Biomarkers lie at the heart of precision medicine. Surprisingly, while rapid genomic profiling is becoming ubiquitous, the development of biomarkers usually involves the application of bespoke techniques that cannot be directly applied to other datasets. There is an urgent need for a systematic methodology to create biologically-interpretable molecular models that robustly predict key phenotypes. Here we present SIMMS : an algorithm that fragments pathways into functional modules and uses these to predict phenotypes. We apply SIMMS to multiple data types across five diseases, and in each it reproducibly identifies known and novel subtypes, and makes superior predictions to the best bespoke approaches. To demonstrate its ability on a new dataset, we profile 33 genes/nodes of the PI3K pathway in 1734 FFPE breast tumors and create a four-subnetwork prediction model. This model out-performs a clinically-validated molecular test in an independent cohort of 1742 patients. SIMMS is generic and enables systematic data integration for robust biomarker discovery. Accurate and actionable biomarkers that integrate diverse molecular, functional and clinical information hold great promise in precision medicine. Here, the authors develop SIMMS, a method for pathway-based cross-disease biomarker discovery. A single disease phenotype can often arise in many ways, allowing a diversity of molecular underpinnings to yield a smaller number of phenotypic consequences. This molecular heterogeneity within a single disease is believed to underlie poor clinical trial results for some therapiesResearchers thus face two challenges. First, molecular markers are needed to personalize and optimize treatment decisions by predicting patient outcome  and response to therapy. Second, clinical heterogeneity in patient phenotypes needs to be molecularly rationalized to allow targeting of the mechanistic underpinnings of disease. For example, if a single pathway is dysregulated in multiple ways, drugs targeting the pathway could be applied.6. Unfortunately, these studies have been limited by noise and disease heterogeneity. Several groups have integrated multiple data types using network and systems biology approaches identifying patient subtypes, with limited post-hoc clinical evaluation25. These algorithms have not yet clearly shown how the interplay between different pathways underpins disease etiology, nor generated biomarkers with systematically demonstrated reproducibility on independent patient cohorts across multiple indications26.Several approaches have been taken to solve these challenges. The most common has been to measure mRNA abundances as a snapshot of cellular state, and to construct a predictive model from themThere is thus an urgent need to generate accurate and actionable biomarkers that integrate diverse molecular, functional and clinical information. We developed a subnetwork-based approach, called SIMMS, which uses arbitrary molecular data types to identify dysregulated pathways and create functional biomarkers. We validate SIMMS across five tumor types and 11,392 patients, using it to create biomarkers from a diverse range of molecular assays and uncovering unanticipated pan-cancer similarities.SIMMS acts upon a collection of subnetwork modules, where each node is a molecule  and each edge is an interaction  between those molecules. Molecular data is projected onto these subnetworks using topology measurements that represent the impact of and synergy between different molecular features. To allow modeling of biological processes with different network architectures, we devised three scoring paradigms: N (nodes/molecules in a subnetwork), E (edges/interactions in a subnetwork) and N + E (both nodes and edges). While the N model assumes independent and additive effects of parts of a subnetwork, the E and N + E models incorporate the impact of dysregulated interactions (Methods). SIMMS fits each one of these models thereby estimating a ‘module-dysregulation score’ (MDS) for each subnetwork that measures their strength of association with a specific disease, phenotype or outcome  were isolated and regarded as a subnetwork. We further removed overlapping subnetworks from this collection resulting in 500 subnetworks across the database . SIMMS can use any network, and we chose to evaluate it using 449 gene-centric pathways from the high-quality, manually-curated NCI-Nature Pathway Interaction databaseP < 0.05, Wilcoxon rank sum test; Supplementary Fig. 5). This suggests that mRNA abundance of functionally-related genesets alone is a strong predictor of patient outcome; here a geneset refers to a set of genes from the same subnetwork. We therefore focused solely on Model N moving forward, while recognizing that in other diseases different subnetwork architectures may be disrupted and therefore require model E or N + E.Our analysis of prognostic subnetworks revealed several properties of tumor network biology. First, there was a global propensity for highly prognostic subnetworks to contain significantly higher number of genes and interactions for Model N and N + E . Sensitivity of these methods against a panel of subnetworks most likely to be associated with patient outcome (having at least three significantly prognostic genes) also confirmed SIMMS’ superiority with highest true positive rate compared to other methods across all four cancer types  revealed well-known oncogenic pathways such as Aurora Kinase A and B signaling, apoptosis, DNA repair, RAS signaling, telomerase regulation and P53 activity in all four tumor types (Supplementary Tables 2HR| > 0.584 and P < 0.05) in each tumor type, 17 recurrently prognostic subnetworks (at least three tumor types) were identified . In total, 7/111 subnetworks demonstrated co-occurrence of platinum resistance in high-risk groups  were highly prognostic (Supplementary Table MKI67 (mRNA abundance) as a surrogate for proliferation, and found strong concordance between MKI67 abundance and SIMMS’ risk scores . We further validated this subnetwork for breast cancer disease-specific survival in an independent cohort of 1970 patients31 , as well as stromal signal   Fig. , both ofBreast = 50, nColon = 75, nNSCLC = 25 and nOvarian = 50), SIMMS’ risk scores were estimated in each disease. These subnetworks revealed a number of highly correlated clusters of subnetworks . Next, multivariate prognostic classifiers  were created for each tumor type thereby further refining the list of highly correlated subnetworks. For each tumor type, subnetwork-based classifiers encompassing multiple pathways successfully predicted patient survival in the fully-independent validation cohorts  were generated regardless of subnetwork size  a biomarker constructed using all these genes collapsed into one composite geneset which was subsequently modeled using SIMMS. For the large multivariate model, breast and ovarian cancer models yielded results similar to SIMMS while colon and NSCLC models were significantly inferior to SIMMS’ models  a biomarker constructed from all the genes (in our pathways database) in multivariate setting using a Cox model with n = 1,970)31. Our prognostic classifier revealed two primary patient clusters with distinct pathway activities encompassing subnetworks derived from cell cycle, signaling, immune and regulatory pathways . Since breast cancer is a heterogeneous disease with distinct molecular and clinical characteristics34, we asked whether SIMMS could identify subtype-specific prognostic markers. To evaluate this, we classified patients into PAM50, ER+ and ER− subtypes and created SIMMS classifiers for each subtype. SIMMS classifiers were able to identify subgroups of patients at a significantly higher risk of relapse  in each of the Luminal-A, Normal-like and ER+ subtypes . It was also effective when simply median-dichotomizing predicted risk scores into low- and high-risk groups . To further verify this, predicted risk groups (Q1-Q4) in the validation cohort were divided into chemotherapy negative and positive arms with further stratification by nodal status. Risk predictions was similar for node-negative/chemotherapy-negative patients  was significantly superior to the IHC-protein model .To benchmark SIMMS’ PI3K modules signature against current clinically-validated approaches, we compared its performance to a clinically-validated protein-based residual risk predictor, IHC438, colorectal (COADREAD)7, kidney (KIRC)39, ovarian (OV)40) along with the Metabric31 breast cancer cohort, each of which included matched mRNA, CNA and clinical data. In order to test pathways harboring multi-modal alterations, we curated previously published pathway modules (MEMo)41 from TCGA studies , there is a need to simultaneously integrate these into unified predictive models. We used four TCGA datasets (breast (BRCA)42. Indeed, a single whole-genome sequencing experiment generates information about single nucleotide variations, copy number aberrations and genomic rearrangements. SIMMS puts this molecular variability into the context of existing knowledge of biological pathways using subnetwork information. Several other groups have considered the value of network models in predicting breast cancer outcome30 and in subtyping glioblastoma43. However no such tools have yet been developed to be generalizable to a broad range of diseases or to arbitrary topological measures that might be used to estimate weights in network-models of biology45 or to work with physical, functional, transcriptional or metabolic networks47. SIMMS provides this generalizability and flexibility by treating molecular profiles as generic features and not just genes.Patients with complex human diseases present highly heterogeneous molecular profiles, ranging from a few aberrant genes to a set of dysregulated pathways. Because many different molecular aberrations can give rise to a single clinical phenotype, the importance of generating multi-modal datasets is increasingly appreciatedpost hoc to characterize the most interesting genes50. Our approach inverts this strategy, taking known pathways a priori and thus creating immediately interpretable and clinically actionable biomarkers12. We recognize that for breast and ovarian cancers, pathway-based models presented here yield similar prognostic association as for some single gene biomarkers. This phenomenon is surprising and potentially confounded to some extent by a number of factors, including differential power across disease types and inter- and intra-tumoural heterogeneity51. Overall, the observed saturation of prognostic signal for some disease types require further evaluation in much larger cohorts, as well as additional tumor types. Nonetheless, our data highlights that prognostic markers based on functionally related genes offer new opportunities to rationalize disease biology and foster discovery of candidate drug targets. For example, by identifying the key signaling nodes within one of the most frequently dysregulated pathways in human cancer52 we are able to demonstrate both improved risk stratification of patients undergoing standard of care treatments , except for two colon cancer datasets (TCGA and Loboda dataset), which were used in their original pre-normalized and log-transformed format. ProbeSet annotation to Entrez IDs was done using custom CDFs56 . The Metabric breast cancer dataset was pre-processed, summarized and quantile-normalized from the raw expression files generated by Illumina BeadStudio. (R packages: beadarray v2.4.2 and illuminaHuman v3.db_1.12.2). Raw Metabric files were downloaded from European genome-phenome archive (EGA) (Study ID: EGAS00000000083). Data files of one Metabric sample were not available at the time of our analysis, and were therefore excluded. All datasets were normalized independently. TCGA breast (BRCA), colon (COADREAD), kidney (KIRC) and ovarian (OV) cancer datasets were downloaded from http://gdac.broadinstitute.org/ . The choice of training and validation sets was driven by maintaining homogeneity in size and platforms, and was further addressed through 10-fold cross validation, as well as permutation analyses. Raw mRNA abundance NanoString counts data were pre-processed using the R package NanoStringNorm57 . A range of pre-processing schemes was assessed to optimize normalization parameters (Supplementary Methods section 4). For breast, NSCLC and ovarian cancers with different survival end-points, overall survival (OS) was used as the survival time variable; for the studies that did not report OS, we used the closest alternative endpoint available in that study . For colon cancer, all studies reported relapse/disease free survival and hence this was used as the survival end-point.All pre-processing was performed in R statistical environment v2.13.0). Raw datasets from breast, colon, NSCLC and ovarian cancer studies (Supplementary Tables .13.0. Ra58 early breast cancer were randomly assigned to receive exemestane (25 mg once daily), or tamoxifen (20 mg once daily) for the first 2.5-3 years followed by exemestane . This study complied with the Declaration of Helsinki, individual ethics committee guidelines, and the International Conference on Harmonization and Good Clinical Practice guidelines; all patients provided informed consent. Distant metastasis free survival (DRFS) was defined as time from randomization to distant relapse or death from breast cancer58.The TEAM trial is a multinational, randomized, open-label, phase III trial in which postmenopausal women with hormone receptor-positive luminal59. After mRNA extraction and NanoString analysis, 3476 samples were available. Patients were randomly assigned to either a training cohort (n = 1734) or the validation cohort (n = 1742) by randomly splitting the 297 NanoString nCounter cartridges into two groups. The training and validation cohorts are statistically indistinguishable from one another and from the overall trial cohort  except that samples were incubated in protease for 3 h instead of 15 minutes. RNA samples were eluted and quantified using a Nanodrop-8000 spectrophotometer . Samples, where necessary, underwent sodium-acetate/ethanol re-precipitation. We selected 33 genes of interest from key functional nodes in the PIK3CA signaling pathway60 and 6 reference genes  sections per case were deparaffinised, tumor areas were macro-dissected and RNA extracted according to Ambion® Recoverall™ Total Nucleic Acid Isolation Kit-RNA extraction protocol (Life Technologies), the remaining studies were divided into two sets; training and validation cohorts (Supplementary Tables z-scores within the scope of each dataset (R package: stats v2.13.0).Following univariate analyses and elimination of redundant patients  whenever, for a given patient, both the interacting genes result in the same risk group.The hazard ratio for all the protein-protein interactions gathered from the NCI-Nature pathway interaction database were estimated using a multivariate Cox proportional hazards model. A Cox model, shown below, was fitted to median dichotomized patient grouping of each of the interacting gene pairs:k:The pathway-based subnetworks modules were scored using three different models. These models estimate a module-dysregulation score (MDS) by incorporating the hazard ratio of nodes and edges that form the subnetwork, say for a given subnetwork module 1- Nodes + Edges2- Nodes onlyn and e represent total number of nodes (genes) and edges (interactions) in a subnetwork, respectively. HR represents the hazard ratios of genes and the protein-protein interactions in a subnetwork  . The subnetworks were ranked by MDS, thereby ranking candidate prognostic features.3- Edges onlyn subnetwork features for each of the three models (section: Subnetwork module-dysregulation score). These features were subsequently used to estimate patient risk scores using Model N + E, N and E. For a patient (t), the risk score for a given subnetwork (riskSN) was estimated using the following models:The subnetwork MDS was used to draw a list of the top 1 - Nodes + Edges2 - Nodes onlyn and e represent the total number of nodes (genes) and edges (interactions) in a subnetwork (SN), respectively. HR is the hazard ratio of genes and the protein-protein interactions  . x and y are the two nodes connected by an edge j and X is the scaled intensity of the molecular profile being modeled  for a patient t.3 - Edges onlyA univariate Cox proportional hazards model was fitted to the training set, and applied to the validation set for each of the subnetworks. The prognostic ability of all three models was compared using non-parametric two sample Wilcoxon rank-sum test.L1-regularization) in 10-fold cross validation setting on the training cohort (R package: glmnet v1.9-8). SIMMS R package supports additional machine learning algorithms including elastic-nets (ridge to LASSO), backward elimination and forward selection (R package: MASS v7.3-12). The fitted coefficients (β) were subsequently used to estimate an overall measure of per patient risk score for the validation set using the following formula:Yij is the ith patient’s risk score for subnetwork j. The training set HRs of the nodes and edges were used to estimate Yij (section: Patient risk score). Next, we median dichotomized the validation cohort into low-risk and high-risk patients (or quartiles) using the median risk score (or quartiles) estimated using the training set. The risk group classification was assessed for potential association with patient survival data using Cox proportional hazards model and Kaplan–Meier survival analysis.In order to prioritize an optimal combination of subnetwork features for SIMMS’ multivariate models, we fitted a Cox model using generalized linear models  were randomly sampled using all 500 subnetworks. The predictive performance of each random classifier was measured as the absolute value of the log2-transformed hazard ratio obtained by fitting a multivariate Cox proportional hazards model using Model N.Jackknifing was performed over the subnetwork marker space for four tumor types; breast, colon, NSCLC and ovarian. Ten million prognostic classifiers , lattice (v0.19-28), latticeExtra (v0.6-16) and VennDiagram (v1.0.0).All plots were created in the R statistical environment (v2.13.0 or above) using R packages BPGSupplementary InformationDescription of Additional Supplementary filesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Supplementary Data 8Supplementary Data 9Supplementary Data 10Supplementary Data 11Supplementary Data 12Supplementary Data 13Supplementary Data 14Supplementary Data 15Supplementary Data 16Supplementary Data 17Supplementary Data 18Supplementary data 19Supplementary Data 19Supplementary Data 20Supplementary Data 21


The precision medicine paradigm is centered on therapies targeted to particular molecular entities that will elicit an anticipated and controlled therapeutic response. However, genetic alterations in the drug targets themselves or in genes whose products interact with the targets can affect how well a drug actually works for an individual patient. To better understand the effects of targeted therapies in patients, we need software tools capable of simultaneously visualizing patient-specific variations and drug targets in their biological context. This context can be provided using pathways, which are process-oriented representations of biological reactions, or biological networks, which represent pathway-spanning interactions among genes, proteins, and other biological entities. To address this need, we have recently enhanced the Reactome Cytoscape app, ReactomeFIViz, to assist researchers in visualizing and modeling drug and target interactions. ReactomeFIViz integrates drug-target interaction information with high quality manually curated pathways and a genome-wide human functional interaction network. Both the pathways and the functional interaction network are provided by Reactome, the most comprehensive open source biological pathway knowledgebase. We describe several examples demonstrating the application of these new features to the visualization of drugs in the contexts of pathways and networks. Complementing previous features in ReactomeFIViz, these new features enable researchers to ask focused questions about targeted therapies, such as drug sensitivity for patients with different mutation profiles, using a pathway or network perspective. For cancer and other complex diseases, a key strategy often used is to harness these alterations to stratify patients into subgroups to receive the most appropriate targeted therapy. However, we currently face many challenges such as identifying rigorous biomarkers to guide treatment decisions, rationally designing combination therapies for improved efficacy, reducing therapeutic toxicity and side effects, and managing both intrinsic and adaptive drug resistance to therapy4. Note that patients may share the same pathway-level dysregulation that is acquired through alterations in different genes in the pathway or in a set of genes that span multiple pathways. While the first scenario is well-suited to pathway analysis, the second would benefit from network methods that encompass across multiple pathways. Both scenarios provide opportunities for biomarker discovery to inform patient stratification, target prioritization, or treatment assignment by applying pathway and network approaches5.Pathway and network analysis approaches to understanding the interactions between genetic variation and therapeutic response offer researchers the unique ability to understand these interactions in their rich biological context. While pathways are groups of biological entities that are connected together to carry out specific functions or biological processes, networks of entities are usually constructed in a systems-manner, covering many pathways at the same time. In cancer, shared oncogenic pathways in patients may provide an opportunity to stratify and identify therapeutic options. For instance, targeting kinases in the MAPK and PI3K/AKT signaling pathways has been very successful for cancer treatment due to the high prevalence of patient mutations in these pathways7. They have also been used to unravel the drug resistance mechanisms. Two notable mechanisms of drug resistance in tumors include gatekeeper mutations in direct drug targets (such as those often found in oncogenic kinases) and mutations in non-drug targets that enable bypass resistance pathways used by tumor cells in response to drug-induced inhibition of pathways8. In the second case, mutations in proteins other than the direct target of a drug can confer resistance by activating downstream and/or parallel pathways9.Pathway- and network-based approaches have also been successfully used in other aspects of targeted therapy. For example, they have been used successfully in prediction of drug side effects and in explaining critical toxicity issues in failed drugs10 and PharmGKB11. ConsensusPathDB aggregates functional interactions including protein interactions, gene-gene interactions, metabolic network interactions, and drug-target interactions from public resources10. With respect to drug-target interactions, ConsensusPathDB allows for selection of interactions for visualization but does not provide