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Data-driven unbiased curation of the TP53 tumor suppressor gene mutation database and validation by ultradeep sequencing of human tumors
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Molecular and Morphological Pathology.
Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology.ORCID iD: 0000-0001-6085-6749
Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Genomics.
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2012 (English)In: Proceedings of the National Academy of Sciences of the United States of America, ISSN 0027-8424, E-ISSN 1091-6490, Vol. 109, no 24, 9551-9556 p.Article in journal (Refereed) Published
Abstract [en]

Cancer mutation databases are expected to play central roles in personalized medicine by providing targets for drug development and biomarkers to tailor treatments to each patient. The accuracy of reported mutations is a critical issue that is commonly overlooked, which leads to mutation databases that include a sizable number of spurious mutations, either sequencing errors or passenger mutations. Here we report an analysis of the latest version of the TP53 mutation database, including 34,453 mutations. By using several data-driven methods on multiple independent quality criteria, we obtained a quality score for each report contributing to the database. This score can now be used to filter for high-confidence mutations and reports within the database. Sequencing the entire TP53 gene from various types of cancer using next-generation sequencing with ultradeep coverage validated our approach for curation. In summary, 9.7% of all collected studies, mostly comprising numerous tumors with multiple infrequent TP53 mutations, should be excluded when analyzing TP53 mutations. Thus, by combining statistical and experimental analyses, we provide a curated mutation database for TP53 mutations and a framework for mutation database analysis.

Place, publisher, year, edition, pages
2012. Vol. 109, no 24, 9551-9556 p.
National Category
Medical and Health Sciences
Identifiers
URN: urn:nbn:se:uu:diva-175985DOI: 10.1073/pnas.1200019109ISI: 000305511300070PubMedID: 22628563OAI: oai:DiVA.org:uu-175985DiVA: diva2:533787
Available from: 2012-06-14 Created: 2012-06-14 Last updated: 2017-12-07Bibliographically approved
In thesis
1. Molecular Characterisation and Prognostic Biomarker Discovery in Human Non-Small Cell Lung Cancer
Open this publication in new window or tab >>Molecular Characterisation and Prognostic Biomarker Discovery in Human Non-Small Cell Lung Cancer
2012 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Non-small cell lung cancer (NSCLC) constitutes a clinically, histologically, and genetically heterogeneous disease entity that represents a major cause of cancer-related death. Early-stage patients, who undergo surgery with curative intent, experience high recurrence rates and the effect of adjuvant treatment is modest. Prognostic biomarkers would be of particular relevance to guide intensified treatment depending on expected outcome and moreover often infer a biological role in tumourigenesis.

This thesis presents a translational study approach to establish a well-characterised NSCLC frozen-tissue cohort and to obtain a profile of each specimen with regard to genome-wide copy number alterations, global gene expression levels and somatic mutations in selected cancer-related genes. Furthermore, the generation of a formalin-fixed, paraffin-embedded tissue microarray enabled validation of findings on the protein level using immunohistochemistry. The comprehensive molecular characterisation, combined with data on clinical parameters, enabled the analysis of biomarkers linked to disease outcome. In Paper I, single nucleotide polymorphism arrays were applied to assess copy number alterations in NSCLC and associations with overall survival in adenocarcinoma and squamous cell carcinoma were described. In Paper II, we evaluated expression levels of selected stromal proteins in NSCLC using immunohistochemistry and the adhesion molecule CD99 was identified as an outcome-related biomarker in two independent cohorts. Paper III presents a strategy for prognostic biomarker discovery based on gene expression profiling, meta-analysis, and validation of protein expression on tissue microarrays, and suggests the putative tumour suppressor CADM1 as a candidate biomarker. In Paper IV, we propose a prognostic role for tumour-infiltrating IGKC-expressing plasma cells in the local tumour microenvironment, indicating an involvement of the humoral immune response in anti-tumor activity. In Paper V, we combined next-generation deep sequencing with statistical analysis of the TP53 database to define novel parameters for database curation.

In summary, this thesis exemplifies the benefits of a translational study approach, based on a comprehensive tumour characterisation, and describes molecular markers associated with clinical outcome in NSCLC.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2012. 68 p.
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Medicine, ISSN 1651-6206 ; 817
Keyword
non-small cell lung cancer, biomarker, prognosis, microarray, copy number aberration
National Category
Other Basic Medicine Cancer and Oncology
Research subject
Medical Science
Identifiers
urn:nbn:se:uu:diva-181912 (URN)978-91-554-8482-8 (ISBN)
Public defence
2012-11-16, Rudbecksalen, Rudbecklaboratoriet, Dag Hammarskjölds väg 20, Uppsala, 13:15 (English)
Opponent
Supervisors
Available from: 2012-10-26 Created: 2012-10-01 Last updated: 2013-06-19Bibliographically approved

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Edlund, KarolinaAmeur, AdamBunikis, IgnasGyllensten, UlfSundström, MagnusMicke, PatrickBotling, Johan

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Molecular and Morphological PathologyScience for Life Laboratory, SciLifeLabDepartment of Immunology, Genetics and PathologyGenomics
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