NCBI Bookshelf. A service of the National Library of Medicine, National Institutes of Health.

Marchionni L, Wilson RF, Marinopoulos SS, et al. Impact of Gene Expression Profiling Tests on Breast Cancer Outcomes. Rockville (MD): Agency for Healthcare Research and Quality (US); 2008 Jan. (Evidence Reports/Technology Assessments, No. 160.)

Cover of Impact of Gene Expression Profiling Tests on Breast Cancer Outcomes

Impact of Gene Expression Profiling Tests on Breast Cancer Outcomes.

Show details

4Discussion

Using the analytical framework described, we evaluated the evidence available on three commercially available gene expression based assays, and on the gene expression profiles underlying these tests. Specifically, our review focused on the MammaPrint® assay, based on the 70-gene prognostic signature developed by van't Veer and colleagues,21,25,58,59 on the Oncotype DX™ assay, based on the 21-gene profile developed by Paik and colleagues, 28,50,53 and on the Breast Cancer Profiling (BCP) assay, based on the two-gene ratio signature developed by Ma et al.61,64

The first question, (is there any direct evidence that these tests in breast cancer patients lead to improvement in outcomes?) is defined as randomized clinical trials comparing the outcomes of patients following standard management to those of patients managed with the aid of the expression-based assays. No such studies have been conducted. Two prospective randomized trials are in progress: TAILORx35 and MINDACT36 were recently initiated to prospectively evaluate the clinical utility of Oncotype DX and MammaPrint, respectively. As described in Chapter 3, TAILORx will provide information on the appropriate RS threshold for recommending adjuvant chemotherapy, and will not directly assess the effect of clinical decisionmaking with and without the test. The data generated may allow indirect inferences to be made. MINDACT will allow more direct inferences on the clinical utility, since its will be compared directly to the use of a conventional risk index. For both trials, patient health outcomes will be endpoints.

The evidence available on the subsequent key questions allowed us to draw conclusions about the specific tests, as well as about the methodology of test development and current and future clinical uses of gene expression assays. Currently established methods for risk stratification of patients with breast cancer rely on a combination of prognostic factors like tumor size, grade, lymph node status, and presence of hormone receptors and the human growth factor receptor 2 (HER-2), such as the St. Gallen Consensus Guidelines5 or Adjuvant! Online.7 The latter also incorporates a nomogram to generate estimates of benefit from specific therapies. A critical question is how much gene expression-based tests add to standard risk assessment methods or guidelines. A second question is how clearly does the current evidence relate to the test's proposed use in a decisionmaking context, i.e., how well defined or homogeneous are the patient populations, in terms of their current therapy and decisions about future therapy? Is it clear how the test information should be implemented, i.e., using cutoffs, as a continuous score, or in combination with other indices? When viewed through the prism of clinical decisionmaking, the current evidence base for these technologies leaves many uncertainties.

Many aspects of expression-based predictors differ in qualitative ways from other kinds of risk predictors. First, the mechanism by which the expression of any particular gene, or combination thereof, is related to outcome is generally less well understood than with standard predictors, as are the methods by which the combinations are chosen. Gene expression levels are markers of activation or inactivation of complex biological processes. As Fan et al.79 demonstrated, similar risk classifications can be achieved with predictors having few or no overlapping genes. Second, there is no “gold standard” for gene expression values; the technologies used here - RT-PCR and microarrays - represent the state of the art. In the end it is less analytic validity (i.e., proximity to a true value) but analytic variability (i.e., variation in the calculated value) that must be understood to predict whether investigational results are likely to be similar to those produced in practice, and whether the results in practice are likely to be stable over time and with broader use. Third, we know little about the stability of the predictive value of such markers over populations with different genetic profiles. Arguments can be made that genetic predictors (particularly from tumors) are likely to be either more or less universal than physiologic ones, so there is still much to be learned about the generalizability of these rules. However, in spite of these differences, the latter half of the developmental pathway for these tests must follow the same principles and procedures as those for any multivariate clinical prediction rule. These have been outlined in detail in the clinical literature,94,95 enshrined in reporting guidelines,96 and articulated with specific respect to expression-based predictors in a series of articles by Simon.68,71,97

The three signatures and assays considered differ not only in the technologies used and their implementations, but also in the nature of the validation studies. An important distinction for all expression-based tests is that between the signature and the licensed test, as offered to a patient. Data about the actual tests offered to the patients are available only for MammaPrint and Oncotype DX, albeit more limited for the former. There is only one published study that used the two-gene index as it is implemented in its marketed version, the BCP assay,61 although it is not clear whether the lab performing the assay in this report was the same as the one with current rights to perform the test. The remaining reports considered the signature, with the expression of the two genes measured and combined in varying ways.6163,72

Recent publications have begun to address the analytic validity of the tests. There is now evidence about several aspects of gene expression measurements for two of the tests (MammaPrint and Oncotype DX.44,45,57,58 The public release of these data is useful as it supports the rationale behind two of the currently available assays and encourages development and publication of similar information for future assays. However, evidence about analytic features of the assays does not obviate the need for continuous monitoring of the experimental procedures involved with such testing. In this regard it is worth mentioning that the U.S. FDA Office of In Vitro Diagnostic evaluation and Safety (OIVD) is developing a Guidance Document on In Vitro Diagnostic Multivariate Index Assays (IVDMIAs) that will affect the development of future assays in the U.S. Moreover, the laboratories offering such assays, as any other laboratory providing diagnostic services must adhere to the Clinical Laboratory Improvement Act (CLIA).

Below follows the discussion of the specific tests and key questions considered in the present report, along with recommendations and conclusions.

Oncotype DX

Oncotype DX, the basis for the “recurrence score,” was first developed, then applied and used as an assay in investigational settings. All evidence about the RS (apart from the comparison study by Fan and colleagues79 and the development studies44)48 were obtained using the same assay that is offered to patients, with sample processing done in the same manner by the same laboratory.

Analytic Validity

Analytic validity evidence now exists for some of the operational/laboratory characteristics/procedures of this test, as well as about its reproducibility, although information about this latter point is limited to a few repeated analyses. These studies demonstrated that the reproducibility of the test across different samples of the same block, and samples from different blocks, is reasonably high.45 The test involves not only the simple assessment of the RNA levels by RT-PCR, but also the preparation of the RNA, following a central review of the specimens shipped to Genomic Health to check for tumor content. No direct evidence is available about the sample preparation aspect of the test, although there is indirect evidence from peer-reviewed literature in the form of the overall success rate of extracting analyzable mRNA, which appears to be fairly high. Centralization is a current strength of Oncotype DX with regard to reproducibility, but additional scrutiny may be needed if other laboratories offer such testing in the future.

