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Originally published as JCO Early Release 10.1200/JCO.2008.20.2812 on February 2 2009 © 2009 American Society of Clinical Oncology.
Twenty-One–Gene Assay: Challenges and Promises in Translating Personal Genomics and Whole-Genome Scans Into Personalized Treatment of Breast CancerDepartment of Surgery, Surgical Oncology Research Unit, Ioannina University School of Medicine, Ioannina, Greece To the Editor: In the September 1 issue of Journal of Clinical Oncology, Goldstein et al1 conclude that the 21-gene assay is a better recurrence predictor than the currently used Adjuvant!—a validated algorithm based on classical clinicopathologic features—and that it can be used for tailoring chemotherapy among individual patients with hormone receptor (HR) –positive operable breast cancer. The use of genomic markers for treatment decision making without consideration of standard conventional factors is not realistic.2 Personalized cancer treatment in an environment of personalized medicine is one of the major goals of clinical research in oncology. Personal genomics, pharmacogenomics, and whole-genome scans have stimulated major interest by national anticancer organizations, industry, academia, and the general public and have contributed to intensive, internationally collaborative research efforts. But despite these global efforts, only a few robust prognostic and predictive biomarkers, like HR and human epidermal growth factor receptor 2 (HER-2), have been validated and widely used in clinical practice.2–4 Although the 21-gene assay represents one, and perhaps the most promising, gene expression profiling marker to reach the level of testing in a large-scale randomized controlled trial (Trial Assigning Individual Option for Treatment [TAILORx]),5 the translation of genomics research into evidence-based clinical use faces multiple challenges.2–4,6,7 In addition to its strengths,5 the study by Goldsteinet al has several limitations: First, it is a retrospective, relatively small study of 465 patients that compared the predictive utility of the 21-gene assay with that of Adjuvant! Second, the study population, including both node-negative and node-positive patients who all received chemohormonal therapy, differs from the original population—HR-positive, node-negative patients who received tamoxifen—in whom the 21-gene assay recurrence score (RS) was developed.8 Third, despite methodological strengths, there are several weaknesses6 in the techniques used to assess the predictive value of the original RS with regard to chemotherapy benefit.9 Fourth, and perhaps most important, neither the original RS study 8,9 nor the present study1 have considered the new standard targeted agents: aromatase inhibitors for HR-positive, postmenopausal patients and trastuzumab for HER-2–postitve patients.10 Because therapeutic strategy is changing, markers developed in tissues of patients who had not received current standard agents have limited value in current clinical practice.11 Therefore, the utility of RS for postmenopausal HR-positive and/or HER-2–positive patients is unknown. Two randomized trials test the efficacy of the 21-gene assay (TAILORx) and the 70-gene signature12 (Microarray in Node-Negative Disease Avoids Chemotherapy [MINDACT] trial). Genomics research in collected biospecimens (biobanks) will add important new information. It was once thought that simply stratifying individual patients into subgroups at high or low risk for developing breast cancer or recurrence and tailoring the most effective preventive or therapeutic intervention would revolutionize management of breast cancer. However, given the heterogeneity and complexity of breast cancer, this goal now appears elusive. Indeed, some individual primary tumors contain small cancer cell subpopulations, like breast cancer stem cells, that vary in metastatic ability and treatment response. Moreover, different deregulated signaling pathways and various biologic processes, including angiogenesis, tumor microenvironment, and dormancy, prove that individualized treatment approaches in solid cancers will require extremely hard work and sophisticated protocols.13 There is promise that the next-generation sequencing technology will provide newer genotyping platforms with more than 1 million single-nucleotide polymorphisms and copy