1
|
Lin YJ, Menon AS, Hu Z, Brenner SE. Variant Impact Predictor database (VIPdb), version 2: Trends from 25 years of genetic variant impact predictors. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.25.600283. [PMID: 38979289 PMCID: PMC11230257 DOI: 10.1101/2024.06.25.600283] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/10/2024]
Abstract
Background Variant interpretation is essential for identifying patients' disease-causing genetic variants amongst the millions detected in their genomes. Hundreds of Variant Impact Predictors (VIPs), also known as Variant Effect Predictors (VEPs), have been developed for this purpose, with a variety of methodologies and goals. To facilitate the exploration of available VIP options, we have created the Variant Impact Predictor database (VIPdb). Results The Variant Impact Predictor database (VIPdb) version 2 presents a collection of VIPs developed over the past 25 years, summarizing their characteristics, ClinGen calibrated scores, CAGI assessment results, publication details, access information, and citation patterns. We previously summarized 217 VIPs and their features in VIPdb in 2019. Building upon this foundation, we identified and categorized an additional 186 VIPs, resulting in a total of 403 VIPs in VIPdb version 2. The majority of the VIPs have the capacity to predict the impacts of single nucleotide variants and nonsynonymous variants. More VIPs tailored to predict the impacts of insertions and deletions have been developed since the 2010s. In contrast, relatively few VIPs are dedicated to the prediction of splicing, structural, synonymous, and regulatory variants. The increasing rate of citations to VIPs reflects the ongoing growth in their use, and the evolving trends in citations reveal development in the field and individual methods. Conclusions VIPdb version 2 summarizes 403 VIPs and their features, potentially facilitating VIP exploration for various variant interpretation applications. Availability VIPdb version 2 is available at https://genomeinterpretation.org/vipdb.
Collapse
Affiliation(s)
- Yu-Jen Lin
- Department of Molecular and Cell Biology, University of California, Berkeley, California 94720, USA
- Center for Computational Biology, University of California, Berkeley, California 94720, USA
| | - Arul S. Menon
- Department of Molecular and Cell Biology, University of California, Berkeley, California 94720, USA
- College of Computing, Data Science, and Society, University of California, Berkeley, California 94720, USA
| | - Zhiqiang Hu
- Department of Plant and Microbial Biology, University of California, Berkeley, California 94720, USA
- Currently at: Illumina, Foster City, California 94404, USA
| | - Steven E. Brenner
- Department of Molecular and Cell Biology, University of California, Berkeley, California 94720, USA
- Center for Computational Biology, University of California, Berkeley, California 94720, USA
- College of Computing, Data Science, and Society, University of California, Berkeley, California 94720, USA
- Department of Plant and Microbial Biology, University of California, Berkeley, California 94720, USA
| |
Collapse
|
2
|
Rieke DT, de Bortoli T, Horak P, Lamping M, Benary M, Jelas I, Rüter G, Berger J, Zettwitz M, Kagelmann N, Kind A, Fabian F, Beule D, Glimm H, Brors B, Stenzinger A, Fröhling S, Keilholz U. Feasibility and outcome of reproducible clinical interpretation of high-dimensional molecular data: a comparison of two molecular tumor boards. BMC Med 2022; 20:367. [PMID: 36274133 PMCID: PMC9590222 DOI: 10.1186/s12916-022-02560-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Accepted: 09/09/2022] [Indexed: 11/29/2022] Open
Abstract
BACKGROUND Structured and harmonized implementation of molecular tumor boards (MTB) for the clinical interpretation of molecular data presents a current challenge for precision oncology. Heterogeneity in the interpretation of molecular data was shown for patients even with a limited number of molecular alterations. Integration of high-dimensional molecular data, including RNA- (RNA-Seq) and whole-exome sequencing (WES), is expected to further complicate clinical application. To analyze challenges for MTB harmonization based on complex molecular datasets, we retrospectively compared clinical interpretation of WES and RNA-Seq data by two independent molecular tumor boards. METHODS High-dimensional molecular cancer profiling including WES and RNA-Seq was performed for patients with advanced solid tumors, no available standard therapy, ECOG performance