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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.)

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Impact of Gene Expression Profiling Tests on Breast Cancer Outcomes.

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Appendix G: Title Review Forms

1.

Record ID: 1781

van 't Veer, L. J., Dai, H., van de Vijver, M. J., He, Y. D., Hart, A. A., Mao, M., Peterse, H. L., van der Kooy, K., Marton, M. J., Witteveen, A. T., Schreiber, G. J., Kerkhoven, R. M., Roberts, C., Linsley, P. S., Bernards, R., and Friend, S. H. Gene expression profiling predicts clinical outcome of breast cancer. Nature 2002;415(6871):530–6.

Does this article POTENTIALLY apply to the key questions?

() POTENTIALLY eligible

() INELIGIBLE

Abstract Review Form

1.

Record ID: 1781

van 't Veer, L. J., Dai, H., van de Vijver, M. J., He, Y. D., Hart, A. A., Mao, M., Peterse, H. L., van der Kooy, K., Marton, M. J., Witteveen, A. T., Schreiber, G. J., Kerkhoven, R. M., Roberts, C., Linsley, P. S., Bernards, R., and Friend, S. H. Gene expression profiling predicts clinical outcome of breast cancer. Nature 2002;415(6871):530–6.

ABSTRACT: Breast cancer patients with the same stage of disease can have markedly different treatment responses and overall outcome. The strongest predictors for metastases (for example, lymph node status and histological grade) fail to classify accurately breast tumours according to their clinical behaviour. Chemotherapy or hormonal therapy reduces the risk of distant metastases by approximately one-third; however, 70–80% of patients receiving this treatment would have survived without it. None of the signatures of breast cancer gene expression reported to date allow for patient-tailored therapy strategies. Here we used DNA microarray analysis on primary breast tumours of 117 young patients, and applied supervised classification to identify a gene expression signature strongly predictive of a short interval to distant metastases (‘poor prognosis’ signature) in patients without tumour cells in local lymph nodes at diagnosis (lymph node negative). In addition, we established a signature that identifies tumours of BRCA1 carriers. The poor prognosis signature consists of genes regulating cell cycle, invasion, metastasis and angiogenesis. This gene expression profile will outperform all currently used clinical parameters in predicting disease outcome. Our findings provide a strategy to select patients who would benefit from adjuvant therapy.

Should this article be REVIEWED? (choose one)

[1] YES:indicate the questions that this article might apply to (below)

This article potentially applies to the following key questions(Choose all that apply)

1.

What is the direct evidence that the Mammaprint or OnctotypeDX gene expression profiling tests in women diagnosed with breast cancer (or any specific subset of this population) lead to improvement in outcomes?

2.

What are the sources of and contributions to analytic variability in these two gene expression-based prognostic estimators for women diagnosed with breast cancer?

3.

What is the clinical validity of these tests in women diagnosed with breast cancer?

a.

How well does this testing predict recurrence rates for breast cancer compared to standard prognostic approaches? Specifically, how much do these tests add to currently known factors or combination indices that predict the probability of breast cancer recurrence, (e.g., tumor type or stage, age, estrogen receptor (ER) and human epidermal growth factor receptor 2 (HER2) status)?

b.

Are there any other factors, which may not be components of standard predictors of recurrence (e.g., race/ethnicity or adjuvant therapy), that affect the clinical validity of these tests, and thereby generalizability of results to different populations?

4.

What is the clinical utility of these tests?

a.

To what degree do the results of these tests predict the response to chemotherapy, and what factors affect the generalizability of that prediction?

b.

What are the effects of using these two tests and the subsequent management options on the following outcomes: testing or treatment related psychological harms, testing or treatment related physical harms, disease recurrence, mortality, utilization of adjuvant therapy, and medical costs.

c.

What is known about the utilization of Mammaprint and OncotypeDX gene expression profiling in women diagnosed with breast cancer in the United States?

d.

What projections have been made in published analyses about the cost-effectiveness of using Mammaprint and OncotypeDX gene expression profiling in women diagnosed with breast cancer?

[2] Unclear/No abstract (promote to article review)

[3] NOT eligible (exclude):indicate reason for exclusion (below)

Reason for EXCLUSION? (choose any that apply)

[1] Study applies only to breast cancer biology

[2] Study only applies to single or multiple gene predictors and does not involve OncotypeDX or Mammaprint profiles

[3] Does not involve OncotypeDX or Mammaprint gene expression profiling tests

[4] Does not involve original data or original data analysis

[5] Does not involve women

[6] Does not involve breast cancer patients

[7] Not English language

[8] Does not apply to the key questions

[9] OTHER______________

[10] Unclear

[4] No, may be useful for BACKGROUND material (pull for hand searching If publish in 2002 or later)

Article Review Form

ARTICLE inclusion/exclusion

Record ID: 750

Reid, J. F., Lusa, L., De Cecco, L., Coradini, D., Veneroni, S., Daidone, M. G., Gariboldi, M., and Pierotti, M. A. Limits of predictive models using microarray data for breast cancer clinical treatment outcome. Journal of the National Cancer Institute 2005;97(12):927–30.

