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Summary CausalPath (causalpath.org) evaluates proteomic measurements against prior knowledge of biological pathways and infers causality between changes in measured features, such as global protein and phospho-protein levels. It uses pathway resources to determine potential causality between observable omic features, which are called prior relations. The subset of the prior relations that are supported by the proteomic profiles are reported and evaluated for statistical significance. The end result is a network model of signaling that explains the patterns observed in the experimental dataset. For complete details on the use and execution of this protocol, please refer to Babur et al. (2021)., Graphical abstract, Highlights • A free open-source tool for exploration of proteomic data • Analysis focuses on causal relationships revealed by proteomic changes • Network visualization and publication-quality figures of CausalPath results, CausalPath (causalpath.org) evaluates proteomic measurements against prior knowledge of biological pathways and infers causality between changes in measured features, such as global protein and phospho-protein levels. It uses pathway resources to determine potential causality between observable omic features, which are called prior relations. The subset of the prior relations that are supported by the proteomic profiles are reported and evaluated for statistical significance. The end result is a network model of signaling that explains the patterns observed in the experimental dataset.

Reliable methods to quantify dynamic signaling changes across diverse pathways are needed to better understand the effects of disease and drug treatment in cells and tissues but are presently lacking. Here, we present SigPath, a targeted mass spectrometry (MS) assay that measures 284 phosphosites in 200 phosphoproteins of biological interest. SigPath probes a broad swath of signaling biology with high throughput and quantitative precision. We applied the assay to investigate changes in phospho‐signaling in drug‐treated cancer cell lines, breast cancer preclinical models, and human medulloblastoma tumors. In addition to validating previous findings, SigPath detected and quantified a large number of differentially regulated phosphosites newly associated with disease models and human tumors at baseline or with drug perturbation. Our results highlight the potential of SigPath to monitor phosphoproteomic signaling events and to nominate mechanistic hypotheses regarding oncogenesis, response, and resistance to therapy., SigPath is a targeted, quantitative mass spectrometry assay that measures 284 phosphosites spanning 200 phosphoproteins with high throughput and quantitative precision across a broad swath of signaling biology of known interest.

Summary: The TP53 tumor suppressor gene is frequently mutated in human cancers. An analysis of five data platforms in 10,225 patient samples from 32 cancers reported by The Cancer Genome Atlas (TCGA) enables comprehensive assessment of p53 pathway involvement in these cancers. More than 91% of TP53-mutant cancers exhibit second allele loss by mutation, chromosomal deletion, or copy-neutral loss of heterozygosity. TP53 mutations are associated with enhanced chromosomal instability, including increased amplification of oncogenes and deep deletion of tumor suppressor genes. Tumors with TP53 mutations differ from their non-mutated counterparts in RNA, miRNA, and protein expression patterns, with mutant TP53 tumors displaying enhanced expression of cell cycle progression genes and proteins. A mutant TP53 RNA expression signature shows significant correlation with reduced survival in 11 cancer types. Thus, TP53 mutation has profound effects on tumor cell genomic structure, expression, and clinical outlook. : Donehower et al. performed a comprehensive analysis of the effects of TP53 gene mutation in 32 cancer types and 10,225 patients from The Cancer Genome Atlas (TCGA). Data synthesized from five different analysis platforms show how mutant TP53 increases genomic instability and induces major pathway signaling changes in cancer cells. Keywords: TP53, TP53 mutation, p53, TCGA, The Cancer Genome Atlas, PanCanAtlas, p53 signaling pathway, chromosomal instability, p53 signature, p53 targets

Adaptation of tumors to therapeutic interventions contributes to dismal long-term patient outcomes. Adaptation to therapy involves co-action of functionally related proteins that together activate cell survival programs and compensate for the therapeutic impact. Oncogenic dependencies to such adaptive events, however, can generate new therapeutic vulnerabilities that can be targeted with drug combinations. The precision medicine approaches in which targeted drugs are matched to pre-existing genomic aberrations fail to address the adaptive responses and resulting vulnerabilities. Here, we provide the mathematical formulation, implementation and validation of the TargetScore method. The TargetScore identifies collective adaptive responses to targeted interventions as concurrent changes of phospho-proteins that are connected within a signaling network. Based on the adaptive responses, the method predicts drug-induced vulnerabilities. Using TargetScore, we inferred the adaptive responses with short-term (i.e., days) stress and long-term (i.e., months) acquired resistance to inhibitors of anti-apoptotic mediators, MCL1 and BCL2. With experiments guided by the predictions, we identified synergistic interactions between inhibitors of PARP, SHP2, and MCL1 in breast cancer cells. TargetScore is readily applicable to existing precision oncology efforts by matching targeted drug combinations to emerging molecular signatures under therapeutic stress.

Technological advances in computing provide major opportunities to accelerate scientific discovery. The wide availability of structured knowledge would allow us to take full advantage of these by enabling efficient human-computer interaction. Traditionally, biological knowledge is captured in publications and knowledge bases, however, the information in articles is not directly accessible to computers, and knowledge bases are constrained by finite resources available for manual curation. To accelerate knowledge capture and communication and to keep pace with the rapid growth of scientific reports, we developed the Biofactoid (biofactoid.org) software suite, which crowdsources structured knowledge in articles from authors. Biofactoid is a web-based system that lets scientists draw a network of interactions between genes, their products, and chemical compounds and employs smart-automation to translate user input into a structured language using the expressive power of a formal ontology. The resulting data is shared via public information resources, enabling author-curated knowledge to be appreciated in the context of all existing computable knowledge. Authors of recently published papers across a range of journals have already contributed their pathway information, much of which is novel and extends existing pathway databases into new biological areas. We envision the adoption of Biofactoid for crowdsourced curation by scientists and publishers as part of an ecosystem of tools that accelerate scientific communication and discovery.AvailabilityBiofactoid server at https://biofactoid.org