status of 0-1, and available fresh-frozen tissue within the DKTK-MASTER Program from 2016 to 2018. Identical molecular profiling data of 40 patients were independently discussed by two molecular tumor boards (MTB) after prior annotation by specialized physicians, following independent, but similar workflows. Identified biomarkers and resulting treatment options were compared between the MTBs and patients were followed up clinically. RESULTS A median of 309 molecular aberrations from WES and RNA-Seq (n = 38) and 82 molecular aberrations from WES only (n = 3) were considered for clinical interpretation for 40 patients (one patient sequenced twice). A median of 3 and 2 targeted treatment options were identified per patient, respectively. Most treatment options were identified for receptor tyrosine kinase, PARP, and mTOR inhibitors, as well as immunotherapy. The mean overlap coefficient between both MTB was 66%. Highest agreement rates were observed with the interpretation of single nucleotide variants, clinical evidence levels 1 and 2, and monotherapy whereas the interpretation of gene expression changes, preclinical evidence levels 3 and 4, and combination therapy yielded lower agreement rates. Patients receiving treatment following concordant MTB recommendations had significantly longer overall survival than patients receiving treatment following discrepant recommendations or physician's choice. CONCLUSIONS Reproducible clinical interpretation of high-dimensional molecular data is feasible and agreement rates are encouraging, when compared to previous reports. The interpretation of molecular aberrations beyond single nucleotide variants and preclinically validated biomarkers as well as combination therapies were identified as additional difficulties for ongoing harmonization efforts.
Collapse
Affiliation(s)
- Damian T Rieke
- Comprehensive Cancer Center, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Chariteplatz 1, 10117, Berlin, Germany. .,Department of Hematology, Oncology and Cancer Immunology, Campus Benjamin Franklin, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Hindenburgdamm 30, 12203, Berlin, Germany. .,Berlin Institute of Health (BIH) at Charité - Universitätsmedizin Berlin, Anna-Louisa-Karsch-Straße 2, 10178, Berlin, Germany. .,German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Heidelberg, Germany.
| | - Till de Bortoli
- Comprehensive Cancer Center, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Chariteplatz 1, 10117, Berlin, Germany
| | - Peter Horak
- German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Heidelberg, Germany.,Department of Translational Medical Oncology, National Center for Tumor Diseases (NCT) Heidelberg and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Mario Lamping
- Comprehensive Cancer Center, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Chariteplatz 1, 10117, Berlin, Germany.,Department of Hematology, Oncology and Cancer Immunology, Campus Benjamin Franklin, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Hindenburgdamm 30, 12203, Berlin, Germany
| | - Manuela Benary
- Comprehensive Cancer Center, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Chariteplatz 1, 10117, Berlin, Germany.,Core Unit Bioinformatics (CUBI), Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Ivan Jelas
- Comprehensive Cancer Center, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Chariteplatz 1, 10117, Berlin, Germany
| | - Gina Rüter
- Comprehensive Cancer Center, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Chariteplatz 1, 10117, Berlin, Germany
| | - Johannes Berger
- Comprehensive Cancer Center, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Chariteplatz 1, 10117, Berlin, Germany
| | - Marit Zettwitz
- Comprehensive Cancer Center, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Chariteplatz 1, 10117, Berlin, Germany
| | - Niklas Kagelmann
- Comprehensive Cancer Center, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Chariteplatz 1, 10117, Berlin, Germany
| | - Andreas Kind
- Comprehensive Cancer Center, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Chariteplatz 1, 10117, Berlin, Germany
| | - Falk Fabian
- Comprehensive Cancer Center, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Chariteplatz 1, 10117, Berlin, Germany
| | - Dieter Beule
- Berlin Institute of Health (BIH) at Charité - Universitätsmedizin Berlin, Anna-Louisa-Karsch-Straße 2, 10178, Berlin, Germany.,Core Unit Bioinformatics (CUBI), Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Hanno Glimm