Clinical Validity

“Clinical validity” is defined here as the ability of a prediction test to accurately predict risk. Whether or not those risk predictions differ enough to justify its use in a clinical setting (i.e., whether discrimination is sufficient) is a second issue. The clinical validity of Oncotype DX has been evaluated in various settings. The first validation study28 used tamoxifen-treated women with ER positive, lymph node negative breast cancer, from the randomized clinical trial NSABP B-14. This study independently validated the prognostic value of the RS, which had been previously tested in the tamoxifen-treated population of NSABP B-20. Perhaps the most important aspect of this population is that it was clinically and prognostically well defined, in that everyone was presumed to be eligible for chemotherapy, and all subjects had similar treatment (i.e., tamoxifen), making for a relatively clear interpretation of the results in terms of both treatment biology and clinical decisionmaking. Predictors of response on a specified therapy are not necessarily prognostic factors independent of that therapy, so studies which mix treated and untreated patients, or patients differently treated, can produce results that do not apply well to either. While this study took place in the past, all measurements were done concurrently independently of the outcome, and so has evidential value quite close to that of a concurrent prospective study. The main issues raised by the non-concurrency are whether the 668 subjects examined were a representative sample of the more than 2000 in the original study, and the degree to which the findings in tamoxifen-treated women will apply to aromatase inhibitor treated patients today, the role of HER-2 testing and treatment, and whether there was anything clinically relevant about how the early stage cancer was diagnosed (e.g., clinically or by mammogram) that might differ today.

While this study reported hazard ratios for the RS in the presence of clinical predictors, it did not provide predictions by the RS cross-classified by those of standardized combination risk predictors to see exactly how many women would be re-classified in risk strata that might change decisions regarding chemotherapy. This information was however presented in poster form in 200465 and it showed that the RS had considerable predictive power beyond that of the St. Gallen or NCCN risk stratification guidelines (n.b. St. Gallen did not include HER-2 at that time). Another poster66 showed the same information for Adjuvant! Online (Tables 13 and 14, Chapter 3). All of these cross-classifications showed that the greatest contribution of the test was likely to be in the reclassification of patients from high to low risk, i.e., in reducing the number of patients who might unnecessarily undergo chemotherapy. It also showed that optimal predictions probably would be achieved with a combination of both expression and clinical predictors. It must be stressed that the cross-classified risk of patients in the low-risk RS groups in these posters does not represent the lowest attainable risk; they are an artifact of the “low risk” category threshold. Patients who had low absolute RS scores would be predicted to have lower risks than the “low risk” category average, and those lower risks are probably low enough for many women classified as high risk by other indices to change their treatment decisions. One very important remaining question is the degree to which the absolute observed risks in this population, particularly in these lowest risk groups, are similar to other populations, and to those whom it is currently being used, i.e. whether the calibration of the test predictions will vary. The low risk arm of the TAILORx trial, in which patients with a RS less than10 will not be treated with adjuvant chemotherapy, will help address this question.

A second large study looked at the clinical performance of the Oncotype DX assay to predict breast cancer death (at 10 years) in a community-based population of ER positive, node-negative patients treated with tamoxifen, confirming the B14 results among the tamoxifen treated patients, and showing predictive value, albeit lesser, in ER positive patients not treated with tamoxifen.50 The Esteva study,48 which showed no predictive value of RS in a small population of patients who received neither tamoxifen nor chemotherapy, showed such anomalous results with standard predictors (i.e. higher grade predicting better prognosis) that its results cannot be regarded as reliable. Finally, the Fan study,79 while testing the RS signature measured by microarrays and not the actual Oncotype DX test based on RT-PCR, showed good discriminatory power in a relatively large, independent dataset, albeit with a heterogeneous mix of treatments, receptor and nodal status. This is the same dataset on which the MammaPrint signature was developed.

These studies in combination provide fairly strong support for the clinical validity of the Oncotype DX test over and above standard predictors, in a well defined population (ER positive, lymph node negative, tamoxifen treated) with clear treatment indication (adjuvant chemotherapy). Exactly how much it adds, however, exactly what proportion of these patients would benefit from its use, and the stability of the observed risk in the various risk categories in other (or current) populations, is not as clear. Discussion will continue below about its use in a clinical setting.

Clinical Utility

Clinical utility is the degree to which a test is predictive of treatment benefit, and hence is a critical foundation for the use of a test in clinical decisionmaking. Prognostic ability itself speaks to this to some degree, as it puts a ceiling on the degree of clinical benefit. For example, if the 10 year distant relapse rate is 5 percent, by definition additional treatment cannot provide more than a 5 percent absolute benefit, and background knowledge about treatment efficacy tells us it will be less. So if the risk of distant recurrence can be reliably established as low enough, this has clinical utility in itself.

However, it is of considerable value to have a direct estimate of the degree of treatment benefit. This can only be done reliably in the context of RCTs, prospectively or retrospectively, as they assess treatment effect in an unbiased manner. This was addressed by Paik et al. in their study of the correlation of the RS with the degree of adjuvant chemotherapy benefit in the context of the NSABP-20 trial.53 This showed that the chemotherapy benefit in ER positive, node negative patients randomized to tamoxifen versus tamoxifen plus chemotherapy was almost entirely restricted to those in the high risk RS category. The CIs in the low and intermediate risk categories were wide and included the possibility of benefit whereas the CI for the high risk group was narrow and showed clear benefit. A statistical interaction was also found with patient age, although those data were not reported. The only caveat is that the tamoxifen arm of this population was part of the training set for the assay, although the outcome measure used in the training set was not treatment benefit. It is not clear whether the information in clinical predictors was optimally used (i.e., as continuous rather than dichotomous variables), but that is unlikely to have accounted for the degree of differential effect predicted by the RS. HER-2 positivity reportedly had no effect on the results. Several other studies evaluated the value of the RS information in different populations of patients to predict other correlates of treatment effect. For example, evaluation of pathologic response after preoperative chemotherapy49,55 supports clinical utility, although that was evaluated in patients in whom chemotherapy was already determined to be necessary.

The NSABP-20 study probably represents as strong evidence as can be derived from already existing data regarding the clinical utility of the Oncotype DX test. While prospective confirmation of these findings are definitely needed as well as analysis of existing patient samples from other completed trials, this provides reasonable justification in the interim for the use of the test by women in this specific population.

Use in clinical decisionmaking. One published study has reported the impact of using the RS on clinical management,56 and there have been examinations of the economic implications of testing.75 In general, studies showing that physicians change recommendations or that woman change treatment decisions in response to their Oncotype DX risk category are minimally informative if the study is not designed to specifically explore the woman's risk thresholds for making that decision. The reported study does not specify what information was conveyed to the patients, i.e., a risk score, the risk category, or the risk itself. If the latter, the number they were told is important to know. In the absence of this information, it is not possible to know the threshold of risk below which most women (or any given proportion) would forego chemotherapy, or conversely, the risks at which they would choose it. In the absence of such information, it cannot be known whether the study is effectively examining compliance with physician recommendations, careful weighing of risks and benefits, or the effect of test marketing.

There are still uncertainties about the optimal use of this test in practice. First, while the cut-offs are valuable for test validation purposes, it is not clear whether the current thresholds actually correspond to the cutoffs that would be derived using a formal decision-analytic approach based on utility assessments. For an individual woman, a risk based on her exact RS value would be preferable, since by definition, those with RS scores near the upper boundary of the “low risk” range have a predicted risk higher than the average of the group, and those with low scores have lower risk. The fact that the boundaries used in the studies may not be optimal for decisionmaking is seen in the different cut-offs used by the TAILORx trial, in which the low-risk group is defined as RS less-than or equal-to10 instead of 18, and the high risk group is defined as greater than 25 instead of 30.

