PRECISE-based pathway signatures, can delineate pan-cancer commonalities and differences in proteomic network biology within and across tumors, demonstrates robust tumor stratification that is both biologically and clinically informative and superior prognostic power compared to existing approaches. Towards establishing the translational relevance of the functional proteome in research and clinical settings, we provide an online, publicly available, comprehensive database and visualization repository of our findings (https://mjha.shinyapps.io/PRECISE/).Personalized (patient-specific) approaches have recently emerged with a precision medicine paradigm that acknowledges the fact that molecular pathway structures and activity might be considerably different within and across tumors. The functional cancer genome and proteome provide rich sources of information to identify patient-specific variations in signaling pathways and activities within and across tumors; however, current analytic methods lack the ability to exploit the diverse and multi-layered architecture of these complex biological networks. We assessed pan-cancer pathway activities for >7700 patients across 32 tumor types from The Cancer Proteome Atlas by developing a personalized cancer-specific integrated network estimation (PRECISE) model. PRECISE is a general Bayesian framework for integrating existing interaction databases, data-driven  These include perturbations of signaling pathways that generate modifications of molecular activities affecting the downstream molecules along specific pathways. The coordinated triggers that regulate the cumulative change in the signaling pathways may result in different phenotypic changes, along with signaling alterations that can be delineated by a deeper understanding of the pathway networks and topologies4. These network structures vary widely within and between different tumors and are important factors in characterizing molecular tumor types and developing therapeutic strategies, especially in cancer6.Cancer is a complex disease initiated and characterized by myriad molecular and genomic events, which often occur within a modular and layered architecture of functional or cell signaling pathwaysglobal (across patients) and local (within patients). Global approaches typically generate population-level summaries (across patients) that rely on different distance metrics, such as functional and topological information of the networks, to measure associations between the target genes and known gene/protein sets of interest that represent various cellular processes. For example, methods based on gene set enrichment analysis9 use functional information that assesses the statistical overrepresentation of genes in a pre-selected list of interest from a reference list of known gene sets without consideration of their topological information in the graph structure. Analogously, several computational methods have been developed that incorporate the known topologic pathways to improve the prioritization of the gene set associations11. Without defining pathways, de novo pathway enrichment analysis14 has been proposed to identify functional modules/sub-networks enriched in biological entities active in a given experimental dataset by using large-scale interaction network.Most systematic endeavors for investigating pathways can be broadly classified into two categories based on the hypothesis, approach and outputs: 15. This type of approach acknowledges the fact that pathway structures and activity may differ considerably within and between patients as well as across tumor sites. For example, patient-specific pathway or meta-gene (MG) analysis has been proposed to infer the patient-specific activity of a gene/pathway/MG by incorporating known (pathway) interactions among genes and multiple types of molecular data18 as well as the functional impact of a mutation within gene/protein networks21. However, these methods only incorporate curated  interaction database and do not consider de novo data-driven causal networks. Moreover, these methods provide patient-specific scores of individual genes/MGs, as opposed to more interpretable cumulative pathway-level summaries that better reflect the complex pathophysiology of tumors.The local category of patient-specific approaches has recently emerged within a precision medicine paradigm. It is the treatment and prevention of disease by accounting for individual variability to match the right drugs or intervention to the right patientsa priori existing knowledge databases, data-driven de novo causal structures and diverse molecular profiling data at proteomic, transcriptomic (mRNA expression), epigenomic (DNA methylation) and microRNA expression levels. The novelty of our methodology lies in combining causal structure learning, Bayesian graphical models and variable selection at both population (cancer-specific) and patient-pathway levels. PRECISE outputs cancer-specific integrated networks, infers patient-specific networks (PRECISE networks), and elicits interpretable pathway-level signatures (PRECISE scores) by summarizing the patient-specific network as a single score that can be used for tumor stratification as well as constructing pathway-based prognostic models in cohorts of patients.In this study, we develop a novel and general Bayesian framework that conflates both global and local approaches: personalized cancer-specific integrated network estimation (PRECISE). PRECISE is a multi-scale approach that integrates multiple sources of information: 22. Predictions of protein levels from RNA levels are weak (with correlations <=0.5), which can be explained by many factors, for example (i) suppression of translation by miRNAs, (ii) rate of degradation of mRNAs may be different from their protein products, (iii) phosphoproteins are dependent on signaling activity and may not correlate well with their transcripts alone, (iv) in the case of protein complexes, the presence or absence of other proteins in the complex may stabilize the complex or destabilize it resulting in altered protein expression22. Furthermore, because the signaling properties of proteins can be modified by post-translational events like phosphorylation, acetylation and ubiquitination, it is difficult to predict their functional impact at the genomic or transcriptomic level23.We delineate the interplay of proteins in signaling pathways in various cancer types and infer patient-specific proteomic pathway activities. While pathways have often been assessed at the DNA and RNA levels, proteomic signaling networks are emerging for two critical reasons. First, the amount and function of proteins are downstream summation of changes that occur at the genomic and transcriptomic levels23. Thus, assessing the cancer-specific topology and structure of proteomic signaling networks and relating them to (prognostic) clinical outcomes are important tasks toward understanding the biological mechanisms behind cancer development and progression and, more importantly, identifying potential therapeutic targets5.Second, the signaling properties of proteins can be further modified by post-translational events e.g. through phosphorylation and other processes, which cannot be predicted at the genomic or transcriptomic levelhttp://tcpaportal.org) of 7,714 samples from 32 tumor types from The Cancer Genome Atlas (TCGA) where-in we analyze multiple pathways of critical importance to the behavior of tumor cells and response to therapy. From the comprehensive exploration of proteomics-based patient-specific pathway signatures across 32 cancer types, we delineate pan-cancer commonalities and differences in key proteomic signaling pathway activities and prognostic power of the patient-specific scores. We demonstrate the superior performance of PRECISE algorithm as compared to other established and current approaches as well as potential clinical utility of our scores across tumor types. We believe that our study is the largest and most comprehensive network-based modeling and exploration of pan-cancer proteomic pathway activities for tumor stratification and prediction.We applied PRECISE to a pan-cancer reverse-phase protein array (RPPA) proteomic data set  as described in the previous TCGA RPPA study for 11 tumor types22. The gene/protein membership for each pathway is listed in Supplementary Table pan-cancer network signaling and rewiring.We applied PRECISE independently on 32 cancer types from TCGA, which are listed in Table connectivity score (CS), which is the constructed as the ratio of the observed number of edges in the network to the total number of possible edges and its permutation p-value  in multiple lineages including colon/rectum cancers (CORE), BRCA, and thyroid cancer (THCA) while only CORE samples provided its significance with We investigated the extent of cross-signaling (among proteins) for each pathway across lineages. For defining a metric of connectivity in the corresponding integrated cancer-specific network, we use edge consistency (EC), which for a given edge is defined as the number of tumor types that hold the particular edge. We compared the EC to the known protein-protein interaction (PPI) score to corroborate with existing evidence as well as find new ones. Figure A pan-cancer analysis was undertaken to determine the extent to which edges (both regulatory and correlative) in each of the pathways are shared across tumor types. To this end, we used the To identify subtypes for the pan-cancer patients and pathway determinants of those subtypes, we developed a tumor stratification algorithm for all of the tumor samples in the 32 lineages based on the PRECISE scores. The PRECISE scores were computed by deriving summary measures from the PRECISE networks (patient-specific), indicating that the entire pathway is suppressed, neutral or activated. For the clustering analysis, we combined the two positive values for the suppressed and activated scores by summation- the combined score for a pathway and patient- thus represents a global perturbation level including activated or suppressed directions of the pathway for the patient. After matching all four platforms, RPPA, mRNA expression, microRNA expression, and DNA methylation, we had 6,844 patients across the 32 lineages. For all patients and all pathways, we constructed a 6,844 × 12 suppressed and activated PRECISE score matrix, with each row corresponding to a patient and each column corresponding to a pathway. This matrix was the input for our clustering analysis, which identified 23 optimal clusters of patients Fig. . We concenrichment probability (EP), which is defined as the posterior probability of enrichment in patients within a particular clinical subtype  in a given cluster, with EP close to 1 if the proportion of patients from the subtype in the cluster is significantly higher than the proportion of patients from outside the subtype in the cluster by random chance (EP close to 0). The detailed information on computing EP is in Section S1.3. The DNA damage response and RAS/MAPK pathways