- German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Heidelberg, Germany.,Department for Translational Medical Oncology, National Center for Tumor Diseases (NCT/UCC), Dresden, Germany.,Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany.,Helmholtz-Zentrum Dresden - Rossendorf (HZDR), Dresden, Germany
| | - Benedikt Brors
- German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | | | - Stefan Fröhling
- German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Heidelberg, Germany.,Department of Translational Medical Oncology, National Center for Tumor Diseases (NCT) Heidelberg and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Ulrich Keilholz
- Comprehensive Cancer Center, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Chariteplatz 1, 10117, Berlin, Germany.,German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Heidelberg, Germany
| |
Collapse
|
3
|
Pasche E, Mottaz A, Caucheteur D, Gobeill J, Michel PA, Ruch P. Variomes: a high recall search engine to support the curation of genomic variants. Bioinformatics 2022; 38:2595-2601. [PMID: 35274687 PMCID: PMC9048643 DOI: 10.1093/bioinformatics/btac146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 02/07/2022] [Accepted: 03/10/2022] [Indexed: 12/02/2022] Open
Abstract
Motivation Identification and interpretation of clinically actionable variants is a critical bottleneck. Searching for evidence in the literature is mandatory according to ASCO/AMP/CAP practice guidelines; however, it is both labor-intensive and error-prone. We developed a system to perform triage of publications relevant to support an evidence-based decision. The system is also able to prioritize variants. Our system searches within pre-annotated collections such as MEDLINE and PubMed Central. Results We assess the search effectiveness of the system using three different experimental settings: literature triage; variant prioritization and comparison of Variomes with LitVar. Almost two-thirds of the publications returned in the top-5 are relevant for clinical decision-support. Our approach enabled identifying 81.8% of clinically actionable variants in the top-3. Variomes retrieves on average +21.3% more articles than LitVar and returns the same number of results or more results than LitVar for 90% of the queries when tested on a set of 803 queries; thus, establishing a new baseline for searching the literature about variants. Availability and implementation Variomes is publicly available at https://candy.hesge.ch/Variomes. Source code is freely available at https://github.com/variomes/sibtm-variomes. SynVar is publicly available at https://goldorak.hesge.ch/synvar. Supplementary information Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Emilie Pasche
- SIB Text Mining Group, Swiss Institute of Bioinformatics, 1206 Geneva, Switzerland.,BiTeM Group, Information Sciences, 1227 Carouge, Switzerland HES-SO/HEG
| | - Anaïs Mottaz
- SIB Text Mining Group, Swiss Institute of Bioinformatics, 1206 Geneva, Switzerland.,BiTeM Group, Information Sciences, 1227 Carouge, Switzerland HES-SO/HEG
| | - Déborah Caucheteur
- SIB Text Mining Group, Swiss Institute of Bioinformatics, 1206 Geneva, Switzerland.,BiTeM Group, Information Sciences, 1227 Carouge, Switzerland HES-SO/HEG
| | - Julien Gobeill
- SIB Text Mining Group, Swiss Institute of Bioinformatics, 1206 Geneva, Switzerland.,BiTeM Group, Information Sciences, 1227 Carouge, Switzerland HES-SO/HEG
| | - Pierre-André Michel
- SIB Text Mining Group, Swiss Institute of Bioinformatics, 1206 Geneva, Switzerland.,BiTeM Group, Information Sciences, 1227 Carouge, Switzerland HES-SO/HEG
| | - Patrick Ruch
- SIB Text Mining Group, Swiss Institute of Bioinformatics, 1206 Geneva, Switzerland.,BiTeM Group, Information Sciences, 1227 Carouge, Switzerland HES-SO/HEG
| |
Collapse
|
5
|
Sänger M, Leser U. Large-scale entity representation learning for biomedical relationship extraction. Bioinformatics 2021; 37:236-242. [PMID: 32726411 DOI: 10.1093/bioinformatics/btaa674] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2020] [Revised: 07/14/2020] [Accepted: 07/21/2020] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION The automatic extraction of published relationships between molecular entities has important applications in many biomedical fields, ranging from Systems Biology to Personalized Medicine. Existing works focused on extracting relationships described in single articles or in single sentences. However, a single record is rarely sufficient to judge upon the biological correctness of a relation, as