TROY, N.Y. — In order to effectively address intractable challenges like cancer, researchers, drug developers, and clinicians need to be able to see how a potential therapeutic works within a living system, ideally in real time. That type of vision and insight is being made possible by engineers at Rensselaer Polytechnic Institute.

A new $2.5 million grant from the National Institutes of Health’s National Cancer Institute (NCI) underscores the influence of Rensselaer researchers in this area, as they continue to develop new and innovative bioimaging techniques that also harness the power of machine learning methods.

This most recent grant will support the further development of a new imaging technique that will allow cancer biologists to observe the molecular, metabolic, and functional behavior of breast cancer cells when a targeted therapeutic — specifically human epidermal growth factor receptor 2 (HER2) —  is introduced. It will be used in preclinical research using non-human models.

A team led by Xavier Intes, a professor of biomedical engineering at Rensselaer and co-director of the Center for Modeling, Simulation and Imaging in Medicine (CeMSIM), will combine imaging technology — specifically quantum detectors — developed by Edoardo Charbon from the École Polytechnique Fédérale de Lausanne in collaboration with Xavier Michalet from UCLA, optical imaging methodologies developed at Rensselaer, and deep learning techniques that enable ultra-fast and user-friendly image formation capabilities.

The technology will be validated and optimized in collaboration with Margarida Barroso, a professor of molecular and cellular physiology at Albany Medical College. The overarching goal is to establish a new imaging platform that can gather information about cellular delivery of a drug to the tumor, how effective the therapeutic is over time, and whether or not the tumor cells develop drug resistance.

“The synergistic team assembled with researchers from Rensselaer, Albany Med, and UCLA is uniquely positioned to leverage the most recent technological developments in optical imaging and AI to tackle some of the most outstanding challenges in cancer research,” Intes said.

This type of imaging technology is necessary for biologists and clinicians to understand how particular therapeutics may or may not target specific cancer cells and to assess drug response. That knowledge is an essential step toward precision and personalized medicine, and it has the potential to enable the development of more effective treatments.

“Although improved therapy has led to better cancer outcomes, resistance to treatment can still lead to cancer relapse and recurrence. This new technology will be able to simultaneously determine drug therapy efficacy and actual drug binding to tumors, allowing us to investigate how tumors adapt or change during therapy, which is key to tackling drug resistance,” Barroso said.

According to Intes, this technique should also be more cost-efficient than other current imaging technologies, which would make it more accessible for other researchers, enabling further scientific discovery; it would also be well suited for use in clinical settings.