Cancer of Unknown Primary (CUP) is a common cancer type that is defined by having no specified primary tumor after a diagnostic workup. CUP patients typically have dismal outcomes and are challenging to treat because many established therapies are cancer specific. In this work, we develop an algorithm (OncoNPC) that uses somatic mutational data from targeted tumor sequencing to accurately classify cancer types using multi-institutional training data. We show that OncoNPC classified subtypes show increased germline risk for the predicted cancer, significant prognostic differences, and longer survival when on matching treatments. These findings suggest that CUPs may exhibit distinct molecular subtypes that can be accurately inferred using genomic data and utilized for patient prognosis or to personalize treatments.
For more, see the threads from Intae Moon as well as the related software and data.
OncoNPC classification accuracy on known tumors (a); OncNPC predicted cancer of unknown primary subtypes show survival differences (b); cancer of unknown primary patients treated consistently with OncoNPC predictions achieved better outcomes (c)