We describe a large-scale community effort to build an open-access, interoperable, and computable repository of COVID-19 molecular mechanisms - the COVID-19 Disease Map. We discuss the tools, platforms, and guidelines necessary for the distributed development of its contents by a multi-faceted community of biocurators, domain experts, bioinformaticians, and computational biologists. We highlight the role of relevant databases and text mining approaches in the enrichment and validation of the curated mechanisms. We describe the contents of the Map and their relevance to the molecular pathophysiology of COVID-19 and the analytical and computational modelling approaches that can be applied for mechanistic data interpretation and predictions. We conclude by demonstrating concrete applications of our work through several use cases and highlight new testable hypotheses.; We describe a large-scale community effort to build an open-access, interoperable, and computable repository of COVID-19 molecular mechanisms - the COVID-19 Disease Map. We discuss the tools, platforms, and guidelines necessary for the distributed development of its contents by a multi-faceted community of biocurators, domain experts, bioinformaticians, and computational biologists. We highlight the role of relevant databases and text mining approaches in the enrichment and validation of the curated mechanisms. We describe the contents of the Map and their relevance to the molecular pathophysiology of COVID-19 and the analytical and computational modelling approaches that can be applied for mechanistic data interpretation and predictions. We conclude by demonstrating concrete applications of our work through several use cases and highlight new testable hypotheses.

Summary We present a computational method to infer causal mechanisms in cell biology by analyzing changes in high-throughput proteomic profiles on the background of prior knowledge captured in biochemical reaction knowledge bases. The method mimics a biologist's traditional approach of explaining changes in data using prior knowledge but does this at the scale of hundreds of thousands of reactions. This is a specific example of how to automate scientific reasoning processes and illustrates the power of mapping from experimental data to prior knowledge via logic programming. The identified mechanisms can explain how experimental and physiological perturbations, propagating in a network of reactions, affect cellular responses and their phenotypic consequences. Causal pathway analysis is a powerful and flexible discovery tool for a wide range of cellular profiling data types and biological questions. The automated causation inference tool, as well as the source code, are freely available at http://causalpath.org., Graphical abstract, Highlights • CausalPath builds mechanistic models from proteomic profiles • It integrates biological pathway models with molecular measurements • It supports logical reasoning with post-translational modifications • A web server, free software, and a source code are available, The bigger picture Molecular profiling of biological organisms provides us with a great amount of information on cellular differences, but converting it to mechanistic insights is still a very challenging task. A prominent approach is to integrate new measurements with the mechanistic knowledge described in the scientific literature and build a model that is supported by both. Although this can be done in many ways, an adept approach will use the literature knowledge in detail and follow high standards of logical reasoning while integrating the known and the new. This article describes an approach that utilizes the details in human biological pathways to identify pairs of changes with a likely cause-effect relation within. The approach automatically converts comparative proteomic and other molecular profiles into hypotheses of differentially active mechanistic relations that explain how the profiles came to be., CausalPath integrates detailed biological pathways with proteomic and other molecular profiles to generate mechanistic models explaining how the observed changes are related. It is applicable to a wide range of contexts and a variety of experiment types. The method can be accessed at causalpath.org.

Our study details the stepwise evolution of gilteritinib resistance in FLT3-mutated acute myeloid leukemia (AML). Early resistance is mediated by the bone marrow microenvironment, which protects residual leukemia cells. Removing these supportive extrinsic ligands drives evolution of late, intrinsic resistance. Whole exome sequencing, CRISPR/Cas, metabolomics, proteomics, and pharmacologic approaches were used to mechanistically define both early and late resistance. Early resistant cells undergo metabolic reprogramming, grow more slowly, and are dependent upon Aurora kinase B (AURKB). Late resistant cells are characterized byexpansion of pre-existing NRAS mutant subclones and continued metabolic reprogramming. Our model closely mirrors the timing and mutations of AML patients treated with gilteritinib.Pharmacological inhibition of AURKB resensitized both early resistant cell cultures and primary leukemia cells from gilteritinib-treated AML patients. These findings support a combinatorial strategy to target early resistant AML cells with AURKB inhibitors and gilteritinib before the expansion of pre-existing resistance mutations occurs.

Technological advances in computing provide major opportunities to complement human reasoning and to dramatically speed up science - but only if structured knowledge is available to enable efficient communication between humans and computers. Traditionally, biological knowledge is captured in publications and knowledge bases. Knowledge in papers is not directly in a computable, structured form; curated structured knowledge bases are limited by manual curation processes. To accelerate knowledge capture and communication and keep pace with the rapid growth of scientific reports, we developed the Biofactoid (biofactoid.org) software suite. Biofactoid accelerates the transfer of knowledge from the minds of authors into computable, widely shared, structured knowledge and can be used as part of the standard publication process. Biofactoid is a web-based system for scientists to compose a structured representation of networks of interactions between genes, their products, and chemical compounds, represented using the expressive power of a formal ontology (BioPAX). The resulting knowledge items are shared via public information resources and can be discovered and analyzed in the context of all existing computable knowledge. We envision adoption of software technology for knowledge capture by scientists and publishers as part of an ecosystem of tools, in which scientific reasoning is supported by efficient knowledge computation.

Platelets engage cues of pending vascular injury through coordinated adhesion, secretion, and aggregation responses. These rapid, progressive changes in platelet form and function are orchestrated downstream of specific receptors on the platelet surface and through intracellular signaling mechanisms that remain systematically undefined. This study brings together cell physiological and phosphoproteomics methods to profile signaling mechanisms downstream of the immunotyrosine activation motif (ITAM) platelet collagen receptor GPVI. Peptide tandem mass tag (TMT) labeling, sample multiplexing, synchronous precursor selection (SPS), and triple stage tandem mass spectrometry (MS3) detected >3000 significant (false discovery rate < 0.05) phosphorylation events on >1300 proteins over conditions initiating and progressing GPVI-mediated platelet activation. With literature-guided causal inference tools, >300 site-specific signaling relations were mapped from phosphoproteomics data among key and emerging GPVI effectors (ie, FcRγ, Syk, PLCγ2, PKCδ, DAPP1). Through signaling validation studies and functional screening, other less-characterized targets were also considered within the context of GPVI/ITAM pathways, including Ras/MAPK axis proteins (ie, KSR1, SOS1, STAT1, Hsp27). Highly regulated GPVI/ITAM targets out of context of curated knowledge were also illuminated, including a system of >40 Rab GTPases and associated regulatory proteins, where GPVI-mediated Rab7 S72 phosphorylation and endolysosomal maturation were blocked by TAK1 inhibition. In addition to serving as a model for generating and testing hypotheses from omics datasets, this study puts forth a means to identify hemostatic effectors, biomarkers, and therapeutic targets relevant to thrombosis, vascular inflammation, and other platelet-associated disease states.

