Publications by authors named "Bülent Arman Aksoy"

28 Publications

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Pathway Commons 2019 Update: integration, analysis and exploration of pathway data.

Nucleic Acids Res 2020 01;48(D1):D489-D497

cBio Center, Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA 02215, USA.

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.
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http://dx.doi.org/10.1093/nar/gkz946DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7145667PMC
January 2020

Cytokit: a single-cell analysis toolkit for high dimensional fluorescent microscopy imaging.

BMC Bioinformatics 2019 Sep 2;20(1):448. Epub 2019 Sep 2.

Microbiology and Immunology Department at Medical University of South Carolina, Charleston, USA.

Background: Multiplexed in-situ fluorescent imaging offers several advantages over single-cell assays that do not preserve the spatial characteristics of biological samples. This spatial information, in addition to morphological properties and extensive intracellular or surface marker profiling, comprise promising avenues for rapid advancements in the understanding of disease progression and diagnosis. As protocols for conducting such imaging experiments continue to improve, it is the intent of this study to provide and validate software for processing the large quantity of associated data in kind.

Results: Cytokit offers (i) an end-to-end, GPU-accelerated image processing pipeline; (ii) efficient input/output (I/O) strategies for operations specific to high dimensional microscopy; and (iii) an interactive user interface for cross filtering of spatial, graphical, expression, and morphological cell properties within the 100+ GB image datasets common to multiplexed immunofluorescence. Image processing operations supported in Cytokit are generally sourced from existing deep learning models or are at least in part adapted from open source packages to run in a single or multi-GPU environment. The efficacy of these operations is demonstrated through several imaging experiments that pair Cytokit results with those from an independent but comparable assay. A further validation also demonstrates that previously published results can be reproduced from a publicly available multiplexed image dataset.

Conclusion: Cytokit is a collection of open source tools for quantifying and analyzing properties of individual cells in large fluorescent microscopy datasets that are often, but not necessarily, generated from multiplexed antibody labeling protocols over many fields of view or time periods. This project is best suited to bioinformaticians or other technical users that wish to analyze such data in a batch-oriented, high-throughput setting. All source code, documentation, and data generated for this article are available under the Apache License 2.0 at https://github.com/hammerlab/cytokit .
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http://dx.doi.org/10.1186/s12859-019-3055-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6720861PMC
September 2019

Cancer-associated mutations in DICER1 RNase IIIa and IIIb domains exert similar effects on miRNA biogenesis.

Nat Commun 2019 08 15;10(1):3682. Epub 2019 Aug 15.

Department of Developmental Biology, Memorial Sloan-Kettering Cancer Center, New York, NY, 10065, USA.

Somatic mutations in the RNase IIIb domain of DICER1 arise in cancer and disrupt the cleavage of 5' pre-miRNA arms. Here, we characterize an unstudied, recurrent, mutation (S1344L) in the DICER1 RNase IIIa domain in tumors from The Cancer Genome Atlas (TCGA) project and MSK-IMPACT profiling. RNase IIIa/b hotspots are absent from most cancers, but are notably enriched in uterine cancers. Systematic analysis of TCGA small RNA datasets show that DICER1 RNase IIIa-S1344L tumors deplete 5p-miRNAs, analogous to RNase IIIb hotspot samples. Structural and evolutionary coupling analyses reveal constrained proximity of RNase IIIa-S1344 to the RNase IIIb catalytic site, rationalizing why mutation of this site phenocopies known hotspot alterations. Finally, examination of DICER1 hotspot endometrial tumors reveals derepression of specific miRNA target signatures. In summary, comprehensive analyses of DICER1 somatic mutations and small RNA data reveal a mechanistic aspect of pre-miRNA processing that manifests in specific cancer settings.
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http://dx.doi.org/10.1038/s41467-019-11610-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6695490PMC
August 2019

Anti-CTLA-4 Activates Intratumoral NK Cells and Combined with IL15/IL15Rα Complexes Enhances Tumor Control.

Cancer Immunol Res 2019 08 25;7(8):1371-1380. Epub 2019 Jun 25.

Immunology, Microenvironment and Metastasis Program, Wistar Cancer Center, The Wistar Institute, Philadelphia, Pennsylvania.