The second uncertainty is the optimal use of conventional predictors. While the RS has been shown to have more value than most predictors, the same studies show that clinical predictors retain predictive value, and clinical prediction models continue to evolve and improve. An improved prediction tool would involve a combination of the expression-based and clinical predictors, but this has not been systematically explored in any study, and absolute risks produced by regression models or stratified tables with all predictors included are generally not reported. As noted previously, cross-classification data using the most updated standardized clinical indices would be one form of such data, although those do not show the risk from combinations of the exact RS and clinical predictor score.

Cost-effectiveness. While our review highlights many gaps in what is known about the clinical utility of using gene expression profiling in women diagnosed with breast cancer, the review also revealed that little is known about the cost-effectiveness of using these tests. Once studies have demonstrated the clinical utility of these gene expression profiles, policy makers and health care providers will need information about the cost-effectiveness of those tests that have proven utility. Such information will be particularly important given the relatively high expected costs of the tests. Oncotype DX, for example, costs more than $3000 for each use of the test.

In our review, we found three published studies that have addressed economic outcomes associated with use of the breast cancer gene expression tests. One study reported that using the 21-gene RT-PCR assay to reclassify patients would be cost-effective for those who were defined by 2005 NCCN criteria as low risk ($31,452 per quality-adjusted life-year (QALY) gained) and would be cost-saving for those who were defined by NCCN criteria as high risk.67 The EPC team had only moderate confidence in these projections because the study did not provide enough information about potential sources of bias in the analysis, allied with the fact that the study was supported by the manufacturer, which may introduce conflict of interest. The 2007 NCCN guideline now indicates that use of chemotherapy in these patients is optional, further diminishing the value of these projections.

The second study reported that use of the 21-gene RT-PCR assay was associated with a cost-utility ratio of $4432 per QALY compared with use of tamoxifen alone, and a gain of 1.71 QALYs with net cost savings when compared with chemotherapy plus tamoxifen.75 The EPC team had little confidence in this analysis, which was supported by the manufacturer, because it did not meet many of the standards that were used for appraising the quality of the analysis.

The third study compared the cost-effectiveness of the Netherlands Cancer Institute gene expression profiling (GEP) assay (MammaPrint) to the U.S. National Institutes of Health (NIH) guidelines for identification of early breast cancer patients who would benefit from adjuvant chemotherapy. The GEP assay was projected to yield a poorer quality-adjusted survival than the NIH guidelines (9.68 vs. 10.08 QALYs) and lower total costs ($29,754 vs. $32,636). To improve quality-adjusted survival, the GEP assay would need to have a sensitivity of at least 95 percent for detecting high risk patients while also having a specificity of at least 51 percent. The EPC team had confidence in the results of this analysis because it met most of the standards for appraising the quality of an economic analysis.

Since the overall body of evidence is inconclusive about the economic outcomes associated with use of breast cancer gene expression tests, this is an area that will require further investigation. Future economic analyses of validated tests should take into consideration existing guidelines for the performance and reporting of such analyses.41 Ideally, the analyses should be performed by investigators who have not received financial support from manufacturers of the tests.

Questions Regarding the Clinical Validity and Utility of the Oncotype DX Assay

1.

Better information is needed about the predictions from combining the RS with current versions of standardized risk predictors, both in the form of cross-classification tables, and perhaps of regression-based combinations that optimize individual risk predictions. Formal development of cutoffs to optimize patient utility are also needed.

2.

While Oncotype DX exhibits a fair bit of risk discrimination (i.e., separating patients into different risk groups), the stability across different populations of the observed absolute risk in patients with a given risk score (i.e., calibration) needs further study. Of greatest interest is the observed risk in the lowest risk groups, since the absolute level of this risk is critical for informed decisionmaking, and patients may forego chemotherapy on the basis of this information.

3.

Data are currently available mainly for tamoxifen-treated patients and for those treated with cyclophosphamide-methotrexate-5-fluorocil chemotherapy. It is important to assess whether RS applies to other hormonal treatments such as aromatase inhibitors, as well as more contemporary chemotherapy regimens using taxanes and anthracyclines.

4.

It is not clear whether RS can be used to help guide treatment of HER-2 positive patients and additional studies are needed, as most of these patients were classified in the high RS group in the initial trials.

5.

While awaiting the TAILORx results, the findings of the Paik 200653 study predicting treatment benefit need independent confirmation, particularly for low and intermediate risk groups.

6.

Studies examining the use of Oncotype DX should provide women and physicians with quantitative risk information and report how this alters clinical decisionmaking. The manner in which this risk information is presented should also be studied.

MammaPrint

Published evidence includes both reports about MammaPrint,5759 as well as studies about the associated 70-gene signature. The manuscripts that used the signature provide useful information about the validity of the biological correlations underlying the profile and suggest that it can be used in a clinical setting, but cannot be considered to be a direct validation of the assay.

The assay is based on the gene signature first proposed in 200221 by investigators at the Netherlands Cancer Institute, using 789 lymph node negative patients, younger than 55 years old, who did not carry a breast cancer gene (BRCA) mutation, and whose tumors were less than 5 cm in diameter. This signature was validated in a second study by the same group, using a series of 295 consecutive stage I or II breast cancer patients, who were either lymph node negative or positive, and who were younger than 53 years.25 This validation was only partial, since the investigators included 61 of the 78 patients used to develop the prognosis profile. The MammaPrint test itself was further validated in a multicenter European study of 302 patients not treated with chemotherapy or tamoxifen, showing that it provided prognostic information beyond that of standard clinical-pathologic indices for those patients.59 Recently, this signature was implemented as a commercial assay, and RNA available from the original cohorts were reanalyzed, yielding consistent results.58 It is the first prognostic test submitted to the FDA under its new, non-binding IVDMIA guidance, and received approval in February 2007.

Analytic Validity

This assay is the first microarray-based test introduced in the field. Two recent papers addressed issues related to the reproducibility of the test within laboratories, as well as across laboratories. Such evidence however was obtained from a limited number of patients and using a moderate number of replication experiments. Results showed a good reproducibility within a laboratory, and a good degree of agreement across laboratories, although RNA labeling emerged as a possible source of variation capable of affecting the results. Whether this issue has an impact on risk classification was not thoroughly investigated, and thus the portability of the result of the assay from one laboratory to another still remains open. A second relevant point is the fact that the only validation study using the MammaPrint assay showed that only about 80 percent of specimens from the field (in this case 5 different European institutions) were analyzable, raising some concern about the analysis of fresh-frozen specimens. As more patients are analyzed by this test, the overall success rate may change. Finally, it must be noted that although this technology requires fresh rather than paraffin embedded specimens, Agendia performs a central pathologic review of the specimens as is performed with FFPE samples at Genomic Health, before evaluation with the test.

