Emerging Technologies in Brain Tumor Management (Podcast)

“The biggest thing that’s happened in the past five to 10 years in brain tumor care has been the genomic revolution hitting our field,” says Andrew Dhawan, MD, PhD, a physician scientist with Cleveland Clinic’s Rose Ella Burkhardt Brain Tumor and Neuro-Oncology Center. “What that means is we are much better able to classify tumors according to their underlying biology as opposed to only what they look like under the microscope. This expanded ability arises from methods like whole exome sequencing and methylation arrays that allow us to really pick up the exact subtype of the tumor so we can more accurately treat a patient’s disease.”

In the latest episode of Cleveland Clinic’s Neuro Pathways podcast, , Dr. Dhawan expands upon these genomic advances and other developments in brain tumor diagnostics and therapy, with an emphasis on the notoriously difficult-to-treat glioblastoma tumor type. In a wide-ranging discussion with podcast host and fellow neuro-oncologist Glen Stevens, DO, PhD, he touches on the following:

• An overview of glioblastoma and its treatment challenges
• Advances in molecular diagnostics through genomics and methylation
• Spinal fluid analysis for tumor biomarkers
• Tumor resistance in neuro-oncology
• Wearable technology for monitoring patients with brain tumors

Click the podcast player above to listen to the 27-minute episode now or read on for an edited excerpt of its transcript. Check out more Neuro Pathways episodes at clevelandclinic.org/neuropodcast or wherever you get your podcasts.

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Excerpt from the podcast

Glen Stevens, DO, PhD: You have a particular interest in the use of technology-based applications in glioblastoma care and patient monitoring. Tell us a bit about your work in this area.

Andrew Dhawan, MD, PhD: One of our focuses is bringing wearable devices — things like smartwatches and smartphones — to the clinic. These devices are widespread and inexpensive tools we can use to get a good window into how people’s health changes over time.

We are specifically looking at that in the context of patients with glioblastoma by monitoring what we call actigraphy measures in real time. These are metrics like walking speed, step count and everything that’s captured by these devices’ sensors, including heart rate, heart rate variability, arm swing, etc. It turns out that we can actually predict complications and, hopefully, tumor progression before it appears on the MRI, with the aim that we could help people decide whether or not therapy should be switched earlier.

One example of a complication we can be alerted to in advance is a thromboembolic event such as pulmonary embolism (PE). A significant change in a patient’s heart rate or heart rate variability, as detected by their wearable device, would be a signal for PE risk. And that’s important because glioblastoma is a highly thrombogenic cancer, with thromboembolism affecting about 30% of patients with glioblastoma. So it’s a major complication we worry about. Fortunately, none of the patients in our study have had a PE at this point, but if they were to, we would look for that type of signal.

Dr. Stevens: There was an interesting study a number of years ago that looked at exercise in patients with grade 3 or 4 astrocytomas. It showed that patients who were able to exercise five days a week, for 30 minutes at a time at a brisk pace, lived twice as long, on average. It's always hard to rule out confounding effects in studies like that, but it raises some questions. Are you monitoring steps with your patients or somehow encouraging them to ambulate more?

Dr. Dhawan: We are not yet nudging patients to do any intervention. That’s part of the next phase of the investigation. But we are monitoring step counts. In fact, step count has been probably our strongest signal thus far of whether the tumor is getting worse, as the amount of walking and movement is one of the first things to go down when a tumor progresses.

Walking steadiness has been the other strong signal we’ve observed. When a large brain lesion like glioblastoma progresses, one of the things that's most affected is the gait circuit. We can measure that really well with actigraphy data, especially with large parietal lesions. So we do see that signal as well.