Antibodies targeting CTLA-4 induce durable responses in some patients with melanoma and are being tested in a variety of human cancers. However, these therapies are ineffective for a majority of patients across tumor types. Further understanding the immune alterations induced by these therapies may enable the development of novel strategies to enhance tumor control and biomarkers to identify patients most likely to respond. In several murine models, including colon26, MC38, CT26, and B16 tumors cotreated with GVAX, anti-CTLA-4 efficacy depends on interactions between the Fc region of CTLA-4 antibodies and Fc receptors (FcR). Anti-CTLA-4 binding to FcRs has been linked to depletion of intratumoral T regulatory cells (Treg). In agreement with previous studies, we found that Tregs infiltrating CT26, B16-F1, and autochthonous melanoma tumors had higher expression of surface CTLA-4 (sCTLA-4) than other T-cell subsets, and anti-CTLA-4 treatment led to FcR-dependent depletion of Tregs infiltrating CT26 tumors. This Treg depletion coincided with activation and degranulation of intratumoral natural killer cells. Similarly, in non-small cell lung cancer (NSCLC) and melanoma patient-derived tumor tissue, Tregs had higher sCTLA-4 expression than other intratumoral T-cell subsets, and Tregs infiltrating NSCLC expressed more sCTLA-4 than circulating Tregs. Patients with cutaneous melanoma who benefited from ipilimumab, a mAb targeting CTLA-4, had higher intratumoral CD56 expression, compared with patients who received little to no benefit from this therapy. Furthermore, using the murine CT26 model we found that combination therapy with anti-CTLA-4 plus IL15/IL15Rα complexes enhanced tumor control compared with either monotherapy.
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http://dx.doi.org/10.1158/2326-6066.CIR-18-0386DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6956982PMC
August 2019

T cells genetically engineered to overcome death signaling enhance adoptive cancer immunotherapy.

J Clin Invest 2019 02 25;129(4):1551-1565. Epub 2019 Feb 25.

Parker Institute for Cancer Immunotherapy, New York, New York, USA.

Across clinical trials, T cell expansion and persistence following adoptive cell transfer (ACT) have correlated with superior patient outcomes. Herein, we undertook a pan-cancer analysis to identify actionable ligand-receptor pairs capable of compromising T cell durability following ACT. We discovered that FASLG, the gene encoding the apoptosis-inducing ligand FasL, is overexpressed within the majority of human tumor microenvironments (TMEs). Further, we uncovered that Fas, the receptor for FasL, is highly expressed on patient-derived T cells used for clinical ACT. We hypothesized that a cognate Fas-FasL interaction within the TME might limit both T cell persistence and antitumor efficacy. We discovered that genetic engineering of Fas variants impaired in the ability to bind FADD functioned as dominant negative receptors (DNRs), preventing FasL-induced apoptosis in Fas-competent T cells. T cells coengineered with a Fas DNR and either a T cell receptor or chimeric antigen receptor exhibited enhanced persistence following ACT, resulting in superior antitumor efficacy against established solid and hematologic cancers. Despite increased longevity, Fas DNR-engineered T cells did not undergo aberrant expansion or mediate autoimmunity. Thus, T cell-intrinsic disruption of Fas signaling through genetic engineering represents a potentially universal strategy to enhance ACT efficacy across a broad range of human malignancies.
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http://dx.doi.org/10.1172/JCI121491DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6436880PMC
February 2019

Coral: Clear and Customizable Visualization of Human Kinome Data.

Cell Syst 2018 09 29;7(3):347-350.e1. Epub 2018 Aug 29.

Thurston Arthritis Research Center, University of North Carolina, Chapel Hill, NC 27599, USA; Department of Cell Biology & Physiology, University of North Carolina, Chapel Hill, NC 27599, USA; Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC 27599, USA. Electronic address:

Protein kinases represent one of the largest gene families in eukaryotes and play roles in a wide range of cell signaling processes and human diseases. Current tools for visualizing kinase data in the context of the human kinome superfamily are limited to encoding data through the addition of nodes to a low-resolution image of the kinome tree. We present Coral, a user-friendly interactive web application for visualizing both quantitative and qualitative data. Unlike previous tools, Coral can encode data in three features (node color, node size, and branch color), allows three modes of kinome visualization (the traditional kinome tree as well as radial and dynamic force networks), and generates high-resolution scalable vector graphics files suitable for publication without the need for refinement using graphics editing software. Due to its user-friendly, interactive, and highly customizable design, Coral is broadly applicable to high-throughput studies of the human kinome. The source code and web application are available at github.com/dphansti/CORAL and phanstiel-lab.med.unc.edu/Coral, respectively.
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http://dx.doi.org/10.1016/j.cels.2018.07.001DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6366324PMC
September 2018

Mutations in Diffuse-Type Gastric Adenocarcinoma Promote Epithelial-to-Mesenchymal Transition.

Clin Cancer Res 2018 12 14;24(24):6556-6569. Epub 2018 Aug 14.

Gastric and Mixed Tumor Service, Department of Surgery, Memorial Sloan-Kettering Cancer Center, New York, New York.

