Prediction of outcome from spatial Protein profiling of triple-negative breast cancers
Our results show that unbiased protein-based and segmentation-free spatial analysis is effective for identifying clinically relevant biomarkers from multiplex tumor images and identifying predictive biology.
Authors
- P. R. H. Robson (ORCID: https://orcid.org/0000-0003-1841-3594)
- Te-Chia Wu (ORCID: https://orcid.org/0000-0001-8302-3985)
- Jan Martínek (ORCID: https://orcid.org/0000-0001-5277-8859)
- Ali Foroughi pour (ORCID: https://orcid.org/0000-0002-3547-0796)
- Jeffrey H. Chuang (ORCID: https://orcid.org/0000-0002-3298-2358)
- Santhosh Sivajothi (ORCID: https://orcid.org/0000-0002-8854-4517)
- Karolina Palucka
- Javad Noorbakhsh
Institutions
- St. Jude Children's Research Hospital (US)
- Jackson Laboratory (US)
- AstraZeneca (United States) (US)
- UConn Health (US)
Publication Details
- Journal
- Communications Medicine
- Published
- 2026-01-29
- DOI
- https://doi.org/10.1038/s43856-026-01400-4
- Primary Topic
- Advanced Biosensing Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Yale University
- Yale Cancer Center
- National Institutes of Health
- National Cancer Institute