were highly activated or suppressed for all BRCA samples, and most samples showed activation or suppression for the apoptosis, PI3K/AKT, and TSC/mTOR pathways  and thyroid carcinoma (THCA) samples were both located primarily in clusters C5 and C8 Fig. . KIRC me30 and, in our study, the mutations were found in KIRC and THCA samples in C8 (60%), compared to no mutations in KIRC and THCA samples in C5 , we computed the enrichment probability (EP), which is defined as the posterior probability that the proportion of samples with a mutation in a cluster is higher than the proportion of samples with a wild-type modification in the cluster Fig. . The mos C5 Fig. . This inPRECISE status—which indicates that the pathway is suppressed, neutral, or activated for the patient, can be decided by the maximal probability. To investigate the prognostic power of the PRECISE status, we performed Kaplan-Meier survival analysis with a log-rank test to determine the difference in survival probabilities among the three groups according to the PRECISE statuses. The direction of the association with PRECISE statuses varied in different tumors, indicating that the effects of the pathways were dependent on the intrinsic characteristics of the tumor, either in terms of overall prognosis or intrinsic proteomic pathway expression patterns  and Receptor tyrosine kinases (RTK) pathways – which have established and critical roles in cancer development and metastasis across multiple tumor type. We correlated PRECISE EMT activated/suppressed score with epithelial tumor types such as Ovarian (OV), Lung Adenocarcinoma (LUAD), and Stomach Adenocarcinoma (STAD), and mesenchymal tumor types such as brain tumors, Glioblastoma Multifome (GBM) and Low Grade Gliomas (LGG). The mesenchymal tumor types such as brain tumors, GBM and LGG tend to have higher PRECISE EMT scores than the epithelial tumors such as OV, LUAD and STAD  and thus enable a fair comparison. Using prior knowledge from protein causal structure learning and existing database helps to improve its prognostic power for most pathways. The difference was the largest in the breast reactive pathway that were defined based on the breast cancer patients: 14 folds increase in p-values when we use no prior  native pathway score, which is defined as the sum of all positive regulatory components minus that of the negative regulatory components in a particular pathway as used and reported by recent literatures33, (3) PARADIGM score, which measures the patient-specific genetic activities incorporating curated pathway interactions among genes using expression16, and (4) Random protein signatures obtained by the method following34 (see Section S1.1). The naïve method gives all proteins equal weight (thus ignoring network information) in estimating the pathway-level score; whereas the native method takes directionality (+/−) into account to yield overall pathway-level scores. PARADIGM uses pathways from the National Cancer Institute Pathway Interaction Database (NCI-PID) and assigns an integrated pathway level (IPL) reflecting the activity of each node by computing patient-specific log-likelihood ratio. The major difference between these methods and our PRECISE algorithm is that we estimate and construct de novo cancer-specific networks (model building) to calibrate the patient-level pathway signatures by exploiting multiple sources of information obtained from cancer-specific multi-platform data, causal structure learning and existing interaction database. It is well established that sub-networks within signaling pathways and their products undergo changes in response to different conditions35. Such network rewiring in cancers are manifested at the level of signaling networks and is not currently well understood5. For FDR < 0.1, PRECISE, naïve and native scores were prognostic for 31, 21, and 4 numbers of tests across all the 12 pathways and lineage combinations  ons Fig. . There wons Fig. . For KIRons Fig. .32 across four tumor types. UCEC, OV, SARC, and UCS – to assess shared/differentiated pathway-based features. There was clear evidence that our PRECISE method preserves tumor-specific signals  better than native and PARADIGM scores and showed more evident pattern of activities across different tumor types . We use the proportions of random signatures that work better than those obtained from PRECISE as another measure of significance; we refer those proportions as concordance score between the predicted value using the PRECISE method and the observed value for each protein in a given sample (Section S1.4). This score takes values between 0 and 1, with a high value indicating that the predicted value has concordance with the observed value. Almost all lineages had concordance scores greater than 0.9; however, uveal melanoma (UVM) had large variations below 0.9, which was likely due to the small sample size of 12   explain tumor-specific pathway networks and connectivity; (2) develop patient-specific pathway networks and scores that are reflective of the molecular underpinnings in various tumor types; (3) are well-calibrated to identify tissue-specific signals, prognostic and clinical utility across multiple tumor types and; (4) general enough to be applied to any modern high-throughput technology that generated complex multivariate data.We develop a general Bayesian framework, called PRECISE, for inferring cancer-specific network structures and topologies of signaling pathways and inferring patient-specific pathway activities. We report the discovery that, through model-based integration of multiple sources of information such as existing protein-protein interactions databases, data-driven a priori knowledge leads to sparser and more biologically interpretable edges. We found a high degree of variation in pathway activity across cancer types, with some signaling pathways showing higher connectivity in certain tumor types than others , which suggests that the data-driven cancer-specific network aligns well with the a priori functional characteristics of this pathway. The RTK pathway and the TSC/mTOR pathway showed high cross-signaling across most major tumor types. The pan-cancer analyses of edges to determine network relationships (both regulatory and correlative) revealed that several network/edge relationships were highly conserved across tumor types, some of which confirmed existing knowledge while others represented novel links with significantly high probability which might warrant further experimental and functional validation  and correlative  edges between the proteins in the pathways. Through Bayesian sparse regression modeling, our approach is able to produce sparse tissue-specific and pathway-specific networks and the incorporation of ion Fig. , S3.Using Bayesian posterior predictive modeling, we de-convolve the patient-specific networks from the global tissue-specific ones and derive network-based scores reflective of the underlying patient-specific pathway-level activities. Using these PRECISE scores in an unsupervised clustering algorithm to assess cross-tumor heterogeneity across all tumor types, we find a high fidelity between the optimal clusters and the tumor lineages, e.g., 23 clusters, 14 of which were enriched with single type of tissue at the site of tumor origin with markedly different pathway activation status, which can help delineate pathway-based therapeutic targets. We found three clusters that represent the 3 subtypes of breast cancer, luminal, HER2 positive and basal, and both of the three clusters and pathway scores for breast reactive and core reactive pathways were significantly associated with patients’ survival times. Patients who have KIRC and THCA tumors were mostly classified into two PRECISE clusters and the PBRM1 gene that is a novel drug target for KIRC was found in patients located in one of the clusters, which indicates that PRECISE effectively classified subtypes across cancers.de novo causal networks, and computed the pathway scores across the multiple pathways for breast cancer patients that showed the clearest patterns of prognostic proteomic signatures and thus enable a fair comparison. We find that using prior knowledge from protein causal structure learning and existing database helps to improve its prognostic power for most pathways. The difference was the largest in the breast reactive pathway that were defined based on the breast cancer patients: 14 folds increase in p-values when we use no prior knowledge.We also investigated different aspects of PRECISE by performing external and internal validations and showing potential clinical utility. For objective external validation of the PRECISE algorithm, we compared PRECISE scores with that of published as well as scores obtained by random (unstructured) perturbations of the data. We found clear evidence that our PRECISE method preserves tumor-specific and clinical subtype-specific signals better than other existing methods and has much higher prognostic power in detecting pathway-specific prognostic signals between and across tumor types as compared to existing methods and random signatures. For the internal validation, we used a diluted version of our PRECISE method without using prior calibration from existing databases and 22. Therefore, direct estimates of RTK pathway activity using proteomics data would potentially be more predictive of tumor response. Importantly, the PRECISE pathway scores, while developed in the absence of clinical data, were robustly able to predict outcomes across patient lineages.For demonstrating potential clinical utility, we investigated the role of epithelial-mesenchymal transition (EMT), which has established critical roles in cancer development and metastasis across multiple tumor type. The mesenchymal tumor types, low-grade gliomas and glioblastoma multiforme tend to have higher PRECISE EMT scores as compared to epithelial-type tumors such as ovarian, lung and stomach adenocarcinomas, which shows PRECISE preserves and accentuates the actual biological mechanism of these pathways in relevant tumor types. Furthermore, we also confirmed that HER2+ breast cancer samples  have high PRECISE RTK scores compared to other breast samples and can potentially be used as a complementary proteomics surrogate for determining HER2- status. Our rationale for using RPPA-based proteomics data rather than mRNA’s, which have been shown to be poor surrogates for estimating protein expression33. This will not only shed light on some of the fundamental cancer mechanisms but also aid drug development and screening. Moreover, integrative analysis of patient and cell-line data, and identification of commonalities and differences in network biology could reveal insights into the specific roles of tumor microenvironment in cancer development and progression. From a methodological perspective, we note that our model assumes a linear relationship between the proteins for simplicity and interpretability. It is possible to extend this approach to allow for more flexible and non-linear dependence structures36.There