Lung squamous cell carcinoma (LSCC) remains a leading cause of cancer death with few therapeutic options. We characterized the proteogenomic landscape of LSCC, providing a deeper exposition of LSCC biology with potential therapeutic implications. We identify NSD3 as an alternative driver in FGFR1-amplified tumors and low-p63 tumors overexpressing the therapeutic target survivin. SOX2 is considered undruggable, but our analyses provide rationale for exploring chromatin modifiers such as LSD1 and EZH2 to target SOX2-overexpressing tumors. Our data support complex regulation of metabolic pathways by crosstalk between post-translational modifications including ubiquitylation. Numerous immune-related proteogenomic observations suggest directions for further investigation. Proteogenomic dissection of CDKN2A mutations argue for more nuanced assessment of RB1 protein expression and phosphorylation before declaring CDK4/6 inhibition unsuccessful. Finally, triangulation between LSCC, LUAD, and HNSCC identified both unique and common therapeutic vulnerabilities. These observations and proteogenomics data resources may guide research into the biology and treatment of LSCC.

Motivation Visualization of cellular processes and pathways is a key recurring requirement for effective biological data analysis. There is a considerable need for sophisticated web-based pathway viewers and editors operating with widely accepted standard formats, using the latest visualization techniques and libraries. Results We developed a web-based tool named Newt for viewing, constructing and analyzing biological maps in standard formats such as SBGN, SBML and SIF. Availability and implementation Newt’s source code is publicly available on GitHub and freely distributed under the GNU LGPL. Ample documentation on Newt can be found on http://newteditor.org and on YouTube.

Activating mutations in the tyrosine kinase receptor FLT3 are observed in ~35% of all acute myeloid leukemia (AML) cases. Multiple FLT3 inhibitors are currently in clinical development and while most patients initially respond well to FLT3 inhibition, resistance inevitably develops in a period of months. During the initial response to FLT3 inhibitors, residual leukemia cells are able to survive and persist in the marrow microenvironment, which facilitates early resistance. Over time, leukemia cells develop intrinsic mechanisms of resistance, that are not dependent upon the microenvironment, which leads to late resistance and disease relapse. In this study, we utilized a two-step model to study the temporal evolution of gilteritinib resistance. To model initial extrinsic early resistance, we cultured the FLT3-ITD+ AML cell lines, MOLM-14 and MV4;11, with ligands secreted by the marrow microenvironment. We previously reported that fibroblast growth factor 2 (FGF2) is secreted from marrow stromal cells and protects FLT3-ITD+ AML cells from quizartinib, a FLT3 inhibitor. FGF2 binds receptor FGFR1, activates MAPK signaling, and promotes ligand-dependent growth (Traer et al. Cancer Res. 2016). We also used FLT3 ligand (FL), which has been shown to reactivate the FLT3 receptor despite the presence of inhibitor. MOLM14 cells were cultured with 100 nM gilteritinib in media alone, or supplemented with 10 ng/ml FGF2 or FL. After 7 weeks, all cultures supplemented with ligand eventually resumed growth (early resistance), whereas MOLM-14 cells without ligand were unable to resume growth.We then removed ligand, which transiently restored sensitivity to gilteritinib. However, after 2 months the cells resumed exponential growth in the absence of extrinsic factors (late resistance). A similar pattern was observed with MV4;11 cells. We analyzed the mechanisms of early and late resistance using a number of orthogonal tools: whole exome sequencing (WES), genome-wide CRISPR/Cas9, proteomics, metabolomics, and small-molecule inhibitor screening. WES identified NRAS mutations in the majority of late resistant cultures (13/15) and a gatekeeper FLT3 mutation was found in 1, consistent with mutations found in patients treated with gilteritinib on the Admiral trial (McMahon et al., Cancer Discov., 2019). To identify if NRAS mutations were pre-existing, ddPCR was used. NRAS mutations were detected at low level ( We further analyzed both early and late resistant lines with genome-wide resensitization CRISPR/Cas screening and global and phospho-proteomics. As expected, CRISPR/Cas screening revealed that NRAS was important for late resistance, which was confirmed by proteomics. In contrast, early resistance revealed multiple hits in metabolic and cell cycle pathways by CRISPR/Cas and global and phospho-proteomics. The unique metabolic signatures (lipid signaling in particular) were verified by metabolomic analyses. CDK proteins were significantly decreased in early resistance, as was the cell cycle. Aurora Kinase B protein (AURKB) was also increased in early resistance, and cells were uniquely sensitive to AURKB small molecule inhibitors and genetic deletion. We then evaluated primary leukemia cells from 11 patients before and after 1-2 months of gilteritinib to evaluate early resistance. Leukemia cells were isolated by CD33 and CD34 beads and subjected to a targeted proteomic analysis (Figure 1). In agreement with our cell line model, early resistant cells had a distinct protein profile, with significant alteration of cell cycle and lipid metabolism proteins after gilteritinib treatment. Primary patient samples also demonstrated robust sensitivity to AURKB inhibitors only after gilteritinib exposure. Our results suggest that developing drug combinations that selectively target early resistance can improve the depth of initial response and may block development of later resistance mutations, thus improving the durability of response to gilteritinib. Figure 1 Disclosures Tyner: Incyte: Research Funding; Janssen: Research Funding; Syros: Research Funding; Seattle Genetics: Research Funding; Petra: Research Funding; Agios: Research Funding; AstraZeneca: Research Funding; Gilead: Research Funding; Aptose: Research Funding; Array: Research Funding; Genentech: Research Funding; Constellation: Research Funding; Takeda: Research Funding. Druker:Henry Stewart Talks: Patents & Royalties; Third Coast Therapeutics: Membership on an entity's Board of Directors or advisory committees; Novartis Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees, Patents & Royalties, Research Funding; McGraw Hill: Patents & Royalties; MolecularMD (acquired by ICON): Consultancy, Current equity holder in publicly-traded company, Membership on an entity's Board of Directors or advisory committees; Millipore (formerly Upstate Biotechnology): Patents & Royalties; Merck & Co: Patents & Royalties; Cepheid: Consultancy, Membership on an entity's Board of Directors or advisory committees; GRAIL: Consultancy, Current equity holder in private company, Membership on an entity's Board of Directors or advisory committees; Dana-Farber Cancer Institute: Patents & Royalties; EnLiven: Consultancy, Research Funding; Gilead Sciences: Consultancy, Membership on an entity's Board of Directors or advisory committees; Vivid Biosciences: Membership on an entity's Board of Directors or advisory committees; Aileron Therapeutics: Membership on an entity's Board of Directors or advisory committees; ALLCRON: Consultancy, Membership on an entity's Board of Directors or advisory committees; Amgen: Current equity holder in publicly-traded company, Membership on an entity's Board of Directors or advisory committees; Aptose Therapeutics Inc. (formerly Lorus): Consultancy, Current equity holder in publicly-traded company, Membership on an entity's Board of Directors or advisory committees; ARIAD: Research Funding; Blueprint Medicines: Consultancy, Current equity holder in private company, Membership on an entity's Board of Directors or advisory committees; Bristol-Myers Squibb: Research Funding; Oregon Health & Science University: Patents & Royalties; Patient True Talks: Consultancy; Pfizer: Research Funding; The RUNX1 Research Program: Membership on an entity's Board of Directors or advisory committees; Iterion Therapeutics (formerly Beta Cat Pharmaceuticals): Membership on an entity's Board of Directors or advisory committees; VB Therapeutics: Membership on an entity's Board of Directors or advisory committees; Leukemia & Lymphoma Society: Research Funding. Traer:Daiichi Sankyo: Membership on an entity's Board of Directors or advisory committees; Genentech: Membership on an entity's Board of Directors or advisory committees; Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees; Agios: Membership on an entity's Board of Directors or advisory committees; Incyte: Research Funding; Notable Labs: Consultancy, Current equity holder in private company; Astellas: Membership on an entity's Board of Directors or advisory committees.