Purpose: Lauren diffuse-type gastric adenocarcinomas (DGAs) are generally genomically stable. We identified lysine (K)-specific methyltransferase 2C () as a frequently mutated gene and examined its role in DGA progression.

Experimental Design: We performed whole exome sequencing on tumor samples of 27 patients with DGA who underwent gastrectomy. Lysine (K)-specific methyltransferase 2C () was analyzed in DGA cell lines and in patient tumors.

Results: was the most frequently mutated gene (11 of 27 tumors [41%]). KMT2C expression by immunohistochemistry in tumors from 135 patients with DGA undergoing gastrectomy inversely correlated with more advanced tumor stage ( = 0.023) and worse overall survival ( = 0.017). KMT2C shRNA knockdown in non-transformed HFE-145 gastric epithelial cells promoted epithelial-to-mesenchymal transition (EMT) as demonstrated by increased expression of EMT-related proteins N-cadherin and Slug. Migration and invasion in gastric epithelial cells following KMT2C knockdown increased by 47- to 88-fold. In the DGA cell lines MKN-45 and SNU-668, which have lost KMT2C expression, KMT2C re-expression decreased expression of EMT-related proteins, reduced cell migration by 52% to 60%, and reduced cell invasion by 50% to 74%. Flank xenografts derived from KMT2C-expressing DGA organoids, compared with wild-type organoids, grew more slowly and lost their infiltrative leading edge. EMT can lead to the acquisition of cancer stem cell (CSC) phenotypes. KMT2C re-expression in DGA cell lines reduced spheroid formation by 77% to 78% and reversed CSC resistance to chemotherapy via promotion of DNA damage and apoptosis.

Conclusions: is frequently mutated in certain populations with DGA. KMT2C loss in DGA promotes EMT and is associated with worse overall survival.
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http://dx.doi.org/10.1158/1078-0432.CCR-17-1679DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6295255PMC
December 2018

Computational Pipeline for the PGV-001 Neoantigen Vaccine Trial.

Front Immunol 2017 18;8:1807. Epub 2018 Jan 18.

Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States.

This paper describes the sequencing protocol and computational pipeline for the PGV-001 personalized vaccine trial. PGV-001 is a therapeutic peptide vaccine targeting neoantigens identified from patient tumor samples. Peptides are selected by a computational pipeline that identifies mutations from tumor/normal exome sequencing and ranks mutant sequences by a combination of predicted Class I MHC affinity and abundance estimated from tumor RNA. The personalized genomic vaccine (PGV) pipeline is modular and consists of independently usable tools and software libraries. We hope that the functionality of these tools may extend beyond the specifics of the PGV-001 trial and enable other research groups in their own neoantigen investigations.
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http://dx.doi.org/10.3389/fimmu.2017.01807DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5778604PMC
January 2018

A Landscape of Metabolic Variation across Tumor Types.

Cell Syst 2018 Mar 27;6(3):301-313.e3. Epub 2018 Jan 27.

cBio Center, Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Computational Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA. Electronic address:

Tumor metabolism is reorganized to support proliferation in the face of growth-related stress. Unlike the widespread profiling of changes to metabolic enzyme levels in cancer, comparatively less attention has been paid to the substrates/products of enzyme-catalyzed reactions, small-molecule metabolites. We developed an informatic pipeline to concurrently analyze metabolomics data from over 900 tissue samples spanning seven cancer types, revealing extensive heterogeneity in metabolic changes relative to normal tissue across cancers of different tissues of origin. Despite this heterogeneity, a number of metabolites were recurrently differentially abundant across many cancers, such as lactate and acyl-carnitine species. Through joint analysis of metabolomic data alongside clinical features of patient samples, we also identified a small number of metabolites, including several polyamines and kynurenine, which were associated with aggressive tumors across several tumor types. Our findings offer a glimpse onto common patterns of metabolic reprogramming across cancers, and the work serves as a large-scale resource accessible via a web application (http://www.sanderlab.org/pancanmet).
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http://dx.doi.org/10.1016/j.cels.2017.12.014DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5876114PMC
March 2018

PI3Kδ Inhibition Enhances the Antitumor Fitness of Adoptively Transferred CD8 T Cells.

Front Immunol 2017 29;8:1221. Epub 2017 Sep 29.

Department of Microbiology and Immunology, Medical University of South Carolina, Charleston, SC, United States.

