In: Eriksson , R , Aagaard , L , Jensen , L J , Borisova , L , Hørlück , D , Brunak , S & Hansen , E H 2014 , ' Discrepancies in listed adverse drug reactions in pharmaceutical product information supplied by the regulatory authorities in Denmark and the USA ' , Pharmacology Research & Perspectives , vol. 2 , no. 3 , e00038 . https://doi.org/10.1002/prp2.38
Pharmaceutical product information (PI) supplied by the regulatory authorities serves as a source of information on safe and effective use of drugs. The objectives of this study were to qualitatively and quantitatively compare PIs for selected drugs marketed in both Denmark and the USA with respect to consistency and discrepancy of listed adverse drug reaction (ADR) information. We compared individual ADRs listed in PIs from Denmark and the USA with respect to type and frequency. Consistency was defined as match of ADRs and of ADR frequency or match could not be ruled out. Discrepancies were defined as ADRs listed only in one country or listed with different frequencies. We analyzed PIs for 40 separate drugs from ten therapeutic groups and assigned the 4003 identified ADRs to System Organ Classes (Medical Dictionary for Regulatory Activities [MedDRA] terminology). Less than half of listed ADRs (n = 1874; 47%) showed consistency. Discrepancies (n = 2129; 53%) were split into ADRs listed only in the USA (n = 1558; 39%), ADRs listed only in Denmark (n = 325; 8%) and ADRs listed with different frequencies (n = 246; 6%). The majority of listed ADRs were of the type "gastrointestinal disorders" and "nervous system disorders". Our results show great differences in PIs for drugs approved in both Denmark and the USA illuminating concerns about the credibility of the publicly available PIs. The results also represent an argument for further harmonization across borders to improve consistency between authority-supplied information.
The catalog of cancer driver mutations in protein-coding genes has greatly expanded in the past decade. However, non-coding cancer driver mutations are less well-characterized and only a handful of recurrent non-coding mutations, most notably TERT promoter mutations, have been reported. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2658 cancer across 38 tumor types, we perform multi-faceted pathway and network analyses of non-coding mutations across 2583 whole cancer genomes from 27 tumor types compiled by the ICGC/TCGA PCAWG project that was motivated by the success of pathway and network analyses in prioritizing rare mutations in protein-coding genes. While few non-coding genomic elements are recurrently mutated in this cohort, we identify 93 genes harboring non-coding mutations that cluster into several modules of interacting proteins. Among these are promoter mutations associated with reduced mRNA expression in TP53, TLE4, and TCF4. We find that biological processes had variable proportions of coding and non-coding mutations, with chromatin remodeling and proliferation pathways altered primarily by coding mutations, while developmental pathways, including Wnt and Notch, altered by both coding and non-coding mutations. RNA splicing is primarily altered by non-coding mutations in this cohort, and samples containing non-coding mutations in well-known RNA splicing factors exhibit similar gene expression signatures as samples with coding mutations in these genes. These analyses contribute a new repertoire of possible cancer genes and mechanisms that are altered by non-coding mutations and offer insights into additional cancer vulnerabilities that can be investigated for potential therapeutic treatments. ; B.J.R. received funding from NIH grants U24CA211000 and R01HG007069. J.M.S. received funding from NIH grants U24CA143858, R01CA180778, and U24CA210990. J.R. received funding from the Ontario Institute for Cancer Research (OICR) Investigator Award provided by the Government of Ontario, Operating Grant from Cancer Research Society (CRS) (#21089), the Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grant (#RGPIN-2016-06485), and the Canadian Institutes of Health Research (CIHR) Project Grant. K.M. received funding from IWT/SBO NEMOA and FWO 3G046318 and G.0371.06 grants. J.M.G.I. received funding from the Novo Nordisk Foundation (NNF17OC0027594 and NNF14CC0001) and the Innovation Fund Denmark (5184-00102B). S.B. received funding from the Novo Nordisk Foundation (NNF17OC0027594 and NNF14CC0001). J.B. received funding from the BioTalent Canada Student Internship Program. A.V. and M.V. received funding from the Joint BSC-IRB-CRG Program in Computational Biology and the Severo Ochoa Award (SEV 2015-0493). M.A.R. was supported in part by the National Cancer Institute of the NIH (Cancer Target Discovery and Development Network grant U01CA217875)
