SFU and UBC researchers collaborate to understand the role of caveolin-1 in cancer

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PICTURE: Ghassan Hamarneh, professor of computer science at SFU, uses his expertise in medical imaging analysis to help UBC researchers understand the role of caveolin-1 (CAV1) in certain types of cancer. view After

Credit: SFU

Ghassan Hamarneh, professor of computer science at SFU, uses his expertise in medical imaging analysis to help UBC researchers understand the role of caveolin-1 (CAV1) in certain types of cancer.

CAV1 is a protein associated with poor performance in aggressive breast and prostate cancers. It is also associated with tumor metastasis and tumor suppression, but it is not known how CAV1 differentiates these two roles.

UBC biologist Ivan Robert Nabi and his team are studying the contribution of CAV1. They do this by using machine learning-based super-resolution microscopy analyzes to study and visualize caveolae and scaffolding – tiny structures that are found in a cell and in which CAV1 is found.

Using a graph-based approach, Hamarneh and the other researchers are able to capture and process three-dimensional coordinates of data, called point clouds, with nanometric resolution to view cellular processes.

Given the complexity and volume of data, manual filtering of all of this data is inefficient. For this reason, researchers use computer vision and artificial intelligence (AI) methods to automatically analyze the data. This is done through classic machine learning approaches where researchers train the machines to examine specific characteristics and feed the data into an algorithm. They also use deep learning approaches in which researchers do not impose their own bias in terms of the features they seek to examine and allow the machine to find out which features stand out.

“With deep learning, the machine discovers features for you, but sometimes those features are not easily interpreted,” says Hamarneh.

“We’re looking at interpretable AI methods, where we take a look at what the AI ​​has discovered and expose it to biologists to get a glimpse of it.”

Since starting their collaboration a few years ago, Hamarneh and Nabi have combined their expertise to develop software tools to make this process possible. Together with SFU postdoctoral researcher Ismail Khater, they developed super-resolution network analysis, which helps researchers analyze biological clusters and can be applied to different biological questions.

Their research efforts have not gone unnoticed. Recently, the research groups received $ 910,000 from the Canadian Institutes of Health Research (CIHR) for the next five years.

“We are very happy with this funding because it allows us to continue this work and, most importantly, to provide salaries to graduate students and post-docs to help us in our research,” says Hamarneh.

“Receiving very positive feedback on our research is encouraging because it validates the work we are doing.”

While the researchers are focused on understanding the role of CAV1 in breast and prostate cancer, they are also interested in how their current research may benefit other areas of biology in the future. .

“Our long-term goal is to facilitate biological discovery, for the improvement of human health, by bridging the gap between computational tools and biological issues,” says Hamarneh.

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* The collaboration between Hamarneh and the Nabi laboratories involved several graduate students and postdoctoral fellows who played a decisive role in this interdisciplinary research.

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