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 [39]http://tcmio.xielab.net.

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

Introduction

   The relationship between the human immune system and cancer can be
   traced back to 150 years ago, when Rudolf Virchow observed immune
   infiltration in human tumors and proposed the concept of mobilizing the
   immune system against cancer ([40]Balkwill and Mantovani, 2001).
   Several decades later, American surgeon William Coley observed
   inflammatory responses and cancer clearance in some patients with
   bacterial infection ([41]Coley, 1891). Due to the limited scientific
   understanding of the immune system, no great progress was made in the
   following century ([42]Hoos, 2016). The milestone for cancer
   immunotherapy came with the identification of immune checkpoints, which
   control T-cell immune responses through on and off switches ([43]Sharma
   and Allison, 2015). Immune checkpoint proteins maintain self-tolerance
   and prevent autoimmunity to protect tissues from damage by the immune
   system in a physiological situation. In cancer cells, the expression of
   checkpoint proteins can be dysregulated within the tumor
   microenvironment, an important factor in immune resistance ([44]Hoos,
   2016). Blockade of immune checkpoints seems to unleash the potential of
   the antitumor immune response in a fashion that is transforming human
   cancer therapeutics. Prominent checkpoint receptors are cytotoxic
   T-lymphocyte associated protein 4 (CTLA4), programmed cell death
   protein 1 (PD-1), and programmed death ligand 1 (PDL-1). In 2011, the
   first checkpoint inhibitor, ipilimumab, an anti-CTLA4 antibody
   developed by Bristol-Myers Squibb (BMS), was approved for treatment of
   melanoma ([45]O’Day et al., 2010). This marked the beginning of the
   cancer immunotherapy revolution and changed the paradigm of anti-cancer
   drug development. Since then, various immunotherapies have been
   approved, and these new therapies have quickly become the standard of
   care for many cancer types ([46]Pardoll, 2012).

   Currently, most of the drugs developed in cancer immunotherapies are
   monoclonal antibodies that block T-cell checkpoint receptors and their
   cognate ligands. However, not all patients benefit from these drugs,
   and they can have adverse effects ([47]Huck et al., 2018). To address
   these issues, a small-molecule approach has been proposed as an adjunct
   therapy, which is complementary to biologic drugs and shows a
   potentially synergistic effect ([48]Huck et al., 2018). Small molecules
   can regulate the adaptive and innate immune system and ultimately
   influence the course of events within the tumor microenvironment.
   Small-molecule drugs show advantages over biological drugs, namely high
   feasibility, greater exposure within the tumor microenvironment,
   diverse formulation, and low cost ([49]Adams et al., 2015). These
   advantages indicate enormous opportunities for small molecules in tumor
   immunotherapy ([50]Adams et al., 2015).

   Traditional Chinese medicine (TCM) has been widely used in China and
   has shown efficacy over a long history of clinical practice. Many herbs
   or ingredients from TCM have exerted immunomodulatory functions and
   antitumor effects via targeting the immune system ([51]Ma et al.,
   2013). Ginsenosides, the effective ingredients of Panax ginseng
   C.A.Mey., have been one of the most extensively studied ingredients
   that enhance the host immune response effect. A direct effect of
   ginsenoside Rg1 on helper T-cell activity and on Th1/Th2 lineage
   development has been identified ([52]Lee and Han, 2006). In addition,
   polysaccharides from Ganoderma lucidum (Ganoderma lucidum (Leyss.ex
   Fr.) Karst.)([53]Wang et al., 2017; [54]Zhang et al., 2019), Fructus
   psoraleae (Cullen corylifolium (L.) Medik./Fabaceae) ([55]Dai et al.,
   2007), Brucea javanica (Brucea javanica (L.) Merr./Simaroubaceae)
   ([56]Dai et al., 2007), and Radix Astragali (Astragalus mongholicus
   Bunge/Fabaceae) ([57]Jiang et al., 2010) were also reported to have
   immunological effects. Cancer patients can benefit from the
   immunomodulatory effects of TCM. A large retrospective cohort study
   found that patients with TCM utilization had a 32% decreased risk of
   death compared with patients without TCM utilization([58]Liao et al.,
   2017). These findings demonstrated that adjunctive therapy with TCM may
   improve overall survival for cancer patients.

