Focus on cancer signals

This year, approximately 240,000 people in the United States will be found to have lung cancer. Some 200,000 of them will be diagnosed with non-small cell lung cancer, which is the second leading cause of death following cardiovascular disease.

Georgia Tech researcher Ahmet Coskun is working to improve the odds for these patients in two recently published studies that focus on understanding why and how patients respond differently to disease and treatments.

“What we’ve learned is that connectivity and communication between molecules and between cells is what really controls everything, whether patients are healthy or not, or how they will respond to drugs,” said Coskun, assistant professor in the Wallace H. Coulter department. of biomedical engineering at Georgia Tech and Emory University.

Published in journals npj Precision Oncology et iSciencethe studies detail the development of tools and techniques to deeply explore the tumor microenvironment at the subcellular level, using the Coskun laboratory’s expertise in combining multiplex cell imaging methods with artificial intelligence.

“We are developing a better understanding of cell signaling and decision-making, and how they are coordinated in the tumor microenvironment, which may lead to more personalized precision treatments for these patients,” said Coskun, who is keenly interested in why some patients respond. to breakthrough immunotherapy drugs, and some don’t.

With this in mind, his team developed SpatialVizScore, a new method described in npj Precision Oncology, to study in depth tumor immunology in cancerous tissues and help identify patients most likely to respond to immunotherapy. It is a significant upgrade from the current standard methodology used by oncology physicians and researchers, Immunoscore.

Score immunity

Immunoscore is used as a prognostic tool, measuring how the body’s immune cells surround and enter a tumor. It has shown promise in predicting a patient’s risk of disease recurrence, a key step in developing a personalized treatment plan. A high score indicates better immune cell infiltration, while a low score indicates a greater risk of recurrence.

But immune cells are moving targets and exhibit a high level of molecular complexity that cannot always be properly captured by conventional Immunoscore methods. With SpatialVizScore, the Coskun team has expanded the scope of immunoscoring.

While the standard method looks at how T cells interact with tumors, Coskun’s system looks at the interactions of additional immune cells, such as macrophages, which have two subtypes – M1 and M2, which are often found in conflict. M1 helps eliminate pathogens, while M2 can promote tumor growth.

Coskun’s multiplex imaging system examines all of this, visualizing how these cells communicate and interact with each other and with cancer cells, not just in and around the tumor, but across the tumor environment.

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“Because cancer cells and immune cells are not always close to each other, we visualize spatial connectivity and we visualize cell communities or neighborhoods,” Coskun said. “But we’re not just looking at a cancer cell interacting with an immune cell. We also look at immuno-immune interactions. By looking at the effects of these different interactions, we can explain the tumor, and we can come to develop a more complete immunoscore.”

Zoom in

In the iScience study, the team moves away from communities and cell neighborhoods. Instead, they zoom in on subcellular protein-protein interaction networks, which can affect signaling pathways in cancer – when molecules in a cell work together to control a cellular function, which can be the cell division or cell death.

Each molecule activates another molecule, and the process is repeated along the “pathway” until the last molecule in line is activated and the cellular function – good or bad – is carried out. Abnormal activation of a pathway can lead to cancer, but some drugs target specific molecules involved and can prevent cancer cells from growing.

Coskun and his team are using their multiplex imaging tools and machine learning to probe protein-protein interactions to decipher the pathogenesis of signaling pathways that contribute to drug resistance in non-small cell lung cancer.

“We can observe and map protein activity,” said Coskun, whose team developed a subcellular resolution imaging technique called rapid multiplexed immunofluorescence (RapMIF).

“Proteins make the decisions that affect our cells,” Coskun added. “We can now see how they communicate, how they affect what our cells ultimately do. It’s a signaling discovery approach that can be used in the design of precision therapies and ultimately help more patients battling cancer.”

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