Identification of Small and Discriminative Gene Signatures for Chemosensitivity Prediction in Breast Cancer
Wei Hu
DOI: 10.4236/jct.2011.22025   PDF    HTML     6,217 Downloads   9,867 Views  


Various gene signatures of chemosensitivity in breast cancer have been discovered. One previous study employed t-test to find a signature of 31 probe sets (27 genes) from a group of patients who received weekly preoperative chemotherapy. Based on this signature, a 30-probe set diagonal linear discriminant analysis (DLDA-30) classifier of pathologic complete response (pCR) was constructed. In this study, we sought to uncover a signature that is much smaller than the 31 probe sets and yet has enhanced predictive performance. A signature of this nature could inform us what genes are essential in response prediction. Genetic algorithms (GAs) and sparse logistic regression (SLR) were employed to identify two such small signatures. The first had 13 probe sets (10 genes) selected from the 31 probe sets and was used to build a SLR predictor of pCR (SLR-13), and the second had 14 probe sets (14 genes) selected from the genes involved in Notch signaling pathway and was used to develop another SLR predictor of pCR (SLR-Notch-14). The SLR-13 and SLR-Notch-14 had a higher accuracy and a higher positive predictive value than the DLDA-30 with much lower P values, suggesting that our two signatures had their own discriminative power with high statistical significance. The SLR prediction model also suggested the dual role of gene RNUX1 in promoting residual disease (RD) or pCR in breast cancer. Our results demonstrated that the multivariable techniques such as GAs and SLR are effective in finding significant genes in chemosensitivity prediction. They have the advantage of revealing the interacting genes, which might be missed by single variable techniques such as t-test.

