Publications by authors named "Selçuk Onur Sümer"

8 Publications

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Accelerating Discovery of Functional Mutant Alleles in Cancer.

Cancer Discov 2018 02 15;8(2):174-183. Epub 2017 Dec 15.

Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, New York.

Most mutations in cancer are rare, which complicates the identification of therapeutically significant mutations and thus limits the clinical impact of genomic profiling in patients with cancer. Here, we analyzed 24,592 cancers including 10,336 prospectively sequenced patients with advanced disease to identify mutant residues arising more frequently than expected in the absence of selection. We identified 1,165 statistically significant hotspot mutations of which 80% arose in 1 in 1,000 or fewer patients. Of 55 recurrent in-frame indels, we validated that novel duplications induced pathway hyperactivation and conferred AKT inhibitor sensitivity. Cancer genes exhibit different rates of hotspot discovery with increasing sample size, with few approaching saturation. Consequently, 26% of all hotspots in therapeutically actionable oncogenes were novel. Upon matching a subset of affected patients directly to molecularly targeted therapy, we observed radiographic and clinical responses. Population-scale mutant allele discovery illustrates how the identification of driver mutations in cancer is far from complete. Our systematic computational, experimental, and clinical analysis of hotspot mutations in approximately 25,000 human cancers demonstrates that the long right tail of biologically and therapeutically significant mutant alleles is still incompletely characterized. Sharing prospective genomic data will accelerate hotspot identification, thereby expanding the reach of precision oncology in patients with cancer. .
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http://dx.doi.org/10.1158/2159-8290.CD-17-0321DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5809279PMC
February 2018

3D clusters of somatic mutations in cancer reveal numerous rare mutations as functional targets.

Genome Med 2017 01 23;9(1). Epub 2017 Jan 23.

Department of Cell Biology, Harvard Medical School, Boston, MA, USA.

Many mutations in cancer are of unknown functional significance. Standard methods use statistically significant recurrence of mutations in tumor samples as an indicator of functional impact. We extend such analyses into the long tail of rare mutations by considering recurrence of mutations in clusters of spatially close residues in protein structures. Analyzing 10,000 tumor exomes, we identify more than 3000 rarely mutated residues in proteins as potentially functional and experimentally validate several in RAC1 and MAP2K1. These potential driver mutations (web resources: 3dhotspots.org and cBioPortal.org) can extend the scope of genomically informed clinical trials and of personalized choice of therapy.
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http://dx.doi.org/10.1186/s13073-016-0393-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5260099PMC
January 2017

A Multi-Method Approach for Proteomic Network Inference in 11 Human Cancers.

PLoS Comput Biol 2016 Feb 29;12(2):e1004765. Epub 2016 Feb 29.

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

Protein expression and post-translational modification levels are tightly regulated in neoplastic cells to maintain cellular processes known as 'cancer hallmarks'. The first Pan-Cancer initiative of The Cancer Genome Atlas (TCGA) Research Network has aggregated protein expression profiles for 3,467 patient samples from 11 tumor types using the antibody based reverse phase protein array (RPPA) technology. The resultant proteomic data can be utilized to computationally infer protein-protein interaction (PPI) networks and to study the commonalities and differences across tumor types. In this study, we compare the performance of 13 established network inference methods in their capacity to retrieve the curated Pathway Commons interactions from RPPA data. We observe that no single method has the best performance in all tumor types, but a group of six methods, including diverse techniques such as correlation, mutual information, and regression, consistently rank highly among the tested methods. We utilize the high performing methods to obtain a consensus network; and identify four robust and densely connected modules that reveal biological processes as well as suggest antibody-related technical biases. Mapping the consensus network interactions to Reactome gene lists confirms the pan-cancer importance of signal transduction pathways, innate and adaptive immune signaling, cell cycle, metabolism, and DNA repair; and also suggests several biological processes that may be specific to a subset of tumor types. Our results illustrate the utility of the RPPA platform as a tool to study proteomic networks in cancer.
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http://dx.doi.org/10.1371/journal.pcbi.1004765DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4771175PMC
February 2016

MutationAligner: a resource of recurrent mutation hotspots in protein domains in cancer.

Nucleic Acids Res 2016 Jan 20;44(D1):D986-91. Epub 2015 Nov 20.

Cancer Research UK, Cambridge Institute, University of Cambridge, Cambridge, CB2 0RE, UK

The MutationAligner web resource, available at http://www.mutationaligner.org, enables discovery and exploration of somatic mutation hotspots identified in protein domains in currently (mid-2015) more than 5000 cancer patient samples across 22 different tumor types. Using multiple sequence alignments of protein domains in the human genome, we extend the principle of recurrence analysis by aggregating mutations in homologous positions across sets of paralogous genes. Protein domain analysis enhances the statistical power to detect cancer-relevant mutations and links mutations to the specific biological functions encoded in domains. We illustrate how the MutationAligner database and interactive web tool can be used to explore, visualize and analyze mutation hotspots in protein domains across genes and tumor types. We believe that MutationAligner will be an important resource for the cancer research community by providing detailed clues for the functional importance of particular mutations, as well as for the design of functional genomics experiments and for decision support in precision medicine. MutationAligner is slated to be periodically updated to incorporate additional analyses and new data from cancer genomics projects.
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http://dx.doi.org/10.1093/nar/gkv1132DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4702822PMC
January 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

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

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