Abstract
The revolution of genomic technologies, including gene expression profiling, high-resolution mapping of genomic imbalances, and next-generation sequencing, allows us to establish molecular portraits of cancer cells with unprecedented accuracy. This generates hope and justifies anticipation that disease diagnosis, prognosis, and the choice of treatment will be adapted to the individual needs of patients based on molecular evidence.
Preoperative treatment strategies are now recommended for a variety of human cancers. Unfortunately, the response of individual tumors to a preoperative treatment is not uniform, and ranges from complete regression to resistance. This poses a considerable clinical dilemma, as patients with a priori resistant tumors could either be spared exposure to radiation or DNA-damaging drugs, i.e., could be referred to primary surgery, or dose-intensified protocols could be pursued. Because the response of an individual tumor as well as therapy-induced side effects represent the major limiting factors of current treatment strategies, identifying molecular markers of response or for treatment toxicity has become exceedingly important.
However, complex phenotypes such as tumor responsiveness to multimodal treatments probably do not depend on the expression levels of just one or a few genes and proteins. Therefore, methods that allow comprehensive interrogation of genetic pathways and networks hold great promise in delivering such tumor-specific signatures, since expression levels of thousands of genes can be monitored simultaneously. Over the past few years, microarray technology has emerged as a central tool in addressing pertinent clinical questions, the answers to which are critical for the realization of a personalized genomic medicine, in which patients will be treated based on the biology of their tumor and their genetic profile (Quackenbush, N Engl J Med 354:2463–72, 2006; Jensen et al., Curr Opin Oncol 18:374–380, 2006; Bol and Ebner, Pharmacogenomics 7:227–235, 2006; Nevins and Potti, Nat Rev Genet 8:601–609, 2007).
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Acknowledgements
The authors would like to thank PD Dr. Jochen Gaedcke, PD Dr. Marian Grade, Dr. Gabriela Salinas-Riester, and Mr. Chan Rong Lai for their advice. This work was supported by the Deutsche Forschungsgemeinschaft (KFO 179).
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Ghadimi, B.M., Jo, P. (2016). Cancer Gene Profiling for Response Prediction. In: Grützmann, R., Pilarsky, C. (eds) Cancer Gene Profiling. Methods in Molecular Biology, vol 1381. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-3204-7_9
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