| 1. |
- Häkkinen, Jari, et al.
(författare)
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Implementation of an Open Source Software solution for Laboratory Information Management and automated RNAseq data analysis in a large-scale Cancer Genomics initiative using BASE with extension package Reggie.
- 2016
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- BackgroundLarge-scale cancer genomics initiatives and next-generation sequencing for transcriptome profiling allow for detailed molecular characterization of tumors, and provide opportunities for clinical tools to improve diagnosis, prognosis, and treatment decisions. Laboratory information, data management, and data sharing in large-scale genomics projects is a challenge. Aiming to introduce such technologies in a clinical setting offer additional challenges associated with requirements of short lead-times and specialized tracking of biomaterials, data, and analysis results.ResultsUsing the free open-source BioArray Software Environment (BASE) and extension package Reggie we have implemented a laboratory information management system and an automated RNAseq data analysis pipeline that successfully manage a large regional cancer genomics initiative. The system manages enrolled cancer patients, tumor biopsies, extraction of nucleic acid, and whole transcriptome RNA-sequencing through to data analysis and quality control. The implementation offers integration of laboratory equipment and operating procedures, and information tracking in a module based fashion enabling efficient and flexible use of personnel resources. The system provides two-factor authentication and transaction control and seamless integration of freely available software for RNAseq analysis such as Tophat, Cufflinks, and Picard. As of February 2016 more than 8000 patients and over 6000 tumor biopsies have been successfully processed. Lead-time from biopsy arrival to summarized reports based on RNAseq data is less than 5 days, in line with regional clinical requirements. BASE and Reggie are freely available and released as open-source under the GNU General Public License and GNU Affero General Public License, respectively.ConclusionUsing free open-source software together with BASE and a customized extension package, Reggie, we have implemented a system capable of managing large collections of quality controlled and curated material for use in research and development and tailored to meet requirements for clinical use. Featuring high degree of automation and interactivity the system allows for resource efficient laboratory procedures and short lead-times with demonstrated use of RNAseq data analyses in a clinical setting.
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| 2. |
- Nacer, Deborah F., et al.
(författare)
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Molecular characteristics of breast tumors in patients screened for germline predisposition from a population-based observational study
- 2023
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Ingår i: Genome Medicine. - 1756-994X. ; 15, s. 1-20
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Tidskriftsartikel (refereegranskat)abstract
- BackgroundPathogenic germline variants (PGVs) in certain genes are linked to higher lifetime risk of developing breast cancer and can influence preventive surgery decisions and therapy choices. Public health programs offer genetic screening based on criteria designed to assess personal risk and identify individuals more likely to carry PGVs, dividing patients into screened and non-screened groups. How tumor biology and clinicopathological characteristics differ between these groups is understudied and could guide refinement of screening criteria.MethodsSix thousand six hundred sixty breast cancer patients diagnosed in South Sweden during 2010–2018 were included with available clinicopathological and RNA sequencing data, 900 (13.5%) of which had genes screened for PGVs through routine clinical screening programs. We compared characteristics of screened patients and tumors to non-screened patients, as well as between screened patients with (n = 124) and without (n = 776) PGVs.ResultsBroadly, breast tumors in screened patients showed features of a more aggressive disease. However, few differences related to tumor biology or patient outcome remained significant after stratification by clinical subgroups or PAM50 subtypes. Triple-negative breast cancer (TNBC), the subgroup most enriched for PGVs, showed the most differences between screening subpopulations (e.g., higher tumor proliferation in screened cases). Significant differences in PGV prevalence were found between clinical subgroups/molecular subtypes, e.g., TNBC cases were enriched for BRCA1 PGVs. In general, clinicopathological differences between screened and non-screened patients mimicked those between patients with and without PGVs, e.g., younger age at diagnosis for positive cases. However, differences in tumor biology/microenvironment such as immune cell composition were additionally seen within PGV carriers/non-carriers in ER + /HER2 − cases, but not between screening subpopulations in this subgroup.ConclusionsCharacterization of molecular tumor features in patients clinically screened and not screened for PGVs represents a relevant read-out of guideline criteria. The general lack of molecular differences between screened/non-screened patients after stratification by relevant breast cancer subsets questions the ability to improve the identification of screening candidates based on currently used patient and tumor characteristics, pointing us towards universal screening. Nevertheless, while that is not attained, molecular differences identified between PGV carriers/non-carriers suggest the possibility of further refining patient selection within certain patient subsets using RNA-seq through, e.g., gene signatures.
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| 3. |
- Vallon-Christersson, Johan, et al.
(författare)
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BASE--2nd generation software for microarray data management and analysis.
- 2009
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Ingår i: BMC Bioinformatics. - : Springer Science and Business Media LLC. - 1471-2105. ; 10:Oct 12
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: Microarray experiments are increasing in size and samples are collected asynchronously over long time. Available data are re-analysed as more samples are hybridized. Systematic use of collected data requires tracking of biomaterials, array information, raw data, and assembly of annotations. To meet the information tracking and data analysis challenges in microarray experiments we reimplemented and improved BASE version 1.2. RESULTS: The new BASE presented in this report is a comprehensive annotable local microarray data repository and analysis application providing researchers with an efficient information management and analysis tool. The information management system tracks all material from biosource, via sample and through extraction and labelling to raw data and analysis. All items in BASE can be annotated and the annotations can be used as experimental factors in downstream analysis. BASE stores all microarray experiment related data regardless if analysis tools for specific techniques or data formats are readily available. The BASE team is committed to continue improving and extending BASE to make it usable for even more experimental setups and techniques, and we encourage other groups to target their specific needs leveraging on the infrastructure provided by BASE. CONCLUSION: BASE is a comprehensive management application for information, data, and analysis of microarray experiments, available as free open source software at http://base.thep.lu.se under the terms of the GPLv3 license.
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