cancers Cancers Cancers cancers 2072-6694 MDPI 10.3390/cancers4041180 cancers-04-01180 Article Annotating Cancer Variants and Anti-Cancer Therapeutics in Reactome Milacic Marija 1 Haw Robin 1 * Rothfels Karen 1 Wu Guanming 1 Croft David 2 Hermjakob Henning 2 D’Eustachio Peter 3 Stein Lincoln 1 Informatics and Bio-computing Platform, Ontario Institute for Cancer Research, Toronto, ON, M5G0A3, Canada; E-Mails: MOrlic-Milacic@oicr.on.ca (M.M.); Karen.Rothfels@oicr.on.ca (K.R.); guanmingwu@gmail.com (G.W.); lincoln.stein@gmail.com (L.S.) European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK; E-Mails: croft@ebi.ac.uk (D.C.); hhe@ebi.ac.uk (H.H.) Department of Biochemistry, NYU School of Medicine, New York, NY 10016, USA; E-Mail: Peter.D’Eustachio@nyumc.org Author to whom correspondence should be addressed; E-Mail: robin.haw@oicr.on.ca ; Tel.: +1-647-260-7985; Fax: +1-416-977-1118. 08 11 2012 12 2012 4 4 1180 1211 08 10 2012 31 10 2012 02 11 2012 © 2012 by the authors; licensee MDPI, Basel, Switzerland. 2012

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/).

Reactome describes biological pathways as chemical reactions that closely mirror the actual physical interactions that occur in the cell. Recent extensions of our data model accommodate the annotation of cancer and other disease processes. First, we have extended our class of protein modifications to accommodate annotation of changes in amino acid sequence and the formation of fusion proteins to describe the proteins involved in disease processes. Second, we have added a disease attribute to reaction, pathway, and physical entity classes that uses disease ontology terms. To support the graphical representation of “cancer” pathways, we have adapted our Pathway Browser to display disease variants and events in a way that allows comparison with the wild type pathway, and shows connections between perturbations in cancer and other biological pathways. The curation of pathways associated with cancer, coupled with our efforts to create other disease-specific pathways, will interoperate with our existing pathway and network analysis tools. Using the Epidermal Growth Factor Receptor (EGFR) signaling pathway as an example, we show how Reactome annotates and presents the altered biological behavior of EGFR variants due to their altered kinase and ligand-binding properties, and the mode of action and specificity of anti-cancer therapeutics.

 pathway database pathway visualization network visualization cancer annotation EGFR signaling 1. Introduction

The development of a malignantly transformed cell from a normal cell is a complex multi-step process that remains incompletely understood [1,2]. “Bottom-up” studies of relevant processes such as control of cell division, cell migration, tissue remodeling, and cell death have allowed the identification and characterization of many individual genes whose malfunction due to mutation or misregulation is associated with malignant transformation [3,4,5,6]. More recently, the development of high-throughput studies that exploit the availability of whole-genome sequencing has enabled “top-down” studies to systematically catalogue somatically mutated genes and altered patterns of gene expression in individual tumors [7,8]. These studies have confirmed the importance of genes identified as key players in the “bottom-up” studies, but have also suggested roles for additional genes and gene combinations not previously associated with processes relevant to malignancy.

Pathway databases have been effectively used to annotate our “bottom-up” understanding of molecular details of processes relevant to cell growth, differentiation, migration, and death. Here, we describe one such database, Reactome, focusing on extensions to this basic annotation strategy to allow the capture of details of disease processes, and on the development of data analysis tools to support the annotation and interpretation of gene sets identified in top-down studies.

Reactome is an open-source, open access, curated and peer-reviewed biological knowledgebase of human reactions, pathways and processes that serves as a platform for pathway visualization and analysis [9,10,11,12]. Reactome provides information about proteins and small molecules and how they participate in pathways to coordinate cellular events. The Reactome database employs a reductionist data model, which represents biology as reactions that convert input physical entities into output physical entities. The Reactome definition of a “reaction” is broad, including binding, dissociation, translocation and degradation, in addition to biochemical transformations of proteins and small molecules. Reactions are linked in causal chains to form pathways which in turn are grouped to represent larger biological processes like intermediary metabolism, innate immunity, solute transport, GPCR signal transduction, and apoptosis [13,14].

Reactome curators, in collaboration with outside expert researchers, annotate new pathways. The molecular details of every reaction are traceable to experimental evidence in the primary literature. If an event has not been directly studied in human systems, the appropriate non-human reaction is annotated and the homologous human one is inferred from it. Every pathway module is peer-reviewed by an additional expert. New and revised modules are publicly released to the Reactome website every quarter. Pathways, reactions, protein and small molecule entities are cross referenced with accession numbers and identifiers to a number of well-established databases, including NCBI Gene [15], Ensembl [16] and UniProt databases [17], UCSC Genome Browser [18], and ChEBI [19]. Physical entities and events are further linked to “Molecular Function”, “Biological Process” and “Cellular Component” ontology terms found in Gene Ontology (GO) [20].

Currently, the pathways in Reactome cover about 25% of the gene products encoded in the human genome, and contain the normal versions of many pathways that can be abnormally activated in cancer, such as “Signaling by EGFR” [21], “Signaling by FGFR” [22], “Signaling by NOTCH” [23], “PIP3 Activates AKT Signaling” [24], “RAF/MAP Kinase Cascade” [25]. We have also annotated a number of pathways that can be inactivated in cancer, such as pathways involving TP53: “Apoptosis” [26] and “Cell Cycle Checkpoints” [27], as well as pathways involving the RB1 protein family: “Mitotic G1-G1/S phases” [28].

Here, we use the epidermal growth factor receptor (EGFR), fibroblast growth factor receptor (FGFR) and PI3K/AKT signaling pathways to illustrate Reactome annotation of cancer pathways. EGFR and FGFR are transmembrane receptor tyrosine kinases. EGFR is activated by several growth factors, including the epidermal growth factor (EGF) [29]. FGFR family members (FGFR1, FGFR2, FGFR3 and FGFR4) are activated by 18 of 22 existing human fibroblast growth factors (FGFs), with each FGFR showing different affinity for individual FGFs [30]. Growth factor binding induces a conformational change that enables dimerization and trans-autophosphorylation on C-tail tyrosine residues of EGFR [31] and FGFRs [32,33,34]. Phosphorylated tyrosines in the C-tails of EGFR and FGFR serve as docking sites for downstream effectors that, upon binding to phosphorylated receptors, activate intracellular signaling cascades that regulate cellular proliferation, differentiation, and survival [30,35,36]. One of the intracellular signaling cascades downstream of EGFR and FGFRs is PI3K/AKT signaling [37,38]. PI3K class IA enzymes are heterodimers composed of a regulatory subunit p85 (encoded by PIK3R1, PIK3R2 or PIK3R3) and a catalytic subunit p110 (encoded by PIK3CA, PIK3CB or PIK3CD) [39]. The catalytic p110 subunit of PI3K becomes activated when inhibitory contacts with the p85 regulatory subunit are relieved by p85 binding to phosphorylated adaptor proteins recruited to activated EGFR or FGFRs [40,41]. Active PI3K class I enzymes phosphorylate PIP2 (phosphatidylinositol-4,5-bisphophate), converting it into PIP3 (phosphatidylinositol-3,4,5-trisphosphate), a reaction negatively regulated by PTEN phosphatase [42]. PIP3 serves as a second messenger that activates AKT (AKT1, AKT2 or AKT3) [43]. AKT family members are cytosolic and nuclear serine/threonine protein kinases involved in phosphorylation-mediated regulation of numerous proteins involved in cell survival and growth [39].

