In the fight against cancer, a new tool has emerged that has changed the treatment landscape. CAR-T cell therapy, first approved for clinical use in 2017, uses a patient’s own remodeled immune cells to fight cancer. It has been shown to be particularly effective against certain types of lymphoma.
Its success represents the continued rise of immunotherapy, a class of treatments that enhance or alter the immune system to fight disease. Now, CAR-T cell therapy and other similar treatments offer new hope in the fight against some of our most challenging diseases.
But the development of these treatments is only possible thanks to the scientists who have devoted their careers to advancing our understanding of the immune system. Jun Huang, an assistant professor of molecular engineering at the Pritzker School of Molecular Engineering at the University of Chicago, is one such researcher.
Armed with new, sophisticated tools, his work could have far-reaching implications, not only for the treatment of cancer, but also for infections and autoimmune diseases more broadly.
New understanding leads to new treatments
Huang’s work has been described as molecular immunology with a biotechnology bias. He studies the basic mechanisms behind the immune system with a particular focus on T cells (a type of white blood cell). He and his team use a combination of advanced microscopy, tailored tools and ingenuity to study immunology at the molecular level, and they apply this knowledge to develop novel treatments.
Huang has already used his research to develop microscopic traps that catch and kill the coronavirus, solve long-unanswered questions about cellular metabolism, develop a new machine learning molecular imaging pipeline that can be used in vaccine development, and transformative techniques to identify CAR T cells. With each advance, Huang gets closer to a goal he set for himself in 2009 during his postdoctoral studies.
“I want to cure cancer and HIV – two major diseases that we cannot beat yet,” he said. “Of course, most people would think that they are very different diseases. But for us, treating both could be a T cell issue. HIV infects CD4 T cells and paralyzes the human immune system, while tumor microenvironments drive T cell dysfunction and inhibit T cell killing of cancer cells. If we can effectively restore T cell functions, we could treat both diseases despite their different natures.”
seeing is believing
The immune system is one of the most complex systems in the human body. Inside, billions of highly specialized cells just a few micrometers in size work together to ward off a constant flood of pathogens such as viruses and bacteria. With so much happening, researchers have yet to unravel some of the more complex mechanisms of our immune system. In the case of CAR-T cell therapy, for example, we don’t fully understand why it’s effective against some cancers but not others.
Huang aims to fill the gaps in our understanding by using multiple cutting-edge technologies and custom internal devices to study immune cells at the molecular level. His results have already opened new doors in cell research.
In May 2020, Huang’s lab combined publicly available software and machine learning techniques to create a pipeline to analyze light sheet microscopy data. Grating light sheet microscopy provides high-resolution 3D videos of cells. Huang’s Lattice Light-Sheet Microscopy Multi-dimensional Analyzes (LaMDA) pipeline effectively processes the vast amounts of data generated by lattice light-sheet microscopy, allowing researchers to use single molecules as data points. LaMDA could have numerous medical applications such as drug testing and vaccine development, in addition to expanding knowledge of T-cell biology.
In June 2021, Huang and his team used a combination of genetically encoded biosensors, machine learning, and high-resolution microscopy to visually observe glycolysis — the process by which cells metabolize glucose — at the molecular level. They found that cells expend more energy when they move and contract, and that they take up glucose through a previously unknown receptor — both findings that could fuel research into a variety of diseases. For example, if doctors could inhibit glycolysis in lung endothelial cells, they could reduce the effects of acute respiratory syndrome in COVID-19 patients.
Huang believes machine learning will be key to advancing our understanding of the immune system and helping researchers like him process the vast amounts of data generated by high-resolution imaging. When discussing his work, Huang points out that his tools are a means to an end – that he doesn’t pursue technology for its own sake, but to answer questions about immunology and ultimately develop useful treatments. It’s a philosophy he arrived at during his postdoctoral studies.
“I worked in an immunology lab and there were a lot of collaborations with MDs and MD/PhDs,” Huang said. “This experience changed my thinking. It made me think, ‘What do the doctors actually think is important? What do patients really need? How can we connect the basic research, the basic research, to provide that?’ As an engineer, I want to do that.”
In early 2020, when the coronavirus pandemic first emerged, researchers everywhere focused on managing the crisis. For Huang and his team, it was a chance to apply their advanced studies in immunology to a new threat. Although they had not previously addressed COVID-19, by their very nature, immunological research can be quickly adapted to emerging diseases. In this case, the researchers directed their study of exosomes, which are small vesicles secreted by cancer cells that suppress the immune system, towards SARS-CoV-2. The team believed they could use the same mechanism to fight the virus that causes COVID.