Optimally discriminative subnetwork markers predict response to chemotherapy

Bioinformatics

Volumn 27, Issue 13, 2011, Pages

  Dao, Phuong ^a   Wang, Kendric ^b,c   Collins, Colin ^b,c   Ester, Martin ^a   Lapuk, Anna ^c   Sahinalp, S Cenk ^a  

^a SIMON FRASER UNIVERSITY   (Canada)

^b UNIVERSITY OF BRITISH COLUMBIA   (Canada)

^c VANCOUVER PROSTATE CENTRE   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

TUMOR MARKER;

ALGORITHM; ARTICLE; BREAST TUMOR; GENE EXPRESSION PROFILING; GENETICS; HUMAN; METABOLISM; METHODOLOGY;

ALGORITHMS; BREAST NEOPLASMS; GENE EXPRESSION PROFILING; HUMANS; TUMOR MARKERS, BIOLOGICAL;

EID: 79959392210     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/btr245     Document Type: Article

References (51)

1. Alon, N. et al. 1995 Color-coding. J. ACM, 42, 844-856.
2. Alon, N. et al. (2008) Biomolecular network motif counting and discovery by color coding. Bioinformatics, 24 (Suppl. 1), i241-i249.
3. Baldi, P. (2000) Assessing the accuracy of prediction algorithms for classification: an overview. Bioinformatics, 16, 412-424.
4. Benini, S. (2001) Inhibition of insulin-like growth factor I receptor increases the antitumor activity of doxorubicin and vincristine against Ewing's sarcoma cells. Clin. Cancer Res., 7, 1790-1797.
5. Bonnefoi, H. et al. (2009) Predictive signatures for chemotherapy sensitivity in breast cancer: are they ready for use in the clinic? Eur. J. Cancer, 45, 1733-1743.
6. Bruckner, S. et al. (2010) Topology-free querying of protein interaction networks. In J. Comput. Biol., 17, 237-252.
7. BW, M. (1975) Comparison of the predicted and observed secondary structure of t4 phage lysozyme. Biochim. Biophys. Acta., 405, 442-451.
8. Chowdhury, S.A. and Koyutürk, M. (2010) Identification of coordinately dysregulated subnetworks in complex phenotypes. In Pacific Symposium on Biocomputing, pp. 133-144.
9. Chowdhury, S.A. (2010) Subnetwork state functions define dysregulated subnetworks in cancer. J. Comput. Biol., 18, 263-281.
10. Chuang, H.Y. et al. (2007) Network-based classification of breast cancer metastasis. Mol. Syst. Biol., 3, 140.
11. Cleator, S. et al. (2006) Gene expression patterns for doxorubicin (Adriamycin) and cyclophosphamide (cytoxan) (AC) response and resistance. Breast Cancer Res. Treat., 95, 229-233.
12. Dao, P. et al. (2009) Quantifying systemic evolutionary changes by color coding confidence-scored ppi networks. In 9th International Workshop on Algorithms in Bioinformatics, pp. 37-48.
13. Dao, P. et al. (2010) Inferring cancer subnetwork markers using density-constrained biclustering. Bioinformatics, 26.
14. Dittrich, M.T. et al. (2008) Identifying functional modules in protein-protein interaction networks: an integrated exact approach. Bioinformatics, 24, i223-i231.
15. Dost, B. et al. (2008) Qnet:Atool for querying protein interaction networks. J. Comput. Biol., 15, 913-925.
16. Dubrovska, A. (2005) TGFbeta1/Smad3 counteracts BRCA1-dependent repair of DNA damage. Oncogene, 24, 2289-2297.
17. Dunn, S.E. et al. (1997) Insulin-like growth factor 1 (IGF-1) alters drug sensitivity of HBL100 human breast cancer cells by inhibition of apoptosis induced by diverse anticancer drugs. Cancer Res., 57, 2687-2693.
18. Ein-Dor, L. et al. (2006) Thousands of samples are needed to generate a robust gene list for predicting outcome in cancer. Proc. Natl Acad. Sci. USA, 103, 5923-5928.
19. Farmer, P. (2009) A stroma-related gene signature predicts resistance to neoadjuvant chemotherapy in breast cancer. Nat. Med., 15, 68-74.
20. Fortney, K. et al. (2010) Inferring the functions of longevity genes with modular subnetwork biomarkers of Caenorhabditis elegans aging. Genome Biol., 11, R13.
21. Gooch, J.L. et al. (1999) Insulin-like growth factor (IGF)-I rescues breast cancer cells from chemotherapy-induced cell death-proliferative and anti-apoptotic effects. Breast Cancer Res. Treat., 56, 1-10.
22. Hess, K.R. (2006) Pharmacogenomic predictor of sensitivity to preoperative chemotherapy with paclitaxel and fluorouracil, doxorubicin, and cyclophosphamide in breast cancer. J. Clin. Oncol., 24, 4236-4244.
23. Irizarry, R.A. et al. (2003) Summaries of affymetrix genechip probe level data. Nucleic Acids Res., 31.
