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Cross-species DNA copy number analyses identifies multiple 1q21-q23 subtype-specific driver genes for breast cancer.

Silva GO, He X, Parker JS, Gatza ML, Carey LA, Hou JP, Moulder SL, Marcom PK, Ma J, Rosen JM, Perou CM - Breast Cancer Res. Treat. (2015)

Bottom Line: Using a novel method called SWITCHplus, we identified subtype-specific DNA CNAs occurring at a 15% or greater frequency, which excluded many well-known breast cancer-related drivers such as amplification of ERBB2, and deletions of TP53 and RB1.Additional criteria that included gene expression-to-copy number correlation, a DawnRank network analysis, and RNA interference functional studies highlighted candidate driver genes that fulfilled these multiple criteria.Specifically, we identified chromosome 1q21-23 as a Basal-like subtype-enriched region with multiple potential driver genes including PI4KB, SHC1, and NCSTN.

View Article: PubMed Central - PubMed

Affiliation: Department of Genetics, University of North Carolina, Chapel Hill, NC, 27599, USA, silvag@email.unc.edu.

ABSTRACT
A large number of DNA copy number alterations (CNAs) exist in human breast cancers, and thus characterizing the most frequent CNAs is key to advancing therapeutics because it is likely that these regions contain breast tumor 'drivers' (i.e., cancer causal genes). This study aims to characterize the genomic landscape of breast cancer CNAs and identify potential subtype-specific drivers using a large set of human breast tumors and genetically engineered mouse (GEM) mammary tumors. Using a novel method called SWITCHplus, we identified subtype-specific DNA CNAs occurring at a 15% or greater frequency, which excluded many well-known breast cancer-related drivers such as amplification of ERBB2, and deletions of TP53 and RB1. A comparison of CNAs between mouse and human breast tumors identified regions with shared subtype-specific CNAs. Additional criteria that included gene expression-to-copy number correlation, a DawnRank network analysis, and RNA interference functional studies highlighted candidate driver genes that fulfilled these multiple criteria. Numerous regions of shared CNAs were observed between human breast tumors and GEM mammary tumor models that shared similar gene expression features. Specifically, we identified chromosome 1q21-23 as a Basal-like subtype-enriched region with multiple potential driver genes including PI4KB, SHC1, and NCSTN. This step-wise computational approach based on a cross-species comparison is applicable to any tumor type for which sufficient human and model system DNA copy number data exist, and in this instance, highlights that a single region of amplification may in fact harbor multiple driver genes.

No MeSH data available.


Related in: MedlinePlus

Data analysis pipeline to identify candidate driver genes within subtype-specific CNAs
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Fig1: Data analysis pipeline to identify candidate driver genes within subtype-specific CNAs

Mentions: To identify subtype-specific CNAs from segmentation data generated by the various copy number array platforms (see Supplemental File 1 for details), we produced an add-on script to the SWITCHdna method of DNA copy number change point detection [13]. We created an R suite of functions called SWITCHplus, which can identify segments of the genome with copy number changes specific for a user-determined set of tumors, thus providing a supervised method for analyzing copy number data. SWITCHplus is provided as a source script in R and available for download at: https://genome.unc.edu/SWITCHplus/. Note, that we did not perform multiple hypothesis testing corrections as we chose alternative biologically based filtering criteria (Fig. 1) based upon cross-species conservation.Fig. 1


Cross-species DNA copy number analyses identifies multiple 1q21-q23 subtype-specific driver genes for breast cancer.

Silva GO, He X, Parker JS, Gatza ML, Carey LA, Hou JP, Moulder SL, Marcom PK, Ma J, Rosen JM, Perou CM - Breast Cancer Res. Treat. (2015)

Data analysis pipeline to identify candidate driver genes within subtype-specific CNAs
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4491106&req=5

Fig1: Data analysis pipeline to identify candidate driver genes within subtype-specific CNAs
Mentions: To identify subtype-specific CNAs from segmentation data generated by the various copy number array platforms (see Supplemental File 1 for details), we produced an add-on script to the SWITCHdna method of DNA copy number change point detection [13]. We created an R suite of functions called SWITCHplus, which can identify segments of the genome with copy number changes specific for a user-determined set of tumors, thus providing a supervised method for analyzing copy number data. SWITCHplus is provided as a source script in R and available for download at: https://genome.unc.edu/SWITCHplus/. Note, that we did not perform multiple hypothesis testing corrections as we chose alternative biologically based filtering criteria (Fig. 1) based upon cross-species conservation.Fig. 1

Bottom Line: Using a novel method called SWITCHplus, we identified subtype-specific DNA CNAs occurring at a 15% or greater frequency, which excluded many well-known breast cancer-related drivers such as amplification of ERBB2, and deletions of TP53 and RB1.Additional criteria that included gene expression-to-copy number correlation, a DawnRank network analysis, and RNA interference functional studies highlighted candidate driver genes that fulfilled these multiple criteria.Specifically, we identified chromosome 1q21-23 as a Basal-like subtype-enriched region with multiple potential driver genes including PI4KB, SHC1, and NCSTN.

View Article: PubMed Central - PubMed

Affiliation: Department of Genetics, University of North Carolina, Chapel Hill, NC, 27599, USA, silvag@email.unc.edu.

ABSTRACT
A large number of DNA copy number alterations (CNAs) exist in human breast cancers, and thus characterizing the most frequent CNAs is key to advancing therapeutics because it is likely that these regions contain breast tumor 'drivers' (i.e., cancer causal genes). This study aims to characterize the genomic landscape of breast cancer CNAs and identify potential subtype-specific drivers using a large set of human breast tumors and genetically engineered mouse (GEM) mammary tumors. Using a novel method called SWITCHplus, we identified subtype-specific DNA CNAs occurring at a 15% or greater frequency, which excluded many well-known breast cancer-related drivers such as amplification of ERBB2, and deletions of TP53 and RB1. A comparison of CNAs between mouse and human breast tumors identified regions with shared subtype-specific CNAs. Additional criteria that included gene expression-to-copy number correlation, a DawnRank network analysis, and RNA interference functional studies highlighted candidate driver genes that fulfilled these multiple criteria. Numerous regions of shared CNAs were observed between human breast tumors and GEM mammary tumor models that shared similar gene expression features. Specifically, we identified chromosome 1q21-23 as a Basal-like subtype-enriched region with multiple potential driver genes including PI4KB, SHC1, and NCSTN. This step-wise computational approach based on a cross-species comparison is applicable to any tumor type for which sufficient human and model system DNA copy number data exist, and in this instance, highlights that a single region of amplification may in fact harbor multiple driver genes.

No MeSH data available.


Related in: MedlinePlus