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Dharanipragada P, Parekh N. In Silico Identification and Functional Characterization of Genetic Variations across DLBCL Cell Lines. Cells 2023; 12:cells12040596. [PMID: 36831263 PMCID: PMC9954129 DOI: 10.3390/cells12040596] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2022] [Revised: 01/12/2023] [Accepted: 01/29/2023] [Indexed: 02/15/2023] Open
Abstract
Diffuse large B-cell lymphoma (DLBCL) is the most common form of non-Hodgkin lymphoma and frequently develops through the accumulation of several genetic variations. With the advancement in high-throughput techniques, in addition to mutations and copy number variations, structural variations have gained importance for their role in genome instability leading to tumorigenesis. In this study, in order to understand the genetics of DLBCL pathogenesis, we carried out a whole-genome mutation profile analysis of eleven human cell lines from germinal-center B-cell-like (GCB-7) and activated B-cell-like (ABC-4) subtypes of DLBCL. Analysis of genetic variations including small sequence variants and large structural variations across the cell lines revealed distinct variation profiles indicating the heterogeneous nature of DLBCL and the need for novel patient stratification methods to design potential intervention strategies. Validation and prognostic significance of the variants was assessed using annotations provided for DLBCL samples in cBioPortal for Cancer Genomics. Combining genetic variations revealed new subgroups between the subtypes and associated enriched pathways, viz., PI3K-AKT signaling, cell cycle, TGF-beta signaling, and WNT signaling. Mutation landscape analysis also revealed drug-variant associations and possible effectiveness of known and novel DLBCL treatments. From the whole-genome-based mutation analysis, our findings suggest putative molecular genetics of DLBCL lymphomagenesis and potential genomics-driven precision treatments.
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Borchert F, Mock A, Tomczak A, Hügel J, Alkarkoukly S, Knurr A, Volckmar AL, Stenzinger A, Schirmacher P, Debus J, Jäger D, Longerich T, Fröhling S, Eils R, Bougatf N, Sax U, Schapranow MP. Knowledge bases and software support for variant interpretation in precision oncology. Brief Bioinform 2021; 22:bbab134. [PMID: 33971666 PMCID: PMC8574624 DOI: 10.1093/bib/bbab134] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2020] [Revised: 03/10/2021] [Accepted: 03/30/2021] [Indexed: 12/12/2022] Open
Abstract
Precision oncology is a rapidly evolving interdisciplinary medical specialty. Comprehensive cancer panels are becoming increasingly available at pathology departments worldwide, creating the urgent need for scalable cancer variant annotation and molecularly informed treatment recommendations. A wealth of mainly academia-driven knowledge bases calls for software tools supporting the multi-step diagnostic process. We derive a comprehensive list of knowledge bases relevant for variant interpretation by a review of existing literature followed by a survey among medical experts from university hospitals in Germany. In addition, we review cancer variant interpretation tools, which integrate multiple knowledge bases. We categorize the knowledge bases along the diagnostic process in precision oncology and analyze programmatic access options as well as the integration of knowledge bases into software tools. The most commonly used knowledge bases provide good programmatic access options and have been integrated into a range of software tools. For the wider set of knowledge bases, access options vary across different parts of the diagnostic process. Programmatic access is limited for information regarding clinical classifications of variants and for therapy recommendations. The main issue for databases used for biological classification of pathogenic variants and pathway context information is the lack of standardized interfaces. There is no single cancer variant interpretation tool that integrates all identified knowledge bases. Specialized tools are available and need to be further developed for different steps in the diagnostic process.
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Affiliation(s)
- Florian Borchert
- Digital Health Center, Hasso Plattner Institute (HPI), University of Potsdam, Prof.-Dr.-Helmert-Str. 2-3, 14482 Potsdam, Germany
| | - Andreas Mock
- Department of Translational Medical Oncology (TMO), National Center for Tumor Diseases (NCT) Heidelberg, German Cancer Research Center (DKFZ) Heidelberg, Im Neuenheimer Feld 460, 69120 Heidelberg, Germany
- Department of Medical Oncology, National Center for Tumor Diseases (NCT) Heidelberg, Heidelberg University Hospital, Im Neuenheimer Feld 460, 69120 Heidelberg, Germany
| | - Aurelie Tomczak
- Institute of Pathology Heidelberg, Heidelberg University Hospital, Im Neuenheimer Feld 224, 69120 Heidelberg, Germany
- Liver Cancer Center Heidelberg, Heidelberg University Hospital, Im Neuenheimer Feld 460, 69120 Heidelberg, Germany
| | - Jonas Hügel
- Department of Medical Informatics, University Medical Center Göttingen, Von-Siebold-Str. 3, 37099 Göttingen, Germany
- Campus Institute Data Science, Göttingen, Germany
| | - Samer Alkarkoukly
- CECAD, Faculty of Medicine and University Hospital Cologne, University of Cologne, Joseph-Stelzmann-Straße 26, 50931 Cologne
| | - Alexander Knurr
- Division of Medical Informatics for Translational Oncology, German Cancer Research Center (DKFZ) Heidelberg, Im Neuenheimer Feld 280, 69120 Heidelberg, Germany
| | - Anna-Lena Volckmar
- Institute of Pathology Heidelberg, Heidelberg University Hospital, Im Neuenheimer Feld 224, 69120 Heidelberg, Germany