In: Koivula , R W , Heggie , A , Barnett , A , Cederberg , H , Hansen , T H , Koopman , A D , Ridderstrale , M , Rutters , F , Vestergaard , H , Gupta , R , Herrgard , S , Heymans , M W , Perry , M H , Rauh , S , Siloaho , M , Teare , H J A , Thorand , B , Bell , J , Brunak , S , Frost , G , Jablonka , B , Mari , A , McDonald , T J , Dekker , J M , Hansen , T , Hattersley , A , Laakso , M , Pedersen , O , Koivisto , V , Ruetten , H , Walker , M , Pearson , E & Franks , P W 2014 , ' Discovery of biomarkers for glycaemic deterioration before and after the onset of type 2 diabetes: rationale and design of the epidemiological studies within the IMI DIRECT Consortium ' , Diabetologia , vol. 57 , no. 6 , pp. 1132-1142 . https://doi.org/10.1007/s00125-014-3216-x
Aims/hypothesis The DIRECT (Diabetes Research on Patient Stratification) Study is part of a European Union Framework 7 Innovative Medicines Initiative project, a joint undertaking between four industry and 21 academic partners throughout Europe. The Consortium aims to discover and validate biomarkers that: (1) predict the rate of glycaemic deterioration before and after type 2 diabetes onset; (2) predict the response to diabetes therapies; and (3) help stratify type 2 diabetes into clearly definable disease subclasses that can be treated more effectively than without stratification. This paper describes two new prospective cohort studies conducted as part of DIRECT. Methods Prediabetic participants (target sample size 2,200-2,700) and patients with newly diagnosed type 2 diabetes (target sample size similar to 1,000) are undergoing detailed metabolic phenotyping at baseline and 18 months and 36 months later. Abdominal, pancreatic and liver fat is assessed using MRI. Insulin secretion and action are assessed using frequently sampled OGTTs in non-diabetic participants, and frequently sampled mixed-meal tolerance tests in patients with type 2 diabetes. Biosamples include venous blood, faeces, urine and nail clippings, which, among other biochemical analyses, will be characterised at genetic, transcriptomic, metabolomic, proteomic and metagenomic levels. Lifestyle is assessed using high-resolution triaxial accelerometry, 24 h diet record, and food habit questionnaires. Conclusinos/interpretation DIRECT will yield an unprecedented array of biomaterials and data. This resource, available through managed access to scientists within and outside the Consortium, will facilitate the development of new treatments and therapeutic strategies for the prevention and management of type 2 diabetes.
The DIRECT (Diabetes Research on Patient Stratification) Study is part of a European Union Framework 7 Innovative Medicines Initiative project, a joint undertaking between four industry and 21 academic partners throughout Europe. The Consortium aims to discover and validate biomarkers that: (1) predict the rate of glycaemic deterioration before and after type 2 diabetes onset; (2) predict the response to diabetes therapies; and (3) help stratify type 2 diabetes into clearly definable disease subclasses that can be treated more effectively than without stratification. This paper describes two new prospective cohort studies conducted as part of DIRECT.
In: Kenner , B , Chari , S T , Kelsen , D , Klimstra , D S , Pandol , S J , Rosenthal , M , Rustgi , A K , Taylor , J A , Yala , A , Abul-Husn , N , Andersen , D K , Bernstein , D , Brunak , S , Canto , M I , Eldar , Y C , Fishman , E K , Fleshman , J , Go , V L W , Holt , J M , Field , B , Goldberg , A , Hoos , W , Iacobuzio-Donahue , C , Li , D , Lidgard , G , Maitra , A , Matrisian , L M , Poblete , S , Rothschild , L , Sander , C , Schwartz , L H , Shalit , U , Srivastava , S & Wolpin , B 2021 , ' Artificial Intelligence and Early Detection of Pancreatic Cancer : 2020 Summative Review ' , Pancreas , vol. 50 , no. 3 , pp. 251-279 . https://doi.org/10.1097/MPA.0000000000001762
ABSTRACT: Despite considerable research efforts, pancreatic cancer is associated with a dire prognosis and a 5-year survival rate of only 10%. Early symptoms of the disease are mostly nonspecific. The premise of improved survival through early detection is that more individuals will benefit from potentially curative treatment. Artificial intelligence (AI) methodology has emerged as a successful tool for risk stratification and identification in general health care. In response to the maturity of AI, Kenner Family Research Fund conducted the 2020 AI and Early Detection of Pancreatic Cancer Virtual Summit (www.pdac-virtualsummit.org) in conjunction with the American Pancreatic Association, with a focus on the potential of AI to advance early detection efforts in this disease. This comprehensive presummit article was prepared based on information provided by each of the interdisciplinary participants on one of the 5 following topics: Progress, Problems, and Prospects for Early Detection; AI and Machine Learning; AI and Pancreatic Cancer-Current Efforts; Collaborative Opportunities; and Moving Forward-Reflections from Government, Industry, and Advocacy. The outcome from the robust Summit conversations, to be presented in a future white paper, indicate that significant progress must be the result of strategic collaboration among investigators and institutions from multidisciplinary backgrounds, supported by committed funders.