Phosphatidylinositol-3-kinase p110δ (PI3Kδ) inhibition by Idelalisib (CAL-101) in hematological malignancies directly induces apoptosis in cancer cells and disrupts immunological tolerance by depleting regulatory T cells. Yet, little is known about the direct impact of PI3Kδ blockade on effector T cells from CAL-101 therapy. Herein, we demonstrate a direct effect of p110δ inactivation CAL-101 on murine and human CD8 T cells that promotes a strong undifferentiated phenotype (elevated CD62L/CCR7, CD127, and Tcf7). These CAL-101 T cells also persisted longer after transfer into tumor bearing mice in both the murine syngeneic and human xenograft mouse models. The less differentiated phenotype and improved engraftment of CAL-101 T cells resulted in stronger antitumor immunity compared to traditionally expanded CD8 T cells in both tumor models. Thus, this report describes a novel direct enhancement of CD8 T cells by a p110δ inhibitor that leads to markedly improved tumor regression. This finding has significant implications to improve outcomes from next generation cancer immunotherapies.
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http://dx.doi.org/10.3389/fimmu.2017.01221DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5626814PMC
September 2017

Contribution of systemic and somatic factors to clinical response and resistance to PD-L1 blockade in urothelial cancer: An exploratory multi-omic analysis.

PLoS Med 2017 05 26;14(5):e1002309. Epub 2017 May 26.

Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York, United States of America.

Background: Inhibition of programmed death-ligand 1 (PD-L1) with atezolizumab can induce durable clinical benefit (DCB) in patients with metastatic urothelial cancers, including complete remissions in patients with chemotherapy refractory disease. Although mutation load and PD-L1 immune cell (IC) staining have been associated with response, they lack sufficient sensitivity and specificity for clinical use. Thus, there is a need to evaluate the peripheral blood immune environment and to conduct detailed analyses of mutation load, predicted neoantigens, and immune cellular infiltration in tumors to enhance our understanding of the biologic underpinnings of response and resistance.

Methods And Findings: The goals of this study were to (1) evaluate the association of mutation load and predicted neoantigen load with therapeutic benefit and (2) determine whether intratumoral and peripheral blood T cell receptor (TCR) clonality inform clinical outcomes in urothelial carcinoma treated with atezolizumab. We hypothesized that an elevated mutation load in combination with T cell clonal dominance among intratumoral lymphocytes prior to treatment or among peripheral T cells after treatment would be associated with effective tumor control upon treatment with anti-PD-L1 therapy. We performed whole exome sequencing (WES), RNA sequencing (RNA-seq), and T cell receptor sequencing (TCR-seq) of pretreatment tumor samples as well as TCR-seq of matched, serially collected peripheral blood, collected before and after treatment with atezolizumab. These parameters were assessed for correlation with DCB (defined as progression-free survival [PFS] >6 months), PFS, and overall survival (OS), both alone and in the context of clinical and intratumoral parameters known to be predictive of survival in this disease state. Patients with DCB displayed a higher proportion of tumor-infiltrating T lymphocytes (TIL) (n = 24, Mann-Whitney p = 0.047). Pretreatment peripheral blood TCR clonality below the median was associated with improved PFS (n = 29, log-rank p = 0.048) and OS (n = 29, log-rank p = 0.011). Patients with DCB also demonstrated more substantial expansion of tumor-associated TCR clones in the peripheral blood 3 weeks after starting treatment (n = 22, Mann-Whitney p = 0.022). The combination of high pretreatment peripheral blood TCR clonality with elevated PD-L1 IC staining in tumor tissue was strongly associated with poor clinical outcomes (n = 10, hazard ratio (HR) (mean) = 89.88, HR (median) = 23.41, 95% CI [2.43, 506.94], p(HR > 1) = 0.0014). Marked variations in mutation loads were seen with different somatic variant calling methodologies, which, in turn, impacted associations with clinical outcomes. Missense mutation load, predicted neoantigen load, and expressed neoantigen load did not demonstrate significant association with DCB (n = 25, Mann-Whitney p = 0.22, n = 25, Mann-Whitney p = 0.55, and n = 25, Mann-Whitney p = 0.29, respectively). Instead, we found evidence of time-varying effects of somatic mutation load on PFS in this cohort (n = 25, p = 0.044). A limitation of our study is its small sample size (n = 29), a subset of the patients treated on IMvigor 210 (NCT02108652). Given the number of exploratory analyses performed, we intend for these results to be hypothesis-generating.

Conclusions: These results demonstrate the complex nature of immune response to checkpoint blockade and the compelling need for greater interrogation and data integration of both host and tumor factors. Incorporating these variables in prospective studies will facilitate identification and treatment of resistant patients.
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http://dx.doi.org/10.1371/journal.pmed.1002309DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5446110PMC
May 2017

Somatic Mutations and Neoepitope Homology in Melanomas Treated with CTLA-4 Blockade.

Cancer Immunol Res 2017 01 12;5(1):84-91. Epub 2016 Dec 12.

Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York.

