Genmab (Netherlands)

Abstract Accurately modeling antibody-antigen interactions requires distinguishing intrinsic binding affinity (“ protein-interaction ”) from protein biophysical properties (“ protein-quality ”), including folding, stability, and expression. However, high-throughput mutational measurements commonly used to train and benchmark computational models often conflate these effects, obscuring the true determinants of molecular recognition. Here, we present an experimental and analytical framework to disentangle protein-interaction effects from protein-quality effects in single-domain antibody (VHH)-antigen binding. Using a large-scale deep mutational scanning (DMS) dataset spanning four VHH-antigen complexes, with single and double mutations in both partners, we introduce control binders to quantify protein-quality changes independently of protein-interaction . This enables decomposition of experimentally measured affinity into protein-interaction and protein-quality components at scale. Leveraging the disentangled dataset, we evaluated state-of-the-art structure- and sequence-based models for protein-quality and protein-interaction prediction and show that their performance largely reflects protein-quality rather than protein-interaction effects. Our results highlight a major confounder in current datasets and suggest that accounting for protein-quality will be essential for training next-generation affinity-prediction models. Nomenclature Antibody related terms Primary VHH : The VHH of a VHH-antigen complex for which the paratope and the epitope weremutated. Control VHH : A second VHH that binds to the same antigen as the primary VHH but has non-overlapping epitope positions and therefore does not bind to any of the mutated antigen positions. Affinity-related terms Real Affinity : “The strength of the interaction between two […] molecules that bind reversibly (interact)” 1 . In the context of antibody-antigen binding, it quantifies interactions between active proteins (which are expressed and correctly folded 2 and are therefore functionally and biologically active (see below). It is commonly quantified by the equilibrium dissociation constant, K D . Observed affinity (°K D ): The interaction strength experimentally measured between two molecules. Unlike real affinity, this value is confounded by the biophysical properties of the individual binding partners, specifically their folding, stability, and expression levels. Consequently, the observed affinity often differs from the real/intrinsic affinity if a significant fraction of the protein population is inactive 3 . NOTE: Unless otherwise specified, °K D is reported in - log10 space. For example, a °K D of -9 corresponds to 10 -9 M or 1nM. Change in observed affinity (Δ°K D ) : The shift in the observed affinity between two proteins upon mutation, reported as the log 10 -transformed fold change. A value of 1 reflects a 10-fold difference, a value of 2 a 100-fold difference, etc. This aggregate change resolves into two distinct biophysical components 2, 4 : Protein-interaction change: The change in the intrinsic thermodynamic affinity between the two binding partners, each in its active state (i.e., the specific change in interface Gibbs free energy because both enthalpy and entropy are considered). Protein-quality change: The change in the fraction of the mutated protein population that is biologically active - meaning it is expressed, correctly folded, and stable 2, 5 . Folding: The process that guides the polypeptide chain toward its native conformation, which is a prerequisite for forming a functional binding site. Stability: The thermodynamic capacity to maintain the folded structure over time and under physiological conditions. Stability (decrease in Gibbs free energy from the unfolded to the folded state) ensures the binding interface remains intact and prevents competing processes such as aggregation 6 . Expression: The steady-state abundance of the protein. This is largely dependent on proper folding and stability, as cellular quality control mechanisms degrade proteins that fail to fold or remain stable at functional concentrations. Change in relative affinity (ΔΔ°K D ) : the differen

Treatment efficacy of patients receiving anti-TNF antibodies is limited by the formation of anti-drug antibodies. These are observed in most adalimumab-treated rheumatoid arthritis patients, despite the adjuvant-free and human sequence-derived nature of the antibody. The class switched phenotype and high affinity of these antibodies suggest CD4 T-cell involvement in their formation. In this study, we investigated the potential epitopes in the functional domain of adalimumab and assessed their actual HLA II presentation and induction of CD4 T-cell responses in exposed patients. The binding strength of overlapping adalimumab-derived peptides to 27 DR and 14 DQ HLA alleles was predicted in silico. 10 strong and 44 medium-binding 10-mer peptides were identified within the variable regions of the heavy and light chain of adalimumab. HLA-DR-mediated antigen presentation of selected peptides by monocyte-derived dendritic cells was determined by mass spectrometry of the peptide pool eluted from isolated HLA-DR complexes. Binding of the variable region peptides of heavy (H41-62) and light chains (L18-39) was demonstrated. The presence of adalimumab-specific CD4 T-cells in adalimumab-experienced patients was investigated via peptide stimulation of peripheral blood mononuclear cells and assessment of T-cell proliferation. Anti-adalimumab CD4 T-cell responses were observed against four variable region peptides in a group of adalimumab-experienced RA patients. Some of these responses were also present in healthy control donors. This study identifies immunologically relevant CD4 T-cell epitopes in the variable region of the human therapeutic antibody adalimumab based on RA patients' reactivity. Modification of these epitopes or concomitant therapy that targets or prevents adalimumab-specific T cell responses could be beneficial for patients with significant anti-drug responses.