| | - Albrecht Stenzinger
- Institute of Pathology Heidelberg, Heidelberg University Hospital, Im Neuenheimer Feld 224, 69120 Heidelberg, Germany
| | - Peter Schirmacher
- Institute of Pathology Heidelberg, Heidelberg University Hospital, Im Neuenheimer Feld 224, 69120 Heidelberg, Germany
- Liver Cancer Center Heidelberg, Heidelberg University Hospital, Im Neuenheimer Feld 460, 69120 Heidelberg, Germany
| | - Jürgen Debus
- Department of Radiation Oncology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany
- National Center for Tumor Diseases (NCT), Heidelberg University Hospital, Im Neuenheimer Feld 460, 69120 Heidelberg, Germany
- Clinical Cooperation Unit Radiation Oncology, German Cancer Research Center (DKFZ) Heidelberg, Im Neuenheimer Feld 280, 69120 Heidelberg, Germany
- Heidelberg Ion-Beam Therapy Center (HIT), Department of Radiation Oncology, Heidelberg University Hospital, Im Neuenheimer Feld 450, 69120 Heidelberg, Germany
- Heidelberg Institute of Radiation Oncology (HIRO), Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany
| | - Dirk Jäger
- Department of Medical Oncology, National Center for Tumor Diseases (NCT) Heidelberg, Heidelberg University Hospital, Im Neuenheimer Feld 460, 69120 Heidelberg, Germany
- Clinical Coorporation Unit Applied Tumor-Immunity, German Cancer Research Center (DKFZ) Heidelberg, Im Neuenheimer Feld 280, 69120 Heidelberg, Germany
| | - Thomas Longerich
- Institute of Pathology Heidelberg, Heidelberg University Hospital, Im Neuenheimer Feld 224, 69120 Heidelberg, Germany
- Liver Cancer Center Heidelberg, Heidelberg University Hospital, Im Neuenheimer Feld 460, 69120 Heidelberg, Germany
| | - Stefan Fröhling
- Department of Translational Medical Oncology (TMO), National Center for Tumor Diseases (NCT) Heidelberg, German Cancer Research Center (DKFZ) Heidelberg, Im Neuenheimer Feld 460, 69120 Heidelberg, Germany
- German Cancer Consortium (DKTK), 69120 Heidelberg, Germany
| | - Roland Eils
- Health Data Science Unit, Heidelberg University Hospital, Im Neuenheimer Feld 267, 69120 Heidelberg, Germany
- Center for Digital Health, Berlin Institute of Health and Charité Universitötsmedizin Berlin, Kapelle-Ufer 2, 10117 Berlin, Germany
| | - Nina Bougatf
- Department of Radiation Oncology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany
- National Center for Tumor Diseases (NCT), Heidelberg University Hospital, Im Neuenheimer Feld 460, 69120 Heidelberg, Germany
- Clinical Cooperation Unit Radiation Oncology, German Cancer Research Center (DKFZ) Heidelberg, Im Neuenheimer Feld 280, 69120 Heidelberg, Germany
- Heidelberg Ion-Beam Therapy Center (HIT), Department of Radiation Oncology, Heidelberg University Hospital, Im Neuenheimer Feld 450, 69120 Heidelberg, Germany
- Heidelberg Institute of Radiation Oncology (HIRO), Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany
| | - Ulrich Sax
- Department of Medical Informatics, University Medical Center Göttingen, Von-Siebold-Str. 3, 37099 Göttingen, Germany
- Campus Institute Data Science, Göttingen, Germany
| | - Matthieu-P Schapranow
- Digital Health Center, Hasso Plattner Institute (HPI), University of Potsdam, Prof.-Dr.-Helmert-Str. 2-3, 14482 Potsdam, Germany
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Hu Z, Yu C, Furutsuki M, Andreoletti G, Ly M, Hoskins R, Adhikari AN, Brenner SE. VIPdb, a genetic Variant Impact Predictor Database. Hum Mutat 2019; 40:1202-1214. [PMID: 31283070 PMCID: PMC7288905 DOI: 10.1002/humu.23858] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2019] [Accepted: 06/27/2019] [Indexed: 12/30/2022]
Abstract
Genome sequencing identifies vast number of genetic variants. Predicting these variants' molecular and clinical effects is one of the preeminent challenges in human genetics. Accurate prediction of the impact of genetic variants improves our understanding of how genetic information is conveyed to molecular and cellular functions, and is an essential step towards precision medicine. Over one hundred tools/resources have been developed specifically for this purpose. We summarize these tools as well as their characteristics, in the genetic Variant Impact Predictor Database (VIPdb). This database will help researchers and clinicians explore appropriate tools, and inform the development of improved methods. VIPdb can be browsed and downloaded at https://genomeinterpretation.org/vipdb.
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Affiliation(s)
- Zhiqiang Hu
- Department of Plant and Microbial Biology, University of California, Berkeley, California 94720, USA
| | - Changhua Yu
- Department of Plant and Microbial Biology, University of California, Berkeley, California 94720, USA
- Department of Bioengineering, University of California, Berkeley, California 94720, USA
| | - Mabel Furutsuki
- Department of Plant and Microbial Biology, University of California, Berkeley, California 94720, USA
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, California 94720, USA
| | - Gaia Andreoletti
- Department of Plant and Microbial Biology, University of California, Berkeley, California 94720, USA
| | - Melissa Ly
- Department of Plant and Microbial Biology, University of California, Berkeley, California 94720, USA
- Division of Data Sciences, University of California, Berkeley, California 94720, USA
| | - Roger Hoskins
- Department of Plant and Microbial Biology, University of California, Berkeley, California 94720, USA
| | - Aashish N. Adhikari
- Department of Plant and Microbial Biology, University of California, Berkeley, California 94720, USA
| | - Steven E. Brenner
- Department of Plant and Microbial Biology, University of California, Berkeley, California 94720, USA
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