scholarly journals A site specific model and analysis of the neutral somatic mutation rate in whole-genome cancer data

2017 ◽  
Author(s):  
Johanna Bertl ◽  
Qianyun Guo ◽  
Malene Juul ◽  
Søren Besenbacher ◽  
Morten Muhlig Nielsen ◽  
...  
Keyword(s):  
Mutation Rate ◽  
Null Model ◽  
Specific Model ◽  
Cancer Development ◽  
Whole Genome ◽  
Site Specific ◽  
Explanatory Variables ◽  
Different Types ◽  
Cancer Types ◽  
Mutational Process

AbstractBackgroundDetailed modelling of the neutral mutational process in cancer cells is crucial for identifying driver mutations and understanding the mutational mechanisms that act during cancer development. The neutral mutational process is very complex: whole-genome analyses have revealed that the mutation rate differs between cancer types, between patients and along the genome depending on the genetic and epigenetic context. Therefore, methods that predict the number of different types of mutations in regions or specific genomic elements must consider local genomic explanatory variables. A major drawback of most methods is the need to average the explanatory variables across the entire region or genomic element. This procedure is particularly problematic if the explanatory variable varies dramatically in the element under consideration.ResultsTo take into account the fine scale of the explanatory variables, we model the probabilities of different types of mutations for each position in the genome by multinomial logistic regression. We analyse 505 cancer genomes from 14 different cancer types and compare the performance in predicting mutation rate for both regional based models and site-specific models. We show that for 1000 randomly selected genomic positions, the site-specific model predicts the mutation rate much better than regional based models. We use a forward selection procedure to identify the most important explanatory variables. The procedure identifies site-specific conservation (phyloP), replication timing, and expression level as the best predictors for the mutation rate. Finally, our model confirms and quantifies certain well-known mutational signatures.ConclusionWe find that our site-specific multinomial regression model outperforms the regional based models. The possibility of including genomic variables on different scales and patient specific variables makes it a versatile framework for studying different mutational mechanisms. Our model can serve as the neutral null model for the mutational process; regions that deviate from the null model are candidates for elements that drive cancer development.

scholarly journals A site specific model and analysis of the neutral somatic mutation rate in whole-genome cancer data

2018 ◽  
Vol 19 (1) ◽  
Author(s):  
Johanna Bertl ◽  
Qianyun Guo ◽  
Malene Juul ◽  
Søren Besenbacher ◽  
Morten Muhlig Nielsen ◽  
...  
Keyword(s):  
Somatic Mutation ◽  
Mutation Rate ◽  
Specific Model ◽  
Whole Genome ◽  
Site Specific ◽  
Cancer Data ◽  
A Site

Identification of BAP1-associated MicroRNAs and Implications in Cancer Development

2019 ◽  
pp. 1-4
Author(s):  
Tikam Chand ◽  
Tikam Chand
Keyword(s):  
Gene Regulation ◽  
In Silico ◽  
Mirna Target ◽  
Target Prediction ◽  
Differential Regulation ◽  
Cancer Development ◽  
Mirna Target Prediction ◽  
Cancer Types ◽  
In Silico Tools

Having role in gene regulation and silencing, miRNAs have been implicated in development and progression of a number of diseases, including cancer. Herein, I present potential miRNAs associated with BAP1 gene identified using in-silico tools such as TargetScan and Exiqon miRNA Target Prediction. I identified fifteen highly conserved miRNA (hsa-miR-423-5p, hsa-miR-3184-5p, hsa-miR-4319, hsa-miR125b-5p, hsa-miR-125a-5p, hsa-miR-6893-3p, hsa-miR-200b-3p, hsa-miR-200c-3p, hsa-miR-505-3p.1, hsa-miR-429, hsa-miR-370-3p, hsa-miR-125a-5p, hsa-miR-141-3p, hsa-miR-200a-3p, and hsa-miR-429) associated with BAP1 gene. We also predicted the differential regulation of these twelve miRNAs in different cancer types.

scholarly journals Lights and Shadows in Immuno-Oncology Drug Development

Cancers ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 691
Author(s):  
Milana Bergamino Sirvén ◽  
Sonia Pernas ◽  
Maggie C. U. Cheang
Keyword(s):  
Drug Development ◽  
Checkpoint Inhibitors ◽  
Late Phase ◽  
New Drugs ◽  
Successful Outcome ◽  
Different Types ◽  
Cancer Types ◽  
Clinical Trial Designs ◽  
Oncology Drug Development ◽  
Critical Aspects

