbadis.2023.166959.
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Fernández-Quintero, Monica L.; Ljungars, Anne; Waibl, Franz; Greiff, Victor; Andersen, Jan Terje & Gjølberg, Torleif
[Show all 16 contributors for this article]
(2023).
Assessing developability early in the discovery process for novel biologics.
mAbs.
ISSN 1942-0862.
15:2171248(1),
p. 1–19.
doi:
10.1080/19420862.2023.2171248.
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Beyond potency, a good developability profile is a key attribute of a biological drug. Selecting and screening for such attributes early in the drug development process can save resources and avoid costly late-stage failures. Here, we review some of the most important developability properties that can be assessed early on for biologics. These include the influence of the source of the biologic, its biophysical and pharmacokinetic properties, and how well it can be expressed recombinantly. We furthermore present in silico, in vitro, and in vivo methods and techniques that can be exploited at different stages of the discovery process to identify molecules with liabilities and thereby facilitate the selection of the most optimal drug leads. Finally, we reflect on the most relevant developability parameters for injectable versus orally delivered biologics and provide an outlook toward what general trends are expected to rise in the development of biologics.
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Rappazzo, C. Garrett; Fernandez-Quintero, Monica L.; Mayer, Andreas; Wu, Nicholas C.; Greiff, Victor & Guthmiller, Jenna J.
(2023).
Defining and studying B cell receptor and TCR interactions.
Journal of Immunology.
ISSN 0022-1767.
211(3),
p. 311–322.
doi:
10.4049/jimmunol.2300136.
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Høye, Eirik; Dagenborg, Vegar Johansen; Kristensen, Annette Torgunrud; Lund-Andersen, Christin; Fretland, Åsmund Avdem & Lorenz, Susanne
[Show all 13 contributors for this article]
(2023).
T cell receptor repertoire sequencing reveals chemotherapy-driven clonal expansion in colorectal liver metastases.
GigaScience.
12.
doi:
10.1093/gigascience/giad032.
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Background - Colorectal liver metastasis (CLM) is a leading cause of colorectal cancer mortality, and the response to immune checkpoint inhibition (ICI) in microsatellite-stable CRC has been disappointing. Administration of cytotoxic chemotherapy may cause increased density of tumor-infiltrating T cells, which has been associated with improved response to ICI. This study aimed to quantify and characterize T-cell infiltration in CLM using T-cell receptor (TCR) repertoire sequencing. Eighty-five resected CLMs from patients included in the Oslo CoMet study were subjected to TCR repertoire sequencing. Thirty-five and 15 patients had received neoadjuvant chemotherapy (NACT) within a short or long interval, respectively, prior to resection, while 35 patients had not been exposed to NACT. T-cell fractions were calculated, repertoire clonality was analyzed based on Hill evenness curves, and TCR sequence convergence was assessed using network analysis.<p> <p>Results - Increased T-cell fractions (10.6% vs. 6.3%) were detected in CLMs exposed to NACT within a short interval prior to resection, while modestly increased clonality was observed in NACT-exposed tumors independently of the timing of NACT administration and surgery. While private clones made up >90% of detected clones, network connectivity analysis revealed that public clones contributed the majority of TCR sequence convergence.<p> <p>Conclusions - TCR repertoire sequencing can be used to quantify T-cell infiltration and clonality in clinical samples. This study provides evidence to support chemotherapy-driven T-cell clonal expansion in CLM in a clinical context.
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Han, Jiami; Masserey, Solène; Shlesinger, Danielle; Kuhn, Raphael; Papadopoulou, Chrysa & Agrafiotis, Andreas
[Show all 14 contributors for this article]
(2022).
Echidna: Integrated simulations of single-cell immune receptor repertoires and transcriptomes.
Bioinformatics Advances.
2(1),
p. 1–10.
doi:
10.1093/bioadv/vbac062.
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Abstract Motivation Single-cell sequencing now enables the recovery of full-length immune receptor repertoires [B cell receptor (BCR) and T cell receptor (TCR) repertoires], in addition to gene expression information. The feature-rich datasets produced from such experiments require extensive and diverse computational analyses, each of which can significantly influence the downstream immunological interpretations, such as clonal selection and expansion. Simulations produce validated standard datasets, where the underlying generative model can be precisely defined and furthermore perturbed to investigate specific questions of interest. Currently, there is no tool that can be used to simulate single-cell datasets incorporating immune receptor repertoires and gene expression. Results We developed Echidna, an R package that simulates immune receptors and transcriptomes at single-cell resolution with user-tunable parameters controlling a wide range of features such as clonal expansion, germline gene usage, somatic hypermutation, transcriptional phenotypes and spatial location. Echidna can additionally simulate time-resolved B cell evolution, producing mutational networks with complex selection histories incorporating class-switching and B cell subtype information. We demonstrated the benchmarking potential of Echidna by simulating clonal lineages and comparing the known simulated networks with those inferred from only the BCR sequences as input. Finally, we simulated immune repertoire information onto existing spatial transcriptomic experiments, thereby generating novel datasets that could be used to develop and integrate methods to profile clonal selection in a spatially resolved manner. Together, Echidna provides a framework that can incorporate experimental data to simulate single-cell immune repertoires to aid software development and bioinformatic benchmarking of clonotyping, phylogenetics, transcriptomics and machine learning strategies. Availability and implementation The R package and code used in this manuscript can be found at github.com/alexyermanos/echidna and also in the R package Platypus (Yermanos et al., 2021). Installation instructions and the vignette for Echidna is described in the Platypus Computational Ecosystem (https://alexyermanos.github.io/Platypus/index.html). Publicly available data and corresponding sample accession numbers can be found in Supplementary Tables S2 and S3. Supplementary information Supplementary data are available at Bioinformatics Advances online.
