scholarly journals Nonmonotonic Pathway Gene Expression Analysis Reveals Oncogenic Role of p27/Kip1 at Intermediate Dose

2017 ◽  
Vol 16 ◽  
pp. 117693511774013 ◽  
Author(s):  
Hien H Nguyen ◽  
Susan C Tilton ◽  
Christopher J Kemp ◽  
Mingzhou Song
Keyword(s):  
Gene Expression ◽  
Pathway Analysis ◽  
Dose Response ◽  
Response Analysis ◽  
Response Curves ◽  
Cancer Pathways ◽  
Pathway Gene ◽  
Mechanistic Basis ◽  
Functional Pathway ◽  
Squamous Cell Papilloma

The mechanistic basis by which the level of p27Kip1 expression influences tumor aggressiveness and patient mortality remains unclear. To elucidate the competing tumor-suppressing and oncogenic effects of p27Kip1 on gene expression in tumors, we analyzed the transcriptomes of squamous cell papilloma derived from Cdkn1b nullizygous, heterozygous, and wild-type mice. We developed a novel functional pathway analysis method capable of testing directional and nonmonotonic dose response. This analysis can reveal potential causal relationships that might have been missed by other nondirectional pathway analysis methods. Applying this method to capture dose-response curves in papilloma gene expression data, we show that several known cancer pathways are dominated by low-high-low gene expression responses to increasing p27 gene doses. The oncogene cyclin D1, whose expression is elevated at an intermediate p27 dose, is the most responsive gene shared by these cancer pathways. Therefore, intermediate levels of p27 may promote cellular processes favoring tumorigenesis—strikingly consistent with the dominance of heterozygous mutations in CDKN1B seen in human cancers. Our findings shed new light on regulatory mechanisms for both pro- and anti-tumorigenic roles of p27Kip1. Functional pathway dose-response analysis provides a unique opportunity to uncover nonmonotonic patterns in biological systems.

Log-Logistic Analysis of Herbicide Dose-Response Relationships

1995 ◽  
Vol 9 (2) ◽  
pp. 218-227 ◽  
Author(s):  
Steven S. Seefeldt ◽  
Jens Erik Jensen ◽  
E. Patrick Fuerst
Keyword(s):  
Dose Response ◽  
Logistic Model ◽  
Response Analysis ◽  
Analysis Method ◽  
Weed Science ◽  
Response Curves ◽  
Herbicide Resistant ◽  
Logistic Analysis ◽  
Analysis Methods ◽  
The Difference

Dose-response studies are an important tool in weed science. The use of such studies has become especially prevalent following the widespread development of herbicide resistant weeds. In the past, analyses of dose-response studies have utilized various types of transformations and equations which can be validated with several statistical techniques. Most dose-response analysis methods 1) do not accurately describe data at the extremes of doses and 2) do not provide a proper statistical test for the difference(s) between two or more dose-response curves. Consequently, results of dose-response studies are analyzed and reported in a great variety of ways, and comparison of results among various researchers is not possible. The objective of this paper is to review the principles involved in dose-response research and explain the log-logistic analysis of herbicide dose-response relationships. In this paper the log-logistic model is illustrated using a nonlinear computer analysis of experimental data. The log-logistic model is an appropriate method for analyzing most dose-response studies. This model has been used widely and successfully in weed science for many years in Europe. The log-logistic model possesses several clear advantages over other analysis methods and the authors suggest that it should be widely adopted as a standard herbicide dose-response analysis method.

scholarly journals Advancing risk assessment: mechanistic dose–response modelling of Listeria monocytogenes infection in human populations

2018 ◽  
Vol 5 (8) ◽  
pp. 180343 ◽  
Author(s):  
Ashrafur Rahman ◽  
Daniel Munther ◽  
Aamir Fazil ◽  
Ben Smith ◽  
Jianhong Wu
Keyword(s):  
Listeria Monocytogenes ◽  
Dose Response ◽  
Geometric Interpretation ◽  
Human Populations ◽  
Response Curves ◽  
Future Experimentation ◽  
Limited Power ◽  
Mechanistic Basis ◽  
Response Modelling

