Clinical and multiple gene expression variables in survival analysis of breast cancer: Analysis with the hypertabastic survival model

BMC Medical Genomics

Karan P Singh¹  Hong Li³  Nadim Nimeh²  Wayne M Eby³  Mohammad A Tabatabai³ 

[1] Department of Medicine, University of Alabama at Birmingham, Birmingham, AL, 35295, USA;Cancer Centers of Southwest Oklahoma, Lawton, OK, 73505, USA;Department of Mathematical Sciences, Cameron University, Lawton, OK, 73505, USA

关键词: Fibroblast core serum response;    ErbB2 overexpression;    Seventy gene signature;    Breast cancer biomarkers;    Gene expression variables;    Hypertabastic survival models;   

Others  :  1121238
DOI  :  10.1186/1755-8794-5-63

 received in 2011-10-28, accepted in 2012-11-27,  发布年份 2012

【 摘 要 】

Background

We explore the benefits of applying a new proportional hazard model to analyze survival of breast cancer patients. As a parametric model, the hypertabastic survival model offers a closer fit to experimental data than Cox regression, and furthermore provides explicit survival and hazard functions which can be used as additional tools in the survival analysis. In addition, one of our main concerns is utilization of multiple gene expression variables. Our analysis treats the important issue of interaction of different gene signatures in the survival analysis.

Methods

The hypertabastic proportional hazards model was applied in survival analysis of breast cancer patients. This model was compared, using statistical measures of goodness of fit, with models based on the semi-parametric Cox proportional hazards model and the parametric log-logistic and Weibull models. The explicit functions for hazard and survival were then used to analyze the dynamic behavior of hazard and survival functions.

Results

The hypertabastic model provided the best fit among all the models considered. Use of multiple gene expression variables also provided a considerable improvement in the goodness of fit of the model, as compared to use of only one. By utilizing the explicit survival and hazard functions provided by the model, we were able to determine the magnitude of the maximum rate of increase in hazard, and the maximum rate of decrease in survival, as well as the times when these occurred. We explore the influence of each gene expression variable on these extrema. Furthermore, in the cases of continuous gene expression variables, represented by a measure of correlation, we were able to investigate the dynamics with respect to changes in gene expression.

Conclusions

We observed that use of three different gene signatures in the model provided a greater combined effect and allowed us to assess the relative importance of each in determination of outcome in this data set. These results point to the potential to combine gene signatures to a greater effect in cases where each gene signature represents some distinct aspect of the cancer biology. Furthermore we conclude that the hypertabastic survival models can be an effective survival analysis tool for breast cancer patients.

【 授权许可】

   
2012 Tabatabai et al.; licensee BioMed Central Ltd.

【 图 表 】

Figure 1.

Figure 2.

Figure 3.

Figure 4.

Figure 5.

Figure 6.

Figure 7.