Immune checkpoint inhibitors are promising treatments for patients with a variety of malignancies. Toward understanding the determinants of response to immune checkpoint inhibitors, it was previously demonstrated that the presence of somatic mutations is associated with benefit from checkpoint inhibition. A hypothesis was posited that neoantigen homology to pathogens may in part explain the link between somatic mutations and response. To further examine this hypothesis, we reanalyzed cancer exome data obtained from our previously published study of 64 melanoma patients treated with CTLA-4 blockade and a new dataset of RNA-Seq data from 24 of these patients. We found that the ability to accurately predict patient benefit did not increase as the analysis narrowed from somatic mutation burden, to inclusion of only those mutations predicted to be MHC class I neoantigens, to only including those neoantigens that were expressed or that had homology to pathogens. The only association between somatic mutation burden and response was found when examining samples obtained prior to treatment. Neoantigen and expressed neoantigen burden were also associated with response, but neither was more predictive than somatic mutation burden. Neither the previously described tetrapeptide signature nor an updated method to evaluate neoepitope homology to pathogens was more predictive than mutation burden. Cancer Immunol Res; 5(1); 84-91. ©2016 AACR.
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http://dx.doi.org/10.1158/2326-6066.CIR-16-0019DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5253347PMC
January 2017

Pan-Cancer Analysis of Mutation Hotspots in Protein Domains.

Cell Syst 2015 Sep 23;1(3):197-209. Epub 2015 Sep 23.

Computational Biology Program, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA. Electronic address:

In cancer genomics, recurrence of mutations in independent tumor samples is a strong indicator of functional impact. However, rare functional mutations can escape detection by recurrence analysis owing to lack of statistical power. We enhance statistical power by extending the notion of recurrence of mutations from single genes to gene families that share homologous protein domains. Domain mutation analysis also sharpens the functional interpretation of the impact of mutations, as domains more succinctly embody function than entire genes. By mapping mutations in 22 different tumor types to equivalent positions in multiple sequence alignments of domains, we confirm well-known functional mutation hotspots, identify uncharacterized rare variants in one gene that are equivalent to well-characterized mutations in another gene, detect previously unknown mutation hotspots, and provide hypotheses about molecular mechanisms and downstream effects of domain mutations. With the rapid expansion of cancer genomics projects, protein domain hotspot analysis will likely provide many more leads linking mutations in proteins to the cancer phenotype.
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http://dx.doi.org/10.1016/j.cels.2015.08.014DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4982675PMC
September 2015

PaxtoolsR: pathway analysis in R using Pathway Commons.

Bioinformatics 2016 04 18;32(8):1262-4. Epub 2015 Dec 18.

Computational Biology Center, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA.

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.
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http://dx.doi.org/10.1093/bioinformatics/btv733DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4824129PMC
April 2016

Chemotherapy Resistance in Diffuse-Type Gastric Adenocarcinoma Is Mediated by RhoA Activation in Cancer Stem-Like Cells.

Clin Cancer Res 2016 02 19;22(4):971-83. Epub 2015 Oct 19.

Department of Surgery, Memorial Sloan-Kettering Cancer Center, New York, New York.

Purpose: The Lauren diffuse type of gastric adenocarcinoma (DGA), as opposed to the intestinal type (IGA), often harbors mutations in RHOA, but little is known about the role of RhoA in DGA.

Experimental Design: We examined RhoA activity and RhoA pathway inhibition in DGA cell lines and in two mouse xenograft models. RhoA activity was also assessed in patient tumor samples.

Results: RhoA activity was higher in DGA compared with IGA cell lines and was further increased when grown as spheroids to enrich for cancer stem-like cells (CSCs) or when sorted using the gastric CSC marker CD44. RhoA shRNA or the RhoA inhibitor Rhosin decreased expression of the stem cell transcription factor, Sox2, and decreased spheroid formation by 78% to 81%. DGA spheroid cells had 3- to 5-fold greater migration and invasion than monolayer cells, and this activity was Rho-dependent. Diffuse GA spheroid cells were resistant in a cytotoxicity assay to 5-fluorouracil and cisplatin chemotherapy, and this resistance could be reversed with RhoA pathway inhibition. In two xenograft models, cisplatin inhibited tumor growth by 40% to 50%, RhoA inhibition by 32% to 60%, and the combination by 77% to 83%. In 288 patient tumors, increased RhoA activity correlated with worse overall survival in DGA patients (P = 0.017) but not in IGA patients (P = 0.612).

Conclusions: RhoA signaling promotes CSC phenotypes in DGA cells. Increased RhoA activity is correlated with worse overall survival in DGA patients, and RhoA inhibition can reverse chemotherapy resistance in DGA CSC and in tumor xenografts. Thus, the RhoA pathway is a promising new target in DGA patients.
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http://dx.doi.org/10.1158/1078-0432.CCR-15-1356DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4823002PMC
February 2016

Perturbation biology nominates upstream-downstream drug combinations in RAF inhibitor resistant melanoma cells.