Clinical Validity

Overall, published evidence supports MammaPrint as a better predictor of the 5-year risk of distant recurrence than traditionally used tumor characteristics or algorithms. However, the cohorts in whom it was developed and validated are more clinically heterogeneous than those used for the Oncotype DX test, with a mix of lymph node status, ER status and current treatment. Additionally, evidence was derived only from patients younger than 55 to 60 years of age. Even so, it is interesting that it had 80 percent concordance with the array-based RS classification when applied to the same patients, although it remains to be seen how well it predicts in cohorts with the same degree of clinical and treatment homogeneity as used in the Oncotype DX development, and which differ from its training set. Evidence about its value in comparison with clinical predictors was assessed in a collaborative study among 5 different institutions in Europe, where data were compared to standard clinical predictors like Adjuvant!.59 The area under the receiver operating characteristic curves was 0.68 for MammaPrint and 0.66 for the Adjuvant! Score. Such estimates indicate that both methods have apparently similar and modest discriminatory power in absolute terms. Similar results were obtained also using the ten year overall survival end point. However, when Adjuvant! and MammaPrint were cross-classified against each other, Adjuvant! had no additional predictive value. Adjustment for other predictors (St. Gallen and the Nottingham Prognostic Index) had a minimal effect on the regression coefficient of MammaPrint score or its significance, but no other data were reported on their incremental value. Of note, no significant heterogeneity in the hazard ratio estimates was shown among centers, although original hazard ratio estimates were significantly higher than those obtained in this validation study. The validation cohort had longer time of observation, included older women, and excluded patients who received adjuvant therapy.

Clinical Utility

No studies evaluated clinical utility of this test.

Use in clinical practice. No studies explored the use of this test in clinical practice.

In summary, MammaPrint is the first commercialized microarray-based gene expression profile with a prognostic purpose. The underlying signature has been evaluated in approximately 700 patients, although MammaPrint itself has only been evaluated in one study of 307 untreated patients. A reanalysis of the original training data of the signature using the marketed test showed a net reclassification of only one patient of 78.58 It is unclear what population of patients would derive benefit from use of the test, and what the magnitude of that benefit would be. Prospective data from trials like MINDACT will be extremely valuable. Overall, published evidence supports MammaPrint as a better predictor of the risk of distant recurrence than traditionally used tumor characteristics or algorithms, but its performance in therapeutically homogeneous populations is not yet known with precision, and it is unclear for how many women the lowest predicted risks are low enough to forgo chemotherapy. No evidence is available to permit conclusions regarding the clinical utility of MammaPrint to select women who will benefit from chemotherapy.

To conclude, the literature on the 70-gene signature includes numerous studies that focused more on its biological underpinning and less on the clinical implications of this gene expression profile, although it has now received FDA approval for clinical use. It has been shown that this signature is maintained along the cancer progression process.82 This profile was directly investigated by two different platforms (microarray and RT-PCR,48), and was successfully re-implemented in two distinct microarray platforms, showing that it has a fair degree of analytic robustness.

Here we summarize open questions as well as research gaps found in the evidence about the clinical validity and utility of the MammaPrint assay and the 70-gene signature.

1.

The prognostic value of the 70-gene signature has been assessed in different populations facing different therapeutic choices. In the analysis by van de Vijver and colleagues, 130 of the 295 patients received adjuvant therapy in a non-randomized fashion. Patients in the original development cohort were not treated, and Buyse validated the marketed assay in untreated patients. It is not yet clear which are the optimal patient populations for the use of this test, exactly what its performance is in those populations, and how many of its predictions would result in different therapeutic decisions. Larger independent validation studies in therapeutically homogeneous groups would be very valuable.

2.

Previous comments noted in the Oncotype DX summary apply here as well, including the presentation of data regarding the test in combination with standard predictors, the use of risk categories instead of a continuous risk measure, and the importance of confirming the stability of the test's calibration in different populations.

H/I Ratio Signature and Breast Cancer Profiling (BCP)

This test, licensed by AviaraDX to Quest Diagnostic, Inc. is based on the two-gene ratio signature originally proposed by Ma and colleagues.64 Specifically, the assay is based on the two-gene index that includes normalization to specific reference genes followed by a mathematical transformation.61 Overall, large collections of patients have been investigated using the signature, but its prognostic and predictive value has been inconsistent; strong in some studies, weak or absent in others. In the Fan study, in which the ratio based on the signature (not the marketed BCP test), it was completely non-predictive where both Oncotype DX and the MammaPrint signatures were. The reason for that may have been a technical failure of the array technology used to simulate the test,98 or the test's value may be restricted to certain populations. The populations in which it has been developed have been heterogeneous, although stratified analyses were used. Differences have been found in its ability to predict in various subgroups of those populations, differences that are not consistent across studies. A major limitation of the evidence is that the signature has been formulated in a variety of ways, as a simple ratio, as an index, by normalizing to a different set of reference genes, or to a standard calibration RNA. In the 2006 study in which the index as is currently marketed was tested,61 statistical methods to find optimal cutoffs were applied, meaning that this assay still requires further external validation. We found no analytic validity data for the BCP assay.

In summary, while this test shows some promise, it must be regarded as being in a developmental phase. It was not clear in the Ma 200661 study whether samples were processed by Quest Diagnostics, Inc. which holds the current license. There are a number of intriguing biological insights and plausible mechanisms to support the rationale for the test, but its consistent value in well-defined clinical settings has not yet been firmly established.

General Comments on Analytic Validity and Laboratory Quality Control

Until recently there were no multi-gene RNA-expression-based assay kits approved by the FDA for use in breast cancer. Such tests are currently offered as laboratory services (“home-brew test”) subject to CLIA general laboratory standards. In February 2007, and again in July 2007, the FDA published draft guidelines on regulation of IVDMIAs, which cover tests combining complex algorithms and data from multiple laboratory tests. The release of these draft guidelines suggests that in the future these tests will be subject to FDA evaluation. Under this model, all the assays to be used to make medical decisions about therapeutic options will be regarded as Class II or III devices and will go through a Pre-Market Approval (PMA) process, and will require specific post-market revision. Based on such draft guidelines, MammaPrint receive IVDMIA approval upon their voluntary submission of data.38,39

Nevertheless, analytic validity is an issue related to quality control in the laboratories where the test is carried out, and these data are not in the literature, but in the laboratories' log books. An effort has been made by Genomic Health, Inc. and Agendia to clarify the laboratory procedures and acknowledge critical issues, but periodic review and reporting of the procedures needs to be established to monitor the reproducibility of the procedures, success rates, and quality control indices.

A critical and often underappreciated analytic issue for the success of these tests is the way specimens are handled. Unlike DNA, RNA is unstable, so the length of time from excision to freezing or fixation, prolonged storage, and other factors related to specimen processing can lead to significant variability in the quality of mRNA available for expression profiling. Even if central labs offering the test are certified and use reliable procedures, preanalytic issues at the sending sites such as specimen acquisition and handling can potentially affect the results of the testing. Both the Oncotype DX and BCP use standard formalin fixed specimens, which tends to be stable, whereas MammaPrint requires fresh tissue. The use of fresh tissue required for gene array testing is challenging and, according to on-line information available from the Agendia website, careful procedures must be used when sampling the tumor to avoid necrotic parts and stromal tissue. Samples are reviewed centrally at Genomic Health and Agendia for tumor content, and BCP is performed after laser capture microdissection. Regardless of the technology used, standardized protocols, use of new reagents specifically designed to preserve mRNA for gene expression profiling, and reduction in RNA degradation (during sample processing, storage, and preparation) are important to assuring reliable measurements of mRNA levels for use in gene expression profiling.

