CCIL Funding Supports Multiscale Imaging to Study Drug-Tolerant Cancer Cells

Researchers at the Cancer Center at Illinois (CCIL) are using advanced imaging to better understand drug-tolerant cells. Cancer cells do not always respond to chemotherapy in the same way. Some cells survive treatment and later contribute to treatment failure or recurrence, making it difficult to identify resistance before it becomes clinically evident.

A project led by CCIL member, Yang Liu, alongside co-principal investigator Chitra Subramanian earned a Planning Grant from one of the CCIL’s merit-based research funding programs to perform this research.

The project “Multiscale high-content imaging to identify molecular signatures in drug-tolerant cancer cells” focuses on drug-tolerant persister cells, which are a type of cancer cells that survive chemotherapy.

“These cells are often referred to as drug-tolerant persisters,” Liu said.

Liu’s team is studying these cells through a multiscale imaging platform, with an original focus on cisplatin resistance in head and neck squamous cell carcinoma (HNSCC).

“We are using a multiscale imaging platform that combines super-resolution chromatin imaging, high-content multiplexed fluorescence imaging, and label-free quantitative phase imaging to study cancer cells across multiple scales,” Liu said. “The original focus was cisplatin resistance in head and neck squamous cell carcinoma. We first studied resistant and parental cancer cell lines and then moved toward validation in organoids and HNSCC tissues.”

The project is based on the idea that resistant cells may show detectable changes during treatment.

“The central idea is that therapy-resistant cancer cells undergo distinct changes in intrinsic epigenetic organization and morphodynamic behavior during treatment, creating imaging-detectable signatures that may reveal emerging resistance before it becomes clinically evident,” Liu said.

One goal of the project is to identify features that resistant or drug-tolerant cells may have in common.

“We want to identify reliable molecular, structural, and spatial signatures of therapeutic resistance,” Liu said. “More specifically, we want to determine whether drug-resistant or drug-tolerant cancer cells share common structural features that can be identified earlier prior to or during the treatment.”

These signatures could eventually help researchers develop predictive biomarkers that can identify resistant tumor states earlier and guide more effective treatment strategies.

The CCIL Planning Grant has already supported progress in identifying differences between resistant and parental cancer cells.

“We have made significant progress,” Liu said. “We showed that drug-resistant cells have more condensed chromatin architecture, with repressive epigenetic marks emerging as major differences between resistant and parental cells.”

Liu’s team has worked to improve imaging and analysis methods for HNSCC tissues.

“We have also advanced tissue-level validation by optimizing highly multiplexed fluorescence imaging for HNSCC tissues, developing an automated fluidic device for staining and imaging, and building computational pipelines for segmentation, phenotype classification, and spatial neighborhood analysis,” Liu said.

These tools allow the researchers to look more closely at how resistant cells are arranged in the tumor environment.

For Liu, the project is part of a larger effort to connect imaging technology with cancer precision medicine.

“This project fits within my broader cancer research vision, which is to develop advanced optical imaging and computational imaging technologies that study cancer across scales, and to translate these technologies into clinically useful tools for precision medicine,” Liu said.

The CCIL funding helped move the work from technology development toward a more translational cancer application.

“The CCIL Planning Grant allowed us to connect our imaging technology to a major clinical problem, which is to predict therapeutic resistance prior to the treatment,” Liu said. “It also helped us move from technology development toward a translational cancer application, using cell lines, 3D models, and ultimately patient-derived tumor models.”

Looking ahead, Liu said the project contributes to a broader effort to build imaging platforms that can identify resistant tumor states earlier.

“I see this work as part of a broader effort to build clinically useful imaging platforms that can identify aggressive or therapy-resistant tumor states earlier, guide treatment selection, and support precision oncology,” Liu said. “The project also strengthens the connection between imaging, molecular measurement, computational biology, and drug discovery, which are all important themes within CCIL.”

Yang Liu

Editor’s notes:

Yang Liu is a Professor of Bioengineering, Affiliate Professor of Electrical and Computer Engineering and the Deputy Director of the Center for Label-free Imaging and Multiscale Biophotonics.

She can be reached at liuy46@illinois.edu.

This story was written by Hailee Munno, CCIL Communications Intern