Elife 2015 Aug 18;4. Epub 2015 Aug 18.

Computational Biology Center, Memorial Sloan Kettering Cancer Center, New York, United States.

Resistance to targeted cancer therapies is an important clinical problem. The discovery of anti-resistance drug combinations is challenging as resistance can arise by diverse escape mechanisms. To address this challenge, we improved and applied the experimental-computational perturbation biology method. Using statistical inference, we build network models from high-throughput measurements of molecular and phenotypic responses to combinatorial targeted perturbations. The models are computationally executed to predict the effects of thousands of untested perturbations. In RAF-inhibitor resistant melanoma cells, we measured 143 proteomic/phenotypic entities under 89 perturbation conditions and predicted c-Myc as an effective therapeutic co-target with BRAF or MEK. Experiments using the BET bromodomain inhibitor JQ1 affecting the level of c-Myc protein and protein kinase inhibitors targeting the ERK pathway confirmed the prediction. In conclusion, we propose an anti-cancer strategy of co-targeting a specific upstream alteration and a general downstream point of vulnerability to prevent or overcome resistance to targeted drugs.
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http://dx.doi.org/10.7554/eLife.04640DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4539601PMC
August 2015

SBGNViz: A Tool for Visualization and Complexity Management of SBGN Process Description Maps.

PLoS One 2015 1;10(6):e0128985. Epub 2015 Jun 1.

Computational Biology Center, Memorial Sloan-Kettering Cancer Center, New York, NY, USA.

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.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0128985PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4451519PMC
April 2016

Systematic identification of cancer driving signaling pathways based on mutual exclusivity of genomic alterations.

Genome Biol 2015 Feb 26;16:45. Epub 2015 Feb 26.

Computational Biology Center, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, Box 460, New York, 10065, USA.

We present a novel method for the identification of sets of mutually exclusive gene alterations in a given set of genomic profiles. We scan the groups of genes with a common downstream effect on the signaling network, using a mutual exclusivity criterion that ensures that each gene in the group significantly contributes to the mutual exclusivity pattern. We test the method on all available TCGA cancer genomics datasets, and detect multiple previously unreported alterations that show significant mutual exclusivity and are likely to be driver events.
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http://dx.doi.org/10.1186/s13059-015-0612-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4381444PMC
February 2015

Integrating biological pathways and genomic profiles with ChiBE 2.

BMC Genomics 2014 Aug 3;15:642. Epub 2014 Aug 3.

Computational Biology Center, Memorial Sloan-Kettering Cancer Center, 1275 York Avenue, Box 460, New York, NY 10065, USA.

Background: Dynamic visual exploration of detailed pathway information can help researchers digest and interpret complex mechanisms and genomic datasets.

Results: ChiBE is a free, open-source software tool for visualizing, querying, and analyzing human biological pathways in BioPAX format. The recently released version 2 can search for neighborhoods, paths between molecules, and common regulators/targets of molecules, on large integrated cellular networks in the Pathway Commons database as well as in local BioPAX models. Resulting networks can be automatically laid out for visualization using a graphically rich, process-centric notation. Profiling data from the cBioPortal for Cancer Genomics and expression data from the Gene Expression Omnibus can be overlaid on these networks.

Conclusions: ChiBE's new capabilities are organized around a genomics-oriented workflow and offer a unique comprehensive pathway analysis solution for genomics researchers. The software is freely available at http://code.google.com/p/chibe.
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http://dx.doi.org/10.1186/1471-2164-15-642DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4131037PMC
August 2014

Prediction of individualized therapeutic vulnerabilities in cancer from genomic profiles.

Bioinformatics 2014 Jul 24;30(14):2051-9. Epub 2014 Mar 24.

Computational Biology Center, Memorial Sloan-Kettering Cancer Center, New York, NY 10065 and Tri-Institutional Training Program in Computational Biology & Medicine, New York, NY 10065, USA.

Motivation: Somatic homozygous deletions of chromosomal regions in cancer, while not necessarily oncogenic, may lead to therapeutic vulnerabilities specific to cancer cells compared with normal cells. A recently reported example is the loss of one of the two isoenzymes in glioblastoma cancer cells such that the use of a specific inhibitor selectively inhibited growth of the cancer cells, which had become fully dependent on the second isoenzyme. We have now made use of the unprecedented conjunction of large-scale cancer genomics profiling of tumor samples in The Cancer Genome Atlas (TCGA) and of tumor-derived cell lines in the Cancer Cell Line Encyclopedia, as well as the availability of integrated pathway information systems, such as Pathway Commons, to systematically search for a comprehensive set of such epistatic vulnerabilities.

