Generalized Evolutionary Classifier for Evolutionary Guided Precision Medicine
The first two DPM moves provide most of the clinical benefit of DPM, reducing the duration required for subclone monitoring. This also leads to an evolutionary classifier selecting patients who will benefit: those in whom DPM and CPM recommendations differ early. These advances bring DPM (and potentially other EGPM approaches) closer to potential clinical testing.
Authors
- Shaymaa Bahnassy (ORCID: https://orcid.org/0000-0001-9882-4195)
- Robert A. Beckman (ORCID: https://orcid.org/0000-0003-0098-9210)
- Rebecca Baab Riggins (ORCID: https://orcid.org/0000-0002-1555-4431)
- Deepak Parashar (ORCID: https://orcid.org/0000-0003-1280-3663)
- Matthew McCoy (ORCID: https://orcid.org/0000-0001-7601-6963)
- Stanley Tam (ORCID: https://orcid.org/0000-0002-9338-436X)
- Chen‐Hsiang Yeang (ORCID: https://orcid.org/0000-0002-9224-5800)
Institutions
- Turing Institute (GB)
- Georgetown University (US)
- Georgetown University Medical Center (US)
- British Library (GB)
- University of Warwick (GB)
- The Alan Turing Institute (GB)
- Institute of Statistical Science, Academia Sinica (TW)
Publication Details
- Journal
- JCO Precision Oncology
- Published
- 2025-03-01
- DOI
- https://doi.org/10.1200/po.23.00714
- Citations
- 5
- Primary Topic
- Cancer Genomics and Diagnostics
- Type
- article
- Field-Weighted Citation Impact
- 4.68