The development of effective targeted therapies for the treatment of basal-like breast cancers remains challenging. Here, we demonstrate that cellular stress engendered by BET inhibition induces an adaptive response leading to MCL1 protein-driven evasion of apoptosis in breast cancers. Consequently, co-targeting MCL1 and BET is highly synergistic in in vitro and in vivo breast cancer models. The mechanism of adaptive response to BET inhibition involves upregulation of critical lipid metabolism enzymes including the rate-limiting enzyme stearoyl-CoA desaturase (SCD). Changes in the lipid metabolism are associated with increases in cell motility and membrane fluidity as well as transitions in cell morphology. The structural changes in the cell membrane leads to re-localization and activation of HER2/EGFR which can be interdicted by inhibiting SCD activity. Active HER2/EGFR, in turn, induces accumulation of MCL1 protein and therapeutic vulnerability to MCL1 inhibitors. The BET protein, lipid metabolism and receptor tyrosine kinase activation cascade is observed in patient cohorts of basal-like and HER2-amplified breast cancers. Drug response and genomics analyses revealed that MCL1 copy number alterations, including low-level gains, are selectively enriched in basal-like breast cancers and associated with effective BET and MCL1 co-targeting. The high frequency of MCL1 chromosomal amplifications (>30%) and gains (>50%) in basal-like breast cancers suggests that BET and MCL1 co-inhibition may have therapeutic utility in this aggressive subtype.

We present a computational method to infer causal mechanisms in cell biology by analyzing changes in high-throughput proteomic profiles on the background of prior knowledge captured in biochemical reaction knowledge bases. The method mimics a biologist's approach of explaining changes in data using prior knowledge in their memory and in the literature, but at the scale of hundreds of thousands of reactions. The identified mechanisms can explain how experimental and physiological perturbations, propagating in a network of reactions, affect cellular responses and their phenotypic consequences. We first demonstrate the validity of the method on cell line perturbation experiments. In discovery mode we then identify 1) differential signaling within breast cancer and ovarian cancer, 2) subtype-specific signaling in these cancers, and 3) recurrent signaling relations across 32 cancer types. Causal pathway analysis is a powerful and flexible discovery tool for a wide range of cellular profiling data types and biological questions. The automated causation inference tool, as well as the source code, are freely available at causalpath.org.

Pathway Commons (https://www.pathwaycommons.org) is an integrated resource of publicly available information about biological pathways including biochemical reactions, assembly of biomolecular complexes, transport and catalysis events and physical interactions involving proteins, DNA, RNA, and small molecules (e.g. metabolites and drug compounds). Data is collected from multiple providers in standard formats, including the Biological Pathway Exchange (BioPAX) language and the Proteomics Standards Initiative Molecular Interactions format, and then integrated. Pathway Commons provides biologists with (i) tools to search this comprehensive resource, (ii) a download site offering integrated bulk sets of pathway data (e.g. tables of interactions and gene sets), (iii) reusable software libraries for working with pathway information in several programming languages (Java, R, Python and Javascript) and (iv) a web service for programmatically querying the entire dataset. Visualization of pathways is supported using the Systems Biological Graphical Notation (SBGN). Pathway Commons currently contains data from 22 databases with 4794 detailed human biochemical processes (i.e. pathways) and ∼2.3 million interactions. To enhance the usability of this large resource for end-users, we develop and maintain interactive web applications and training materials that enable pathway exploration and advanced analysis.

Pathway Commons (https://www.pathwaycommons.org) is an integrated resource of publicly available information about biological pathways including biochemical reactions, assembly of biomolecular complexes, transport and catalysis events and physical interactions involving proteins, DNA, RNA, and small molecules (e.g., metabolites and drug compounds). Data is collected from multiple providers in standard formats, including the Biological Pathway Exchange (BioPAX) language and the Proteomics Standards Initiative Molecular Interactions format, and then integrated. Pathway Commons provides biologists with (1) tools to search this comprehensive resource, (2) a download site offering integrated bulk sets of pathway data (e.g., tables of interactions and gene sets), (3) reusable software libraries for working with pathway information in several programming languages (Java, R, Python, and Javascript), and (4) a web service for programmatically querying the entire dataset. Visualization of pathways is supported using the Systems Biological Graphical Notation (SBGN). Pathway Commons currently contains data from 22 databases with 4,794 detailed human biochemical processes (i.e., pathways) and ∼2.3 million interactions. To enhance the usability of this large resource for end-users, we develop and maintain interactive web applications and training materials that enable pathway exploration and advanced analysis.