are several refinements and generalizations possible for the PRECISE methodology. In this article, we only focused on patient tumor samples. An interesting next step would be to investigate the functional proteome of the cancer cell lines that has recently been collectedavailability of data and material section below).To the best of our knowledge, this is the first comprehensive exploration of pathway-specific network signatures for stratification and prediction using the cancer functional proteome. A more robust tumor stratification and pathway selection will emerge as more comprehensive set of functional pathway information from more mature proteomic data are generated across the entire proteome. Our PRECISE framework is a step towards the major goal of precision medicine — to find a disease treatment and its optimal targets while taking into account individual variability. Another contribution of this method is toward establishing the translational relevance of the functional proteome in research and clinical settings. We have also developed an R package for PRECISE and developed an online data and visualization repository, that compiles a comprehensive database of networks, scores, and pathway signatures that are easily accessible to the scientific and research community  were downloaded and preprocessed using ‘DownloadRNASeqData’ and ‘ProcessRNASeqData’ functions for gene-level expression. For microRNA expression data, miRNASeq data (generated by illuminaga or illuminahiseq) were downloaded using the ‘DownloadmiRNASeqData’ function. Using the ‘ProcessmiRNASeqData’ function, we processed the miRNASeq data, using the Hg19 reference genomes to map the reads. Then we annotated the microRNAs to genes using the ‘microRNA’ package in R version 3.2.0. DNA methylation data (generated by Illumina Infinium HumanMethylation 27 K or 450 K) were downloaded and preprocessed using ‘DownloadMethylationData’, ‘DownloadMethylation27Data’ and ‘DownloadMethylation450Data’.We used a pan-cancer reverse-phase protein array (RPPA) proteomic data set of 7,714 samples from 32 tumor types from The Cancer Genome Atlas (TCGA) . As an illustrative example, Supplementary Fig. We developed a general Bayesian framework to estimate cancer-specific and patient-specific networks and elicit patient-level pathway scores by integrating data arising from multiple platforms and incorporating existing network information. With Pan-cancer TCGA proteomic, genomic and epigenomic data, PRECISE uses a multi-scale approach Fig. . The impBriefly, we use a Bayesian regression model, in which each protein is regressed on covariates that include the protein regulators learned from the data-driven causal network and the PPIs (as prior information), and features from upstream data. The selected regulators for all proteins constitute cancer-specific integrated network. Given the cancer-type-specific network, we deconvolve the patient-specific PRECISE scores, using the status of each protein (node) of the networks as suppressed, neutral, or activated. We use these PRECISE scores for patient stratification and clinical outcome prediction. PRECISE is a general framework to achieve patient-level and pathway-level signatures by sequential estimations of cancer-specific  and patient-specific  networks. The novelty of our methodology lies in conflating causal structure learning, Bayesian graphical models and variable selection at both population (cancer-specific) and patient-pathway levels. The integrative modeling strategy involves smooth calibration of the prior by applying a causal structure learning method on resampled data, defining a patient-specific network using cancer-specific network with patient-specific labels on the nodes (proteins) with the estimated activation statuses of proteins, and subsequently estimating patient-specific pathway and network scores.i to protein j, where protein i is a regulator of protein j or protein j is a target of protein i. We decide the weights prior inclusion probabilities for the protein regulators based on data-driven causal structure and known PPI scores (discussed in the next section). Because both i for n patients and includes Ki covariate vectors  and microRNA expression. For protein i, the 13We aimed to estimate integrated cancer-specific networks using Bayesian regression methods on each of the proteins with other molecular profiling data. For regulatory relationship among proteins, let g reflects the prior on g implies deviation from 38 to infer the posterior network. Specifically, for each regression of protein I, its integrated cancer-specific regulators that have edges directed toward the protein i are defined by the proteins or other covariates that have a posterior inclusion probability  greater than 0.5. Therefore, among proteins, the network includes both directed regulatory edges - when regulator proteins of a protein are not targets of the protein and correlative edges- where-in both proteins in a link are regulators and targets.The hyper-parameter i for a patient j (A PRECISE network is the integrated cancer-specific network with patient-specific labels on the nodes (proteins). Specifically, the activation statuses of the nodes are evaluated by estimating the posterior predictive density of each of the proteins for each patient. To determine the activation status of a protein p genes, the pathway activity scores for a patient j is given byPRECISE pathway status- which indicates that the pathway is suppressed, neutral, or activated for the patient, can be decided by maximum of the three PRECISE pathway scores, j.To compute pathway activity scores for each patient, we derive summary measures from the PRECISE networks obtained from the Step 2, indicating that the entire pathway is suppressed, neutral and activated. Under the PRECISE networks, the number of nodes that have edges from protein To decide the prior inclusion probabilities for protein regulators  of Step 1.2. Draw a random sub-sample , which provides known and predicted PPIs. Each interaction is associated with a combined confidence score that integrates the various types of evidence such as genomic context, high-throughput experiments, co-expression, and previous knowledge. STRING v10 data were downloaded. As the weighted prior network, we collected all PPIs in the database, where each PPI has a score ranging from 0 to 1. A higher score means higher confidence. Note that the PPI network from STRING database only serves as a prior since the PPI scores in the STRING DB, ranged from 0 to 1 are used in combination with the de novo cancer-specific PPI scores, also ranged from 0 to 1 from the causal structural learning described above in each type of cancers.In combination with the data-driven causal structure, we collected the PPIs from the STRING database (16) on the known network, we use the soft-thresholding approach by calibrating the priors of regressions based on the average interaction scores from data-driven de novo cancer-specific causal structure and existing global PPI information. Finally, our methodology is general enough to be combined with any PPI database that is relevant to scientific context.Our PRECISE estimation method for cancer-specific networks conflates Bayesian variable selection and causal structure learning. Thus, if the data does not support a particular a priori network structure, it will exhibit low posterior probabilities in the final estimands and will thus be naturally filtered out. Instead of performing hard-thresholding (e.g. PARADIGMp genes and a patient j, we combined the activated and suppressed PRECISE scores, 43.The PRECISE method provides processed and highly interpretable pathway-level functional summaries that compress the regulatory relations from DNA methylation, mRNA/microRNA expression, and protein regulators. Using the resulting PRECISE score matrix as an input data, we obtain robust pan-cancer stratification. For a pathway with Supplementary Information


INDRA-IPM (Interactive Pathway Map) is a web-based pathway map modeling tool that combines natural language processing with automated model assembly and visualization. INDRA-IPM contextualizes models with expression data and exports them to standard formats.http://pathwaymap.indra.bio. Source code is available at http://github.com/sorgerlab/indra_pathway_map. The underlying web service API is available at http://api.indra.bio:8000.INDRA-IPM is available at: Bioinformatics online. Disease or process-specific pathway maps are commonly used to communicate mechanistic information about interacting genes and proteins  Interactive Pathway Map (INDRA-IPM), a web-based pathway modeling tool that builds on the capabilities of INDRA  systems; users can choose between the REACH  and edges representing mechanistic relationships among them. The graph is displayed using CytoscapeJS . Nodes rTo leverage automated assembly for diverse modeling tasks, INDRA-IPM exports models as SBML, SBGN, BNGL, Kappa, PySB and CX. These formats are widely used in computational biology for modeling, simulating and visualizing pathways. Users also have the option of storing maps on the Network Data Exchange (NDEx)  and relative expression levels to color intensity (greater color saturation denotes higher expression). Cancer Cell Line Encyclopedia  to a database identifier using named entity recognition. This makes it possible to connect a pathway map to standard external resources via uniform identifiers. For example, by clicking on a node, a tooltip appears with links to HGNC, UniProt and CiteAb, allowing users to access details about the constituents of a pathway and identify reagents useful for experiments (e.g. antibodies).www.openbel.org). Users are therefore able to access literature support for relationships specified in natural language descriptions.Clicking on an edge in a pathway map retrieves support for that interaction by querying a database of interactions aggregated by INDRA. This database includes information gathered from reading literature at scale . The INDRA-IPM map automatically follows the same visual conventions as the hand-drawn map, hierarchically organizing the graph and spatially grouping related nodes to reduce clutter. In addition, INDRA-IPM substantially extends the original map by providing access to supporting evidence sourced by INDRA, linking elements to external data resources and provide context from CCLE data. The RAS pathway map is available as a built-in example in INDRA-IPM.To facilitate integration of INDRA-IPM with other tools, we make it available a Web-based API that accesses reading, assembly and export functions of INDRA-IPM. This work was funded under the DARPA Big Mechanism and CwC Programs [W911NF-14-1-0397 and W911NF-15-1-0544] and by NIH [P50-GM107618].Conflict of Interest: P.K.S. holds equity in Merrimack Pharmaceuticals, Glencoe Software, Applied Biomath and RareCyte Inc. P.K.S. declares that none of these relationships are directly or indirectly related to the content of this article.btz289_Supplementary_DataClick here for additional data file.


