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Prostate Cancer Lab #52: Remote Monitoring and Deep Data (Mike Snyder)

We try to actually follow people’s baseline and look for these shifts.” – Mike Snyder

The goal is to try and understand what a healthy profile looks like. How does it change over time? How does it compare between different people? What happens when people first get ill? And importantly, for trying to transform the healthcare system, can these advanced technologies, like genome sequencing and wearables, be better used to manage people’s health?” – Mike Snyder

Meeting Summary

Advanced cancer patients are interested in monitoring their health, and there are a growing number of tools to help them.

Mike Snyder, PhD, is uniquely qualified to talk about disease monitoring technologies and the data that they generate. He is the Chair of the Department of Genetics, and Director, Center for Genomics and Personalized Medicine at Stanford University School of Medicine. He is an expert on everything related to the “digital self” in healthcare: on the state of developments in remote monitoring, sequencing and other “omics”, and novel medical devices, such as wearables. These technologies and the big data they generate hold the promise to transform healthcare and detect health problems early. His major research involves collecting and analyzing data on people’s DNA, activity levels, diet, stress, and other environmental factors longitudinally and looking for any shifts that may indicate health issues before they become serious.

What can you measure to monitor your health?

Your goal should be to try to understand what your healthy profile looks like. How does it change over time? How does it compare with other people? What happens when you first get ill?

Your health is influenced by your DNA, your genome, and lots of other things: your activity, the food you eat, stress, and environmental responses. You can quantify a lot of this easily, like your genome and your activity. Some of the measurement systems are clunky, but it’s quantifiable. You can also quantify the effects of these things by taking deep data measurements.

  • From your blood, using mass spectrometry and other methods, you can profile proteins, metabolites (substances made or used when the body breaks down food, drugs, or chemicals, or its own tissue), and lipids (the breakdown and storage of fats).
  • From tissue or blood, you can sequence your genome, transcriptome, and proteome.
  • From peripheral blood (blood circulating through your body), you can isolate mononuclear cells (PBMCs, blood cells with round nuclei), allowing you to measure your immune system cells (monocytes, lymphocytes, and macrophages).
  • From blood plasma (the part without the cells), you can measure proteins and cytokines, which are small proteins that are important to the immune system and blood cell controllers.
  • From fecal, urine, gut, nasal, tongue, and skin samples, you can follow your microbiome.
  • From questionnaires, you can track your feelings, pain, nutrition, exercise, and symptoms.
  • From advanced tests, such as stress echocardiograms and glucose control measures, you can track deeper clinical status.
  • From wearables, you can track heart rate and rhythm, blood pressure, oxygen saturation, skin temperature, quality of sleep, total steps in a day, amount of exercise, and exercise response. With a continuous glucose monitor, you can track your glycemic response to what you eat.


How often should you be taking these health measures?

You should track your data longitudinally. Wearables can track you all the time. Metabolic tests can be run every month. Otherwise every six months may be enough while you’re healthy. Then if an adverse event comes along, like a viral infection, you should take more samples. (This is what the Snyder Lab does, though they do not know the real answer.)

How can you use your health data?

Increasing the data you gather from health monitoring tools can help you:

  • Diagnose: For example, Mike Snyder is type two diabetic, which was predicted from a genome sequence and then got picked up through profiling. Mike also detected when he had Lyme disease pre-symptomatically, because his heart rate went up, and his blood oxygen dropped. This was picked up with a pulse oximeter, although you can now get it from a watch.
  • Provide an early warning: Seeing things are off before people have symptoms. For example, early lymphoma, pre-cancers that can convert to aggressive cancers, and heart issues have been detected. Mike’s lab developed a COVID predictor based on raised heart rate.
  • Monitor: For example, through longitudinal profiling, a case of early pancreatic cancer was detected. Other cancers can be monitored for recurrence.
  • Personalize treatment: For example, your normal temperature is probably higher or lower than 98.6, which has been the generally accepted normal temperature. Everybody reacted differently to an Ensure shake.


How could microsampling be used to monitor a particular disease, like prostate cancer?

Microsampling is a fantastic opportunity for semi-continuous monitoring and could give you useful surrogate indicators. For example, one of the challenges metastatic prostate cancer patients face is getting tissue to support diagnostics because their lesions are typically in their bones. Today, they rely on cfDNA tests to identify oncogenic and resistant mutations that characterize their solid tumors. This is a huge step forward, but we would want to explore whether or not blood-based proteomic microsampling could detect solid tumor protein expressions that are currently limited to tissue samples.

However, there are challenges to designing a microsampling and monitoring protocol for a particular disease. What to measure is as important as how often to measure it, as well as how to look for fluctuations in whatever is monitored that may be meaningful. Some commercial vendors provide monitoring, but for specific analytes or measures that may or not be the most informative. For example, there is no obvious answer to the obvious biomarker(s) for prostate cancer. To find them, we would look for the most homogeneous patient population and treatment pattern with distinct outcomes of response versus non-response. Then we would develop an observational study to see what kind of data would separate the two. The key is a simple clinical trial design to keep the study fairly small and reasonably easy to recruit patients.

We are exploring convening a group to discuss this possible trial design. The study would need prostate oncologists and clinical trial statisticians.

The information and opinions expressed on this website or platform, or during discussions and presentations (both verbal and written) are not intended as health care recommendations or medical advice by Prostate Cancer Lab, its principals, presenters, participants, or representatives for any medical treatment, product, or course of action. You should always consult a doctor about your specific situation before pursuing any health care program, treatment, product or other course of action that might affect your health.

Meeting Recording

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