January 9, 2023 - Podcast

Episode 335 — Using AI to improve cancer immunotherapy

Solid tumors represent the vast majority of human cancers. However, current cancer immunotherapy screening methods overlook the capacity of the immune cells -- called T cells -- to penetrate the solid tumor tissue.

That’s why an interdisciplinary team of researchers led by Indiana University bioengineer Feng Guo has developed a tool that could lead to improved cancer immunotherapy. The prototype platform facilitates automated drug screening and real-time, 3D imaging and analysis of interactions between immune cells and cancer cells.

Guo, an intelligent systems engineering professor, says the researchers can use the platform to see how different therapies impact the killing of target cancer cells — even tumor infiltration, which is unique. The platform uses microfluidics — often referred to as lab-on-a-chip technology — combined with a type of artificial intelligence called deep learning. Microfluidics is the technology of fluid manipulation in microscopic channels, essentially scaling down different laboratory functions onto one microchip. Deep learning is machine learning based on computing systems inspired by biological neural networks. Together, these technologies allow the platform to quickly and autonomously identify potential cancer immunotherapy drugs and test how they will perform on a cellular level.

The researchers trained a deep-learning algorithm using clinical data, including digitized images of solid tumors and patient survival data. Then, they integrated the algorithm with the microfluidic platform, which can model tumor immunity and screen new immunotherapeutics that promote both T cell tumor infiltration and the killing of cancer cells. The researchers call it “intelligent microfluidics.”

They say they’re excited about the new platform and its potential to address immunotherapy for solid tumors, as well as in health fields beyond oncology, such as immunology, neurology, tissue engineering and more.