Integrating Functional Testing and Genomic Profiling to Guide Cancer Treatment

In an interview with CancerNetwork®, at the Big Ten CRC Summit, VK Gadi, MD, PhD, deputy director of the University of Illinois Cancer Center, explained how functional drug testing fits alongside genomic profiling in guiding cancer treatment. Gadi, an early researcher in patient-derived organoids, has described genomic sequencing as a static map of a tumor's mutations. Functional modeling, by contrast, tests how a patient's living cancer cells respond to drugs.

Gadi called the 2 approaches "absolutely complementary" and cautioned against relying on one over the other. He described functional testing as a low-throughput strategy: a tumor must be obtained, grown in culture, and exposed to many drugs, which limits how quickly and at what scale it can be done. Genomic profiling, he said, now costs only dollars per patient and yields a large volume of information quickly, although the bioinformatics remain challenging. He added that AI is expected to help with that analysis. Integrating the 2, he said, should reveal patterns that can inform treatment decisions.

Gadi is a professor in the Division of Hematology/Oncology, Department of Internal Medicine, at the University of Illinois College of Medicine and a member of the Translational Oncology Program at the University of Illinois Cancer Center.

Transcript:

Functional testing and genomics are absolutely complementary, and I caution people against relying on one over the other. When you look at functional testing, the genomics can clearly inform you as to the right next thing to do, but it also works the other way around.

At scale, functional testing is hard. It is what I call a low-throughput strategy. You have to get a tumor. You have to grow it in a culture dish. You then have to expose it to 100 drugs or what have you. There are limits to how quickly, efficiently, and accurately we can do that. There are people who can, but it is a low-throughput exercise.

Genomics is at the other end of the spectrum. The cost of goods to do this work is now enormously reduced; we are talking dollars a patient. The bioinformatics is hard and challenging, but now AI is going to help us with that as well. So you have a high-throughput way of getting a lot of knowledge, which may not have all the insights you need, and then you have this slightly lower-throughput way that maybe gives you maximal knowledge. We just have to integrate the 2, and then we will see patterns that make sense, and then we can make treatment decisions based on that.