functionality for overlaying drugs to a pathway of interest. PharmGKB provides a curated drug-centric view of pathways that aggregates information for a particular drug’s targeted pathways, according to the disease for which the drug is designed11. However, these resources do not provide a means of selecting and filtering drug-target interaction evidence and do not overlay drug-target information to all pathways.In each of these applications, it is imperative to be able to visualize the intended effect of drugs on pathways through its designated targets. Currently available resources that offer such visualization include ConsensusPathDB12, Petri-Nets13, flux-based analysis (FBA)14, probabilistic graphical models (PGMs)15, and Boolean Networks17. ODE modeling is the most sophisticated because it is able to generate both quantitative and dynamic behaviors of model entities. However, lack of initial concentrations and key parameters often limits the application of this approach to small pathways and networks. While Petri-Net and FBA approaches have been used in metabolic network modeling, the sensitivity of these methods to the underlying network structures makes it difficult to apply them to signal transduction pathways because of the incompleteness in many signaling networks due to current limited knowledge. PGMs have been used to integrate multiple 'omics data types together for pathway impact analysis15. However, the need to learn model parameters makes this approach difficult, if not impossible, in many applications due to small sample sizes and/or incomplete network structure. Furthermore, PGMs cannot handle dynamic modeling well since inference results from these models reflect "beliefs" for states of model variables and do not involve kinetic behavior as produced by dynamic modeling.In addition to visualizing both primary and secondary or “off” targets of drugs in the contexts of pathways and networks, we also need to be able to investigate the effect of perturbation either via one drug or a combination of drugs. For this purpose, we need to perform pathway mathematical modeling. In recent decades, several pathway modeling approaches have been developed, including models leveraging ordinary differential equations (ODEs)18, Boolean networks provide a method of modeling biological pathways and networks using Boolean variables. Each biological entity in a Boolean network can be in either the active (1) or inactive (0) state, which is determined by logic-based rules involving the network’s functional interactions. A two-state-based Boolean network can be extended to a fuzzy logic model through introduction of continuous variables with values between 0 and 1, inclusively, and applied to biological use cases that involve continuous variables19.Boolean network modeling is one of most widely used modeling approaches and has been applied to several large-scale network modeling efforts due to its simplicity and high efficiency. Originally introduced by Kauffman20, a Cytoscape21 app, was developed to perform pathway- and network-based data analysis and visualization for cancer and other complex disease data using Reactome pathways22 and its complementary resource, the Reactome Functional Interaction (FI) network23. Recent enhancements to ReactomeFIViz provide features for visualization of drug-target interactions for approved clinical drugs in the contexts of pathways and networks and modeling the effect of drug action on the pathway activities , which are retrieved by using the “Fetch Cancer Drugs” feature in ReactomeFIViz to visualize drug-target interactions collected in the Cancer Targetome. As displayed in29, and supported by assay values less than 100 nM. This view suggests a list of potential drug candidates for researchers to perturb the activity of the SCF-KIT signaling pathway. Alternatively, we may start with a pathway of interest and investigate all drugs that target components of the pathway within a selected affinity range. This perspective allows a broad view for assessing how confident we may be able to find a drug targeted to a particular pathway, as many pathways have only weakly or no interacting drugs32 to model a drug’s perturbation on the activity of a particular pathway. We conducted two constrained fuzzy logic simulations: a reference simulation without considering drug action, and a perturbation simulation considering drug action. To model drug action within the simulation, we mapped drug-target interaction affinity values as “activation” or “inhibition” strength according to the drug’s action mechanism and its assay values collected in the drug data sources. We then used this information to modify the transfer functions related to the drug's target(s) in the fuzzy logic model. Finally, we calculated the relative impact scores for individual entities in the pathway by comparing the model that includes drug effects to the one that does not. For details, seeMethods.Overlaying drug-target interactions onto a pathway reveals potential drug action targets in the pathway. However, the topological structure of a pathway may be complicated and contain one or more feedback loops. This makes it difficult to predict the dynamic effect of a drug on pathway activity by inspection only. To assist researchers in performing computational modeling of drug-induced pathway perturbation, we developed an automated approach to convert Reactome pathways into Boolean networks (BNs). These BNs were then subject to constrained fuzzy logic simulation33. These regulated pathways include RAF/MAP kinase signaling, PIP3-activated AKT signaling, and the JAK/STAT signaling pathway. The Reactome-annotated pathway “Signaling by SCF-KIT” includes a set of linked reactions that produce entities activating these regulated pathways.To illustrate this capability, we take the example of modeling the perturbation induced by the kinase inhibitor sorafenib on the “Signaling by SCF-KIT” pathway. Stem cell factor (SCF), together with its receptor, c-KIT, a tyrosine kinase receptor, regulates pathways related to proliferation, migration, survival, and differentiation of multiple cell types34. Evidence shows that sorafenib binds strongly to c-KIT ;2. Default setup with sorafenib (sorafenib_1 as drug-perturbed state);3. https://reactome.org/PathwayBrowser/#/R-HSA-1433557&SEL=R-HSA-58196&PATH=R-HSA-162582,R-HSA-9006934) from 1.0 to 0.5. No sorafenib was applied (reference_2 as PRKCA-perturbed state). PRKCA is protein kinase C alpha, which phosphorylates KIT and therefore inhibits KIT’s kinase activity (https://reactome.org/PathwayBrowser/#/R-HSA-1433557);Default setup with a reduced initial value of PRKCA [cytosol] (4. Same as in 3) except that sorafenib was applied (sorafenib_2 as drug/PRKCA-perturbed state). 35. As expected, we saw these attractors in all four simulations. In the first drug perturbation modeling  , which was annotated as an activator for pathway “PIP3 activates AKT signaling”. We observed similar behavior as in the case of “p-STAT dimers”: reducing the initial value of PRKCA increased the activity of “PIP3”. The application of sorafenib modestly increased PIP3's activity using default PKCA activity level (1.0), but reduced its activity when PRKCA’s activity was reduced. However, the relative impact scores for PIP3 are much larger compared to the case of the p-STAT dimers described above, most likely because this entity has a converged single stable activity, in contrast to p-STAT dimer’s cycle attractor.We also performed analysis for another output of this pathway, “PIP3”  network was constructed by extracting functional relationships from manually curated pathways in Reactome and several other pathway databases, and then combining them with predicted interactions based on a machine learning approach, Naïve Bayes ClassifierExtended data: Table S1). EGFR is targeted by both monoclonal antibody drugs and kinase inhibitors37. 14 of the 18 drugs that interact with targets in the network bind to EGFR  may be prioritized as a target for ovarian cancer. It has roles in multiple pathways that are significantly enriched for mutated genes, including “L1CAM interactions”, “Focal adhesions”, “Calcium signaling pathway”, “PI3K-AKT signaling pathway”, and “HIF-1 signaling pathway” (to EGFR . EGFR isExtended data: Table S1). An alternative strategy for prioritizing drugs interacting with this FI network would be to select drugs interacting with targets in multiple, disjoint pathways to potentially achieve a greater coverage of key genes in the network. For instance, the drug sunitinib malate targets both “TRAF2 And NCK Interacting Kinase” (TNIK) and “P21 (RAC1) Activated Kinase 3” (PAK3) , which aPathway and network-based approaches for precision medicine offer a holistic view of drug action, providing biological contextualization for drug-target interactions. We have developed a user-friendly, integrative software platform by enhancing a popular Cytoscape app, ReactomeFIViz, for the community to perform pathway and network-based drug-target interaction visualization, modeling, and analysis.22 is one of the most popular biological pathway databases, providing high quality manually curated biological pathways that cover over 50% of known human genes. Pathways in Reactome are annotated according to biochemical reactions, which are connected together to form networks. We developed a scheme to automatically convert reaction-based pathways in Reactome into Boolean networks and then perform constrained fuzzy logic based simulation32. Our approach is generic and can be adopted for other reaction-based pathway databases (e.g. Panther Pathways41) and pathways provided in the BioPAX format42, the community standard for pathway data exchange.ReactomeExtended data: Figure S1). Future improvements will also include expansions for learning and/or optimizing parameters based on omics data and improving handling of entity set members for tissue-specific simulation by annotating tissue-specific information. Furthermore, collaboration with bench scientists will help to validate and refine the computational models.Future improvements to our Boolean network-based fuzzy logic modeling approach will find a better measure to quantify the impact of drug perturbation on entity activities. The current relative impact score may be too sensitive when the area under curve of the fuzzy logic values vs time steps generated from the reference simulation is very close to 0, as shown for the PIP3 example , European Medicines Agency (EMA), and the Japan Pharmaceuticals and Medical Devices Agency (PMDA). Target information in DrugCentral is mined from bioactivity resources such as ChEMBL and IUPHAR, and may also be supplemented with manually curated target information from drug approval labels. Overall, DrugCentral  includes information for 2102 compounds, 1550 targets, and 13,407 drug-target interactions. The drug-target interaction file supported by ReactomeFIViz, drug.target.interaction.08292017.tsv, was downloaded from the DrugCentral web site  in 2017.The DrugCentral36. Pathway enrichment p-values in ReactomeFIViz were calculated using the binomial test and were false discovery rate (FDR)-corrected based on the Benjamini-Hochberg approach50.For the functional interaction network in20 based on the PARADIGM approach15. A typical reaction in Reactome has one or more inputs, one catalyst, one or more activators, one or more inhibitors, and one or more outputs. A logical “AND” relationship was created among inputs, the catalyst, and activators. Inhibitors were negated and then added into this “AND” relationship. To make the relationship simple, if multiple