Analysis of gene regulatory networks allows the identification of master transcriptional factors that control specific groups of genes. In this work, we inferred a gene regulatory network from a large dataset of breast cancer samples to identify the master transcriptional factors that control the genes within signal transduction pathways. The focus in a particular subset of relevant genes constitutes an extension of the original Master Regulator Inference Algorithm (MARINa) analysis. This modified version of MARINa utilizes a restricted molecular signature containing genes from the 25 human pathways in KEGG's signal transduction category. Our breast cancer RNAseq expression dataset consists of 881 samples comprising tumors and normal mammary gland tissue. The top 10 master transcriptional factors found to regulate signal transduction pathways in breast cancer we identified are: TSHZ2, HOXA2, MEIS2, HOXA3, HAND2, HOXA5, TBX18, PEG3, GLI2, and CLOCK. The functional enrichment of the regulons of these master transcriptional factors showed an important proportion of processes related to morphogenesis. Our results suggest that, as part of the aberrant regulation of signaling pathways in breast cancer, pathways similar to the regulation of cell differentiation, cardiovascular system development, and vasculature development may be dysregulated and co-opted in favor of tumor development through the action of these transcription factors. Breast cancers originate from healthy cells that are somehow reprogrammed to acquire unlimited proliferation and self-renewal capacity, among other properties, altogether referred to as "hallmarks of cancer" . These pSignal transduction pathways (STPs) are intricate molecular mechanisms that allow cells to sense specific signals, producing cellular actions in response and serve an important role in the integration of information . Among tSTPs themselves can be regulated through the action of transcription factors (TFs) that modulate the transcription of groups of genes participating in them . As STPsThe analysis of the structure of a gene regulatory network that contains TFs and their targets, together with information of differential expression values, allows the identification of TFs with the greatest influence over the differences in expression. Those TFs are denominated transcriptional master regulators (TMRs). The Master Regulator Inference Algorithm (MARINa)  can infeIn breast cancer, where multiple transcription factors with hundreds or even thousands of targets are simultaneously deregulated, an integrative approach can help us to understand the biology underlying this disease. Modified MARINa analysis is complemented with the use of statistical enrichment analysis to look at the possible biological functions that are affected by the regulons of the TMRs . The infNOISeq . A regulons network is a regulon set, which is formed by the union of many regulons.To obtain a regulon set from the data, we used the expression matrix of the tumor samples and a list of transcription factors in the TFCheckpoint curated database . From thp value was below 0.005.As a first step, transcription factors are associated with other genes expressed in the tissue. We used the mutual information-based algorithm ARACNe  which caMutual information can detect both indirect and direct relationships. ARACNe constrains the number of indirect interactions applying the data processing inequality theorem (DPI), which considers that, in a triangle of interactions, the weakest one has a greater probability of being indirect if its difference is large with respect to the other two interactions . We applaracne2regulon function in the viper R package (The type of association (activation or repression) of the transcription factors is determined from the Spearman correlation of the TF with the levels of expression of all its targets . This ca package . This fusignal transduction pathways category in the KEGG database . For this work we built a molecular signature using only those genes annotated within the database . For therability .To estimate the probability that a gene set enrichment score depends on the biological context and thus is not merely random, a null model was generated by random permutation between cases and control samples and recalculating differential expression values .p value for each regulon was estimated by evaluating the enrichment score (ES) with reference to the distribution of scores of the null model  which produces a ranking of the regulons for this specific subset. It is important to note that the regulons network used as input is the same as in regular MARINa, but the ranking is focused on the specific gene signature. The set of genes that constitute each regulon may include genes that are not in the molecular signature and can be part of a more diverse range of biological functions than signal transduction. This is the reason why we performed a subsequent enrichment analysis of the regulons with all KEGG human pathways.p = 0.05 after false discovery rate (FDR) correction.An overrepresented pathway is defined as one for which we found significantly more genes within a test set than the number expected from a random sampling  hence, wa priori or imposed from a biological knowledge database like KEGG or GO. The co-expression of genes belonging to a function or pathway in different network modules has been previously observed  which produces a ranking of the regulons for this specific subset of genes. The regulons network used as input is the same as in regular MARINa, but the ranking is constrained with the use of the specific gene signature. The sets of genes that constitute each regulon can vary in size from tens to several hundreds of genes and may include genes that are not in the molecular signature but are part of biological functions other than signal transduction. The statistical overrepresentation analysis allows the reduction of dimensionality from large lists of individual genes to fewer discernible biological functions, which is necessary given the large number of genes included in the network and the possibility of multiple annotations for each gene.Pathways in cancer (hsa05200) with a coincidence of 121 genes. This pathway was not considered in the construction of the gene signature, although the enrichment is to be expected since it is a very large pathway composed of genes from many other signal transduction pathways and because we initially constrained our analysis to regulons that contained at least 20 genes of the molecular signature .The top 10 regulons contain 4,052 genes associated by statistical dependencies. To know which pathways are most likely being regulated, we performed an overrepresentation analysis for all human KEGG pathways. This helped us to know which of the signal transduction pathways are represented, as well as other co-regulated pathways. The pathway with the most statistically significant enrichment was Cell cycle (hsa04110) and Focal adhesion (hsa04510) follow in the top 3 enrichments. Also enriched are signaling pathways present within our molecular signature and that are known to be important in the development of cancer, such as PI3K-AKT signaling pathway (hsa04151), Phospholipase D signaling pathway (hsa04072), and Hedgehog signaling pathway (hsa04340) (Other pathways such as sa04340) Table 2.During the processes associated with tissue remodeling, signals such as morphogens, cytokines, and growth factors are present in the cell's environment. These activate signal transduction pathways that in turn induce changes within the cell . In the The most significantly enriched processes of each TMR regulon are presented in Nine out of our 10 TMRs are recognized for taking part in morphogenetic processes . EnrichmEnriched GO biological processes are obtained from the molecular signature of the signal transduction pathways. The top places are occupied by processes associated with morphogenesis. These results are in line with the idea that tumors bear aberrations of growth, differentiation, and organization of cell populations. These are basic processes that are tightly coordinated and controlled during embryogenesis as well as in adult tissues. A similar idea has been previously proposed by Vinnitsky (1993), with the name of "oncogerminative theory of cancer development" . It suggEnrichments of GO biological processes for each individual regulon in the top 10 show a recurrent theme in processes associated with morphogenesis . These rIn addition, our results show that 10 of the 11 members of the Homeobox A family are included within the top 10 TMR regulons Figure 6With the generation of gene regulatory networks and a molecular signature centered on signal transduction pathways, we present a list of genes and their transcriptional regulators that may be modulated signal transduction pathways in breast cancer. This information may be helpful in the study of this disease where pathway analysis showed that all pathways from KEGG in this category are deregulated in a large dataset of breast cancer samples.We identified TSHZ2, HOXA2, MEIS2, HOXA3, HAND2, HOXA5, TBX18, PEG3, GLI2, and CLOCK as the top 10 TMRs regulating signal transduction pathways in breast cancer. These genes regulate 30% of the genes in these pathways. CLOCK is the only TMR in the top 10 that shows a positive regulation of its predicted targets, while the other top TMRs show negative regulation relationships, although the molecular mechanisms by which those TMRs act should be explored in future studies.The enrichment analysis of the top 10 TMR regulons recovers information about processes that are well recognized in cancer . These results are reassuring in the sense that we know we are recovering relevant biological information from the phenotypes under study instead of random associations. Furthermore, the analysis of individual regulons allows the identification of specific molecules that may be playing key roles. This seems to be the case with genes in the HOXA family, which are within the top 10 TMRs and as part of regulons. Genes of this family are known for their role in morphogenetic processes as well as in the maintenance of adult tissues and with altered expression in tumors.We present a modified version of MARINa that utilizes a specific gene signature with genes in KEGG's Signal Transduction category. The reason for this is to narrow the search of TMRs to those most relevant to signal transduction pathways. This approach, however, is not limited to a particular pathway or process and the molecular signature can be modified to reflect other research questions relative to specific pathways or processes.Signal transduction pathways serve an important role as information integrators in the cell. Components and their interactions are thus of great interest and subject of a thorough study. Furthermore, signal transduction pathways are susceptible to external pharmacological modulation. An understanding of the regulation of the pathways may be helpful in the search for therapeutic targets.Publicly available datasets were analyzed in this study. Data comes from TCGA/GDC public repositories. The list of GDC samples used in this work is in the DT-C implemented methods, performed calculations, analyzed results, and drafted the paper. HT implemented methods and analyzed and discussed the results. TV-C contributed to the discussion and drafting of the paper. EH-L designed and supervised the study, contributed to the analysis of the results and discussion, and revised the manuscript. All authors read and approved the final version of the manuscript.This work was supported by CONACYT (grant no. 285544/2016), as well as by federal funding from the National Institute of Genomic Medicine (Mexico). Additional support has been granted by the Laboratorio Nacional de Ciencias de la Complejidad (grant no. 232647/2014 CONACYT). EH-L is the recipient of the 2016 Marcos Moshinsky Fellowship in the Physical Sciences.