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Dahal-Koirala, Shiva; Balaban, Gabriel; Neumann, Ralf Stefan; Scheffer, Lonneke; Lundin, Knut & Greiff, Victor
[Show all 9 contributors for this article]
(2022).
TCRpower: quantifying the detection power of T-cell receptor sequencing with a novel computational pipeline calibrated by spike-in sequences.
Briefings in Bioinformatics.
ISSN 1467-5463.
23(2),
p. 1–14.
doi:
10.1093/bib/bbab566.
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T-cell receptor (TCR) sequencing has enabled the development of innovative diagnostic tests for cancers, autoimmune diseases and other applications. However, the rarity of many T-cell clonotypes presents a detection challenge, which may lead to misdiagnosis if diagnostically relevant TCRs remain undetected. To address this issue, we developed TCRpower, a novel computational pipeline for quantifying the statistical detection power of TCR sequencing methods. TCRpower calculates the probability of detecting a TCR sequence as a function of several key parameters: in-vivo TCR frequency, T-cell sample count, read sequencing depth and read cutoff. To calibrate TCRpower, we selected unique TCRs of 45 T-cell clones (TCCs) as spike-in TCRs. We sequenced the spike-in TCRs from TCCs, together with TCRs from peripheral blood, using a 5′ RACE protocol. The 45 spike-in TCRs covered a wide range of sample frequencies, ranging from 5 per 100 to 1 per 1 million. The resulting spike-in TCR read counts and ground truth frequencies allowed us to calibrate TCRpower. In our TCR sequencing data, we observed a consistent linear relationship between sample and sequencing read frequencies. We were also able to reliably detect spike-in TCRs with frequencies as low as one per million. By implementing an optimized read cutoff, we eliminated most of the falsely detected sequences in our data (TCR α-chain 99.0% and TCR β-chain 92.4%), thereby improving diagnostic specificity. TCRpower is publicly available and can be used to optimize future TCR sequencing experiments, and thereby enable reliable detection of disease-relevant TCRs for diagnostic applications.
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Pennell, Matt; Rodriguez, Oscar L.; Watson, Corey T. & Greiff, Victor
(2022).
The evolutionary and functional significance of germline immunoglobulin gene variation.
Trends in immunology.
ISSN 1471-4906.
44(1),
p. 7–21.
doi:
10.1016/j.it.2022.11.001.
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The recombination between immunoglobulin (IG) gene segments determines an individual’s naïve antibody repertoire and, consequently, (auto)antigen recognition. Emerging evidence suggests that mammalian IG germline variation impacts humoral immune responses associated with vaccination, infection, and autoimmunity – from the molecular level of epitope specificity, up to profound changes in the architecture of antibody repertoires. These links between IG germline variants and immunophenotype raise the question on the evolutionary causes and consequences of diversity within IG loci. We discuss why the extreme diversity in IG loci remains a mystery, why resolving this is important for the design of more effective vaccines and therapeutics, and how recent evidence from multiple lines of inquiry may help us do so.
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Kanduri, Chakravarthi; Pavlović, Milena; Scheffer, Lonneke; Motwani, Keshav; Chernigovskaia, Maria & Greiff, Victor
[Show all 7 contributors for this article]
(2022).
Profiling the baseline performance and limits of machine learning models for adaptive immune receptor repertoire classification.
GigaScience.
11(05).
doi:
10.1093/gigascience/giac046.
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Background Machine learning (ML) methodology development for the classification of immune states in adaptive immune receptor repertoires (AIRRs) has seen a recent surge of interest. However, so far, there does not exist a systematic evaluation of scenarios where classical ML methods (such as penalized logistic regression) already perform adequately for AIRR classification. This hinders investigative reorientation to those scenarios where method development of more sophisticated ML approaches may be required. Results To identify those scenarios where a baseline ML method is able to perform well for AIRR classification, we generated a collection of synthetic AIRR benchmark data sets encompassing a wide range of data set architecture-associated and immune state–associated sequence patterns (signal) complexity. We trained ≈1,700 ML models with varying assumptions regarding immune signal on ≈1,000 data sets with a total of ≈250,000 AIRRs containing ≈46 billion TCRβ CDR3 amino acid sequences, thereby surpassing the sample sizes of current state-of-the-art AIRR-ML setups by two orders of magnitude. We found that L1-penalized logistic regression achieved high prediction accuracy even when the immune signal occurs only in 1 out of 50,000 AIR sequences. Conclusions We provide a reference benchmark to guide new AIRR-ML classification methodology by (i) identifying those scenarios characterized by immune signal and data set complexity, where baseline methods already achieve high prediction accuracy, and (ii) facilitating realistic expectations of the performance of AIRR-ML models given training data set properties and assumptions. Our study serves as a template for defining specialized AIRR benchmark data sets for comprehensive benchmarking of AIRR-ML methods.
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Akbar, Rahmad; Bashour, Habib; Rawat, Puneet; Robert, Philippe Paul Auguste; Smorodina, Eva & Cotet, Tudor-Stefan
[Show all 15 contributors for this article]
(2022).
Progress and challenges for the machine learning-based design of fit-for-purpose monoclonal antibodies.
mAbs.
ISSN 1942-0862.
14:e2008790(1),
p. 1–35.
doi:
10.1080/19420862.2021.2008790.