ABSTRACT Antibody-epitope conjugates (AECs) proved to be a promising new therapeutic strategy to redirect virus-specific CD8+ T cells toward cancer cells by delivering T-cell epitopes. To be able to redirect a larger fraction of the virus-specific T-cell population, it is beneficial to deliver a broader selection of T-cell epitopes. We investigated two different methods to generate AECs with two distinct virus-specific T-cell epitopes fused to one antibody. Epitopes were either placed in a tandem-like fashion at the C-terminus of the AEC (t-AEC) or bispecific-AECs (bs-AECs) were generated via controlled Fab-arm exchange to generate bs-AECs with two identical antigen binding domains, but two distinct epitopes on each Fab-arm. Our study revealed that maintaining a free epitope terminus was required for efficient delivery of the virus-specific T-cell epitopes. Consequently, viral-epitope delivery using t-AECs was suboptimal as the concatenated epitopes were less effectively delivered to the target cells. However, well-defined bs-AECs containing both CMV and EBV epitopes were successfully generated and both in vitro and in vivo efficacy was evaluated. Our results demonstrate that bispecific-AECs can efficiently deliver EBV and CMV epitopes simultaneously to multiple cancer cell lines from different origins, thereby redirecting and activating two distinct populations of virus-specific T cells. Furthermore, our in vivo findings indicate that when both virus-specific T-cell populations are present and tumor cells express the proteases required for efficient epitope delivery, bs-AECs exhibit similar efficacy in reducing tumor burden compared to AECs. To conclude, our study demonstrates the feasibility of redirecting two groups of virus-specific T cells using a single antibody and highlights the potential of bs-AECs both in vitro and in vivo.

Abstract Molecular profiling of formalin-fixed paraffin-embedded (FFPE) tumors in clinical research is performed with low-plex immunohistochemistry or genome-wide transcriptomics. The application of unbiased, highly multiplexed proteomics to FFPE is limited by sample input requirements, assay throughput and complex bioinformatics. Here we report a proteomic method amenable to low-input FFPE profiling which complements transcriptomics. We applied Biognosys’ mass-spectrometry platform (TrueDiscovery, DIA-MS) to samples encountered in routine histopathology laboratories (e.g. B-cell malignancies [n = 35], and breast, pancreatic and colorectal tumors [n=8]). FFPE samples were deparafinzed, de-crosslinked followed by trypsin digestion, and resulted peptides were analyzed by directDIA. Impact of tissue staining by Hematoxylin and Eosin (H&E) and acid decalcification was also assessed. We compared a deep-proteome workflow on a Thermo Scientific Orbitrap Exploris 480 to a novel high throughput workflow on a Bruker timsTOF HT. The deep-proteome workflow with single-slide FFPE tissues (median area 35mm2) generated comprehensive proteome coverage: ∼12,000 proteins detected overall and ∼7,000 detected in the majority of samples. The high throughput Bruker workflow delivered similar data with 80% less input in 20% of the time. Proteomes from H&E stained, cover-slipped slides were nearly identical to adjacent unstained tissue. Interestingly, proteomics profiling was successful in acid decalcified leukemia bone marrow biopsies whereas RNA was not recoverable. To assess the detection of key tissue biomarkers for therapeutic decision-making, we compiled a list of FDA approved antibody based therapeutic targets and found 20 of 21 were detectable in our samples (e.g. PDL1, PD1, LAG3, CD19, CD20 and CD38). Similarly, >80% of 55 immuno-oncology targets undergoing clinical development were detected. A key effector function of antibody therapeutics is complement-dependent cytotoxicity. The presence of complement components was confirmed by proteomics, however, some were undetectable by transcriptomics. Diffuse Large B-cell Lymphoma samples in this study were molecularly subtyped for a key prognostic, Cell of Origin (COO), using a CLIA validated RNA assay (Lymph2Cx). Well studied proteins (e.g. MUM1 and CD10) showed expected distribution based on patient COO, demonstrating proteomics can recapitulate established subtyping methods. We demonstrate that proteomics profiling can be conducted on single FFPE slides even with pre-processed tissue. This high-throughput workflow enables real-time analysis of Phase I trials or large tissue banks. Furthermore, proteome profiling provides an opportunity to consolidate testing for multiple therapeutic targets instead of serial IHC assays, maximizing the use of precious tissue biopsies. Citation Format: Matthew Loya, Tine Casneuf, Helene Bon, Manling Ma-Edmonds, Angelo Harris, Daniel Martin, Anantharaman Muthuswamy, Dominique Kamber, Martin Mehnert, Amaury Lachaud, Yuehan Feng, Mark Fereshteh, Omar Jabado. Single-slide FFPE proteomic profiling enables therapeutic target quantification and molecular subtyping in non-Hodgkin’s lymphoma and bone marrow tumor biopsies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 2990.