The development of effective targeted therapies for the treatment of basal-like breast cancers remains challenging. Here, we demonstrate that BET inhibition induces a multi-faceted adaptive response program leading to MCL1 protein-driven evasion of apoptosis in breast cancers. Consequently, co-targeting MCL1 and BET is highly synergistic inin vitroandin vivobreast cancer models. Drug response and genomics analyses revealed that MCL1 copy number alterations, including low-level gains, are selectively enriched in basal-like breast cancers and associated with effective BET and MCL1 co-targeting. The mechanism of adaptive response to BET inhibition involves upregulation of critical lipid metabolism enzymes including the rate-limiting enzyme stearoyl-CoA desaturase (SCD). Changes in the lipid metabolism are associated with increases in cell motility and membrane fluidity as well as transitions in cell morphology and adhesion. The structural changes in the cell membrane leads to re-localization and activation of HER2/EGFR which can be interdicted by inhibiting SCD activity. Active HER2/EGFR, in turn, induces accumulation of MCL1 protein and therapeutic vulnerability to MCL1 inhibitors. The BET protein, lipid metabolism and receptor tyrosine kinase activation cascade is observed in patient cohorts of basal-like and HER2-amplified breast cancers. The high frequency of MCL1 chromosomal amplifications (>30%) and gains (>50%) in basal-like breast cancers suggests that BET and MCL1 co-inhibition may have therapeutic utility in this aggressive subtype.

Mutual exclusivity analysis of genomic mutations has proven useful for detecting driver alterations in cancer patient cohorts. Here we demonstrate, for the first time, that this pattern is also present among de novo mutations in autism spectrum disorder. We analyzed three large whole genome sequencing studies and identified mutual exclusivity patterns within the most confident set of autism-related genes, as well as in the circadian clock and PI3K/AKT signaling pathways.

Intracellular signaling systems associated with the platelet glycoprotein VI receptor (GPVI) are essential to hemostasis and also initiate atherothrombotic events in cardiovascular disease contexts. While many separable molecular entities linked to GPVI-mediated platelet activation have been characterized, a systemic understanding of GPVI signaling over the context of the entire platelet proteome remains far from complete. To test the hypothesis that specific, intact signaling systems downstream of GPVI regulate platelet function, we used a combination of tandem mass tag (TMT) labeling and high-resolution, multidimensional mass spectrometry and informatics tools to quantify and model protein phosphorylation events reproducibly measurable in platelets from healthy human subjects (n=5) in response to the GPVI specific agonist collagen-related peptide (CRP). Following CRP treatment, platelets rapidly regulated the phosphorylation state of 1004 serine, threonine and tyrosine residues on 542 proteins (fold change > 1.5; false discovery rate < 0.05). In addition to regulatory sites on kinases, phospholipases and other well-known targets, the majority of phosphorylation events following CRP stimulation occurred on proteins with uncharacterized roles in platelet function. CausalPath analysis of this data mapped 46 high confidence pathway relations, suggesting that Syk, PKCs and MAPKs ultimately drive the phosphorylation of several novel effectors specifically downstream of GPVI in platelet regulation, including CRMP-2, RAD23B and caldesmon. In addition to comprehensively mapping out known signaling relations while illuminating previously unrecognized regulatory mechanisms of platelet function around GPVI, our work provides a platform to generate novel, mechanistic and testable hypotheses from omics data sets for subsequent discovery-driven efforts to identify platelet-based therapeutic and biomarker targets relevant to cardiovascular disease risk, progression and mortality.

Upon encountering physiological cues associated with damaged or inflamed endothelium, blood platelets set forth intracellular responses to ultimately support hemostatic plug formation and vascular repair. To gain insights into the molecular events underlying platelet function, we used a combination of interactome, pathway analysis, and other systems biology tools to analyze associations among proteins functionally modified by reversible phosphorylation upon platelet activation. While an interaction analysis mapped out a relative organization of intracellular mediators in platelet signaling, pathway analysis revealed directional signaling relations around protein kinase C (PKC) isoforms and mitogen-activated protein kinases (MAPKs) associated with platelet cytoskeletal dynamics, inflammatory responses, and hemostatic function. Pathway and causality analysis further suggested that platelets activate a specific p38-MK2 axis to phosphorylate RTN4 (reticulon-4, also known as Nogo), a Bcl-xl sequestration protein and critical regulator of endoplasmic reticulum (ER) physiology. In vitro, we find that platelets drive a p38-MK2-RTN4-Bcl-xl pathway associated with the regulation of the ER and platelet phosphatidylserine exposure. Together, our results support the use of pathway tools in the analysis of omics data sets as a means to help generate novel, mechanistic, and testable hypotheses for platelet studies while uncovering RTN4 as a putative regulator of platelet cell physiological responses.

Measurement of changes in protein levels and in post-translational modifications, such as phosphorylation, can be highly informative about the phenotypic consequences of genetic differences or about the dynamics of cellular processes. Typically, such proteomic profiles are interpreted intuitively or by simple correlation analysis. Here, we present a computational method to generate causal explanations for proteomic profiles using prior mechanistic knowledge in the literature, as recorded in cellular pathway maps. To demonstrate its potential, we use this method to analyze the cascading events after EGF stimulation of a cell line, to discover new pathways in platelet activation, to identify influential regulators of oncoproteins in breast cancer, to describe signaling characteristics in predefined subtypes of ovarian and breast cancers, and to highlight which pathway relations are most frequently activated across 32 cancer types. Causal pathway analysis, that combines molecular profiles with prior biological knowledge captured in computational form, may become a powerful discovery tool as the amount and quality of cellular profiling rapidly expands. The method is freely available at http://causalpath.org.