The rapidly evolving landscape of immuno-oncology (IO) is redefining the treatment of a number of cancer types. IO treatments are becoming increasingly complex, with different types of drugs emerging beyond checkpoint inhibitors. However, many of the new drugs either do not progress from phase I-II clinical trials or even fail in late-phase trials. We have identified at least five areas in the development of promising IO treatments that should be redefined for more efficient designs and accelerated approvals. Here we review those critical aspects of IO drug development that could be optimized for more successful outcome rates in all cancer types. It is important to focus our efforts on the mechanisms of action, types of response and adverse events of these novel agents. The use of appropriate clinical trial designs with robust biomarkers of response and surrogate endpoints will undoubtedly facilitate the development and subsequent approval of these drugs. Further research is also needed to establish biomarker-driven strategies to select which patients may benefit from immunotherapy and identify potential mechanisms of resistance.

scholarly journals WATER VAPOUR WEIGHTED MEAN TEMPERATURE MODEL FOR GPS-DERIVED INTEGRATED WATER VAPOUR IN PENINSULAR MALAYSIA

2017 ◽  
Vol XLII-4/W5 ◽  
pp. 127-135 ◽  
Author(s):  
T. A. Musa ◽  
M. H. Mazlan ◽  
Y. D. Opaluwa ◽  
I. A. Musliman ◽  
Z. M. Radzi
Keyword(s):  
Water Vapour ◽  
Diurnal Cycle ◽  
Specific Model ◽  
Regional Model ◽  
Peninsular Malaysia ◽  
Site Specific ◽  
Weighted Mean Temperature ◽  
Integrated Water Vapour ◽  
Relationship Of ◽  
The Impact

This paper presents the development of T<sub>M</sub> model by using the radiosonde stations from Peninsular Malaysia. Two types of T<sub>M</sub> model were developed; site-specific and regional models. The result revealed that the estimation from site-specific model has small improvement compared to the regional model, indicating that the regional model is adequately to use in estimation of GPS-derived IWV over Peninsular Malaysia. Meanwhile, this study found that the diurnal cycle of T<sub>S</sub> has influenced the T<sub>M</sub>&amp;ndash;T<sub>S</sub> relationship. The separation between daytime and nighttime observation can improve the relationship of T<sub>M</sub>&amp;ndash;T<sub>S</sub>. However, the impact of diurnal cycle to IWV estimation is less than 1&amp;thinsp;%. The T<sub>M</sub> model from Global and Tropic also been evaluated. The Tropic T<sub>M</sub> model is superior to be utilized as compared to the Global T<sub>M</sub> model.

scholarly journals NIMBus: a Negative Binomial Regression based Integrative Method for Mutation Burden Analysis

2020 ◽  
Author(s):  
Jing Zhang ◽  
Jason Liu ◽  
Patrick McGillivray ◽  
Caroline Yi ◽  
Lucas Lochovsky ◽  
...  
Keyword(s):  
Mutation Rate ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Whole Genome ◽  
Rate Heterogeneity ◽  
Mutation Burden ◽  
Binomial Regression ◽  
Mutational Hotspots ◽  
Dna Elements ◽  
Integrative Method

ABSTRACTBackgroundIdentifying frequently mutated regions is a key approach to discover DNA elements influencing cancer progression. However, it is challenging to identify these burdened regions due to mutation rate heterogeneity across the genome and across different individuals. Moreover, it is known that this heterogeneity partially stems from genomic confounding factors, such as replication timing and chromatin organization. The increasing availability of cancer whole genome sequences and functional genomics data from the Encyclopedia of DNA Elements (ENCODE) may help address these issues.ResultsWe developed a Negative binomial regression-based Integrative Method for mutation Burden analysiS (NIMBus). Our approach addresses the over-dispersion of mutation count statistics by (1) using a Gamma-Poisson mixture model to capture the mutation-rate heterogeneity across different individuals and (2) estimating regional background mutation rates by regressing the varying local mutation counts against genomic features extracted from ENCODE.We applied NIMBus to whole-genome cancer sequences from the PanCancer Analysis of Whole Genomes project (PCAWG) and other cohorts. It successfully identified well-known coding and noncoding drivers, such as TP53 and the TERT promoter. To further characterize the burdening of non-coding regions, we used NIMBus to screen transcription factor binding sites in promoter regions that intersect DNase I hypersensitive sites (DHSs). This analysis identified mutational hotspots that potentially disrupt gene regulatory networks in cancer. We also compare this method to other mutation burden analysis methods.ConclusionNIMBus is a powerful tool to identify mutational hotspots. The NIMBus software and results are available as an online resource at github.gersteinlab.org/nimbus.