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Although the therapeutic efficacy and commercial success of monoclonal antibodies (mAbs) are tremendous, the design and discovery of new candidates remain a time and cost-intensive endeavor. In this regard, progress in the generation of data describing antigen binding and developability, computational methodology, and artificial intelligence may pave the way for a new era of in silico on-demand immunotherapeutics design and discovery. Here, we argue that the main necessary machine learning (ML) components for an in silico mAb sequence generator are: understanding of the rules of mAb-antigen binding, capacity to modularly combine mAb design parameters, and algorithms for unconstrained parameter-driven in silico mAb sequence synthesis. We review the current progress toward the realization of these necessary components and discuss the challenges that must be overcome to allow the on-demand ML-based discovery and design of fit-for-purpose mAb therapeutic candidates.
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Wilman, Wiktoria; Wróbel, Sonia; Bielska, Weronika; Deszynski, Piotr; Dudzic, Paweł & Jaszczyszyn, Igor
[Show all 13 contributors for this article]
(2022).
Machine-designed biotherapeutics: opportunities, feasibility and advantages of deep learning in computational antibody discovery.
Briefings in Bioinformatics.
ISSN 1467-5463.
23:bbac267(4),
p. 1–20.
doi:
10.1093/bib/bbac267.
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Abstract Antibodies are versatile molecular binders with an established and growing role as therapeutics. Computational approaches to developing and designing these molecules are being increasingly used to complement traditional lab-based processes. Nowadays, in silico methods fill multiple elements of the discovery stage, such as characterizing antibody–antigen interactions and identifying developability liabilities. Recently, computational methods tackling such problems have begun to follow machine learning paradigms, in many cases deep learning specifically. This paradigm shift offers improvements in established areas such as structure or binding prediction and opens up new possibilities such as language-based modeling of antibody repertoires or machine-learning-based generation of novel sequences. In this review, we critically examine the recent developments in (deep) machine learning approaches to therapeutic antibody design with implications for fully computational antibody design.
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Perruzza, Lisa; Strati, Francesco; Raneri, Matteo; Li, Hai; Gargari, Giorgio & Rezzonico-Jost, Tanja
[Show all 17 contributors for this article]
(2022).
Apyrase-mediated amplification of secretory IgA promotes intestinal homeostasis.
Cell reports.
40:111112(3),
p. 1–25.
doi:
10.1016/j.celrep.2022.111112.
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Secretory immunoglobulin A (SIgA) interaction with commensal bacteria conditions microbiota composition and function. However, mechanisms regulating reciprocal control of microbiota and SIgA are not defined. Bacteria-derived adenosine triphosphate (ATP) limits T follicular helper (Tfh) cells in the Peyer’s patches (PPs) via P2X7 receptor (P2X7R) and thereby SIgA generation. Here we show that hydrolysis of extracellular ATP (eATP) by apyrase results in amplification of the SIgA repertoire. The enhanced breadth of SIgA in mice colonized with apyrase-releasing Escherichia coli influences topographical distribution of bacteria and expression of genes involved in metabolic versus immune functions in the intestinal epithelium. SIgA-mediated conditioning of bacteria and enterocyte function is reflected by differences in nutrient absorption in mice colonized with apyrase-expressing bacteria. Apyrase-induced SIgA improves intestinal homeostasis and attenuates barrier impairment and susceptibility to infection by enteric pathogens in antibiotic-induced dysbiosis. Therefore, amplification of SIgA by apyrase can be leveraged to restore intestinal fitness in dysbiotic conditions.
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Rognes, Torbjørn; Scheffer, Lonneke; Greiff, Victor & Sandve, Geir Kjetil
(2022).
CompAIRR: ultra-fast comparison of adaptive immune receptor repertoires by exact and approximate sequence matching.
Bioinformatics.
ISSN 1367-4803.
38(17),
p. 4230–4232.
doi:
10.1093/bioinformatics/btac505.
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Abstract Motivation Adaptive immune receptor (AIR) repertoires (AIRRs) record past immune encounters with exquisite specificity. Therefore, identifying identical or similar AIR sequences across individuals is a key step in AIRR analysis for revealing convergent immune response patterns that may be exploited for diagnostics and therapy. Existing methods for quantifying AIRR overlap scale poorly with increasing dataset numbers and sizes. To address this limitation, we developed CompAIRR, which enables ultra-fast computation of AIRR overlap, based on either exact or approximate sequence matching. Results CompAIRR improves computational speed 1000-fold relative to the state of the art and uses only one-third of the memory: on the same machine, the exact pairwise AIRR overlap of 104 AIRRs with 105 sequences is found in ∼17 min, while the fastest alternative tool requires 10 days. CompAIRR has been integrated with the machine learning ecosystem immuneML to speed up commonly used AIRR-based machine learning applications. Availability and implementation CompAIRR code and documentation are available at https://github.com/uio-bmi/compairr. Docker images are available at https://hub.docker.com/r/torognes/compairr. The code to replicate the synthetic datasets, scripts for benchmarking and creating figures, and all raw data underlying the figures are available at https://github.com/uio-bmi/compairr-benchmarking. Supplementary information Supplementary data are available at Bioinformatics online.
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Grevys, Algirdas; Frick, Rahel; Mester, Simone; Flem-Karlsen, Karine; Nilsen, Jeannette & Foss, Stian
[Show all 13 contributors for this article]
(2022).
Antibody variable sequences have a pronounced effect on cellular transport and plasma half-life.
iScience.
25:103746(2),
p. 1–22.
doi:
10.1016/j.isci.2022.103746.
