Human MHC Class II and Invariant Chain Knock-in Mice Mimic Rheumatoid Arthritis with Allele Restriction in Immune Response and Arthritis Association.

Transgenic mice expressing human major histocompatibility complex class II (MHCII) risk alleles are widely used in autoimmune disease research, but limitations arise due to non-physiologic expression. To address this, physiologically relevant mouse models are established via knock-in technology to explore the role of MHCII in diseases like rheumatoid arthritis. The gene sequences encoding the ectodomains are replaced with the human DRB1*04:01 and 04:02 alleles, DRA, and CD74 (invariant chain) in C57BL/6N mice. The collagen type II (Col2a1) gene is modified to mimic human COL2. Importantly, DRB1*04:01 knock-in mice display physiologic expression of human MHCII also on thymic epithelial cells, in contrast to DRB1*04:01 transgenic mice. Humanization of the invariant chain enhances MHCII expression on thymic epithelial cells, increases mature B cell numbers in spleen, and improves antigen presentation. To validate its functionality, the collagen-induced arthritis (CIA) model is used, where DRB1*04:01 expression led to a higher susceptibility to arthritis, as compared with mice expressing DRB1*04:02. In addition, the humanized T cell epitope on COL2 allows autoreactive T cell-mediated arthritis development. In conclusion, the humanized knock-in mouse faithfully expresses MHCII, confirming the DRB1*04:01 alleles role in rheumatoid arthritis and being also useful for studying MHCII-associated diseases.

Sequence enrichment profiles enable target-agnostic antibody generation for a broad range of antigens.

Phenotypic drug discovery (PDD) enables the target-agnostic generation of therapeutic drugs with novel mechanisms of action. However, realizing its full potential for biologics discovery requires new technologies to produce antibodies to all, a priori unknown, disease-associated biomolecules. We present a methodology that helps achieve this by integrating computational modeling, differential antibody display selection, and massive parallel sequencing. The method uses the law of mass action-based computational modeling to optimize antibody display selection and, by matching computationally modeled and experimentally selected sequence enrichment profiles, predict which antibody sequences encode specificity for disease-associated biomolecules. Applied to a phage display antibody library and cell-based antibody selection, ∼105 antibody sequences encoding specificity for tumor cell surface receptors expressed at 103-106 receptors/cell were discovered. We anticipate that this approach will be broadly applicable to molecular libraries coupling genotype to phenotype and to the screening of complex antigen populations for identification of antibodies to unknown disease-associated targets.

Vectorized Treg-depleting αCTLA-4 elicits antigen cross-presentation and CD8+ T cell immunity to reject 'cold' tumors.

BACKGROUND: Immune checkpoint blockade (ICB) is a clinically proven concept to treat cancer. Still, a majority of patients with cancer including those with poorly immune infiltrated 'cold' tumors are resistant to currently available ICB therapies. Cytotoxic T lymphocyte-associated antigen-4 (CTLA-4) is one of few clinically validated targets for ICB, but toxicities linked to efficacy in approved αCTLA-4 regimens have restricted their use and precluded full therapeutic dosing. At a mechanistic level, accumulating preclinical and clinical data indicate dual mechanisms for αCTLA-4; ICB and regulatory T cell (Treg) depletion are both thought to contribute efficacy and toxicity in available, systemic, αCTLA-4 regimens. Accordingly, strategies to deliver highly effective, yet safe αCTLA-4 therapies have been lacking. Here we assess and identify spatially restricted exposure to a novel strongly Treg-depleting, checkpoint-blocking, vectorized αCTLA-4, as a highly efficacious and potentially safe strategy to target CTLA-4. METHODS: A novel human IgG1 CTLA-4 antibody (4-E03) was identified using function-first screening for monoclonal antibodies (mAbs) and targets associated with superior Treg-depleting activity. A tumor-selective oncolytic vaccinia vector was then engineered to encode this novel, strongly Treg-depleting, checkpoint-blocking, αCTLA-4 antibody or a matching surrogate antibody, and Granulocyte-macrophage colony-stimulating factor (GM-CSF) (VVGM-αCTLA-4). RESULTS: The identified 4-E03 antibody showed significantly stronger Treg depletion, but equipotent checkpoint blockade, compared with clinically validated αCTLA-4 ipilimumab against CTLA-4-expressing Treg cells in a humanized mouse model in vivo. Intratumoral administration of VVGM-αCTLA-4 achieved tumor-restricted CTLA-4 receptor saturation and Treg depletion, which elicited antigen cross-presentation and stronger systemic expansion of tumor-specific CD8+ T cells and antitumor immunity compared with systemic αCTLA-4 antibody therapy. Efficacy correlated with FcγR-mediated intratumoral Treg depletion. Remarkably, in a clinically relevant mouse model resistant to systemic ICB, intratumoral VVGM-αCTLA-4 synergized with αPD-1 to reject cold tumors. CONCLUSION: Our findings demonstrate in vivo proof of concept for spatial restriction of Treg depletion-optimized immune checkpoint blocking, vectorized αCTLA-4 as a highly effective and safe strategy to target CTLA-4. A clinical trial evaluating intratumoral VVGM-αhCTLA-4 (BT-001) alone and in combination with αPD-1 in metastatic or advanced solid tumors has commenced.

Deep Mining of Complex Antibody Phage Pools Generated by Cell Panning Enables Discovery of Rare Antibodies Binding New Targets and Epitopes.

Phage display technology is a common approach for discovery of therapeutic antibodies. Drug candidates are typically isolated in two steps: First, a pool of antibodies is enriched through consecutive rounds of selection on a target antigen, and then individual clones are characterized in a screening procedure. When whole cells are used as targets, as in phenotypic discovery, the output phage pool typically contains thousands of antibodies, binding, in theory, hundreds of different cell surface receptors. Clonal expansion throughout the phage display enrichment process is affected by multiple factors resulting in extremely complex output phage pools where a few antibodies are highly abundant and the majority is very rare. This is a huge challenge in the screening where only a fraction of the antibodies can be tested using a conventional binding analysis, identifying mainly the most abundant clones typically binding only one or a few targets. As the expected number of antibodies and specificities in the pool is much higher, complementing methods, to reach deeper into the pool, are required, called deep mining methods. In this study, four deep mining methods were evaluated: 1) isolation of rare sub-pools of specific antibodies through selection on recombinant proteins predicted to be expressed on the target cells, 2) isolation of a sub-pool enriched for antibodies of unknown specificities through depletion of the primary phage pool on recombinant proteins corresponding to receptors known to generate many binders, 3) isolation of a sub-pool enriched for antibodies through selection on cells blocked with antibodies dominating the primary phage pool, and 4) next-generation sequencing-based analysis of isolated antibody pools followed by antibody gene synthesis and production of rare but enriched clones. We demonstrate that antibodies binding new targets and epitopes, not discovered through screening alone, can be discovered using described deep mining methods. Overall, we demonstrate the complexity of phage pools generated through selection on cells and show that a combination of conventional screening and deep mining methods are needed to fully utilize such pools. Deep mining will be important in future phenotypic antibody drug discovery efforts to increase the diversity of identified antibodies and targets.