Integrative analysis of survival-associated gene sets in breast cancer

BMC Medical Genomics

Chao Cheng²  Shao Ke Lou¹  Matthew H Ung¹  Frederick S Varn¹ 

[1] Department of Genetics, Geisel School of Medicine at Dartmouth, Hanover 03755, New Hampshire, USA;Norris Cotton Cancer Center, Geisel School of Medicine at Dartmouth, Lebanon 03766, New Hampshire, USA

关键词: Survival prediction;    Prognosis;    Gene sets;    Breast cancer;   

Others  :  1137818
DOI  :  10.1186/s12920-015-0086-0

 received in 2014-10-16, accepted in 2015-02-24,  发布年份 2015

【 摘 要 】

Background

Patient gene expression information has recently become a clinical feature used to evaluate breast cancer prognosis. The emergence of prognostic gene sets that take advantage of these data has led to a rich library of information that can be used to characterize the molecular nature of a patient’s cancer. Identifying robust gene sets that are consistently predictive of a patient’s clinical outcome has become one of the main challenges in the field.

Methods

We inputted our previously established BASE algorithm with patient gene expression data and gene sets from MSigDB to develop the gene set activity score (GSAS), a metric that quantitatively assesses a gene set’s activity level in a given patient. We utilized this metric, along with patient time-to-event data, to perform survival analyses to identify the gene sets that were significantly correlated with patient survival. We then performed cross-dataset analyses to identify robust prognostic gene sets and to classify patients by metastasis status. Additionally, we created a gene set network based on component gene overlap to explore the relationship between gene sets derived from MSigDB. We developed a novel gene set based on this network’s topology and applied the GSAS metric to characterize its role in patient survival.

Results

Using the GSAS metric, we identified 120 gene sets that were significantly associated with patient survival in all datasets tested. The gene overlap network analysis yielded a novel gene set enriched in genes shared by the robustly predictive gene sets. This gene set was highly correlated to patient survival when used alone. Most interestingly, removal of the genes in this gene set from the gene pool on MSigDB resulted in a large reduction in the number of predictive gene sets, suggesting a prominent role for these genes in breast cancer progression.

Conclusions

The GSAS metric provided a useful medium by which we systematically investigated how gene sets from MSigDB relate to breast cancer patient survival. We used this metric to identify predictive gene sets and to construct a novel gene set containing genes heavily involved in cancer progression.

【 授权许可】

   
2015 Varn et al.; licensee BioMed Central.

【 图 表 】

Figure 1.

Figure 2.

Figure 3.

Figure 4.

Figure 5.

Figure 6.

Figure 7.

Figure 8.