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Monoclonal IgG antibodies are the fastest growing class of biologics, but large differences exist in their plasma half-life in humans. Thus, to design IgG antibodies with favorable pharmacokinetics, it is crucial to identify the determinants of such differences. Here, we demonstrate that the variable region sequences of IgG antibodies greatly affect cellular uptake and subsequent recycling and rescue from intracellular degradation by endothelial cells. When the variable sequences are masked by the cognate antigen, it influences both their transport behavior and binding to the neonatal Fc receptor (FcRn), a key regulator of IgG plasma half-life. Furthermore, we show how charge patch differences in the variable domains modulate both binding and transport properties and that a short plasma half-life, due to unfavorable charge patches, may partly be overcome by Fc-engineering for improved FcRn binding.
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Weber, Cédric R.; Rubio, Teresa; Wang, Longlong; Zhang, Wei; Robert, Philippe Paul Auguste & Akbar, Rahmad
[Show all 15 contributors for this article]
(2022).
Reference-based comparison of adaptive immune receptor repertoires.
Cell Reports Methods.
2(8).
doi:
10.1016/j.crmeth.2022.100269.
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B and T cell receptor (immune) repertoires can represent an individual’s immune history. While current repertoire analysis methods aim to discriminate between health and disease states, they are typically based on only a limited number of parameters. Here, we introduce immuneREF: a quantitative multidimensional measure of adaptive immune repertoire (and transcriptome) similarity that allows interpretation of immune repertoire variation by relying on both repertoire features and cross-referencing of simulated and experimental datasets. To quantify immune repertoire similarity landscapes across health and disease, we applied immuneREF to >2,400 datasets from individuals with varying immune states (healthy, [autoimmune] disease, and infection). We discovered, in contrast to the current paradigm, that blood-derived immune repertoires of healthy and diseased individuals are highly similar for certain immune states, suggesting that repertoire changes to immune perturbations are less pronounced than previously thought. In conclusion, immuneREF enables the population-wide study of adaptive immune response similarity across immune states.
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Akbar, Rahmad; Robert, Philippe Paul Auguste; Weber, Cédric R.; Widrich, Michael; Frank, Robert & Pavlovic, Milena
[Show all 19 contributors for this article]
(2022).
In silico proof of principle of machine learning-based antibody design at unconstrained scale.
mAbs.
ISSN 1942-0862.
14:e2031482(1),
p. 1–18.
doi:
10.1080/19420862.2022.2031482.
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Generative machine learning (ML) has been postulated to become a major driver in the computational design of antigen-specific monoclonal antibodies (mAb). However, efforts to confirm this hypothesis have been hindered by the infeasibility of testing arbitrarily large numbers of antibody sequences for their most critical design parameters: paratope, epitope, affinity, and developability. To address this challenge, we leveraged a lattice-based antibody-antigen binding simulation framework, which incorporates a wide range of physiological antibody-binding parameters. The simulation framework enables the computation of synthetic antibody-antigen 3D-structures, and it functions as an oracle for unrestricted prospective evaluation and benchmarking of antibody design parameters of ML-generated antibody sequences. We found that a deep generative model, trained exclusively on antibody sequence (one dimensional: 1D) data can be used to design conformational (three dimensional: 3D) epitope-specific antibodies, matching, or exceeding the training dataset in affinity and developability parameter value variety. Furthermore, we established a lower threshold of sequence diversity necessary for high-accuracy generative antibody ML and demonstrated that this lower threshold also holds on experimental real-world data. Finally, we show that transfer learning enables the generation of high-affinity antibody sequences from low-N training data. Our work establishes a priori feasibility and the theoretical foundation of high-throughput ML-based mAb design.
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Dorraji, Seyed Esmaeil; Borgen, Elin; Segura-Pena, Dario; Rawat, Puneet; Smorodina, Eva & Dunn, Claire
[Show all 10 contributors for this article]
(2022).
Development of a High-Affinity Antibody against the Tumor-Specific and Hyperactive 611-p95HER2 Isoform.
Cancers.
14:4859(19),
p. 1–24.
doi:
10.3390/cancers14194859.
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Simple Summary
In the present study, we addressed the unmet need for a molecular antibody (mAb) with high affinity and specificity against a truncated hyperactive isoform of human epidermal growth factor receptor 2 (HER2), called 611-carboxy terminal fragment (CTF)-p95HER2. Patients with p95HER2+ breast cancer are at risk of developing metastatic breast cancer with a poor prognosis and resistance to therapies targeting full-length HER2. We have generated a mAb named Oslo-2, which react specifically with 611-CTF-p95HER2 and has a high affinity. We also characterized the antigenic determinant (epitope) on the p95HER2 protein and the antigen-binding site (paratope) on the Oslo-2 mAb. The antibody can be used to develop antibody- or cell-based therapies targeting p95HER2, as well as a diagnostic assay to identify p95HER2+ disease.
Abstract
The expression of human epidermal growth factor receptor 2 (HER2) is a key classification factor in breast cancer. Many breast cancers express isoforms of HER2 with truncated carboxy-terminal fragments (CTF), collectively known as p95HER2. A common p95HER2 isoform, 611-CTF, is a biomarker for aggressive disease and confers resistance to therapy. Contrary to full-length HER2, 611-p95HER2 has negligible normal tissue expression. There is currently no approved diagnostic assay to identify this subgroup and no therapy targeting this mechanism of tumor escape. The purpose of this study was to develop a monoclonal antibody (mAb) against 611-CTF-p95HER2. Hybridomas were generated from rats immunized with cells expressing 611-CTF. A hybridoma producing a highly specific Ab was identified and cloned further as a mAb. This mAb, called Oslo-2, gave strong staining for 611-CTF and no binding to full-length HER2, as assessed in cell lines and tissues by flow cytometry, immunohistochemistry and immunofluorescence. No cross-reactivity against HER2 negative controls was detected. Surface plasmon resonance analysis demonstrated a high binding affinity (equilibrium dissociation constant 2 nM). The target epitope was identified at the N-terminal end, using experimental alanine scanning. Further, the mAb paratope was identified and characterized with hydrogen-deuterium-exchange, and a molecular model for the (Oslo-2 mAb:611-CTF-p95HER2) complex was generated by an experimental-information-driven docking approach. We conclude that the Oslo-2 mAb has a high affinity and is highly specific for 611-CTF-p95HER2. The Ab may be used to develop potent and safe therapies, overcoming p95HER2-mediated tumor escape, as well as for developing diagnostic assays.
