Numerous studies have demonstrated sex differences in drug reactions to the same drug treatment, steering away from the traditional view of one-size-fits-all medicine. A premise of this study is that the sex of a patient influences difference in disease characteristics and risk factors. In this study, we intend to exploit and to obtain better sex-specific biomarkers from gene-expression data. We propose a procedure to isolate a set of important genes as sex-specific genomic biomarkers, which may enable more effective patient treatment. A set of sex-specific genes is obtained by a variable importance ranking using a combination of cross-validation methods. The proposed procedure is applied to three gene-expression datasets. 1. Introduction Personalized medicine is defined by the use of genomic signatures of patients to assign effective therapies in order to achieve the best medical outcomes for individual patients, thus improving public health. Despite the variety of clinical, morphological, and molecular parameters used to classify human malignancies, patients receiving the same diagnosis can have markedly different clinical courses and treatment responses. Since there is no simple way to determine who will have an adverse reaction, the current system of “one-size-fits-all-” diagnoses is simply not good enough. An increasing number of studies have demonstrated sex differences in drug reactions to the same drug treatment. Migeon [1] implied that males and females responded differently to drug treatments and that sex plays a key role in cancer. In addition, females are historically less studied subjects due to the complication of estrous cycle, and therefore such studies would further benefit women’s health and promote public health. Recent advancements in biotechnology have accelerated the search for molecular biomarkers useful in the diagnosis and treatment of disease. Molecular biomarkers of disease risk and status are critical to an accurate treatment by identifying patients most likely to benefit from particular drugs or experience adverse reactions. Because medicine is always practiced on individuals rather than populations, the goal is to change the assignment of therapies from a population-based approach to an individualized approach. Gene-expression data can be used to identify patients with a good disease prognosis, thereby preventing some patients from unnecessary therapies and toxicity. For example, gene-expression profiling was used to predict clinical outcomes in pediatric patients with acute myeloid leukemia and to find genes whose aberrant
References
[1]
B. R. Migeon, “Why females are mosaics, x-chromosome inactivation, and sex differences in disease,” Gender Medicine, vol. 4, no. 2, pp. 97–105, 2007.
[2]
T. Yagi, A. Morimoto, M. Eguchi et al., “Identification of a gene expression signature associated with pediatric AML prognosis,” Blood, vol. 102, no. 5, pp. 1849–1856, 2003.
[3]
A. L. Blum and P. Langley, “Selection of relevant features and examples in machine learning,” Artificial Intelligence, vol. 97, no. 1-2, pp. 245–271, 1997.
[4]
R. Kohavi and G. H. John, “Wrappers for feature subset selection,” Artificial Intelligence, vol. 97, no. 1-2, pp. 273–324, 1997.
[5]
H. Ahn and H. Moon, “Classification: supervised learning with high-dimensional biological data,” in Statistical Bioinformatics, A Guide for Life and Biomedical Science Researchers, J. K. Lee, Ed., Chapter 8, John Wiley & Sons, Chichester, UK, 2009.
[6]
S. Dudoit, J. Fridlyand, and T. P. Speed, “Comparison of discrimination methods for the classification of tumors using gene expression data,” Journal of the American Statistical Association, vol. 97, no. 457, pp. 77–86, 2002.
[7]
H. Liu, J. Li, and L. Wong, “A comparative study on feature selection and classification methods using gene expression profiles and proteomic patterns,” Genome Informatics Series, vol. 13, pp. 51–60, 2002.
[8]
I. Guyon, J. Weston, S. Barnhill, and V. Vapnik, “Gene selection for cancer classification using support vector machines,” Machine Learning, vol. 46, no. 1–3, pp. 389–422, 2002.
[9]
C.-A. N. Tsai, C.-H. Chen, T.-C. Lee, I.-C. Ho, U.-C. Yang, and J. J. Chen, “Gene selection for sample classifications in microarray experiments,” DNA and Cell Biology, vol. 23, no. 10, pp. 607–614, 2004.
[10]
L. Breiman, “Random forests,” Machine Learning, vol. 45, no. 1, pp. 5–32, 2001.
[11]
R. Díaz-Uriarte and S. Alvarez de Andrés, “Gene selection and classification of microarray data using random forest,” BMC Bioinformatics, vol. 7, article 3, 2006.
[12]
S. Baek, H. Moon, H. Ahn, R. L. Kodell, C.-J. Lin, and J. J. Chen, “Identifying high-dimensional biomarkers for personalized medicine via variable importance ranking,” Journal of Biopharmaceutical Statistics, vol. 18, no. 5, pp. 853–868, 2008.
[13]
S. Baek, C.-A. Tsai, and J. J. Chen, “Development of biomarker classifiers from high-dimensional data,” Briefings in Bioinformatics, vol. 10, no. 5, pp. 537–546, 2009.
[14]
J. D. Patel, P. B. Bach, and M. G. Kris, “Lung cancer in us women: a contemporary epidemic,” Journal of the American Medical Association, vol. 291, no. 14, pp. 1763–1768, 2004.
[15]
C. Haslinger, N. Schweifer, S. Stilgenbauer et al., “Microarray gene expression profiling of B-cell chronic lymphocytic leukemia subgroups defined by genomic aberrations and VH mutation status,” Journal of Clinical Oncology, vol. 22, no. 19, pp. 3937–3949, 2004.
[16]
J. Homsi, M. Kashani-Sabet, J. L. Messina, and A. Daud, “Cutaneous melanoma: prognostic factors,” Cancer Control, vol. 12, no. 4, pp. 223–229, 2005.
[17]
Y. Zhao and R. Simon, “BRB-ArrayTools Data Archive for human cancer gene expression: a unique and efficient data sharing resource,” Cancer Informatics, vol. 6, pp. 9–15, 2008.
