Speakers: Andrew Magis, Director of Data Science at the Institute for Systems Biology
Original Broadcast date: April 20 2021
This webinar discusses a study that sought to identify early biomarkers for cancer by analyzing pre-diagnosis samples from seemingly healthy individuals who were later diagnosed with cancer.
Andrew Magis shares details of the study, which used the Olink platform to analyze 1,196 proteins in longitudinal plasma samples from participants in a commercial wellness program, including samples collected pre-diagnosis from ten cancer patients and 69 controls.
The study found that for three individuals ultimately diagnosed with metastatic breast, lung, or pancreatic cancer, CEACAM5 was a persistent longitudinal outlier as early as 26.5 months pre-diagnosis. Meanwhile, CALCA, a biomarker for medullary thyroid cancer, was hypersecreted in metastatic pancreatic cancer at least 16.5 months pre-diagnosis. Another marker, ERBB2, spiked in metastatic breast cancer between 10 and four months pre-diagnosis.
Dr. Magis discusses how these results support the value of deep phenotyping seemingly healthy individuals in order to prospectively infer disease transitions.
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