outputs, activators, or inhibitors are annotated for a reaction, we introduced accessory nodes for them. Therefore, a typical reaction inIn Reactome, the biochemical reaction is the basic unit for pathway annotation. Pathways are annotated as a set of biochemical reactions that are linked together. We developed a scheme to automatically convert Reactome pathways into Boolean networks  by adoptinput1 · input2 · catalyst · activator_acc · !inhibitor_acc = output_accactivator1 + activator2 = activator_accinhibitor1 + inhibitor2 = inhibitor_accoutput_acc = output1output_acc = output2Reactome has annotated many complex assembly reactions. However, some complexes are annotated directly for reactions without explicit description of how subunits are assembled together. We expanded a complex like this into an “AND” relationship among its subunits and generated the following Boolean relationship for the complex as illustrated inSubunit1 · Subunit2 · Subunit3 = Complex51 used to collect a set of entities with the same function in a biochemical reaction and is routinely used to collect protein isoforms. We expanded an EntitySet instance into an “OR” relationship among annotated members:EntitySet is a class in the Reactome data modelMember1 + Member2 + Member3 = EntitySetThus, by converting biochemical reactions, complexes, and EntitySets into a set of Boolean relationships, we converted a Reactome pathway into a Boolean network.32 to simulate the dynamic behavior of Boolean networks that are automatically converted from Reactome pathways by extending two-state Boolean variables to continuous variables with values between 0 and 1, inclusively. This allows our modeling approach to be applied to continuous ‘omics data types and drug-target assay values without the need for discretization. Based on the minimum supporting assay values for drug-target interactions loaded from the drug data sources, we calculated inhibition or activation strength by using the following hard-coded piecewise function:We adopted the constrained fuzzy logic simulation approachf(input) may be a Hill function32 or an Identity function. For the four simulations conducted with the “Signaling by SCF-KIT” pathway, the Identity function was used.FunctionTo measure the perturbation impact caused by drug action, we developed a relative impact score based on the time step plots for individual entities in the pathway. We conducted the simulation twice: one with drugs as the perturbation, and another without drugs as the reference. The relative impact score was then calculated as follows :Our test results indicated that the above defined relative impact score, ranging between -1.0 and 1.0, inclusively, converged within a limited number of time steps in most cases.https://spring.io) along with the Hibernate OR  mapping (http://hibernate.org); the front end provides all user interfaces.We adopted the three-tier software architecture: The data tier provides drug-target interactions from the Cancer Targetome and DrugCentral, contents from the Reactome database, and the Reactome FI network; The server tier fetches the content from the data tier and then send it to the front-end app via a RESTful API (application programming interface). The API also provides methods for Boolean network construction and fuzzy logic simulation. The server was implemented upon the Spring framework  hosted athttps://github.com/saezlab/CNORfuzzy by Saez Lab. The simulation ran until either the maximum difference between two consecutive iterations was less than 1.0 × 10-6, or the total number of iterations reached the larger of 100 or 1.2 × total number of variables in the Boolean network. To calculate the relative impact scores, we expanded the plots of fuzzy variable values vs. time steps based on reached attractors by 20 time steps each time. This expansion stopped when the maximum difference between two consecutive expansions was either less than 0.01, or the expansion reached a total of 1000 time steps. As the default setup for simulation, 1.0 was assigned as the initial values for those entities annotated as inputs of reactions forming loops or inputs that are not annotated as outputs in the pathway.Our implementation of Boolean network in Java referred to the Java implementation of open source CellNOptRhttps://java.com) and Eclipse (https://www.eclipse.org) as the integrated development environment (IDE) for programming. ReactomeFIViz is released through the Cytoscape App Store (http://apps.cytoscape.org/apps/reactomefiplugin). The code is open source, hosted at GitHub (https://github.com/reactome-fi/CytoscapePlugIn).We used Java 8 .License: Creative Commons Attribution 4.0 International (CC BY 4.0) License ), PAK3 (in brown), or TNIK (in green), as discussed in the main text. (R) indicates pathways collected from Reactome, (K) from KEGG (1), (N) from NCI Pathway Interaction Database (2), (P) from Panther Pathways.1. Kanehisa M, Furumichi M, Tanabe M, Sato Y, Morishima K. KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res. 2017 Jan 4;45(D1):D353–61.et al. PID: the Pathway Interaction Database. Nucleic Acids Res. 2009 Jan;37(Database issue):D674–9.2. Schaefer CF, Anthony K, Krupa S, Buchoff J, Day M, Hannay T,Figure S1. Constrained fuzzy logic simulation results for entity PIP3 [plasma membrane]  in the “Signaling by SCF-KIT” pathway from 4 simulations. A: Simulations conducted with the default setup provided by ReactomeFIVIz. B: Same as A except the initial value of PRKCA was reduced from 1.0 to 0.5. Logic fuzzy values prior to time step 11 are the same for all four simulations, which is 0.0.Creative Commons Zero "No rights reserved" data waiver (CC0 1.0 Public domain dedication).Data are available under the terms of the 1. Please cite those that are relevant for the present work, and also describe the added value of ReactomeFIViz in the context of precision medicine applications.There are several drug-target interaction network visualization and drug repurposing analysis tools available as web-based software implementations, for instance, those reviewed in our recent articleCancer Targetome aggregates drug-target data from various databases in different formats; for example, DrugBank simply reports binary drug-target interactions and does not provide quantitative bioactivity measurements . Similarly, IUPHAR/BPS Guide to Pharmacology reports median bioactivity values in negative log. Please describe how these data were combined?It remains unclear how the evidence from multiple bioactivity assays  are summarized into an interaction strength for pathway analyses . This is important especially when there are multiple, perhaps contradicting evidence from multiple assays for the same drug-target pair; which assay is considered most informative in such cases?We have initiated an open-data platform, called DrugTargetCommons (DTC), with aim to annotate bioactivity data with assay information to enable more harmonized compound-target analyses. This community-based effort involves both approved drugs and investigational compounds, and perhaps something the authors would like to consider in the future version of ReactomeFIViz.The drug-target interaction network analyses shown in Fig. 7 are interesting, but it remains unclear whether the extracted drug-target information includes also mutant targets, or is the visualization based only on wild-type targets? Selective targeting of the mutant protein (e.g. oncogene) often leads to better efficacy and less side effects, especially if the protein is essential in healthy cells. For instance, DTC includes more than 200 mutant targets with binding affinities for hundreds of drugs.Introduction mentions several current challenges in precision medicine, such as identifying biomarkers to guide treatment decisions, rationally designing combination therapies for improved efficacy, reducing therapeutic toxicity and side effects, and managing both intrinsic and adaptive drug resistance. The authors should summarize in Discussion which of these challenges ReactomeFIViz can currently address, and how, and which will be topics of future research.  Specific comments:Figure 1: the text is so small in the panels that it becomes unreadable.Figure 2: several targets are marked with same colors, can you redefine coloring based on pathways?This manuscript introduces an enhanced Cytoscape plugin, called ReactomeFIViz, which enables researchers to visualize drug-target interactions according to strength of supporting binding assay evidence, visualize drug-target interactions in the contexts of pathways and functional interaction networks, and to perform Boolean Network modeling to investigate the impact of drugs on pathways. This seems like a highly useful tool for the community, and I have only a couple of comments on how to improve the manuscript, as well as a few suggestions on potential future versions of ReactomeFIViz.We confirm that we have read this submission and believe that we have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. In this article, Blucher et al. present important updates to the ReactomeFIViz app for Cytoscape. They added several very useful features to this popular plugin that now enables easy visualization of drug and their targets in the context of biological pathways. In addition to retrieving and overlaying the drug-target information on the Reactome pathways, the updated app provides convenient visual representations of several layers of related information. Moreover, they added the capability to model the impact of single or multiple drugs on the pathways using a simple yet elegant approach based on Boolean Network modeling. Overall, the authors have done a great job in providing a thorough description of the new capabilities of the software.The fuzzy logic modeling examples are very well described but It might be useful if the description included some discussion on the biological interpretation of these results, especially in the context of precision medicine.I found the online documentation for the reactomeFIViz documentation very useful to follow the analyses discussed in the manuscript. However, I was not able to test the “systems pathway impact analysis” which kept returning an HTTP status 500 error message. I am not sure if it’s a user-specific error, but it might be worth checking.Some of the drug-target overlays are very difficult to read because of high edge-density. It might help if the edges were semi-transparent.  Consider using a color-blind friendly palette for visualizing simulation outputs, as the red-green gradient may be challenging for some users to differentiate. I only have some minor comments and observations:I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.  Discuss the possibility of zoom in/out the signaling pathways, considering that multiple signaling pathways are often activated in a specific disease. If it is possible to list/visualize all drugs targeting on all the pathways, and users can check the details, like the google map.For the drug-target interaction, it is better to indicate the target names directly beside the color bar (because it is hard to match so many colors to drug names in the legend area)Minor: the resolution of Figure 1 is low. The subfigures (though these sub-figures are presented again the manuscript) are not clear. In this study, the authors improved a cytoscape network tool to analyze and visualize drug target interaction in the context of signaling pathways. The detailed examples are very helpful to indicate the importance of the functions of the tool. Overall it is well written. A couple of comments are as follows:I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.