   The rapid development of immuno-oncology (IO) has led to increasing
   demand for informatics techniques for the analysis of IO targets,
   drugs, tumors, and the tumor microenvironment ([59]Hammerbacher and
   Snyder, 2017). In order to track and understand the current IO agents
   in clinical development, the Cancer Research Institute presented an
   outline of the landscape of immuno-oncology drug development based on
   trusted and publicly available data sources ([60]Tang et al., 2018b;
   [61]Tang et al., 2018a; [62]Xin Yu et al., 2019). The Cancer Immunome
   Atlas (TCIA) was developed, which aims to provide comprehensive
   immunogenomic analyses of next-generation sequencing data for solid
   cancers ([63]Charoentong et al., 2017). Also developed was TIMER, a
   comprehensive resource for the systematical analysis of immune
   infiltrates across diverse cancer types ([64]Li et al., 2017). These
   informatics resources provide comprehensive information on IO, which
   help the cancer research community with improving efficiency and with
   innovation.

   Previous studies have illustrated the important roles of TCMs in immune
   regulation and have proposed a promising future for them in cancer
   immuno-therapies. However, to date, there has not been a comprehensive
   database of TCM for immuno-oncology. To address this challenge, we
   collected the IO targets and their small-molecule ligands, and
   information on those ligands was further mapped to the chemical
   ingredients of TCM. Comprehensive analysis of the relationship between
   TCM and cancer immunity was conducted. All these collected data were
   deposited in a web-based publicly accessible database, TCMIO, and
   cheminformatics and bioinformatics tools were integrated into the
   database for user analysis ([65]Figure 1).

Figure 1.

   [66]Figure 1
   [67]Open in a new tab

   Overall architecture of the development of the TCMIO database.

Materials and Methods

Data Preparation

   The IO targets were extracted from the literature ([68]Tang et al.,
   2018b; [69]Tang et al., 2018a). For each target, the protein name and
   gene name were standardized using the public database UniProt
   ([70]Bateman et al., 2015). The ligands for each target were extracted
   from ChEMBL (version 24.0) ([71]Gaulton et al., 2017), with an activity
   threshold of 10 μM. The activity types only include Ki, Kd, IC[50],
   EC[50], and potency. The prescriptions and herbs were extracted from
   the Chinese Pharmacopoeia (version 2015). Kew Medicinal Plant Names
   Services ([72]https://mpns.science.kew.org/mpns-portal/) were used to
   collect the species names of the TCM. The chemical ingredients were
   downloaded from TCMAnalyzer ([73]Liu et al., 2018), which integrates
   data from TCMSP ([74]Ru et al., 2014) and TCMID([75]Huang et al.,
   2018).

Network-Based Inference Approach to Target Prediction

   Since most TCM-derived natural products have few or no known IO
   targets, we exploited our in-house network-based predictive models to
   infer targets for each TCM ingredient. These models were developed to
   identify new targets of natural products via a balanced
   substructure-drug-target network-based inference (bSDTNBI) approach
   ([76]Fang et al., 2017b), which could prioritize potential targets for
   known drugs and new chemical entities (NCEs) by resource-diffusion
   processes of the substructure drug-target network ([77]Wu et al.,
   2016). During this process, two parameters, α and β, were imported to
   balance the initial resource allocation of different node types and the
   weighted values of different edge types. In addition, the parameter γ
   was also utilized to balance the influence of hub nodes. The final
   parameter values adopted for bSDTNBI were α = β =0.1, γ = –0.5, and k =
   2. Among the four network models developed with different types of
   fingerprints, bSDTNBI_KR performed best, with the highest values of P
   (0.049), R (0.752), eP (27.02), eR (27.24), and AUC (0.959). Thus,
   bSDTNBI_KR was used to predict the top 50 targets for each TCM
   ingredient. Finally, we mapped all the predicted targets into manually
   curated IO targets to construct an herb–ingredient–target network.

Cheminformatics Mining Tools

   Chemical structure mining tools are required to investigate the
   chemical ingredients of TCM and the ligands of the IO targets. TCMIO
   integrates Bingo, which is one of the most popular modern
   cheminformatics application tools. Similarity, substructure, and
   full-structure searches are supported for mining the structures of TCM
   ingredients and ligands. Chemical similarity searching is based on the
   concept that structurally similar compounds usually having similar
   properties or biological activities ([78]Maggiora et al., 2014).
   Similarity search estimates the similarity of the molecules by
   comparing their fingerprints using the default Tanimoto metric. Users
   can choose the appropriate similarity thresholds based on their
   requirements. A lower threshold will identify more targets but may
   bring lower accuracy, and vice versa.

Bioinformatics Mining Tools

   In order to investigate the mechanism of actions of TCM, gene
   enrichment analysis tools were added into TCMIO. The Database for
   Annotation, Visualization, and Integrated Discovery (DAVID) was
   integrated ([79]Huang et al., 2009). DAVID provides a comprehensive set
   of functional annotation tools for understanding the biological meaning
   behind a list of genes. For TCM or a prescription, the experimentally
   validated or predicted IO target genes could be used as input. TCMIO
   uses the Python web service of DAVID to process the input genes and
   performs KEGG pathway enrichment analysis. For each pathway, the genes
   involved in the pathway, P-value, fold enrichment, Benjamini value, and
   false discovery rate (FDR) are provided.

