Circulating tumour cells (CTC) are shed into the vasculature from primary tumours, and have been shown to contribute to the formation of metastatic lesions in model systems. Monitoring these circulating cells therefore presents, in principle, a means to monitor a tumour's metastatic potential in real time. Similar to the heterogeneity of cellular subpopulations within an individual tumour, CTCs within an individual also exhibit heterogeneity, containing subpopulations having varying relevance to the development of metastatic disease. Recent studies show that specific subpopulations of CTCs possess metastatic potential, while other subpopulations of circulating epithelial cells may be relatively benign. Similarly, the levels of surface proteins on CTCs are heterogeneous and dynamic: they are observed to change as a function of disease stage and response to therapy. In particular, the epithelial-mesenchymal transition (EMT) appears to be a dynamic process in CTCs, and the markers that correspond to these two states vary and contribute to the phenotypic heterogeneity of CTCs. Using a microfluidic device, the velocity valley (VV) chip, that was developed in our group, we now have the ability to profile a CTC population from blood samples and by sorting these cells based on expression of surface markers. This novel technology has enabled us to capture and study CTCs within various ranges of EMT. In this proposal, our goal is to fully develop the VV chip technology into a fully integrated device for CTC population profiling, CTC detection, and molecular analysis. This will be accomplished through integration of companion technologies allowing for sensitive on-chip electrochemical detection and genetic analysis of CTCs. Manufacturing methods for the device will be investigated for production at high-scale. In addition, automation for sample analysis and detection will be developed enabling the full realization of the device in a clinical or research setting. Finally the device will be validated with clinical samples from prostate and breast cancer patients. This project will include the collaboration of a multidisciplinary team of six researchers and clinicians for device development, manufacturing, and clinical testing. As a team, the researchers will work to develop and validate this diagnostic platform. At the completion of this project a clinical research tool will be produced capable of profiling a patient's CTC population and providing molecular and genetic information on the EMT for that population in a single automated device with capture, profiling, and detection capabilities.

Public Health Relevance

Circulating tumor cells are shed into the bloodstream by growing tumors. We are developing a new device that can isolate these cells and classify their biochemical state. In this project, we are advancing a CTC analysis/profiling device to commercialization by re-engineering it to be amenable to high volume manufacturing and integrating a CTC detection system that will be user-friendly and straightforward to automate. This device will provide essential molecular information to clinical researchers studying the impact of epithelial to mesenchymal transition of a patients CTC population on disease progression.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Exploratory/Developmental Grants Phase II (R33)
Project #
5R33CA204574-03
Application #
9627947
Study Section
Special Emphasis Panel (ZCA1)
Program Officer
Sorg, Brian S
Project Start
2017-03-15
Project End
2021-02-28
Budget Start
2019-03-01
Budget End
2021-02-28
Support Year
3
Fiscal Year
2019
Total Cost
Indirect Cost
Name
University of Toronto
Department
Type
DUNS #
259999779
City
Toronto
State
ON
Country
Canada
Zip Code
M5 1S8
Kermanshah, Leyla; Poudineh, Mahla; Ahmed, Sharif et al. (2018) Dynamic CTC phenotypes in metastatic prostate cancer models visualized using magnetic ranking cytometry. Lab Chip 18:2055-2064
Labib, Mahmoud; Mohamadi, Reza M; Poudineh, Mahla et al. (2018) Single-cell mRNA cytometry via sequence-specific nanoparticle clustering and trapping. Nat Chem 10:489-495