Logo image
Back
A simple circuit to sustain intact tumor microenvironments for complex drug interrogations
Preprint   Open access

A simple circuit to sustain intact tumor microenvironments for complex drug interrogations

A Leila Sarvestani, Samantha Ruff, Kirsten Remmert, Martha Teke, Emily Verbus, Areeba Saif, Marcial Garmendia-Cedillos, Alex Rossi, Ashley Rainey, Krystle Nomie, …
bioRxiv
Cold Spring Harbor Laboratory, 1.2
10/28/2025
DOI: 10.1101/2025.10.26.684624
PMCID: PMC12636357
PMID: 41279433
url
https://doi.org/10.1101/2025.10.26.684624View
Preprint (Author's original) This preprint has not been evaluated by subject experts through peer review. Preprints may undergo extensive changes and/or become peer-reviewed journal articles. Open Access

Abstract

Deep learning and large language models can integrate complex datasets to uncover biological insights that are often undetectable through conventional analyses. With application to translational cancer research, these computational tools have positioned 3D patient-derived tumor avatars front and center as crucial data input sources. However, a major challenge remains: the lack of standardization in media composition in 3D patient-derived tumor models unpredictably affects cell behavior and limit the utility beyond predicting treatment responses. To address this unmet need, we developed a simple, reproducible perfusion circuit system to approximate in vivo physiology using autologous patient plasma. With peritoneal metastases and core needle biopsies across multiple tumor histologies, we demonstrate preservation of the tumor microenvironment for up to 48 hours using multi-modal interrogation techniques. With proof-of-concept experiments, we display the system’s ability to unveil complex drug-dependent biology within this time window. Standardizable, physiologically relevant platforms for 3D patient-derived tumor avatars will yield unprecedented insights through the integration of data from broad groups of patients and the use of an expanding armamentarium of artificial intelligence capabilities.
 Cancer Biology

Details

Metrics

2 Record Views
Logo image