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Biology subjects

Hanna, E.

Publications and source records attributed to Hanna, E..

3 recordsLinked to original sources

Hyper-migratory CAR T cells alleviate ovarian cancer metastatic burden and improve prognosis

Cellular immunotherapy has shown remarkable efficacy in hematological malignancies but remains limited by infiltration issues in solid tumors, leading to poor treatment efficacy. We have recently shown that mesothelin-targeting CAR T cells co-transduced with cytokine-binding synthetic velocity receptors (VRs, referred to as CAR TV cells) demonstrate increased motility, improved infiltration in solid primary tumors, and lead to a better anti-tumor effect compared to CAR T cells that do not express VRs. However, it is metastasis that causes the vast majority of cancer related deaths and is difficult to target clinically, indicating an urgent unmet need. We show that these CAR T cells engineered to be hyper-migratory using VRs are highly effective against liver metastasis of ovarian cancer along different stages of the metastatic cascade. Mesothelin-targeting CAR TV cells expressing synthetic or native receptors responsive to the cytokine Interleukin-5 improved the survival of mice bearing an extremely high or terminal level of metastatic burden compared to CAR T cells that did not express VRs. Against newly established metastatic lesions and lesions undergoing metastatic outgrowth, CAR TV cells showed a robust anti-cancer effect resulting in an improved prognosis compared to control CAR T cells. Histopathological assessments showed a substantial reduction of metastasis number and lesion size with CAR TV treatment, concomitant with increased immune cell infiltration in the metastatic regions. Our work demonstrates the efficacy of high-motility CAR T cells in a metastatic setting and extends their scope to the treatment of metastasis of solid tumors.

bioengineering↗

CODAvision: best practices and a user-friendly interface for rapid, customizable segmentation of medical images

Image-based machine learning tools have emerged as powerful resources for analyzing medical images, with deep learning-based semantic segmentation commonly utilized to enable spatial quantification of structures in images. However, customization and training of segmentation algorithms requires advanced programming skills and intricate workflows, limiting their accessibility to many investigators. Here, we present a protocol and software for automatic segmentation of medical images guided by a graphical user interface (GUI) using the CODAvision algorithm. This workflow simplifies the process of semantic segmentation of microanatomical structures by enabling users to train highly customizable deep learning models without extensive coding expertise. The protocol outlines best practices for creating robust training datasets, configuring model parameters, and optimizing performance across diverse biomedical image modalities. CODAvision enhances the usability of the CODA algorithm (Nature Methods, 2022) by streamlining parameter configuration, model training, and performance evaluation, automatically generating quantitative results and comprehensive reports. We expand beyond the original implementation of CODA to serial histology by demonstrating robust performance across numerous medical image modalities and diverse biological questions. We provide sample results in data types including histology, magnetic resonance imaging (MRI), and computed tomography (CT). We demonstrate the diverse use of this tool in applications including quantification of metastatic burden in in vivo models and deconvolution of spot-based spatial transcriptomics datasets. This protocol is designed for researchers with interest in rapid design of highly customizable semantic segmentation algorithms and a basic understanding of programming and anatomy.

bioinformatics↗

Single Cell Portal: an interactive home for single-cell genomics data

Single-cell omics research has the power to leave a deep impact on modern healthcare. Sharing data widely and freely advances this progress in both the academic and clinical spheres. We developed the Single Cell Portal (SCP) to maximize the impact of this work. SCP enables data sharing, supports dynamic results visualization, and facilitates scientific exploration across a large repository of single-cell datasets. SCPs data contributors maintain full control over how their data are shared and presented, without requiring web development expertise. Finally, SCP supports the entire lifecycle of a research project, from sparking an idea, to fine-tuning the data with collaborators, to sharing results in an accessible and interactive way. This paper highlights the most valuable ways in which SCP helps to advance single-cell research.

scientific communication and education↗