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


With the generation of large-scale sequencing data, there are more opportunities to study the molecular mechanisms of cancer development. It is necessary to investigate the potential impact of DNA methylation, copy number variation (CNV), and gene-expression changes in the molecular states of oncogenic dysfunctional subpathways. We propose a novel method, Identification of Cancer Dysfunctional Subpathways (ICDS), by integrating multi-omics data and pathway topological information to identify dysfunctional subpathways. We first calculated gene-risk scores by integrating the three following types of data: DNA methylation, CNV, and gene expression. Second, we performed a greedy search algorithm to identify the key dysfunctional subpathways within pathways for which the discriminative scores were locally maximal. Finally, a permutation test was used to calculate the statistical significance level for these key dysfunctional subpathways. We validated the effectiveness of ICDS in identifying dysregulated subpathways using datasets from liver hepatocellular carcinoma (LIHC), head-neck squamous cell carcinoma (HNSC), cervical squamous cell carcinoma, and endocervical adenocarcinoma. We further compared ICDS with methods that performed the same subpathway identification algorithm but only considered DNA methylation, CNV, or gene expression . With these analyses, we confirmed that ICDS better identified cancer-associated subpathways than the three other methods, which only considered one type of data. Our ICDS method has been implemented as a freely available R-based tool ( Cancer is a complex disease involving multiple biological processes and multiple factors, including genomic, epigenomic, and transcriptomic aberrations associated with cancer formation and development . IdentifBiological pathways are models containing structural information between genes, such as interactions, regulation, modifications, and binding properties. In addition, genes in the same pathway usually coordinately achieve a particular function. With the appearance of some traditional pathway-analysis tools, such as GSEA  and SPIAde novo pathway. For instance, de novo pathway enrichment extracted sub-networks enriched in biological entities active by combining experimental data with a large-scale interaction network , to identify dysfunctional subpathways by integrating multi-omics data and pathway topological information. In ICDS, the first step is to calculate gene-risk scores to evaluate the contribution of genes to cancer states by considering the following three molecular characterizations: DNA methylation, CNV, and gene expression. In the second step, we convert the KEGG pathway into an undirected-pathway network with genes as nodes and biological relationships as edges, and use a greedy search algorithm to search for candidate dysfunctional subpathways within the pathways for which the discriminative scores are locally maximal. Finally, a perturbation test is used to calculate statistical significance for these dysfunctional subpathways. We applied the ICDS method to liver hepatocellular carcinoma (LIHC), head-neck squamous cell carcinoma (HNSC), and cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) datasets, and compared our results with three analytical methods that only used DNA methylation, CNV, or gene expression to calculate subpathway-activity scores . Through these analyses, we confirmed that ICDS could better identify cancer-associated subpathways compared to the other three methods.1. We downloaded TCGA RNA-seq level-3 data, which were processed and normalized and used the Reads Per Kilobase per Million mapped reads (RPKM) values for the gene-expression levels. Finally, there were 19,754 genes used in 424 LIHC, 546 HNSC, and 309 CESC samples. CNV profiling was estimated using the GISTIC2 method  with a range from 0 to 1, in which M is methylated allele frequencies and U is unmethylated allele frequencies. Overall, higher β-values indicate higher methylation. For three datasets, we removed genes with values of zero in more than 80% of the samples. In this paper, we also use the data from HNSC and CESC samples, which were processed using the above procedure. Detailed data information is shown in We downloaded TCGA level-3 Illumina HumanMethylation450 Bead Array data for DNA methylation. The LIHC DNA methylation level-3 dataset contain β-values for 20,105 genes from 429 samples, which included 50 normal samples and 379 lung-cancer samples. The β-values are calculated by The KEGG pathway database contains experimentally verified pathway structural information . We collected 294 KEGG biological pathways, and each pathway was converted to an undirected network with genes as nodes and biological relationships as edges on the basis of pathway structural information using the “iSubpathwayMiner” system , 2013.t-test . According to results of GISTIC2 analysis, the sample was then divided into a copy-number-variated group and an un-variated group for each gene, and then the differential expression level of the gene in the two groups was calculated by Student’s t-test (denoted by cnvp). It is difficult to define the quantitative relationship and relative degree of each factor’s influence on tumorigenesis, so we assume that gene expression, CNV, and DNA Methylation equally contribute to the cancer development. The gene risk score (RS) was calculated by integrating the above three p-values with Fisher’s combined probability test. This method computed a combined statistic S from the adjusted p-values obtained from the three individual datasets as shown in Equation (1). Usually, the statistic S followed a χ2 distribution with 2k degrees of freedom, and we then calculated the null hypothesis p-value of the statistic S. Finally, we converted the p-value to a z-score according to the inverse-normal cumulative-distribution function (CDF), and the z-score was taken as the RS of each gene in cancer.There are many factors influencing tumorigenesis, such as gene expression, CNV and DNA methylation. For each gene, we calculated its risk score in cancer by considering the following three types of genetic molecular features: gene expression, CNV, and DNA methylation. With the above data, we used the Student’s t-test  to calcuRS of genes were obtained by the above method, considering gene expression, CNV and methylation. For a KEGG pathway, we performed a greedy algorithm to search for dysfunctional subpathways within the pathways for which the discriminative scores were locally maximal. Specifically, the search algorithm started from a seed gene i which had a significantly high risk score (p < 0.001) and expanded iteratively, after which it selected one of the neighbors of the seed gene to form the current subpathway. For a subpathway k, the activity score (kAS) was the average of the RS of the member genes in the subpathway, calculated by Equation (2):Previous studies have confirmed that subpathways can provide more detailed biological information than whole pathways. In this study, we proposed a novel method to combine gene-risk score with pathway topological structure to infer subpathway activities. The i is the index of the gene in the subpathway k, while n is the number of genes involved in the subpathway. At each iteration, the algorithm adopted a gene from the neighbors of genes in the current subpathway, which produced maximal increases between k+1AS and kAS. The search algorithm will stops when no additional gene increases in the score k+1AS over (1+r) kAS or the distance in the current subpathway between any two nodes is greater than 3 in order to keep the search locally. The improvement rate r is chosen to avoid too large subpathway region, resulting in the addition of redundant weak information. The parameter r = 0.05 has been demonstrated to be appropriate in the greedy heuristic algorithm applied in the biological network (In Equation (2),  network . When thk_perm1AS. This test was used to test the correlation between real subpathways and disease phenotype. In this study, we performed 10,000 perturbations for this test and calculated statistically significant p-value = M/N, in which M is the number of k_perm1AS greater than the real subpathway score kAS, and N is the number of perturbations. In addition, the second test perturbed the gene names in the pathway to which the subpathway belonged, and recalculated the activity score of the subpathway, denoted as k_perm2AS. This test was used to test the correlation between real subpathways and pathway structure. We also performed 10,000 perturbations and the score of each real kAS was indexed on the null distribution of all k_perm2AS whose p-values could be evaluated. The p-values were adjusted using the false discovery rate (FDR) method proposed by Benjamini and Hochberg to correct for multiple comparisons  as the