190 research outputs found
Author Correction: Reconstructing evolutionary trajectories of mutation signature activities in cancer using TrackSig
No full textMulti-cancer risk stratification based on national health data: a retrospective modelling and validation study
No full textAuthor Correction: The evolutionary history of 2,658 cancers (Nature, (2020), 578, 7793, (122-128), 10.1038/s41586-019-1907-7)
No full textIn the published version of this paper, the members of the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium were listed in the Supplementary Information; however, these members should have been included in the main paper. The original Article has been corrected to include the members and affiliations of the PCAWG Consortium in the main paper; the corrections have been made to the HTML version of the Article but not the PDF version. Additional minor corrections to affiliations have been made to the PDF and HTML versions of the original Article for consistency of information between the PCAWG list and the main paper.0info:eu-repo/semantics/publishe
Waiting Time Models of Cancer Progression
No full textCancer progression is an evolutionary process driven by mutation and selection in a population of tumor cells. In multistage models of cancer progression, each stage is associated with the occurrence of genetic alterations and their fixation in the population. The accumulation of mutations is described using conjunctive Bayesian networks, an exponential family of waiting time models in which the occurrence of mutations is constrained by a partial temporal order. Two opposing limit cases arise if mutations either follow a linear order or occur independently. Exact analytical expressions for the waiting time until a specific number of mutations have accumulated are derived in these limit cases as well as for the general conjunctive Bayesian network. In a stochastic population genetics model that accounts for mutation and selection, waves of clonal expansions sweep through the population at equidistant intervals. An approximate analytical expression for the waiting time is compared to the results obtained with conjunctive Bayesian networks.Bayesian network, cancer, genetic progression, multistage theory, Wright-Fisher process,
Bayesian Cox Regression for Large-scale Inference with Applications to Electronic Health Records
No full textThe Cox model is an indispensable tool for time-to-event analysis,
particularly in biomedical research. However, medicine is undergoing a profound
transformation, generating data at an unprecedented scale, which opens new
frontiers to study and understand diseases. With the wealth of data collected,
new challenges for statistical inference arise, as datasets are often high
dimensional, exhibit an increasing number of measurements at irregularly spaced
time points, and are simply too large to fit in memory. Many current
implementations for time-to-event analysis are ill-suited for these problems as
inference is computationally demanding and requires access to the full data at
once. Here we propose a Bayesian version for the counting process
representation of Cox's partial likelihood for efficient inference on
large-scale datasets with millions of data points and thousands of
time-dependent covariates. Through the combination of stochastic variational
inference and a reweighting of the log-likelihood, we obtain an approximation
for the posterior distribution that factorizes over subsamples of the data,
enabling the analysis in big data settings. Crucially, the method produces
viable uncertainty estimates for large-scale and high-dimensional datasets. We
show the utility of our method through a simulation study and an application to
myocardial infarction in the UK Biobank.Comment: 20 pages, 5 figures, 4 table
Multiparameter prediction of myeloid neoplasia risk
No full text: The myeloid neoplasms encompass acute myeloid leukemia, myelodysplastic syndromes and myeloproliferative neoplasms. Most cases arise from the shared ancestor of clonal hematopoiesis (CH). Here we analyze data from 454,340 UK Biobank participants, of whom 1,808 developed a myeloid neoplasm 0-15 years after recruitment. We describe the differences in CH mutational landscapes and hematology/biochemistry test parameters among individuals that later develop myeloid neoplasms (pre-MN) versus controls, finding that disease-specific changes are detectable years before diagnosis. By analyzing differences between 'pre-MN' and controls, we develop and validate Cox regression models quantifying the risk of progression to each myeloid neoplasm subtype. We construct 'MN-predict', a web application that generates time-dependent predictions with the input of basic blood tests and genetic data. Our study demonstrates that many individuals that develop myeloid neoplasms can be identified years in advance and provides a framework for disease-specific prognostication that will be of substantial use to researchers and physicians
Identification of prognostic phenotypes of esophageal adenocarcinoma in two independent cohorts
No full textBackground & Aims: most
patients with esophageal adenocarcinoma (EAC) present de novo. Although
this could be due to inadequate screening strategies, the precise
reason for this observation is not clear. We compared survival of
patients with prevalent EAC with and without synchronous Barrett
esophagus (BE) with intestinal metaplasia (IM) at the time of EAC
diagnosis.Methods: clinical
data were studied using Cox proportional hazards regression to evaluate
the effect of synchronous BE-IM on EAC survival independent of age,
sex, TNM stage, and tumor location. Two cohorts from the Mayo Clinic and
a UK multicenter prospective cohort were included.Results: the
Mayo Clinic cohort had 411 patients with EAC, and 49.3% with BE-IM
showed a survival benefit compared with those without (hazard ratio [HR]
0.44, 95% confidence interval [CI] 0.34–0.57, P < .001). In
a multivariable analysis, BE-IM was associated with better survival
independent of age, sex, stage, and tumor location and length (adjusted
HR 0.66, 95% CI 0.5–0.88, P = .005). The UK cohort included1417
patients, and 45% with BE-IM showed a survival benefit compared with
those without (hazard ratio 0.59, 95% CI 0.5–0.69, P <
.001), with continued significance in multivariable analysis that
included age, sex, stage, and tumor location (adjusted HR 0.77, 95% CI
0.64–0.93, P = .006).Conclusion: two
types of EAC can be characterized based on the presence or absence of
Barrett epithelium. These findings have implications for understanding
the etiology of EAC, determining prognosis, and developing optimal
clinical strategies to identify patients at risk
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