Overall Implications and Recommendations

The discussion above covers issues specific to the tests under examination, but there are some larger issues whose consideration is motivated by this analysis that groups involved in assessing the value of these tests should be aware of.

Assay Validation

In general, it is clear that validation studies need to deal with populations for whom the decision-making implications of various risk groupings are clear. The studies examined herein have established the proof-of-concept that tumor gene expression has prognostic value, but for all tests except Oncotype DX, both validation and development studies have been on mixed populations, without sufficient sample sizes to stratify into large enough homogeneous groups to guide clinical decisionmaking. In addition, validation samples are often re-used by other investigators; the pool of such samples in the public domain needs to be greatly expanded.

Potential for Scale Problems

One problem that may be faced in the future is that of the consequences of an increase in demand for these tests. Scaling up the production could represent a challenge for the reproducibility and reliability of the tests in any setting, especially if more than one laboratory will offer the assays, since procedures to warrant inter-lab reproducibility will be needed. Not only analytical aspects will need monitoring, but also procedures involving specimen evaluation prior to testing. With a larger number of tests, for instance, the ability to reliably perform the central pathologic review might become an issue, while in the case of MammaPrint the availability of the current reference RNA could potentially become a limiting factor.

Genetic Variability and Gene Expression

It is unknown whether gene expression profiles are more or less likely than more traditional biomarkers to be generalizable beyond the populations in which they were initially developed. Gene expression may reflect fundamental biological tumor features, and thus be relatively stable across ethnic groups. However, gene expression patterns have also been associated with specific genetic mutations (i.e., BRCA1), indicating that specific DNA mutations or polymorphisms21,99 may affect the performance of a signature. This speaks to the importance of validating these tests in populations with varying genetic background. Biological and genetic evidence potentially addressing these issues is expected to become available in the form of single nucleotide polymorphism (SNP) arrays coupled to expression arrays.

The Need for Databases, Reproducibility, and Standards

MammaPrint® is the first assay based on microarrays that has completed the path from the bench to FDA approval for clinical application. For data storage, the MIAME standards32 represent the basis for the proper collection and storage of microarray data, and should be used to develop procedures going forward for the archiving of the tests performed in real patients, much as databases have been developed to facilitate outcomes research to complement clinical trials. Consideration should be given to the development of databases with complete data on each patient (absent identifiers), including all the analyses performed, laboratory logs, the raw and processed data, and all the information about procedures and analyses that have been performed to produce a risk estimate from a tumor sample. These apply equally to the other two assays, differing only in the type of data that would be stored.

Where Is the Field Going?

The current evidence for the feasibility of such gene expression based tests in clinical settings, along with the demand for better tools to manage patients, is leading to both an evolution of the available tests, and the addition of novel alternative tests. The number of publications is growing, and several alternative signatures not considered here have already been proposed for breast cancer as well as for other neoplasms. We can expect many new tests, as well as new uses for the assays that already exist. More genes might be added to the signatures, and in the particular case of MammaPrint this will be possible without changing the experimental procedures, since the array contains thousands more genes than the ones that are incorporated in the 70-gene signature. In this regard, we might also expect other modifications: subsets of the current signatures might be proposed as alternatives to current clinical risk factors, or be proposed in different populations or for different purposes. For Oncotype DX, a natural evolution could be related to its use as an alternative to immunohistochemistry and/or pathology to evaluate tumor Grade, S-phase index, ER, PR, and HER-2 expression, since such genes are part of the set included in the assay. Reporting of individual gene expression results may also prove useful. A great deal more work needs to be done on the prediction of therapeutic benefit, which is the ultimate goal of all such tests.

“Comparative Effectiveness” Studies

The emphasis in virtually all of the papers and in our evidence assessment is on the establishment of the value of each of these predictors over standard clinical predictors. However, as gene expression tests mature and proliferate, an important question will be how they compare to each other, and whether there is value in their combination. In the therapeutic domain, this has been called “comparative effectiveness” research. Such research has traditionally been difficult to fund by government or by industry, because it may not hold out as much therapeutic promise as new discoveries, and because industry understandably is not anxious to fund head-to-head comparisons with competitive products. This same dynamic could easily take hold in the risk prediction arena, with a proliferation of licensed prediction indices without any clear notion of what new ones are contributing over previous tests. Development of future expression-based predictors should make clear their incremental value over pre-existing methods. In the absence of better oversight of test development, physicians and patient are likely to be awash in new tests that all claim to offer similar guidance, or perhaps new guidance in previously neglected clinical subsets, with no way to sort out those claims.

Conclusion

The introduction of these gene-expression tests have ushered in a new era in which many conventional clinical markers and predictors may be seen merely as surrogates for more fundamental genetic and physiologic processes. The multidimensional nature of these predictors demands both large numbers of clinically homogeneous patients to the used in the validation process, and exceptional rigor and discipline. Every study provides an opportunity to tweak a genetic signature, but we must find the right balance between speed of innovation and development of scientifically and clinically reliable tools. Going forward, it will be important to harness, if possible, as much genetic and clinical information on patients who undergo these tests to facilitate each goal without unduly sacrificing the other.

