GRAIL and University of Oxford to Present Long-Term Data From the SYMPLIFY Study Evaluating the Galleri® Multi-Cancer Early Detection Test in Symptomatic Individuals at the Early Detection of Cancer Conference (EDCC)

Core Insights - The SYMPLIFY study revealed that one-third of participants initially thought to have false-positive results were later diagnosed with cancer during follow-up, highlighting the importance of proactive follow-up on positive multi-cancer early detection (MCED) results [1][3][6] - The updated positive predictive value (PPV) of the Galleri test in this symptomatic population is reported to be 84.2%, an increase from the previous 75.5% [1][5][6] - Galleri's Cancer Signal Origin (CSO) prediction accurately identified the location of cancer in almost all cases initially considered false positives, reinforcing the test's diagnostic capabilities [1][3][6] Study Overview - SYMPLIFY is the first large-scale evaluation of an MCED test in symptomatic individuals referred for diagnostic follow-up due to cancer suspicion, involving over 6,000 participants [2][7] - The study was non-interventional, meaning test results were not disclosed to physicians during the diagnostic process [2] Follow-Up Results - In a 24-month follow-up, 35.4% of participants with initially reported false-positive results were later diagnosed with cancer, leading to a reduction in false positives from 79 to 51 [6][7] - Of the 28 participants diagnosed with cancer, 27 had correct CSO predictions, which could have facilitated faster diagnoses [6][7] Clinical Implications - The findings emphasize the potential of the Galleri test as a diagnostic tool for individuals with non-specific cancer symptoms, suggesting it could enhance clinical decision-making in primary care [7] - The study's results support the need for continued follow-up after a cancer signal is detected to identify cancers that may be missed during initial evaluations [7] Company Background - GRAIL, Inc. is focused on early cancer detection using next-generation sequencing and machine learning, aiming to alleviate the global burden of cancer [9] - The Galleri test can detect over 50 types of cancer before symptoms appear, significantly increasing cancer detection rates when combined with standard screenings [10]