Stroma-associated master regulators of molecular subtypes predict patient prognosis in ovarian cancer

Scientific Reports

Volumn 5, Issue , 2015, Pages

  Zhang, Shengzhe ^a,b   Jing, Ying ^a   Zhang, Meiying ^a   Zhang, Zhenfeng ^a,c   Ma, Pengfei ^a   Peng, Huixin ^a   Shi, Kaixuan ^b   Gao, Wei Qiang ^a,b   Zhuang, Guanglei ^a  

^a SHANGHAI JIAO TONG UNIVERSITY SCHOOL OF MEDICINE   (China)

^b SHANGHAI JIAO TONG UNIVERSITY   (China)

^c Shanghai Institute of Digestive Disease   (China)

Author keywords

[No Author keywords available]

Indexed keywords

TUMOR MARKER;

ALGORITHM; CLASSIFICATION; FEMALE; GENE EXPRESSION PROFILING; GENE EXPRESSION REGULATION; GENE REGULATORY NETWORK; GENETICS; HUMAN; MORTALITY; OVARY TUMOR; PATHOLOGY; PHYSIOLOGY; PROGNOSIS; STROMA CELL; SURVIVAL RATE; TUMOR MICROENVIRONMENT;

ALGORITHMS; BIOMARKERS, TUMOR; FEMALE; GENE EXPRESSION PROFILING; GENE EXPRESSION REGULATION, NEOPLASTIC; GENE REGULATORY NETWORKS; HUMANS; OVARIAN NEOPLASMS; PROGNOSIS; STROMAL CELLS; SURVIVAL RATE; TUMOR MICROENVIRONMENT;

EID: 84946226597     PISSN: None     EISSN: 20452322     Source Type: Journal    
DOI: 10.1038/srep16066     Document Type: Article

References (51)