PubMed, a repository and search engine for biomedical literature, now indexes >1 million articles each year. This exceeds the processing capacity of human domain experts, limiting our ability to truly understand many diseases. We present Reach, a system for automated, large-scale machine reading of biomedical papers that can extract mechanistic descriptions of biological processes with relatively high precision at high throughput. We demonstrate that combining the extracted pathway fragments with existing biological data analysis algorithms that rely on curated models helps identify and explain a large number of previously unidentified mutually exclusive altered signaling pathways in seven different cancer types. This work shows that combining human-curated ‘big mechanisms’ with extracted ‘big data’ can lead to a causal, predictive understanding of cellular processes and unlock important downstream applications.

Introduction: Upon activation, platelets undergo specific morphological alterations critical to hemostatic plug and thrombus formation via actin cytoskeletal reorganizations driven by the Rho GTPases Rac1, Cdc42 and RhoA. Here we investigate roles for Rho-specific guanine nucleotide dissociation inhibitor proteins (RhoGDIs) in regulating platelet function. Methods and Hypothesis: Through an approach combining pharmacology, cell biology and systems biology methods we assessed the hypothesis that RhoGDI proteins regulate Rho GTPase-driven platelet functions downstream of platelet integrin and glycoprotein receptors. Results: We find that platelets express two RhoGDI family members, RhoGDI and Ly-GDI. Antibody interference and platelet spreading experiments suggest a specific role for Ly-GDI in platelet function. Intracellular staining and super resolution microscopy assays find that Ly-GDI displays an asymmetric, polarized localization that largely overlaps with Rac1 and Cdc42 as well as microtubules and protein kinase C (PKC) in platelets adherent to fibrinogen. Signaling studies based on interactome and pathways analyses also support a regulatory role for Ly-GDI in platelets, as Ly-GDI is phosphorylated at PKC substrate motifs in a PKC-dependent manner in response to the platelet collagen receptor glycoprotein (GP)VI-specific agonist collagen-related peptide. Notably, inhibition of PKC diffuses the polarized organization of Ly-GDI in spread platelets relative to its colocalization with Rac1 and Cdc42. Conclusion: In conclusion, our results support roles for Ly-GDI as a localized regulator of Rho GTPases in platelets and link PKC and Rho GTPase signaling systems to platelet function.

On activation at sites of vascular injury, platelets undergo morphological alterations essential to hemostasis via cytoskeletal reorganizations driven by the Rho GTPases Rac1, Cdc42, and RhoA. Here we investigate roles for Rho-specific guanine nucleotide dissociation inhibitor proteins (RhoGDIs) in platelet function. We find that platelets express two RhoGDI family members, RhoGDI and Ly-GDI. Whereas RhoGDI localizes throughout platelets in a granule-like manner, Ly-GDI shows an asymmetric, polarized localization that largely overlaps with Rac1 and Cdc42 as well as microtubules and protein kinase C (PKC) in platelets adherent to fibrinogen. Antibody interference and platelet spreading experiments suggest a specific role for Ly-GDI in platelet function. Intracellular signaling studies based on interactome and pathways analyses also support a regulatory role for Ly-GDI, which is phosphorylated at PKC substrate motifs in a PKC-dependent manner in response to the platelet collagen receptor glycoprotein (GP) VI–specific agonist collagen-related peptide. Additionally, PKC inhibition diffuses the polarized organization of Ly-GDI in spread platelets relative to its colocalization with Rac1 and Cdc42. Together, our results suggest a role for Ly-GDI in the localized regulation of Rho GTPases in platelets and hypothesize a link between the PKC and Rho GTPase signaling systems in platelet function.

Understanding the mechanisms responsible for a cellular behavior often begins with observations of genes and gene products. Depending on the type of experiment, the number of resulting genes can be small, but increasingly, researchers are faced with many thousands of measurements, as in the case of transcriptomic or protein-DNA binding observations. Here, we describe ways to pair experimental results consisting of one or more genes with analysis tools with the overall aim being to make results more biologically interpretable. In certain cases, experimental approaches such as screens for essential genes can generate one or a few ‘genes of interest’ and there is a desire to understand their relationship to one another as well as discover links to additional, interesting genes. To this end, ‘GeneMANIA’ is a web tool that accepts gene names and returns a network visualization of related genes based on similarity in expression, localization, protein domains and those involved in physical interactions. Likewise, ‘PCViz’ is a web tool that displays a network of interactions drawn from Pathway Commons, a web resource for pathway and interaction knowledge. In cases where experiments generate a lengthy list of genes, for instance, transcriptomic measurements, there is a desire to understand their relevance to a phenotype of interest. Pathway enrichment analysis methods aim to summarize gene lists as pathways, which have a closer link to cell function. An online ‘Guide’ by Pathway Commons includes workflows that illustrate how to chain together software tools to identify pathways from the corresponding gene-level data then organize and summarize the pathway-level results in an interactive visualization known as an Enrichment Map. For those wishing to drill-down to individual pathways, Pathway Commons offers a set of web apps, including ‘Search’ that enables users to query by keyword and visualize ranked search results. Ongoing development of web apps aims to enhance the accessibility to pathways and integrate support for analysis and visualization of experimental data. The full complement of data, tools and resources offered by Pathway Commons in support of pathway analysis are described. Citation Format: Augustin Luna, Jeffrey V. Wong, Emek Demir, Igor Rodchenkov, Özgün Babur, Chris Sander, Gary D. Bader. Interpreting gene lists from -omics experiments [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 3451.

Protein post-translational modifications critically regulate a range of physiological and disease processes. In addition to tyrosine, serine and threonine phosphorylation, reversible N-ε acylation and alkylation of protein lysine residues also modulate diverse aspects of cellular function. Studies of lysine acyl and alkyl modifications have focused on nuclear proteins in epigenetic regulation; however, lysine modifications are also prevalent on cytosolic proteins to serve increasingly apparent, although less understood roles in cell regulation. Here, we characterize the methyl-lysine (meK) proteome of anucleate blood platelets. With high-resolution, multiplex mass spectrometry methods, we identify 190 mono-, di- and tri-meK modifications on 150 different platelet proteins – including 28 meK modifications quantified by tandem mass tag (TMT) labeling. In addition to identifying meK modifications on calmodulin (CaM), GRP78 (HSPA5, BiP) and EF1A1 that have been previously characterized in other cell types, we also uncover more novel modifications on cofilin, drebin-like protein (DBNL, Hip-55), DOCK8, TRIM25 and numerous other cytoplasmic proteins. Together, our results and analyses support roles for lysine methylation in mediating cytoskeletal, translational, secretory and other cellular processes. Mass spectrometry data for this study have been deposited into the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD012217.