Development of cancer treatments such as antibody-based therapy relies on several factors across the drug-target axis, including the specificity of target expression and characterization of downstream signaling pathways. While existing tools for analyzing and visualizing transcriptomic data offer evaluation of individual gene-level expression, they lack a comprehensive assessment of pathway-guided analysis, relevant for single- and dual-targeting therapeutics. Here, we introduce VIBE (VIsualization of Bulk RNA Expression data), an R package which provides a thorough exploration of both individual and combined gene expression, supplemented by pathway-guided analyses. VIBE's versatility proves pivotal for disease stratification and therapeutic targeting in cancer and other diseases. VIBE offers a wide array of functions that streamline the visualization and analysis of transcriptomic data for single- and dual-targeting therapies. Its intuitive interface allows users to evaluate the expression of target genes and their associated pathways across various cancer indications, aiding in target and disease prioritization. Metadata, such as treatment or number of prior lines of therapy, can be easily incorporated to refine the identification of patient cohorts hypothesized to derive benefit from a given drug. We demonstrate how VIBE can be used to assist in indication selection and target identification in three user case studies using both simulated and real-world data. VIBE integrates statistics in all graphics, enabling data-informed decision-making. VIBE facilitates detailed visualization of individual and cohort-level summaries such as concordant or discordant expression of two genes or pathways. Such analyses can help to prioritize disease indications that are amenable to treatment strategies such as bispecific or monoclonal antibody therapies. With this tool, researchers can enhance indication selection and potentially accelerate the development of novel targeted therapies with the goal of precision, personalization, and ensuring treatments align with an individual patient's disease state across a spectrum of disorders. Explore VIBE's full capabilities using the vignettes on the GitLab repository (https://gitlab.com/genmab-public/vibe ).

BackgroundT-cell engaging bispecific antibodies have been successful in hematological malignancies in part due to the accessibility of both T-cells and target cancer cells. However, their potential in solid tumors is limited by challenges such as tissue penetration and T-cell activation within the immunosuppressive tumor microenvironment (TME). Traverse Biotech is developing TB-Bs1, a CD3xROR2 bispecific antibody generated using the DuoBody(R) platform, an established platform that has led to four FDA-approved bispecific antibodies. TB-Bs1 is produced through controlled Fab Arm Exchange, resulting in a bispecific antibody with pharmacokinetic properties comparable to wild type IgG1.TB-Bs1 targets the Receptor Tyrosine Kinase-like Orphan Receptor 2 (ROR2), a non-canonical Wnt-binding receptor expressed during embryogenesis that is upregulated in select cancers, often correlating with poor prognosis. Immunohistochemistry analysis shows significant ROR2 expression in multiple primary solid tumors, including sarcomas, ovarian cancer, non-small cell lung cancer (NSCLC) and colon cancer. TB-Bs1 is the first highly specific ROR2 engaging antibody with a low-affinity anti-CD3 T-cell engaging arm. Preliminary in vitro data demonstrates TB-Bs1 dose-dependent T-cell mediated cytotoxicity against multiple ROR2-expressing human cancer cell lines. We are now evaluating ROR2 as a CD3 T-cell engager target in vivo for ROR2-expressing tumors, with a proof-of-concept study in humanized NSG™ mice bearing NCI-H1650 orthotopic tumors.MethodsWe established a humanized mouse model to evaluate the efficacy of TB-Bs1. Using NCI-H1650 human lung adenocarcinoma and human donor-derived peripheral blood monocytes (PBMCs) engrafted in NSG™ mice. Treatments include a human IgG isotype control, and 9 additional groups receiving hPBMCs from either donor 2 or donor 4 followed by intravenous treatment with TB-Bs1 at weekly doses ranging from 0.1 mg/kg to 10 mg/kg. Primary endpoints are tumor volume reduction and T-cell-mediated cytotoxicity. Flow cytometry will assess human T-cell engraftment and ROR2 expression on tumor cells. Graft-versus-host disease and body weight changes will be monitored throughout the study.ResultsImmunohistochemistry analysis showed ROR2 incidence between 25% and 100% in various solid tumor indications. We have confirmed that TB-Bs1 can effectively direct human CD3+T cells to kill ROR2-expressing tumor cells in vitro. Now, we now investigate whether our in vivo study will further demonstrate TB-Bs1 has anti-tumor efficacy of ROR2+ tumor cells in our established humanized mouse model.ConclusionsThis study aims to validate ROR2 as a target in cancer immunotherapy and provide a therapeutic proof-of-concept for T-cell engager therapy in a preclinical humanized mouse model of a ROR2-positive tumor.