1. Bowtell, D. D. The genesis and evolution of high-grade serous ovarian cancer. Nat Rev Cancer 10, 803-808 (2010
2. Liu, J. & Matulonis, U. A. New strategies in ovarian cancer: translating the molecular complexity of ovarian cancer into treatment advances. Clin Cancer Res 20, 5150-5156 (2014
3. Bookman, M. A. et al. Better therapeutic trials in ovarian cancer. J Natl Cancer Inst 106, dju029 (2014
4. Coleman, R. L., Monk, B. J., Sood, A. K. & Herzog, T. J. Latest research and treatment of advanced-stage epithelial ovarian cancer. Nat Rev Clin Oncol 10, 211-224 (2013
5. Pignata, S. et al. Chemotherapy in epithelial ovarian cancer. Cancer Lett 303, 73-83 (2011
6. Riester, A. et al. Age below 40 or a recently proposed clinical prediction score cannot bypass adrenal venous sampling in primary aldosteronism. J Clin Endocrinol Metab 99, E1035-1039 (2014
7. Waldron, L. et al. Comparative meta-Analysis of prognostic gene signatures for late-stage ovarian cancer. J Natl Cancer Inst 106 (2014
8. Tothill, R. W. et al. Novel molecular subtypes of serous and endometrioid ovarian cancer linked to clinical outcome. Clin Cancer Res 14, 5198-5208 (2008
9. Tan, T. Z. et al. Functional genomics identifies five distinct molecular subtypes with clinical relevance and pathways for growth control in epithelial ovarian cancer. EMBO Mol Med 5, 983-998 (2013
10. Cancer Genome Atlas Research, N. Integrated genomic analyses of ovarian carcinoma. Nature 474, 609-615 (2011
11. Konecny, G. E. et al. Prognostic and therapeutic relevance of molecular subtypes in high-grade serous ovarian cancer. J Natl Cancer Inst 106 (2014
12. Sadanandam, A. et al. A colorectal cancer classification system that associates cellular phenotype and responses to therapy. Nat Med 19, 619-625 (2013
13. De Sousa, E. M. F. et al. Poor-prognosis colon cancer is defined by a molecularly distinct subtype and develops from serrated precursor lesions. Nat Med 19, 614-618 (2013
14. Collisson, E. A. et al. Subtypes of pancreatic ductal adenocarcinoma and their differing responses to therapy. Nat Med 17, 500-503 (2011
15. Tlsty, T. D. & Coussens, L. M. Tumor stroma and regulation of cancer development. Annu Rev Pathol 1, 119-150 (2006
16. Finak, G. et al. Stromal gene expression predicts clinical outcome in breast cancer. Nat Med 14, 518-527 (2008
17. Hanahan, D. & Coussens, L. M. Accessories to the crime: functions of cells recruited to the tumor microenvironment. Cancer Cell 21, 309-322 (2012
18. Isella, C. et al. Stromal contribution to the colorectal cancer transcriptome. Nat Genet 47, 312-319 (2015
19. Calon, A. et al. Stromal gene expression defines poor-prognosis subtypes in colorectal cancer. Nat Genet 47, 320-329 (2015
20. Verhaak, R. G. et al. Integrated genomic analysis identifies clinically relevant subtypes of glioblastoma characterized by abnormalities in PDGFRA, IDH1, EGFR, and NF1. Cancer Cell 17, 98-110 (2010
21. Tusher, V. G., Tibshirani, R. & Chu, G. Significance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad Sci USA 98, 5116-5121 (2001
22. Tibshirani, R., Hastie, T., Narasimhan, B. & Chu, G. Diagnosis of multiple cancer types by shrunken centroids of gene expression. Proc Natl Acad Sci USA 99, 6567-6572 (2002
23. Crijns, A. P. et al. Survival-related profile, pathways, and transcription factors in ovarian cancer. PLoS Med 6, e24 (2009
24. Carter, S. L. et al. Absolute quantification of somatic DNA alterations in human cancer. Nat Biotechnol 30, 413-421 (2012
25. Yoshihara, K. et al. Inferring tumour purity and stromal and immune cell admixture from expression data. Nat Commun 4, 2612 (2013
26. Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA 102, 15545-15550 (2005
27. Ricci, F. et al. Patient-derived ovarian tumor xenografts recapitulate human clinicopathology and genetic alterations. Cancer Res 74, 6980-6990 (2014
28. Basso, K. et al. Reverse engineering of regulatory networks in human B cells. Nat Genet 37, 382-390 (2005
29. Carro, M. S. et al. The transcriptional network for mesenchymal transformation of brain tumours. Nature 463, 318-325 (2010
30. Fletcher, M. N. et al. Master regulators of FGFR2 signalling and breast cancer risk. Nat Commun 4, 2464 (2013
31. Barbie, D. A. et al. Systematic RNA interference reveals that oncogenic KRAS-driven cancers require TBK1. Nature 462, 108-112 (2009
32. Hanzelmann, S., Castelo, R. & Guinney, J. GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics 14, 7 (2013
33. Verhaak, R. G. et al. Prognostically relevant gene signatures of high-grade serous ovarian carcinoma. J Clin Invest 123, 517-525 (2013
34. Yeung, T. L. et al. TGF-beta modulates ovarian cancer invasion by upregulating CAF-derived versican in the tumor microenvironment. Cancer Res 73, 5016-5028 (2013
35. Brodsky, A. S. et al. Expression profiling of primary and metastatic ovarian tumors reveals differences indicative of aggressive disease. PLoS One 9, e94476 (2014
36. Ahmed, A. A. et al. The extracellular matrix protein TGFBI induces microtubule stabilization and sensitizes ovarian cancers to paclitaxel. Cancer Cell 12, 514-527 (2007
37. Ganzfried, B. F. et al. curatedOvarianData: clinically annotated data for the ovarian cancer transcriptome. Database (Oxford) 2013, bat013 (2013
38. Bonome, T. et al. A gene signature predicting for survival in suboptimally debulked patients with ovarian cancer. Cancer Res 68, 5478-5486 (2008
39. Yoshihara, K. et al. Gene expression profile for predicting survival in advanced-stage serous ovarian cancer across two independent datasets. PLoS One 5, e9615 (2010
40. Denkert, C. et al. A prognostic gene expression index in ovarian cancer - validation across different independent data sets. J Pathol 218, 273-280 (2009
41. Puisieux, A., Brabletz, T. & Caramel, J. Oncogenic roles of EMT-inducing transcription factors. Nat Cell Biol 16, 488-494 (2014
42. Ikushima, H., Negishi, H. & Taniguchi, T. The IRF family transcription factors at the interface of innate and adaptive immune responses. Cold Spring Harb Symp Quant Biol 78, 105-116 (2013
43. Yoshida, T. & Georgopoulos, K. Ikaros fingers on lymphocyte differentiation. Int J Hematol 100, 220-229 (2014
44. Murphy, T. L., Tussiwand, R. & Murphy, K. M. Specificity through cooperation: BATF-IRF interactions control immuneregulatory networks. Nat Rev Immunol 13, 499-509 (2013
45. Kang, J., D'Andrea, A. D. & Kozono, D. A DNA repair pathway-focused score for prediction of outcomes in ovarian cancer treated with platinum-based chemotherapy. J Natl Cancer Inst 104, 670-681 (2012
46. Jin, N. et al. Network-based survival-Associated module biomarker and its crosstalk with cell death genes in ovarian cancer. Sci Rep 5, 11566 (2015
47. Brunet, J. P., Tamayo, P., Golub, T. R. & Mesirov, J. P. Metagenes and molecular pattern discovery using matrix factorization. Proc Natl Acad Sci USA 101, 4164-4169 (2004
48. Wilkerson, M. D. & Hayes, D. N. ConsensusClusterPlus: a class discovery tool with confidence assessments and item tracking. Bioinformatics 26, 1572-1573 (2010
49. Reich, M. et al. GenePattern 2.0. Nat Genet 38, 500-501 (2006
50. Bhat, K. P. et al. The transcriptional coactivator TAZ regulates mesenchymal differentiation in malignant glioma. Genes Dev 25, 2594-2609 (2011
51. Castro, M. A., Wang, X., Fletcher, M. N., Meyer, K. B. & Markowetz, F. RedeR: R/Bioconductor package for representing modular structures, nested networks and multiple levels of hierarchical associations. Genome Biol 13, R29 (2012