Hampton, A. | bioRxiv Science

bioRxiv2024

Bispecific GD2 x B7-H3 Antibody Improves Tumor Targeting and Reduces Toxicity while Maintaining Efficacy for Neuroblastoma

The current treatment for neuroblastoma involves an immunotherapy regimen that includes a monoclonal antibody that recognizes disialoganglioside (GD2), expressed at high levels on neuroblastoma. GD2 is not present on most normal tissues but is expressed on nerves. Thus, anti-GD2 treatment causes substantial, dose-limiting, neuropathic pain. B7-H3 is overexpressed on multiple tumor types, including neuroblastoma, with minimal normal cell expression and is absent on nerves. We designed a bispecific antibody (bsAb) that requires simultaneous binding of these two tumor antigens to achieve tight-binding of tumor cells. Our preclinical research shows that when compared to an anti-GD2 monospecific antibody, the GD2xB7-H3 bsAb has improved tumor specificity with similar efficacy and reduced toxicity. Since this bsAb does not bind to nerves, it may be possible to administer increased or additional doses beyond the tolerable dose of monospecific anti-GD2 antibodies, which could improve therapeutic efficacy and quality of life for patients with neuroblastoma.

cancer biology↗

bioRxiv2023

HyperPRI: A Dataset of Hyperspectral Images for Underground Plant Root Study

Collecting and analyzing hyperspectral imagery (HSI) of plant roots over time can enhance our understanding of their function, responses to environmental factors, turnover, and relationship with the rhizosphere. Current belowground red-green-blue (RGB) root imaging studies infer such functions from physical properties like root length, volume, and surface area. HSI provides a more complete spectral perspective of plants by capturing a high-resolution spectral signature of plant parts, which have extended studies beyond physical properties to include physiological properties, chemical composition, and phytopathology. Understanding crop plants physical, physiological, and chemical properties enables researchers to determine high-yielding, drought-resilient genotypes that can withstand climate changes and sustain future population needs. However, most HSI plant studies use cameras positioned above ground, and thus, similar belowground advances are urgently needed. One reason for the sparsity of belowground HSI studies is that root features often have limited distinguishing reflectance intensities compared to surrounding soil, potentially rendering conventional image analysis methods ineffective. Here we present HyperPRI, a novel dataset containing RGB and HSI data for in situ, non-destructive, underground plant root analysis using ML tools. HyperPRI contains images of plant roots grown in rhizoboxes for two annual crop species - peanut (Arachis hypogaea) and sweet corn (Zea mays). Drought conditions are simulated once, and the boxes are imaged and weighed on select days across two months. Along with the images, we provide hand-labeled semantic masks and imaging environment metadata. Additionally, we present baselines for root segmentation on this dataset and draw comparisons between methods that focus on spatial, spectral, and spatialspectral features to predict the pixel-wise labels. Results demonstrate that combining HyperPRIs hyperspectral and spatial information improves semantic segmentation of target objects.

plant biology↗