Chromosome abnormalities in ovarian adenocarcinoma: III. Using breakpoint data to infer and test mathematical models for oncogenesis

Simon, Richard H.; Desper, Richard; Papadimitriou, Christos H.; Peng, A; Alberts, David S.; Taetle, Raymond; Trent, Jeffrey M.; Schäffer, Alejandro A.

doi:10.1002/(sici)1098-2264(200005)28:1<106::aid-gcc13>3.0.co;2-s

Cited by 47 publications

(33 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…A major difficulty with Bayesian network models is that they generally have many more paths than do tree models, causing an exponential number of parameters to estimate and overfitting of the data. Several assumptions were thus proposed to reduce the number of parameters when learning Bayesian network models [21,29,35] .…”

Section: Comparison Of Mathematical Modeling Methodsmentioning

confidence: 99%

“…The events which are consistently near the root in both trees are inferred to be the early events, and the events which tend to cluster together in both trees are deduced to mark subclasses of tumors [17] . Both methods are preferred to be used, so as to boost confidence when they make consistent predictions, and raise doubts when they make inconsistent predictions [21] .…”

Section: Oncogenetic Tree Modelsmentioning

confidence: 99%

“…Since proposed, tree models have been used by many researchers to explore the molecular mechanism of tumor development, including the CGH data from renal cell carcinoma [20,22] , hereditary breast cancers [23] , bladder cancer [24] , head and neck squamous cell cancer [25] , nasopharyngeal carcinoma [14,26] , thymoma [27] , and rat mammary tumors [28] , and breakpoint data from ovarian cancer [21] . These models have been used to identify early events, as well as marker events of subtypes of tumors, defining the sequence of these events and the involved pathways.…”

Section: Oncogenetic Tree Modelsmentioning

confidence: 99%

“…The models have been applied as directed acyclic graph (DAG) models previously by Simon et al [21] and Radmacher et al [34] .…”

Section: Bayesian Network Modelsmentioning

confidence: 99%

See 3 more Smart Citations

Mathematical modeling of carcinogenesis based on chromosome aberration data

2009

Chin. J. Cancer Res.

View full text Add to dashboard Cite

Objective:The progression of human cancer is characterized by the accumulation of genetic instability. An increasing number of experimental genetic molecular techniques have been used to detect chromosome aberrations. Previous studies on chromosome abnormalities often focused on identifying the frequent loci of chromosome alterations, but rarely addressed the issue of interrelationship of chromosomal abnormalities. In the last few years, several mathematical models have been employed to construct models of carcinogenesis, in an attempt to identify the time order and cause-and-effect relationship of chromosome aberrations. The principles and applications of these models are reviewed and compared in this paper. Mathematical modeling of carcinogenesis can contribute to our understanding of the molecular genetics of tumor development, and identification of cancer related genes, thus leading to improved clinical practice of cancer.

show abstract

Section: Comparison Of Mathematical Modeling Methodsmentioning

confidence: 99%

Section: Oncogenetic Tree Modelsmentioning

confidence: 99%

Section: Oncogenetic Tree Modelsmentioning

confidence: 99%

“…The models have been applied as directed acyclic graph (DAG) models previously by Simon et al [21] and Radmacher et al [34] .…”

Section: Bayesian Network Modelsmentioning

confidence: 99%

See 2 more Smart Citations

Mathematical modeling of carcinogenesis based on chromosome aberration data

2009

Chin. J. Cancer Res.

View full text Add to dashboard Cite

show abstract

“…Previously published methods include the linear model [7], the oncogenetic tree (oncotree) approach [9], [10], [11], [12], various Bayesian graphical approaches [13], [14], and some clustering-based methods [15], [16]. Based upon the seminal work in delineating the temporal sequence of events in colorectal cancer by Vogelstein and colleagues, the linear model assumes that there exists a single, most likely order of mutations, and that all of these mutations arise in sequential order [7].…”

Section: Introductionmentioning

confidence: 99%

A Mathematical Methodology for Determining the Temporal Order of Pathway Alterations Arising during Gliomagenesis

et al. 2012

View full text Add to dashboard Cite

Human cancer is caused by the accumulation of genetic alterations in cells. Of special importance are changes that occur early during malignant transformation because they may result in oncogene addiction and thus represent promising targets for therapeutic intervention. We have previously described a computational approach, called Retracing the Evolutionary Steps in Cancer (RESIC), to determine the temporal sequence of genetic alterations during tumorigenesis from cross-sectional genomic data of tumors at their fully transformed stage. Since alterations within a set of genes belonging to a particular signaling pathway may have similar or equivalent effects, we applied a pathway-based systems biology approach to the RESIC methodology. This method was used to determine whether alterations of specific pathways develop early or late during malignant transformation. When applied to primary glioblastoma (GBM) copy number data from The Cancer Genome Atlas (TCGA) project, RESIC identified a temporal order of pathway alterations consistent with the order of events in secondary GBMs. We then further subdivided the samples into the four main GBM subtypes and determined the relative contributions of each subtype to the overall results: we found that the overall ordering applied for the proneural subtype but differed for mesenchymal samples. The temporal sequence of events could not be identified for neural and classical subtypes, possibly due to a limited number of samples. Moreover, for samples of the proneural subtype, we detected two distinct temporal sequences of events: (i) RAS pathway activation was followed by TP53 inactivation and finally PI3K2 activation, and (ii) RAS activation preceded only AKT activation. This extension of the RESIC methodology provides an evolutionary mathematical approach to identify the temporal sequence of pathway changes driving tumorigenesis and may be useful in guiding the understanding of signaling rearrangements in cancer development.

show abstract

Cytogenetics and molecular genetics of ovarian cancer

Wang

2002

Am. J. Med. Genet.

View full text Add to dashboard Cite

Genetic alterations identified in human ovarian tumors by conventional banding, fluorescence in situ hybridization, comparative genomic hybridization, chromosome microdissection, loss of heterozygosity, chromosome microcell-mediated chromosome transfer, and microarray gene expression analysis are summarized and correlated. The significance of these findings with respect to pathologic classification and clinical application are discussed.

show abstract

Chromosome abnormalities in ovarian adenocarcinoma: III. Using breakpoint data to infer and test mathematical models for oncogenesis

Cited by 47 publications

References 20 publications

Mathematical modeling of carcinogenesis based on chromosome aberration data

Mathematical modeling of carcinogenesis based on chromosome aberration data

A Mathematical Methodology for Determining the Temporal Order of Pathway Alterations Arising during Gliomagenesis

Cytogenetics and molecular genetics of ovarian cancer

Contact Info

Product

Resources

About