| 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. |
- Kanai, M, et al.
(author)
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- 2023
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swepub:Mat__t
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| 3. |
- Hare, Matthew JL, et al.
(author)
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Glucose-independent ethnic differences in HbA(1c), in people without known diabetes
- 2013
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In: Diabetes Care. - : American Diabetes Association. - 0149-5992 .- 1935-5548. ; 36:6, s. 1534-1540
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Journal article (peer-reviewed)abstract
- OBJECTIVE To determine whether glucose-independent differences in HbA(1c) exist between people of African, South Asian, and Chinese ethnicities.RESEARCH DESIGN AND METHODS Data from 6,701 people aged 19-78 years, without known diabetes, from Mauritius, and participating in the population-based Non-Communicable Disease Surveys of the main island and the island of Rodrigues were included. Participants were African (n = 1,219 from main island, n = 1,505 from Rodrigues), South Asian (n = 3,820), and Chinese (n = 157). Survey data included HbA(1c), plasma glucose during oral glucose tolerance testing (OGTT), anthropometry, demographics, and medical and lifestyle history.RESULTS Mean HbA(1c), after adjustment for fasting and 2-h plasma glucose and other factors known to influence HbA(1c), was higher in Africans from Rodrigues (6.1%) than in South Asians (5.7%, P < 0.001), Chinese (5.7%, P < 0.001), or Africans from the main island of Mauritius (5.7%, P < 0.001). The age-standardized prevalence of diabetes among Africans from Rodrigues differed substantially depending on the diagnostic criteria used [OGTT 7.9% (95% CI 5.8-10.0); HbA(1c) 17.3% (15.3-19.2)]. Changing diagnostic criteria resulted in no significant change in the prevalence of diabetes within the other ethnic groups.CONCLUSIONS People of African ethnicity from Rodrigues have higher HbA(1c) than those of South Asian or African ethnicity from the main island of Mauritius for reasons not explained by plasma glucose during an OGTT or traditional factors known to affect glycemia. Further research should be directed at determining the mechanism behind this disparity and its relevance to clinical outcomes.
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| 4. |
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| 5. |
- Rees, CA, et al.
(author)
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Derivation and validation of a novel risk assessment tool to identify children aged 2-59 months at risk of hospitalised pneumonia-related mortality in 20 countries
- 2022
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In: BMJ global health. - : BMJ. - 2059-7908. ; 7:4
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Journal article (peer-reviewed)abstract
- Existing risk assessment tools to identify children at risk of hospitalised pneumonia-related mortality have shown suboptimal discriminatory value during external validation. Our objective was to derive and validate a novel risk assessment tool to identify children aged 2–59 months at risk of hospitalised pneumonia-related mortality across various settings.MethodsWe used primary, baseline, patient-level data from 11 studies, including children evaluated for pneumonia in 20 low-income and middle-income countries. Patients with complete data were included in a logistic regression model to assess the association of candidate variables with the outcome hospitalised pneumonia-related mortality. Adjusted log coefficients were calculated for each candidate variable and assigned weighted points to derive the Pneumonia Research Partnership to Assess WHO Recommendations (PREPARE) risk assessment tool. We used bootstrapped selection with 200 repetitions to internally validate the PREPARE risk assessment tool.ResultsA total of 27 388 children were included in the analysis (mean age 14.0 months, pneumonia-related case fatality ratio 3.1%). The PREPARE risk assessment tool included patient age, sex, weight-for-age z-score, body temperature, respiratory rate, unconsciousness or decreased level of consciousness, convulsions, cyanosis and hypoxaemia at baseline. The PREPARE risk assessment tool had good discriminatory value when internally validated (area under the curve 0.83, 95% CI 0.81 to 0.84).ConclusionsThe PREPARE risk assessment tool had good discriminatory ability for identifying children at risk of hospitalised pneumonia-related mortality in a large, geographically diverse dataset. After external validation, this tool may be implemented in various settings to identify children at risk of hospitalised pneumonia-related mortality.
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