Cited by 118

The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.

Phosphosignature Predicts Dasatinib Response in Non-small Cell Lung Cancer

Klammer, Martin, Kaminski, Marc, Zedler, Alexandra, Oppermann, Felix, Blencke, Stephanie, Marx, Sandra, Müller, Stefan, Tebbe, Andreas, Godl, Klaus, Schaab, Christoph·2012·Molecular & Cellular Proteomics

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1074/mcp.m111.016410

Cites

4 of 4 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Phosphorylation at corresponding sites in SNX1, SNX3, SNX12, and SNX17 has been reported in various cancer types.

    “phosphorylation at corresponding sites in SNX1, SNX3, SNX12, and SNX17 has been reported in various cancer types (47, 48, 49, 50, 51, 52)”

    From Phosphorylation of SNX17 impedes activation of Retriever-mediated sorting (2025) · cites this paper as [50]

    The Dasatinib Sensitivity tables and Discussion report phosphorylation sites in other proteins, but do not address SNX1, SNX3, SNX12, or SNX17 at corresponding sites. Searches for those SNX names and for sorting-ne xin phosphorylation found no passage supporting the claim across cancer types. (LLM, conf=0.72)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Flagged first, then by citations

Research works printed but not linked: 57

  1. Katzel, 2009, Recent advances of novel targeted therapy in non-small cell lung cancer, J. Hematol. Oncol., 2, 2, 10.1186/1756-8722-2-2 · DOI
  2. Reichert, 2007, Development trends for monoclonal antibody cancer therapeutics, Nat. Rev. Drug Discov., 6, 349, 10.1038/nrd2241 · DOI
  3. Ross, 1999, HER-2/neu (c-erb-B2) gene and protein in breast cancer, Am. J. Clin. Pathol., 112, S53
  4. Cobleigh, 1999, Multinational study of the efficacy and safety of humanized anti-HER2 monoclonal antibody in women who have HER2-overexpressing metastatic breast cancer that has progressed after chemotherapy for metastatic disease, J. Clin. Oncol., 17, 2639, 10.1200/JCO.1999.17.9.2639 · DOI
  5. Dressman, 2007, An integrated genomic-based approach to individualized treatment of patients with advanced-stage ovarian cancer, J. Clin. Oncol., 25, 517, 10.1200/JCO.2006.06.3743 · DOI
  6. Olsen, 2006, Global, in vivo, and site-specific phosphorylation dynamics in signaling networks, Cell, 127, 635, 10.1016/j.cell.2006.09.026 · DOI
  7. Macek, 2009, Global and site-specific quantitative phosphoproteomics: Principles and applications, Annu. Rev. Pharmacol. Toxicol., 49, 199, 10.1146/annurev.pharmtox.011008.145606 · DOI
  8. Schaab, 2011, Analysis of phosphoproteomics data, Methods Mol. Biol., 696, 41, 10.1007/978-1-60761-987-1_3 · DOI
  9. Kaminska, 2005, MAPK signalling pathways as molecular targets for anti-inflammatory therapy: From molecular mechanisms to therapeutic benefits, Biochim. Biophys. Acta, 1754, 253, 10.1016/j.bbapap.2005.08.017 · DOI · Paper page
  10. Ferlay, 2010, Estimates of cancer incidence and mortality in Europe in 2008, Eur. J. Cancer, 46, 765, 10.1016/j.ejca.2009.12.014 · DOI
  11. Jemal, 2008, Cancer statistics, 2008, CA Cancer J. Clin., 58, 71
  12. Sharma, 2009, Proteomics strategy for quantitative protein interaction profiling in cell extracts, Nat. Methods, 6, 741, 10.1038/nmeth.1373 · DOI
  13. Bantscheff, 2007, Quantitative chemical proteomics reveals mechanisms of action of clinical ABL kinase inhibitors, Nat. Biotechnol., 25, 1035, 10.1038/nbt1328 · DOI
  14. Johnson, 2010, Phase II study of dasatinib in patients with advanced non-small-cell lung cancer, J. Clin. Oncol., 28, 4609, 10.1200/JCO.2010.30.5474 · DOI
  15. Andersen, 2010, Pathway-based identification of biomarkers for targeted therapeutics: Personalized oncology with PI3K pathway inhibitors, Sci. Transl. Med., 2, 43ra55, 10.1126/scitranslmed.3001065 · DOI
  16. Sos, 2009, Predicting drug susceptibility of non-small cell lung cancers based on genetic lesions, J. Clin. Invest., 119, 1727, 10.1172/JCI37127 · DOI