scholarly journals Interplay Between Mitochondrial Peroxiredoxins and ROS in Cancer Development and Progression

2019 ◽  
Vol 20 (18) ◽  
pp. 4407 ◽  
Author(s):  
Tayaba Ismail ◽  
Youni Kim ◽  
Hongchan Lee ◽  
Dong-Seok Lee ◽  
Hyun-Shik Lee
Keyword(s):  
Redox Balance ◽  
Cancer Development ◽  
Cancer Therapies ◽  
Lung Cancers ◽  
Micro Rnas ◽  
Cancer Types ◽  
High Level ◽  
Cancerous Cells ◽  
Ros Scavengers ◽  
Cellular Organelles

Mitochondria are multifunctional cellular organelles that are major producers of reactive oxygen species (ROS) in eukaryotes; to maintain the redox balance, they are supplemented with different ROS scavengers, including mitochondrial peroxiredoxins (Prdxs). Mitochondrial Prdxs have physiological and pathological significance and are associated with the initiation and progression of various cancer types. In this review, we have focused on signaling involving ROS and mitochondrial Prdxs that is associated with cancer development and progression. An upregulated expression of Prdx3 and Prdx5 has been reported in different cancer types, such as breast, ovarian, endometrial, and lung cancers, as well as in Hodgkin’s lymphoma and hepatocellular carcinoma. The expression of Prdx3 and Prdx5 in different types of malignancies involves their association with different factors, such as transcription factors, micro RNAs, tumor suppressors, response elements, and oncogenic genes. The microenvironment of mitochondrial Prdxs plays an important role in cancer development, as cancerous cells are equipped with a high level of antioxidants to overcome excessive ROS production. However, an increased production of Prdx3 and Prdx5 is associated with the development of chemoresistance in certain types of cancers and it leads to further complications in cancer treatment. Understanding the interplay between mitochondrial Prdxs and ROS in carcinogenesis can be useful in the development of anticancer drugs with better proficiency and decreased resistance. However, more targeted studies are required for exploring the tumor microenvironment in association with mitochondrial Prdxs to improve the existing cancer therapies and drug development.

scholarly journals Causal Mediation Analysis

2011 ◽  
Vol 11 (4) ◽  
pp. 605-619 ◽  
Author(s):  
Raymond Hicks ◽  
Dustin Tingley
Keyword(s):  
Mediation Analysis ◽  
Parametric Models ◽  
Sensitivity Analyses ◽  
Statistical Methodology ◽  
Mediation Effects ◽  
Explanatory Variables ◽  
Causal Mediation ◽  
Different Types ◽  
Recent Developments ◽  
Causal Mediation Analysis

Estimating the mechanisms that connect explanatory variables with the explained variable, also known as “mediation analysis,” is central to a variety of social-science fields, especially psychology, and increasingly to fields like epidemiology. Recent work on the statistical methodology behind mediation analysis points to limitations in earlier methods. We implement in Stata computational approaches based on recent developments in the statistical methodology of mediation analysis. In particular, we provide functions for the correct calculation of causal mediation effects using several different types of parametric models, as well as the calculation of sensitivity analyses for violations to the key identifying assumption required for interpreting mediation results causally.

scholarly journals Comprehensive analysis of indels in whole-genome microsatellite regions and microsatellite instability across 21 cancer types

2020 ◽  
Vol 30 (3) ◽  
pp. 334-346 ◽  
Author(s):  
Akihiro Fujimoto ◽  
Masashi Fujita ◽  
Takanori Hasegawa ◽  
Jing Hao Wong ◽  
Kazuhiro Maejima ◽  
...  
Keyword(s):  
Microsatellite Instability ◽  
Comprehensive Analysis ◽  
Whole Genome ◽  
Cancer Types

scholarly journals Epigenetic modifications of histones in cancer

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Zibo Zhao ◽  
Ali Shilatifard
Keyword(s):  
Histone Modifications ◽  
Functional Significance ◽  
Epigenetic Modifications ◽  
Cancer Development ◽  
Clinical Implications ◽  
Biological Processes ◽  
Disease Pathogenesis ◽  
Different Types ◽  
Histone Modifiers ◽  
The Many

AbstractThe epigenetic modifications of histones are versatile marks that are intimately connected to development and disease pathogenesis including human cancers. In this review, we will discuss the many different types of histone modifications and the biological processes with which they are involved. Specifically, we review the enzymatic machineries and modifications that are involved in cancer development and progression, and how to apply currently available small molecule inhibitors for histone modifiers as tool compounds to study the functional significance of histone modifications and their clinical implications.

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