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A novel tree-based procedure for deciphering the genomic spectrum of clinical disease entities.

Mbogning C, Perdry H, Toussile W, Broët P - J Clin Bioinforma (2014)

Bottom Line: The proposed procedure with a Bayesian Information Criterion (BIC) achieved good performances to detect the hidden structure as compared to the original procedure.The novel procedure was used for analyzing patterns of copy-number alterations in lung adenocarcinomas, with respect to Kirsten Rat Sarcoma Viral Oncogene Homolog gene (KRAS) mutation status, while controlling for a cohort effect.Our procedure performs well in a general framework and is simple to implement.

View Article: PubMed Central - HTML - PubMed

Affiliation: Abirisk consortium WP4, 14-16 Avenue Paul-Vaillant-Couturier, 94807 Villejuif, France ; Inserm U669, 14-16 Avenue Paul-Vaillant-Couturier, 94807 Villejuif, France.

ABSTRACT

Background: Dissecting the genomic spectrum of clinical disease entities is a challenging task. Recursive partitioning (or classification trees) methods provide powerful tools for exploring complex interplay among genomic factors, with respect to a main factor, that can reveal hidden genomic patterns. To take confounding variables into account, the partially linear tree-based regression (PLTR) model has been recently published. It combines regression models and tree-based methodology. It is however computationally burdensome and not well suited for situations for which a large number of exploratory variables is expected.

Methods: We developed a novel procedure that represents an alternative to the original PLTR procedure, and considered different selection criteria. A simulation study with different scenarios has been performed to compare the performances of the proposed procedure to the original PLTR strategy.

Results: The proposed procedure with a Bayesian Information Criterion (BIC) achieved good performances to detect the hidden structure as compared to the original procedure. The novel procedure was used for analyzing patterns of copy-number alterations in lung adenocarcinomas, with respect to Kirsten Rat Sarcoma Viral Oncogene Homolog gene (KRAS) mutation status, while controlling for a cohort effect. Results highlight two subgroups of pure or nearly pure wild-type KRAS tumors with particular copy-number alteration patterns.

Conclusions: The proposed procedure with a BIC criterion represents a powerful and practical alternative to the original procedure. Our procedure performs well in a general framework and is simple to implement.

No MeSH data available.


Related in: MedlinePlus

Flow-chart of the four steps of the method.
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Figure 1: Flow-chart of the four steps of the method.

Mentions: In the following we present our novel procedure with different selection criteria. The first two steps are similar to those of the original PLTR procedure (Chen et al.) whereas the last two steps are new. The four steps are summarized in Figure 1 and presented in details below.


A novel tree-based procedure for deciphering the genomic spectrum of clinical disease entities.

Mbogning C, Perdry H, Toussile W, Broët P - J Clin Bioinforma (2014)

Flow-chart of the four steps of the method.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4129184&req=5

Figure 1: Flow-chart of the four steps of the method.
Mentions: In the following we present our novel procedure with different selection criteria. The first two steps are similar to those of the original PLTR procedure (Chen et al.) whereas the last two steps are new. The four steps are summarized in Figure 1 and presented in details below.

Bottom Line: The proposed procedure with a Bayesian Information Criterion (BIC) achieved good performances to detect the hidden structure as compared to the original procedure.The novel procedure was used for analyzing patterns of copy-number alterations in lung adenocarcinomas, with respect to Kirsten Rat Sarcoma Viral Oncogene Homolog gene (KRAS) mutation status, while controlling for a cohort effect.Our procedure performs well in a general framework and is simple to implement.

View Article: PubMed Central - HTML - PubMed

Affiliation: Abirisk consortium WP4, 14-16 Avenue Paul-Vaillant-Couturier, 94807 Villejuif, France ; Inserm U669, 14-16 Avenue Paul-Vaillant-Couturier, 94807 Villejuif, France.

ABSTRACT

Background: Dissecting the genomic spectrum of clinical disease entities is a challenging task. Recursive partitioning (or classification trees) methods provide powerful tools for exploring complex interplay among genomic factors, with respect to a main factor, that can reveal hidden genomic patterns. To take confounding variables into account, the partially linear tree-based regression (PLTR) model has been recently published. It combines regression models and tree-based methodology. It is however computationally burdensome and not well suited for situations for which a large number of exploratory variables is expected.

Methods: We developed a novel procedure that represents an alternative to the original PLTR procedure, and considered different selection criteria. A simulation study with different scenarios has been performed to compare the performances of the proposed procedure to the original PLTR strategy.

Results: The proposed procedure with a Bayesian Information Criterion (BIC) achieved good performances to detect the hidden structure as compared to the original procedure. The novel procedure was used for analyzing patterns of copy-number alterations in lung adenocarcinomas, with respect to Kirsten Rat Sarcoma Viral Oncogene Homolog gene (KRAS) mutation status, while controlling for a cohort effect. Results highlight two subgroups of pure or nearly pure wild-type KRAS tumors with particular copy-number alteration patterns.

Conclusions: The proposed procedure with a BIC criterion represents a powerful and practical alternative to the original procedure. Our procedure performs well in a general framework and is simple to implement.

No MeSH data available.


Related in: MedlinePlus