References and Included Studies

1.
Jemal A, Siegel R, Ward E. et al. Cancer statistics, 2007. CA Cancer J Clin. 2007;57(1):43–66. [PubMed: 17237035]
2.
Berry DA, Cirrincione C, Henderson IC. et al. Estrogen-receptor status and outcomes of modern chemotherapy for patients with node-positive breast cancer. JAMA. 2006;295(14):1658–67. [PMC free article: PMC1459540] [PubMed: 16609087]
3.
Eifel P, Axelson JA, Costa J. et al. National Institutes of Health Consensus Development Conference Statement: adjuvant therapy for breast cancer, November 1–3, 2000. J Natl Cancer Inst. 2001;93(13):979–89. [PubMed: 11438563]
4.
National Institutes of Health (NIH) Consensus Development Criteria web site. Available at: http://consensus​.nih​.gov/2000/2000AdjuvantTherapyBreastCancer114html.htm. Accessed July 25, 2007.
5.
Goldhirsch A, Glick JH, Gelber RD. et al. Meeting Highlights: International Expert Consensus on the Primary Therapy of Early Breast Cancer 2005. Breast. 2005;14(6):643.
6.
Carlson RW, Anderson BO, Burstein HJ. et al. Invasive breast cancer. J Natl Compr Canc Netw. 2007;5(3):246–312. [PubMed: 17439758]
7.
Ravdin PM, Siminoff LA, Davis GJ. et al. Computer program to assist in making decisions about adjuvant therapy for women with early breast cancer. J Clin Oncol. 2001;19(4):980–91. [PubMed: 11181660]
8.
Adjuvant!, Inc. Adjuvant! Online. Available at: http://www​.adjuvantonline.com. Accessed July 25, 2007.
9.
Wolff AC, Hammond ME, Schwartz JN. et al. American Society of Clinical Oncology/College of American Pathologists guideline recommendations for human epidermal growth factor receptor 2 testing in breast cancer. J Clin Oncol. 2007;25(1):118–45. [PubMed: 17159189]
10.
Braxton S, Bedilion T. The integration of microarray information in the drug development process. Curr Opin Biotechnol. 1998;9(6):643–9. [PubMed: 9889142]
11.
Mirnics K. Microarrays in brain research: the good, the bad and the ugly. Nat Rev Neurosci. 2001;2(6):444–7. [PubMed: 11389480]
12.
Mirnics K, Middleton FA, Lewis DA. et al. Analysis of complex brain disorders with gene expression microarrays: schizophrenia as a disease of the synapse. Trends Neurosci. 2001;24(8):479–86. [PubMed: 11476888]
13.
Schulze A, Downward J. Navigating gene expression using microarrays—a technology review. Nat Cell Biol. 2001;3(8):E190–5. [PubMed: 11483980]
14.
van Berkum NL, Holstege FC. DNA microarrays: raising the profile. Curr Opin Biotechnol. 2001;12(1):48–52. [PubMed: 11167072]
15.
DeRisi J, Penland L, Brown PO. et al. Use of a cDNA microarray to analyse gene expression patterns in human cancer. Nat Genet. 1996;14(4):457–60. [PubMed: 8944026]
16.
Alizadeh A, Eisen M, Davis RE. et al. The lymphochip: a specialized cDNA microarray for the genomic-scale analysis of gene expression in normal and malignant lymphocytes. Cold Spring Harb Symp Quant Biol. 1999;64:71–8. [PubMed: 11232339]
17.
Alizadeh AA, Ross DT, Perou CM. et al. Towards a novel classification of human malignancies based on gene expression patterns. J Pathol. 2001;195(1):41–52. [PubMed: 11568890]
18.
Rew DA. DNA microarray technology in cancer research. Eur J Surg Oncol. 2001;27(5 ):504–8. [PubMed: 11504524]
19.
Hu Z, Fan C, Oh DS. et al. The molecular portraits of breast tumors are conserved across microarray platforms. BMC Genomics. 2006;7:96. [PMC free article: PMC1468408] [PubMed: 16643655]
20.
Sorlie T, Perou CM, Tibshirani R. et al. Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc Natl Acad Sci U S A. 2001;98(19):10869–74. [PMC free article: PMC58566] [PubMed: 11553815]
21.
van 't Veer LJ, Dai H, van de Vijver MJ. et al. Gene expression profiling predicts clinical outcome of breast cancer. Nature. 2002;415(6871):530–6. [PubMed: 11823860]
22.
Chang HY, Nuyten DS, Sneddon JB. et al. Robustness, scalability, and integration of a wound-response gene expression signature in predicting breast cancer survival. Proc Natl Acad Sci U S A. 2005;102(10):3738–43. [PMC free article: PMC548329] [PubMed: 15701700]
23.
Sotiriou C, Wirapati P, Loi S. et al. Gene expression profiling in breast cancer: understanding the molecular basis of histologic grade to improve prognosis. J Natl Cancer Inst. 2006;98(4):262–72. [PubMed: 16478745]
24.
Huang E, Cheng SH, Dressman H. et al. Gene expression predictors of breast cancer outcomes. Lancet. 2003;361(9369):1590–6. [PubMed: 12747878]
25.
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. 2002;347(25):1999–2009. [PubMed: 12490681]
26.
Bustin SA. Absolute quantification of mRNA using real-time reverse transcription polymerase chain reaction assays. J Mol Endocrinol. 2000;25(2):169–93. [PubMed: 11013345]
27.
Schena M, Shalon D, Davis RW. et al. Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science. 1995;270(5235):467–70. [PubMed: 7569999]
28.
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. 2004;351(27):2817–26. [PubMed: 15591335]
29.
Baldwin D, Crane V, Rice D. A comparison of gel-based, nylon filter and microarray techniques to detect differential RNA expression in plants. Curr Opin Plant Biol. 1999;2(2):96–103. [PubMed: 10322196]
30.
Watson A, Mazumder A, Stewart M. et al. Technology for microarray analysis of gene expression. Curr Opin Biotechnol. 1998;9(6):609–14. [PubMed: 9889134]
31.
Schena M, Heller RA, Theriault TP. et al. Microarrays: biotechnology's discovery platform for functional genomics. Trends Biotechnol. 1998;16(7):301–6. [PubMed: 9675914]
32.
Brazma A, Hingamp P, Quackenbush J. et al. Minimum information about a microarray experiment (MIAME)-toward standards for microarray data. Nat Genet. 2001;29(4):365–71. [PubMed: 11726920]
33.
Bammler T, Beyer RP, Bhattacharya S. et al. Standardizing global gene expression analysis between laboratories and across platforms. Nat Methods. 2005;2(5):351–6. [PubMed: 15846362]
34.
Irizarry RA, Warren D, Spencer F. et al. Multiple-laboratory comparison of microarray platforms. Nat Methods. 2005;2(5):345–50. [PubMed: 15846361]
35.
National Cancer Institute. The TAILORx Breast Cancer Trial. Available at: http://www​.cancer.gov​/clinicaltrials/digestpage/TAILORx. Accessed August 14, 2007.
36.
TransBIG. MINDACT. Available at: http://www​.breastinternationalgroup​.org/TransBIG/Mindact​.aspx. Accessed August 15, 2007.
37.
Food and Drug Administration, Center for Devices and Radiological Health. Available at: http://www​.fda.gov/cdrh/. Accessed July 25, 2007.
38.
Food and Drug Administration, Center for Devices and Radiological Health. 510(k) Substantial Equivalence Determination Decision Summary, No. k062694. Available at: http://www.fda.gov/cdrh/reviews/K062694.pdf. Accessed July 25, 2007.
39.