The utility of characterizing the effects of strain variation and individual/subgroup susceptibility on dose–response outcomes has motivated the search for new approaches beyond the popular use of the exponential dose–response model for listeriosis. While descriptive models can account for such variation, they have limited power to extrapolate beyond the details of particular outbreaks. By contrast, this study exhibits dose–response relationships from a mechanistic basis, quantifying key biological factors involved in pathogen–host dynamics. An efficient computational algorithm and geometric interpretation of the infection pathway are developed to connect dose–response relationships with the underlying bistable dynamics of the model. Relying on in vitro experiments as well as outbreak data, we estimate plausible parameters for the human context. Despite the presence of uncertainty in such parameters, sensitivity analysis reveals that the host response is most influenced by the pathogen–immune system interaction. In particular, we show how variation in this interaction across a subgroup of the population dictates the shape of dose–response curves. Finally, in terms of future experimentation, our model results provide guidelines and highlight vital aspects of the interplay between immune cells and particular strains of Listeria monocytogenes that should be examined.

scholarly journals Why do pathway methods work better than they should?

2020 ◽  
Author(s):  
Bence Szalai ◽  
Julio Saez-Rodriguez
Keyword(s):  
Gene Expression ◽  
Transcription Factors ◽  
Pathway Analysis ◽  
Information Value ◽  
Classical Pathway ◽  
Protein Activity ◽  
Pathway Activity ◽  
Analysis Methods ◽  
Pathway Gene ◽  
Gene Sets

AbstractDifferent pathway analysis methods are frequently applied to cancer gene expression data to identify dysregulated pathways. In most cases these methods infer pathway activity changes based on the gene expression of pathway members. However, pathways are constituted by signaling proteins, and their activity - not their abundance - defines the activity of the pathway; the association between gene expression and protein activity is in turn limited and not well characterised. Other methods infer pathway activity from the expression of the genes whose transcription is regulated by the pathway of interest, which seems a more adequate proxy of activity. Despite these potential limitations, membership based pathway methods are frequently used and often provide statistically significant results.Here, we submit that pathway based methods are not effective because of the correlation between the gene expression of pathway members and the activity of the pathway, but because pathway member gene sets overlap with the genes regulated by transcription factors (regulons). This implies that pathway methods do not inform about the activity of the pathway of interest, but instead the downstream effects of changes in the activities of transcription factors.To support our hypothesis, we show that the higher the overlap to transcription factor regulons, the higher the information value of pathway gene sets. Furthermore, removing these overlapping genes reduces the information content of pathway gene sets, but not vice versa. Our results suggest that results of classical pathway analysis methods should be interpreted with caution, and instead methods using pathway regulated genes for activity inference should be prioritised.Graphical abstract

scholarly journals Differences in Activation of Intracellular Signalling in Primary Human Retinal Endothelial Cells Between Isoforms of VEGFA-165

2019 ◽  
Author(s):  
Wendelin Dailey ◽  
Roberto Shunemann ◽  
Fang Yang ◽  
Megan Moore ◽  
Austen Knapp ◽  
...  
Keyword(s):  
Gene Expression ◽  
Endothelial Cells ◽  
Dose Response ◽  
Target Gene ◽  
Intracellular Signalling ◽  
Signalling Pathways ◽  
Response Curves ◽  
Retinal Endothelial Cells ◽  
Intracellular Signalling Pathways ◽  
Maximum Activation

