Quantitative Models to Define Cancer Cell Heterogeneity and Predict Patient Drug Responses

This project resulted in the successful development of an integrated suite of completely new capabilities that enable quantification of patient tumor cell therapy response, ex vivo. We leveraged this brand new approach to patient tumor cell characterization to successfully discriminate cell responses to multiple common and novel MM therapies allowing us to competitively seek major NIH funding to more rigorously test the predictive capabilities of this approach.

– Dr. Shigeki Miyamoto

At a Glance

Multiple myeloma is considered an incurable cancer that forms in plasma cells in bone marrow which has a significant impact in Wisconsin. One of the challenges associated with treating this and other cancers is knowing how individual patients are likely to respond to different drugs. The goal of this research was to develop a new, comprehensive approach for characterizing patient drug responses.

As a result of the project, a suite of new capabilities to quantify patient tumor cell therapy response was developed, allowing researchers to determine how cells respond to common therapies and seek more funding to test the predictive capabilities of this approach. In the future, patients could see more effective treatment as a result of this and derivative work.

The Challenge

When treating cancer, a fundamental challenge is predicting how the tumors in an individual patient will respond to different drugs. Cancer cell dynamics are complex and ever-changing and need to be characterized accurately so that the best possible treatment can be administered. However, there is currently no way to quantify live cancer cell characteristics ex vivo in a large-scale, tumor-specific manner for individual patients. Thus, a new approach is needed to make this kind of analysis possible and create a mathematical model that will help scientists predict patient drug responses going forward. To develop this system, multiple myeloma was chosen as a specific test application because of this type of cancer’s impact in Wisconsin, a dire need for better ways to predict patient responses to therapy for multiple myeloma and the unique strengths of the multidisciplinary research team at UW–Madison.

Project Goals

The goal of this project was to develop a new experimental paradigm in the field of cancer research for analyzing patient samples to improve cancer care. Specifically, the researchers wanted to characterize live tumor cells ex vivo using a suite of techniques that collectively form a strategy called “cytoprofiling.” The techniques included time lapse microscopy, ex vivo culture technology, image analysis and informatics. Their specific aims were two-fold. In Aim 1, they sought to develop a cytoprofiling approach for a drug called Velcade. For this drug, the dose, cell density, cell exposure times and the presence of tumor microenvironment cell types had already been optimized. The goal then was to optimize the cytoprofiling approach (viable dyes, image capture and image analysis) and begin to develop and test machine learning approaches for predicting clinical responses. In Aim 2, they sought to use cytoprofiling to predict patient responses to two other common multiple myeloma drugs, Revlimid and Dexamethasone, by first optimizing drug doses and then developing machine learning models to predict therapy responses.

Results

As a result of this project, an integrated suite of completely new capabilities that enable quantification of patient tumor cell therapy responses was developed. The approach was successfully used to discriminate cell responses to multiple common and new therapies for multiple myeloma, which allowed the researchers to competitively seek and receive a fundable score toward major funding from the National Institutes of Health. More specifically, multiple new technologies that enable comprehensive molecular and phenotypic characterization of patient tumor cell therapy responses were developed. These advances transformed the approach to tumor cell characterization taken in this study from a prototype to an optimized and robust platform that could be applied across many patients for sensitive drug response characterization. These technologies are the focus of manuscripts in preparation for publication and are expected to have a significant impact on the field of cancer research by describing new methods for patient cell analysis.

Lasting Impact

Several new technologies and protocols for cancer research and quantifying patient tumor responses to different drugs have emerged from this work. As a result of this work and derivative work, it is possible that future multiple myeloma and cancer patients may see better outcomes. Some of the technologies developed as a result of this work are listed below:

1. A thin-layer poly (ethylene glycol) platform for multiplexed single-cell cytometry and analysis: This technology enables efficient use and storage of patient samples for in-depth analysis, and will be published.
2. A novel, robust and highly sensitive approach to nuclear localization quantification: This strategy was needed to study response to common multiple myeloma therapies. It will be the focus of its own publication, and has also already been submitted as a supporting technology for novel research on NF-?B signaling, which is a key pathway of drug resistance in multiple myeloma.
3. A new symmetry metric for cytoprofiling: This metric can be used for cytoprofiling but has broad potential beyond cell biology and life sciences, to almost anywhere images are used (e.g., internet search engines and satellite image analysis), and will be published.
4. Collagen-based co-culture methodology: Time lapse microscopy of living cells in culture is important for viewing functional cell response to drugs. This method is a key advancement that will be detailed in a final publication to convey the results of the system for characterizing patient tumor cell drug response (i.e., cytoprofiling).
5. Quantitative phase imaging for label-free quantification of drug response: This advancement has allowed the researchers to eliminate the use of most fluorescent dyes, enhancing their ability to reliably detect drug response in patient cells.
6. Development of a highly-biocompatible biochemical metric of cell viability: This advancement was critical to providing a biochemical readout of cell death to complement the more experimental approaches to quantification of cell response, and will be described as part of the paper describing cytoprofiling for patient sample analysis.
7. Approach to survival curve endpoint analysis for sensitively discriminating cell response to drugs: this approach helped the researchers identify how tumor cells responded (i.e., how long they lived and their characteristics) to different drugs with high confidence.