【 参考文献 】

• [1]Tabatabai MA, Bursac Z, Williams DK, Singh KP: Hypertabastic survival model. Theor. Biol. and Med. Model 2007, 4:40. BioMed Central Full Text
• [2]Bursac Z, Tabatabai M, Williams DK, Singh K: A simulation study of performance of hypertabastic and hyperbolastic survival models in comparison with classic survival models. In Proc. 2008 American statistical assoc. Biometrics section (CD-ROM). Alexandria, VA: American Statistical Association; 2009:617-622. Alexandria, VA
• [3]Tabatabai M, Eby W, Nimeh N, Singh K: Role of metastasis in hypertabastic survival analysis of breast cancer: Interactions with clinical and gene expression variables. Cancer Growth and Metastasis 2012, 5:1-17.
• [4]Sǿrlie T, Perou CM, Tibshirani R, et al.: Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. PNAS 2001, 98:10869-10874.
• [5]Van’t Veer LJ, Dai H, van de Vijver MJ, et al.: Gene expression profiling predicts clinical outcome of breast cancer. Nature 2002, 415:530-536.
• [6]Sotiriou C, Neo SY, McShane LM, et al.: Breast cancer classification and prognosis based on gene expression profiles from a population-based study. PNAS 2003, 100:10393-10398.
• [7]Wang Y, Klijn JGM, Zhang Y, et al.: Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer. Lancet 2005, 365:671-679.
• [8]Dvorak HF: Tumors: Wounds that do not heal. NEJM 1986, 315:1650-1659.
• [9]Chang HY, Sneddon JB, Alizadeh AA, et al.: Gene expression signature of fibroblast serum response predicts human cancer progression: Similarities between tumors and wounds. PLoS Biology 2004, 2:206-214.
• [10]Chang HY, Nuyten DS, Sneddon JB, et al.: Robustness, scalability, and integration of a wound-response gene expression signature in predicting breast cancer survival. PNAS 2005, 102:3738-3743.
• [11]Sotiriou C, Piccart MJ: Taking gene-expression profiling to the clinic: when will molecular signatures become relevant to patient care? Nat Rev Cancer 2007, 7:545-553.
• [12]Fan C, Prat A, Parker JS, et al.: Building prognostic models for breast cancer patients using clinical variables and hundreds of gene expression signatures. BMC Medical Genomics 2011, 4(3):1-15.
• [13]Fumagalli D, Sotiriou C: Treatment of pT1N0 breast cancer: multigene predictors to assess risk of relaps. Annals Oncol 2010, 21:vii103-vii106.
• [14]Haibe-Kains B, Desmedt C, Piette F, et al.: Comparison of prognostic gene expression signatures for breast cancer. BMC Genomics 2008, 9:394. BioMed Central Full Text
• [15]Fan C, Oh DS, Wessels L, et al.: Concordance among gene-expression based predictors for breast cancer. NEJM 2006, 355:560-569.
• [16]Haibe-Kains B, Desmedt C, Rothé F, et al.: A fuzzy gene expression-based computational approach improves breast cancer prognostication. Genome Biol 2010, 11:R18. BioMed Central Full Text
• [17]Cardoso F, van’t Veer L, Rutgers E, et al.: Clinical application of the 70-gene profile: the MINDACT trial. J Clin Oncol 2008, 26:729-735.
• [18]Sparano JA, Paik S: Development of the 21-gene assay and its application in clinical practice and clinical trials. J Clin Oncol 2008, 26:721-728.
• [19]Finak G, Bertos N, Pepin F, et al.: Stromal gene expression predicts clinical outcome in breast cancer. Nature Med 2008, 14:518-527.
• [20]Teschendorff AE, Miremadi A, Pinder SE, et al.: An immune response gene expression module identifies a good prognosis subtype in oestrogen receptor negative breast cancer. Genome Biol 2007, 8:R157. BioMed Central Full Text
• [21]Loi S, Haibe-Kains B, Majjaj S, et al.: PlK3CA mutations associated with gene signature of low mTORC1 signaling and better outcomes in estrogen receptor-positive breast cancer. PNAS 2010, 107:10208-10213.
• [22]Györffy B, Surowiak P, Lage H: Application of microarrays for the prediction of therapy response in breast cancer. Cancer Genom & Proteom 2005, 2:255-264.
• [23]Rathnagiriswaran S, Wan Y, Abraham J, et al.: A population-based gene signature is predictive of breast cancer survival and chemoresponse. Intl J Oncol 2010, 36:607-616.
• [24]Sotiriou C, Powles TJ, Dowsett M, et al.: Gene expression profiles derived from fine needle aspiration correlate with response to systemic chemotherapy in breast cancer. Breast Cancer Res 2002, 4(R3):1-8.
• [25]Hatzis C, Pusztai L, Valero V, et al.: A genomic predictor of response and survival following taxane-anthracycline chemotherapy for invasive breast cancer. JAMA 2011, 305(18):1873-1881.
• [26]Chang JC, Wooten EC, Tsimelzou A, et al.: Patterns of resistance and incomplete resistance to decetaxel (Taxotere) by gene expression profiling in breast cancer patients. J Clin Oncol 2005, 23:1169-1177.
• [27]van de Vijver MJ, He YD, van’t Veer LJ, et al.: A gene-expression signature as a predictor of survival in breast cancer. NEJM 2002, 347:1999-2009.
• [28]Akikake A: New look at statistical model identification. IEEE Trans Autom Control 1974, 19(6):716-723.
• [29]Ross JS, Fletcher JA, Linette GP, et al.: The Her-2/neu gene and protein in breast cancer 2003: Biomarker and target of therapy. Oncologist 2003, 8:307-325.
• [30]Arteaga CL, Moulder SL, Yakes FM: HER (erbB) tyrosine kinase inhibitors in the treatment of breast cancer. Semin Oncol 2002, 29(3 Suppl. 11):4-10.
• [31]Zhou BP, Hung MC: Dysregulation of cellular signaling by HER2/neu in breast cancer. Semin Oncol 2003, 30(Suppl 16):38-48.