TRANSCUP: A Scalable Workflow for Predicting Cancer of Unknown Primary Based on Next-Generation Transcriptome Profiling

Abstract Cancer of unknown primary site (CUP) accounts for 5% of all cancer diagnoses. These patients may benefit from more precise treatment when primary cancer site was identified. Advances in high-throughput sequencing have enabled cost-effective sequencing the transcriptome for clinical application. Here, I present a free, scalable and extendable software for CUP predication called TRANSCUP, which enables (1) Raw data processing, (2) Read mapping, (3) Quality re-port, (4) Gene expression quantification, (5) Random forest machine learning model building for cancer type classification. TRANSCUP achieved high accuracy, sensitivity and specificity for tumor type classification based on external RNA-seq datasets. It has potential for broad clinical application for solving the CUP problem. TRANSCUP is open-source and freely available at https://github.com/plsysu/TRANSCUP