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- 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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| 4. |
- Blach, S., et al.
(author)
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Global change in hepatitis C virus prevalence and cascade of care between 2015 and 2020: a modelling study
- 2022
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In: Lancet Gastroenterology & Hepatology. - : Elsevier BV. - 2468-1253. ; 7:5, s. 396-415
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Journal article (peer-reviewed)abstract
- Background Since the release of the first global hepatitis elimination targets in 2016, and until the COVID-19 pandemic started in early 2020, many countries and territories were making progress toward hepatitis C virus (HCV) elimination. This study aims to evaluate HCV burden in 2020, and forecast HCV burden by 2030 given current trends. Methods This analysis includes a literature review, Delphi process, and mathematical modelling to estimate HCV prevalence (viraemic infection, defined as HCV RNA-positive cases) and the cascade of care among people of all ages (age =0 years from birth) for the period between Jan 1, 2015, and Dec 31, 2030. Epidemiological data were collected from published sources and grey literature (including government reports and personal communications) and were validated among country and territory experts. A Markov model was used to forecast disease burden and cascade of care from 1950 to 2050 for countries and territories with data. Model outcomes were extracted from 2015 to 2030 to calculate population-weighted regional averages, which were used for countries or territories without data. Regional and global estimates of HCV prevalence, cascade of care, and disease burden were calculated based on 235 countries and territories. Findings Models were built for 110 countries or territories: 83 were approved by local experts and 27 were based on published data alone. Using data from these models, plus population-weighted regional averages for countries and territories without models (n=125), we estimated a global prevalence of viraemic HCV infection of 0.7% (95% UI 0.7-0.9), corresponding to 56.8 million (95% UI 55.2-67.8) infections, on Jan 1, 2020. This number represents a decrease of 6.8 million viraemic infections from a 2015 (beginning of year) prevalence estimate of 63.6 million (61.8-75.8) infections (0.9% [0.8-1.0] prevalence). By the end of 2020, an estimated 12.9 million (12.5-15.4) people were living with a diagnosed viraemic infection. In 2020, an estimated 641 000 (623 000-765 000) patients initiated treatment. Interpretation At the beginning of 2020, there were an estimated 56.8 million viraemic HCV infections globally. Although this number represents a decrease from 2015, our forecasts suggest we are not currently on track to achieve global elimination targets by 2030. As countries recover from COVID-19, these findings can help refocus efforts aimed at HCV elimination. Copyright (C) 2022 Elsevier Ltd. All rights reserved.
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| 5. |
- Mahajan, Anubha, et al.
(author)
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Multi-ancestry genetic study of type 2 diabetes highlights the power of diverse populations for discovery and translation
- 2022
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In: Nature Genetics. - : Springer Nature. - 1061-4036 .- 1546-1718. ; 54:5, s. 560-572
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Journal article (peer-reviewed)abstract
- We assembled an ancestrally diverse collection of genome-wide association studies (GWAS) of type 2 diabetes (T2D) in 180,834 affected individuals and 1,159,055 controls (48.9% non-European descent) through the Diabetes Meta-Analysis of Trans-Ethnic association studies (DIAMANTE) Consortium. Multi-ancestry GWAS meta-analysis identified 237 loci attaining stringent genome-wide significance (P < 5 x 10(-9)), which were delineated to 338 distinct association signals. Fine-mapping of these signals was enhanced by the increased sample size and expanded population diversity of the multi-ancestry meta-analysis, which localized 54.4% of T2D associations to a single variant with >50% posterior probability. This improved fine-mapping enabled systematic assessment of candidate causal genes and molecular mechanisms through which T2D associations are mediated, laying the foundations for functional investigations. Multi-ancestry genetic risk scores enhanced transferability of T2D prediction across diverse populations. Our study provides a step toward more effective clinical translation of T2D GWAS to improve global health for all, irrespective of genetic background. Genome-wide association and fine-mapping analyses in ancestrally diverse populations implicate candidate causal genes and mechanisms underlying type 2 diabetes. Trans-ancestry genetic risk scores enhance transferability across populations.
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| 8. |
- Grossmann, Igor, et al.
(author)
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Insights into the accuracy of social scientists' forecasts of societal change
- 2023
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In: Nature Human Behaviour. - : Springer Nature. - 2397-3374. ; 7, s. 484-501
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Journal article (peer-reviewed)abstract
- How well can social scientists predict societal change, and what processes underlie their predictions? To answer these questions, we ran two forecasting tournaments testing the accuracy of predictions of societal change in domains commonly studied in the social sciences: ideological preferences, political polarization, life satisfaction, sentiment on social media, and gender-career and racial bias. After we provided them with historical trend data on the relevant domain, social scientists submitted pre-registered monthly forecasts for a year (Tournament 1; N = 86 teams and 359 forecasts), with an opportunity to update forecasts on the basis of new data six months later (Tournament 2; N = 120 teams and 546 forecasts). Benchmarking forecasting accuracy revealed that social scientists' forecasts were on average no more accurate than those of simple statistical models (historical means, random walks or linear regressions) or the aggregate forecasts of a sample from the general public (N = 802). However, scientists were more accurate if they had scientific expertise in a prediction domain, were interdisciplinary, used simpler models and based predictions on prior data. How accurate are social scientists in predicting societal change, and what processes underlie their predictions? Grossmann et al. report the findings of two forecasting tournaments. Social scientists' forecasts were on average no more accurate than those of simple statistical models.
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- Tschiderer, L., et al.
(author)
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The Prospective Studies of Atherosclerosis (Proof-ATHERO) Consortium: Design and Rationale
- 2020
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In: Gerontology. - : S. Karger AG. - 0304-324X .- 1423-0003. ; 66:5, s. 447-459
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Journal article (peer-reviewed)abstract
- Atherosclerosis - the pathophysiological mechanism shared by most cardiovascular diseases - can be directly or indirectly assessed by a variety of clinical tests including measurement of carotid intima-media thickness, carotid plaque, ankle-brachial index, pulse wave velocity, and coronary artery calcium. The Prospective Studies of Atherosclerosis (Proof-ATHERO) consortium (https://clinicalepi.i-med.ac.at/research/proof-athero/) collates de-identified individual-participant data of studies with information on atherosclerosis measures, risk factors for cardiovascular disease, and incidence of cardiovascular diseases. It currently comprises 74 studies that involve 106,846 participants from 25 countries and over 40 cities. In summary, 21 studies recruited participants from the general population (n = 67,784), 16 from high-risk populations (n = 22,677), and 37 as part of clinical trials (n = 16,385). Baseline years of contributing studies range from April 1980 to July 2014; the latest follow-up was until June 2019. Mean age at baseline was 59 years (standard deviation: 10) and 50% were female. Over a total of 830,619 person-years of follow-up, 17,270 incident cardiovascular events (including coronary heart disease and stroke) and 13,270 deaths were recorded, corresponding to cumulative incidences of 2.1% and 1.6% per annum, respectively. The consortium is coordinated by the Clinical Epidemiology Team at the Medical University of Innsbruck, Austria. Contributing studies undergo a detailed data cleaning and harmonisation procedure before being incorporated in the Proof-ATHERO central database. Statistical analyses are being conducted according to pre-defined analysis plans and use established methods for individual-participant data meta-analysis. Capitalising on its large sample size, the multi-institutional collaborative Proof-ATHERO consortium aims to better characterise, understand, and predict the development of atherosclerosis and its clinical consequences. (c) 2020 S. Karger AG, Basel
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