  17. Ong, 2002, Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics, Mol. Cell. Proteomics, 1, 376, 10.1074/mcp.M200025-MCP200 · DOI · Paper page
  18. Cox, 2008, MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification, Nat. Biotechnol., 26, 1367, 10.1038/nbt.1511 · DOI · Paper page
  19. Benjamini, 1995, Controlling the false discovery rate: A practical and powerful approach to multiple testing, J. R. Statist. Soc., B 57, 289, 10.1111/j.2517-6161.1995.tb02031.x · DOI · Paper page
  20. Harris, 2004, The Gene Ontology (GO) database and informatics resource, Nucleic Acids Res., 32, D258, 10.1093/nar/gkh036 · DOI
  21. Kanehisa, 2000, KEGG: Kyoto Encyclopedia of Genes and Genomes, Nucleic Acids Res., 28, 27, 10.1093/nar/28.1.27 · DOI · Paper page
  22. Al-Shahrour, 2007, From genes to functional classes in the study of biological systems, BMC Bioinformatics, 8, 114, 10.1186/1471-2105-8-114 · DOI
  23. Klammer, M., Godl, K., Tebbe, A., Schaab, C., Identifying differentially regulated subnetworks from phosphoproteomic data. BMC Bioinformatics 11, 351 · DOI
  24. Zhou, 2007, A global approach to identify differentially expressed genes in cDNA (two-color) microarray experiments, Bioinformatics, 23, 2073, 10.1093/bioinformatics/btm292 · DOI
  25. Jensen, 2009, STRING 8: A global view on proteins and their functional interactions in 630 organisms, Nucleic Acids Res., 37, D412, 10.1093/nar/gkn760 · DOI
  26. Abeel, 2010, Robust biomarker identification for cancer diagnosis with ensemble feature selection methods, Bioinformatics, 26, 392, 10.1093/bioinformatics/btp630 · DOI
  27. Geiger, 2010, Super-SILAC mix for quantitative proteomics of human tumor tissue, Nat. Methods, 7, 383, 10.1038/nmeth.1446 · DOI · Paper page
  28. Ramaswamy, 2001, Multiclass cancer diagnosis using tumor gene expression signatures, Proc. Natl. Acad. Sci. U.S.A., 98, 15149, 10.1073/pnas.211566398 · DOI
  29. Hutter, 2004, Prediction of mechanisms of action of antibacterial compounds by gene expression profiling, Antimicrob. Agents Chemother., 48, 2838, 10.1128/AAC.48.8.2838-2844.2004 · DOI
  30. Thuerigen, 2006, Gene expression signature predicting pathologic complete response with gemcitabine, epirubicin, and docetaxel in primary breast cancer, J. Clin. Oncol., 24, 1839, 10.1200/JCO.2005.04.7019 · DOI
  31. Molinaro, 2005, Prediction error estimation: A comparison of resampling methods, Bioinformatics, 21, 3301, 10.1093/bioinformatics/bti499 · DOI
  32. Lange, 2008, Selected reaction monitoring for quantitative proteomics: A tutorial, Mol. Syst. Biol., 4, 222, 10.1038/msb.2008.61 · DOI
  33. Hüttenhain, 2009, Perspectives of targeted mass spectrometry for protein biomarker verification, Curr. Opin. Chem. Biol., 13, 518, 10.1016/j.cbpa.2009.09.014 · DOI
  34. Uhlen, 2010, Towards a knowledge-based Human Protein Atlas, Nat. Biotechnol., 28, 1248, 10.1038/nbt1210-1248 · DOI · Paper page
  35. Buettner, 2008, Inhibition of Src family kinases with dasatinib blocks migration and invasion of human melanoma cells, Mol. Cancer Res., 6, 1766, 10.1158/1541-7786.MCR-08-0169 · DOI
  36. Shor, 2007, Dasatinib inhibits migration and invasion in diverse human sarcoma cell lines and induces apoptosis in bone sarcoma cells dependent on SRC kinase for survival, Cancer Res., 67, 2800, 10.1158/0008-5472.CAN-06-3469 · DOI
  37. Johnson, 2005, Dasatinib (BMS-354825) tyrosine kinase inhibitor suppresses invasion and induces cell cycle arrest and apoptosis of head and neck squamous cell carcinoma and non-small cell lung cancer cells, Clin. Cancer Res., 11, 6924, 10.1158/1078-0432.CCR-05-0757 · DOI
  38. Dans, 2001, Tyrosine phosphorylation of the β4 integrin cytoplasmic domain mediates Shc signaling to extracellular signal-regulated kinase and antagonizes formation of hemidesmosomes, J. Biol. Chem., 276, 1494, 10.1074/jbc.M008663200 · DOI
  39. Chung, 2002, Integrin (alpha 6 beta 4) regulation of eIF-4E activity and VEGF translation: A survival mechanism for carcinoma cells, J. Cell Biol., 158, 165, 10.1083/jcb.200112015 · DOI