Web-Based Database Implementation

   TCMIO is implemented as a web-based publicly accessible database that
   can be accessed through the major web browsers. Beego, an open-source
   framework for rapid web development in Golang, is used for backend
   services. RESTful APIs are provided in TCMIO, and detailed usage
   information and examples are described on a downloads page. All data
   are stored in a PostgreSQL open-source relational database (version
   10.5). ipmDraw ([80]http://ipmdraw.iprexmed.com) is used as a structure
   drawing tool. The ChemDoodle web component ([81]Burger, 2015) is used
   for chemical structure visualization. Bingo
   ([82]https://lifescience.opensource.epam.com/bingo) is used as the
   molecular search cartridge owing to the state-of-the-art indexing
   algorithms within its underlying database server and due to it
   supporting high-performance similarity, substructure, and full
   structure search functions. The tools used for constructing the TCMIO
   database are summarized in [83]Supplementary Table S1.

Results and Discussion

Data Statistics

   After data preparation and standardization, a total of 400 unique IO
   targets derived from the literature were obtained, which can be
   classified into 34 protein families. TCMIO contains 157,195
   ligand-target interactions, extracted from ChEMBL, connecting 126,973
   ligands against 164 IO targets (164/400). There are an average of 958.5
   ligands for each target ([84]Figure 2A). In addition, a total of 1,493
   TCM formulas (prescriptions) as well as 618 herbs collected in our
   previous work were also integrated into TCMIO. 16,437 unique
   TCM-derived ingredients were obtained, and their similarities against
   ligands were calculated. The result showed that 68% of herb ingredients
   are similar to at least one ligand using the similarity threshold value
   of 0.8. This percentage increases to 88% with the threshold value at
   0.7. As shown in [85]Figure 2B, 20% of ingredients have at least 50
   similar ligands with a threshold of 0.7 compared to 10 with a threshold
   of 0.8. The structural similarity between chemical ingredients of TCM
   and the ligands of IO targets show promising prospects for TCMs and
   their ingredients in the development of immuno-therapy agents. The data
   entries in TCMIO are summarized in [86]Table 1.

Figure 2.

   [87]Figure 2
   [88]Open in a new tab

   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.

Table 1.

   Data statistics in TCMIO.
   Type Entries Descriptions
   Targets 400 Immuno-oncology targets derived from the literature and
   standardized using the UniProt database
   Prescriptions 1493 Prescriptions extracted from the Chinese
   Pharmacopoeia (2015 Edition)
   TCMs 618 TCMs extracted from the Chinese Pharmacopoeia (2015 Edition)
   Prescription-TCM-Relations 13403 Prescription–TCM relations extracted
   from the Chinese Pharmacopoeia (2015 Edition)
   Ligands 126972 Small molecule ligands against IO targets extracted from
   the ChEMBL database as an SDF file
   Ingredients 16437 Ingredients of TCMs extracted from various public TCM
   databases
   TCM-Ingredient-Relations 32847 TCM-ingredient relations extracted from
   public TCM databases
   Ingredient-Target-Relations 41527 Ingredient–target (IO) relations
   based on network prediction
   Ligand-Target-Relations 157195 Ligand–target (IO) relations extracted
   from the ChEMBL database
   [89]Open in a new tab

Web Interface

Browse

   TCMIO provides interactive tables in which users can browse the
   targets, ligands, ingredients, herbs, and prescriptions. For each data
   entry, more information is provided in the details page. Meanwhile,
   public resource links are also provided for easy user access. For IO
   targets, the target IDs in ChEMBL ([90]Gaulton et al., 2017), UniProt
   ([91]Bateman et al., 2015), and KEGG([92]Kanehisa et al., 2017) are
   provided. Drug information for targets can be accessed through a
   DrugBank ([93]Wishart et al., 2018) link. Structural information for
   targets can be accessed through a PDB([94]Rose et al., 2012) code. For
   ligands, the structure and the chemical properties, including molecular
   weight, hydrogen bond donor, hydrogen bond acceptor, and lipid-water
   partition coefficient AlogP, are given. For herbs, the part of the
   source used, property, flavor, channel tropism, effect, and indication
   are provided ([95]Figure 3). For prescriptions, the TCM components,
   indication, and effect are given. The browse page provides
   comprehensive information in an easy to access format.