Motivation While existing network visualization tools enable the exploration of cancer genomics data, most biologists prefer simplified, curated pathway diagrams, such as those featured in many manuscripts from The Cancer Genome Atlas (TCGA). These pathway diagrams typically summarize how a pathway is altered in individual cancer types, including alteration frequencies for each gene. Results To address this need, we developed the web-based tool PathwayMapper, which runs in most common web browsers. It can be used for viewing pre-curated cancer pathways, or as a graphical editor for creating new pathways, with the ability to overlay genomic alteration data from cBioPortal. In addition, a collaborative mode is available that allows scientists to co-operate interactively on constructing pathways, with support for concurrent modifications and built-in conflict resolution. Availability and Implementation The PathwayMapper tool is accessible at http://pathwaymapper.org and the code is available on Github (https://github.com/iVis-at-Bilkent/pathway-mapper). Supplementary information Supplementary data are available at Bioinformatics online.

SMMART is a precision medicine research program focused on understanding the evolution of actionable biology and mechanisms of resistance in human tumors during therapy. This is accomplished through in depth functional, ‘omic and multiscale image analysis of longitudinal samples acquired during treatment. Here we present a case report detailing the insights that can be gained from the comparative analysis of pre- and post-treatment biopsy specimens in a late-stage metastatic breast cancer patient. To understand the molecular evolution of cancer, we interrogated genomics with targeted and whole exome sequencing, transcriptomics with RNA and gene-fusion sequencing, and proteomics with reverse phase protein arrays. To understand cellular organization and architectural changes, we employed multi-scale imaging tools, including scanning electron microscopy (SEM), cyclic immunofluorescence, immune cell profiling with cyclic immunohistochemistry, and traditional pathological assessment. During the course of treatment, we monitored patient response to therapy with clinical imaging, circulating tumor DNA sequencing and cancer protein assessment. Individual assays revealed key aspects of how this individual's cancer evolved under therapeutic pressure. For example, mutational profiling revealed the patterns of clonal evolution and the acquisition of new genetic driver events. 2D and 3D SEM showed changes in ECM organization, macropinocytosis, mitochondrion size, number and density and number and organization of filopodia-like protrusions. We used a 30-color cyclic immunofluorescence analysis to identify differences in cancer cell proliferation and differentiation state, as well as the composition and organization of infiltrating immune cells. In addition, integrative analyses of multiple data types provided insight into the evolution of actionable biology within this patient's disease. This included changes in the suitability of the patient for immune checkpoint inhibitors as well as specific tyrosine kinase inhibitors. The comprehensive molecular and architectural characterization of an individual patient's cancer at multiple time points provides biologically novel and clinically relevant insight into the ways in which cancers become resistant to treatment. Citation Format: Brett Johnson, Jamie Keck, Max Morris, Kiara Siex, Annette Kolodzie, Swapnil Parmar, Jessica Riesterer, Koei Chin, Summer Gibbs, Laura Heiser, Paul Spellman, Kyle Ellrott, Ozgun Babur, Emek Demir, Adam Margolin, Jeremy Goecks, Lisa Coussens, Raymond Bergan, Joe Gray. SMMART: Serial measurements of molecular and architectural responses to therapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3296.

Drug resistance to targeted therapies, either intrinsic (existing before treatment) or acquired (resulting from adaptation to therapy), is a major challenge in patient care. Drug combinations offer a possible solution to prevent drug resistance by blocking multiple routes to tumor proliferation. Discovery of effective combinations remains a challenging task due to complexity of the underlying biological processes and inter-tumor heterogeneity. Here, we developed a statistical pathway analysis method that (i) reveals pathways involved in drug response and adaptive resistance and (ii) nominates combination targets and therapies to down-regulate the resistance pathways. The method is based on the rationale that (i) use of therapies targeting specific genomic aberrations can lead to compensatory responses (e.g. through feedback loop activation in the short term or the development of oncogenic alterations in the long term) leading to drug resistance, and (ii) collective changes in pathway activities are better predictors of resistance and mitigation strategies than individual responses. We construct a pathway model of signaling interactions for measured molecular species through automated extraction of pathway information from Pathway Commons plus manual expert curation. Next, we combine the pathway model with the cell-type-specific drug response data to calculate a TargetScore (TS) for each protein. The score quantifies the adaptive pathway responses to a perturbation by integrating the change in the level of a (phospho)protein along with its pathway neighborhood in response to the single drug. A high TS corresponds to involvement in adaptive response (e.g. upregulation of RTK expression by MEK inhibitor via a feedback loop) and a low TS corresponds to the activity of the drug (e.g. inhibition of ERK phosphorylation by MEK inhibitor). Finally, by identifying the sub-pathways with enriched, high TS values, we determine the adaptive resistance pathways. We test the resulting predictions (combinations of the original single drug with drugs targeting members of the resistance pathways) experimentally. TargetScore is amenable to calculations for hundreds of samples treated with individual (or combinations of) drugs in multiple doses and/or time points and assayed for hundreds to thousands of molecular entities (mRNA or proteomic). Analysis of longitudinal data may allow us to trace the evolution of drug resistance and, potentially, the optimum time points for intervention. The current protocol is defined with a focus on proteomic RPPA data, but can be adapted to other kinds of molecular data associated with adaptive responses. We applied our method to BET-BRD inhibition in ovarian cancer to compare resistance/response pathways in cells with varying degrees sensitivity. The analysis nominated cell-type-specific, anti-resistance combinations involving BET inhibitors. Citation Format: Augustin Luna, Özgün Babur, Gonghong Yan, Emek Demir, Chris Sander, Anil Korkut. Discovery of adaptive resistance pathways and anti-resistance combination therapies in cancer from phosphoproteomic data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 2838.

