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Identification of Collaborative Driver Pathways in Breast Cancer

Overview
Journal BMC Genomics
Publisher Biomed Central
Specialty Genetics
Date 2014 Jul 19
PMID 25034939
Citations 5
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Abstract

Background: An important challenge in cancer biology is to computationally screen mutations in cancer cells, separating those that might drive cancer initiation and progression, from the much larger number of bystanders. Since mutations are large in number and diverse in type, the frequency of any particular mutation pattern across a set of samples is low. This makes statistical distinctions and reproducibility across different populations difficult to establish.

Results: In this paper we develop a novel method that promises to partially ameliorate these problems. The basic idea is although mutations are highly heterogeneous and vary from one sample to another, the processes that are disrupted when cells undergo transformation tend to be invariant across a population for a particular cancer or cancer subtype. Specifically, we focus on finding mutated pathway-groups that are invariant across samples of breast cancer subtypes. The identification of informative pathway-groups consists of two steps. The first is identification of pathways significantly enriched in genes containing non-synonymous mutations; the second uses pathways so identified to find groups that are functionally related in the largest number of samples. An application to 4 subtypes of breast cancer identified pathway-groups that can highly explicate a particular subtype and rich in processes associated with transformation.

Conclusions: In contrast to previous methods that identify pathways across a set of samples without any further validation, we show that mutated pathway-groups can be found in each breast cancer subtype and that such groups are invariant across the majority of samples. The algorithm is available at http://www.visantnet.org/misi/MUDPAC.zip.

Citing Articles

CDKN1B mutation and copy number variation are associated with tumor aggressiveness in luminal breast cancer.

Viotto D, Russo F, Anania I, Segatto I, Rampioni Vinciguerra G, DallAcqua A J Pathol. 2020; 253(2):234-245.

PMID: 33140857 PMC: 7839435. DOI: 10.1002/path.5584.


Characterization of potential driver mutations involved in human breast cancer by computational approaches.

Rajendran B, Deng C Oncotarget. 2017; 8(30):50252-50272.

PMID: 28477017 PMC: 5564847. DOI: 10.18632/oncotarget.17225.


Mining pathway associations for disease-related pathway activity analysis based on gene expression and methylation data.

Lee H, Shin M BioData Min. 2017; 10:3.

PMID: 28168005 PMC: 5286825. DOI: 10.1186/s13040-017-0127-7.


Mutated Pathways as a Guide to Adjuvant Therapy Treatments for Breast Cancer.

Liu Y, Hu Z, DeLisi C Mol Cancer Ther. 2015; 15(1):184-9.

PMID: 26625895 PMC: 4707054. DOI: 10.1158/1535-7163.MCT-15-0601.


Evaluation and integration of cancer gene classifiers: identification and ranking of plausible drivers.

Liu Y, Tian F, Hu Z, DeLisi C Sci Rep. 2015; 5:10204.

PMID: 25961669 PMC: 4650817. DOI: 10.1038/srep10204.

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