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RESEARCH ARTICLE
Genomic profiling of murine mammary tumors identifies potential personalized drug targets for p53-deficient mammary cancers
Adam D. Pfefferle, Yash N. Agrawal, Daniel C. Koboldt, Krishna L. Kanchi, Jason I. Herschkowitz, Elaine R. Mardis, Jeffrey M. Rosen, Charles M. Perou
Disease Models & Mechanisms 2016 9: 749-757; doi: 10.1242/dmm.025239
Adam D. Pfefferle
1Department of Pathology and Laboratory Medicine, University of North Carolina, Chapel Hill, NC 27599, USA
2Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC 27599, USA
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  • ORCID record for Adam D. Pfefferle
Yash N. Agrawal
2Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC 27599, USA
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Daniel C. Koboldt
3The McDonnell Genome Institute, Washington University School of Medicine, St Louis, MO 63108, USA
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Krishna L. Kanchi
3The McDonnell Genome Institute, Washington University School of Medicine, St Louis, MO 63108, USA
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Jason I. Herschkowitz
4Department of Biomedical Sciences, University at Albany, Rensselaer, NY 12144, USA
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Elaine R. Mardis
3The McDonnell Genome Institute, Washington University School of Medicine, St Louis, MO 63108, USA
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Jeffrey M. Rosen
5Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX 77030, USA
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Charles M. Perou
1Department of Pathology and Laboratory Medicine, University of North Carolina, Chapel Hill, NC 27599, USA
2Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC 27599, USA
6Department of Genetics, University of North Carolina, Chapel Hill, NC 27599, USA
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  • For correspondence: cperou@med.unc.edu
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    Fig. 1.

    Human counterparts of Trp53-null transplant tumors. (A) Genes highly expressed within each Trp53-null transplant class were identified using a two-class (class x versus all others) SAM analysis (FDR 0%) across our 385-sample murine microarray dataset. The standardized average of these gene signatures was calculated across more than 3000 human tumors and displayed by intrinsic subtype. (B) Tumor differentiation scores (D-Scores) (Prat et al., 2010) were calculated for all 385 murine samples and displayed by intrinsic class. The D-Scores of the three Trp53-null transplant classes were compared using a Student's t-test.

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    Fig. 2.

    Murine Trp53-null tumor datasets. Sequencing and microarray technologies were used to produce four Trp53-null tumor datasets of varying sizes: (i) whole genome sequencing (n=12), (ii) exome sequencing (n=25), (iii) DNA copy-number microarray (n=43) and (iv) gene expression microarray (n=43). The intrinsic class of each sample is displayed on the dendrogram, with colored boxes being previously identified human subtype counterparts (Pfefferle et al., 2013). The hierarchical clustering location of each p53-null tumor within the datasets is displayed as a vertical black strip. *The Trp53-null transplant model produces heterogeneous tumors that primarily develop into one of these three murine expression subtypes. For each dataset, the number of tumors studied from each of the three murine classes highlighted by ‘*’ is displayed on the right-hand side of the figure.

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    Fig. 3.

    Chromosome structural-variation analysis. Displayed are circos plots of the structural variants (SVs) enriched within (A) p53null-BasalEx, (B) p53null-Claudin-lowEx and (C) p53null-LuminalEx tumors as determined by a two-class (class x versus all others) Fisher's exact test. The genes affected by these SVs are listed under each circos plot.

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    Fig. 4.

    DNA copy-number analysis. Displayed in genomic order are the median class DNA copy-number levels for (A) p53null-BasalEx, (B) p53null-Claudin-lowEx and (C) p53null-LuminalEx tumors. DNA copy-number changes enriched within each of the three Trp53-null transplant classes were identified using a two-class (class x versus all others) SAM analysis. Genomic regions of significant gain are labeled in red and regions of significant loss are labeled in green.

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    Fig. 5.

    Conserved DNA aberrations with human basal-like tumors. Pearson correlations between DNA copy number and gene expression were determined for all genes within the significant regions of gain and loss from Fig. 4. Genes with a correlation greater than or equal to 0.5 are displayed in genomic order. The heatmap corresponds to DNA copy-number abundance. The percentage of human non-basal and human basal-like tumors affected by amplification or deletion of these genes is displayed. Human basal-like enriched events (indicated by ***) were determined using a two-class Fisher's exact test (P<0.05). CL, p53null-Claudin-lowEx.

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    Fig. 6.

    Candidate drug targets. (A) Candidate-drug-target filtering steps. (B) Final drug-target candidates for p53null-BasalEx and p53null-LuminalEx tumors. (C) Correlation of DNA and RNA for Met. (D) Change in tumor volume after 14 days of continuous crizotinib treatment displayed as box-and-whisker plots.

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Keywords

  • Basal-like breast cancer
  • Exome sequencing
  • Genetically engineered mouse models
  • p53
  • Personalized genomics
  • Whole-genome sequencing

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RESEARCH ARTICLE
Genomic profiling of murine mammary tumors identifies potential personalized drug targets for p53-deficient mammary cancers
Adam D. Pfefferle, Yash N. Agrawal, Daniel C. Koboldt, Krishna L. Kanchi, Jason I. Herschkowitz, Elaine R. Mardis, Jeffrey M. Rosen, Charles M. Perou
Disease Models & Mechanisms 2016 9: 749-757; doi: 10.1242/dmm.025239
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RESEARCH ARTICLE
Genomic profiling of murine mammary tumors identifies potential personalized drug targets for p53-deficient mammary cancers
Adam D. Pfefferle, Yash N. Agrawal, Daniel C. Koboldt, Krishna L. Kanchi, Jason I. Herschkowitz, Elaine R. Mardis, Jeffrey M. Rosen, Charles M. Perou
Disease Models & Mechanisms 2016 9: 749-757; doi: 10.1242/dmm.025239

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