Figure 3.

   [96]Figure 3
   [97]Open in a new tab

   Snapshot of the browse page.

Structure

   TCMIO aims to mine the relations of the TCM and IO through chemical
   structure mining tools. In the structure page, the user can conduct a
   structure search against ingredients or ligands. The widely used
   substructure and similarity search functions are provided. The user can
   draw a substructure or full structure of interest and then identify all
   the TCMs or targets with a structure query ([98]Figure 4). All of the
   output TCMs share that query structure, which suggests that these TCMs
   may have the same biological functions. The output protein list is the
   potential IO targets of the query structure, suggesting the potential
   molecular mechanisms of this compound.

Figure 4.

   [99]Figure 4
   [100]Open in a new tab

   Snapshot of the structure page.

MOA

   TCMIO provides mechanism of action (MOA) exploration for TCMs in
   immuno-oncology based on the network pharmacology approach. On the MOA
   page, the user can submit herbs or prescriptions, and the ingredients
   and their target information,including the experimentally validated and
   predicted IO target using network-based approach. The targets are
   submitted to the DAVID web server for further gene enrichment analysis.
   Finally, TCMIO generates an herb–ingredient–target network ([101]Figure
   5) and interactive tables ([102]Figure 6) for visualizing the
   relationships among them.

Figure 5.

   [103]Figure 5
   [104]Open in a new tab

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

Figure 6.

   [105]Figure 6
   [106]Open in a new tab

   Snapshots of pathways on the MOA page.

Case Studies

   In this section, we select a natural product (curcumin) and a Chinese
   herb (Lycium barbarum L.) as case studies to showcase how TCMIO
   facilitates research of TCM in immuno-oncology through target
   identification and MOA exploration.

Curcumin

   Curcumin is a phenolic compound mainly isolated from the natural
   pleiotropic herb Curcuma longa L. Accumulating studies and clinical
   trials ([107]https://clinicaltrials.gov) have demonstrated the
   potential therapeutic effects of curcumin against multiple tumors, such
   as breast cancer, prostate cancer, and pancreatic cancer ([108]Devassy
   et al., 2015; [109]Fang et al., 2017a). Recently, curcumin was
   identified as an effective immunomodulator to regulate the
   immunosuppressive tumor microenvironment ([110]Liao et al., 2018;
   [111]Wei et al., 2018). TCMIO has collected five known IO targets for
   curcumin from the ChEMBL database ([112]Table 2). Besides, TCMIO allows
   users to identify potential IO targets of an input ligand through the
   substructure or fingerprint similarity search functions. Here, by
   setting the similarity >= 0.8, three new IO targets of curcumin are
   predicted, namely nuclear factor erythroid 2-related factor 2 (NFE2L2),
   prostaglandin G/H synthase 2 (PTGS2), and signal transducer and
   activator of transcription 3 (STAT3). Interestingly, a recent study has
   reported that curcumin can exhibit significant inhibitory activity on
   IL-6-induced STAT3 activation with an IC[50] value of 1.6 µM,
   indicating its potential to be a candidate for the treatment of cancer
   related to the IL-6/STAT3 signaling pathway ([113]Jang et al., 2019).
   The rest of the predicted targets provide novel potential proteins
   involved in cancer immunity by curcumin, which deserve to be further
   validated by experimental assays.

Table 2.

   Immuno-Oncology (IO) targets of curcumin identified by TCMIO.
   Target Name ChEMBL ID Type Activity (nM)
   Epidermal growth factor receptor CHEMBL203 Known IC[50] = 8650
   Hypoxia-inducible factor 1-alpha CHEMBL426 Known Potency=6309.6
   Mitogen-activated protein kinase 1 CHEMBL4040 Known Potency=31622.8
   Thyrotropin receptor CHEMBL1963 Known Potency=10000
   Toll-like receptor 9 CHEMBL5804 Known IC[50] = 8362
   Nuclear factor erythroid 2-related factor 2 CHEMBL1075094 Predicted N/A
   Prostaglandin G/H synthase 2 CHEMBL230 Predicted N/A
   Signal transducer and activator of transcription 3 CHEMBL4026 Predicted
   N/A
   [114]Open in a new tab

Lycium barbarum L.