ABSTRACT:

Should this article be REVIEWED? (choose one)

[1] YES:indicate the questions that this article might apply to (below)

This article potentially applies to the following key questions(Choose all that apply)

1.

What is the direct evidence that the Mammaprint or OnctotypeDX gene expression profiling tests in women diagnosed with breast cancer (or any specific subset of this population) lead to improvement in outcomes?

2.

What are the sources of and contributions to analytic variability in these two gene expression-based prognostic estimators for women diagnosed with breast cancer?

3.

What is the clinical validity of these tests in women diagnosed with breast cancer?

a.

How well does this testing predict recurrence rates for breast cancer compared to standard prognostic approaches? Specifically, how much do these tests add to currently known factors or combination indices that predict the probability of breast cancer recurrence, (e.g., tumor type or stage, age, estrogen receptor (ER) and human epidermal growth factor receptor 2 (HER2) status)?

b.

Are there any other factors, which may not be components of standard predictors of recurrence (e.g., race/ethnicity or adjuvant therapy), that affect the clinical validity of these tests, and thereby generalizability of results to different populations?

4.

What is the clinical utility of these tests?

a.

To what degree do the results of these tests predict the response to chemotherapy, and what factors affect the generalizability of that prediction?

b.

What are the effects of using these two tests and the subsequent management options on the following outcomes: testing or treatment related psychological harms, testing or treatment related physical harms, disease recurrence, mortality, utilization of adjuvant therapy, and medical costs.

c.

What is known about the utilization of Mammaprint and OncotypeDX gene expression profiling in women diagnosed with breast cancer in the United States?

d.

What projections have been made in published analyses about the cost-effectiveness of using Mammaprint and OncotypeDX gene expression profiling in women diagnosed with breast cancer?

[2] Unclear/No abstract (promote to article review)

[3] NOT eligible (exclude):indicate reason for exclusion (below)

Reason for EXCLUSION? (choose any that apply)

[1] Study applies only to breast cancer biology

[2] Study only applies to single or multiple gene predictors and does not involve OncotypeDX or Mammaprint profiles

[3] Does not involve OncotypeDX or Mammaprint gene expression profiling tests

[4] Does not involve original data or original data analysis

[5] Does not involve women

[6] Does not involve breast cancer patients

[7] Not English language

[8] Does not apply to the key questions

[9] OTHER______________

[10] Unclear

[4] No, may be useful for BACKGROUND material (pull for hand searching If publish in 2002 or later)

Data Abstraction Tables

Population Characteristics

Study, YearInterventionGeneral CharacteristicsDiagnosis(es)Treatments and Outcomes

Study Design

Study, YearCountryStudy period (data collection period)Study TypePopulation size, NBlinded (Y/N)Study purpose

Data Extraction Tables

Clinical Validity/Utility

Study, yearContextMethodsResultsConclusions

Analytic Validity

Study, yearMeasureConclusions

Quality Assessment Matrix

SectionMeasure
Patients
  • Describes population characteristics
  • Describes participant recruitment
  • Describes participant sampling
  • Describes inclusion/exclusion criteria
  • Describes treatments received
  • Describes randomization.
Materials and Methods
  • Describes the reference standard.
  • Describes technical specifications of material and methods involved.
  • Describes type of biological material used (including control samples).
  • Includes definition of and rationale for the units, cutoffs and/or categories of the results of the index tests and the reference standard.
  • Describes blinding.
  • Describes methods for calculating or comparing measures.
  • Describes methods for calculating test reproducibility.
  • Describes methods of preservation and storage
  • Specifies the assay method used and provides (or references) a detailed protocol, including specific reagents or kits used, quality control procedures, reproducibility assessments, quantitation methods, and scoring and reporting protocols.
Results
  • Describes the flow of patients through the study, including the number of patients included in each stage of the analysis(both overall and for each subgroup extensively examined).
  • Describes distributions of basic demographic characteristics (at least age and sex), standard (disease-specific) prognostic variables, and tumor marker, including numbers of missing values.
  • Presents univariate analyses showing the relation between the marker and outcome, with the estimated effect (e.g. hazard ratio and survival probability).
  • Provides similar analyses for all other variables being analyzed (for the effect of a tumor marker on a time-to-event outcome, a Kaplan-Meier plot is recommended).
  • For key multivariable analyses, report estimated effects (e.g. hazard ratio) with confidence intervals for the marker and, at least for the final model.
  • Provides estimated effects with confidence intervals from an analysis in which the marker and standard prognostic variables are included, regardless of their significance.
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