Food and Drug Administration, Center for Devices and Radiological Health. 510(k) Submission for MammaPring Service in the U.S. Summary, No. k070675. Available at: http://www.fda.gov/cdrh/pdf7/K070675.pdf. Accessed July 25, 2007.
40.
Berlin JA. Does blinding of readers affect the results of meta-analyses? University of Pennsylvania Meta-analysis Blinding Study Group. Lancet. 1997;350(9072):185–6. [PubMed: 9250191]
41.
Philips Z, Ginnelly L, Sculpher M et al. Review of guidelines for good practice in decision-analytic modelling in health technology assessment. Health Technol Assess 2004;8(36):iii–iv, ix–xi, 1–158. [PubMed: 15361314]
42.
Bossuyt PM, Reitsma JB, Bruns DE. et al. Towards complete and accurate reporting of studies of diagnostic accuracy: the STARD initiative. Fam Pract. 2004;21(1):4–10. [PubMed: 14760036]
43.
McShane LM, Altman DG, Sauerbrei W. et al. REporting recommendations for tumour MARKer prognostic studies (REMARK) Br J Cancer. 2005;93(4):387–91. [PMC free article: PMC2361579] [PubMed: 16106245]
44.
Cronin M, Pho M, Dutta D. et al. Measurement of gene expression in archival paraffin-embedded tissues: development and performance of a 92-gene reverse transcriptase-polymerase chain reaction assay. Am J Pathol. 2004;164(1):35–42. [PMC free article: PMC1602211] [PubMed: 14695316]
45.
Cronin M, Sangli C, Liu ML. et al. Analytical Validation of the Oncotype DX Genomic Diagnostic Test for Recurrence Prognosis and Therapeutic Response Prediction in Node-Negative, Estrogen Receptor-Positive Breast Cancer. Clin Chem. 2007;53(6):1084–91. [PubMed: 17463177]
46.
Braxton S, Bedilion T. The integration of microarray information in the drug development process. Curr Opin Biotechnol. 1998;9(6):643–9. [PubMed: 9889142]
47.
Cobleigh MA, Tabesh B, Bitterman P. et al. Tumor gene expression and prognosis in breast cancer patients with 10 or more positive lymph nodes. Clin Cancer Res. 2005;11(24 Pt 1):8623–31. [PubMed: 16361546]
48.
Esteva FJ, Sahin AA, Cristofanilli M. et al. Prognostic role of a multigene reverse transcriptase-PCR assay in patients with node-negative breast cancer not receiving adjuvant systemic therapy. Clin Cancer Res. 2005;11(9):3315–9. [PubMed: 15867229]
49.
Gianni L, Zambetti M, Clark K. et al. Gene expression profiles in paraffin-embedded core biopsy tissue predict response to chemotherapy in women with locally advanced breast cancer. J Clin Oncol. 2005;23(29):7265–77. [PubMed: 16145055]
50.
Habel LA, Shak S, Jacobs MK. et al. A population-based study of tumor gene expression and risk of breast cancer death among lymph node-negative patients. Breast Cancer Res. 2006;8(3):R25. [PMC free article: PMC1557737] [PubMed: 16737553]
51.
Mina L, Soule SE, Badve S et al. Predicting response to primary chemotherapy: gene expression profiling of paraffin-embedded core biopsy tissue. Breast Cancer Res Treat 2006. [PubMed: 17039265]
52.
Bertone P, Stolc V, Royce TE. et al. Global identification of human transcribed sequences with genome tiling arrays. Science. 2004;306(5705):2242–6. [PubMed: 15539566]
53.
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. 2006;24(23):3726–34. [PubMed: 16720680]
54.
Sorlie T, Tibshirani R, Parker J. et al. Repeated observation of breast tumor subtypes in independent gene expression data sets. Proc Natl Acad Sci U S A. 2003;100(14):8418–23. [PMC free article: PMC166244] [PubMed: 12829800]
55.
Chang JC, Makris A, Gutierrez MC et al. Gene expression patterns in formalin-fixed, paraffin-embedded core biopsies predict docetaxel chemosensitivity in breast cancer patients. Breast Cancer Res Treat 2007. [PubMed: 17468949]
56.
Oratz R and Dev P. Impact of Oncotype DXTM Recurrence Score on Decision Making in Early-Stage Breast Cancer. Journal of Oncology Practice in press. [PMC free article: PMC2793805] [PubMed: 20859407]
57.
Ach RA, Floore A, Curry B. et al. Robust interlaboratory reproducibility of a gene expression signature measurement consistent with the needs of a new generation of diagnostic tools. BMC Genomics. 2007;8(1):148. [PMC free article: PMC1904205] [PubMed: 17553173]
58.
Glas AM, Floore A, Delahaye LJ. et al. Converting a breast cancer microarray signature into a high-throughput diagnostic test. BMC Genomics. 2006;7:278. [PMC free article: PMC1636049] [PubMed: 17074082]
59.
Buyse M, Loi S, van't Veer L. et al. Validation and clinical utility of a 70-gene prognostic signature for women with node-negative breast cancer. J Natl Cancer Inst. 2006;98(17):1183–92. [PubMed: 16954471]
60.
Hughes TR, Marton MJ, Jones AR. et al. Functional discovery via a compendium of expression profiles. Cell. 2000;102(1):109–26. [PubMed: 10929718]
61.
Ma XJ, Hilsenbeck SG, Wang W. et al. The HOXB13:IL17BR expression index is a prognostic factor in early-stage breast cancer. J Clin Oncol. 2006;24(28):4611–9. [PubMed: 17008703]
62.
Goetz MP, Suman VJ, Ingle JN. et al. A two-gene expression ratio of homeobox 13 and interleukin-17B receptor for prediction of recurrence and survival in women receiving adjuvant tamoxifen. Clin Cancer Res. 2006;12(7 Pt 1):2080–7. [PubMed: 16609019]
63.
Jerevall PL, Brommesson S, Strand C et al. Exploring the two-gene ratio in breast cancer-independent roles for HOXB13 and IL17BR in prediction of clinical outcome. Breast Cancer Res Treat 2007. [PubMed: 17453342]
64.
Ma XJ, Wang Z, Ryan PD. et al. A two-gene expression ratio predicts clinical outcome in breast cancer patients treated with tamoxifen. Cancer Cell. 2004;5(6):607–616. [PubMed: 15193263]
65.
Paik S, Shak S, and Tang G. Risk classification of breast cancer patients by teh recurrence score assay: caomparison to guidelines based on patient age, tumor size, and tumor grade. Abstract presented at Annual San Antonio Breast Cancer Symposium; December 8–11, 2004. San Antonio, TX. Absrtract 104.
66.
Bryant,et al., 2005. Toward a More Rational Selection ofTailored Adjuvant Therapy. Poster Presentation. St. Gallens Conference, March 2005.
67.
Hornberger J, Cosler LE, Lyman GH. Economic analysis of targeting chemotherapy using a 21-gene RT-PCR assay in lymph-node-negative, estrogen-receptor-positive, early-stage breast cancer. Am J Manag Care. 2005;11(5):313–24. [PubMed: 15898220]
68.
Simon Z, Sipka S, Gergely L. et al. Investigation of monoclonal gammopathy of undetermined significance: a single-centre study. Clin Lab Haematol. 2006;28(3):164–9. [PubMed: 16706932]
69.
Reid JF, Lusa L, De Cecco L. et al. Limits of predictive models using microarray data for breast cancer clinical treatment outcome. J Natl Cancer Inst. 2005;97(12):927–30. [PubMed: 15956654]
70.
Sotiriou C, Neo SY, McShane LM. et al. Breast cancer classification and prognosis based on gene expression profiles from a population-based study. Proc Natl Acad Sci U S A. 2003;100(18):10393–8. [PMC free article: PMC193572] [PubMed: 12917485]
71.
Simon R. Development and validation of therapeutically relevant multi-gene biomarker classifiers. J Natl Cancer Inst. 2005;97(12):866–7. [PubMed: 15956642]
72.
Jansen MP, Sieuwerts AM, Look MP. et al. HOXB13-to-IL17BR expression ratio is related with tumor aggressiveness and response to tamoxifen of recurrent breast cancer: a retrospective study. J Clin Oncol. 2007;25(6):662–8. [PubMed: 17308270]
73.
Sieuwerts AM, Meijer-van Gelder ME, Timmermans M. et al. How ADAM-9 and ADAM-11 differentially from estrogen receptor predict response to tamoxifen treatment in patients with recurrent breast cancer: a retrospective study. Clin Cancer Res. 2005;11(20):7311–21. [PubMed: 16243802]
74.
Carlson RW, Anderson BO, Burstein HJ. et al. Breast cancer. J Natl Compr Canc Netw. 2005;3(3):238–89. [PubMed: 16002000]
75.
Lyman GH, Cosler LE, Kuderer NM. et al. Impact of a 21-gene RT-PCR assay on treatment decisions in early-stage breast cancer: an economic analysis based on prognostic and predictive validation studies. Cancer. 2007;109(6):1011–8. [PubMed: 17311307]
76.
Oestreicher N, Ramsey SD, Linden HM. et al. Gene expression profiling and breast cancer care: what are the potential benefits and policy implications? Genet Med. 2005;7(6):380–9. [PubMed: 16024969]
77.
Brauer CA, Rosen AB, Greenberg D, Neumann PJ. Trends in the measurement of health utilities in published cost-utility analyses. Value Health. 2006;9(4):213–8. [PubMed: 16903990]
78.
Torrance GW, Feeny D. Utilities and quality-adjusted life years. Int J Technol Assess Health Care. 1989;5(4):559–75. [PubMed: 2634630]
79.
Fan C, Oh DS, Wessels L. et al. Concordance among gene-expression-based predictors for breast cancer. N Engl J Med. 2006;355(6):560–9. [PubMed: 16899776]
80.
Espinosa E, Vara JA, Redondo A. et al. Breast cancer prognosis determined by gene expression profiling: a quantitative reverse transcriptase polymerase chain reaction study. J Clin Oncol. 2005;23(29):7278–85. [PubMed: 16129846]
81.
Eden P, Ritz C, Rose C. et al. “Good Old” clinical markers have similar power in breast cancer prognosis as microarray gene expression profilers. Eur J Cancer. 2004;40(12):1837–41. [PubMed: 15288284]
82.
Weigelt B, Hu Z, He X. et al. Molecular portraits and 70-gene prognosis signature are preserved throughout the metastatic process of breast cancer. Cancer Res. 2005;65(20):9155–8. [PubMed: 16230372]
83.
Nuyten DS, Kreike B, Hart AA. et al. Predicting a local recurrence after breast-conserving therapy by gene expression profiling. Breast Cancer Res. 2006;8(5):R62. [PMC free article: PMC1779489] [PubMed: 17069664]
84.
Chang HY, Sneddon JB, Alizadeh AA. et al. Gene expression signature of fibroblast serum response predicts human cancer progression: similarities between tumors and wounds. PLoS Biol. 2004;2(2):E7. [PMC free article: PMC314300] [PubMed: 14737219]
85.
Chi JT, Wang Z, Nuyten DSA. et al. Gene expression programs in response to hypoxia: Cell type specificity and prognostic significance in human cancers. PLoS Med. 2006;3(3):395–409. [PMC free article: PMC1334226] [PubMed: 16417408]
86.
Naderi A, Teschendorff AE, Barbosa-Morais NL et al. A gene-expression signature to predict survival in breast cancer across independent data sets. Oncogene 2006. [PubMed: 16936776]
87.
Sun Y, Goodison S, Li J. et al. Improved breast cancer prognosis through the combination of clinical and genetic markers. Bioinformatics. 2007;23(1):30–7. [PMC free article: PMC3431620] [PubMed: 17130137]
88.
Esteva FJ, Sahin AA, Rassidakis GZ. et al. Jun Activation Domain Binding Protein 1 Expression Is Associated with Low p27Kip1 Levels in Node-Negative Breast Cancer. Clin Cancer Res. 2003;9(15):5652–5659. [PubMed: 14654548]
89.
Wang J, Buchholz TA, Middleton LP. et al. Assessment of histologic features and expression of biomarkers in predicting pathologic response to anthracycline-based neoadjuvant chemotherapy in patients with breast carcinoma. Cancer. 2002;94(12):3107–14. [PubMed: 12115341]
90.
Harvey JM, Clark GM, Osborne CK. et al. Estrogen receptor status by immunohistochemistry is superior to the ligand-binding assay for predicting response to adjuvant endocrine therapy in breast cancer. J Clin Oncol. 1999;17(5):1474–81. [PubMed: 10334533]
91.
Mohsin SK, Weiss H, Havighurst T. et al. Progesterone receptor by immunohistochemistry and clinical outcome in breast cancer: a validation study. Mod Pathol. 2004;17(12):1545–54. [PubMed: 15272277]
92.
Allred DC, Harvey JM, Berardo M, Clark GM. Prognostic and predictive factors in breast cancer by immunohistochemical analysis. Mod Pathol. 1998;11(2):155–68. [PubMed: 9504686]
93.
Allred DC, Harvey JM, Berardo M. et al. Prognostic and predictive factors in breast cancer by immunohistochemical analysis. Mod Pathol. 1998;11(2):155–68. [PubMed: 9504686]
94.
Laupacis A, Sekar N, Stiell IG. Clinical prediction rules. A review and suggested modifications of methodological standards. JAMA. 1997;277(6):488–94. [PubMed: 9020274]
95.
Harrell FE Jr, Lee KL, Mark DB. Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat Med. 1996;15(4):361–87. [PubMed: 8668867]
96.
McShane LM, Altman DG, Sauerbrei W. et al. Reporting recommendations for tumor marker prognostic studies (REMARK) J Natl Cancer Inst. 2005;97(16):1180–4. [PubMed: 16106022]
97.
Dupuy A, Simon RM. Critical review of published microarray studies for cancer outcome and guidelines on statistical analysis and reporting. J Natl Cancer Inst. 2007;99(2):147–57. [PubMed: 17227998]
98.
Goetz MP, Ingle JN, Couch FJ. Gene-expression-based predictors for breast cancer. N Engl J Med. 2007;356(7):752. author reply 752–3. [PubMed: 17301312]
99.
Hedenfalk I, Duggan D, Chen Y. et al. Gene-expression profiles in hereditary breast cancer. N Engl J Med. 2001;344(8):539–48. [PubMed: 11207349]
100.
Auer H, Lyianarachchi S, Newsom D. et al. Chipping away at the chip bias: RNA degradation in microarray analysis. Nat Genet. 2003;35(4):292–3. [PubMed: 14647279]
101.
Baldwin D, Crane V, Rice D. A comparison of gel-based, nylon filter and microarray techniques to detect differential RNA expression in plants. Curr Opin Plant Biol. 1999;2( 2):96–103. [PubMed: 10322196]
102.
Klein JP. Small sample moments of the estimators of the variance of the Kaplan-Meier and Nelson-Aalen estimators. Scand J Stat. 1991;18:333–40.

Views

  • PubReader
  • Print View
  • Cite this Page

Related information

  • PMC
    PubMed Central citations
  • PubMed
    Links to PubMed

Recent Activity

Your browsing activity is empty.

Activity recording is turned off.

Turn recording back on

See more...