Biology subjects

Gerber, D. E.

Publications and source records attributed to Gerber, D. E..

4 recordsLinked to original sources

bioRxiv2024

A novel and potent MICA/B antibody is therapeutically effective in KRAS LKB1 mutant lung cancer models

Concurrent KRAS LKB1 (STK11, KL) mutant Non-Small Cell Lung Cancers (NSCLC) is particularly difficult to treat and does not respond well to current immune checkpoint blockade (ICB) therapies. This is due to numerous mechanisms including low antigen presentation limiting T cell mediated killing. To activate anti-tumor immunity, we targeted tumor cell - natural killer (NK) cell interactions. We tested whether a novel antibody based therapeutic strategy that predominantly activates natural killer (NK) cells demonstrates efficacy in pre-clinical mouse models of KL NSCLC. NK cells rely on binding of ligands, such as Major Histocompatibility Complex (MHC) class I-related chain A or B (MICA/B), to the activating receptor NKG2D. Importantly MICA and MICB are widely expressed in elevated levels across NSCLC subtypes including KL lung cancers. Proteases with the tumor microenvironment (TME) can cleave these proteins rendering tumor cells less visible to NK cells. We therefore developed a MICA monoclonal antibody, AHA-1031, which utilizes two NK cell activating receptors. AHA1031 prevents ligand shedding without interfering with binding to NKG2D while targeting cancer cells to antibody mediated cell dependent cytotoxicity (ADCC). Our therapeutic novel antibody has significant monotherapy activity in KL cancer models including xenografts of human cell lines and patient derived xenografts. Activating NK cells through MICA/B stabilization and inducing ADCC offers an alternative and potent therapy option in KL tumors. MICA/B are shed across different tumors making this therapeutic strategy universally applicable.

immunology↗

bioRxiv2024

An Artificial Intelligence Model for Profiling the Landscape of Antigen-binding Affinities of Massive BCR Sequencing Data

The interaction between antigens and antibodies (B cell receptors, BCRs) is the key step underlying the function of the humoral immune system in cancers and other biological contexts. The capability to profile the landscape of antigen-binding affinities of a vast number of BCRs will provide a powerful tool to reveal novel insights at unprecedented levels and will yield powerful tools for translational development. However, current experimental approaches for detecting antibody-antigen interactions are costly and time-consuming, and can only achieve low-to-mid throughput. On the other hand, bioinformatics tools in the field of antibody informatics mostly focus on optimization of antibodies given known binding antigens, which is a very different research question and of limited scope. In this work, we developed an innovative Artificial Intelligence tool, Cmai, to address the prediction of binding between antibodies and antigens that can be scaled to high-throughput sequencing data. We devised a biomarker metric based on the output from Cmai applied to high-throughput BCR sequencing data to predict the landscape of the antigen-binding affinities of the BCRs. We found that extracellular antigens on malignant tumor cells are inducing B cell infiltrations, and the infiltrating B cells have a greater tendency to co-localize with tumor cells expressing these antigens. We further found that the abundance of tumor antigen-targeting antibodies is predictive of immune-checkpoint inhibitor (ICI) treatment response. On the other hand, we found that, during immune-related adverse events (irAEs) caused by ICI treatment, humoral immunity is preferentially responsive to intracellular antigens from the organs affected by the irAEs. Overall, we developed a powerful tool for elucidating the antigen binding landscape of BCR repertoires, and it shall also propell antibody-centric translational applications for cancers and other diseases.

immunology↗

bioRxiv2024

Differences in systemic immune parameters in individuals with lung cancer according to race

IntroductionRacial and ethnic disparities in the presentation and outcomes of lung cancer are widely known. To evaluate potential factors contributing to these observations, we measured systemic immune parameters in Black and White patients with lung cancer. MethodsPatients scheduled to receive cancer immunotherapy were enrolled in a multi-institutional prospective biospecimen collection registry. Clinical and demographic information were obtained from electronic medical records. Pre-treatment peripheral blood samples were collected and analyzed for cytokines using a multiplex panel and for immune cell populations using mass cytometry. Differences between Black and White patients were determined and corrected for multiple comparisons. ResultsA total of 187 patients with non-small cell lung cancer (Black, 19; White, 168) were included in the analysis. There were no significant differences in baseline characteristics between Black and White patients. Compared to White patients, Black patients had significantly lower levels of CCL23 and CCL27, and significantly higher levels of CCL8, CXCL1, CCL26, CCL25, CCL1, IL-1 b, CXCL16, and IFN-{gamma} (all P<0.05, FDR<0.1). Black patients also exhibited greater populations of non-classical CD16+ monocytes, NKT-like cells, CD4+ cells, CD38+ monocytes, and CD57+ gamma delta T cells (all P<0.05). ConclusionsBlack and White patients with lung cancer exhibit several differences in immune parameters, with Black patients exhibiting greater levels of numerous pro-inflammatory cytokines and cell populations. The etiology and clinical significance of these differences warrant further evaluation.

cancer biology↗

bioRxiv2023

pan-MHC and cross-Species Prediction of T Cell Receptor-Antigen Binding

Profiling the binding of T cell receptors (TCRs) of T cells to antigenic peptides presented by MHC proteins is one of the most important unsolved problems in modern immunology. Experimental methods to probe TCR-antigen interactions are slow, labor-intensive, costly, and yield moderate throughput. To address this problem, we developed pMTnet-omni, an Artificial Intelligence (AI) system based on hybrid protein sequence and structure information, to predict the pairing of TCRs of {beta} T cells with peptide-MHC complexes (pMHCs). pMTnet-omni is capable of handling peptides presented by both class I and II pMHCs, and capable of handling both human and mouse TCR-pMHC pairs, through information sharing enabled this hybrid design. pMTnet-omni achieves a high overall Area Under the Curve of Receiver Operator Characteristics (AUROC) of 0.888, which surpasses competing tools by a large margin. We showed that pMTnet-omni can distinguish binding affinity of TCRs with similar sequences. Across a range of datasets from various biological contexts, pMTnet-omni characterized the longitudinal evolution and spatial heterogeneity of TCR-pMHC interactions and their functional impact. We successfully developed a biomarker based on pMTnet-omni for predicting immune-related adverse events of immune checkpoint inhibitor (ICI) treatment in a cohort of 57 ICI-treated patients. pMTnet-omni represents a major advance towards developing a clinically usable AI system for TCR-pMHC pairing prediction that can aid the design and implementation of TCR-based immunotherapeutics.

immunology↗