Keywords: HER2; p95HER2; antibody; tumor-specific; breast cancer; cancer stem cells; HDX-MS; epitope and paratope mapping; docking
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Mester, Simone; Evers, Mitchell; Meyer, Saskia; Nilsen, Jeannette; Greiff, Victor & Sandlie, Inger
[Show all 8 contributors for this article]
(2021).
Extended plasma half-life of albumin-binding domain fused human IgA upon pH-dependent albumin engagement of human FcRn in vitro and in vivo.
mAbs.
ISSN 1942-0862.
13:1893888(1),
p. 1–14.
doi:
10.1080/19420862.2021.1893888.
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Albumin has a serum half-life of 3 weeks in humans. This feature can be used to improve the pharmacokinetics of shorter-lived biologics. For instance, an albumin-binding domain (ABD) can be used to recruit albumin. A prerequisite for such design is that the ABD-albumin interaction does not interfere with pH-dependent binding of albumin to the human neonatal Fc receptor (FcRn), as FcRn acts as the principal regulator of the half-life of albumin. Thus, there is a need to know how ABDs act in the context of fusion partners and human FcRn. Here, we studied the binding and transport properties of human immunoglobulin A1 (IgA1), fused to a Streptococcus protein G-derived engineered ABD, in in vitro and in vivo systems harboring human FcRn. IgA has great potential as a therapeutic protein, but its short half-life is a major drawback. We demonstrate that ABD-fused IgA1 binds human FcRn pH-dependently and is rescued from cellular degradation in a receptor-specific manner in the presence of albumin. This occurs when ABD is fused to either the light or the heavy chain. In human FcRn transgenic mice, IgA1-ABD in complex with human albumin, gave 4-6-fold extended half-life compared to unmodified IgA1, where the light chain fusion showed the longest half-life. When the heavy chain-fused protein was pre-incubated with an engineered human albumin with improved FcRn binding, cellular rescue and half-life was further enhanced. Our study reveals how an ABD, which does not interfere with albumin binding to human FcRn, may be used to extend the half-life of IgA.
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Snapkov, Igor; Chernigovskaia, Maria; Sinitcyn, Pavel; Le, Quy Khang; Nyman, Tuula Anneli & Greiff, Victor
(2021).
Progress and challenges in mass spectrometry-based analysis of antibody repertoires.
Trends in Biotechnology.
ISSN 0167-7799.
00,
p. 1–19.
doi:
10.1016/j.tibtech.2021.08.006.
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Humoral immunity is divided into the cellular B cell and protein-level antibody responses. High-throughput sequencing has advanced our understanding of both these fundamental aspects of B cell immunology as well as aspects pertaining to vaccine and therapeutics biotechnology. Although the protein-level serum and mucosal antibody repertoire make major contributions to humoral protection, the sequence composition and dynamics of antibody repertoires remain underexplored. This limits insight into important immunological and biotechnological parameters such as the number of antigen-specific antibodies, which are for example, relevant for pathogen neutralization, microbiota regulation, severity of autoimmunity, and therapeutic efficacy. High-resolution mass spectrometry (MS) has allowed initial insights into the antibody repertoire. We outline current challenges in MS-based sequence analysis of antibody repertoires and propose strategies for their resolution.
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Mikocziova, Ivana; Greiff, Victor & Sollid, Ludvig Magne
(2021).
Immunoglobulin germline gene variation and its impact on human disease.
Genes and Immunity.
ISSN 1466-4879.
22(4),
p. 205–217.
doi:
10.1038/s41435-021-00145-5.
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Immunoglobulins (Ig) play an important role in the immune system both when expressed as antigen receptors on the cell surface of B cells and as antibodies secreted into extracellular fluids. The advent of high-throughput sequencing methods has enabled the investigation of human Ig repertoires at unprecedented depth. This has led to the discovery of many previously unreported germline Ig alleles. Moreover, it is becoming clear that convergent and stereotypic antibody responses are common where different individuals recognise defined antigenic epitopes with the use of the same Ig V genes. Thus, germline V gene variation is increasingly being linked to the differential capacity of generating an effective immune response, which might lead to varying disease susceptibility. Here, we review recent evidence of how germline variation in Ig genes impacts the Ig repertoire and its subsequent effects on the adaptive immune response in vaccination, infection, and autoimmunity.
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Robert, Philippe Paul Auguste; Kunze-Schumacher, Heike; Greiff, Victor & Krueger, Andreas
(2021).
Modeling the dynamics of T-cell development in the thymus.
Entropy.
23:437(4).
doi:
10.3390/e23040437.
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The thymus hosts the development of a specific type of adaptive immune cells called T cells. T cells orchestrate the adaptive immune response through recognition of antigen by the highly variable T-cell receptor (TCR). T-cell development is a tightly coordinated process comprising lineage commitment, somatic recombination of Tcr gene loci and selection for functional, but non-self-reactive TCRs, all interspersed with massive proliferation and cell death. Thus, the thymus produces a pool of T cells throughout life capable of responding to virtually any exogenous attack while preserving the body through self-tolerance. The thymus has been of considerable interest to both immunologists and theoretical biologists due to its multi-scale quantitative properties, bridging molecular binding, population dynamics and polyclonal repertoire specificity. Here, we review experimental strategies aimed at revealing quantitative and dynamic properties of T-cell development and how they have been implemented in mathematical modeling strategies that were reported to help understand the flexible dynamics of the highly dividing and dying thymic cell populations. Furthermore, we summarize the current challenges to estimating in vivo cellular dynamics and to reaching a next-generation multi-scale picture of T-cell development.