EGFR, FGFRs, PIK3CA, PIK3R1 and AKT1 are proto-oncogenes, frequently activated in cancer through gain-of-function mutations and/or overexpression. PTEN is an established tumor suppressor gene, with a frequent loss of function in cancer [44]. Gain-of-function mutations in EGFR [45,46] and FGFRs [47,48,49,50,51] usually act by conferring ligand-independent activation or by increasing tyrosine kinase catalytic activity. Mutations in PIK3R1 or PIK3CA abolish inhibitory interactions between the regulatory and catalytic subunit of PI3K [52,53,54,55,56], resulting in PI3K activity in the absence of growth factor stimulation. AKT1 gain of function mutations increase AKT1 affinity for PIP2, allowing AKT1 activation in the absence of PI3K activity and PIP3 generation [57]. PTEN loss-of-function mutations usually affect the phosphatase domain, impairing PTEN catalytic activity and removal of PIP3 [58].

Small molecule therapeutics and recombinant antibodies are being developed as potential treatments for cancers driven by increased activity of EGFR, FGFR and/or PI3K/AKT. Gefitinib and erlotinib, small tyrosine kinase inhibitors, are approved for the clinical treatment of cancers harboring specific EGFR mutations. A recombinant antibody, cetuximab, is approved for the clinical treatment of cancers that overexpress wild-type EGFR [59]. Small molecules that inhibit the catalytic activity of FGFRs [60], PI3K and AKT [61] are currently undergoing clinical trials or are in pre-clinical development.

We have extended the Reactome data model and enhanced the web tools to permit the annotation and visualization of the altered biological behavior of protein variants. These enhancements can be applied to any molecular abnormality due to germline or somatic mutation, as well as to abnormalities due to expression of foreign proteins encoded by genomes of infectious agents like viruses or intracellular parasites.

 2. Results and Discussion 2.1. Annotation of Cancer-Perturbed Pathways

Pathways that stimulate cell growth, cell division and survival, and maintenance of undifferentiated state are activated in cancer through gain-of-function mutations in participating proto-oncogenes and/or their overexpression. On the other hand, pathways that negatively regulate cell division, growth and survival, or promote cellular differentiation are inactivated through loss-of-function mutations in tumor suppressor genes and/or their downregulation. To capture these two groups of cancer effectors, we have added new classes of data to the Reactome database.

2.1.1. Extension of Protein Modifications to Accommodate Annotation of Changes in Amino Acid Sequence

The protein modification class in the Reactome data model was constructed to support annotation of covalent co- and post-translational modifications of proteins such as the phosphorylation of serine residues. To allow for annotation of mutant proteins, two new subclasses of modifications were introduced: Replaced Residue and Fragment Modification (Figure 1a). The Replaced Residue class is used to annotate amino acid substitutions in a protein sequence. A Replaced Residue instance associates a specific coordinate of a protein sequence with two PSI-MOD ontology [62] attributes: the first identifies the amino acid found at that position in the normal protein and the second attribute identifies the amino acid that replaces it in the mutant protein. For example, the most frequently found mutation in EGFR is the substitution of a leucine residue at position 858 with an arginine residue in the catalytic domain of EGFR. This mutation disrupts autoinhibitory interactions, facilitating adoption of an active conformation [63]. The Reactome record for EGFR L858R (Figure 1b) indicates this amino acid substitution.

The FragmentModification subclass includes two subclasses, FragmentInsertionModification and FragmentDeletionModification. FragmentInsertionModification is used to annotate insertions of amino acid residues in a protein sequence. FragmentDeletionModification is used to annotate removal of amino acid residues. PIK3R1 Y463_L466del is a variant of the PI3K regulatory subunit p85alpha found in endometrial cancer (Figure 1c). This PIK3R1 mutant lacks four amino acid residues in the inter-SRC homology 2 (iSH2) domain. PIK3R1 is able to bind the catalytic subunit of PI3K, PIK3CA (p110alpha), but does not inhibit it, resulting in the constitutive activity of PI3K, in the absence of growth factors [64]. The deletion coordinates are indicated in the Reactome record for PIK3R1 Y463_L466del mutant.

Annotation of cancer mutations. Reactome records are not displayed in their entirety due to space limitations. (a) Subclasses of protein modifications contained in Reactome class Abstract Modified Residue. Currently, Reactome website displays all subclasses of protein modifications in the single field “Post-translational modification(s)”. Future changes to the website will allow chemical modifications to be distinguished from effects of mutations. (b) Reactome record for EGFR L858R caused by a missense mutation that replaces leucine residue at position 858 with arginine. (c) Reactome record for PIK3R1 Y463_L466del caused by an in-frame intragenic deletion in PIK3R1 that removes amino acid residues from position 463 to position 466, as captured by the Fragment Deletion Modification instance. (d) Reactome record for BCR-FGFR1 fusion protein. Truncation of the wild-type BCR protein sequence is shown by altered end coordinate. FGFR1 fragment fused to BCR is annotated as an insertion using FragmentInsertionModification class.

The FragmentModification class can also be used to annotate fusion proteins. For example, the translocation t(8;22)(p11;q11) in chronic myeloid leukemia produces a BCR-FGFR1 fusion that consists of the first four exons of BCR and exons 9–18 of FGFR1 [65]. The BCR-FGFR1 fusion protein is annotated as an Entity with Accessioned Sequence (Figure 1d) that consists of a truncated BCR protein, starting at position 1 and ending at position 584 of the reference UniProt sequence P11274 (human BCR). Then, a FragmentInsertionModification instance defines insertion of amino acids 429–822 of the UniProt reference sequence P11362 (human FGFR1) at position 585 of BCR (Figure 1d).

On the Reactome website, selecting a physical entity or an event node by clicking on a pathway diagram brings up a record for that particular instance in the details pane, which appears by clicking on the yellow triangle at the bottom of the Pathway Browser page. Selecting EGFRvIII in the diagram (Figure 2a), brings up Reactome information on this mutant protein, as well as interactive cross references that direct users to other Reactome website pages or other databases of interest (Figure 2b). Each cancer-related disease variant record cross-references available records in the Catalogue of Somatic Mutations in Cancer (COSMIC) database (Table 1) [66]. The EGFRvIII record displayed on Reactome website links to COSMIC record 21351, which provides information on nucleotide sequence changes and tumor samples in which this mutation was reported.

