| 1. |
- Crescitelli, Rossella, 1985, et al.
(författare)
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Extracellular vesicle DNA from human melanoma tissues contains cancer-specific mutations
- 2022
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Ingår i: Frontiers in Cell and Developmental Biology. - : Frontiers Media SA. - 2296-634X. ; 10
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Tidskriftsartikel (refereegranskat)abstract
- Liquid biopsies are promising tools for early diagnosis and residual disease monitoring in patients with cancer, and circulating tumor DNA isolated from plasma has been extensively studied as it has been shown to contain tumor-specific mutations. Extracellular vesicles (EVs) present in tumor tissues carry tumor-derived molecules such as proteins and nucleic acids, and thus EVs can potentially represent a source of cancer-specific DNA. Here we identified the presence of tumor-specific DNA mutations in EVs isolated from six human melanoma metastatic tissues and compared the results with tumor tissue DNA and plasma DNA. Tumor tissue EVs were isolated using enzymatic treatment followed by ultracentrifugation and iodixanol density cushion isolation. A panel of 34 melanoma-related genes was investigated using ultra-sensitive sequencing (SiMSen-seq). We detected mutations in six genes in the EVs (BRAF, NRAS, CDKN2A, STK19, PPP6C, and RAC), and at least one mutation was detected in all melanoma EV samples. Interestingly, the mutant allele frequency was higher in DNA isolated from tumor-derived EVs compared to total DNA extracted directly from plasma DNA, supporting the potential role of tumor EVs as future biomarkers in melanoma.
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| 2. |
- Filges, Stefan, 1991
(författare)
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Next generation molecular diagnostics using ultrasensitive sequencing
- 2022
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Massively parallel sequencing enables the exploration of the genetic heterogeneity within microbial, viral and tumor cell populations. Detecting circulating tumor DNA in blood and other body fluids has the potential to revolutionize molecular diagnostics. However, these liquid biopsies typically contain only minute amounts of highly degraded DNA and standard sequencing approaches lack the resolution to detect rare genetic variants. The overall goal of this thesis was to develop an ultrasensitive sequencing approach with single molecule resolution that requires only minimal amounts of material. To this end, we developed the simple multiplexed PCR-based barcoding of DNA for ultrasensitive mutation detection by next-generation sequencing protocol (SiMSen-Seq). SiMSen-Seq achieves ultrasensitive detection of nucleotide variants by attaching unique molecular identifiers to target DNA molecules using PCR primers. SiMSen-Seq is enabled by highly optimized reaction conditions and the use of a stem-loop structure that prevents the UMI from forming non-specific PCR products. We showed that ultrasensitive variant detection is attained mainly by using UMI, while gains in sensitivity from using high-fidelity polymerases were minor. We also demonstrated that oligonucleotide quality is essential in numerous molecular applications, including SiMSen-Seq. Next generation diagnostics tools also demand optimized preanalytical conditions to achieve the necessary variant detection sensitivity, while remaining fast, simple, and cost efficient. Therefore, we established a workflow for cell-free DNA analysis and developed quantitative PCR-based quality controls to evaluate each experimental step. We also developed a bioinformatics pipeline for processing any type of targeted sequencing data containing unique molecular identifiers, including barcode clustering, error correction, variant calling, and visualization. Next, we used SiMSen-Seq in applications requiring ultrasensitive mutant detection. We first employed SiMSen-Seq to experimentally confirm that UV light rapidly induces highly recurrent mutations within a specific promotor motif. These mutations remained sub-clonal even after weeks of cell culture, arguing against a tumor-driving role. Our results highlight the importance of sequence context for the interpretation of somatic variants in cancer. We also showed that ctDNA can be used as a clinical biomarker for tumor burden and to monitor treatment efficacy in uveal melanoma. Patients with high ctDNA levels had worse overall survival, demonstrating the clinical utility of circulating tumor-DNA-based liquid biopsy analysis. In conclusion, we showed that SiMSen-Seq is a simple, flexible, low-DNA input protocol that enables rare variant detection to address a multitude of clinical and basic research questions.
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| 3. |
- Österlund, Tobias, 1984, et al.
(författare)
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UMIErrorCorrect and UMIAnalyzer: Software for Consensus Read Generation, Error Correction, and Visualization Using Unique Molecular Identifiers
- 2022
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Ingår i: Clinical Chemistry. - : Oxford University Press (OUP). - 0009-9147 .- 1530-8561. ; 68:11, s. 1425-1435
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Tidskriftsartikel (refereegranskat)abstract
- Background Targeted sequencing using unique molecular identifiers (UMIs) enables detection of rare variant alleles in challenging applications, such as cell-free DNA analysis from liquid biopsies. Standard bioinformatics pipelines for data processing and variant calling are not adapted for deep-sequencing data containing UMIs, are inflexible, and require multistep workflows or dedicated computing resources. Methods We developed a bioinformatics pipeline using Python and an R package for data analysis and visualization. To validate our pipeline, we analyzed cell-free DNA reference material with known mutant allele frequencies (0%, 0.125%, 0.25%, and 1%) and public data sets. Results We developed UMIErrorCorrect, a bioinformatics pipeline for analyzing sequencing data containing UMIs. UMIErrorCorrect only requires fastq files as inputs and performs alignment, UMI clustering, error correction, and variant calling. We also provide UMIAnalyzer, a graphical user interface, for data mining, visualization, variant interpretation, and report generation. UMIAnalyzer allows the user to adjust analysis parameters and study their effect on variant calling. We demonstrated the flexibility of UMIErrorCorrect by analyzing data from 4 different targeted sequencing protocols. We also show its ability to detect different mutant allele frequencies in standardized cell-free DNA reference material. UMIErrorCorrect outperformed existing pipelines for targeted UMI sequencing data in terms of variant detection sensitivity. Conclusions UMIErrorCorrect and UMIAnalyzer are comprehensive and customizable bioinformatics tools that can be applied to any type of library preparation protocol and enrichment chemistry using UMIs. Access to simple, generic, and open-source bioinformatics tools will facilitate the implementation of UMI-based sequencing approaches in basic research and clinical applications.
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