seed genes in the pathway network for the subpathway search algorithm . Subpathways were selected which satisfied two permutation tests with FDR1 < 0.001 and FDR2 < 0.001 out of the 10,000 permutations. ICDS identified 19 dysfunctional subpathways associated with LIHC, belonging to 12 entire pathways   . The abnf AMPD1) .genep = 1.53e-35), and both the expression of CCL20 and CCR2 are regulated by differential methylation . Importantly, the ICDS method not only recognized subregions of differential gene expressions but also detected some genetically or epigenetically diverse regions . Another subpathway of the chemokine signaling pathway was path:04062_4, which contains 9 genes . Next, we used the same procedure as above to find significant subpathways and used the same parameter settings. Using the methods of ICDS-G and ICDS-M, we obtained three and one significant subpathways, respectively, and the entire pathways they belonged to were all found by the ICDS method . Using t 9 genes . We founThe HNSC datasets were obtained from TCGA, and their detailed information is shown in methy = 4.79e-12) in HNSC, and low expression of THRA is known to activate PIK3R1, which provides instructions for synthesizing a subunit of phosphatidylinositol 3-kinase (PI3K). PI3K signaling is important for many cell activities, including cell growth, division, and migration  (Path:04919_4 is a significant subpathways  belonginigration . However.46e-06) .methyp = 9.25e-34) and ALDH1A2 (methyp = 1.65e-13). Other components in the same subpathway, path: 00830_3, mainly showed differences in the degree of expression compared to normal samples, such as AOX1 (genep = 3.11e-18) and ADH4 (genep = 2.75e-38) . Using the methods of ICDS-G and ICDS-M, we obtained two significant subpathways and the pathways they belonged to were also found by the ICDS method. However, 13 subpathways identified by ICDS were missing from all of the other methods  . For exa.75e-38) . TherefoWe applied ICDS to identify dysfunctional subpathways in CESC . With the threshold of FDR1 < 0.001, we obtained four significant subpathways that had just exceeded the threshold FDR2 , and allgenep = 9.92e-32), TACR2 (genep = 3.82e-08), and HTR2B (genep = 2.76e-26). Moreover, with CESC samples, AVPR1A, which is a GPCR in cells, mainly showed differences in methylation and expression compared to normal samples. Many studies have shown that the abnormal expression and activity of GPCRs is associated with the development and progression of cancers , each of which represents a network of interactions between genes, proteins, and chemical molecules. The main purpose of this study was to discover important dysfunctional subregions based on the pathway topological structure. ICDS used Fisher’s combined probability test to integrate gene expression, CNVs, and methylation to calculate the RS of genes. As the gene expression, CNV and DNA methylation are not completely independent, and thus the independence assumption of Fisher’s combined probability test may be violated here. This may be a limitation of our ICDS method. Alternatively, the Brown’s method , subpathway size (5 < size < 100), and overlap between subpathways , which can be set by a user of the ICDS package.In this study, the employed method was based on s method  can alsoc tumors . To avoiWe applied the ICDS method to LIHC, HNSC, and CESC datasets. Based on these analyses, we demonstrated that ICDS can effectively identify dysfunctional subpathways correlated with a cancer phenotype. For the HNSC dataset, the subpathway path:04062_1 was the most significant subpathway and included 41 genes belonging to chemokine signaling pathway. Studies have confirmed that the chemokine signaling pathway is a critical component of tumor progression. These genes did not simultaneously have changes in copy number, methylation, and gene expression. However, these subregions could still be found through our integration algorithm, which is the most prominent advantage of our method. To further validate the ICDS method, we compared it with three other methods that only considered one type of data – gene expression, CNV, or DNA methylation – named as ICDS-G, ICDS-CNV, and ICDS-M, respectively. The results showed that the ICDS method was able to identify new risk subpathways associated with cancer that were simultaneously neglected by the other three methods. Thus, it is essential to integrate multi-omics data to identify additional dysfunctional subpathways in cancer. In the future, we will involve other omics data, such as proteomics, to improve our ICDS method.3. If users are considering using the ICDS method, they need to input three datasets of gene expression, copy number, and methylation. The ICDS-package will produce a prioritized list of subpathways. With this method, ICDS is used to find key subpathways related to cancer phenotypes, and it is expected that it can be used to mine for key subnetworks within some prior networks  based on integrating DNA methylation, CNV, and gene expression data. In addition, ICDS may identify key subpathways as biomarkers to distinguish high and low risk cancer patients. For this purpose, researchers should input the molecular profile of genes with different stage samples, such as patients in different stages of glioma. Therefore, we have developed a free and robust tool to identify dysfunctional subpathways in cancer by integrated multi-omics data.To provide users with convenient and simple analytical tools, we have integrated the ICDS, ICDS-G, ICDS-CNV, and ICDS-M methods into an available R-based package on CRANJH, YZ, and LC conceived and designed the study. SL and BZ developed the software. YY analyzed the data and implemented the methodology. YJ revised the manuscript. YZ provided constructive discussions. JH and LC drafted the manuscript. All the authors read and agreed to the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


To transmit information efficiently in a changing environment, the retina adapts to visual contrast by adjusting its gain, latency and mean response. Additionally, the temporal frequency selectivity, or bandwidth changes to encode the absolute intensity when the stimulus environment is noisy, and intensity differences when noise is low. We show that the On pathway of On-Off retinal amacrine and ganglion cells is required to change temporal bandwidth but not other adaptive properties. This remarkably specific adaptive mechanism arises from differential effects of contrast on the On and Off pathways. We analyzed a biophysical model fit only to a cell’s membrane potential, and verified pharmacologically that it accurately revealed the two pathways. We conclude that changes in bandwidth arise mostly from differences in synaptic threshold in the two pathways, rather than synaptic release dynamics as has previously been proposed to underlie contrast adaptation. Different efficient codes are selected by different thresholds in two independently adapting neural pathways. In neural circuits, multiple computations are often produced simultaneously by multiple nonlinear mechanisms, making it difficult to assign specific functions to specific neural components. To overcome this barrier in the retina, we used a computational model that reveals internal nonlinear processing. Adaptation to the variance of a signal entails multiple separate properties and mechanisms, one aspect of which is to change the temporal response from more integrating to more differentiating. This adaptive change in temporal bandwidth is known to improve information transmission when the noise level changes. Using the model and pharmacological manipulation, we show that this change in temporal bandwidth can be explained by different thresholds in two parallel neural pathways, rather than properties of vesicle release as have previously been proposed. Our findings provide a rare example where one can disentangle and assign specific computational properties to specific circuit elements in an adaptive circuit.Sensory systems have the task of encoding stimuli in a changing environment. Neural circuits meet this challenge by changing their neural code in a number of ways that can be explained theoretically by principles of efficient coding. Many sensory environments are composed of strong signals, including high luminance, high contrast, fast velocity or loud auditory stimuli. In each of these cases, in order to use a cell’s dynamic range more efficiently, sensory neurons adapt to the strong stimulus in multiple ways –4.Four distinct changes in the neural code during contrast adaptation have been described in sensory neurons, including in the vertebrate retina, fly visual system and the auditory cortex ,5–9. AdaOne obstacle to understanding the sources of these adaptive properties is that they involve nonlinear dynamic properties of the system. A second complication is that signals in the retina are merged through multiple neural pathways, each potentially with its own adaptive properties. A third challenge is that multiple changes in the neural code occur together, making it difficult to understand if any of the changes have specific mechanisms not common to other changes in encoding. Thus is it difficult to gain insights into the interactions of neural components without the use of a computational model. Models have played a key role in the understanding of how nonlinear dynamical systems perform computations, including in single cell processes such as phototransduction  and actiDepletion of synaptic vesicles is thought to be a key source of contrast adaptation, both in terms of a change in gain, offset ,18 and tHere we analyze On-Off amacrine and ganglion cells, which we find to have strong adaptive changes in their preferred temporal feature with contrast. We report a specfic dissociation between the four adaptive properties that requires the On pathway, in that blocking the On pathway abolishes the shift in temporal bandwidth, but leaves changes in gain, offset and the speed of response virtually intact. Our analysis first concludes that the shift in bandwidth with contrast arises from a changing ratio of activation of the On and Off pathway. Thus the temporal derivative that is computed during high contrast is largely a difference between two neural pathways.To understand the source of this differential activation of neural pathways in the intact system without pharmacological manipulation, we analyzed a