 2.1.2. Associating Disease Attributes with Physical Entities and Events

All physical entities related to disease variants, such as proteins, sets of proteins, and protein complexes are tagged with disease attributes (Table 1), using a term from the Disease Ontology (DO) [67]. This DO record provides, when possible, a link to the synonymous disease record in the National Cancer Institute Thesaurus (NCIt) [68]. The disease attribute of the physical entity is assigned to all reactions and pathways in which it participates.

Besides providing information on disease involvement of specific proteins and directing users to more detailed disease descriptions, a disease attribute annotation enables users to search Reactome database for proteins and events associated with a specific disease. For example, in Figure 2b, a DO instance “adult glioblastoma multiforme” is associated with EGFRvIII. Clicking on the “adult glioblastoma multiforme” link displayed on Reactome website (Figure 2b) provides a DO identifier for this disease instance (3075) and also lists all other proteins in Reactome database whose mutant forms are associated with adult glioblastoma multiforme (Figure 2c). Thus, Reactome provides cancer researchers with a quick access to cancer type-specific disease variants and information on the mechanism of action for each variant annotated.

 2.1.3. Mode of Action and Specificity of Anti-Cancer Therapeutics

The Reactome data model allows for annotation of small molecules and antibodies used as anti-cancer therapeutics, as well as the annotation of their specific mode of action. We have annotated nine small tyrosine kinase inhibitors (TKIs) used to inhibit EGFR kinase activity in cancer [59,69], as well as the recombinant antibody cetuximab [70] (Figure 3a). In addition, we annotated five benzaquinoid ansamycins that inhibit the HSP90 chaperone protein that stabilizes EGFR mutant proteins [71], twelve anti-FGFR TKIs [60], one anti-FGFR recombinant antibody [72], ten small molecules that inhibit the catalytic subunit of PI3K [61], and three small molecules that inhibit AKT [61] (Table 2).

Disease information presented interactively on the Reactome website. (a) Selecting an entity in the pathway diagram, EGFRvIII mutant in this case and opening the Reactome details pane by clicking on the yellow triangle at the bottom of the Pathway Browser page brings up a record for the selected instance. A pathway hierarchy displayed on the left hand side shows how the selected instance is related to the rest of the pathway content. (b) Reactome record, displayed in the details pane, provides information on the selected entity, including cross-references to other databases such as COSMIC, UniProt, GO. (c) Cross-reference to Disease Ontology: clicking on a disease attribute, such as “adult glioblastoma multiforme”, provides a Disease Ontology (DO) identifier for this disease instance (3075) and lists all proteins in Reactome database associated with adult glioblastoma multiforme.

Mode of action and specificity of anti-EGFR cancer therapeutics. (a) Anti-EGFR therapeutics differ in their specificity for EGFR cancer variants, as well as in their mode of action (non-covalent vs. covalent binding). (b) Classification of EGFR-binding small tyrosine kinase inhibitors (TKIs) according to spectrum and reversibility of their binding.

For each anti-EGFR TKI, we specify whether it associates with the EGFR catalytic domain through formation of a covalent (irreversible) bond or through a non-covalent interaction (reversible). We also specify whether a TKI is EGFR-specific or whether it can inhibit other receptor tyrosine kinases besides EGFR (EGFRplus). Each small molecule instance we annotate is associated with the Chemical Entities of Biological Interest (ChEBI) database identifier [19]. On the Reactome website, a link to a corresponding ChEBI record is displayed after the name of each small molecule. Clicking on the ChEBI link associated with gefitinib (Figure 3b) directs the user to the gefitinib information in ChEBI, displaying its molecular structure and additional information not directly captured by Reactome.

EGFR cancer mutants in Reactome are classified into sets based on their sensitivity to various TKIs (Figure 3a). Ligand responsive EGFR mutants sensitive to non-covalent TKIs can be inhibited by low concentrations of non-covalent (reversible) TKIs that do not significantly affect the function of wild-type EGFR and therefore produce minimal side effects. Ligand responsive EGFR mutants resistant to non-covalent TKIs can be inhibited by covalent (irreversible) TKIs. As can be seen from the diagram (Figure 3a), concentrations of irreversible TKIs that inhibit EGFR mutants also inhibit the function of the wild-type protein, causing more severe side effects, as described in event summations. Cetuximab is used for treatment of cancers that overexpress wild-type EGFR protein, usually due to amplification of the EGFR locus [59,70].

cancers-04-01180-t001_Table 1

Cancer-related disease variants published by Reactome to date. A total of ~150 cancer mutants have been published since the start of the project in December 2010.