In this manuscript, we summarize the discussions and key messages from developed in the e-environment team of the European Bioanalysis forum, which were the basis of a subsequent workshop on cloud applications in a regulated bioanalysis lab environment, hosted by the European Bioanalysis Forum e-environment team at their 16th Open Symposium in Barcelona, Spain in November 2023. The purpose of our discussions is to provide further insight and understanding on the status of having cloud applications implemented in a regulated bioanalysis laboratory and the challenges experienced. The discussions highlight the importance of cross functional collaboration during the entire process of cloud implementation and some of the uncertainties in the different functions' roles and responsibilities.

The mechanism of action of bispecific antibodies (bsAbs) directing T-cell immunity to solid tumors is incompletely understood. Here, we screened a series of CD3xHER2 bsAbs using extracellular matrix (ECM) embedded breast cancer tumoroid arrays exposed to healthy donor-derived T-cells. An initial phase of random T-cell movement throughout the ECM (day 1–2), was followed by a bsAb-dependent phase of active T-cell recruitment to tumoroids (day 2–4), and tumoroid killing (day 4–6). Low affinity HER2 or CD3 arms were compensated for by increasing bsAb concentrations. Instead, a bsAb binding a membrane proximal HER2 epitope supported tumor killing whereas a bsAb binding a membrane distal epitope did not, despite similar affinities and intra-tumoroid localization of the bsAbs, and efficacy in 2D co-cultures. Initial T-cell-tumor contact through effective bsAbs triggered a wave of subsequent T-cell recruitment. This critical surge of T-cell recruitment was explained by paracrine signaling and preceded a full-scale T-cell tumor attack.

Existing guidelines on the preparation (Planning Research and Experimental Procedures on Animals: Recommendations for Excellence (PREPARE)) and reporting (Animal Research: Reporting of In Vivo Experiments (ARRIVE)) of animal experiments do not provide a clear and standardized approach for refinement during in vivo cancer studies, resulting in the publication of generic methodological sections that poorly reflect the attempts made at accurately monitoring different pathologies. Compliance with the 3Rs guidelines has mainly focused on reduction and replacement; however, refinement has been harder to implement. The Oncology Best-practices: Signs, Endpoints and Refinements for in Vivo Experiments (OBSERVE) guidelines are the result of a European initiative supported by EurOPDX and INFRAFRONTIER, and aim to facilitate the refinement of studies using in vivo cancer models by offering robust and practical recommendations on approaches to research scientists and animal care staff. We listed cancer-specific clinical signs as a reference point and from there developed sets of guidelines for a wide variety of rodent models, including genetically engineered models and patient derived xenografts. In this Consensus Statement, we systematically and comprehensively address refinement and monitoring approaches during the design and execution of murine cancer studies. We elaborate on the appropriate preparation of tumor-initiating biologicals and the refinement of tumor-implantation methods. We describe the clinical signs to monitor associated with tumor growth, the appropriate follow-up of animals tailored to varying clinical signs and humane endpoints, and an overview of severity assessment in relation to clinical signs, implantation method and tumor characteristics. The guidelines provide oncology researchers clear and robust guidance for the refinement of in vivo cancer models.