experimental evidence might be weak or only valid in a certain context. Furthermore, statements may be more speculative than confirmative, and different articles often contradict each other. Experts therefore always take the complete literature into account to take a reliable decision upon a relationship. It is an open research question how to do this effectively in an automatic manner. RESULTS We propose two novel relation extraction approaches which use recent representation learning techniques to create comprehensive models of biomedical entities or entity-pairs, respectively. These representations are learned by considering all publications from PubMed mentioning an entity or a pair. They are used as input for a neural network for classifying relations globally, i.e. the derived predictions are corpus-based, not sentence- or article based as in prior art. Experiments on the extraction of mutation-disease, drug-disease and drug-drug relationships show that the learned embeddings indeed capture semantic information of the entities under study and outperform traditional methods by 4-29% regarding F1 score. AVAILABILITY AND IMPLEMENTATION Source codes are available at: https://github.com/mariosaenger/bio-re-with-entity-embeddings. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Mario Sänger
- Computer Science Department, Knowledge Management in Bioinformatics, Humboldt-Universität zu Berlin, Berlin 10099, Germany
| | - Ulf Leser
- Computer Science Department, Knowledge Management in Bioinformatics, Humboldt-Universität zu Berlin, Berlin 10099, Germany
| |
Collapse
|
6
|
Zhang J, Yao Y, He H, Shen J. Clinical Interpretation of Sequence Variants. CURRENT PROTOCOLS IN HUMAN GENETICS 2020; 106:e98. [PMID: 32176464 PMCID: PMC7431429 DOI: 10.1002/cphg.98] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Clinical interpretation of DNA sequence variants is a critical step in reporting clinical genetic testing results. Application of next-generation sequencing technology in molecular genetic testing has facilitated diagnoses of genetic disorders in clinical practice. However, the large number of DNA sequence variants detected in clinical specimens, many of which have never been seen before, make clinical interpretation challenging. Recommendations by the American College of Medical Genetics and Genomics and the Association for Molecular Pathology (ACMG/AMP) have been widely adopted by clinical laboratories around the world to guide clinical interpretation of sequence variants. The ClinGen Sequence Variant Interpretation Working Group and various disease-specific variant curation expert panels have also developed specifications for the ACMG/AMP recommendations. Despite these efforts to standardize variant interpretation in clinical practice, different laboratories may subjectively use professional judgment to determine which criteria are applicable when classifying a variant. In addition, clinicians and researchers who are not familiar with the variant interpretation process may have difficulty understanding clinical genetic reports and communicating the clinical significance of genetic testing results. Here we provide a step-by-step protocol for clinical interpretation of sequence variants, including practical examples. By following this protocol, clinical laboratory geneticists can interpret the clinical significance of sequence variants according to the ACMG/AMP recommendations and ClinGen framework. Furthermore, this article will help clinicians and researchers to understand variant classification in clinical genetic testing reports and evaluate the quality of the reports. © 2020 by John Wiley & Sons, Inc. Basic Protocol: Interpreting the clinical significance of sequence variants Support Protocol: Reevaluating the clinical significance of sequence variants.
Collapse
Affiliation(s)
- Junyu Zhang
- Department of Reproductive Genetics, International Peace Maternity and Child Health Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
- Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
| | - Yanyi Yao
- Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
- Medical Genetics Center, Maternal and Child Health Hospital of Hubei Province, Wuhan, China
| | - Haixian He
- Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
- Department of Otorhinolaryngology, Qilu Hospital of Shandong University, Jinan, China
- NHC Key Laboratory of Otorhinolaryngology, Shandong University, Jinan, China
| | - Jun Shen
- Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
| |
Collapse
|