  40. Dutta, 2008, A key tyrosine (Y1494) in the β4 integrin regulates multiple signaling pathways important for tumor development and progression, Cancer Res., 68, 8779, 10.1158/0008-5472.CAN-08-2125 · DOI
  41. Tagliabue, 1998, Prognostic value of α6β4 integrin expression in breast carcinomas is affected by laminin production from tumor cells, Clin. Cancer Res., 4, 407
  42. Lu, 2008, Analysis of integrin β4 expression in human breast cancer: Association with basal-like tumors and prognostic significance, Clin. Cancer Res., 14, 1050, 10.1158/1078-0432.CCR-07-4116 · DOI
  43. Van Waes, 1991, The A9 antigen associated with aggressive human squamous carcinoma is structurally and functionally similar to the newly defined integrin α6β4, Cancer Res., 51, 2395
  44. Hornbeck, 2004, PhosphoSite: A bioinformatics resource dedicated to physiological protein phosphorylation, Proteomics, 4, 1551, 10.1002/pmic.200300772 · DOI
  45. Miki, 2000, IRSp53 is an essential intermediate between Rac and WAVE in the regulation of membrane ruffling, Nature, 408, 732, 10.1038/35047107 · DOI
  46. Yamagishi, 2004, A novel actin bundling/filopodium-forming domain conserved in insulin receptor tyrosine kinase substrate p53 and missing in metastasis protein, J. Biol. Chem., 279, 14929, 10.1074/jbc.M309408200 · DOI
  47. Blomquist, 2000, Identification and characterization of a novel Rho-specific guanine nucleotide exchange factor, Biochem. J., 352, 319, 10.1042/bj3520319 · DOI
  48. Niu, 2003, G Protein betagamma subunits stimulate p114RhoGEF, a guanine nucleotide exchange factor for RhoA and Rac1: Regulation of cell shape and reactive oxygen species production, Circ. Res., 93, 848, 10.1161/01.RES.0000097607.14733.0C · DOI
  49. Bergamaschi, 2006, iASPP preferentially binds p53 proline-rich region and modulates apoptotic function of codon 72-polymorphic p53, Nat. Genet., 38, 1133, 10.1038/ng1879 · DOI
  50. Wu, 2005, Integrative genomics revealed RAI3 is a cell growth-promoting gene and a novel P53 transcriptional target, J. Biol. Chem., 280, 12935, 10.1074/jbc.M409901200 · DOI
  51. Berg, 2011, Use of formalin-fixed and paraffin-embedded tissues for diagnosis and therapy in routine clinical settings, Methods Mol. Biol., 785, 109, 10.1007/978-1-61779-286-1_8 · DOI
  52. Luo, 2006, Dasatinib (BMS-354825) pharmacokinetics and pharmacodynamic biomarkers in animal models predict optimal clinical exposure, Clin. Cancer Res., 12, 7180, 10.1158/1078-0432.CCR-06-1112 · DOI · Paper page
  53. Luo, 2008, Identification and validation of phospho-SRC, a novel and potential pharmacodynamic biomarker for dasatinib (SPRYCEL), a multi-targeted kinase inhibitor, Cancer Chemother. Pharmacol., 62, 1065, 10.1007/s00280-008-0699-5 · DOI
  54. Herold, 2011, Phase II trial of dasatinib in patients with metastatic breast cancer using real-time pharmacodynamic tissue biomarkers of Src inhibition to escalate dosing, Clin. Cancer Res., 17, 6061, 10.1158/1078-0432.CCR-11-1071 · DOI
  55. Kitteringham, 2009, Multiple reaction monitoring for quantitative biomarker analysis in proteomics and metabolomics, J. Chromatogr. B Analyt. Technol. Biomed. Life Sci., 877, 1229, 10.1016/j.jchromb.2008.11.013 · DOI
  56. Ostasiewicz, 2010, Proteome, phosphoproteome, and N-glycoproteome are quantitatively preserved in formalin-fixed paraffin-embedded tissue and analyzable by high-resolution mass spectrometry, J. Proteome Res., 9, 3688, 10.1021/pr100234w · DOI
  57. Gámez-Pozo, 2011, Protein phosphorylation analysis in archival clinical cancer samples by shotgun and targeted proteomics approaches, Mol. Biosyst., 7, 2368, 10.1039/c1mb05113j · DOI

Cited by

Sorted by citations

Metadata

Authors
Klammer, M., Kaminski, M., Zedler, A., Oppermann, F., Blencke, S., Marx, S., Müller, S., Tebbe, A., Godl, K., & Schaab, C.
Venue
Molecular & Cellular Proteomics
Publisher
Elsevier BV
Published
2012-09
Volume
11
Issue
9
Pages
651-668
ISSN
1535-9476
Cited by
118
References
4
Language
en

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review