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| 2. |
- Campbell, PJ, et al.
(author)
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Pan-cancer analysis of whole genomes
- 2020
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In: Nature. - : Springer Science and Business Media LLC. - 1476-4687 .- 0028-0836. ; 578:7793, s. 82-
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Journal article (peer-reviewed)abstract
- Cancer is driven by genetic change, and the advent of massively parallel sequencing has enabled systematic documentation of this variation at the whole-genome scale1–3. Here we report the integrative analysis of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). We describe the generation of the PCAWG resource, facilitated by international data sharing using compute clouds. On average, cancer genomes contained 4–5 driver mutations when combining coding and non-coding genomic elements; however, in around 5% of cases no drivers were identified, suggesting that cancer driver discovery is not yet complete. Chromothripsis, in which many clustered structural variants arise in a single catastrophic event, is frequently an early event in tumour evolution; in acral melanoma, for example, these events precede most somatic point mutations and affect several cancer-associated genes simultaneously. Cancers with abnormal telomere maintenance often originate from tissues with low replicative activity and show several mechanisms of preventing telomere attrition to critical levels. Common and rare germline variants affect patterns of somatic mutation, including point mutations, structural variants and somatic retrotransposition. A collection of papers from the PCAWG Consortium describes non-coding mutations that drive cancer beyond those in the TERT promoter4; identifies new signatures of mutational processes that cause base substitutions, small insertions and deletions and structural variation5,6; analyses timings and patterns of tumour evolution7; describes the diverse transcriptional consequences of somatic mutation on splicing, expression levels, fusion genes and promoter activity8,9; and evaluates a range of more-specialized features of cancer genomes8,10–18.
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| 3. |
- Gerstung, M, et al.
(author)
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The evolutionary history of 2,658 cancers
- 2020
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In: Nature. - : Springer Science and Business Media LLC. - 1476-4687 .- 0028-0836. ; 578:7793, s. 122-
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Journal article (peer-reviewed)abstract
- Cancer develops through a process of somatic evolution1,2. Sequencing data from a single biopsy represent a snapshot of this process that can reveal the timing of specific genomic aberrations and the changing influence of mutational processes3. Here, by whole-genome sequencing analysis of 2,658 cancers as part of the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA)4, we reconstruct the life history and evolution of mutational processes and driver mutation sequences of 38 types of cancer. Early oncogenesis is characterized by mutations in a constrained set of driver genes, and specific copy number gains, such as trisomy 7 in glioblastoma and isochromosome 17q in medulloblastoma. The mutational spectrum changes significantly throughout tumour evolution in 40% of samples. A nearly fourfold diversification of driver genes and increased genomic instability are features of later stages. Copy number alterations often occur in mitotic crises, and lead to simultaneous gains of chromosomal segments. Timing analyses suggest that driver mutations often precede diagnosis by many years, if not decades. Together, these results determine the evolutionary trajectories of cancer, and highlight opportunities for early cancer detection.
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| 4. |
- Yakneen, S, et al.
(author)
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Butler enables rapid cloud-based analysis of thousands of human genomes
- 2020
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In: Nature biotechnology. - : Springer Science and Business Media LLC. - 1546-1696 .- 1087-0156. ; 38:3, s. 288-
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Journal article (peer-reviewed)abstract
- We present Butler, a computational tool that facilitates large-scale genomic analyses on public and academic clouds. Butler includes innovative anomaly detection and self-healing functions that improve the efficiency of data processing and analysis by 43% compared with current approaches. Butler enabled processing of a 725-terabyte cancer genome dataset from the Pan-Cancer Analysis of Whole Genomes (PCAWG) project in a time-efficient and uniform manner.
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| 6. |
- Fiorina, B., et al.
(author)
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Challenging modeling strategies for LES of non-adiabatic turbulent stratified combustion
- 2015
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In: Combustion and Flame. - : Elsevier BV. - 0010-2180 .- 1556-2921. ; 162:11, s. 4264-4282
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Journal article (peer-reviewed)abstract
- Five different low-Mach large eddy simulations are compared to the turbulent stratified flame experiments conducted at the Technical University of Darmstadt (TUD). The simulations were contributed by TUD, the Institute for Combustion Technology (ITV) at Aachen, Lund University (LUND), the EM2C laboratory at Ecole Centrale Paris, and the University of Duisburg-Essen (UDE). Combustion is modeled by a premixed flamelet tabulation with local flame thickening (TUD), a premixed flamelet progress variable approach coupled to a level set method (ITV), a 4-steps mechanism combined with implicit LES (LUND), the F-TACLES model that is based on filtered premixed flamelet tabulation (EM2C), and a flame surface density approach (UDE). An extensive comparison of simulation and experimental data is presented for the first two moments of velocity, temperature, mixture fraction, and major species mass fractions. The importance of heat-losses was assessed by comparing simulations for adiabatic and isothermal boundary conditions at the burner walls. The adiabatic computations predict a flame anchored on the burner lip, while the non-adiabatic simulations show a flame lift-off of one half pilot diameter and a better agreement with experimental evidence for temperature and species concentrations. Most simulations agree on the mean flame brush position, but it is evident that subgrid turbulence must be considered to achieve the correct turbulent flame speed. Qualitative comparisons of instantaneous snapshots of the flame show differences in the size of the resolved flame wrinkling patterns. These differences are (a) caused by the influence of the LES combustion model on the flame dynamics and (b) by the different simulation strategies in terms of grid, inlet condition and numerics. The simulations were conducted with approaches optimized for different objectives, for example low computational cost, or in another case, short turn around. (C) 2015 The Combustion Institute. Published by Elsevier Inc. All rights reserved.
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