# Hydrogel‐embedded precision‐cut lung slices support ex vivo culture of in vivo‐induced premalignant lung lesions

## TL;DR

A new method using hydrogels to culture lung tissue with early cancer-like lesions allows long-term study and testing of prevention strategies.

## Contribution

Hydrogel-embedded precision-cut lung slices enable 6-week ex vivo culture of in vivo-generated premalignant lung lesions.

## Key findings

- PMLs in hydrogel-embedded PCLS maintained viability, gene expression, and proliferation for 6 weeks.
- Iloprost treatment in the model reduced PML size and proliferation, matching in vivo results.
- Degradable hydrogels supported dynamic interactions and response to prevention agents.

## Abstract

Lung cancer is the leading cause of global cancer death, and prevention strategies are key to reducing mortality. Medical prevention of premalignant lesion (PML) progression may have a larger impact than treatment on mortality by targeting high‐risk populations and reducing their lung cancer risk. PMLs are difficult to study in humans but are easily accessible in murine preclinical carcinogenesis studies. Precision‐cut lung slices (PCLS) are underutilized as an ex vivo model for lung cancer studies due to limited culture time. Embedding PCLS within bioengineered hydrogels extends PCLS viability and functionality for up to 6 weeks. Here, we embedded PCLS with PMLs generated from urethane‐exposed mice in cell‐degradable and nondegradable hydrogels to study viability and responsiveness to PML interception over 6 weeks. PMLs in hydrogel‐embedded PCLS maintained viability, gene expression, and proliferation. Treatment of hydrogel‐embedded PCLS containing urethane‐induced PMLs with iloprost, a known lung cancer prevention agent, recapitulated in vivo gene expression and activity. Our studies also showed that iloprost reduced proliferation and PML size in PCLS embedded in degradable hydrogels. These results demonstrate that hydrogel‐embedded PCLS models support long‐term culture of in vivo generated PMLs. This modeling approach will improve preclinical studies of cancer biology and prevention across cancer types.