TCMIO: A Comprehensive Database of Traditional Chinese Medicine on Immuno-Oncology

Abstract

Advances in immuno-oncology (IO) are making immunotherapy a powerful tool for cancer treatment. With the discovery of an increasing number of IO targets, many herbs or ingredients from traditional Chinese medicine (TCM) have shown immunomodulatory function and antitumor effects via targeting the immune system. However, knowledge of underlying mechanisms is limited due to the complexity of TCM, which has multiple ingredients acting on multiple targets. To address this issue, we present TCMIO, a comprehensive database of Traditional Chinese Medicine on Immuno-Oncology, which can be used to explore the molecular mechanisms of TCM in modulating the cancer immune microenvironment. Over 120,000 small molecules against 400 IO targets were extracted from public databases and the literature. These ligands were further mapped to the chemical ingredients of TCM to identify herbs that interact with the IO targets. Furthermore, we applied a network inference-based approach to identify the potential IO targets of natural products in TCM. All of these data, along with cheminformatics and bioinformatics tools, were integrated into the publicly accessible database. Chemical structure mining tools are provided to explore the chemical ingredients and ligands against IO targets. Herb-ingredient-target networks can be generated online, and pathway enrichment analysis for TCM or prescription is available. This database is functional for chemical ingredient structure mining and network analysis for TCM. We believe that this database provides a comprehensive resource for further research on the exploration of the mechanisms of TCM in cancer immunity and TCM-inspired identification of novel drug leads for cancer immunotherapy. TCMIO can be publicly accessed at http://tcmio.xielab.net.

Keywords: bioinformatics; cancer immunotherapy; cheminformatics; database; immuno-oncology; medicinal herbs; traditional Chinese medicine.

Figure 1

Overall architecture of the development of the TCMIO database.

Figure 2

Data statistics. (A) The distribution of numbers of ligands for targets in ChEMBL. (B) The structurally similar ligands count against ingredients with different similarity thresholds (0.7 and 0.8); 20% of ingredients have at least 50 similar ligands with a threshold of 0.7 and 10 similar ligands with a threshold of 0.8.

Figure 3

Snapshot of the browse page.

Figure 4

Snapshot of the structure page.

Figure 5

Snapshot of an herb (Lycium barbarum L.)–ingredient–target network on the MOA page.

Figure 6

Snapshots of pathways on the MOA page.

Figure 7

Top 20 pathways of KEGG enrichment with the lowest P-value for Lycium barbarum L., as obtained by TCMIO.