Abstract 4177: High-throughput in vivo monitoring of splenomegaly and tumor burden in a murine breast cancer model

Abstract

Abstract Splenomegaly is a valuable parameter in mouse model cancer studies, as it can reflect the magnitude of cancer burden, hematopoietic activity, immune status, and can provide an indirect measure of cancer treatment efficacy. In liquid cancer models specifically (leukemia/lymphoma), spleen sizing is important as it is usually the only measure of in vivo tumor burden that can be assessed noninvasively without genetic labeling of tumor cells. Magnetic resonance imaging (MRI) and contrast-enhanced computed tomography (CT) are highly effective at evaluating spleen size in small animals. However, these techniques can impose limitations on large-cohort study designs due to high cost, long scan time, and ionizing radiation. Ultrasound is an alternative method with lower cost, high frame rate, and no ionization, but can suffer from poor inter-user variability, limited 2D field-of-view, and low throughput due to manual operation. In this work, we evaluated a novel 3D automated ultrasound scanner (Vega, Revvity, Inc.) for rapid assessment of splenomegaly in a murine cancer model. Female BALB/c mice (n=10) were implanted with 5e5 4T1 (ATCC) cells on the right flank. Mice were imaged at five timepoints over 25 days to track longitudinal progression of both spleen and tumor enlargement. Images were segmented manually in 3D using the “Slice Draw” and “Fill Between Slices” effects in SonoEQ v2.0.1 (Revvity, Inc.) to quantify tissue volume. After the final timepoint, mice were euthanized, and spleen weight was measured ex vivo. An additional cohort of age-matched mice without tumors (n=7) were imaged at the final timepoint to serve as controls. Imaging revealed a substantial enlargement of the spleen over time, from 30 ± 3 mm3 to 545 ± 105 mm3, representing an 18-fold increase. In the same time span, tumor volume grew to 1966 ± 87 mm3. Correlation analysis demonstrated good agreement between ultrasound volumes and ex vivo spleen weights (R² = 0.90). The results in this study demonstrate that noninvasive, nonlabelled quantification of murine spleen size with automated ultrasound is feasible, robust, and fast. The average time to acquire the spleen image for each mouse was less than 30 seconds. Future studies will apply this workflow in liquid cancer models, as well as explore using machine learning approaches to automatically segment spleen borders for faster data analysis. Citation Format: Craig McMannus, Tomek J. Czernuszewicz, Ryan C. Gessner, Jeffrey D. Peterson. High-throughput in vivo monitoring of splenomegaly and tumor burden in a murine breast cancer model [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4177.