Despite considerable research efforts, pancreatic cancer is associated with a dire prognosis and a 5-year survival rate of only 10%. Early symptoms of the disease are mostly nonspecific. The premise of improved survival through early detection is that more individuals will benefit from potentially curative treatment. Artificial intelligence (AI) methodology has emerged as a successful tool for risk stratification and identification in general health care. In response to the maturity of AI, Kenner Family Research Fund conducted the 2020 AI and Early Detection of Pancreatic Cancer Virtual Summit (www.pdac-virtualsummit.org) in conjunction with the American Pancreatic Association, with a focus on the potential of AI to advance early detection efforts in this disease. This comprehensive presummit article was prepared based on information provided by each of the interdisciplinary participants on one of the 5 following topics: Progress, Problems, and Prospects for Early Detection; AI and Machine Learning; AI and Pancreatic Cancer—Current Efforts; Collaborative Opportunities; and Moving Forward—Reflections from Government, Industry, and Advocacy. The outcome from the robust Summit conversations, to be presented in a future white paper, indicate that significant progress must be the result of strategic collaboration among investigators and institutions from multidisciplinary backgrounds, supported by committed funders.
In: Koivula , R W , Heggie , A , Barnett , A , Cederberg , H , Hansen , T H , Koopman , A D , Ridderstråle , M , Rutters , F , Vestergaard , H , Gupta , R , Herrgård , S , Heymans , M W , Perry , M H , Rauh , S , Siloaho , M , Teare , H J A , Thorand , B , Bell , J , Brunak , S , Frost , G , Jablonka , B , Mari , A , McDonald , T J , Dekker , J M , Hansen , T , Hattersley , A , Laakso , M , Pedersen , O , Koivisto , V , Ruetten , H , Walker , M , Pearson , E , Franks , P W & DIRECT Consortium 2014 , ' Discovery of biomarkers for glycaemic deterioration before and after the onset of type 2 diabetes : rationale and design of the epidemiological studies within the IMI DIRECT Consortium ' Diabetologia , vol 57 , no. 6 , pp. 1132-1142 . DOI:10.1007/s00125-014-3216-x
Aims/hypothesis The DIRECT (Diabetes Research on Patient Stratification) Study is part of a European Union Framework 7 Innovative Medicines Initiative project, a joint undertaking between four industry and 21 academic partners throughout Europe. The Consortium aims to discover and validate biomarkers that: (1) predict the rate of glycaemic deterioration before and after type 2 diabetes onset; (2) predict the response to diabetes therapies; and (3) help stratify type 2 diabetes into clearly definable disease subclasses that can be treated more effectively than without stratification. This paper describes two new prospective cohort studies conducted as part of DIRECT. Methods Prediabetic participants (target sample size 2,200–2,700) and patients with newly diagnosed type 2 diabetes (target sample size ~1,000) are undergoing detailed metabolic phenotyping at baseline and 18 months and 36 months later. Abdominal, pancreatic and liver fat is assessed using MRI. Insulin secretion and action are assessed using frequently sampled OGTTs in non-diabetic participants, and frequently sampled mixed-meal tolerance tests in patients with type 2 diabetes. Biosamples include venous blood, faeces, urine and nail clippings, which, among other biochemical analyses, will be characterised at genetic, transcriptomic, metabolomic, proteomic and metagenomic levels. Lifestyle is assessed using high-resolution triaxial accelerometry, 24 h diet record, and food habit questionnaires. Conclusions/interpretation DIRECT will yield an unprecedented array of biomaterials and data. This resource, available through managed access to scientists within and outside the Consortium, will facilitate the development of new treatments and therapeutic strategies for the prevention and management of type 2 diabetes.