24. Jensen, L. (2009) String 8-a global view on proteins and their functional interactions in 630 organisms. Nucleic Acids Res., 37, D412-D416.
25. Johnson, W.E. et al. (2007) Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics, 8, 118-127.
26. Kavallaris, M. (2010) Microtubules and resistance to tubulin-binding agents. Nat. Rev. Cancer, 10, 194-204.
27. King, A.D. et al. (2004) Protein complex prediction via cost-based clustering. Bioinformatics, 20, 3013-3020.
28. Lee, J.K. et al. (2007) A strategy for predicting the chemosensitivity of human cancers and its application to drug discovery. Proc. Natl Acad. Sci. USA, 104, 13086-13091.
29. Liedtke, C. et al. (2010) Clinical evaluation of chemotherapy response predictors developed from breast cancer cell lines. Breast Cancer Res. Treat., 121, 301-309.
30. Liedtke, C. (2008) Response to neoadjuvant therapy and long-term survival in patients with triple-negative breast cancer. J. Clin. Oncol., 26, 1275-1281.
31. Longley, D.B. et al. (2003) 5-fluorouracil: mechanisms of action and clinical strategies. Nat. Rev. Cancer, 3, 330-338.
32. McGrogan, B.T. et al. (2008) Taxanes, microtubules and chemoresistant breast cancer. Biochim. Biophys. Acta, 1785, 96-132.
33. Minotti, G. et al. (2004) Anthracyclines: molecular advances and pharmacologic developments in antitumor activity and cardiotoxicity. Pharmacol. Rev., 56, 185-229.
34. Munro, A.F. et al. (2010) Targeting anthracyclines in early breast cancer: new candidate predictive biomarkers emerge. Oncogene, 29, 5231-5240.
35. Niemeier, L.A. et al. (2010) Androgen receptor in breast cancer: expression in estrogen receptor-positive tumors and in estrogen receptor-negative tumors with apocrine differentiation. Mod. Pathol., 23, 205-212.
36. Pedram, A. et al. (2007) A conserved mechanism for steroid receptor translocation to the plasma membrane. J. Biol. Chem., 282, 22278-22288.
37. Popovici, V. (2010) Effect of training-sample size and classification difficulty on the accuracy of genomic predictors. Breast Cancer Res., 12, R5.
38. Prasad, T.S. et al. (2009) Human Protein Reference Database and Human Proteinpedia as discovery tools for systems biology. Methods Mol. Biol., 577, 67-79.
39. Qiu, Y. et al. (2009) Identifying differentially expressed pathways via a mixed integer linear programming model. 3, 475-486.
40. Scott, J. et al. (2006) Efficient algorithms for detecting signaling pathways in protein interaction networks. J. Comput. Biol., 13, 133-144.
41. Shi, L. (2010) The MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models. Nat. Biotechnol., 28, 827-838.
42. Shitashige, M. et al. (2001) Dissociation of Bax from a Bcl-2/Bax heterodimer triggered by phosphorylation of serine 70 of Bcl-2. J. Biochem., 130, 741-748.
43. Shlomi, T. et al. (2006) Qpath: a method for querying pathways in a protein-protein interaction network. BMC Bioinformatics, 7, 199.
44. Sorlie, T. (2006) Distinct molecular mechanisms underlying clinically relevant subtypes of breast cancer: gene expression analyses across three different platforms. BMC Genomics, 7, 127.
45. Spirin, V. and Mirny, L.A. (2003) Protein complexes and functional modules in molecular networks. Proc. Natl Acad. Sci. USA, 100, 12123-12128.
46. Su, J. et al. (2010) Identification of diagnostic subnetwork markers for cancer in human protein-protein interaction network. BMC Bioinformatics, 11 (Suppl. 6), S8.
47. Sui, M. et al. (2007) Estrogen receptor alpha mediates breast cancer cell resistance to paclitaxel through inhibition of apoptotic cell death. Cancer Res., 67, 5337-5344.
48. Ulitsky, I. et al. (2008) Detecting disease-specific dysregulated pathways via analysis of clinical expression profiles. In 5th Annual International Conference on Research in Computational Molecular Biology, pp. 347-359.
49. van't Veer, L.J. and Bernards, R. (2008) Enabling personalized cancer medicine through analysis of gene-expression patterns. Nature, 452, 564-570.
50. Wu, Z. et al. (2010) A systems biology approach to identify effective cocktail drugs. BMC Syst. Biol., 4 (Suppl. 2), S7.
51. Yang, C.Q. et al. (1996) The expression and characterization of human recombinant proinsulin-like growth factor II and a mutant that is defective in the O-glycosylation of its E domain. Endocrinology, 137, 2766-2773.