Disease Variant COSMIC Identifier(s) Mutation Type Disease Reactome Pathway Name
EGFR A289D mutant 21685 Missense Glioblastoma Signaling by EGFR in Cancer
EGFR A289T mutant 21686 Missense Glioblastoma, oligodendroglioma Signaling by EGFR in Cancer
EGFR A289V mutant 21687 Missense Glioblastoma Signaling by EGFR in Cancer
EGFR D770_N771insNPG mutant Insertion Lung cancer Signaling by EGFR in Cancer
EGFR D770_N771insNPH mutant 48920 Insertion Lung cancer Signaling by EGFR in Cancer
EGFR E746_A750del mutant 6223, 129800, 6225 Deletion Breast, head and neck, kidney, lung, ovarian, salivary gland and thyroid cancer Signaling by EGFR in Cancer
EGFR E746_A750del; T790M mutant Deletion; Missense Lung cancer Signaling by EGFR in Cancer
EGFR E746_S752delinsV mutant 18492, 18426, 12384, 85797 Deletion Lung cancer Signaling by EGFR in Cancer
EGFR E746_T751delinsA mutant 20845, 12678, 13549 Deletion Head and neck, lung cancer Signaling by EGFR in Cancer
EGFR G598V mutant 34167, 21690 Missense Glioblastoma Signaling by EGFR in Cancer
EGFR G719A mutant 6239, 13448 Missense Lung cancer Signaling by EGFR in Cancer
EGFR G719C mutant 6253, 20881 Missense Lung cancer Signaling by EGFR in Cancer
EGFR G719S mutant 6252, 13983 Missense Colorectal, lung cancer Signaling by EGFR in Cancer
EGFR L747_A750delinsP mutant 13562, 12382, 12422 Deletion Head and neck, lung cancer Signaling by EGFR in Cancer
EGFR L747_P753delinsS mutant 13564, 12370 Deletion Head and neck, lung, prostate cancer Signaling by EGFR in Cancer
EGFR L747_S752del mutant 13984, 6255 Deletion Lung cancer Signaling by EGFR in Cancer
EGFR L747_T751del mutant 24432, 12369, 6254, 23571 Deletion Lung cancer Signaling by EGFR in Cancer
EGFR L747_T751delinsP mutant 24573, 12383, 22944 Deletion Lung cancer Signaling by EGFR in Cancer
EGFR L858R mutant 6224, 12979 Missense Breast, lung, ovarian, stomach, thymus and thyroid cancer, mesothelioma Signaling by EGFR in Cancer
EGFR L858R;T790M mutant Missense; Missense Lung cancer Signaling by EGFR in Cancer
EGFR L861Q mutant 6213, 13173 Missense Lung cancer, glioblastoma Signaling by EGFR in Cancer
EGFR M766_A767insASV mutant Insertion Lung cancer Signaling by EGFR in Cancer
EGFR R108K mutant 21683, 34166 Missense Glioblastoma Signaling by EGFR in Cancer
EGFR T263P mutant 21684 Missense Glioblastoma Signaling by EGFR in Cancer
EGFR V738_K739insKIPVAI mutant Insertion Lung cancer Signaling by EGFR in Cancer
EGFRvIII mutant 21351 Deletion Lung cancer, glioblastoma Signaling by EGFR in Cancer
BCR-FGFR1 fusion mutant Translocation Myeloid leukemia Signaling by FGFR in Disease
CNTRL-FGFR1 fusion mutant Translocation Myeloid leukemia Signaling by FGFR in Disease
CPSF6-FGFR1 fusion mutant Translocation Myeloid leukemia Signaling by FGFR in Disease
CUX1-FGFR1 fusion mutant Translocation Myeloid leukemia Signaling by FGFR in Disease
FGFR1 K656E mutant 35673 Missense Glioblastoma Signaling by FGFR in Disease
FGFR1 N546K mutant 19176 Missense Glioblastoma, hypochondroplasia Signaling by FGFR in Disease
FGFR1 P252S mutant Missense Melanoma Signaling by FGFR in Disease
FGFR1 P252T mutant 12834 Missense Lung cancer Signaling by FGFR in Disease
FGFR1 R576W mutant 19177 Missense Glioblastoma Signaling by FGFR in Disease
FGFR1OP-FGFR1 fusion mutant Translocation Myeloid leukemia Signaling by FGFR in Disease
FGFR1OP2-FGFR1 fusion mutant Translocation Myeloid leukemia Signaling by FGFR in Disease
FGFR1c P252R mutant Missense Pfeiffer syndrome Signaling by FGFR in Disease
FGFR2 K660E mutant 36909 Missense Endometrial cancer Signaling by FGFR in Disease
FGFR2 K660M mutant 49175 Missense Cervical cancer Signaling by FGFR in Disease
FGFR2 K660N mutant 49173 Missense Endometrial cancer, Crouzon syndrome Signaling by FGFR in Disease
FGFR2 L764fs*4STOP mutant Frameshift Endometrial cancer Signaling by FGFR in Disease
FGFR2 N549H mutant Missense Crouzon syndrome Signaling by FGFR in Disease
FGFR2 N549K mutant 36912, 36902 Missense Endometrial cancer Signaling by FGFR in Disease
FGFR2 S267P mutant Missense Stomach cancer, Crouzon syndrome Signaling by FGFR in Disease
FGFR2 W290C mutant 41286 Missense Lung cancer, Pfeiffer syndrome Signaling by FGFR in Disease
FGFR2b P253R mutant 49170 Missense Endometrial cancer, acrocephalosyndactylia Signaling by FGFR in Disease
FGFR2b S252W mutant 36903, 41289 Missense Endometrial, ovarian cancer, acrocephalosyndactylia, craniosynostosis Signaling by FGFR in Disease
FGFR2b S373C mutant 36905 Missense Endometrial cancer Signaling by FGFR in Disease
FGFR2b Y376C mutant 36904, 41290 Missense Endometrial, ovarian cancer Signaling by FGFR in Disease
FGFR2c A314D mutant 49171 Missense Endometrial cancer Signaling by FGFR in Disease
FGFR2c A314S mutant Missense Bone development disease Signaling by FGFR in Disease
FGFR2c A315S mutant Missense Syndactyly Signaling by FGFR in Disease
FGFR2c A315T mutant 30777 Missense Endometrial cancer Signaling by FGFR in Disease
FGFR2c P253R mutant 49170 Missense Endometrial cancer, acrocephalosyndactylia Signaling by FGFR in Disease
FGFR2c S252W mutant 41289, 36903 Missense Endometrial, ovarian cancer, acrocephalosyndactylia, craniosynostosis Signaling by FGFR in Disease
FGFR2c S372C mutant Missense Beare-Stevenson cutis gyrata syndrome Signaling by FGFR in Disease
FGFR2c W290G mutant Missense Crouzon syndrome, Pfeiffer syndrome Signaling by FGFR in Disease
FGFR2c Y375C mutant Missense Beare-Stevenson cutis gyrata syndrome Signaling by FGFR in Disease
FGFR3 795fs*139STOP mutant Frameshift Multiple myeloma, thanatophoric dysplasia Signaling by FGFR in Disease
FGFR3 A391E mutant 721 Missense Bladder cancer, Crouzon syndrome Signaling by FGFR in Disease
FGFR3 G370C mutant 716, 35897 Missense Bladder cancer, thanatophoric dysplasia Signaling by FGFR in Disease
FGFR3 G380R mutant 24842, 24812 Missense Bladder cancer, multiple myeloma, achondroplasia Signaling by FGFR in Disease
FGFR3 G382D mutant 727 Missense Multiple myeloma Signaling by FGFR in Disease
FGFR3 K650E mutant 719, 35899 Missense Bladder, testicular cancer, multiple myeloma, thanatophoric dysplasia Signaling by FGFR in Disease
FGFR3 K650M mutant 720, 85791 Missense Bladder, testicular cancer, multiple myeloma, thanatophoric dysplasia Signaling by FGFR in Disease
FGFR3 K650N mutant Missense Bladder, testicular cancer, hypochondroplasia Signaling by FGFR in Disease
FGFR3 K650Q mutant 726 Missense Bladder cancer, hypochondroplasia Signaling by FGFR in Disease
FGFR3 K650T mutant 731 Missense Bladder, testicular cancer, hypochondroplasia Signaling by FGFR in Disease
FGFR3 R248C mutant 714, 35896 Missense Bladder cancer, multiple myeloma, thanatophoric dysplasia Signaling by FGFR in Disease
FGFR3 S371C mutant 17461, 35898 Missense Bladder cancer, thanatophoric dysplasia Signaling by FGFR in Disease
FGFR3 Y373C mutant 718, 29428 Missense Bladder cancer, multiple myeloma, thanatophoric dysplasia Signaling by FGFR in Disease
FGFR3b G697C mutant 24802 Missense Head and neck cancer Signaling by FGFR in Disease
FGFR3b S249C mutant 715, 29427 Missense Bladder, cervical, head and neck, prostate cancer, thanatophoric dysplasia Signaling by FGFR in Disease
FGFR3c P250R mutant Missense Acrocephalosyndactylia, craniosynostosis Signaling by FGFR in Disease
FGFR4 N535D mutant Missense Rhabdomyosarcoma Signaling by FGFR in Disease
FGFR4 N535K mutant Missense Rhabdomyosarcoma Signaling by FGFR in Disease
FGFR4 V550E mutant Missense Rhabdomyosarcoma Signaling by FGFR in Disease
FGFR4 V550L mutant Missense Rhabdomyosarcoma Signaling by FGFR in Disease
FGFR4 Y367C mutant Missense Breast cancer Signaling by FGFR in Disease
LRRFIP1-FGFR1 fusion mutant Translocation Myeloid leukemia Signaling by FGFR in Disease
MYO18A-FGFR1 fusion mutant Translocation Myeloid leukemia Signaling by FGFR in Disease
TRIM24-FGFR1 fusion mutant Translocation Myeloid leukemia Signaling by FGFR in Disease
ZMYM2-FGFR1 fusion mutant Translocation Myeloid leukemia Signaling by FGFR in Disease
