| 1. |
- 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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| 2. |
- Bar, N., et al.
(author)
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A reference map of potential determinants for the human serum metabolome
- 2020
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In: Nature. - : Nature Research. - 0028-0836 .- 1476-4687. ; 588:7836, s. 135-140
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Journal article (peer-reviewed)abstract
- The serum metabolome contains a plethora of biomarkers and causative agents of various diseases, some of which are endogenously produced and some that have been taken up from the environment1. The origins of specific compounds are known, including metabolites that are highly heritable2,3, or those that are influenced by the gut microbiome4, by lifestyle choices such as smoking5, or by diet6. However, the key determinants of most metabolites are still poorly understood. Here we measured the levels of 1,251 metabolites in serum samples from a unique and deeply phenotyped healthy human cohort of 491 individuals. We applied machine-learning algorithms to predict metabolite levels in held-out individuals on the basis of host genetics, gut microbiome, clinical parameters, diet, lifestyle and anthropometric measurements, and obtained statistically significant predictions for more than 76% of the profiled metabolites. Diet and microbiome had the strongest predictive power, and each explained hundreds of metabolites—in some cases, explaining more than 50% of the observed variance. We further validated microbiome-related predictions by showing a high replication rate in two geographically independent cohorts7,8 that were not available to us when we trained the algorithms. We used feature attribution analysis9 to reveal specific dietary and bacterial interactions. We further demonstrate that some of these interactions might be causal, as some metabolites that we predicted to be positively associated with bread were found to increase after a randomized clinical trial of bread intervention. Overall, our results reveal potential determinants of more than 800 metabolites, paving the way towards a mechanistic understanding of alterations in metabolites under different conditions and to designing interventions for manipulating the levels of circulating metabolites.
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| 3. |
- Gilbert, M. R., et al.
(author)
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Perspectives on multiscale modelling and experiments to accelerate materials development for fusion
- 2021
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In: Journal of Nuclear Materials. - : Elsevier BV. - 0022-3115 .- 1873-4820. ; 554
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Research review (peer-reviewed)abstract
- Prediction of material performance in fusion reactor environments relies on computational modelling, and will continue to do so until the first generation of fusion power plants come on line and allow long-term behaviour to be observed. In the meantime, the modelling is supported by experiments that attempt to replicate some aspects of the eventual operational conditions. In 2019, a group of leading experts met under the umbrella of the IEA to discuss the current position and ongoing challenges in modelling of fusion materials and how advanced experimental characterisation is aiding model improvement. This review draws from the discussions held during that workshop. Topics covering modelling of irradiation-induced defect production and fundamental properties, gas behaviour, clustering and segregation, defect evolution and interactions are discussed, as well as new and novel multiscale simulation approaches, and the latest effort s to link modelling to experiments through advanced observation and characterisation techniques.
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| 4. |
- Alexandrov, Ludmil B, et al.
(author)
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The repertoire of mutational signatures in human cancer
- 2020
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In: Nature. - : Springer Science and Business Media LLC. - 0028-0836 .- 1476-4687. ; 578:7793, s. 94-101
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Journal article (peer-reviewed)abstract
- Somatic mutations in cancer genomes are caused by multiple mutational processes, each of which generates a characteristic mutational signature1. Here, as part of the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium2 of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA), we characterized mutational signatures using 84,729,690 somatic mutations from 4,645 whole-genome and 19,184 exome sequences that encompass most types of cancer. We identified 49 single-base-substitution, 11 doublet-base-substitution, 4 clustered-base-substitution and 17 small insertion-and-deletion signatures. The substantial size of our dataset, compared with previous analyses3-15, enabled the discovery of new signatures, the separation of overlapping signatures and the decomposition of signatures into components that may represent associated-but distinct-DNA damage, repair and/or replication mechanisms. By estimating the contribution of each signature to the mutational catalogues of individual cancer genomes, we revealed associations of signatures to exogenous or endogenous exposures, as well as to defective DNA-maintenance processes. However, many signatures are of unknown cause. This analysis provides a systematic perspective on the repertoire of mutational processes that contribute to the development of human cancer.