Cancer arises through accumulation of somatically acquired genetic mutations. An important question is to delineate the temporal order of somatic mutations during carcinogenesis, which contributes to better understanding of cancer biology and facilitates identification of new therapeutic targets. Although a number of statistical and computational methods have been proposed to estimate the temporal order of mutations, they do not account for the differences in the functional impacts of mutations and thus are likely to be obscured by the presence of passenger mutations that do not contribute to cancer progression. In addition, many methods infer the order of mutations at the gene level, which have limited power due to the low mutation rate in most genes.Probabilistic Approach for estimating the Temporal Order of Pathway mutations by leveraging functional Annotations of mutations (PATOPA). PATOPA infers the order of mutations at the pathway level, wherein it uses a probabilistic method to characterize the likelihood of mutational events from different pathways occurring in a certain order. The functional impact of each mutation is incorporated to weigh more on a mutation that is more integral to tumor development. A maximum likelihood method is used to estimate parameters and infer the probability of one pathway being mutated prior to another. Simulation studies and analysis of whole exome sequencing data from The Cancer Genome Atlas (TCGA) demonstrate that PATOPA is able to accurately estimate the temporal order of pathway mutations and provides new biological insights on carcinogenesis of colorectal and lung cancers.In this paper, we develop a PATOPA provides a useful tool to estimate temporal order of mutations at the pathway level while leveraging functional annotations of mutations. Carcinogenesis is a complex process which involves somatic mutations in a number of key biological pathways and processes. Better understanding the temporal order of somatic mutation occurrences is very important to study the biological mechanism of cancer development and to inform new therapeutic targets. For some cancer types, the temporal order of mutations have been well studied. For example, colorectal cancer is frequently initiated by mutations that affect the Wnt signaling pathway, and then progress upon subsequent mutations in genes involved in MAPK, PI3K, TGF-beta, and p53 signaling pathways . HoweverLarge-scale somatic mutation profiling via whole-exome or whole-genome sequencing has provided an unprecedented opportunity for using statistical and computational methods to study carcinogenesis. A number of methods have been developed to infer temporal order of somatic mutations based on cross-sectional genomic sequencing data. One class of methods use a single oncogenetic tree or a mixture of trees to characterize temporal order of mutations –4. A strAll the aforementioned methods infer tumor progression at the gene level. However, different patients have mutations in different genes and mutation rates for most genes are very low. Therefore, the power of gene level analysis is usually low. One main reason for this mutational heterogeneity is the mutual exclusivity of gene mutations in a biological pathway , 14. DifA major limitation of current methods is that all mutations are treated equally. A gene or pathway is considered to be functionally altered as long as a non-synonymous mutation occurs in the gene or pathway. However, many non-synonymous mutations are passenger mutations that do not contribute to cancer progression. Failing to control for such noise may lead to spurious results. The impact of passenger mutations may get worse at the pathway level because of an elevated noise level due to the increased number of mutations . One appIn this paper, we propose PATOPA, a probabilistic method to characterize the temporal order of mutations at the pathway level. PATOPA incorporates the functional annotation of each mutation and weigh more on mutations that are likely to be functional. To our knowledge, this is the first attempt to incorporate functional impact into mutation temporal order analysis. Simulation studies are performed to evaluate the accuracy of PATOPA on estimating the temporal order of pathway mutations and to assess the effect of functional impact scores on the estimation. PATOPA has been applied to colorectal and lung whole exome sequencing datasets from TCGA.P on the ordering of functional mutational events, where the  element of the matrix, pk,i, indicates the probability of the kth functional mutation occurring in the ith pathway. Based on this pivotal probability matrix, we calculate the temporal order probability of one pathway being altered before or after another pathway. Finally, we use a partial order plot to summarize the temporal order of all the pathways.An overview of PATOPA is provided in Fig. A2 and B3 only have one mutation each, and the mutations are in different patients. Therefore, there is no sufficient information to confidently determine the temporal order of mutations in these two genes. In contrast, when pooling genes from the same pathway together, the mutation frequency increases substantially for both pathways so that the temporal order estimation becomes more feasible. The second extension is to incorporate functional annotation information to improve the inference. As shown in Fig. The idea of PATOPA can be illustrated by an example of determining the temporal order of pathways A and B in Fig. P, where the size of the matrix is determined by the number of pathways and the number of functional mutation events. Without control of these two numbers, P can contain a large number of parameters, which makes the estimation computationally intensive. We used the following two approaches to reduce number of parameters in P. Firstly, we performed our analysis for each pair of pathways seperately to estimate P and temporal order probabilities. This approach substantially reduces the number of columns in P, and therefore is computationally much more efficient. To examine the performance of this approach, we used TCGA rectal cancer data and analyzed 9 key cancer pathways from the Kyoto Encyclopedia of Genes and Genome (KEGG) database [PATOPA involves estimation of the parameter matrix database . A more pk,i=pK,i for k>K and estimated an averaged distribution for mutations occurring after the K-th step. To choose an appropriate K value, we again used the TCGA rectal cancer data and compared estimated temporal order probabilities between adjacent K values for 36 pairs of the 9 key pathways. Figure K≥4. In addition, for each pair of pathways, we calculated the probability distribution of number of functional mutations they would contain ), simultaneously with (P(A=B)), and after (P(A>B)) pathway B being altered. These probability values were set as true values to simulate data based on the following procedure. Firstly, the number of functional and non-functional mutations in a patient were generated based on the empirical distributions in TCGA rectal cancer data, respectively. Secondly, functional mutations were assigned to pathways in a temporal order, where the kth functional mutation was assigned to pathway A with probability pk,A, or to pathway B with probability pk,B. Thirdly, non-functional mutations were randomly assigned to the two pathways with probabilities qi, the probability that a randomly sampled non-functional mutation is from pathway i for i=A or B. Fourthly, the functional impact score of each gene mutation was assigned as the conditional probability of observing this specific mutation given that there was a functional/non-functional mutation in the pathway that this gene belonged to.Simulation studies were conducted to evaluate the performance of PATOPA in determining the temporal order of two pathways, A and B. Our goal was to use simulated datasets that we knew the true pathway order probabilities to investigate whether PATOPA was able to uncover those probabilities when analyzing the datasets. To mimic real world situation, we set the true pathway order probabilities based on TCGA rectal cancer mutation data from the p53 signaling  and cell cycle  pathways. Specifically, we applied PATOPA to TCGA rectal cancer data to estimate P(A<B), P(A=B) and P(A>B). To quantify the difference between true probability values we set when simulating the data and estimated probability values from PATOPA, we define the bias as the mean absolute difference between true and estimated probability values across 100 simulations. As shown in Table We simulated data of sample size 50, 100, 200 or 400 with 100 replicates at each sample size. We applied PATOPA to simulated datasets to estimate P(A<B) decreased and P(A>B) increased. Specifically, when functional impact scores of all pathway A mutations were decreased by 0.5, P(A<B) decreased to 0.25. When scores were increased by 0.5, P(A<B) increased to 0.7. The results demonstrate the substantial impact of functional impact score that can lead to distinct inference on the temporal order of pathways.We performed another set of simulations to assess the effect of functional impact score on the estimation of temporal order probabilities. Under the same simulation setting described in the previous subsection, we increased or decreased the PolyPhen-2 scores of all mutations in the p53 signaling pathway (pathway A). The resulting probabilities are presented in Fig. To further evaluate the performance of PATOPA, we applied it to TCGA whole exome sequencing data for colorectal and lung cancer data. We considered 9 key cancer pathways in our analysis. In addition to the canonical molecular signaling pathways of wingless-related integration site (WNT), mitogen-activated protein kinase (MAPK), phosphoinositide 3-kinase (PI3K), transforming growth factor beta (TGF-beta), p53 and vascular endothelial growth factor (VEGF), we also included pathways involving the processes of apoptosis, adherens junction and cell cycle. The apoptosis pathway is an indicator of turnover of both normal and tumor cells; the adherens junction pathway is an important factor for tumor invasion; and the cell cycle pathway suggests the process of cell growth in the tumor. Genes in each of the 9 pathways, which are listed in Additional file The TCGA project provided the whole exome sequencing data of 461 colon and 172 rectal tumor samples. Since we aimed to better understand the similarity and difference of carcinogenesis between colon and rectal cancers, we analyzed these two datasets separately. For each cancer type, we deleted the top 16% hyper-mutated samples because the carcinogenesis process of these tumors involve different sequences of genetic events . We applTo better illustrate how the incorporation of functional impact scores benefited our analysis, we studied the distribution of PolyPhen-2 scores for each pathway using TCGA rectal and colon cancer datasets, see Figure S12 in Additional file Non-small cell lung cancer is another significant cancer type to our interest, which is primarily composed of two clinically and pathologically different subtype groups, i.e. lung adenocarcinoma and lung squamous cell carcinoma. We applied PATOPA to the whole-exome sequencing data of 585 lung adenocarcinoma samples and 504 lung squamous cell carcinoma samples in TCGA. The resulting partial order plots are shown in Fig. We compared PATOPA with an existing method, H-CBN . We focuInferring temporal order of driver mutations during carcinogenesis is an important task in the analysis of whole genome/exome sequencing data. Considering mutations at the pathway level rather than individual gene level is biologically meaningful and can substantially increase the power of the analysis. We focused on 9 KEGG cancer-related pathways in our data analysis. But our method is generally applicable to study alterations of other biological pathways or gene sets including de novo driver gene sets identified from computational algorithms , 29.In our analysis, we used PolyPhen-2 score  to charaOur data analysis did not account for cancer subtypes –33. It wPATOPA infers temporal