IDH1 R132C mutant 28747, 41294 Missense Glioblastoma The citric acid (TCA) cycle and respiratory electron transport
IDH1 R132H mutant 28746, 41291 Missense Glioblastoma The citric acid (TCA) cycle and respiratory electron transport
IDH1 R132L mutant 28750 Missense Glioblastoma The citric acid (TCA) cycle and respiratory electron transport
IDH1 R132S mutant 28748 Missense Glioblastoma The citric acid (TCA) cycle and respiratory electron transport
PIK3CA E542K mutant 760, 29329 Missense Bladder, breast, cervical, colorectal, endometrial, esophageal, gallbladder, head and neck, kidney, liver, lung, ovarian, penis, pharynx, pituitary, skin sweat gland, stomach, thyroid cancer, glioblastoma, lymphocytic leukemia PI3K/AKT Signaling in Cancer
PIK3CA E542Q mutant 17442 Missense Breast, colorectal, endometrial, lung cancer PI3K/AKT Signaling in Cancer
PIK3CA E542V mutant 762 Missense Breast, colorectal, endometrial, ovarian cancer PI3K/AKT Signaling in Cancer
PIK3CA E545A mutant 12458 Missense Breast, colorectal, endometrial, esophageal, lung, ovarian, prostate, thyroid cancer, glioblastoma, hepatoblastoma, synovial sarcoma PI3K/AKT Signaling in Cancer
PIK3CA E545G mutant 764 Missense Bladder, breast, colorectal, endometrial, head and neck, larynx, pituitary, stomach cancer, myeloid leukemia, non-Hodgkin lymphoma, retinoblastoma PI3K/AKT Signaling in Cancer
PIK3CA E545K mutant 763, 29328 Missense Bladder, breast. cervical, colorectal, endometrial, esophageal, gallbladder, head and neck, kidney, lung, ovarian, pancreatic, penis, pharynx, skin, stomach, sweat gland, thyroid cancer, melanoma, glioblastoma, medulloblastoma, myeloma, pituitary adenoma PI3K/AKT Signaling in Cancer
PIK3CA E545Q mutant 27133 Missense Bladder, breast, colorectal, esophageal, head and neck, ovarian, thyroid cancer PI3K/AKT Signaling in Cancer
PIK3CA E545V mutant 144201 Missense Ovarian cancer PI3K/AKT Signaling in Cancer
PIK3CA H1047L mutant 776, 30744 Missense Bladder, breast, colorectal, endometrial, head and neck, liver, lung, ovarian, pharynx, thyroid cancer, glioblastoma, non-Hodgkin lymphoma, pituitary adenoma PI3K/AKT Signaling in Cancer
PIK3CA H1047R mutant 775, 29325 Missense Bladder, breast, cervical, colorectal, endometrial, gallbladder, head and neck, liver, lung, ovarian, pancreatic, pharynx, prostate, stomach, thyroid cancer, glioblastoma, medulloblastoma, melanoma, meningioma, neuroectodermal tumor, non-Hodgkin lymphoma, pituitary adenoma PI3K/AKT Signaling in Cancer
PIK3CA H1047Y mutant 774, 29326 Missense Breast, colorectal, endometrial, lung, ovarian cancer, glioblastoma PI3K/AKT Signaling in Cancer
PIK3CA M1043I mutant 773, 29313, 94984 Missense Bladder, breast, cervical, colorectal, endometrial, lung, ovarian, thyroid cancer, glioblastoma PI3K/AKT Signaling in Cancer
PIK3CA M1043T mutant 12463 Missense Ovarian, stomach cancer, glioblastoma PI3K/AKT Signaling in Cancer
PIK3CA M1043V mutant 12591, 30743 Missense Breast, colorectal, endometrial, head and neck, lung, ovarian, pharynx cancer, glioblastoma PI3K/AKT Signaling in Cancer
PIK3CA Q546E mutant 6147 Missense Breast, cervical, colorectal, endometrial cancer PI3K/AKT Signaling in Cancer
PIK3CA Q546H mutant 24712, 30740 Missense Cervical, colorectal, endometrial cancer PI3K/AKT Signaling in Cancer
PIK3CA Q546K mutant 766, 30738 Missense Breast, colorectal, endometrial, lung, ovarian, stomach cancer, lymphocytic leukemia PI3K/AKT Signaling in Cancer
PIK3CA Q546L mutant 25041, 85754 Missense Breast, colorectal, gallbladder, head and neck cancer PI3K/AKT Signaling in Cancer
PIK3CA Q546P mutant 767 Missense Breast, colorectal, endometrial, ovarian cancer, glioma PI3K/AKT Signaling in Cancer
PIK3CA Q546R mutant 12459, 30739 Missense Breast. colorectal, endometrial, prostate, stomach cancer PI3K/AKT Signaling in Cancer
PIK3CA R38C mutant 744 Missense Colorectal cancer PI3K/AKT Signaling in Cancer
PIK3CA R38G mutant 40945 Missense Glioblastoma PI3K/AKT Signaling in Cancer
PIK3CA R38H mutant 745, 49022 Missense Breast, colorectal, endometrial cancer PI3K/AKT Signaling in Cancer
PIK3CA R38S mutant 87310 Missense Stomach cancer PI3K/AKT Signaling in Cancer
PIK3R1 D560H mutant 125378 Missense Pharynx cancer PI3K/AKT Signaling in Cancer
PIK3R1 D560Y mutant 335765 Missense Glioblastoma PI3K/AKT Signaling in Cancer
PIK3R1 G376R mutant 35827, 132923 Missense Endometrial cancer, glioblastoma PI3K/AKT Signaling in Cancer
PIK3R1 H450_E451del mutant 39296 Deletion Endometrial cancer, glioblastoma PI3K/AKT Signaling in Cancer
PIK3R1 K459del mutant 87216 Deletion Endometrial cancer PI3K/AKT Signaling in Cancer
PIK3R1 N564D mutant 42912 Missense Colorectal, endometrial cancer, glioblastoma PI3K/AKT Signaling in Cancer
PIK3R1 N564K mutant 35808 Missense Glioblastoma PI3K/AKT Signaling in Cancer
PIK3R1 R574_T576del mutant 87219 Deletion Endometrial cancer PI3K/AKT Signaling in Cancer
PIK3R1 R574I mutant 85927 Missense Colorectal cancer PI3K/AKT Signaling in Cancer
PIK3R1 R574T mutant 87544 Missense Bladder, breast cancer PI3K/AKT Signaling in Cancer
PIK3R1 Y463_L466del mutant 87228 Deletion Endometrial cancer PI3K/AKT Signaling in Cancer
AKT1 E17K mutant 33765, 34142 Missense Breast, colorectal, ovarian cancer PI3K/AKT Signaling in Cancer
PTEN R130G mutant 5219 Missense Endometrial, lung, ovarian cancer, glioblastoma PI3K/AKT Signaling in Cancer
PTEN R130Q mutant 5033 Missense Breast, colorectal, endometrial, ovarian, thyroid cancer, glioma, histiocytoma PI3K/AKT Signaling in Cancer
PTEN R130L mutant 5216 Missense Breast, endometrial cancer, Cowden syndrome PI3K/AKT Signaling in Cancer
PTEN C124S mutant 5224, 5271 Missense Endometrial, thyroid cancer, glioblastoma PI3K/AKT Signaling in Cancer
PTEN C124R mutant Missense Thyroid adenoma, Cowden syndrome PI3K/AKT Signaling in Cancer
PTEN R173H mutant 5039 Missense Endometrial, ovarian cancer, glioma PI3K/AKT Signaling in Cancer
PTEN R173C mutant 5089, 24682 Missense Endometrial cancer, glioblastoma, lymphocytic leukemia, melanoma PI3K/AKT Signaling in Cancer
PTEN R173P mutant 12735 Missense Testicular cancer PI3K/AKT Signaling in Cancer
PTEN S170N mutant 5045 Missense Endometrial cancer, glioblastoma PI3K/AKT Signaling in Cancer
PTEN S170R mutant Missense Bannayan-Riley-Ruvalcaba syndrome PI3K/AKT Signaling in Cancer
PTEN H123Y mutant 5078 Missense Endometrial cancer PI3K/AKT Signaling in Cancer
PTEN G129E mutant 28917 Missense Endometrial cancer PI3K/AKT Signaling in Cancer
PTEN G129R mutant 5092 Missense Thyroid cancer, glioblastoma PI3K/AKT Signaling in Cancer
PTEN H93Y mutant 5043 Missense Endometrial cancer, glioma, medulloblastoma PI3K/AKT Signaling in Cancer
PTEN H93A mutant Missense Cancer PI3K/AKT Signaling in Cancer
PTEN H93R mutant 5060 Missense Glioblastoma, autism spectrum disorders PI3K/AKT Signaling in Cancer
PTEN H93D mutant 5283 Missense Endometrial cancer PI3K/AKT Signaling in Cancer
PTEN H93Q mutant 5186 Missense Glioblastoma PI3K/AKT Signaling in Cancer
PTEN R130P mutant 5277 Missense Breast, endometrial, glioblastoma PI3K/AKT Signaling in Cancer
PTEN C124F mutant 13578 Missense Lung cancer PI3K/AKT Signaling in Cancer
PTEN C124Y mutant 5140 Missense Stomach cancer PI3K/AKT Signaling in Cancer
PTEN S170I mutant 5218 Missense Glioblastoma PI3K/AKT Signaling in Cancer
PTEN S170G mutant 5063 Missense Glioblastoma PI3K/AKT Signaling in Cancer
PTEN G129V mutant 5276 Missense Endometrial cancer PI3K/AKT Signaling in Cancer
PTEN R130* mutant 21342, 5152 Nonsense Cervical, colorectal, endometrial, lung, ovarian, prostate, thyroid cancer, glioblastoma, medulloblastoma, leimyosarcoma PI3K/AKT Signaling in Cancer
PTEN R233* mutant 5154, 21343 Nonsense Cervical, colorectal, endometrial, lung, ovarian cancer, glioblastoma, histiocytoma, lymphocytic leukemia, PI3K/AKT Signaling in Cancer
PTEN R335* mutant 5775, 5151 Nonsense Head and neck, stomach cancer, glioblastoma, melanoma, Burkitt lymphoma, lymphocytic leukemia PI3K/AKT Signaling in Cancer
cancers-04-01180-t002_Table 2