   Lycium barbarum L., also known as Wolfberry, Lycium chinense, and Goji
   berries, is traditionally utilized in Asian as a medicinal herb for its
   poly-pharmacological qualities, including an antitumor effect
   ([115]Tang et al., 2012). Numerous in vivo studies have confirmed that
   its main components, L. barbarum polysaccharides (LBPs), can enhance
   immunity and inhibit tumor growth ([116]Cheng et al., 2015; [117]Deng
   et al., 2018). However, the underlying mechanism of its antitumor
   immunity effect has not yet been fully elucidated. TCMIO integrates a
   useful systems pharmacology analysis function in the MOA page that
   provides convenient tools for uncovering the mechanisms of TCM in
   cancer immunity. As shown in [118]Figure 5, the ingredient-target
   network of Lycium barbarum L. constructed by TCMIO consists of 761
   interactions connecting 242 ingredients and 57 protein targets. KEGG
   enrichment analysis ([119]Supplementary Table S2) indicates the high
   correlativity between Lycium barbarum L. and multiple cancers, such as
   prostate cancer (P = 1.69E-08), pancreatic cancer (P = 5.29E-07), and
   melanoma (P = 1.53E-05). Among the top 20 enriched pathways presented
   in [120]Figure 7, the PI3K-Akt signaling pathway (P = 3.08E-09) is an
   important intracellular signaling pathway for regulating immune cell
   effector function and the tumor microenvironment([121]Collins et al.,
   2018; [122]O’Donnell et al., 2018). Therapeutic inhibition of the
   PI3K-Akt signaling pathway could augment tumor immunosurveillance by
   preventing the activation of immunosuppression and enhancing antitumor
   immune-intrinsic properties ([123]Xue et al., 2015). Besides, the VEGF
   signaling pathway (P = 6.28E-06) has been reported to exert modulatory
   effects on immune cells, including effector T cells, regulatory T cells
   (Tregs), myeloid-derived suppressor cells (MDSCs), tumor-associated
   macrophages (TAMs), and mast cells. Agents targeting the VEGF signaling
   pathway are becoming a promising therapeutic strategy for restoring
   antitumor immunity ([124]Yang et al., 2018). Overall, the KEGG
   enrichment analysis performed by TCMIO suggests the potential cancer
   immunity pathways that may be mediated by Lycium barbarum L.

Figure 7.

   [125]Figure 7
   [126]Open in a new tab

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

Conclusion

   Small molecules have shown great potential to stimulate the immune
   system to respond to various cancers. Widely used traditional herbs
   have displayed immunomodulatory functions and extensive antitumor
   effects, which indicates that exploring the relations of traditional
   herbs and immuno-oncology would expand our understanding of the MOAs of
   herbs and accelerate traditional medicine-inspired natural product drug
   discovery for cancer treatment. In this work, we developed a
   comprehensive database TCMIO, which integrates TCM data (prescription,
   herbs, and ingredients) and immuno-oncology data (IO targets and their
   small-molecule ligands). Moreover, cheminformatics and bioinformatics
   services were developed for data analysis. Various structure search
   methods are provided for mining the structures of traditional herbal
   ingredients and IO targeted ligands, which will help users to explore
   the herbs and design multi-target drugs. Herb–ingredient–target
   networks and pathway enrichment analysis for herb or prescription are
   provided for elucidating the MOAs of herbs. Currently, since the TCMs
   in TCMIO are only derived from the Chinese Pharmacopoeia (2015
   Edition), many medicinal plants with immunomodulatory functions are not
   included here. This could be a future direction for TCMIO. To our
   knowledge, TCMIO is the first comprehensive database of herbs for use
   in immuno-oncology, and we believe that this database will bring
   benefits to the research communities of traditional herbs and
   immuno-oncology.

Data Availability Statement

   All datasets generated for this study are included in the
   article/[127]Supplementary Material.

Author Contributions

   LX and JF contributed to the conception and design of the study. ZL and
   JD developed the web-based database. ZL, LC, BL, XF and QW collected
   and processed the data. ZL and CC performed data analysis. ZL, CC, and
   JF wrote the manuscript. All authors contributed to manuscript revision
   and read and approved the submitted version.

Funding

   This work has been supported by the National Natural Science Foundation
   of China (Grant No. 81703416, 81903912, and 81603318), the GDAS'
   Project of Science and Technology Development (Grant No.
   2019GDASYL-0103009), the Youth Scientific Research Training Project of
   GZUCM (No. 2019QNPY05), and the Medical Scientific Research Foundation
   of Guangdong Province of China (Grant No. A2017071).

Conflict of Interest

   Author JD was employed by the company Beijing Jingpai Technology Co.,
   Ltd.

   The remaining authors declare that the research was conducted in the
   absence of any commercial or financial relationships that could be
   construed as a potential conflict of interest.

Supplementary Material

   The Supplementary Material for this article can be found online at:
   [128]https://www.frontiersin.org/articles/10.3389/fphar.2020.00439/full
   #supplementary-material
   [129]Click here for additional data file.^ (24.4KB, docx)

References