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Amoriello, Roberta; Chernigovskaia, Mariia; Greiff, Victor; Carnasciali, Alberto; Massacesi, Luca & Barilaro, Alessandro
[Show all 13 contributors for this article]
(2021).
TCR repertoire diversity in Multiple Sclerosis: High-dimensional bioinformatics analysis of sequences from brain, cerebrospinal fluid and peripheral blood.
EBioMedicine.
68:103429,
p. 1–13.
doi:
10.1016/j.ebiom.2021.103429.
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Background T cells play a key role in the pathogenesis of multiple sclerosis (MS), a chronic, inflammatory, demyelinating disease of the central nervous system (CNS). Although several studies recently investigated the T-cell receptor (TCR) repertoire in cerebrospinal fluid (CSF) of MS patients by high-throughput sequencing (HTS), a deep analysis on repertoire similarities and differences among compartments is still missing. Methods We performed comprehensive bioinformatics on high-dimensional TCR Vβ sequencing data from published and unpublished MS and healthy donors (HD) studies. We evaluated repertoire polarization, clone distribution, shared CDR3 amino acid sequences (CDR3s-a.a.) across repertoires, clone overlap with public databases, and TCR similarity architecture. Findings CSF repertoires showed a significantly higher public clones percentage and sequence similarity compared to peripheral blood (PB). On the other hand, we failed to reject the null hypothesis that the repertoire polarization is the same between CSF and PB. One Primary-Progressive MS (PPMS) CSF repertoire differed from the others in terms of TCR similarity architecture. Cluster analysis splits MS from HD. Interpretation In MS patients, the presence of a physiological barrier, the blood-brain barrier, does not impact clone prevalence and distribution, but impacts public clones, indicating CSF as a more private site. We reported a high Vβ sequence similarity in the CSF-TCR architecture in one PPMS. If confirmed it may be an interesting insight into MS progressive inflammatory mechanisms. The clustering of MS repertoires from HD suggests that disease shapes the TCR Vβ clonal profile. Funding This study was partly financially supported by the Italian Multiple Sclerosis Foundation (FISM), that contributed to Ballerini-DB data collection (grant #2015 R02).
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Slabodkin, Andrei; Chernigovskaia, Maria; Mikocziova, Ivana; Akbar, Rahmad; Scheffer, Lonneke & Pavlović, Milena
[Show all 16 contributors for this article]
(2021).
Individualized VDJ recombination predisposes the available Ig sequence space.
Genome Research.
ISSN 1088-9051.
31(12),
p. 2209–2225.
doi:
10.1101/gr.275373.121.
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The process of recombination between variable (V), diversity (D), and joining (J) immunoglobulin (Ig) gene segments determines an individual's naive Ig repertoire and, consequently, (auto)antigen recognition. VDJ recombination follows probabilistic rules that can be modeled statistically. So far, it remains unknown whether VDJ recombination rules differ between individuals. If these rules differed, identical (auto)antigen-specific Ig sequences would be generated with individual-specific probabilities, signifying that the available Ig sequence space is individual specific. We devised a sensitivity-tested distance measure that enables inter-individual comparison of VDJ recombination models. We discovered, accounting for several sources of noise as well as allelic variation in Ig sequencing data, that not only unrelated individuals but also human monozygotic twins and even inbred mice possess statistically distinguishable immunoglobulin recombination models. This suggests that, in addition to genetic, there is also nongenetic modulation of VDJ recombination. We demonstrate that population-wide individualized VDJ recombination can result in orders of magnitude of difference in the probability to generate (auto)antigen-specific Ig sequences. Our findings have implications for immune receptor–based individualized medicine approaches relevant to vaccination, infection, and autoimmunity.
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Neumeier, Daniel; Pedrioli, Alessandro; Genovese, Alessandro; Sandu, Ioana; Ehling, Roy & Hong, Kai-Lin
[Show all 19 contributors for this article]
(2021).
Profiling the specificity of clonally expanded plasma cells during chronic viral infection by single-cell analysis.
European Journal of Immunology.
ISSN 0014-2980.
52(2),
p. 297–311.
doi:
10.1002/eji.202149331.
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Plasma cells and their secreted antibodies play a central role in the long-term protection against chronic viral infection. However, due to experimental limitations, a comprehensive description of linked genotypic, phenotypic, and antibody repertoire features of plasma cells (gene expression, clonal frequency, virus specificity, and affinity) has been challenging to obtain. To address this, we performed single-cell transcriptome and antibody repertoire sequencing of the murine BM plasma cell population following chronic lymphocytic choriomeningitis virus infection. Our single-cell sequencing approach recovered full-length and paired heavy- and light-chain sequence information for thousands of plasma cells and enabled us to perform recombinant antibody expression and specificity screening. Antibody repertoire analysis revealed that, relative to protein immunization, chronic infection led to increased levels of clonal expansion, class-switching, and somatic variants. Furthermore, antibodies from the highly expanded and class-switched (IgG) plasma cells were found to be specific for multiple viral antigens and a subset of clones exhibited cross-reactivity to nonviral and autoantigens. Integrating single-cell transcriptome data with antibody specificity suggested that plasma cell transcriptional phenotype was correlated to viral antigen specificity. Our findings demonstrate that chronic viral infection can induce and sustain plasma cell clonal expansion, combined with significant somatic hypermutation, and can generate cross-reactive antibodies.