Anti-cancer therapeutics published by Reactome to date. A total of 39 small molecule inhibitors and 2 recombinant antibodies have been published since the start of the project in December 2010.

Anti-Cancer Therapeutic Reference Molecule Identifier Specificity Reactome Pathway Name
17-AAG ChEBIa: 64153 HSP90 Signaling by EGFR in Cancer
17-DMAG ChEBI:65324 HSP90 Signaling by EGFR in Cancer
Afatinib ChEBI:61390 EGFR, ERBB2 Signaling by EGFR in Cancer
Canertinib ChEBI:61399 Pan-ERBB Signaling by EGFR in Cancer
Cetuximab Recombinant antibody EGFR Signaling by EGFR in Cancer
Erlotinib ChEBI:114785 EGFR Signaling by EGFR in Cancer
Gefitinib ChEBI:49668 EGFR Signaling by EGFR in Cancer
Geldanamycin ChEBI:5292 HSP90 Signaling by EGFR in Cancer
HKI-272 ChEBI:61390 EGFR, ERBB2 Signaling by EGFR in Cancer
Herbimycin A ChEBI:5674 HSP90 Signaling by EGFR in Cancer
Lapatinib ChEBI:49603 EGFR, ERBB2 Signaling by EGFR in Cancer
Pelitinib ChEBI:38927 EGFR Signaling by EGFR in Cancer
Vandetanib ChEBI:49960 EGFR, VEGFR Signaling by EGFR in Cancer
WZ4002 ChEBI:61400 EGFR Signaling by EGFR in Cancer
IPI-504 Pending HSP90 Signaling by EGFR in Cancer
AZ 2171 ChEBI:556867 FGFR, PDGFR, VEGFR. KIT Signaling by FGFR in Disease
Brivanib ChEBI:443041 FGFR, VEGFR Signaling by FGFR in Disease
Brivanib alaninate ChEBI:270995 FGFR, VEGFR Signaling by FGFR in Disease
Dovitinib ChEBI:594834 FGFR, FLT3, VEGFR, PDGFR, KIT, CSFR Signaling by FGFR in Disease
E3810 Pending FGFR, VEGFR Signaling by FGFR in Disease
E7080 ChEBI:816009 FGFR VEGFR, PDGFR Signaling by FGFR in Disease
Masitinib ChEBI:63450 FGFR3, PDGFR, KIT Signaling by FGFR in Disease
GP369 Recombinant antibody FGFR2b Signaling by FGFR in Disease
Midostaurin ChEBI:63452 FGFR, FLT3, PDGFR, VEGFR, KIT, PKCA Signaling by FGFR in Disease
PD173074 ChEBI:63448 Pan-FGFR Signaling by FGFR in Disease
AZD4547 ChEBI:63453 Pan-FGFR Signaling by FGFR in Disease
BGJ398 ChEBI:63451 Pan-FGFR Signaling by FGFR in Disease
SU5402 ChEBI:63449 FGFR, VEGFR Signaling by FGFR in Disease
GSK1059615 Pending Pan-PI3K PI3K/AKT Signaling in Cancer
BEZ235 Pending PI3K Class I, mTOR PI3K/AKT Signaling in Cancer
BGT226 Pending PI3K Class I, mTOR PI3K/AKT Signaling in Cancer
BKM120 Pending PI3K Class I PI3K/AKT Signaling in Cancer
XL765 Pending PI3K Class I, mTOR PI3K/AKT Signaling in Cancer
XL147 Pending PI3K Class I PI3K/AKT Signaling in Cancer
GDC0941 ChEBI:65326 PI3K Class I PI3K/AKT Signaling in Cancer
PX-866 ChEBI:65345 PIK3CA, PIK3CD, PIK3CG PI3K/AKT Signaling in Cancer
LY294002 ChEBI:65329 Pan-PI3K PI3K/AKT Signaling in Cancer
wortmannin ChEBI:52289 Pan-PI3K PI3K/AKT Signaling in Cancer
Perifosine ChEBI:428891 AKT PI3K/AKT Signaling in Cancer
MK2206 ChEBI:716367 AKT PI3K/AKT Signaling in Cancer
Triciribine ChEBI:65310 AKT PI3K/AKT Signaling in Cancer
2.2. Other Disease Pathways in Reactome

