- Oral presentation
- Open Access
Present situation and future of genetic profiling for prognosis and treatment
- GN Hortobagyi1
© BioMed Central 2005
- Published: 27 May 2005
- Breast Cancer
- Estrogen Receptor
- Hormone Receptor Status
- Estrogen Receptor Status
Classification and staging systems are important in oncology to predict clinical behavior and determine prognosis. In addition, they may contribute to the selection of optimal treatment strategies. Much clinical and translational research over the past 30 years was directed at establishing or refining prognostic and predictive factors for breast cancer. Initially, tumor related factors such as size, grade, lymph node involvement, and hormone receptor status were considered in the determination of prognosis. Patient characteristics, such as age, menopausal status and performance status, also contributed to these estimates. Some factors such as estrogen receptor (ER) status were shown to be better predictive factors than prognostic factors. Thus, although ER-positive tumors have a slightly better prognosis during the early years of follow up than do ER-negative ones, the major application of ER status is to predict response to endocrine therapy. A variety of biochemical and molecular factors were reported to have prognostic or predictive ability over the past 20 years. These included cathepsin D, HER2, EGFR, p53, UPA, PAI, and many others. Of these, only HER2 was consistently validated as a prognostic factor, as well as a predictor of response to the monoclonal antibody trastuzumab (Herceptin). Developing, assessing, and discarding these various putative prognostic and/or predictive factors was the result of an enormous investment of time and effort of many scientists from many countries around the world. Considering that only one new prognostic/predictive factor was universally adopted over the past 25 years (HER2 status), it must be concluded that this is an enormously inefficient process.
The Human Genome Project was a major milestone in the history of medicine. Both the genetic information obtained and the technological advances that took place during this large multicenter effort have had enormous influence over all fields of medicine. For the field of prognostication and prediction in breast cancer, the major consequence was the development of technology that led to the simultaneous evaluation of gene expression for hundreds and, more recently, thousands of genes. In fact, recently launched gene arrays include the entire human genome. Thus, we have the opportunity to assess, in a small tumor sample, the expression profile of all known human genes. There are multiple technological platforms under evaluation for this purpose, and the results obtained with one cannot automatically be substituted for results obtained with another platform. Nevertheless, on the basis of several reports, it can be stated that gene expression profiling of human breast cancer provides valuable information in the following areas:
1. Molecular classification of primary breast cancer
2. Identification of multiple distinct prognostic subgroups
3. Determination of expression level of several genes of interest (ER, PR, HER2, etc.)
4. Identification of genetic networks
5. Prediction of response to chemotherapy
The initial reports were based on small patient numbers that presented substantial statistical challenges for adequate estimation of end-points and to prevent frequent false-positive or false-negative results. More recent analyses have included several dozen and up to a few hundred patients. These reports provide greater statistical power and greater reliability. However, these reports still represent retrospective analyses of subsets of patients, and prospective validation is still sorely needed. Reports are beginning to appear comparing the performance of different platforms on the same tumor samples and considering the same end-points. The source of tumor material, the manner in which it was handled before testing, and the amount of tissue needed for reliable testing are all under intense scrutiny. Gene profiling with currently available platforms includes a number of genes or gene segments of uncertain function (ESTs). These provide an excellent opportunity to assess the functional value of these genes and enrich our understanding of their biological function. Many centers and groups are assessing the potential of molecular profiling in the prediction of response to therapy. As technology evolves, this type of information will transform the way we think of breast cancer, the way we assess and stage primary and metastatic breast cancer, and the manner in which we select the best combination and sequence of therapies to obtain optimal therapeutic results. Today's costs, while substantial, are rapidly falling and newer technology will make these assays much more accessible. Furthermore, because multiple relevant markers can be determined using a single assay, it is likely that gene expression profiling will be more cost-effective than currently used diagnostic and prognostic tests.
The major challenges in gene profiling are still in developing and using the most appropriate statistical methods for data analysis. The need for handling tens of thousands or hundreds of thousands of data points, especially originating from a much smaller number of tumors, is daunting and mistaken conclusions might be reached in the absence of optimal analytical techniques.
Finally, prospective validation of the clinical utility of gene profiling for classification, determination of prognosis, and selection of optimal therapies for individual patients will require large, prospective, multicenter, controlled clinical trials. If successful, these will take us one step closer to individualized medicine.
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