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. 2017 Mar 28;8:14864.
doi: 10.1038/ncomms14864.

Proteogenomic Integration Reveals Therapeutic Targets in Breast Cancer Xenografts

Free PMC article

Proteogenomic Integration Reveals Therapeutic Targets in Breast Cancer Xenografts

Kuan-Lin Huang et al. Nat Commun. .
Free PMC article

Erratum in

  • Corrigendum: Proteogenomic integration reveals therapeutic targets in breast cancer xenografts.
    Huang KL, Li S, Mertins P, Cao S, Gunawardena HP, Ruggles KV, Mani DR, Clauser KR, Tanioka M, Usary J, Kavuri SM, Xie L, Yoon C, Qiao JW, Wrobel J, Wyczalkowski MA, Erdmann-Gilmore P, Snider JE, Hoog J, Singh P, Niu B, Guo Z, Sun SQ, Sanati S, Kawaler E, Wang X, Scott A, Ye K, McLellan MD, Wendl MC, Malovannaya A, Held JM, Gillette MA, Fenyö D, Kinsinger CR, Mesri M, Rodriguez H, Davies SR, Perou CM, Ma C, Townsend RR, Chen X, Carr SA, Ellis MJ, Ding L. Huang KL, et al. Nat Commun. 2017 Apr 25;8:15479. doi: 10.1038/ncomms15479. Nat Commun. 2017. PMID: 28440318 Free PMC article. No abstract available.


Recent advances in mass spectrometry (MS) have enabled extensive analysis of cancer proteomes. Here, we employed quantitative proteomics to profile protein expression across 24 breast cancer patient-derived xenograft (PDX) models. Integrated proteogenomic analysis shows positive correlation between expression measurements from transcriptomic and proteomic analyses; further, gene expression-based intrinsic subtypes are largely re-capitulated using non-stromal protein markers. Proteogenomic analysis also validates a number of predicted genomic targets in multiple receptor tyrosine kinases. However, several protein/phosphoprotein events such as overexpression of AKT proteins and ARAF, BRAF, HSP90AB1 phosphosites are not readily explainable by genomic analysis, suggesting that druggable translational and/or post-translational regulatory events may be uniquely diagnosed by MS. Drug treatment experiments targeting HER2 and components of the PI3K pathway supported proteogenomic response predictions in seven xenograft models. Our study demonstrates that MS-based proteomics can identify therapeutic targets and highlights the potential of PDX drug response evaluation to annotate MS-based pathway activities.

Conflict of interest statement

C.M. Perou is on the board of directors, has ownership interest (including patents), and is a consultant/advisory board member for bioclassifier LLC. The other authors declare no competing financial interests.