In addition to cancer, Reactome also collects and provides information on communicable diseases. Currently featured infection-related Reactome pathways are “HIV Infection”, “Influenza Infection”, “Botulinum Neurotoxicity”, and “Latent Infection with Mycobacterium tuberculosis”. The pathway “Signaling by FGFR in Disease” contains, besides information on FGFR in cancer, the information on FGFR mutations and their functional implication in various developmental disorders, such as Pfeiffer syndrome and Crouzon syndrome. Reactome has recently published “Abnormal Metabolism in Phenylketonuria” and “Mucopolysaccharidoses” pathways, thereby introducing metabolic genetic diseases.

 2.3. Enhancing the Reactome Pathway Browser for Display of Disease Variants

The Reactome Pathway Browser, based upon the Systems Biology Graphical Notation (SBGN) [73], permits the navigation and analysis of Reactome data, in a similar manner to Google Maps. SBGN is a standard graphical representation of biological pathway and network models. The Pathway Browser was adapted to enable display of disease variants and disease-related events involving proteins. A pathway diagram is shared between a wild-type pathway, for example “Signaling by EGFR”, and the corresponding disease pathway, “Signaling by EGFR in Cancer”. A disease attribute, attached to events involving cancer, instructs the browser to hide disease events when a user selects a wild-type pathway view (Figure 4a). When a user selects a disease pathway view, disease events appear in the diagram while all normal events are shaded gray. All disease events and physical entities with disease tags are outlined in red for easier visualization (Figure 4b).

Display of wild-type and disease pathway diagrams. (a) A cancer disease attribute, assigned to events involved in cancer, instructs the browser to hide disease events when a user selects to view a wild-type pathway. (b) When a user selects to view a disease pathway, disease events appear in the wild type diagram, while all normal events are shaded. All disease events and physical entities with disease tags are outlined in red for easier visualization.

Physical entity and reaction nodes within the pathway diagrams are interactive. Clicking on either feature displays specific information and additional links out to external databases in the “Details” Panel, which opens by clicking on the yellow triangle at the bottom of the Pathway Browser page (Figure 2). Context sensitive menus, accessible through the right click on a selected entity, provide additional information about the physical entity in the pathway: a catalogue of other pathways in Reactome in which the selected entity participates; a list of the entities that contribute to the macromolecular complex; a catalogue of interactors of the selected entity; and the option to export a list of interactors of the selected entity. The latter two features of the context sensitive menu increase protein coverage and associated variant annotations. The Molecular Interaction Overlay (MI Overlay), accessible through “Analyze, Annotate & Upload” button of the Pathway Browser, displays proteins interacting with the manually annotated protein components of a Reactome pathway. This network overlay tool employs PSICQUIC (Proteomics Standard Initiative Common QUery InterfaCe) to apply an interactive display of interaction data from an external database such as IntAct [74] into Reactome pathway diagrams. Other sources of interaction data include protein-protein and protein-drug/small molecule interactions; a user-supplied list can also be displayed. By displaying interaction data from ChEMBL, a database of bioactive drug-like molecules (Figure 5) [75], the MI Overlay feature provides an opportunity to identify protein variant-drug interactions, identify novel cancer targets or off-target effects, or pharmaceuticals that can moderate perturbed reactions or pathways experimentally.

AKT1 E17K mutant-small molecule interactions. When ChEMBL is selected as the interaction database, the MI Overlay displays small molecules from ChEMBL as interactors of AKT1 E17K variant protein of the PI3K/AKT Signaling in Cancer pathway. The nodes of the mini network are interactive; clicking the node to the left of the green arrow will link out to the Staurosporine protein kinase inhibitor record at ChEMBL.

 2.4. Reactome Cancer-Perturbed Pathways Support Pathway Visualization and Analysis

The Pathway Browser provides an intuitive and interactive pathway visualization system, promoting a variety of web-based data analyses of user-supplied experimental data. The Pathway Analysis tool provides two alternate functions to analyze lists of genes. First, in the identifier (ID) mapping mode, a user-supplied set of gene or protein identifiers can be mapped to Reactome events. Second, in the overrepresentation analysis mode, users can determine which pathways are statistically overrepresented in a gene/protein list. The Expression Analysis tool will aid with the biological interpretation of large-scale cancer genome sequencing, genomics and proteomics experiments. For example, this tool allows users to visualize expression data (or any other numeric value, e.g., differential expression) superimposed on the Reactome pathway diagram. Reactome applies an orthology-based computational algorithm to curated human data to infer pathways in 22 diverse model organisms. The Species Comparison tool allows users to visually compare and contrast human pathways with these predicted model organism pathways. As additional cancer-perturbed pathways are added to Reactome, this method of “inferred” curation will provide a platform from which to study molecular disease mechanisms across the evolutionary spectrum. Reactome data is available for downloading and manipulation by third party visualization and analysis tools, including Cytoscape, Vanted and CellDesigner [76,77,78].

