A Bayesian Approach for Identifying Driver Mutations within Oncogenic Pathways through Mutual Exclusivity.

See related article by Sparks et al., [Cancer Res 1998;58:1130–4][1] . Visit the Cancer Research 75th Anniversary [timeline][2]. In the mid 1990s, the human genome project was well underway, but it would still be more than 5 years before the first draft of the human genome was published and more

Improved hierarchical Bayesian modeling framework with arbitrary polynomial chaos for probabilistic model updating

In this brief, a hierarchical Bayesian network modeling framework is formulated for large-scale process monitoring and decision making, which includes a basic layer and a functional layer. First, the whole process is decomposed into different units, where local Bayesian networks are constructed, providing monitoring information and decision-making capability for the upper layer. The network structure is determined automatically based on the process data in each local unit of the basic layer. Then, through incorporating the topological structure of the process, a functional Bayesian network is further constructed to infer the information from the basic layer, which can be customized according to user demands, such as fault detection, fault diagnosis, and classification of operating status. The performance of the proposed method is evaluated through a benchmark process.

Mitochondrial dysfunction is involved in many complex diseases. Efficient and accurate evaluation of mitochondrial functionality is crucial for understanding pathology as well as facilitating novel therapeutic developments. As a popular platform, Seahorse extracellular flux (XF) analyzer is widely used for measuring mitochondrial oxygen consumption rate (OCR) in living cells. A hidden feature of Seahorse XF OCR data is that it has a complex data structure, caused by nesting and crossing between measurement cycles, wells and plates. Surprisingly, statistical analysis of Seahorse XF data has not received sufficient attention, and current methods completely ignore the complex data structure, impairing the robustness of statistical inference. To rigorously incorporate the complex structure into data analysis, here we developed a Bayesian hierarchical modeling framework, OCRbayes, and demonstrated its applicability based on analysis of published data sets. We showed that OCRbayes can analyze Seahorse XF OCR experimental data derived from either single or multiple plates. Moreover, OCRbayes has potential to be used for diagnosing patients with mitochondrial diseases.

Background: BRCA1 and BRCA2 are tumor suppressor genes which play a role in the repair of DNA double stranded breaks and the maintenance of genomic stability. Inherited BRCA mutations increase the risk of development of pancreatic cancer. In sporadic tumors BRCA mutations occur late in pancreatic tumorigenesis. Few studies to date have examined molecular heterogeneity within pancreatic tumors with respect to founder and progressor mutations. This is the first study to examine heterogeneity of mutations in pancreatic carcinomas associated with germline BRCA mutations. Design: Cases of pancreatic tumors arising in four patients with previously confirmed germline BRCA1 or BRCA2 mutations were identified from our case files. Representative sections of formalin-fixed, paraffin embedded tissue from surgical resection specimens were reviewed and 3 sections of tumor from different geographic regions and 1 section of non-tumor tissue were selected for molecular analysis for each case. Samples were manually macrodissected and genomic DNA was isolated. Genomic DNA was used for target capture and amplification of 212 amplicons targeting mutational hotspots in 48 genes using the TruSeq Amplicon Cancer Panel. Next generation sequencing was performed on the MiSeq Benchtop Sequencer and analyzed with NextGENe software. Results: In all cases concordant results were observed in all three tumor samples, with no evidence of intratumoral heterogeneity for the genes tested. In one patient with a BRCA1 germline mutation, TP53 and SMAD4 mutations were identified. In two patients with germline BRCA2 mutations, two additional driver mutations were identified involving KRAS, TP53, or SMAD4. In one case with BRCA2 germline mutation, no additional mutations were identified. Conclusion: Pancreatic tumors arising in the background of germline BRCA mutations had between 1-3 driver mutations per tumor, which is consistent with the reported numbers of driver mutations in sporadically arising pancreatic tumors. No mutual exclusivity among the mutated genes was observed. Mutations observed include KRAS codon 12 mutation, TP53 mutation (within the DNA binding domain), and SMAD4 mutation. Taken together, these results suggest that pancreatic tumors occurring in BRCA patients show similar mutational profiles of key oncogenes and tumor suppressor genes as seen in sporadic tumors with little heterogeneity with respect to these driver mutations. Citation Format: Susan Hsiao, Anne Koehne de Gonzalez, Mahesh M. Mansukhani, Robert L. Fine, Helen E. Remotti. Molecular characterization of pancreatic tumors arising in the background of germline BRCA mutations. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 5590. doi:10.1158/1538-7445.AM2014-5590