Figure 1
Figure 1. Modelling human breast cancer with patient-derived xenografts (n=24).
(a) Illustration of generation and proteogenomic characterization of breast cancer xenograft models. (b) Somatic mutations of significantly mutated genes of human breast tumour were recapitulated in xenograft models. Mutation data for 23 WHIMs are shown (exome data were not available for WHIM47). (c) Variant allele fraction analysis showed clonal representation was consistent between human breast tumour and xenografts. Genomic driver events, including missenses and truncations in TP53 and PIK3CA, were retained in the xenograft models. Each colour represents one xenograft sample.
Figure 2
Figure 2. Proteogenomic correlation analysis in PDX samples.
(a) Correlation between mRNA and iTRAQ protein expression levels identified pathways with significantly concordant or discordant mRNA-protein expressions. Genes were aligned along the x axis by the rank of their Spearman correlation coefficient between mRNA and protein expression levels. Each colour represents one significantly associated pathway, and each bar represents one gene in the pathway. (b) Proteogenomic summary of xenograft shows relationships among mutation, CNV (normalized log-R ratio), mRNA (log-transformed and normalized RSEM values), proteomic (normalized log2 ratio to reference), and phosphoproteomic expression (normalized log2 ratio to reference) levels of breast cancer-related genes in 24 PDX samples across 4 intrinsic subtypes. Expression values from each dataset were calculated as described (Methods) and truncated to a maximum of 10 and a minimum of −10 for visualization.
Figure 3
Figure 3. Transcriptomic and proteomic clustering of breast cancer PDX and human samples.
(a) Transcriptomic clustering of PDX breast tumours based on the PAM50 gene expression markers. (b) Proteomic clustering of PDX breast tumours based on the top 436 variably expressed proteins. (c) Phosphoproteomic clustering of PDX breast tumours based on the top 1,737 variably expressed phosphosites. (d) Proteomic clustering using only 133 non-differential expressed proteins between WHIM and human breast tumour samples. The clustering reproduced the basal-enriched and luminal-enriched clusters, where PDX (n=24) and TCGA human breast tumour samples (n=77) cluster based on their subtypes. The non-differentially expressed proteins were identified through a t-test with FDR>0.3 between the PDX and the TCGA human tumour samples. The PDX tumours are labelled by their WHIM IDs whereas the human tumours are not labelled.
Figure 4
Figure 4. Activated signalling pathways detected through pathway phosphorylation enrichment analysis.
(a) Activation of the MAPK signalling pathway in WHIM9. (b) Activation of the Ras signalling pathway in WHIM12. (c) Activation of the NFκB pathway in WHIM17. (d) Activation of the NFκB pathway in WHIM46. Phosphorylation levels of each protein in the pathway relative to the cohort of 24 PDX models are shown by the colour scale of red (high) to blue (low). Proteins with no phosphorylation data are coloured in green.
Figure 5
Figure 5. Druggable events identified by expression outlier analysis.
Druggable outlier events identified at (a) the mRNA and protein and (b) protein and phosphopeptide expression levels. Each colour represents one xenograft sample. Key outlier events validated in this study are labelled by text. (c) Immunochemistry staining verified outlier expression of HER2 in WHIM8 and WHIM35, RAF1 in WHIM9, and FGFR2 in WHIM16. Scale bar: 50 μm.
Figure 6
Figure 6. Druggable targets identified through proteogenomic analysis in PDX and human breast cancer.
(a) Outlier analysis revealed potentially druggable events in the RTK, PI3K, MAPK signalling, genome integrity pathways at various frequency and magnitudes across four breast cancer subtypes. Selected genes with any outlier score greater than 2.5 or in the key oncogenic pathways, including the PI3K, RTK, MAPK signalling pathways, are shown. (b) Comparison of protein expression outliers of selected druggable genes in PDX and human breast tumours. (c) Comparison of overexpressed phosphosite outliers of selected druggable genes in PDX and human breast tumours. Key outlier events reaching the outlier definition threshold or validated in this study are labelled by text. For the box plots in (b) and (c), the center line indicates median of the protein/phosphosite expression in the human and PDX cohort. The upper and lower hinges correspond to the 25th and 75th percentiles; the upper whisker corresponds to 1.5 × IQR (inter-quartile range) above the 25 percentile and the lower whisker corresponds to 1.5 × IQR below the 75 percentile.
Figure 7
Figure 7. Targeted treatments of breast cancer xenografts.
(a) Western blot of HER2 protein and HER2 p.Y1248 expression levels in 5 WHIM models (WHIM6, WHIM8, WHIM12, WHIM14 and WHIM35). (b) In vivo treatment responses to lapatinib in 4 PDX models including two HER2 positive lines (WHIM8 and WHIM35) and two basal lines (WHIM6 and WHIM14). The response is measured in fold change (%) of tumour volumes after 2 weeks of vehicle or lapatinib treatment. (c) Immunochemistry staining of AKT phosphorylation status in WHIM16, WHIM18 and WHIM20 in response to PI3K inhibitor TAK-117, mTOR inhibitor TAK128, and TAK-117/TAK-128 combined. (d) In vivo treatment responses to PI3K/mTOR inhibitors (TAK-117/TAK-128) of WHIM16, WHIM18 and WHIM20 (n=8-9 for each control or experimental group). Values were represented by average tumour volume (mm3) every 3 days following treatment (error bars: s.e.m.).

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    1. Green E. D. & Guyer M. S. National Human Genome Research, I. Charting a course for genomic medicine from base pairs to bedside. Nature 470, 204–213 (2011). - PubMed
    1. Garnett M. J. et al. . Systematic identification of genomic markers of drug sensitivity in cancer cells. Nature 483, 570–575 (2012). - PMC - PubMed
    1. Rubio-Perez C. et al. . In silico prescription of anticancer drugs to cohorts of 28 tumor types reveals targeting opportunities. Cancer Cell 27, 382–396 (2015). - PubMed
    1. Simon R. & Roychowdhury S. Implementing personalized cancer genomics in clinical trials. Nat. Rev. Drug Discov. 12, 358–369 (2013). - PubMed
    1. Chin L., Andersen J. N. & Futreal P. A. Cancer genomics: from discovery science to personalized medicine. Nat. Med. 17, 297–303 (2011). - PubMed

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