ToPP: Tumor online Prognostic Analysis Platform for Prognostic Feature Selection and Clinical Patient Subgroup Selection

Abstract

Tumor heterogeneity among different patients result in different response to anti-cancer therapeutics and survival. To identify features that are associated with prognosis is essential to precision medicine by providing clues for target identification, drug discovery. Here, we developed a Tumor online Prognostic analysis Platform (ToPP) to explore the prognosis associated genetic features in multi-omic levels in different tumor types. The ToPP platform integrated eight multi-omics features and clinical data for 55 tumor types. It provides multiple ways for customized prognostic studies, including 1) Prognostic analysis based on multi-omics features such as genetic variation (mutation, CNV, methylation) and clinical characteristics; 2) Automatic construction of prognostic model with variable selection, transformation and model evaluation for both public data and custom data; 3) Pancancer prognostic analysis in multi-omics data based on tumor related pathways; 4) Explore the impact of different levels of feature combinations on patient prognosis; 5) More sophisticated prognostic analysis according to regulatory network such as miRNA-gene regulation relationship, gene mutation-expression relationship; 6) Prognostic study of custom data and comparison with public datasets. ToPP provides a comprehensive source and easy-to-use interface for tumor prognosis research, with one-stop service of multi-omics, subtype and online prognostic modeling. The web-server is freely available at http://www.biostatistics.online/topp/index.php. \n \nFunding: This work was supported by Shanghai Municipal Health Commission and Collaborative Innovation Cluster Project (No. 2019CXJQ02). It was also supported by National Natural Science Foundation of China grants (Nos. 31878209, 31671377), Shanghai Municipal Science and Technology Major Project (Grant No. 2017SHZDZX01, 20692191500), Beihang University & Capital Medical University Plan (BHME-201904) and the Open Research Fund of Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE, ECNU. \n \nDeclaration of Interest: The authors declare that they have no competing interests.