Luigi Marchionni, MD, PhD; Renee F. Wilson, MSc; Antonio C. Wolff, MD; Spyridon Marinopoulos, MD, MBA; Giovanni Parmigiani, PhD; Eric B. Bass, MD, MPH; Steven N. Goodman, MD, MHS, PhD
Disclaimer: The authors of this report are responsible for its content. Statements in the report should not be construed as endorsement by the Agency for Healthcare Research and Quality or the U.S. Department of Health and Human Services.
Grant Support: This project was funded under contract no. 290-02-0018 from the Agency for Healthcare Research and Quality, U.S. Department of Health and Human Services.
Potential Financial Conflicts of Interest: None disclosed.
Requests for Single Reprints: Steven N. Goodman, MD, MHS, PhD, Johns Hopkins University School of Medicine, 550 Building, Room 11-03, Baltimore, MD 21205; e-mail, firstname.lastname@example.org.
Current Author Addresses: Drs. Marchionni and Wolff: Johns Hopkins University School of Medicine, Oncology Cancer Biology, Baltimore, MD 21287.
Ms. Wilson, Dr. Marinopoulos, and Dr. Bass: Johns Hopkins University School of Medicine, General Internal Medicine, Baltimore, MD 21287.
Dr. Parmigiani: Johns Hopkins University, School of Medicine Oncology Informatics, Baltimore, MD 21287.
Dr. Goodman: Johns Hopkins University School of Medicine, Oncology Biostatistics, Baltimore, MD 21287.
Three gene expression–based prognostic breast cancer tests have been licensed for use.
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).
MEDLINE, EMBASE, and Cochrane databases (from 1990 through January 2007), Web sites of test manufacturers, and discussion with the manufacturers.
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.
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.
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).
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.
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.
Technologies used for high-throughput gene expression analysis.
A. Breast cancer tumors are sampled at the treatment location and shipped to the central laboratory doing the assay, where pathologic review is done to assess cancer cell contents, followed by RNA preparation and integrity evaluation. Suitable samples are used to quantify RNA levels, thus assessing gene expression. When a gene is expressed, the transcription complex copies its DNA sequence into complementary RNA transcripts that are translated into proteins. High-throughput gene expression analysis aims to quantify messenger RNA (mRNA) populations in a given tissue. B. DNA microarray is the molecular biology technique enabling gene expression analysis in MammaPrint. RNA is labeled with fluorescent dye and hybridized against thousands of different nucleotide sequences corresponding to different genes and arrayed on a solid surface (that is, a modified microscope glass slide). On hybridization, fluorescence emitted by
single locations on the microarray is used to estimate gene expression levels. In MammaPrint, a 2-color design is used, and RNA expression is estimated as a relative ratio between the sample and a reference RNA. For each patient, triplicate measurements are obtained from 2 microarrays inverting the labeling scheme. C. Real-time reverse transcriptase polymerase chain reaction (PCR) is the enabling technology to assess gene expression in Oncotype DX and H/I. This technique is based on reverse transcription (RT) (see Glossary) of a specific mRNA into the complementary DNA (cDNA) molecule, which is used as a template in PCR. The production of double-stranded DNA is accompanied by emission of light, which is recorded throughout the process and correlates to the amount of DNA that is produced. The higher the initial amount of RNA, the earlier light is emitted
during RT-PCR, a measurable difference that allows gene expression to be quantitated. D. Gene expression levels are mathematically transformed into indexes predicting disease recurrence.
Appendix Table 1. Studies on the Oncotype DX Gene Expression Test
Appendix Table 2. Studies on the MammaPrint Gene Expression Test
Appendix Table 3. Studies on the H/I Gene Expression Test
Systematic search strategy and results.
Table 1. Patient Reclassification by Gene Expression Testing with Oncotype DX
Table 2. Kaplan–Meier Analysis of Survival Stratified by MammaPrint and Adjuvant! Online
Table 3. Future Issues
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Luigi Marchionni, Renee F. Wilson, Antonio C. Wolff, Spyridon Marinopoulos, Giovanni Parmigiani, Eric B. Bass, et al. Systematic Review: Gene Expression Profiling Assays in Early-Stage Breast Cancer. Ann Intern Med. 2008;148:358–369. doi: 10.7326/0003-4819-148-5-200803040-00208
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Published: Ann Intern Med. 2008;148(5):358-369.
Breast Cancer, Hematology/Oncology.
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