order of pathway mutations based on mutation frequencies across a cohort of patients. It does not account for the intra-tumor heterogeneity , 35, whiIn this article, we have proposed PATOPA, a new probabilistic method for inferring the temporal order of pathway mutations during carcinogenesis based on whole genome/exome sequencing data and functional impact scores of mutations. The method can be a useful tool to help researchers better understand the process of tumor development. The result obtained by applying our method to TCGA rectal cancer whole-exome sequencing data is mostly consistent with the multi-step process of colorectal carcinogenesis established by previous research, which provides a degree of validation of the ability of our method to recover mutation order of pathways from a cross-sectional dataset.N pathways. We first consider the case that all the pathways do not have any gene in overlap. An extension of our method to the case of overlapping pathways is provided at the end of this subsection. Let i of patient j for i=1,…,N, j=1,…,M, and j. Let kth mutation is functional  is independent of For the second term in equation (1), we assume A being alterred prior to pathway B, denoted by P(A<B), isGA<B is the subset of pathway mutation sequences satisfying that the first functional mutation in A occurs before the first functional mutation in B occurs.An estimate of A and B, with an overlapped subset A∩B. We regroup the genes into three mutually exclusive sets: A′=A∩Bc, AB=A∩B, and B′=Ac∩B, and perform the analysis on those three sets. Functional mutations in A′ and B′ are able to delineate the temporal order of A and B. Functional mutations in AB are considered as altering both A and B simultaneously and are used to estimate such probability, i.e. P(A=B).The aforementioned method requires that the pathways have no overlap. However, many pathways in biological databases, such as KEGG , have ovWe visualize the result of our analysis of each cancer type with a partial order plot . Each nopk,i is obtained by maximizing the likelihood function : Linux or WindowsProgramming language: R, COther requirements: R 3.3.2, gcc 4.2.1License: GNU GPLAny restrictions to use by non-academics: NoneAdditional file 1 Figures S1-S9 display the “core” pathway genes in each of the 9 key cancer pathways used in the paper. Figure S10 shows the distribution of the number of functional mutations in pairs of pathways. Figure S11 compares the temporal orders of pathway alterations inferred by PATOPA with the literature. Figure S12 shows the distribution of PolyPhen-2 scores in each pathway. Figure S13 shows the inferred temporal order of pathway mutations from TCGA rectal and colon data based on H-CBN.Additional file 2 Table S1 provides a list of “core” pathway genes in each of the 9 key cancer pathways considered in the paper.


Apical dominance, the process by which the growing apical zone of the shoot inhibits bud outgrowth, involves an intricate network of several signals in the shoot. Auxin originating from plant apical region inhibits bud outgrowth indirectly. This inhibition is in particular mediated by cytokinins and strigolactones, which move from the stem to the bud and that respectively stimulate and repress bud outgrowth. The action of this hormonal network is itself modulated by sugar levels as competition for sugars, caused by the growing apical sugar sink, may deprive buds from sugars and prevents bud outgrowth partly by their signaling role. In this review, we analyze recent findings on the interaction between light, in terms of quantity and quality, and apical dominance regulation. Depending on growth conditions, light may trigger different pathways of the apical dominance regulatory network. Studies pinpoint to the key role of shoot-located cytokinin synthesis for light intensity and abscisic acid synthesis in the bud for R:FR in the regulation of bud outgrowth by light. Our analysis provides three major research lines to get a more comprehensive understanding of light effects on bud outgrowth. This would undoubtedly benefit from the use of computer modeling associated with experimental observations to deal with a regulatory system that involves several interacting signals, feedbacks, and quantitative effects. As sessile organisms, plants have to adapt to their growth environment. One important way is to adapt their branching architecture, above and below grounds, to accommodate endogenous  and exogenous  constraints. In this process, branching regulation plays a crucial role as it defines strategies whereby plants colonize the underground and aerial spaces. Different environmental factors have been shown to impact this process, such as mineral or water supply to the roots, light, or temperature . In the During growth, apical meristems of plant axes produce sequences of phytomers. One phytomer is composed of an internode with its axillary leaf and one or several axillary buds. Once initiated, axillary buds themselves may in turn enter growth immediately (sylleptic buds), or they can remain latent (proleptic buds) until some external event to the buds triggers their outgrowth . This laLight in particular has been recognized as a major modulator of the expression of apical dominance for decades. For example, increasing light intensity in photosynthetically active radiation (PAR) often results in an increase of the total number of lateral branches that develop on a given axis, thus reducing apical dominance . LikewisThe nature of the physiological or biophysical mechanisms whereby light interacts with the process of apical dominance and participates to releasing axillary bud latency is still largely elusive. A better understanding of these mechanisms requires identifying how light interacts with the physiological mechanisms regulating apical dominance. Two major putative mechanisms of apical dominance have been debated in the literature over the last decades. First, it has been experimentally shown on a variety of plant species that apical dominance is mediated by the plant hormone auxin, produced at the growing apex, and transported downward through the vascular tissues of the stem . In thisIn this review, we analyze how the effect of light on bud outgrowth has been interpreted in the context of the two main paradigms thought to be at the origin of apical dominance . While previous review mainly focused on light effects in the vicinity of the bud , this reAuxin, a plant hormone produced in the apical region and transported downwards through the stem, has long been considered as the orchestrator of apical dominance in plants . While dAuxin acts in an interconnected way with two other hormones, cytokinins (CKs) and strigolactones (SLs). CKs act as shoot-branching inducers that have an antagonistic effect to auxin on bud outgrowth . SLs actAuxin cannot enter buds  and indiIn a theory known as “the second messenger theory,” auxin in the nodal segment adjacent to the bud down-regulates CKs and up-regulates SLs, which are both supposed to migrate into the adjacent bud to control its outgrowth. The direct action of CKs and SLs in buds is supported by exogenous application of CKs and SLs on buds, which stimulated and inhibited their outgrowth, respectively . FurtherTB1/BRC1) in the bud (for reviews OsTB1/FC1 (Oryza sativum Teosinte1/Fine Culm1) in rice was insensitive to SLs  were observed displaying reduced CKs in the xylem sap and higher expression of SL biosynthesis genes, while exogenous SL supply repressed SL biosynthesis  is well known for its role in plant adaptation to abiotic stresses , and gibArabidopsis  displayed higher bud outgrowth frequency (The role of ABA as an inhibitor of bud outgrowth was long hypothesized based on the observations that exogenous ABA supply inhibits bud outgrowth  and thatrequency . Similarrequency .AXR1), MORE AXILLARY BRANCHED (MAX) signaling (MAX2), and BRANCHED1 (BRC1) gene (AtBRC1 directly induces ABA synthesis in the bud by upregulating the expression of 9-CIS-EPOXICAROTENOID DIOXIGENASE 3 (NCED3), which encodes a key ABA-synthesis enzyme  gene . AtBRC1 s enzyme . ABA mays enzyme , which ms enzyme . ABA is s enzyme . This ras enzyme . In barls enzyme . These fBRC1 acts at least partly by up-regulating ABA biosynthesis.So far, many studies have focused on understanding how auxin, synthesized by growing apical organs and transported downwards through the stem in annual plants, acts to inhibit the outgrowth of a bud without entering the bud during apical dominance. They highlighted an intricate regulatory network described in Arabidopsis  in bidopsis . From a bidopsis . Intriguus auxin .Besides these well-studied auxin-dependent pathways in the vicinity of the bud, some studies have also highlighted the important role of roots as a source of SLs and CKs. They indicate that auxin-related apical dominance in the shoot may be modulated by root-derived CKs and SLs, as a way to adjust apical dominance in the aerial part to soil nutrient status . Furtherper unit area . However, CK level in the stem and auxin export from the bud to the stem are unlikely to be the main mediators of sugar promoting effect on bud outgrowth. Without sucrose, CK supply to rose nodal segments in vitro did not induce bud outgrowth, and sucrose could not antagonize the auxin-dependent repression of CK levels in the stem  and sugar acts by suppressing SL response, captured the diversity of observed bud outgrowth responses to sugar and hormones in a quantitative manner. Further studies are required to decipher the exact targets of sugars, but the SL signaling-related gene MAX2 and the integrator gene BRC1 that are downstream of SLs and down-regulated under high sucrose conditions for different species may be involved  exhibited lower suppression of bud outgrowth by low R:FR than the wild type  or signaling (max2) mutants as compared to wild-type Arabidopsis   . This isRhCKX1) . In addiRhCKX1) . Interesmeristem .BRC1 repression and PIN up-regulation  (BRC1 in the bud and up-regulated PIN1 expression in the stem for rose decapitated plants (BRC1 in Arabidopsis (MAX2 and up-regulated sugar metabolism-related genes (Light-induced bud outgrowth may involve the two CK-related processes controlling bud outgrowth: apacity) . Indeed,d plants . In linebidopsis . In addied genes , consisted genes . For rosed genes . All theBesides CKs located in bud vicinity, it is likely that root-derived CKs contribute to bud outgrowth stimulation in response to light intensity. Indeed, the concentration in root-derived CK forms  increases in stems and buds in these conditions ; howeverStrong evidence is given about a main role of competition for carbohydrates, indicated by the source-sink ratio, in bud outgrowth regulation in garden pea and grasses . The carin vitro in darkness did not induce bud outgrowth , have proved meaningful to address the complexity of developmental systems as a collection of interacting constituents (at molecular or cellular level for example). FSPMs make it possible to identify and test various hypotheses on the local interaction rules and to compare qualitatively and quantitatively, with the experiments, the result emerging from these simulated interactions at an integrated level. This approach has been successfully used in the last decade to study various aspects of plant development such as flowering and inflorescence architecture development , phyllotAS, CG, FB, SD-M, SS, and JB searched for articles, synthetized them, and wrote parts of the review; SS gave the initial impetus for writing a review around light, bud outgrowth, and plant physiology; CG and JB structured the review; JB directed the work.This review was conducted in the framework of a PhD thesis funded by the Environment and Agronomy department of the French National Institute for Agricultural Research (INRA), the French Region Pays de la Loire, and the program “Objectif Végétal, Research, Education and Innovation in Pays de la Loire” .The authors declare that the research was conducted in the absence of any personal, professional, or financial relationships that could potentially be construed as a conflict of interest.