I come from a family in which there have been no scientists or doctors. I was interested, however, in biology at school and started my scientific career by training in medicine at Oxford University and Guys Hospital, London. Practising as a doctor reinforced my curiosity about the biological processes underlying human disease. As a consequence, I pursued a clinical vocation in histopathology, a discipline that couples exposure to the sights and smells of the autopsy room with a daily journey into the often beautiful, sometimes ugly world of healthy and diseased human tissues under the microscope. After an introduction to general histopathology in Nick Wright's department at the Hammersmith Hospital, London, I completed my postgraduate medical training in neuropathology with Peter Lantos at the Maudsley Hospital, London.Peering at the nuclei of cancer cells under the microscope, for me it was a matter of fascination that hidden within them were the key events converting normal cells into cancer cells, and frustration because they were out of reach. Many of the tissue samples examined by pathologists are from cancers. The clonal theory of cancer development and the general role of DNA mutations in generating cancer cell clones had been established by 1986 when I was working as a junior pathologist. Indeed, the first mutated cancer gene, HRAS , had recently been identified through application of the, then new, technologies of recombinant DNA technology. Peering at the nuclei of cancer cells under the microscope, for me it was a matter of fascination that hidden within them were the key events converting normal cells into cancer cells, and frustration because they were out of reach. So, I took 3 years break from medicine to study for a PhD, learning the methods and thinking of molecular oncology in Colin Cooper's laboratory at the Institute …

Thetdistribution is a useful extension of the normal distribution, which can be used for statistical modeling of data sets with heavy tails, and provides robust estimation. In this paper, in view of the advantages of Bayesian analysis, we propose a new robust coefficient estimation and variable selection method based on Bayesian adaptive Lassotregression. A Gibbs sampler is developed based on the Bayesian hierarchical model framework, where we treat thetdistribution as a mixture of normal and gamma distributions and put different penalization parameters for different regression coefficients. We also consider the Bayesiantregression with adaptive group Lasso and obtain the Gibbs sampler from the posterior distributions. Both simulation studies and real data example show that our method performs well compared with other existing methods when the error distribution has heavy tails and/or outliers.

Many bioinformatics problems implicitly depend on estimating large-scale covariance matrix. The traditional approaches tend to give rise to high variance and low accuracy due to "overfitting." We cast the large-scale covariance matrix estimation problem into the Bayesian hierarchical model framework, and introduce dependency between covariance parameters. We demonstrate the advantages of our approaches over the traditional approaches using simulations and OMICS data analysis.

Psychedelic drugs are re-emerging as promising scientific and clinical tools. However, despite a rapidly expanding literature on their therapeutic value, the neural mechanisms underlying psychedelic effects remain unclear. Resting-state functional magnetic resonance imaging studies of acute psychedelic effects, conducted independently by several research groups, have so far yielded fragmented and sometimes inconsistent findings. Here, to help facilitate greater convergence, we conducted a ‘mega-analysis’ integrating 11 independent resting-state functional magnetic resonance imaging datasets across five psychedelic drugs (psilocybin, lysergic acid diethylamide, mescaline, N,N-dimethyltryptamine and ayahuasca) from research groups spanning three continents and five countries. By applying a uniform preprocessing pipeline and a Bayesian hierarchical modeling framework, we discovered several common features in the induced alterations to brain function across drugs and sites. Most prominently, we identified a core signature of increased functional connectivity between transmodal (default, frontoparietal and limbic) and unimodal networks (visual and somatomotor), with subnetwork specificity. Furthermore, key subcortical regions (thalamus, caudate and putamen) and the cerebellum exhibited altered coupling with sensorimotor networks. In contrast to several single-site reports, Bayesian modeling revealed weak-to-moderate and selective reductions in within-network functional connectivity, with substantial variability across drugs and networks. Together, these findings extend past work by demonstrating that psychedelics reconfigure large-scale cortical organization while selectively engaging subcortical circuitry. This study provides the most comprehensive synthesis of psychedelic brain action to date, helping resolve inconsistencies and offering a probabilistic map of how psychedelics alter large-scale brain organization. We hereby provide a cornerstone to benchmark and shepherd future psychedelic neuroimaging research.

Motivation: The number of missense mutations being identified in cancer genomes has greatly increased as a consequence of technological advances and the reduced cost of whole-genome/whole-exome sequencing methods. However, a high proportion of the amino acid substitutions detected in cancer genomes have little or no effect on tumour progression (passenger mutations). Therefore, accurate automated methods capable of discriminating between driver (cancer-promoting) and passenger mutations are becoming increasingly important. In our previous work, we developed the Functional Analysis through Hidden Markov Models (FATHMM) software and, using a model weighted for inherited disease mutations, observed improved performances over alternative computational prediction algorithms. Here, we describe an adaptation of our original algorithm that incorporates a cancer-specific model to potentiate the functional analysis of driver mutations.Results: The performance of our algorithm was evaluated using two separate benchmarks. In our analysis, we observed improved performances when distinguishing between driver mutations and other germ line variants (both disease-causing and putatively neutral mutations). In addition, when discriminating between somatic driver and passenger mutations, we observed performances comparable with the leading computational prediction algorithms: SPF-Cancer and TransFIC.Availability and implementation: A web-based implementation of our cancer-specific model, including a downloadable stand-alone package, is available at http://fathmm.biocompute.org.uk.Contact:fathmm@biocompute.org.ukSupplementary information:Supplementary data are available at Bioinformatics online.