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W. Hu, "Identification of Small and Discriminative Gene Signatures for Chemosensitivity Prediction in Breast Cancer," Journal of Cancer Therapy, Vol. 2 No. 2, 2011, pp. 196-202. doi: 10.4236/jct.2011.22025.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] M. Chanrion, V. Negre, H. Fontaine, N. Salvetat, F. Bibeau, G. Mac Grogan, L. Mauriac, D. Katsaros, F. Molina, C. Theillet and J. M. Darbon, “A Gene Expression Signature That Can Predict the Recurrence of Tamoxifen-Treated Primary Breast Cancer,” Clinical Cancer Research, Vol. 14, No. 6, 2008, pp. 1744-1752. doi:10.1158/1078-0432.CCR-07-1833
[2] S. P. Linke, T. M. Bremer, C. D. Herold, G. Sauter and C. Diamond, “A Multimarker Model to Predict Outcome in Tamoxifen-Treated Breast Cancer Patients,” Clinical Cancer Research, Vol. 12, No. 4, 2006, pp. 1175-1183. doi:10.1158/1078-0432.CCR-05-1562
[3] P. E. L?nning, S. Knappskog, V. Staalesen, R. Chrisanthar and J. R. Lillehaug, “Breast Cancer Prognostication and Prediction in the Postgenomic Era,” Annals of Oncology, Vol. 18, No. 8, 2007, pp. 1293-1306. doi:10.1093/annonc/mdm013
[4] M. A. Folgueira, D. M. Carraro, H. Brentani, D. F. Patr?o, E.M. Barbosa, M. M. Netto, J. R. Caldeira, M. L. Katayama, F. A. Soares, C. T. Oliveira, L. F. Reis, J. H. Kaiano, L. P. Camargo, R. Z. Vêncio, I. M. Snitcovsky, F. B. Makdissi, P. J. e Silva, J. C. Góes and M. M. Brentani, “Gene Expression Profile Associated with Response to Doxorubicin-Based Therapy in Breast Cancer,” Clinical Cancer Research, Vol. 11, No. 20, 2005, pp. 7434-7443. doi:10.1158/1078-0432.CCR-04-0548
[5] H. K. Dressman, C. Hans, A. Bild, J. A. Olson, E. Rosen, P. K. Marcom, V. B. Liotcheva, E. L. Jones, Z. Vujaskovic, J. Marks, M. W. Dewhirst, M. West, J. R. Nevins and K. Blackwell, “Gene Expression Profiles of Multiple Breast Cancer Phenotypes and Response to Neoadjuvant Chemotherapy,” Clinical Cancer Research, Vol. 12, No. 3, 2006, pp. 819-826. doi:10.1158/1078-0432.CCR-05-1447
[6] O. Thuerigen, A. Schneeweiss, G. Toedt, P. Warnat, M. Hahn, H. Kramer, B. Brors, C. Rudlowski, A. Benner, F. Schuetz, B. Tews, R. Eils, H.-P. Sinn, C. Sohn and P. Lichter, “Gene Expression Signature Predicting Pathologic Complete Response with Gemcitabine, Epirubicin, and Docetaxel in Primary Breast Cancer,” Journal of Clinical Oncology, Vol. 24, No. 12, 2006, pp. 1839-1845. doi:10.1200/JCO.2005.04.7019
[7] S. Rathnagiriswaran, Y. W. Wan, J. Abraham, V. Castranova, Y. Qian and N. L. Guo, “A Population-Based Gene Signature is Predictive of Breast Cancer Survival and Chemoresponse,” International Journal of Oncology, Vol. 36, No. 3, 2010, pp. 607-616.
[8] K. R. Hess, K. Anderson, W. F. Symmans, et al., “Pharmacogenomic Predictor of Sensitivity to Preoperative Chemotherapy with Paclitaxel and Fluorouracil, Doxorubicin, and Cyclophosphamide in Breast Cancer,” Journal of Clinical Oncology, Vol. 24, No. 26, 2006, pp. 4236-4244.
[9] R. Tibshirani, “Regression Shrinkage and Selection via the Lasso,” Journal of the Royal Statistical Society B, Vol. 58, No. 1, 1996, pp. 267-288.
[10] G. C. Cawley and L. C. Talbot, “Gene Selection in Cancer Classification Using Sparse Logistic Regression with Bayesian Regularization,” Bioinformatics, Vol. 22, No. 19, 2006, pp. 2348-2355. doi:10.1093/bioinformatics/btl386
[11] S. Stylianou, R. B. Clarke, et al., “Activation of Notch Signaling in Human Breast Cancer,” Cancer Research, Vol. 66, No. 3, 2006, pp. 1517-1525. doi:10.1158/0008-5472.CAN-05-3054
[12] K. Brennan and A. M. C. Brown, “Is There a Role for Notch Signalling in Human Breast Cancer?” Breast Cancer Research, Vol. 5, No. 2, 2003, pp. 69-75. doi:10.1186/bcr559
[14] Y. H. Ou, P.-H. Chung, et al., “The Candidate Tumor Suppressor BTG3 Is a Transcriptional Target of P53 That Inhibits E2F1,” The EMBO Journal, Vol. 26, No. 17, 2007, pp. 3968-3980. doi:10.1038/sj.emboj.7601825
[15] J. Starkova, J. Madzo, G. Cario, T. Kalina, A. Ford, M. Zaliova, O. Hrusak and J. Trka, “The Identification of (ETV6)/RUNX1-Regulated Genes in Lymphopoiesis Using Histone Deacetylase Inhibitors In ETV6/RUNX1- Positive Lymphoid Leukemic Cells,” Clinical Cancer Research, Vol. 13, No. 6, 2007, pp. 1726-1735. doi:10.1158/1078-0432.CCR-06-2569
[16] F. M. Mikhail, K. K. Sinha, Y. Saunthararajah and G. Nucifora, “Normal and Transforming Functions of RUNX1: A Perspective,” Journal of Cell Physiology, Vol. 207, No. 3, 2006, pp. 582-593. doi:10.1002/jcp.20538
[17] N. S. Goldstein, D. Decker, D. Severson, et al., “Molecular Classification System Identifies Invasive Breast Carcinoma Patients Who Are Most Likely and Those Who Are Least Likely to Achieve a Complete Pathologic Response after Neoadjuvant Chemotherapy,” Cancel, Vol. 110, No. 8, 2007, pp. 1687-1696. doi:10.1002/cncr.22981

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