number variants. Genome-wide association studies that used platforms with smaller number of single-nucleotide polymorphisms/copy number variants have discovered novel genetic risk variants. Although each risk variant confers only a small effect on cancer risk, and thus it is irrelevant, their combination may have clinical implications.14,15 Completion of breast cancer genetic mapping and functional studies to define the role of key genes and signaling pathways will enable future personalized studies. Comparison of whole-genome scans from relapsed nonresponders and relapse-free responders in large-scale, prospective, unbiased studies that record both "classic" clinicopathologic features and novel genetic risk variants might result in the development of individualized therapeutic approaches preventing both locoregional and distant recurrences.3,7,16,17 The results of the two currently underway randomized trials on genomic biomarkers, TAILORx and MINDACT, will substantially contribute to evidence-based decisions in the treatment of early breast cancer. AUTHOR'S DISCLOSURES OF POTENTIAL CONFLICTS OF INTEREST The author(s) indicated no potential conflicts of interest. Acknowledgment There is no research support for this article. REFERENCES
1. Goldstein LJ, Gray R, Badve S, et al: Prognostic utility of the 21-gene assay in hormone receptor-positive operable breast cancer compared with classical clinicopathologic features. J Clin Oncol 26:4063–4071, 2008. 2. Bustin SA: Molecular medicine, gene-expression profiling and molecular diagnostics: Putting the cart before the horse. Biomark Med 2:201–207, 2008.[CrossRef] 3. Roukos DH. Personal genomics and genome-wide association studies: Novel discoveries but limitations for practical personalized medicine. Ann Surg Oncol. doi: 10.1245/s10434-008-0109-6 [epub ahead of print on August 23, 2008].[CrossRef] 4. Roukos DH, Murray S, Briasoulis E: Molecular genetic tools shape a roadmap towards a more accurate prognostic prediction and personalized management of cancer. Cancer Biol Ther 6:308–312, 2007.[Medline] 5. Paik S, Tang G, Fumagalli D: An ideal prognostic test for estrogen receptor–positive breast cancer? J Clin Oncol 26:4058–4059, 2008. 6. Ioannidis JP: Gene expression profiling for individualized breast cancer chemotherapy: Success or not? Nat Clin Pract Oncol 3:538–539, 2006.[CrossRef][Medline] 7. Roukos DH, Lykoudis E, Liakakos T: Genomics and challenges toward personalized breast cancer local control. J Clin Oncol 26:4360–4361, 2008. 8. Paik S, Shak S, Tang G, et al: A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. N Engl J Med 351:2817–2826, 2004. 9. Paik S, Tang G, Shak S, et al: Gene expression and benefit of chemotherapy in women with node-negative, estrogen receptor-positive breast cancer. J Clin Oncol 24:3726–3734, 2006. 10. Goldhirsch A, Wood WC, Gelber RD, et al: Progress and promise: Highlights of the international expert consensus on the primary therapy of early breast cancer 2007. Ann Oncol 18:1133–1144, 2007. 11. Roukos DH: HER2 and response to paclitaxel in node-positive breast cancer. N Engl J Med 358:197; 2008. 12. van de Vijver MJ, He YD, van't Veer LJ, et al: A gene-expression signature as a predictor of survival in breast cancer. N Engl J Med 347:1999–2009, 2002. 13. Roukos DH: Innovative genomic-based model for personalized treatment of gastric cancer: Integrating current standards and new technologies. Expert Rev Mol Diagn 8:29–39, 2008.[CrossRef][Medline] 14. Hunter DJ, Khoury MJ, Drazen JM: Letting the genome out of the bottle: Will we get our wish? N Engl J Med 358:105–107, 2008. 15. Pharoah PD, Antoniou AC, Easton DF, Ponder BA: Polygenes, risk prediction, and targeted prevention of breast cancer. N Engl J Med 358:2796–2803, 2008. 16. Roukos DH: Genetics and genome-wide association studies: Surgery-guided algorithm and promise for future breast cancer personalized surgery. Expert Rev Mol Diagn 8:587–597, 2008.[CrossRef][Medline] 17. Roukos DH: Assessing both genetic variation (SNPs/CNVs) and gene-environment interactions may lead to personalized gastric cancer prevention. Expert Rev Mol Diagn 9:1–6, 2009.[CrossRef][Medline]
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Copyright © 2009 by the American Society of Clinical Oncology, Online ISSN: 1527-7755. Print ISSN: 0732-183X
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