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Arnaout, Ramy A.; Prak, Eline T. Luning; Schwab, Nicholas; Rubelt, Florian; Greiff, Victor & Pavlović, Milena
[Show all 32 contributors for this article]
(2021).
The Future of Blood Testing Is the Immunome.
Frontiers in Immunology.
12:626793,
p. 1–6.
doi:
10.3389/fimmu.2021.626793.
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It is increasingly clear that an extraordinarily diverse range of clinically important conditions—including infections, vaccinations, autoimmune diseases, transplants, transfusion reactions, aging, and cancers—leave telltale signatures in the millions of V(D)J-rearranged antibody and T cell receptor [TR per the Human Genome Organization (HUGO) nomenclature but more commonly known as TCR] genes collectively expressed by a person’s B cells (antibodies) and T cells. We refer to these as the immunome . Because of its diversity and complexity, the immunome provides singular opportunities for advancing personalized medicine by serving as the substrate for a highly multiplexed, near-universal blood test. Here we discuss some of these opportunities, the current state of immunome-based diagnostics, and highlight some of the challenges involved. We conclude with a call to clinicians, researchers, and others to join efforts with the Adaptive Immune Receptor Repertoire Community (AIRR-C) to realize the diagnostic potential of the immunome.
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Øjlert, Åsa Kristina; Nebdal, Daniel J.H.; Snapkov, Igor; Olsen, Vibeke; Kidman, Joel & Greiff, Victor
[Show all 8 contributors for this article]
(2021).
Dynamic changes in the T cell receptor repertoire during treatment with radiotherapy combined with an immune checkpoint inhibitor.
Molecular Oncology.
ISSN 1574-7891.
p. 1–11.
doi:
10.1002/1878-0261.13082.
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Previous studies have indicated a synergistic effect between radiotherapy and immunotherapy. A better understanding of how this combination affects the immune system can help to clarify its role in the treatment of metastatic cancer. We performed T cell receptor (TCR) sequencing on 46 sequentially collected samples from 15 patients with stage IV non-small cell lung cancer, receiving stereotactic body radiotherapy combined with a programmed cell death ligand-1 (PD-L1) inhibitor. TCR repertoire diversity was assessed using Rényi diversity curves and the Shannon diversity index. TCR clones were tracked over time. We found decreasing or stable diversity in the best responders, and an increase in diversity at progression in patients with an initial response. Expansion of TCR clones was more often seen in responders. Several patients also developed new clones of high abundance. This seemed to be more related to radiotherapy than to immune checkpoint blockade. In summary, we observed similar dynamics in the TCR repertoire as have been described with immunotherapy alone. In addition, the occurrence of new unique clones of high abundance after radiotherapy may indicate that radiotherapy functions as a personalized cancer vaccine.
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Greiff, Victor
(2024).
Finding the needle in a needlestack: Deciphering antigen and disease information encoded in immune receptor repertoires.
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Federico, Lorenzo; Malone, Brandon; Tennøe, Simen; Chaban, Viktoriia; Osen, Julie Røkke & Gainullin, Murat
[Show all 13 contributors for this article]
(2024).
Corrigendum: Experimental validation of immunogenic SARS-CoV-2 T cell epitopes identified by artificial intelligence (Frontiers in Immunology, (2023), 14, (1265044), 10.3389/fimmu.2023.1265044).
Frontiers in Immunology.
15.
doi:
10.3389/fimmu.2024.1377041.
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Natali, Eriberto Noel; Horst, Alexander; Meier, Patrick; Greiff, Victor; Nuvolone, Mario & Babrak, Lmar Marie
[Show all 8 contributors for this article]
(2024).
Publisher Correction: The dengue-specific immune response and antibody identification with machine learning (npj Vaccines, (2024), 9, 1, (16), 10.1038/s41541-023-00788-7).
npj Vaccines.
9(1).
doi:
10.1038/s41541-024-01027-3.
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Erasmus, M. Frank; Ferrara, Fortunato; D’Angelo, Sara; Spector, Laura; Leal-Lopes, Camila & Teixeira, André A.
[Show all 17 contributors for this article]
(2024).
Correction to: Insights into next generation sequencing guided antibody selection strategies (Scientific Reports, (2023), 13, 1, (18370), 10.1038/s41598-023-45538-w).
Scientific Reports.
14(1).
doi:
10.1038/s41598-024-53751-4.
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Natali, Eriberto Noel; Horst, Alexander; Meier, Patrick; Greiff, Victor; Nuvolone, Mario & Babrak, Lmar Marie
[Show all 8 contributors for this article]
(2024).
Correction to: The dengue-specific immune response and antibody identification with machine learning (npj Vaccines, (2024), 9, 1, (16), 10.1038/s41541-023-00788-7).
npj Vaccines.
9(1).
doi:
10.1038/s41541-024-00820-4.
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Mhanna, Vanessa; Bashour, Habib; Quý, Khang Lê; Barennes, Pierre; Rawat, Puneet & Greiff, Victor
[Show all 7 contributors for this article]
(2024).
Correction to: Adaptive immune receptor repertoire analysis (Nature Reviews Methods Primers, (2024), 4, 1, (6), 10.1038/s43586-023-00284-1).
Nature Reviews Methods Primers.
doi:
10.1038/s43586-024-00299-2.
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Greiff, Victor
(2023).
Adaptive immune receptor repertoire profiling for diagnostics and therapeutics discovery and design.
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Vu, Mai Ha; Akbar, Rahmad; Robert, Philippe; Sandve, Geir Kjetil Ferkingstad; Haug, Dag Trygve Truslew & Greiff, Victor
(2023).
