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Tsiatsianis GC, Chan CSY, Mouratidis I, Chantzi N, Tsiatsiani AM, Yee NS, Zaravinos A, Kantere V, Georgakopoulos-Soares I. Peptide absent sequences emerging in human cancers. Eur J Cancer 2024; 196:113421. [PMID: 37952501 DOI: 10.1016/j.ejca.2023.113421] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Revised: 11/01/2023] [Accepted: 11/01/2023] [Indexed: 11/14/2023]
Abstract
Early diagnosis of cancer can significantly improve survival of cancer patients; however sensitive and highly specific biomarkers for cancer detection are currently lacking for most cancer types. Nullpeptides are short peptides that are absent from the human proteome. Here, we examined the emergence of nullpeptides during cancer development. We analyzed 3,600,964 somatic mutations across 10,064 whole exome sequencing tumor samples spanning 32 cancer types. We analyze RNA-seq data from primary tumor samples to identify the subset of nullpeptides that emerge in highly expresed genes. We show that nullpeptides, and particularly the subset that is highly recurrent across cancer patients, can be identified in tumor biopsy samples. We find that cancer genes show an excess of nullpeptides and detect nullpeptide hotspots in specific loci of oncogenes and tumor suppressors. We also observe that recurrent nullpeptides are more likely to be found in neoantigens, which have been shown to be effective targets for immunotherapy, suggesting that they can be used to prioritize candidates. Our findings provide evidence for the utility of nullpeptides as cancer detection and therapeutic biomarkers.
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Affiliation(s)
- Georgios Christos Tsiatsianis
- Institute for Personalized Medicine, Department of Biochemistry and Molecular Biology, The Pennsylvania State University College of Medicine, Hershey, PA, USA; National Technical University of Athens, School of Electrical and Computer Engineering, Athens, Greece
| | - Candace S Y Chan
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Ioannis Mouratidis
- Institute for Personalized Medicine, Department of Biochemistry and Molecular Biology, The Pennsylvania State University College of Medicine, Hershey, PA, USA
| | - Nikol Chantzi
- Institute for Personalized Medicine, Department of Biochemistry and Molecular Biology, The Pennsylvania State University College of Medicine, Hershey, PA, USA
| | - Anna Maria Tsiatsiani
- National Technical University of Athens, School of Electrical and Computer Engineering, Athens, Greece; School of Medicine, National and Kapodistrian University of Athens, Athens, Greece
| | - Nelson S Yee
- Division of Hematology-Oncology, Department of Medicine, Penn State Health Milton S. Hershey Medical Center, Next-Generation Therapies Program, Penn State Cancer Institute, Hershey, PA, USA
| | - Apostolos Zaravinos
- Department of Life Sciences, School of Sciences, European University Cyprus, Nicosia 1516, Cyprus; Cancer Genetics, Genomics and Systems Biology Laboratory, Basic and Translational Cancer Research Center (BTCRC), Nicosia 1516, Cyprus
| | - Verena Kantere
- School of Electrical Engineering and Computer Science, Faculty of Engineering, University of Ottawa, Canada
| | - Ilias Georgakopoulos-Soares
- Institute for Personalized Medicine, Department of Biochemistry and Molecular Biology, The Pennsylvania State University College of Medicine, Hershey, PA, USA.
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Krommyda M, Kantere V. Spatial Data Management in IoT Systems: Solutions and Evaluation. Int J Semantic Computing 2021. [DOI: 10.1142/s1793351x21300016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
As the Internet of Things (IoT) systems gain in popularity, an increasing number of Big Data sources are available. Ranging from small sensor networks designed for household use to large fully automated industrial environments, the IoT systems create billions of measurements each second making traditional storage and indexing solutions obsolete. While research around Big Data has focused on scalable solutions that can support the datasets produced by these systems, the focus has been mainly on managing the volume and velocity of these data, rather than providing efficient solutions for their retrieval and analysis. A key characteristic of these data, which is, more often than not, overlooked, is the spatial information that can be used to integrate data from multiple sources and conduct multi-dimensional analysis of the collected information. We present here the solutions currently available for the storage and indexing of spatial datasets produced by the IoT systems and we discuss their applicability in real-world scenarios.
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Affiliation(s)
- Maria Krommyda
- School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece
| | - Verena Kantere
- School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece
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Abstract
As more and more datasets become available, their utilization in different applications increases in popularity. Their volume and production rate, however, means that their quality and content control is in most cases non-existing, resulting in many datasets that contain inaccurate information of low quality. Especially, in the field of conversational assistants, where the datasets come from many heterogeneous sources with no quality assurance, the problem is aggravated. We present here an integrated platform that creates task- and topic-specific conversational datasets to be used for training conversational agents. The platform explores available conversational datasets, extracts information based on semantic similarity and relatedness, and applies a weight-based score function to rank the information based on its value for the specific task and topic. The finalized dataset can then be used for the training of an automated conversational assistance over accurate data of high quality.
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Affiliation(s)
- Maria Krommyda
- School of Electrical and Computer Engineering, National Technological University of Athens, Athens, Greece
| | - Verena Kantere
- School of Electrical and Computer Engineering, National Technological University of Athens, Athens, Greece
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Kantere V, Bousounis D, Sellis T. Mapping Discovery Over Revealing Schemas. INT J COOP INF SYST 2016. [DOI: 10.1142/s0218843015500069] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
In a world of wide-scale information sharing, data are described in different formats, i.e. data structures, values and schemas. Querying such sources entails techniques that can bridge the data formats. Some existing techniques deal with schema mapping and view complementary aspects of the problem. Important ones, consider producing all the possible mappings for a pair of schemas, insinuating accompanying semantics in the mappings and adapting correct mappings as schemas evolve. In this work, we consider the problem of discovering mappings as schemas of autonomous sources are gradually revealed. Using as an example setting an overlay of peer databases, we present a schema mapping solution that discovers correct mappings as peer schemas are gradually revealed to remote peers. Mapping discovery is schema-centric and incorporates new semantics as they are unveiled. Mapping experience is reused and possible mappings are ranked so that the best choice is presented to the user. The experimental study confirms the suitability of the proposed solution to dynamic settings of heterogeneous sources.
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Affiliation(s)
- Verena Kantere
- Centre Universitaire d’Informatique University of Geneva, Switzerland
| | - Dimos Bousounis
- School of Computer and Electrical Engineering, Swiss Federal Institute of Technology Zurich, Switzerland
| | - Timos Sellis
- School of Computer Science and Information Technology, Royal Melbourne Institute of Technology, Australia
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Kantere V, Sellis T. Data Exchange Issues in Peer-to-Peer Database Systems. ENTERP INF SYST-UK 2008. [DOI: 10.1007/978-3-540-77581-2_3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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