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| 5. |
- Murumagi, A, et al.
(author)
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Drug response profiles in patient-derived cancer cells across histological subtypes of ovarian cancer: real-time therapy tailoring for a patient with low-grade serous carcinoma
- 2023
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In: British journal of cancer. - : Springer Science and Business Media LLC. - 1532-1827 .- 0007-0920. ; 128:4, s. 678-690
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Journal article (peer-reviewed)abstract
- Many efforts are underway to develop novel therapies against the aggressive high-grade serous ovarian cancers (HGSOCs), while our understanding of treatment options for low-grade (LGSOC) or mucinous (MUCOC) of ovarian malignancies is not developing as well. We describe here a functional precision oncology (fPO) strategy in epithelial ovarian cancers (EOC), which involves high-throughput drug testing of patient-derived ovarian cancer cells (PDCs) with a library of 526 oncology drugs, combined with genomic and transcriptomic profiling. HGSOC, LGSOC and MUCOC PDCs had statistically different overall drug response profiles, with LGSOCs responding better to targeted inhibitors than HGSOCs. We identified several subtype-specific drug responses, such as LGSOC PDCs showing high sensitivity to MDM2, ERBB2/EGFR inhibitors, MUCOC PDCs to MEK inhibitors, whereas HGSOCs showed strongest effects with CHK1 inhibitors and SMAC mimetics. We also explored several drug combinations and found that the dual inhibition of MEK and SHP2 was synergistic in MAPK-driven EOCs. We describe a clinical case study, where real-time fPO analysis of samples from a patient with metastatic, chemorefractory LGSOC with a CLU-NRG1 fusion guided clinical therapy selection. fPO-tailored therapy with afatinib, followed by trastuzumab and pertuzumab, successfully reduced tumour burden and blocked disease progression over a five-year period. In summary, fPO is a powerful approach for the identification of systematic drug response differences across EOC subtypes, as well as to highlight patient-specific drug regimens that could help to optimise therapies to individual patients in the future.
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| 8. |
- Li, T. T., et al.
(author)
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Pathogenic antibody response to glucose-6-phosphate isomerase targets a modified epitope uniquely exposed on joint cartilage
- 2023
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In: Annals of the Rheumatic Diseases. - : BMJ. - 0003-4967 .- 1468-2060. ; 82:6, s. 799-808
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Journal article (peer-reviewed)abstract
- ObjectivesTo identify the arthritogenic B cell epitopes of glucose-6-phosphate isomerase (GPI) and their association with rheumatoid arthritis (RA). MethodsIgG response towards a library of GPI peptides in patients with early RA, pre-symptomatic individuals and population controls, as well as in mice, were tested by bead-based multiplex immunoassays and ELISA. Monoclonal IgG were generated, and the binding specificity and affinity were determined by ELISA, gel size exclusion chromatography, surface plasma resonance and X-ray crystallography. Arthritogenicity was investigated by passive transfer experiments. Antigen-specific B cells were identified by peptide tetramer staining. ResultsPeptide GPI(293-307) was the dominant B cell epitope in K/BxN and GPI-immunised mice. We could detect B cells and low levels of IgM antibodies binding the GPI(293-307) epitopes, and high affinity anti-GPI(293-307) IgG antibodies already 7 days after GPI immunisation, immediately before arthritis onset. Transfer of anti-GPI(293-307) IgG antibodies induced arthritis in mice. Moreover, anti-GPI(293-307) IgG antibodies were more frequent in individuals prior to RA onset (19%) than in controls (7.5%). GPI(293-307)-specific antibodies were associated with radiographic joint damage. Crystal structures of the Fab-peptide complex revealed that this epitope is not exposed in native GPI but requires conformational change of the protein in inflamed joint for effective recognition by anti-GPI(293-307) antibodies. ConclusionsWe have identified the major pathogenic B cell epitope of the RA-associated autoantigen GPI, at position 293-307, exposed only on structurally modified GPI on the cartilage surface. B cells to this neo-epitope escape tolerance and could potentially play a role in the pathogenesis of RA.
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