We develop a space‐time Bayesian hierarchical modeling (BHM) framework for two flood risk attributes—seasonal daily maximum flow and the number of events that exceed a threshold during a season (NEETM)—at a suite of gauge locations on a river network. The model uses generalized extreme value (GEV) and Poisson distributions as marginals for these flood attributes with non‐stationary parameters. The rate parameters of the Poisson distribution and location, scale, and shape parameters of the GEV are modeled as linear functions of suitable covariates. Gaussian copulas are applied to capture the spatial dependence. The best covariates are selected using the Watanabe‐Akaike information criterion (WAIC). The modeling framework results in the posterior distribution of the flood attributes at all the gauges and various lead times. We demonstrate the utility of this modeling framework to forecast the flood risk attributes during the summer peak monsoon season (July‐August) at five gauges in the Narmada River basin (NRB) of West‐Central India for several lead times (0–3 months). As potential covariates, we consider climate indices such as El Niño–Southern Oscillation (ENSO), the Indian Ocean Dipole (IOD), and the Pacific Warm Pool Region (PWPR) from antecedent seasons, which have shown strong teleconnections with the Indian monsoon. We also include new indices related to the East Pacific and West Indian Ocean regions depending on the lead times. We show useful long lead skill from this modeling approach which has a strong potential to enable robust risk‐based flood mitigation and adaptation strategies 3 months before flood occurrences.

Driver mutations are the genetic variants responsible for oncogenesis, but how specific somatic mutational events arise in cells remains poorly understood. Mutational signatures derive from the frequency of mutated trinucleotides in a given cancer sample, and they provide an avenue for investigating the underlying mutational processes that operate in cancer. Here we analyse somatic mutations from 7,815 cancer exomes from The Cancer Genome Atlas (TCGA) across 26 cancer types. We curate a list of 50 known cancer driver mutations by analysing recurrence in our cohort and annotations of known cancer-associated genes from the Cancer Gene Census, IntOGen database and Cancer Genome Interpreter. We then use these datasets to perform binary univariate logistic regression and establish the statistical relationship between individual driver mutations and known mutational signatures across different cancer types. Our analysis led to the identification of 39 significant associations between driver mutations and mutational signatures (P < 0.004, with a false discovery rate of < 5%). We first validate our methodology by establishing statistical links for known and novel associations between driver mutations and the mutational signature arising from Polymerase Epsilon proofreading deficiency. We then examine associations between driver mutations and mutational signatures for AID/APOBEC enzyme activity and deficient mismatch repair. We also identify negative associations (odds ratio < 1) between mutational signatures and driver mutations, and here we examine the role of aging and cigarette smoke mutagenesis in the generation of driver mutations in IDH1 and KRAS in brain cancers and lung adenocarcinomas respectively. Our study provides statistical foundations for hypothesised links between otherwise independent biological processes and we uncover previously unexplored relationships between driver mutations and mutagenic processes during cancer development. These associations give insights into how cancers acquire advantageous mutations and can provide direction to guide further mechanistic studies into cancer pathogenesis.

The KRAS mutation stands out as one of the most influential oncogenic mutations, which directly regulates the hallmark features of cancer and interacts with other cancer-causing driver mutations. However, there remains a lack of precise information on their cooccurrence with mutated variants of KRAS and any correlations between KRAS and other driver mutations. To enquire about this issue, we delved into cBioPortal, TCGA, UALCAN, and Uniport studies. We aimed to unravel the complexity of KRAS and its relationships with other driver mutations. We noticed that G12D and G12V are the prevalent mutated variants of KRAS and coexist with the TP53 mutation in PAAD and CRAD, while G12C and G12V coexist with LUAD. We also noticed similar observations in the case of PIK3CA and APC mutations in CRAD. At the transcript level, a positive correlation exists between KRAS and PIK3CA and between APC and KRAS in CRAD. The existence of the co-mutation of KRAS and other driver mutations could influence the signaling pathway in the neoplastic transformation. Moreover, it has immense prognostic and predictive implications, which could help in better therapeutic management to treat cancer.

An Empirical Analysis of Search Engine Advertising: Sponsored Search in Electronic Markets

The Soyang Dam is a main multi-purpose dam for preventing floods and supplying water to the metropolitan area, including Seoul, located in the Han River basin. This research explored the predictability of streamflow that plays a critical role in the reservoir operation of the Soyang Dam in South Korea. A novel stochastic approach was used to offer skillful season-ahead streamflow forecasting during the monsoon season (June-July-August, JJA) using climate state variables (e.g., SST, SLP, and wind anomalies) and dynamic climate forecasts simulated from global climate models (GCMs), as predictors. Further, we employes autoregressive exogenous stochastic volatility(ARXSV) model for streamflow prediction with the predictors that were identified within a Hierarchical Bayesian modeling framework. A cross-validation experiment under different synoptic patterns is performed to test the efficacy of the proposed modeling process. Finally, this study will investigate the effectiveness of streamflow forecasts as a precursor of the hydrological drought condition for the upcoming season.&amp;#160;AcknowledgementThis research was supported by a grant(2022-MOIS63-001) of Cooperative Research Method and Safety Management Technology in National Disaster funded by Ministry of Interior and Safety(MOIS, Korea).&amp;#160;This work was partially funded by the Korea Meteorological Administration Research and Development Program under Grant KMI 2018-07010.