Integrated proteotranscriptomics of breast cancer reveals globally increased protein-mRNA concordance associated with subtypes and survival

[1]  Yiling Lu,et al.  Characterization of Human Cancer Cell Lines by Reverse-phase Protein Arrays. , 2017, Cancer cell.

[2]  Michael L. Gatza,et al.  Proteogenomics connects somatic mutations to signaling in breast cancer , 2016, Nature.

[3]  Eytan Ruppin,et al.  System-wide Clinical Proteomics of Breast Cancer Reveals Global Remodeling of Tissue Homeostasis. , 2016, Cell systems.

[4]  M. Mann,et al.  Proteomic maps of breast cancer subtypes , 2016, Nature Communications.

[5]  Manuel Mayr,et al.  Comparative analysis of statistical methods used for detecting differential expression in label-free mass spectrometry proteomics. , 2015, Journal of proteomics.

[6]  Su-In Lee,et al.  The proteomic landscape of triple-negative breast cancer. , 2015, Cell reports.

[7]  Wei Li,et al.  Dynamic analyses of alternative polyadenylation from RNA-seq reveal a 3′-UTR landscape across seven tumour types , 2014, Nature Communications.

[8]  Michael L. Gatza,et al.  An integrated genomics approach identifies drivers of proliferation in luminal subtype human breast cancer , 2014, Nature Genetics.

[9]  Jeffrey R. Whiteaker,et al.  Proteogenomic characterization of human colon and rectal cancer , 2014, Nature.

[10]  Maria P. Pavlou,et al.  Integrating meta-analysis of microarray data and targeted proteomics for biomarker identification: application in breast cancer. , 2014, Journal of proteome research.

[11]  J. Foekens,et al.  Comparative Proteome Analysis Revealing an 11-Protein Signature for Aggressive Triple-Negative Breast Cancer , 2014, Journal of the National Cancer Institute.

[12]  Sarah J. Kurley,et al.  MYC-driven accumulation of 2-hydroxyglutarate is associated with breast cancer prognosis. , 2014, The Journal of clinical investigation.

[13]  Pei Wang,et al.  Demonstrating the feasibility of large-scale development of standardized assays to quantify human proteins , 2013, Nature Methods.

[14]  J. Foekens,et al.  Quantitative proteomic analysis of microdissected breast cancer tissues: comparison of label-free and SILAC-based quantification with shotgun, directed, and targeted MS approaches. , 2013, Journal of proteome research.

[15]  K. Shokat,et al.  Myc and mTOR converge on a common node in protein synthesis control that confers synthetic lethality in Myc-driven cancers , 2013, Proceedings of the National Academy of Sciences.

[16]  Michal Sheffer,et al.  Pathway-based personalized analysis of cancer , 2013, Proceedings of the National Academy of Sciences.

[17]  D. Speicher,et al.  Identification of Multiple Novel Protein Biomarkers Shed by Human Serous Ovarian Tumors into the Blood of Immunocompromised Mice and Verified in Patient Sera , 2013, PloS one.

[18]  Donald A. Berry,et al.  PAM50 proliferation score as a predictor of weekly paclitaxel benefit in breast cancer , 2013, Breast Cancer Research and Treatment.

[19]  M. Warmoes,et al.  Proteomics of Genetically Engineered Mouse Mammary Tumors Identifies Fatty Acid Metabolism Members as Potential Predictive Markers for Cisplatin Resistance* , 2013, Molecular & Cellular Proteomics.

[20]  Steven J. M. Jones,et al.  Comprehensive molecular portraits of human breast tumors , 2012, Nature.

[21]  Matthias Mann,et al.  Proteomic portrait of human breast cancer progression identifies novel prognostic markers. , 2012, Cancer research.

[22]  P. Gimotty,et al.  A xenograft mouse model coupled with in-depth plasma proteome analysis facilitates identification of novel serum biomarkers for human ovarian cancer. , 2012, Journal of proteome research.

[23]  M. Selbach,et al.  Global quantification of mammalian gene expression control , 2011, Nature.

[24]  D. Felsher,et al.  MYC as a regulator of ribosome biogenesis and protein synthesis , 2010, Nature Reviews Cancer.

[25]  Luis Serrano,et al.  Correlation of mRNA and protein in complex biological samples , 2009, FEBS letters.

[26]  E. Marcotte,et al.  Global signatures of protein and mRNA expression levelsw , 2009 .

[27]  Sarah A. Pendergrass,et al.  A Core MYC Gene Expression Signature Is Prominent in Basal-Like Breast Cancer but Only Partially Overlaps the Core Serum Response , 2009, PloS one.

[28]  C. Mayr,et al.  Widespread Shortening of 3′UTRs by Alternative Cleavage and Polyadenylation Activates Oncogenes in Cancer Cells , 2009, Cell.

[29]  Julie E Goodman,et al.  Association of breast cancer outcome with status of p53 and MDM2 SNP309. , 2006, Journal of the National Cancer Institute.

[30]  M. Cronin,et al.  A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. , 2004, The New England journal of medicine.

[31]  Jean YH Yang,et al.  Bioconductor: open software development for computational biology and bioinformatics , 2004, Genome Biology.

[32]  Van,et al.  A gene-expression signature as a predictor of survival in breast cancer. , 2002, The New England journal of medicine.

[33]  R. Tibshirani,et al.  Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[34]  Christian A. Rees,et al.  Molecular portraits of human breast tumours , 2000, Nature.

[35]  Steven J. M. Jones,et al.  Comprehensive molecular portraits of human breast tumours , 2013 .

[36]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .