3591 Background: Metastatic cancers require further diagnosis to determine their primary tumor sites. However, the tissue-of-origin for around 5% tumors could not be identified by routine medical diagnosis according. With the development of machine learning techniques and the accumulation of big cancer data from TCGA and GEO, it is now feasible to predict cancer tissue-of-origin by computational tools. Methods: Developed a computational framework to infer tumor tissue-o. Results: Applied TOOme to the TCGA data containing 7,008 non-metastatic samples across 20 solid tumors including BLCA, BRCA, CESC, COAD, GBM and so on. 74 genes by gene expression profile and 6 genes by gene mutation are selected by the random forest process, which can be divided into two categories: (1) cancer type specific genes, which are highly expressed or mutated only in one specific cancer and (2) those expressed or mutated in several cancers with different levels of expression or mutation rates. Function analysis indicates that the selected genes are significantly enriched in gland development, urogenital system development, hormone metabolic process, thyroid hormone generation prostate hormone generation and so on. According to the multiple-label classification method, random forest performs the best with a 10-fold cross-validation prediction accuracy of 96%. We also use the 19 metastatic samples from TCGA and 256 cancer samples downloaded from GEO as independent testing data, for which TOOme achieves a prediction accuracy of 89%. The cross-validation validation accuracy is better than those using gene expression (i.e., 95%) and gene mutation (83%) alone. Conclusions: TOOme provides a quick yet accurate alternative to traditional medical methods in inferring cancer tissue-of-origin. In addition, the methods combining somatic mutation and gene expressions outperform those using gene expression or mutation alone.