Results: Based on homozygous deletions affecting metabolic enzymes in 16 TCGA cancer studies and 972 cancer cell lines, we identified 4104 candidate metabolic vulnerabilities present in 1019 tumor samples and 482 cell lines. Up to 44% of these vulnerabilities can be targeted with at least one Food and Drug Administration-approved drug. We suggest focused experiments to test these vulnerabilities and clinical trials based on personalized genomic profiles of those that pass preclinical filters. We conclude that genomic profiling will in the future provide a promising basis for network pharmacology of epistatic vulnerabilities as a promising therapeutic strategy.

Availability And Implementation: A web-based tool for exploring all vulnerabilities and their details is available at http://cbio.mskcc.org/cancergenomics/statius/ along with supplemental data files.
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http://dx.doi.org/10.1093/bioinformatics/btu164DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4080742PMC
July 2014

Emerging landscape of oncogenic signatures across human cancers.

Nat Genet 2013 Oct;45(10):1127-33

Computational Biology Program, Memorial Sloan-Kettering Cancer Center, New York, New York, USA.

Cancer therapy is challenged by the diversity of molecular implementations of oncogenic processes and by the resulting variation in therapeutic responses. Projects such as The Cancer Genome Atlas (TCGA) provide molecular tumor maps in unprecedented detail. The interpretation of these maps remains a major challenge. Here we distilled thousands of genetic and epigenetic features altered in cancers to ∼500 selected functional events (SFEs). Using this simplified description, we derived a hierarchical classification of 3,299 TCGA tumors from 12 cancer types. The top classes are dominated by either mutations (M class) or copy number changes (C class). This distinction is clearest at the extremes of genomic instability, indicating the presence of different oncogenic processes. The full hierarchy shows functional event patterns characteristic of multiple cross-tissue groups of tumors, termed oncogenic signature classes. Targetable functional events in a tumor class are suggestive of class-specific combination therapy. These results may assist in the definition of clinical trials to match actionable oncogenic signatures with personalized therapies.
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http://dx.doi.org/10.1038/ng.2762DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4320046PMC
October 2013

Using biological pathway data with paxtools.

PLoS Comput Biol 2013 19;9(9):e1003194. Epub 2013 Sep 19.

Computational Biology Center, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America.

A rapidly growing corpus of formal, computable pathway information can be used to answer important biological questions including finding non-trivial connections between cellular processes, identifying significantly altered portions of the cellular network in a disease state and building predictive models that can be used for precision medicine. Due to its complexity and fragmented nature, however, working with pathway data is still difficult. We present Paxtools, a Java library that contains algorithms, software components and converters for biological pathways represented in the standard BioPAX language. Paxtools allows scientists to focus on their scientific problem by removing technical barriers to access and analyse pathway information. Paxtools can run on any platform that has a Java Runtime Environment and was tested on most modern operating systems. Paxtools is open source and is available under the Lesser GNU public license (LGPL), which allows users to freely use the code in their software systems with a requirement for attribution. Source code for the current release (4.2.0) can be found in Software S1. A detailed manual for obtaining and using Paxtools can be found in Protocol S1. The latest sources and release bundles can be obtained from biopax.org/paxtools.
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http://dx.doi.org/10.1371/journal.pcbi.1003194DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3777916PMC
April 2014

Pattern search in BioPAX models.

Bioinformatics 2014 Jan 16;30(1):139-40. Epub 2013 Sep 16.

Computational Biology Center, Memorial Sloan-Kettering Cancer Center, New York, NY 10065, USA, Tri-Institutional Training Program in Computational Biology and Medicine, New York, NY 10065, USA and Banting and Best Department of Medical Research, The Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada.

Motivation: BioPAX is a standard language for representing complex cellular processes, including metabolic networks, signal transduction and gene regulation. Owing to the inherent complexity of a BioPAX model, searching for a specific type of subnetwork can be non-trivial and difficult.

Results: We developed an open source and extensible framework for defining and searching graph patterns in BioPAX models. We demonstrate its use with a sample pattern that captures directed signaling relations between proteins. We provide search results for the pattern obtained from the Pathway Commons database and compare these results with the current data in signaling databases SPIKE and SignaLink. Results show that a pattern search in public pathway data can identify a substantial amount of signaling relations that do not exist in signaling databases.

Availability: BioPAX-pattern software was developed in Java. Source code and documentation is freely available at http://code.google.com/p/biopax-pattern under Lesser GNU Public License.
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http://dx.doi.org/10.1093/bioinformatics/btt539DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3866551PMC
January 2014

PiHelper: an open source framework for drug-target and antibody-target data.

