Nicholas R Rydzewski, Yue Shi, Chenxuan Li, Matthew R Chrostek, Hamza Bakhtiar, Kyle T Helzer, Matthew L Bootsma, Tracy J Berg, Paul M Harari, John M Floberg, Grace C Blitzer, David Kosoff, Amy K Taylor, Marina N Sharifi, Menggang Yu, Joshua M Lang, Krishnan R Patel, Deborah E Citrin, Kaitlin E Sundling, Shuang G Zhao
Histopathologic diagnosis and classification of cancer plays a critical role in guiding treatment. Advances in next-generation sequencing have ushered in new complementary molecular frameworks. However, existing approaches do not independently assess both site-of-origin (e.g. prostate) and lineage (e.g. adenocarcinoma) and have minimal validation in metastatic disease, where classification is more difficult. Utilizing gradient-boosted machine learning, we developed ATLAS, a pair of separate AI Tumor Lineage and Site-of-origin models from RNA expression data on 8249 tumor samples...
March 13, 2024: Communications Biology