In: Eriksen , R , Perez , I G , Posma , J M , Haid , M , Sharma , S , Prehn , C , Thomas , L E , Koivula , R W , Bizzotto , R , Mari , A , Giordano , G N , Pavo , I , Schwenk , J M , De Masi , F , Tsirigos , K D , Brunak , S , Viñuela , A , Mahajan , A , McDonald , T J , Kokkola , T , Rutter , F , Teare , H , Hansen , T H , Fernandez , J , Jones , A , Jennison , C , Walker , M , McCarthy , M I , Pedersen , O , Ruetten , H , Forgie , I , Bell , J D , Pearson , E R , Franks , P W , Adamski , J , Holmes , E & Frost , G 2020 , ' Dietary metabolite profiling brings new insight into the relationship between nutrition and metabolic risk : An IMI DIRECT study ' , EBioMedicine , vol. 58 , 102932 . https://doi.org/10.1016/j.ebiom.2020.102932
Background: Dietary advice remains the cornerstone of prevention and management of type 2 diabetes (T2D). However, understanding the efficacy of dietary interventions is confounded by the challenges inherent in assessing free living diet. Here we profiled dietary metabolites to investigate glycaemic deterioration and cardiometabolic risk in people at risk of or living with T2D. Methods: We analysed data from plasma collected at baseline and 18-month follow-up in individuals from the Innovative Medicines Initiative (IMI) Diabetes Research on Patient Stratification (DIRECT) cohort 1 n = 403 individuals with normal or impaired glucose regulation (prediabetic) and cohort 2 n = 458 individuals with new onset of T2D. A dietary metabolite profile model (T pred ) was constructed using multivariable regression of 113 plasma metabolites obtained from targeted metabolomics assays. The continuous T pred score was used to explore the relationships between diet, glycaemic deterioration and cardio-metabolic risk via multiple linear regression models. Findings: A higher T pred score was associated with healthier diets high in wholegrain (β=3.36 g, 95% CI 0.31, 6.40 and β=2.82 g, 95% CI 0.06, 5.57) and lower energy intake (β=-75.53 kcal, 95% CI -144.71, -2.35 and β=-122.51 kcal, 95% CI -186.56, -38.46), and saturated fat (β=-0.92 g, 95% CI -1.56, -0.28 and β=–0.98 g, 95% CI -1.53, -0.42 g), respectively for cohort 1 and 2. In both cohorts a higher T pred score was also associated with lower total body adiposity and favourable lipid profiles HDL-cholesterol (β=0.07 mmol/L, 95% CI 0.03, 0.1), (β=0.08 mmol/L, 95% CI 0.04, 0.1), and triglycerides (β=-0.1 mmol/L, 95% CI -0.2, -0.03), (β=-0.2 mmol/L, 95% CI -0.3, -0.09), respectively for cohort 1 and 2. In cohort 2, the T pred score was negatively associated with liver fat (β=-0.74%, 95% CI -0.67, -0.81), and lower fasting concentrations of HbA1c (β=-0.9 mmol/mol, 95% CI -1.5, -0.1), glucose (β=-0.2 mmol/L, 95% CI -0.4, -0.05) and insulin (β=-11.0 pmol/mol, 95% CI -19.5, -2.6). Longitudinal analysis showed at 18-month follow up a higher T pred score was also associated lower total body adiposity in both cohorts and lower fasting glucose (β=-0.2 mmol/L, 95% CI -0.3, -0.01) and insulin (β=-9.2 pmol/mol, 95% CI -17.9, -0.4) concentrations in cohort 2. Interpretation: Plasma dietary metabolite profiling provides objective measures of diet intake, showing a relationship to glycaemic deterioration and cardiometabolic health. Funding: This work was supported by the Innovative Medicines Initiative Joint Undertaking under grant agreement no. 115,317 (DIRECT), resources of which are composed of financial contribution from the European Union's Seventh Framework Programme (FP7/2007–2013) and EFPIA companies.
Understanding the peopling of the Americas remains an important and challenging question. Here, we present (14)C dates, and morphological, isotopic and genomic sequence data from two human skulls from the state of Minas Gerais, Brazil, part of one of the indigenous groups known as 'Botocudos'. We find that their genomic ancestry is Polynesian, with no detectable Native American component. Radiocarbon analysis of the skulls shows that the individuals had died prior to the beginning of the 19th century. Our findings could either represent genomic evidence of Polynesians reaching South America during their Pacific expansion, or European-mediated transport. ; GeoGenetics members were supported by the Lundbeck Foundation, the Danish National Research Foundation (DNRF94) and the European Union (FP7/2007-2013/317184 and 319209). A.S.M. was supported by a fellowship from the Swiss National Science Foundation (PBSKP3_143529); M.D. by the US National Science Foundation (grant DBI-1103639); P.L.J. by the National Institutes of Health (grant K99 GM104158); V.F.G. by a Strategic Training for Advanced Genetic Epidemiology (STAGE) fellowship, University of Toronto. ; This is the final version. It was first published by Elsevier at http://www.sciencedirect.com/science/article/pii/S0960982214012743