BACKGROUND: Three gene expression-based prognostic breast cancer tests have been licensed for use. PURPOSE: To summarize evidence on the validity and utility of 3 gene expression-based prognostic breast cancer tests: Oncotype DX (Genomic Health, Redwood City, California), MammaPrint (Agendia BV, Amsterdam, the Netherlands), and H/I (AvariaDX, Carlsbad, California). DATA SOURCES: MEDLINE, EMBASE, and Cochrane databases (from 1990 through January 2007), Web sites of test manufacturers, and discussion with the manufacturers. STUDY SELECTION: Original data studies published in English that addressed prognostic accuracy and discrimination or treatment benefit prediction of any of the 3 tests in women with breast cancer. DATA EXTRACTION: Information was extracted about the clinical characteristics of the study population (particularly clinical and therapeutic homogeneity), tumor characteristics, and whether the marketed test or underlying signature was evaluated. DATA SYNTHESIS: The tests are based on distinct gene lists, using 2 different technologies. Overall, the body of evidence showed that this new generation of tests may improve prognostic and therapeutic prediction, but the tests are at different stages of development. Evidence shows that the tests offer clinically relevant, improved risk stratification over standard predictors. Oncotype DX has the strongest evidence, closely followed by MammaPrint and H/I (which is still maturing). LIMITATIONS: For all tests, the relationship of predicted to observed risk in different populations and their incremental contribution over conventional predictors, optimal implementation, and relevance to patients receiving current therapies need further study. CONCLUSION: Gene expression technologies show great promise to improve predictions of prognosis and treatment benefit for women with early-stage breast cancer. More information is needed on the extent of improvement in prediction, characteristics of women in whom the tests should be used, and how best to incorporate test results into decision making about breast cancer treatment.
BACKGROUND: Three gene expression-based prognostic breast cancer tests have been licensed for use. PURPOSE: To summarize evidence on the validity and utility of 3 gene expression-based prognostic breast cancer tests: Oncotype DX (Genomic Health, Redwood City, California), MammaPrint (Agendia BV, Amsterdam, the Netherlands), and H/I (AvariaDX, Carlsbad, California). DATA SOURCES: MEDLINE, EMBASE, and Cochrane databases (from 1990 through January 2007), Web sites of test manufacturers, and discussion with the manufacturers. STUDY SELECTION: Original data studies published in English that addressed prognostic accuracy and discrimination or treatment benefit prediction of any of the 3 tests in women with breast cancer. DATA EXTRACTION: Information was extracted about the clinical characteristics of the study population (particularly clinical and therapeutic homogeneity), tumor characteristics, and whether the marketed test or underlying signature was evaluated. DATA SYNTHESIS: The tests are based on distinct gene lists, using 2 different technologies. Overall, the body of evidence showed that this new generation of tests may improve prognostic and therapeutic prediction, but the tests are at different stages of development. Evidence shows that the tests offer clinically relevant, improved risk stratification over standard predictors. Oncotype DX has the strongest evidence, closely followed by MammaPrint and H/I (which is still maturing). LIMITATIONS: For all tests, the relationship of predicted to observed risk in different populations and their incremental contribution over conventional predictors, optimal implementation, and relevance to patients receiving current therapies need further study. CONCLUSION: Gene expression technologies show great promise to improve predictions of prognosis and treatment benefit for women with early-stage breast cancer. More information is needed on the extent of improvement in prediction, characteristics of women in whom the tests should be used, and how best to incorporate test results into decision making about breast cancer treatment.
Authors: Manfred Schmitt; Karin Mengele; Apostolos Gkazepis; Rudolf Napieralski; Viktor Magdolen; Ute Reuning; Nadia Harbeck Journal: Breast Care (Basel) Date: 2008-10-15 Impact factor: 2.860
Authors: Tatyana V Nasedkina; Natalia A Guseva; Olga A Gra; Olga N Mityaeva; Alexander V Chudinov; Alexander S Zasedatelev Journal: Mol Diagn Ther Date: 2009 Impact factor: 4.074
Authors: Zachary A Gurard-Levin; Laurence O W Wilson; Vera Pancaldi; Sophie Postel-Vinay; Fabricio G Sousa; Cecile Reyes; Elisabetta Marangoni; David Gentien; Alfonso Valencia; Yves Pommier; Paul Cottu; Geneviève Almouzni Journal: Mol Cancer Ther Date: 2016-05-16 Impact factor: 6.261
Authors: Michael D Scott; Rinku Dutta; Manas K Haldar; Anil Wagh; Thomas R Gustad; Benedict Law; Daniel L Friesner; Sanku Mallik Journal: Analyst Date: 2012-10-12 Impact factor: 4.616