Linguistically inspired roadmap for building biologically reliable protein language models.
Nature Machine Intelligence.
5(5),
p. 485–496.
doi:
10.1038/s42256-023-00637-1.
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Erasmus, M. Frank; Ferrara, Fortunato; D’Angelo, Sara; Spector, Laura; Leal‑Lopes, Camila & Teixeira, André A.
[Show all 17 contributors for this article]
(2023).
Author Correction: Insights into next generation sequencing guided antibody selection strategies (Scientific Reports, (2023), 13, 1, (18370), 10.1038/s41598-023-45538-w).
Scientific Reports.
13(1).
doi:
10.1038/s41598-023-49214-x.
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Greiff, Victor
(2022).
A Compact Vocabulary of Paratope-Epitope Interactions Enables Predictability of Antibody-Antigen Binding.
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Greiff, Victor
(2022).
Towards targeted computational and machine-learning- based antibody specificity and developability design.
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Sandve, Geir Kjetil Ferkingstad; Greiff, Victor; Haff, Ingrid Hobæk & Vu, Mai Ha
(2022).
Deciphering the Immune System Through Linguistics-Inspired Statistical Machine Learning.
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Torgersen, Eivind; Kjetil, Sandve Geir; Greiff, Victor; Pavlovic, Milena & Scheffer, Lonneke
(2022).
Fra bare én blodprøve kan kunstig intelligens gi diagnose for mange ulike sykdommer.
Titan.uio.no.
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Doeland, Elin Martine; Sandve, Geir Kjetil Ferkingstad & Greiff, Victor
(2022).
Immunforsvaret har skjulte mønstre om sykdom og infeksjoner.
Forskning.no.
ISSN 1891-635X.
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Sandve, Geir Kjetil Ferkingstad & Greiff, Victor
(2022).
Access to ground truth at unconstrained size makes simulated data as indispensable as experimental data for bioinformatics methods development and benchmarking.
Bioinformatics.
ISSN 1367-4803.
38(21),
p. 4994–4996.
doi:
10.1093/bioinformatics/btac612.
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Greiff, Victor
(2021).
Development of Machine Learning Methods for the Analysis, Prediction and Generation of Antibody Repertoires.
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Smorodina, Eva & Greiff, Victor
(2026).
Structural Rules of Antibody Function.
Universitetet i Oslo.
ISSN 9788234808647.
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Antibodies are highly specific biomolecules composed of two heavy and two light chains, whose complementarity-determining regions (CDRs) form the antigen-binding site. The remarkable ability of antibodies to recognize almost any target molecule has led to their central role in diagnostics and therapeutic development. Despite significant experimental advances, therapeutic antibody discovery remains a time-consuming and low-throughput process, heavily dependent on iterative laboratory screening and animal testing. Computational design promises to accelerate this process, yet its success remains limited due to the complexity and flexibility of antibody-antigen interactions. Traditional physics-based methods have provided valuable structural insights but rely heavily on empirical estimations and require significant resources. Novel data-driven methods provide practical solutions, but their black-box nature limits mechanistic understanding. Both currently achieve low predictive success rates, highlighting the need for alternative approaches. This thesis argues that this limited success stems from an incomplete understanding of the three-dimensional and biochemical rules governing antibody-antigen binding. Rather than introducing another predictive model, it focuses on uncovering the mechanistic principles that define effective binding, integrating experimental and computational approaches across three scales: structural properties of unbound antibodies, structural determinants of affinity and specificity at binding interfaces, and molecular dynamics simulations establishing quantitative structure-based rules validated against experimental data. This work produced the first quantitative set of structural rules capable of differentiating high- from low-affinity antibody binders in agreement with wet lab observations, even when applied to modeled rather than experimental structures. It contributes to advancing the next generation of computational antibody design, guided by deeper biological understanding rather than empirical approximation.
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Quy, Khang Le; Greiff, Victor & Snapkov, Igor
(2025).
Integrative system immunology approaches to characterize immunoglobulin repertoires in health and disease.
Universitetet i Oslo.
ISSN 9788234806735.
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B-cell receptors and antibodies are essential elements of the adaptive immune system, pivotal in pathogen recognition and neutralization, and widely utilized in diagnostics and therapeutic development. Their broad diversity, generated through various processes during immune cell development, demands effective profiling and characterization. High-throughput sequencing has emerged as a leading technique to capture B-cell receptor diversity, with single-cell sequencing further enhancing resolution and context for understanding immune responses. Despite the potential of these technologies, challenges such as undersampling and data preprocessing persist. Concurrently, mass spectrometry offers a unique lens to explore the effector antibody repertoire, complementing sequencing yet remains underutilized with uncertainties surrounding its analytical performance in antibody samples.
In this thesis, Khang Le Quy focused on evaluating sequencing methodologies, particularly single-cell sequencing, delineating its capabilities and limitations. A novel integration of antibody bottom-up mass spectrometry data with sequencing datasets is introduced, presenting a complementary approach that broadens analytical scope in immune repertoire studies. Large-scale benchmarking of bottom-up mass spectrometry using ground-truth data is conducted, delivering insights into performance and future recommendations. Applying these methodologies, the thesis examines immune imprinting phenomena via sequential influenza infections, shedding light on how prior exposures influence immune responses.
Collectively, this work highlights the transformative impact of merging advanced sequencing and mass spectrometry techniques on immunological research. paving the way for advancing the precision and scope of immune system analysis necessary for future biomedical breakthroughs.
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Published
Jan. 2, 2018 3:34 PM
- Last modified
Sep. 19, 2022 1:04 PM