previously reported computational model that captures all adaptive properties of On-Off ganglion cells to changing contrast . This moTo assess why the two pathways adapted differently to contrast, we analyzed differences in the computational components of the two pathways. One might expect that because adaptive properties have been assigned to the kinetics block that differential adaptation must come from different adaptive kinetics in the On and Off pathways, representing differential properties of vesicle release. However, we find that the different thresholds in the two pathways are the primary cause of the change from the more integrating to more differentiating response, rather than the dynamics of synaptic release as has previously been proposed ,20. ThesA randomly flickering visual stimulus was presented from a standard cathode ray tube to the isolated salamander retina. The stimulus was spatially uniform and white in color with an intensity that changed every 30 ms, and was drawn from a Gaussian distribution with a constant mean. Every 20 s, the temporal contrast changed to one of 15 contrasts by varying the standard deviation of the distribution. We recorded the intracellular membrane potential response from On-Off amacrine and ganglion cells for at least 300 s , S1 Fig.To measure the influence of the On neural pathway to contrast adaptation, we presented the stimulus in a control condition, and while blocking the On pathway using L-AP4 (APB), a metabotropic glutamate receptor agonist that blocks synaptic input to On bipolar cells.To quantify different adaptive properties at different contrasts, we used the standard approach of computing a linear nonlinear (LN) model, consisting of a temporal filter that represents the average feature conveyed by a neuron, and a static, or time-independent nonlinear function that captures threshold, gain and any saturation . An LN mWe then examined the change in temporal bandwidth produced by contrast adaptation by analyzing the linear filter. As previously reported ,26, the We then analyzed the spiking responses of ganglion cells with and without APB that included both On/Off cells and Off cells with no On pathway input, which were recorded with a multielectrode array in response to a spatiotemporal stimulus. We first identified the relative strength of the On and Off pathways using a uniform field flash, and computed the ratio of the firing rate of the On and Off responses . Then weAs in the case of membrane potential recordings, high contrast shifted the temporal bandwidth to higher temporal frequencies . Across We further analyzed bipolar cell recordings with the expectation that since they receive input from a single pathway, they would not have large changes in temporal frequency with contrast. Consistent with this idea, although bipolar cells showed changes in gain that are smaller than that of ganglion cells as previously reported ,28, theyFrom both intracellular membrane potential recordings and extracellular recordings, we conclude that cells with strong On and Off input have the highest shift in temporal frequency during contrast adaptation. Furthermore, blocking the On pathway specifically reduces this shift in temporal frequency but not the adaptive properties of changes in gain, delay or offset.F(BON)+G(BOFF)+H, where F(BON) and G(BOFF) are the additive contribution from depolarizing and hyperpolarizing bipolar cells, respectively, and H is the contribution from nonlinear interactions between the two pathways. The application of APB reveals input from G(BOFF) alone, subtracting both F and H. If there were no interactions between the two pathways (H = 0), then APB could reveal the separate contributions of both Off and On pathways, F and G. In the intact circuit, however there are many potential nonlinear interactions that could occur between On and Off pathways, as numerous types of amacrine cells deliver inhibition from one pathway to another, known as ‘crossover inhibition’ [To consider how one can interpret our pharmacological results, in general, the response of a cell is ibition’ . Thus onibition’  , whereas at high contrast, the contributions of the two pathways were more similar (42 ± 2% for On and 58 ± 2% for Off). Between the extremes of low and high contrast, we then examined in greater detail how the magnitude of each pathway changed as a function of contrast. As the contrast increased, first the output amplitude of the Off pathway increased more rapidly than that of the On pathway . Then thBy definition, an LN model is a non-adapting system, whereas an LNK system shows adaptation. Therefore, one might think that differential adaptation would naturally be caused by differences in the parameters of the kinetics block in the two pathways. However, the adaptive properties of an LNK pathway are a result of both the nonlinearity and the kinetics block . We thusWe measured how the output magnitude of the nonlinearity varied as a function of its input in the two pathways. Comparing the two nonlinearities, the On pathway had a higher threshold as previously reported for salamander On-Off ganglion cells , and a mu(t) to the two kinetics blocks, and measured in each pathway the magnitude of the output A(t), the active state. Previously it was shown that because of the threshold nonlinearity, an increase in contrast causes an increase in both the mean and standard deviation of u(t). However, only the mean 〈u〉 controls adaptation in the kinetics block, thereby controlling the standard deviation of the output A(t) [u〉. To generate u(t), we used a nonlinearity N0 with a threshold at zero for both kinetics blocks. This caused both the mean and standard deviation of u(t) to increase linearly with contrast. When 〈u〉 increased, the standard deviation of the kinetics block output A(t) increased quickly at first, but then increased with a decreasing rate as the kinetics block adapted  times more than the Off pathway across different mean inputs. Based on this separate analysis of the different stages of processing, differences in the output of the two pathways with contrast appeared to be caused more by the different nonlinearities than by the different kinetics blocks.To compare the independent contribution of the kinetics block in each pathway, we presented a standardized input put A(t) . Thus we adapted . CompariHowever, we cannot assume that the above effects of the nonlinearity and kinetics block will combine additively in the system. Thus we tested the source of differential adaptation in the two pathways by exchanging different components between the two pathways. We exchanged either the nonlinearities or kinetics blocks between pathways, and then measured the resulting effects on each pathway. First, we switched the kinetics blocks while keeping the filters and nonlinearities fixed, which caused a small change in the output of the two pathways as a function of contrast . Then wec and output magnitude σ for each pathway. We computed the average slope α of σ(c), reflecting the average increase in output magnitude with contrast. If there was no adaptation, the standard deviation σ(c) would be proportional to the contrast with a slope of one, and so the more shallow slope of the Off pathway indicated more adaptation (c), measuring the decay constant β for how fast σ(c) began to plateau with contrast. This indicates how rapidly adaptation occurred as contrast increased, with a smaller value indicating that adaptation developed at a lower contrast.We further quantified these effects by computing two parameters of the relationship between the contrast, aptation . Then weC,βC), when the kinetics blocks were exchanged  and when the nonlinearities were exchanged . In the control condition, the two pathways differed in the change in output αc by a factor of 3.2 ± 0.2, with the On pathway having a greater slope reflecting less adaptation. Exchanging the kinetics caused the slope αK to be only a factor of 1.3 ± 0.06 different than αC, a change of 30%. This factor was computed by averaging αC(ON)/αK(ON) and αK(OFF)/αC(OFF). In comparison, changing the nonlinearities caused the slope αN to be a factor 2.94 ± 0.34 different from αc in the control case, a change of 194%. Thus the change produced by exchanging the nonlinearities was 6.5 times that produced by exchanging the kinetics blocks. We then compared the control rate of adaptation βc for the two pathways, which differed by a factor of 5.7 ± 0.9. Exchanging the kinetics blocks caused the rate βK to be only a factor of 1.26 ± 0.07 different than βc, whereas exchanging the nonlinearity caused βN to be a factor of 4.26 ± 0.72 different than βc. Thus, exchanging the nonlinearities caused a change in the adaptive rate 12.5 times greater than exchanging the kinetics. We conclude that the amount of adaptive change in temporal differentiation is controlled primarily by the different nonlinearities in the two pathways, with a smaller contribution from the different adaptive kinetics. In comparison, the contrast at which the adaptive change in the temporal filter occurs is controlled almost exclusively by the differences in the two nonlinearities.This does not suggest that synaptic kinetics plays a small role in other adaptive properties. Across contrasts, the action of the kinetics block contributes to changing the speed of the temporal filter and the gain ,20. ThesOur results reveal a number of findings about contrast adaptation in On Off retinal ganglion cells, a system with multiple adaptive properties and mechanisms. First, adaptive changes in temporal bandwidth are generated in a manner distinct from changes in gain, the speed of the response and the mean response level. Second, changes in temporal bandwidth specifically require the On pathway, whereas other adaptive changes do not. Thus, we report a remarkably specific relationship between a mechanism, the On pathway, and one of multiple nonlinear properties of adaptation. Third, contrast causes the On and Off pathways to adapt to contrast differently, changing the ratio of On to Off input at high contrast. Because the On pathway has a more delayed average temporal filter than the Off pathway, a greater On pathway contribution results in a more biphasic filter for the entire system. Fourth, we verified using pharmacology and a model that On-Off ganglion cells under a uniform stimulus behave as though they have the computational structure of two independent adapting