Understanding the mechanisms responsible for a cellular behaviour often begins with observations of genes and gene products. Depending on the type of experiment, the number of resulting genes can be small, but increasingly, researchers are faced with many thousands of measurements, as in the case of transcriptomic or protein-DNA binding observations. Here, we describe ways to pair experimental results consisting of one or more genes with analysis tools with the overall aim being to make results more biologically interpretable. In certain cases, experimental approaches such as screens for essential genes can generate one or a few ‘genes of interest' and there is a desire to understand their relationship to one another as well as discover links to additional, interesting genes. To this end, ‘GeneMANIA' is a web tool that accepts gene names and returns a network visualization of related genes based on similarity in expression, localization, protein domains and those involved in physical interactions. Likewise, ‘PCViz' is a web tool that displays a network of interactions drawn from Pathway Commons, a web resource for pathway and interaction knowledge. In cases where experiments generate a lengthy list of genes, for instance, transcriptomic measurements, there is a desire to understand their relevance to a phenotype of interest. Pathway enrichment analysis methods aim to summarize gene lists as pathways, which have a closer link to cell function. An online ‘Guide' by Pathway Commons includes workflows that illustrate how to chain together software tools to identify pathways from the corresponding gene-level data then organize and summarize the pathway-level results in an interactive visualization known as an Enrichment Map. For those wishing to drill-down to individual pathways, Pathway Commons offers a set of web apps, including ‘Search' that enables users to query by keyword and visualize ranked search results. Ongoing development of web apps aims to enhance the accessibility to pathways and integrate support for analysis and visualization of experimental data. The full complement of data, tools and resources offered by Pathway Commons in support of pathway analysis are described. Citation Format: Jeffrey V. Wong, Augustin Luna, Emek Demir, Igor Rodchenkov, Özgün Babur, Chris Sander, Gary D. Bader. How can you interpret gene lists from -omics experiments [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 1284.

Purpose: PaxtoolsR package enables access to pathway data represented in the BioPAX format and made available through the Pathway Commons webservice for users of the R language to aid in advanced pathway analyses. Features include the extraction, merging and validation of pathway data represented in the BioPAX format. This package also provides novel pathway datasets and advanced querying features for R users through the Pathway Commons webservice allowing users to query, extract and retrieve data and integrate these data with local BioPAX datasets. Availability and implementation: The PaxtoolsR package is compatible with versions of R 3.1.1 (and higher) on Windows, Mac OS X and Linux using Bioconductor 3.0 and is available through the Bioconductor R package repository along with source code and a tutorial vignette describing common tasks, such as data visualization and gene set enrichment analysis. Source code and documentation are at http://www.bioconductor.org/packages/paxtoolsr. This plugin is free, open-source and licensed under the LGPL-3. Contact: paxtools@cbio.mskcc.org or lunaa@cbio.mskcc.org

Motivation: As the scientific curiosity in genome studies shifts toward identification of functions of the genomes in large scale, data produced about cellular processes at molecular level has been accumulating with an accelerating rate. In this regard, it is essential to be able to store, integrate, access and analyze this data effectively with the help of software tools. Clearly this requires a strong ontology that is intuitive, comprehensive and uncomplicated. Results: We define an ontology for an intuitive, comprehensive and uncomplicated representation of cellular events. The ontology presented here enables integration of fragmented or incomplete pathway information via collaboration, and supports manipulation of the stored data. In addition, it facilitates concurrent modifications to the data while maintaining its validity and consistency. Furthermore, novel structures for representation of multiple levels of abstraction for pathways and homologies is provided. Lastly, our ontology supports efficient querying of large amounts of data. We have also developed a software tool named pathway analysis tool for integration and knowledge acquisition (PATIKA) providing an integrated, multi-user environment for visualizing and manipulating network of cellular events. PATIKA implements the basics of our ontology. Availability: PATIKA version 1.0 beta is available upon request at http://www.patika.org

Background Information about cellular processes and pathways is becoming increasingly available in detailed, computable standard formats such as BioPAX and SBGN. Effective visualization of this information is a key recurring requirement for biological data analysis, especially for -omic data. Biological data analysis is rapidly migrating to web based platforms; thus there is a substantial need for sophisticated web based pathway viewers that support these platforms and other use cases. Results Towards this goal, we developed a web based viewer named SBGNViz for process description maps in SBGN (SBGN-PD). SBGNViz can visualize both BioPAX and SBGN formats. Unique features of SBGNViz include the ability to nest nodes to arbitrary depths to represent molecular complexes and cellular locations, automatic pathway layout, editing and highlighting facilities to enable focus on sub-maps, and the ability to inspect pathway members for detailed information from EntrezGene. SBGNViz can be used within a web browser without any installation and can be readily embedded into web pages. SBGNViz has two editions built with ActionScript and JavaScript. The JavaScript edition, which also works on touch enabled devices, introduces novel methods for managing and reducing complexity of large SBGN-PD maps for more effective analysis. Conclusion SBGNViz fills an important gap by making the large and fast-growing corpus of rich pathway information accessible to web based platforms. SBGNViz can be used in a variety of contexts and in multiple scenarios ranging from visualization of the results of a single study in a web page to building data analysis platforms. © 2015 Sari et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Motivation: Availability of the sequences of entire genomes shifts the scientific curiosity towards the identification of function of the genomes in large scale as in genome studies. In the near future, data produced about cellular processes at molecular level will accumulate with an accelerating rate as a result of proteomics studies. In this regard, it is essential to develop tools for storing, integrating, accessing, and analyzing this data effectively. Results: We define an ontology for a comprehensive representation of cellular events. The ontology presented here enables integration of fragmented or incomplete pathway information and supports manipulation and incorporation of the stored data, as well as multiple levels of abstraction. Based on this ontology, we present the architecture of an integrated environment named Patika (Pathway Analysis Tool for Integration and Knowledge Acquisition). Patika is composed of a server-side, scalable, object-oriented database and client-side editors to provide an integrated, multi-user environment for visualizing and manipulating network of cellular events. This tool features automated pathway layout, functional computation support, advanced querying and a user-friendly graphical interface. We expect that Patika will be a valuable tool for rapid knowledge acquisition, microarray generated large-scale data interpretation, disease gene identification, and drug development. Availability: A prototype of Patika is available upon request from the authors. Contact: patika@cs.bilkent.edu.tr * To whom correspondence should be addressed.