【 参考文献 】

• [1]Liotta L, Petricoin E: Molecular profiling of human cancer. Nat Rev Genet 2000, 1:48-56.
• [2]van ’t Veer LJ, Dai H, van de Vijver MJ, He YD, Hart AA, Mao M, et al.: Gene expression profiling predicts clinical outcome of breast cancer. Nature 2002, 415:530-6.
• [3]Paik S, Shak S, Tang G, Kim C, Baker J, Cronin M, et al.: A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. N Engl J Med 2004, 351:2817-26.
• [4]Ma XJ, Wang Z, Ryan PD, Isakoff SJ, Barmettler A, Fuller A, et al.: A two-gene expression ratio predicts clinical outcome in breast cancer patients treated with tamoxifen. Cancer Cell 2004, 5:607-16.
• [5]Rhodes DR, Yu J, Shanker K, Deshpande N, Varambally R, Ghosh D, et al.: Large-scale meta-analysis of cancer microarray data identifies common transcriptional profiles of neoplastic transformation and progression. Proc Natl Acad Sci U S A 2004, 101:9309-14.
• [6]Chang HY, Nuyten DS, Sneddon JB, Hastie T, Tibshirani R, Sorlie T, et al.: Robustness, scalability, and integration of a wound-response gene expression signature in predicting breast cancer survival. Proc Natl Acad Sci U S A 2005, 102:3738-43.
• [7]Glinsky GV, Berezovska O, Glinskii AB: Microarray analysis identifies a death-from-cancer signature predicting therapy failure in patients with multiple types of cancer. J Clin Invest 2005, 115:1503-21.
• [8]Wang Y, Klijn JG, Zhang Y, Sieuwerts AM, Look MP, Yang F, et al.: Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer. Lancet 2005, 365:671-9.
• [9]Miller LD, Smeds J, George J, Vega VB, Vergara L, Ploner A, et al.: An expression signature for p53 status in human breast cancer predicts mutation status, transcriptional effects, and patient survival. Proc Natl Acad Sci U S A 2005, 102:13550-5.
• [10]Sotiriou C, Wirapati P, Loi S, Harris A, Fox S, Smeds J, et al.: Gene expression profiling in breast cancer: understanding the molecular basis of histologic grade to improve prognosis. J Natl Cancer Inst 2006, 98:262-72.
• [11]Teschendorff AE, Naderi A, Barbosa-Morais NL, Pinder SE, Ellis IO, Aparicio S, et al.: A consensus prognostic gene expression classifier for ER positive breast cancer. Genome Biol 2006, 7:R101. BioMed Central Full Text
• [12]Naderi A, Teschendorff AE, Barbosa-Morais NL, Pinder SE, Green AR, Powe DG, et al.: A gene-expression signature to predict survival in breast cancer across independent data sets. Oncogene 2007, 26:1507-16.
• [13]Liu R, Wang X, Chen GY, Dalerba P, Gurney A, Hoey T, et al.: The prognostic role of a gene signature from tumorigenic breast-cancer cells. N Engl J Med 2007, 356:217-26.
• [14]Ma XJ, Salunga R, Dahiya S, Wang W, Carney E, Durbecq V, et al.: A five-gene molecular grade index and HOXB13:IL17BR are complementary prognostic factors in early stage breast cancer. Clin Cancer Res 2008, 14:2601-8.
• [15]Sotiriou C, Pusztai L: Gene-expression signatures in breast cancer. N Engl J Med 2009, 360:790-800.
• [16]Weigel MT, Dowsett M: Current and emerging biomarkers in breast cancer: prognosis and prediction. Endocr Relat Cancer 2010, 17:R245-62.
• [17]Parker JS, Mullins M, Cheang MC, Leung S, Voduc D, Vickery T, et al.: Supervised risk predictor of breast cancer based on intrinsic subtypes. J Clin Oncol 2009, 27:1160-7.
• [18]Kittaneh M, Montero AJ, Gluck S: Molecular profiling for breast cancer: a comprehensive review. Biomarkers Cancer 2013, 5:61-70.
• [19]Iwamoto T, Bianchini G, Booser D, Qi Y, Coutant C, Shiang CY, et al.: Gene pathways associated with prognosis and chemotherapy sensitivity in molecular subtypes of breast cancer. J Natl Cancer Inst 2011, 103:264-72.
• [20]Zhao X, Rodland EA, Sorlie T, Naume B, Langerod A, Frigessi A, et al.: Combining gene signatures improves prediction of breast cancer survival. PLoS One 2011, 6:e17845.
• [21]Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, et al.: Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A 2005, 102:15545-50.
• [22]Hanahan D, Weinberg RA: Hallmarks of cancer: the next generation. Cell 2011, 144:646-74.
• [23]Cheng C, Yan X, Sun F, Li LM: Inferring activity changes of transcription factors by binding association with sorted expression profiles. BMC Bioinformatics 2007, 8:452. BioMed Central Full Text
• [24]Zhu M, Liu CC, Cheng C: REACTIN: regulatory activity inference of transcription factors underlying human diseases with application to breast cancer. BMC Genomics 2013, 14:504. BioMed Central Full Text
• [25]Khaleel SS, Andrews EH, Ung M, Direnzo J, Cheng C: E2F4 regulatory program predicts patient survival prognosis in breast cancer. Breast Cancer Res 2014, 16:486. BioMed Central Full Text
• [26]Ur-Rehman S, Gao Q, Mitsopoulos C, Zvelebil M: ROCK: a resource for integrative breast cancer data analysis. Breast Cancer Res Treat 2013, 139:907-21.
• [27]van de Vijver MJ, He YD, van’t Veer LJ, Dai H, Hart AA, Voskuil DW, et al.: A gene-expression signature as a predictor of survival in breast cancer. N Engl J Med. 2002, 347:1999-2009.
• [28]Schmidt M, Bohm D, von Torne C, Steiner E, Puhl A, Pilch H, et al.: The humoral immune system has a key prognostic impact in node-negative breast cancer. Cancer Res. 2008, 68:5405-13.
• [29]Desmedt C, Piette F, Loi S, Wang Y, Lallemand F, Haibe-Kains B, et al.: Strong time dependence of the 76-gene prognostic signature for node-negative breast cancer patients in the TRANSBIG multicenter independent validation series. Clin Cancer Res. 2007, 13:3207-14.
• [30]da Huang W, Sherman BT, Lempicki RA: Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 2009, 4:44-57.
• [31]Donato M, Xu Z, Tomoiaga A, Granneman JG, Mackenzie RG, Bao R, et al.: Analysis and correction of crosstalk effects in pathway analysis. Genome Res. 2013, 23:1885-93.
• [32]Ein-Dor L, Zuk O, Domany E: Thousands of samples are needed to generate a robust gene list for predicting outcome in cancer. Proc Natl Acad Sci U S A. 2006, 103:5923-8.
• [33]Michiels S, Koscielny S, Hill C: Prediction of cancer outcome with microarrays: a multiple random validation strategy. Lancet. 2005, 365:488-92.
• [34]Karakas B, Weeraratna A, Abukhdeir A, Blair BG, Konishi H, Arena S, et al.: Interleukin-1 alpha mediates the growth proliferative effects of transforming growth factor-beta in p21 null MCF-10A human mammary epithelial cells. Oncogene. 2006, 25:5561-9.
• [35]Chuang HY, Lee E, Liu YT, Lee D, Ideker T: Network-based classification of breast cancer metastasis. Mol Syst Biol 2007, 3:140.
• [36]Perou CM, Sorlie T, Eisen MB, van de Rijn M, Jeffrey SS, Rees CA, et al.: Molecular portraits of human breast tumours. Nature 2000, 406:747-52.