The analysis of cancer genomic data has long suffered “the curse of dimensionality.” Sample sizes for most cancer genomic studies are a few hundreds at most while there are tens of thousands of genomic features studied. Various methods have been proposed to leverage prior biological knowledge, such as pathways, to more effectively analyze cancer genomic data. Most of the methods focus on testing marginal significance of the associations between pathways and clinical phenotypes. They can identify informative pathways but do not involve predictive modeling. In this article, we propose a Pathway-based Kernel Boosting (PKB) method for integrating gene pathway information for sample classification, where we use kernel functions calculated from each pathway as base learners and learn the weights through iterative optimization of the classification loss function. We apply PKB and several competing methods to three cancer studies with pathological and clinical information, including tumor grade, stage, tumor sites and metastasis status. Our results show that PKB outperforms other methods and identifies pathways relevant to the outcome variables. High-throughput genomic technologies have enabled cancer researchers to study the associations between genes and clinical phenotypes of interest. A large number of cancer genomic data sets have been collected with both genomic and clinical information from the patients. The analyses of these data have yielded valuable insights on cancer mechanisms, subtypes, prognosis and treatment response.Although many methods have been developed to identify genes informative of clinical phenotypes and build prediction models from these data, it is often difficult to interpret the results with single-gene focused approaches, as one gene is often involved in multiple biological processes and the results are not robust when the signals from individual genes are weak. As a result, pathway-based methods have gained much popularity . A pathGiven that many pathways likely contribute to the onset and progression of a disease ,9,10. ItIn this paper, we propose a Pathway-based Kernel Boosting (PKB) method for sample classification. In our boosting framework, we use the second order approximation of the loss function instead of the first order approximation used in the usual gradient descent boosting method, which allows for deeper descent at each step. We introduce two types of regularizations +Ω(f)=1N∑i=1Nqnd Zhang ). IntuitWe then optimize the regularized loss for the best increment directionGiven the direction, we find the deepest descent step length by minimizing over the original loss functionThe core step of PKB is the optimization of the regularized loss function  Biological Process pathways. These databases provide lists of pathways with emphasis on different biological aspects, including molecular interactions and involvement in biological processes. The number of pathways from these databases ranges from 200 to 700. There is considerable overlap between pathways. To eliminate redundant information and control the overlap between pathways, we applied a preprocessing step to the databases with details provided in the Similar to the simulation studies, we compared the performances from different methods based on three fold cross validations following the same procedure as elaborated in We provide more detailed introductions to the data sets and clinical variables and interpretations of results by PKB in the following. For brevity of the article, we focus on presenting results for three outcomes, one from each data set and leave the other two in the Metabric is a breast cancer study that involved more than 2000 patients with primary breast tumors . The datWe then applied PKB to samples in subtype Lum B, where the sample sizes for the two classes were most balanced . For input gene expression data, we used the normalized mRNA expression (microarray) data for 24,368 genes provided in the data set. The model using GO Biological Process pathways and radial basis function (rbf) kernel yielded the best performance (error rate 27.4%). To obtain the pathways most relevant to tumor grade, we calculated the average pathway weights from the cross validation and sorted them from highest to lowest. Top fifteen pathways with the highest weights are presented in the first columns of Among all pathways, the cell aggregation and sequestering of metal ion pathways are the top two pathways in terms of the estimated pathway weights. Previous research has shown that cell aggregation contributes to the inhibition of cell death and anoikis-resistance, thereby promoting tumor cell proliferation. Genes in the cell aggregation pathway include TGFB2, MAPK14, FGF4 and FGF6, which play important roles in the regulation of cell differentiation and fate . MoreoveGlioma is a type of cancer developed in the glial cells in brain. As glioma tumor grows, it compresses normal brain tissue and can lead to disabling or fatal results. We applied our method to a lower Grade glioma data set from TCGA, where only grades 2 and 3 samples were collected  . After rThe estimated pathway weights indicate that the cell adhesion pathway and the neuropeptide signaling pathway have the strongest association with glioma grade. Genes in the cell adhesion pathways generally govern the activities of cell adhesion molecules. Turning off the expression of cell-cell adhesion molecules is one of the hallmarks of tumor cells, by which tumor cells can inhibit antigrowth signals and promote proliferation. Previous studies have shown that deletion of carcinoembryonic antigen-related cell adhesion molecule 1 (CEACAM1) gene can contribute to cancer progression . Cell AdThe next application of PKB is to a TCGA cutaneous melanoma dataset . MelanomTwo complement pathways, lectin induced complement pathway and classical complement pathway, came out from the PKB model as the most significant pathways. Proteins in complement system participate in a variety of biological processes of metastasis, such as epithelial-mesenchymal transition (EMT). EMT is an important process in the initiation stage of metastasis, through which cells in primary tumor lose cell-cell adhesion and gain invasive properties. Complement activation by tumor cells can recruit stromal cells to the tumor and induce EMT. Furthermore, complement proteins can mediate the degradation of extracellular matrix, thereby promoting tumor metastasis .In this paper, we have introduced the PKB model as a method to perform classification analysis of gene expression data, as well as identify pathways relevant to the clinical outcomes of interest. PKB usually yields sparse models in terms of the number of pathways, which enhances interpretability of the results. Moreover, the pathway weights as defined in Two types of regularizations are introduced in the optimization step of PKB, in order to select simple model with good fitting. Computation efficiency of the two methods depends on the regularization strengh: when regularization is strong, the SNP data ).There are several limitations of the current PKB approach. First of all, when constructing base learners from pathways, we use fixed bandwidth parameters (inverse of the number of genes in each pathway) in the kernel functions. Ideally, we would like the model to auto-determine the parameters. However, the number of such parameters is equal to the number of pathways, which is often too large to tune efficiently. Therefore, it remains a challenging task for future research. Second, we currently only use pathway as a criterion to group genes and within each pathway, all genes are treated equally. It is conceivable that the genes interact with each other through an underlying interaction network and intuitively, genes in the hub should get more weights compared to genes on the periphery. With the network information available, it is possible to build more sensible kernel functions as base learners . Third,


Shuangbai Tablets (SBT), a traditional herbal mixture, has shown substantial clinical efficacy. However, a systematic mechanism of its active ingredients and pharmacological mechanisms of action against proteinuria continues being lacking. A network pharmacology approach was effectual in discovering the relationship of multiple ingredients and targets of the herbal mixture. This study aimed to identify key targets, major active ingredients, and pathways of SBT against proteinuria by network pharmacology approach combined with thin layer chromatography (TLC). Human phenotype (HP) disease analysis, gene ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, and molecular docking were used in this study. To this end, a total of 48 candidate targets of 118 active ingredients of SBT were identified. Network analysis showed PTGS2, ESR1, and NOS2 to be the three key targets, and beta-sitosterol, quercetin, and berberine were the three major active ingredients; among them one of the major active ingredients, quercetin, was discriminated by TLC. These results of the functional enrichment analysis indicated that the most relevant disease including these 48 candidate proteins is proteinuria, SBT treated proteinuria by sympathetically regulating multiple biological pathways, such as the HIF-1, RAS, AGE-RAGE, and VEGF signaling pathways. Additionally, molecular docking validation suggested that major active ingredients of SBT were capable of binding to HIF-1A and VEGFA of the main pathways. Consequently, key targets, major active ingredients, and pathways based on data analysis of SBT against proteinuria were systematically identified confirming its utility and providing a new drug against proteinuria. Proteinuria is an established marker of kidney damage and a risk factor for progression of chronic kidney disease (CKD) –3. AlbumDespite some advancements in the understanding of proteinuria pathogenesis, there is still a lack of therapies that work against proteinuria other than using the renin-angiotensin system (RAS) inhibitor as the gold standard , such asImperatae Rhizoma (IRH), Hedysarum multijugum Maxim (HMM), Acanthopanax senticosus (ASE), Caragana sinica Rehd (CSR), Achyranthis Bidentatae Radix (ABR), Radix Paeoniae Rubra (RPR), Paeoniae Radix Alba (PRA), Fructus lipuidambaris (FRU), Lycopi Herba (LHE), Tetrapanacis Medulla (TME), Polygonum perfoliatum L (PPL), Sambucus williamsii Hance (SWH), Dipsaci Radix (DRA), Stephaniae Tetrandrae Radix (STR), and Chuanxiong Rhizoma (CRH). Furthermore, some single-flavor herbs or ingredients in SBT, such as Astragaloside IV of HMM, paeoniflorin of PRA, and CRH have also been revealed an exact therapeutic effect against proteinuria [Recently, Traditional Chinese Medicine (TCM) has played an important role in the treatment of proteinuria via its multicomponent, multitarget, and multipathway approach , 19–23. teinuria –27.However, active components and potential pharmacological mechanisms of action of SBT against proteinuria have not been identified. In this study, human phenotype (HP) disease analysis, gene ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, and molecular docking were applied to investigate the bioactive constituents and the underlying mechanisms of SBT for the treatment of proteinuria, combined with thin layer chromatography (TLC), to identify a single active ingredient. The flow diagram of this study is shown in SBT (MUST-1901001) was provided by Ruijin Hospital, Shanghai Jiaotong University, China. Quercetin  was purchased by China National Institute for Biological Products Standard. Reagent methanol (MUST-20180105), hydrochloric acid (MUST-20190104), ethyl acetate (MUST-20170609), formic acid (MUST-20140212), and toluene (MUST-20160106) supplied by Lingfeng Chemical Reagent Co., Ltd. . Ethanol (MUST-20190530) and aluminum trichloride (MUST-20100127) were supplied by Klings and Qiangshun Chemical Reagent Co., Ltd. , respectively. Distilled water was used, and all other reagents were of analytical reagent.http://lsp.nwsuaf.edu.cn/tcmsp.php) [http://tcm.lifescience.ntu.edu.tw/) [The active ingredients of each herb in SBT were obtained using TCMSP (msp.php) , accordimsp.php)  and drugmsp.php) , as welledu.tw/)  and relaedu.tw/) –37.http://www.swisstargetprediction.ch) databases with Homo sapiens [The targets of active ingredients were searched in TCMSP and SwissTargetPrediction  [https://www.kegg.jp/) [https://www.disgenet.org/) [https://www.drugbank.ca/) [https://www.uniprot.org/) [Genes associated with proteinuria were searched on four existing human disease resources, including Online Mendelian Inheritance in Man Database  , the Kyoegg.jp/) , DisGeNEet.org/)  databaseank.ca/) . After dot.org/) .https://www.nigms.nih.gov/). We calculated the number of overlapped active ingredients in 15 herbs, as well as the number of overlapped candidate targets in active ingredients, and the top three were considered as the major active ingredients and the key targets, respectively.Candidate targets against proteinuria were identified by matching the targets of the active ingredients of SBT and the genes related to proteinuria. Next, the “herbs-active ingredients-candidate targets” comprehensive network was established using Cytoscape software  [The interacting protein candidate targets against proteinuria were obtained from the String database (db.org/) –47 in whdb.org/) –50. Calchttps://biit.cs.ut.ee/gprofiler/) [We performed HP and online GO term enrichment analysis by placing all central targets into g:Profiler (ofiler/) –53. KEGGofiler/) .HP enrichment analysis is a web that provides a standardized vocabulary of phenotypic abnormalities encountered in human disease which can reveal the correlation between central targets and proteinuria. GO term is able to classify functions into three aspects: molecular functions (molecular activities of gene products), cellular components (where gene products are active), and biological processes (pathways and larger processes made up of the activities of multiple gene products).http://systemsdock.unit.oist.jp/) [After identifying the pathways, in order to certify the reliability of the above enrichment analysis, we docked the factors in the pathways and the major active ingredients by SystemsDock (ist.jp/) , 56, whiist.jp/) .https://pubchem.ncbi.nlm.nih.gov/) and protein PDB ID of the factors in the pathways download from String and PDB database [https://www.rcsb.org/).SystemsDock is a web server for network pharmacology-based prediction and analysis, which applies high-precision docking simulation and molecular pathway map to comprehensively characterize the ligand selectivity and to illustrate how a ligand acts on a complex molecular network. Structure files in the formats of 2D of the major active ingredients download from PubChem  (https:/database  (https:/30 tablets of SBT were dissolved in 50 ml (methanol-hydrochloric acid (25%) (4 : 1)) solution and heated under reflux for 1 hour. The residue was dissolved (20 ml distilled water) and extracted twice (20 ml of ethyl acetate). Next, ethyl acetate solution was washed (10 ml of distilled water), discarding the aqueous solution. Finally, the dissolved residue (2 ml of methanol) was prepared as SBT sample.Standard sample of quercetin  was prepared by dissolving the solutes in methanol.μl) and quercetin standard sample (2 μl) were spotted manually on the chromatographic plates. The mixture of toluene + ethyl acetate + formic acid (25 : 20 : 1) was used as the mobile phase. The plates were developed vertically at room temperature (20°C) to a distance of 10 cm and then dried for 20 h at room temperature (20°C). Finally, it was sprayed with 10% aluminum trichloride in ethanol and placed under ultraviolet light (356 nm) for examination.According to the thin layer chromatography  test, the solutions of SBT samples  signaling pathway (37.5%), renin-angiotensin system (RAS) signaling pathway (25%), advanced glycation end products-receptor of advanced glycation end products (AGE-RAGE) signaling pathway (12.5%), aldosterone-regulated sodium reabsorption (12.5%), and vascular endothelial growth factor (VEGF) signaling pathway (12.5%) see .Molecular docking of bate-sitosterol, quercetin, and berberine with HIF-1A and VEGFA, respectively, all docking scores were not only greater than local ligand scores, but also greater than 5.52 shown in . The resIn the TLC chromatogram, there are fluorescent spots in the chromatogram of the SBT sample corresponding with quercetin standard sample both in position and color shown in .Network pharmacology is a mechanism research method for TCM, based on the characteristics of the comprehensive regulation of “multi-ingredient, multitarget, and multipathway” –62. In tIn our study, beta-sitosterol contained in 9 herbs (HMM and PRA), quercetin contained in HMM and PPL, and berberine contained in ABR acted on 15, 11, and 11 candidate targets against proteinuria, respectively. In addition, 92.1% of 118 active ingredients acted on at least two candidate targets . More imPrevious studies have demonstrated that beta-sitosterol has been widely used in the treatment of proteinuria resulting from diabetes and kidney disease –65. Berbμg·mL−1 were detected in SBT in our previously quality standard study [It is worth mentioning that quercetin in SBT was detected in our TLC results, and studies confirmed it was able to treat proteinuria as well as improve renal function through regulating HIF-1A and VEGFA factors in rat models –71, whicrd study . Studiesrd study  and paeord study . All of In our study, PTGS2, ESR1, and NOS2 were overlapped by 74, 59, and 37 active ingredients, respectively. Moreover, 68.75 % of 48 candidate targets of SBT can be overlapped by at least two active ingredients in SBT , which dIt is reported that PTGS2  was involved in podocyte injury and various renal pathological processes such as renal interstitial fibrosis, showing a potential therapeutic target against lupus nephritis-induced nephropathy , 75. Coiβ1, causing damage in renal function, vascular and structural [NOS2 could produce NO which simultaneously stimulates the increased expression of COX-2 and transforming growth factor-ructural . Relatedructural –85. Otheructural  and PPAPructural .All these studies supported our results that SBT worked against proteinuria in a multitarget synergistic way. Whether three active ingredients act against proteinuria through regulating PTGS2, ESR1, and NOS2, it needs further study.The HIF-1 signaling pathway and RAS signaling pathway were identified as mechanisms of SBT against proteinuria from ClueGO results. Studies have reported that high expression of HIF-1A in renal tubular epithelial cells can aggravate glomerular hypertrophy and extracellular matrix accumulation, increasing urinary protein excretion . More imβ which promotes glomerular sclerosis and renal tubular interstitial fibrosis in progressive nephropathy [In addition, many studies –97 have hropathy . HoweverInhibition of RAS can also regulate the high glucose-induced VEGF signaling pathway . VEGF-A,In conclusion, the “multi-ingredient, multitarget, and multipathway” of SBT against proteinuria were effectively elucidated by the network pharmacology approach. Beta-sitosterol is one of the major active ingredients used to treat proteinuria caused by IgA nephropathy, by targeting ERS1 through its anti-immune effects. Berberine reduces proteinuria and kidney damage caused by diabetes and hypertension through two key targets, PTGS2 and NOS2, which improve podocyte injury. Quercetin, contained in SBT through TLC verification, reduces proteinuria by regulating HIF-1 and VEGF in nephrons. Additionally, blocking RAS, a gold standard for treating proteinuria caused by CKD, can prevent HIF-1 and prevent VEGF from being activated. Blocking AGE-RAGE to prevent the autocrine signaling of VEGF can treat proteinuria caused by diabetic nephropathy. Thus, we predicted that SBT plays a therapeutic role in proteinuria caused by IgA nephropathy, CKD, and diabetic nephropathy and prevents complications such as cardiovascular diseases. All these contribute to the utility of SBT and may provide a new drug against proteinuria. However, more experimental studies are still needed to verify these findings.