pathways whose outputs are summed together. This is a computational conclusion, and the true circuit structure may differ. Finally, analysis of that model indicates that the key component that causes differential adaptation between the On and Off pathways is a difference in threshold, rather than differences in nonlinear dynamics. This implies that the differences in the threshold of the bipolar cell terminal as compared to the baseline membrane potential are responsible for the change in temporal bandwidth, as opposed to differences in the dynamics of synaptic release in the two pathways.We have used pharmacology to block the signals traveling through one receptor type, and then infer the independent contributions of the blocked and unblocked pathways. It is important to note that the subtractive procedure we have performed to make this inference is not generally valid unless the two pathways do sum together independently. Thus, the use of the LNK model to verify this computational structure, and pharmacology to validate this aspect of the model is a necessary step in the interpretation of the two summed pathways. Crossover inhibition between the On and Off pathways is observed in many ganglion cells . HoweverOne reason, however, to think the effect of crossover inhibition is not strong in On-Off ganglion cells is that crossover inhibition often creates a more linear cell , whereasAn alternative computational structure has been proposed to account for contrast adaptation in ganglion cells using a divisive effect of one visual feature on another . This DiSystems that can be accurately captured with a linear-nonlinear (LN) model do not change their properties of temporal filtering or sensitivity with a change in stimulus statistics. Thus, a change in the parameters of the LN model has served as a definition of adaptation ,26,41,42A number of studies in different species have shown that On and Off pathways have differences in their nonlinear properties. On ganglion cells are more linear and have lower thresholds in salamander, mouse and primate ,37,44, aProperties of synaptic vesicle release have been shown to produce all types of adaptive properties discussed here. Depletion of synaptic vesicles can cause changes in gain, the elevated rate constants of release as a function of high calcium concentration can speed the overall system dynamics, and slow replenishment can create a homeostatic effect on the average response. Synaptic depression could also potentially cause some level of a change in temporal bandwidth, in that at high release rate, synaptic depression can create a more transient response, thus attenuating low temporal frequencies. Thus, although all types of observed adaptive changes could potentially be carried out by synapses in a single pathway, the large shift in the temporal bandwidth we measure here required a summation of two neural pathways that adapt differently to contrast. Because the effect of the dynamics of synaptic vesicle release, and the flexibility of the effect of the rate constants on adaptive behavior, one might think that the different adaptive dynamics in the On and Off pathways could arise from differences in synaptic release. Our computational analysis, however, indicates that the higher threshold of the On pathway in On-Off cells, which has previously been reported, gives rise to that differential adaptation.The LNK model breaks down the cell’s response into smaller subsystems, first by separating On and Off pathways, then further compartmentalizing computational function into feature selection, nonlinear distortion and thresholding, and adaptation. The accuracy of this model, its ability to capture adaptive properties and the correspondence of its components to distinct neural pathways  allow usDifferent types of mechanisms can generate changes in temporal differentiation. Photoreceptors have a more biphasic response at higher luminance , yet useThe rules of efficient coding are known to change in a sophisticated way when the signal strength changes. Due to the dominance of low temporal frequencies in natural visual scenes, it can be an efficient strategy to remove these slow correlations, thus giving more equal weight to high and low frequency signals ,9. This The switch from one to two neural pathways is highly similar to that found in the auditory cortex, where the stimulus-response relationship changes from monophasic to biphasic at higher signal strength . In thatOur results show that distinct properties of contrast adaptation are controlled by distinct mechanisms acting in concert, with the baseline set of properties of changing gain speed and offset present in individual neural pathways, and temporal bandwidth changing arising from the recruitment of a high threshold pathway. Thus, the system is layered, with a baseline rule of efficient coding implemented in single pathways, and a second-level rule selected or rejected by the high threshold of a second pathway. Different rules of efficient coding are selected by different combinations of neural pathways.Electrophysiology. This study was performed in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health, and the Stanford institutional animal care and use committee (IACUC) protocol (11619). For intracellular recording, the intact salamander retina of either sex was held in place under a transparent dialysis membrane containing several 150–300 μm holes. Intracellular electrodes were filled with 2 M potassium acetate (200–300 MΩ) and guided into the retina under infrared illumination viewed through a CCD camera. Bipolar cells, On-Off amacrine or ganglion cells were identified by their flash response and level in the retina. Ganglion cells were recorded in the ganglion cell layer, and On-Off amacrine cells were recorded in the inner nuclear layer. Intracellular recordings had resting membrane potentials more hyperpolarized than– 65 mV, and were made from eight On-Off cells including four ganglion cells and four amacrine cells. Results were similar for the two cell types, and are pooled in all analyses. Recordings overall ranged from 10–90 minutes in duration.M (~ 8 mW/m2) and standard deviation W [W/M, and changed randomly every 20 s to a value between 0.05 and 0.35, drawn from a uniform distribution. The stimulus lasted 300 s (15 contrast levels) and the identical stimulus sequence was repeated at least two times.Visual Stimulation. A spatially uniform visual stimulus was projected from a video monitor onto the retina. The stimulus intensity changed every 30 ms, and was drawn from a Gaussian probability distribution with constant mean intensity, iation W . ContrasFLN(t) is different than the component linear filter of the LNK model, FLNK(t). Linear temporal filters were computed as described [s(t) was normalized to have zero mean, and a standard deviation equal to the contrast. The filter, FLN(t), was computed as the correlation between s(t) and the response r(t) normalized by the autocorrelation of the stimulus. The Fourier transform of the filter was computed as,s(t), g(t),g(t) and s(t) were equal,s(t), was passed through a linear temporal filter, FLNK(t) and a static nonlinearity, NLNK(g),x) is the error function defined for cumulative Gaussian distributions, a, b1 and b2 are parameters of the model, and κ was a constant equivalent to the overall variance of the filtered stimulus g(t). The exponent of a varies between zero and twenty, and the activation rate constant ka in the denominator scales the nonlinearity so that its maximum is one. Thus, the output of the nonlinearity, u(t), scales the activation rate in the kinetics block between zero and ka.Standard Linear-Nonlinear (LN) model. A linear temporal filter was computed that most closely approximates the entire response of a cell or pathway. This filter escribed . The stiribution . This cascribed, , where an. As in , the nonP(t) is a column vector of m fractional state occupancies such that Q is an m×m transition matrix containing the rate constants Qij that control the transitions between states i and j, with r′(t) was equal to one of the state occupancies scaled to a response in millivolts,c and d are a scaling and offset term for the entire recording.The kinetics block of the model is a Markov process defined byStates and rate constants are diagrammed in wON and wOFF and summed to give the final output of the model. As previously described [The change in state occupancy was thus determined asescribed , a k-folS1 FigA. Two superimposed traces of the flash response from an example On-Off ganglion cell. Bar indicates the time of light On. B. Intracellular recording of a ganglion cell responding to two repeated presentations of the same white noise stimulus sequence consisting of a uniform field at high contrast (35%). Also shown is the subthreshold membrane potential of one of these repeats extracted for further analysis. C. Same as B for the transition (dotted line) from high (28%) to low (5%) contrast. A slow recovery from hyperpolarization can be seen in the 5% contrast traces.(PDF)Click here for additional data file.S2 FigA. Spatiotemporal filter of an example On-Off ganglion cell. B. LN models from the cell responding to high (35%) and low (5%) contrast in a control condition (top) and in the presence of APB (bottom). The average time course of the cell is shown computed as the first principal component of the spatiotemporal filter.(PDF)Click here for additional data file.S3 FigA. Gain change between high and low contrast for ganglion cell spiking shown as a function of the On/Off ratio of each cell. Results shown in a control condition (solid symbols and lines) and with APB (open symbols and dotted lines). Lines are fits to the data. B. Change in the time to the first negative peak between high and low contrast shown as a function of the On/Off ratio of each cell. C. Slow offset during adaptation to high or low contrast as a function of the On/Off ratio of each cell.(PDF)Click here for additional data file.S4 FigA. Two superimposed traces of the flash response from an example Off bipolar cell. Bar indicates the time of light On. B. LN model for bipolar cells responding to a uniform field stimulus as in C. Median temporal frequency computed from the linear temporal filter, as a function of contrast, averaged over all cells. D. Normalized gain as a function of contrast, averaged over six bipolar cells.(PDF)Click here for additional data file.