ABSTRACTPurposeThere are reports that a b-isoform of Vascular Endothelial Growth Factor-A-165 (VEGFA165b) is predominant in normal human vitreous, switching to the a-isoform (VEGFA165a) in the vitreous of some diseased eyes. While these isoforms appear to have a different ability to activate the VEGF-Receptor-2 (VEGFR2) in various endothelial cells, the nature of their ability to activate intracellular signalling pathways is not fully characterized, especially in retinal endothelial cells. We determined their activation potential for two key intracellular signalling pathways (MAPK, AKT) over complete dose-response curves and compared potential effects on the expression of several VEGFA165 target genes in primary human retinal microvascular endothelial cells (HRMECs).MethodsTo determine full dose-response curves for the activation of MAPK (ERK1/2), AKT and VEGFR2, direct in-cell western assays were developed using primary Human Retinal Microvascular Endothelial Cells (HRMECs). Potential differences in dose-response effects on gene expression markers related to endothelial cell / leukocyte adhesion (ICAM1, VCAM1 and SELE) and tight-junctions (CLDN5 and OCLN) were tested by quantitative-PCR.ResultsActivation dose-response analysis revealed much stronger activation of MAPK, AKT and VEGFR2 by the a-isoform at lower doses. MAPK activation in primary HRMECs displayed a sigmoidal dose-response to a range of VEGFA165a concentrations spanning 10-250 pM, which shifted higher into the 100-5,000 pM range with VEGFA165b. Similar maximum activation of MAPK was achieved by both isoforms at high concentration. Maximum activation of AKT by VEGFA165b was only half of the maximum activation from VEGFA165a. At a lower intermediate dose, where VEGFA165a activated intracellular signalling stronger than VEGFA165b, the changes to VEGFA target gene expression was generally greater with VEGFA165a.ConclusionsIn primary HRMECs, VEGFA165a could maximally activate MAPK and AKT at lower concentrations where VEGFA165b had relatively little effect. The timing for maximal activation of MAPK was similar for both isoforms, which is different than reprorted for non-retinal endothelial cells. While VEGFA165a and VEGFA165b are limited to the sequence of their six C-terminal six amino acids, this results in a large difference in their ablility to activate at least two key intracellular signalling pathways and potentially VEGF target gene expression in primary human retinal endothelial cells.

scholarly journals Automated Dose-Response Analysis of the Relative Hepatic Gene Expression Potency of TCDF in C57BL/6 Mice

2009 ◽  
Vol 112 (1) ◽  
pp. 221-228 ◽  
Author(s):  
Lyle D. Burgoon ◽  
Qi Ding ◽  
Alhaji N'jai ◽  
Ed Dere ◽  
Ashley R. Burg ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dose Response ◽  
Response Analysis ◽  
Hepatic Gene Expression

scholarly journals A regularized functional regression model enabling transcriptome-wide dosage-dependent association study of cancer drug response

2020 ◽  
Author(s):  
Evanthia Koukouli ◽  
Dennis Wang ◽  
Frank Dondelinger ◽  
Juhyun Park
Keyword(s):  
Gene Expression ◽  
Regression Model ◽  
Cell Line ◽  
Cell Lines ◽  
Dose Response ◽  
Drug Response ◽  
Drug Sensitivity ◽  
Cancer Drug ◽  
Functional Regression ◽  
Response Curves

AbstractCancer treatments can be highly toxic and frequently only a subset of the patient population will benefit from a given treatment. Tumour genetic makeup plays an important role in cancer drug sensitivity. We suspect that gene expression markers could be used as a decision aid for treatment selection or dosage tuning. Using in vitro cancer cell line dose-response and gene expression data from the Genomics of Drug Sensitivity in Cancer (GDSC) project, we build a dose-varying regression model. Unlike existing approaches, this allows us to estimate dosage-dependent associations with gene expression. We include the transcriptomic profiles as dose-invariant covariates into the regression model and assume that their effect varies smoothly over the dosage levels. A two-stage variable selection algorithm (variable screening followed by penalised regression) is used to identify genetic factors that are associated with drug response over the varying dosages. We evaluate the effectiveness of our method using simulation studies focusing on the choice of tuning parameters and cross-validation for predictive accuracy assessment. We further apply the model to data from five BRAF targeted compounds applied to different cancer cell lines under different dosage levels. We highlight the dosage-dependent dynamics of the associations between the selected genes and drug response, and we perform pathway enrichment analysis to show that the selected genes play an important role in pathways related to tumourgenesis and DNA damage response.Author SummaryTumour cell lines allow scientists to test anticancer drugs in a laboratory environment. Cells are exposed to the drug in increasing concentrations, and the drug response, or amount of surviving cells, is measured. Generally, drug response is summarized via a single number such as the concentration at which 50% of the cells have died (IC50). To avoid relying on such summary measures, we adopted a functional regression approach that takes the dose-response curves as inputs, and uses them to find biomarkers of drug response. One major advantage of our approach is that it describes how the effect of a biomarker on the drug response changes with the drug dosage. This is useful for determining optimal treatment dosages and predicting drug response curves for unseen drug-cell line combinations. Our method scales to large numbers of biomarkers by using regularisation and, in contrast with existing literature, selects the most informative genes by accounting for drug response at untested dosages. We demonstrate its value using data from the Genomics of Drug Sensitivity in Cancer project to identify genes whose expression is associated with drug response. We show that the selected genes recapitulate prior biological knowledge, and belong to known cancer pathways.