 3. Experimental Section

Using the previously curated human EGFR pathway, which included a number of annotations for EGFR and downstream signaling by SHC1, GRB2, PLCG1 and CBL, as a template from which to extend the EGFR pathway, we imported this dataset into the Reactome Curator Tool [11]. Briefly, the curator tool provides Reactome curators with all the necessary tools to access the Reactome database and annotate data in agreement with the Reactome data model. Curators identified research articles and reviews in PubMed that were relevant to the annotation of the cancer-perturbed EGFR, FGFR and PI3K/AKT pathways. Once publications had been reviewed, a list of cancer-related proteins, small molecules and macromolecular complexes was prepared. Additional queries were performed in UniProt and ChEBI to identify the reference entity proteins and small molecules, respectively that would be used to construct the reactions of the cancer-perturbed EGFR pathway. Additional attributes of a reaction were captured. For example, details of the input and output entity(s), the catalytic or regulatory protein(s), the cellular location(s) of the reactants, a textual summation describing the reaction and the supporting literature reference(s). The Disease Ontology terms that match literature references and COSMIC records for annotated cancer variants were assigned as disease attributes to physical entities and events involving these mutant proteins. Oncogenic overexpression of proteins as a consequence of gene amplification is usually not explicitly shown in pathway diagrams, but is captured in text summations that accompany cancer pathways.

 4. Conclusions

Reactome is a highly reliable, curated database of biological pathways. Through our website, we provide access to pathway and network data analysis tools for visualizing pathway data and interpreting experimental data sets. All Reactome data and software is openly available with no licensing required.

In view of the potential applicability of pathway and network analyses to identify and characterize novel cancer targets, Reactome has integrated and expanded the pathway gene product-function annotation and pathway curation to promote comprehensive and effective characterization of cancer targets, their related relationships and pathways. Our curation efforts thus far have focused on the EGFR pathway (including the EGFR, ERBB2, ERBB3, ERBB4 receptors), FGFR and PI3K-AKT signaling and their downstream effector genes. Reactome curators will enhance our curation of other cancer-perturbed pathways, such as apoptosis, cell cycle checkpoints, and other signaling pathways, including BMP, PDGF, NOTCH, VEGF, WNT, Rho-GTPase, and TGF-beta. Furthermore, as the Ontario Institute for Cancer Research and its partners in the International Cancer Genome Consortium (ICGC) [79,80] sequence various tumor genomes, new cancer-related candidate pathways will be identified and curated into Reactome. Existing Reactome pathways are updated on a regular basis, and additional cancer variants and anti-cancer drugs implicated in EGFR, FGFR and PI3K/AKT pathways will be included as information on their function becomes available.

Reactome is not the only pathway database to curate pathway data relevant to cancer and disease. Cancer-perturbed signaling pathways can be found in KEGG, Panther, MetaCyc, and NCI-PID [81,82,83,84]. The Reactome data model, however, provides a more detailed framework for the curation of the knowledge relevant to cancer-related pathways, a visualization environment to display pathway data, and a suite of analysis tools for the interpretation of experimental cancer data sets.

A number of other bioinformatics databases such as Mouse Genome Informatics (MGI) [85] and Comparative Toxicogenomics Database (CTD) [86] have established disease curation pipelines, employing OMIM. OMIM is a detail-orientated database of disease annotation, widely used by the clinical community, but it lacks the structure and features of an ontology that would otherwise make it a perfect data source to systematically reference disease. Curation of human disease requires an establishment of a widely accessible and structured vocabulary (or ontology) that consists of knowledge that is familiar to Reactome’s end user, flexible to future Reactome annotation updates, and open to semantic reasoning. One such ontology is the Disease Ontology. Reactome will continue to work with the research community to support the development and continuous improvement of human disease ontologies and will link out to the relevant cancer and disease-related databases, to advance our own annotation consistency. In future versions of Reactome, we may also cross-reference NCIt [68] directly for cancer-related physical entities and events. The Disease Ontology does provide NCIt identifiers when possible, but disease terms captured by the Disease Ontology and NCIt do not completely overlap. Cross-referencing different ontologies will make our disease annotations more comprehensive and stable. Since some amount of overlap exists between disease terms in any disease ontology, the overlap is reflected in our current annotation of disease attributes. This is not ideal and we are developing guidelines to standardize the use of disease terms in Reactome. As far as anti-cancer therapeutics are concerned, we do not capture their approval for clinical use other than in text summations, as this is outside the scope of Reactome project. However, cross-referencing a drug database, such as PharmaGKB [87] would provide Reactome users with easy access to clinically relevant drug information, and is currently under our consideration.

We are working on further improvements to the Reactome pathway browser to produce more compact images and to be able to share one diagram between the wild-type pathway and several disease pathways with different etiologies. Furthermore, we are making additions to the Molecular Interaction Overlay to promote visual linkages between pathway entities and disease annotations, such as OMIM. Network-based methods have been used extensively in genomic and proteomic studies to analyze challenging and complex datasets. Reactome provides the Functional Interaction (FI) network plug-in for Cytoscape, which can identify network patterns related to diseases, including cancer [88]. Future expansion of the FI network with interactions based upon Reactome cancer-related pathways should significantly improve coverage, enhance the functionality of the analysis, and enrich the functional annotations supported by the FI network plug-in. Reactome will continue to develop novel and useful technologies for the querying, visualization and analysis of experimental datasets, in the context of not only normal but also disease pathways.

 Acknowledgments

The data, data model, and data analysis tools described in this article are the product of the collaborative work of curators and software developers at the Ontario Institute for Cancer Research (Lincoln Stein, Michael Caudy, Marc Gillespie, Robin Haw, Marija Milacic, Bruce May, Karen Rothfels, Heeyeon Song, Joel Weiser, Guanming Wu), the European Bioinformatics Institute (Henning Hermjakob, David Croft, Antonio Fabregat-Mundo, Phani Garapati, Bijay Jassal, Steven Jupe) and the NYU School of Medicine (Peter D’Eustachio, Lisa Matthews, Veronica Shamovsky). We are grateful to the many scientists who collaborated with us as authors and reviewers to build the content on the knowledgebase, and to our colleagues at GO, ChEBI, and UniProt. We are especially grateful to Lisa Matthews and Marc Gillespie for helpful discussions of disease annotation strategies and to two anonymous reviewers for their comments on an earlier version of this manuscript. This work was supported by grants from the National Human Genome Research Institute at the National Institutes of Health [grant number U41 HG003751], the European Union FP7 project “LipidomicNet” [grant agreement number 202272, and Ontario Research (GL2) Fund. Funding for this open access charge: National Human Genome Research Institute at the National Institutes of Health [grant number U41 HG003751].

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