Bioinformatics 2013 Aug 12;29(16):2071-2. Epub 2013 Jun 12.

Computational Biology Center, Memorial Sloan-Kettering Cancer Center, NY 10065, USA.

Motivation: The interaction between drugs and their targets, often proteins, and between antibodies and their targets, is important for planning and analyzing investigational and therapeutic interventions in many biological systems. Although drug-target and antibody-target datasets are available in separate databases, they are not publicly available in an integrated bioinformatics resource. As medical therapeutics, especially in cancer, increasingly uses targeted drugs and measures their effects on biomolecular profiles, there is an unmet need for a user-friendly toolset that allows researchers to comprehensively and conveniently access and query information about drugs, antibodies and their targets.

Summary: The PiHelper framework integrates human drug-target and antibody-target associations from publicly available resources to help meet the needs of researchers in systems pharmacology, perturbation biology and proteomics. PiHelper has utilities to (i) import drug- and antibody-target information; (ii) search the associations either programmatically or through a web user interface (UI); (iii) visualize the data interactively in a network; and (iv) export relationships for use in publications or other analysis tools.

Availability: PiHelper is a free software under the GNU Lesser General Public License (LGPL) v3.0. Source code and documentation are at http://bit.ly/pihelper. We plan to coordinate contributions from the community by managing future releases.
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http://dx.doi.org/10.1093/bioinformatics/btt345DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3722529PMC
August 2013

Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal.

Sci Signal 2013 Apr 2;6(269):pl1. Epub 2013 Apr 2.

Computational Biology Center, Memorial Sloan-Kettering Cancer Center, New York, NY 10065, USA.

The cBioPortal for Cancer Genomics (http://cbioportal.org) provides a Web resource for exploring, visualizing, and analyzing multidimensional cancer genomics data. The portal reduces molecular profiling data from cancer tissues and cell lines into readily understandable genetic, epigenetic, gene expression, and proteomic events. The query interface combined with customized data storage enables researchers to interactively explore genetic alterations across samples, genes, and pathways and, when available in the underlying data, to link these to clinical outcomes. The portal provides graphical summaries of gene-level data from multiple platforms, network visualization and analysis, survival analysis, patient-centric queries, and software programmatic access. The intuitive Web interface of the portal makes complex cancer genomics profiles accessible to researchers and clinicians without requiring bioinformatics expertise, thus facilitating biological discoveries. Here, we provide a practical guide to the analysis and visualization features of the cBioPortal for Cancer Genomics.
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http://dx.doi.org/10.1126/scisignal.2004088DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4160307PMC
April 2013

Using MEMo to discover mutual exclusivity modules in cancer.

Curr Protoc Bioinformatics 2013 Mar;Chapter 8:Unit 8.17

Computational Biology Center, Memorial Sloan-Kettering Cancer Center, New York, New York, USA.

Although individual tumors show surprisingly diverse genomic alterations, these events tend to occur in a limited number of pathways, and alterations that affect the same pathway tend to not co-occur in the same patient. While pathway analysis has been a powerful tool in cancer genomics, our knowledge of oncogenic pathway modules is incomplete. To systematically identify such modules, we have developed a novel method, Mutual Exclusivity Modules in Cancer (MEMo). The method searches and identifies modules characterized by three properties: (1) member genes are recurrently altered across a set of tumor samples; (2) member genes are known to or are likely to participate in the same biological process; and (3) alteration events within the modules are mutually exclusive. MEMo integrates multiple data types and maps genomic alterations to biological pathways. MEMo's mutual exclusivity uses a statistical model that preserves the number of alterations per gene and per sample. The MEMo software, source code and sample data sets are available for download at: http://cbio.mskcc.org/memo.
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http://dx.doi.org/10.1002/0471250953.bi0817s41DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5563973PMC
March 2013

The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data.

Cancer Discov 2012 May;2(5):401-4

Computational Biology Center, Memorial Sloan-Kettering Cancer Center, New York, New York 10021, USA.

The cBio Cancer Genomics Portal (http://cbioportal.org) is an open-access resource for interactive exploration of multidimensional cancer genomics data sets, currently providing access to data from more than 5,000 tumor samples from 20 cancer studies. The cBio Cancer Genomics Portal significantly lowers the barriers between complex genomic data and cancer researchers who want rapid, intuitive, and high-quality access to molecular profiles and clinical attributes from large-scale cancer genomics projects and empowers researchers to translate these rich data sets into biologic insights and clinical applications.
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http://dx.doi.org/10.1158/2159-8290.CD-12-0095DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3956037PMC
May 2012