scholarly journals Revisiting inconsistency in large pharmacogenomic studies

2015 ◽  
Author(s):  
Zhaleh Safikhani ◽  
Mark Freeman ◽  
Petr Smirnov ◽  
Nehme El-Hachem ◽  
Adrian She ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Lines ◽  
Dose Response ◽  
Drug Sensitivity ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Drug Dose ◽  
Response Curves ◽  
Sensitivity Data ◽  
Pharmacogenomic Studies

Background: In 2012, two large pharmacogenomic studies, the Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Cell Line Encyclopedia (CCLE), were published, each reported gene expression data and measures of drug response for a large number of drugs and hundreds of cell lines. In 2013, we published a comparative analysis that reported gene expression profiles for the 471 cell lines profiled in both studies and dose response measurements for the 15 drugs characterized in the common cell lines by both studies. While we found good concordance in gene expression profiles, there was substantial inconsistency in the drug responses reported by the GDSC and CCLE projects. Our paper was widely discussed and we received extensive feedback on the comparisons that we performed. This feedback, along with the release of new data, prompted us to revisit our initial analysis. Here we present a new analysis using these expanded data in which we address the most significant suggestions for improvements on our published analysis: that drugs with different response characteristics should have been treated differently, that targeted therapies and broad cytotoxic drugs should have been treated differently in assessing consistency, that consistency of both molecular profiles and drug sensitivity measurements should both be compared across cell lines to accurately assess differences in the studies, that we missed some biomarkers that are consistent between studies, and that the software analysis tools we provided with our analysis should have been easier to run, particularly as the GDSC and CCLE released additional data. Methods: For each drug, we used published sensitivity data from the GDSC and CCLE to separately estimate drug dose-response curves. We then used two statistics, the area between drug dose-response curves (ABC) and the Matthews correlation coefficient (MCC), to robustly estimate the consistency of continuous and discrete drug sensitivity measures, respectively. We also used recently released RNA-seq data together with previously published gene expression microarray data to assess inter-platform reproducibility of cell line gene expression profiles. Results: This re-analysis supports our previous finding that gene expression data are significantly more consistent than drug sensitivity measurements. The use of new statistics to assess data consistency allowed us to identify two broad effect drugs -- 17-AAG and PD-0332901 -- and three targeted drugs -- PLX4720, nilotinib and crizotinib -- with moderate to good consistency in drug sensitivity data between GDSC and CCLE. Not enough sensitive cell lines were screened in both studies to robustly assess consistency for three other targeted drugs, PHA-665752, erlotinib, and sorafenib. Concurring with our published results, we found evidence of inconsistencies in pharmacological phenotypes for the remaining eight drugs. Further, to discover "consistency" between studies required the use of multiple statistics and the selection of specific measures on a case-by-case basis. Conclusion: Our results reaffirm our initial findings of an inconsistency in drug sensitivity measures for eight of fifteen drugs screened both in GDSC and CCLE, irrespective of which statistical metric was used to assess correlation. Taken together, our findings suggest that the phenotypic data on drug response in the GDSC and CCLE continue to present challenges for robust biomarker discovery. This re-analysis provides additional support for the argument that experimental standardization and validation of pharmacogenomic response will be necessary to advance the broad use of large pharmacogenomic screens.

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