Biology subjects

King, A. P.

Publications and source records attributed to King, A. P..

3 recordsLinked to original sources

bioRxiv2025

Multimodal Approach for Identification and Validation of Hepatocellular Carcinoma Targets for Radiotheranostics

Identifying tumor selective targets is critical for the development of precision diagnostic and therapeutic agents in oncology. Despite advances in precision oncology elsewhere, there are no FDA-approved hepatocellular carcinoma (HCC)-selective treatments. HCC is the most common type of liver cancer and accounts for significant morbidity and mortality worldwide. Here, we sought to integrate bulk (371 cases) and single cell RNA sequencing (scRNAseq, n=2 datasets, 34 cases, 102,956 cells) of patient samples to enrich for molecules that are overexpressed in HCC, which could serve as HCC-selective targets. To guide definitions of tumor and normal cell clusters with higher fidelity, we also imported a normal liver scRNAseq dataset. Using this integrated approach, we identified several HCC-selective plasma membrane molecules. To validate these targets, we performed immunohistochemical staining of HCC and normal tissue microarrays and confirmed HCC-selective staining of identified targets. Next, we verified the presence of these targets in several commercially available HCC cell lines by flow cytometry and western blot. Finally, we designed, engineered, and tested novel antibody-based positron emission tomography (immunoPET) agents to these targets in various murine models of liver cancer. Our findings confirm that we can leverage this multimodal approach to identify and validate of HCC-selective targets, which can be used to develop tumor-selective diagnostic and therapeutic radiopharmaceuticals, or radiotheranostics, and other precision oncology agents. One Sentence SummaryA multimodal pipeline defines and validates tumor-selective surface targets for radiotheranostic use in hepatocellular carcinoma.

cancer biology↗

bioRxiv2024

A general approach to reduce off-target radioactivity in vivo via Tetrazine-Knock-Out (TKO)

Monoclonal antibodies have had a remarkable impact on cancer therapy due to their high target specificity. However, their large molecular weight results in slow blood clearance, which can take weeks to clear from circulation. As companion nuclear imaging and diagnostic tools, these characteristics force delayed imaging and the use of isotopes with long half-lives such as 89Zr. For optimal clinical application, it is desirable that radioimmunoconjugates remain in the blood for just enough time to accumulate adequately in target tissues, while non-targeted or circulating radioactivity is ideally rapidly excreted from the body to maximize imaging contrast and minimize radiation dose to healthy tissues. We addressed the current challenges of antibody-based imaging by developing rituximab radioimmunoconjugates that accumulate sufficient activity for tumor imaging within 24 h of administration, while clearing circulating radioactivity via administration of a small molecule clearing agent. Rituximab, an anti-CD20 monoclonal antibody, is used as standard first-line therapy for diffuse large B-cell lymphoma. CD20 is expressed by 95% of B-lymphocytes and their malignant counterparts, making it a therapeutic target for B-cell malignancies. We attached 125I, 68Ga, and 89Zr to rituximab using a "clickable" linker containing trans-cyclooctene and tested the ability of tetrazines to induce the inverse electron demand Diels-Alder reaction (iEDDA) after antibody administration. This "tetrazine-knock-out" (TKO) approach liberates the radioactivity from rituximab in the bloodstream, resulting in its rapid renal excretion which enhances target-to-background ratios, and minimizes off-target radiation exposure. Due to the internalization of the radioimmunoconjugate in CD20+ tumor cells, no substantial clearance was observed from Raji xenografts. We characterized different leaving groups, several cellular models and antibodies with distinct internalizaing properties. The TKO approach opens opportunities to use radiolabeled antibodies for low-abundance or heterogeneously expressed biologic targets and may allow radioimmunotherapy (RIT) for targets traditionally untenable due to dose-limiting toxicities. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=94 SRC="FIGDIR/small/596510v1_ufig1.gif" ALT="Figure 1"> View larger version (27K): org.highwire.dtl.DTLVardef@11bc2f0org.highwire.dtl.DTLVardef@19b4f72org.highwire.dtl.DTLVardef@17cee32org.highwire.dtl.DTLVardef@fa4491_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioengineering↗

bioRxiv2022

Automated atlas-based multi-label fetal cardiac vesselsegmentation in Congenital Heart Disease

Congenital heart disease (CHD) is the most commonly diagnosed birth defect. T2w black blood MRI provides optimal vessel visualisation, aiding prenatal CHD diagnosis. Common clinical practice involves manual segmentation of fetal heart and vessels for visualisation and reporting purposes. We propose an automated multi-label fetal cardiac vessels deep learning segmentation approach for T2w black blood MRI. Our network is trained using single-label manual segmentations obtained through current clinical practice, combined with a multi-label anatomical atlas with desired multi-label segmentation protocol. Our framework combines deep learning label propagation with 3D residual U-Net segmentation to produce high-quality multi-label output well adapted to the individual subject anatomy. We train and evaluate the network using forty fetal subjects with suspected coarctation of the aorta, achieving a dice score of 0.79 {+/-} 0.02 for the fetal cardiac vessels region. The proposed network outperforms the label propagation and achieves a statistically equivalent performance to a 3D residual U-Net trained exclusively on manual single-label data (p-value>0.05). This multi-label framework therefore represents an advancement over the single-label approach, providing label-specific anatomical information, particularly useful for assessing specific anomaly areas in CHD.

bioengineering↗