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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

Copy number frequency landscape plots from SWITCHplus showing mouse group-specific CNAs. Segments of group-specific copy number gains are plotted above the x-axis in red and segments of copy number loss are plotted below the x-axis in green. Regions shaded gray indicate segments that are not group-specific or highly frequent (greater than or equal to 15 %). The frequency of alterations in each mouse group is indicated on the y-axis from 0 to 100 %. a C3Tag, b Neu/PyMT, c p53-Basal, d p53-Luminal, e Myc, f Wnt1, and g Claudin-Low copy number landscapes
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Fig2: Copy number frequency landscape plots from SWITCHplus showing mouse group-specific CNAs. Segments of group-specific copy number gains are plotted above the x-axis in red and segments of copy number loss are plotted below the x-axis in green. Regions shaded gray indicate segments that are not group-specific or highly frequent (greater than or equal to 15 %). The frequency of alterations in each mouse group is indicated on the y-axis from 0 to 100 %. a C3Tag, b Neu/PyMT, c p53-Basal, d p53-Luminal, e Myc, f Wnt1, and g Claudin-Low copy number landscapes

Mentions: To identify subtype-specific, and mouse group-specific regions of DNA copy number gains and/or losses we developed a new bioinformatics visualization tool called SWITCHplus. Applying this tool to the mouse dataset identified group-specific DNA copy number changes for each of the seven expression-defined groups (Fig. 2). These results suggest that most mouse groups are characterized by numerous DNA copy number changes, many of which are specific to a given model/group (Supplemental Table 4). However, by comparing the copy number landscape between mouse groups, we also identified CNAs that were present in multiple models (Fig. 2; Supplemental Table 4), which can be considered as common CNAs of murine mammary oncogenesis. Therefore, these data support the notion that common spontaneous events may occur within different GEM mammary models irrespective of the initiating genetic event (i.e., transgene). Consistent with previous work, we identified multiple GEM mammary p53 groups based on gene expression patterns [18, 19]. Interestingly, these p53 groups demonstrated not only differences in mRNA expression patterns, but also exhibited differences in the DNA copy number landscapes (Fig. 2c, d). Additionally, we noticed that the p53-Luminal, p53-Basal, and C3Tag groups contained more group-specific CNAs than any of the other mouse groups (Supplemental Table 4); this observation is likely due to the loss of TP53 in these three groups. On average, each mouse group exhibited nearly twice the number of group-specific copy number gains versus losses.Fig. 2


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)

Copy number frequency landscape plots from SWITCHplus showing mouse group-specific CNAs. Segments of group-specific copy number gains are plotted above the x-axis in red and segments of copy number loss are plotted below the x-axis in green. Regions shaded gray indicate segments that are not group-specific or highly frequent (greater than or equal to 15 %). The frequency of alterations in each mouse group is indicated on the y-axis from 0 to 100 %. a C3Tag, b Neu/PyMT, c p53-Basal, d p53-Luminal, e Myc, f Wnt1, and g Claudin-Low copy number landscapes
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig2: Copy number frequency landscape plots from SWITCHplus showing mouse group-specific CNAs. Segments of group-specific copy number gains are plotted above the x-axis in red and segments of copy number loss are plotted below the x-axis in green. Regions shaded gray indicate segments that are not group-specific or highly frequent (greater than or equal to 15 %). The frequency of alterations in each mouse group is indicated on the y-axis from 0 to 100 %. a C3Tag, b Neu/PyMT, c p53-Basal, d p53-Luminal, e Myc, f Wnt1, and g Claudin-Low copy number landscapes
Mentions: To identify subtype-specific, and mouse group-specific regions of DNA copy number gains and/or losses we developed a new bioinformatics visualization tool called SWITCHplus. Applying this tool to the mouse dataset identified group-specific DNA copy number changes for each of the seven expression-defined groups (Fig. 2). These results suggest that most mouse groups are characterized by numerous DNA copy number changes, many of which are specific to a given model/group (Supplemental Table 4). However, by comparing the copy number landscape between mouse groups, we also identified CNAs that were present in multiple models (Fig. 2; Supplemental Table 4), which can be considered as common CNAs of murine mammary oncogenesis. Therefore, these data support the notion that common spontaneous events may occur within different GEM mammary models irrespective of the initiating genetic event (i.e., transgene). Consistent with previous work, we identified multiple GEM mammary p53 groups based on gene expression patterns [18, 19]. Interestingly, these p53 groups demonstrated not only differences in mRNA expression patterns, but also exhibited differences in the DNA copy number landscapes (Fig. 2c, d). Additionally, we noticed that the p53-Luminal, p53-Basal, and C3Tag groups contained more group-specific CNAs than any of the other mouse groups (Supplemental Table 4); this observation is likely due to the loss of TP53 in these three groups. On average, each mouse group exhibited nearly twice the number of group-specific copy number gains versus losses.Fig. 2

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