A bioengineering researcher develops diagnostic tools to predict and prevent breast cancer metastasis.

Currently, breast cancer metastasis is the cause of 90% of breast cancer deaths. Metastasis is what happens when the cancer spreads to other parts of the body from where it originated, and right now there are no therapies that can specifically treat it. Also, there are no current diagnostics that can predict which patients will develop metastases. Associate Professor of Bioengineering Bojana Gligorijevic is working to address this challenge. “Our dream is to be able to predict and prevent metastasis,” said Gligorijevic. She’s working to create new breast cancer predictive diagnostic tools, which would have the potential to prevent overtreatment and lower the now high mortality rate of metastatic breast cancer deaths by predicting metastasis and stopping it before it actually happens.

A Personal Connection

While Gligorijevic was pursuing her PhD, she developed imaging techniques for malaria research and met dozens of other researchers in the field. Though, during her program, she never met anyone who was infected with malaria. “I wanted to work on something that would provide a more direct impact for people I know and care about. My aunt and a few other women I knew were suffering from breast cancer at the time,” said Gligorijevic. In 2019, Gligorijevic received a $2 million research award from the National Cancer Institute of the National Institutes of Health, and just last year she was awarded a $791,000 four-year Research Scholar Grant from the American Cancer Society. 

Picture of Bojana Gligorijevic

Bojana Gligorijevic

Associate Professor of Bioengineering

“My goal was to build a research program in microscopy of cancer, where students from engineering, biology, physics, chemistry, medicine can all have a role in fighting against cancer, using their different backgrounds. There aren’t many places in the world where you can do that, as it requires integration between a university, a medical school and a cancer center.”

Getting Ahead of Disease

She explained that most therapies now are still rather traditional, meaning they measure how much the tumor shrank after treatment, and metastasis is diagnosed by using imaging techniques. By the time that metastasis is diagnosed, though, it already contains billions of cells that make it extremely difficult to successfully treat.

She and her team are developing markers for the early stages of metastasis, before any cells leave the primary tumor site. One of the markers is based on invadopodia, which are small structures that have long been hypothesized as necessary for metastasis to happen. 

“Invadopodia are protrusions formed on tumor cells and are able to degrade the fibers of connective tissue. By using our unique windows that we implant in mice as well as in vivo, real-time microscopy, we showed for the first time that invadopodia in vivo assembles around blood vessels, where the fibers of connective tissue are highly cross-linked. We demonstrated that invadopodia precede metastasis and that the elimination of invadopodia stops metastasis in vivo,” said Gligorijevic.

Essentially, detection of invadopodia can be used to predict and prevent metastasis. Gligorijevic is also working to understand how invadopodia materialize in the first place and once they’re there, how to find them.

“To prevent invadopodia from ever forming, we are working to understand how they emerge and what stimulates their assembly … Invadopodia only occurs during the early period of cell growth, called G1 phase. So if a patient has invadopodia, they should not be treated with drugs that arrest cells (stop growth) in G1, as this may accelerate metastasis,” said Gligorijevic.

A Partnered Approach

To be able to see invadopodia in patients, Gligorijevic is working with someone very close to her—her husband, Associate Professor of Bioengineering Erkan Tüzel. They’re developing a seven-color imaging assay, with machine and deep learning models, which is one of Tüzel lab’s focus areas. (Machine learning is a method of data analysis where the algorithms learn from the data they collect and optimize automatically. Deep learning is a subcategory of machine learning that imitates how human brains learn and can process immense amounts of data to make predictions.) Gligorijevic is able to access patient tissue through collaboration with the Biosample Repository Facility at Fox Chase Cancer Center, and Tüzel provides automated image analysis, machine learning and computational models.

Targeted Imaging

“What sets us apart is the high resolution of imaging, as we work on the cellular and subcellular level. Of course, it is almost impossible to acquire and analyze images of billions of cells in the tissue, so we work smart. We do a first sweep at low resolution, similar to standard diagnostics, and machine learning helps us identify the niche or the subregion where the high resolution is required,” said Gligorijevic.

Narrowing down where they should focus the high-resolution imaging allows them to effectively identify and target invadopodia, which will appear in a different color than the other cell compartments. “We hope that one day, we will be able to predict which patients need invadopodia-targeted therapies to prevent or minimize metastasis, and mortality that comes with overtreatment and/or metastasis,” said Gligorijevic. 

The next step is to optimize the imaging workflow for patient samples and how machine learning is applied to the collected images, which will allow Gligorijevic and Tüzel to provide a number reflecting the probability for the patient to develop metastasis, from the tumor resection or biopsy. She also explains that this kind of work isn’t possible everywhere.

Interdisciplinary Impact

“I did my undergrad degree in chemistry, doctorate in biophysics and postdoc in cancer biology, and my goal was to build a research program in microscopy of cancer, where students from engineering, biology, physics, chemistry, medicine can all have a role in fighting against cancer, using their different backgrounds,” Gligorijevic said. “There aren’t many places in the world where you can do that, as it requires integration between a university, a medical school and a cancer center.” 

Many of her other collaborators come from the Fox Chase Cancer Center as well as the Lewis Katz School of Medicine, such as Edna Cukierman and Xavier Grana, to name a few.

“Fox Chase Cancer Center has been extremely supportive in terms of building my community of cancer researchers, state-of-the-art facilities, and discussing current therapies with doctors who work both in-clinic and as researchers,” she said. 

And, her dream of bringing students of different backgrounds together to do this work has been possible at Temple. Graduate and undergraduate students have the opportunity to work directly with Gligorijevic in her Cancer Microscopy and Mechanobiology Lab. 

“Students are the research. We are here to train them, mentor them, support them and help them fulfill their dreams. Each student has both independent and team projects and depending on their background and talents, they all contribute to the goal that is larger than any of us—to predict and prevent metastasis,” said Gligorijevic.

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