Reproducibility of Gene Expression Signature–Based Predictions in Replicate Experiments

Anderson, K.; Hess, Kenneth R.; Kapoor, Mini; Tirrell, Stephen; Courtemanche, Jean; Wang, Bailiang; Wu, Yun; Gong, Yun; Hortobágyi, Gabriel N.; Symmans, W. Fraser; Pusztai, Lajos

doi:10.1158/1078-0432.ccr-05-1539

Cited by 33 publications

(18 citation statements)

References 24 publications

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“…The replicate profiles also showed a high degree of concordance [correlation coefficients, 0.94 (0.85-0.99)], indicating that the MSA is likely to have good long-term technical reproducibility. This level of consistency is similar to that achieved by other microarray platforms (25,39) and suggests that the technical performance of the MSA is likely to be robust toward different operators and sample types.…”

Section: Resultssupporting

confidence: 78%

“…Reassuringly, broadly similar molecular classes have been discovered (22) that are largely conserved across microarray platforms (23) and ethnic populations (24), and the reproducibility of gene expression signature -based predictions has been confirmed in replicate experiments (25,26). Compared with conventional assays, molecular profiling platforms offer the potential advantage of measuring multiple biomarkers and signatures in a single test.…”

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confidence: 94%

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Clinical Validation of a Customized Multiple Signature Microarray for Breast Cancer

Tan

et al. 2008

Clinical Cancer Research

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Purpose: Current histopathologic systems for classifying breast tumors require evaluation of multiple variables and are often associated with significant interobserver variability. Recent studies suggest that gene expression profiles may represent a promising alternative for clinical cancer classification. Here, we investigated the use of a customized microarray as a potential tool for clinical practice. Experimental Design: We fabricated custom 188-gene microarrays containing expression signatures for three breast cancer molecular subtypes [luminal/estrogen receptor (ER) positive, human epidermal growth factor receptor 2 (HER2), and ''basaloid''], the Nottingham prognostic index (NPI-ES), and low histologic grade (TuM1). The reliability of these multiple-signature arrays (MSA) was tested in a prospective cohort of 165 patients with primary breast cancer. Results: The MSA-ER signature exhibited a high concordance of 90 % with ER immunohistochemistry reported on diagnosis (P < 0.001). This remained unchanged at 89% (P < 0.001) when the immunohistochemistry was repeated using current laboratory standards. Expression of the HER2 signature showed a good correlation of 76 % with HER2 fluorescence in situ hybridization (FISH; ratio z2.2; P < 0.001), which further improved to 89% when the ratio cutoff was raised to z5. A proportion of low-level FISH-amplified samples (ratio, 2.2-5) behaved comparably to FISH-negative samples by HER2 signature expression, HER2 quantitative reverse transcription-PCR, and HER2 immunohistochemistry. Luminal/ER+ tumors with high NPI-ES expression were associated with high NPI scores (P = 0.001), and luminal/ER+ TuM1-expressing tumors were significantly correlated with low histologic grade (P = 0.002) and improved survival outcome in an interim analysis (hazard ratio, 0.2; P = 0.019). Conclusion: The consistency of the MSA platform in an independent patient population suggests that custom microarrays could potentially function as an adjunct to standard immunohistochemistry and FISH in clinical practice.Carcinoma of the breast is a major cause of worldwide morbidity and mortality in females (1). Two important factors in clinical breast cancer classification include determining the estrogen receptor (ER) and the human epidermal growth factor receptor 2 (HER2) status of tumors because both ER and HER2 are prognostic biomarkers (2, 3) and important predictive markers of treatment response to antihormonal and trastuzumab (HER2 monoclonal antibody) therapy, respectively (4, 5). At present, routinely used histopathologic methods such as ER immunohistochemistry and HER2 fluorescence in situ hybridization (FISH) are known to be associated with a large degree of variability. For example, inaccuracies of up to 20% have been reported for HER2 testing in both standard clinical settings and clinical trials (6) due to discrepancies in preanalytic protocols (7) and variations between different observers (8) and laboratories (9, 10). Other limitations of current assays include their reliance on meas...

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Section: Resultssupporting

confidence: 78%

mentioning

confidence: 94%

Clinical Validation of a Customized Multiple Signature Microarray for Breast Cancer

Tan

et al. 2008

Clinical Cancer Research

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“…As part of an ongoing pharmacogenomic marker validation study, patients treated with preoperative T/FAC chemotherapy were offered fine needle aspiration (FNA) of the primary breast tumor or ipsilateral axillary lymph node metastasis before starting treatment. RNA was extracted from the FNA samples, and gene expression profiling was done on Affymetrix U133A chips as previously described (10,11). A total of 74 patients were included in the current analysis; 51 from the original validation set and 23 additional new cases.…”

Section: Methodsmentioning

confidence: 99%

Thirty-Gene Pharmacogenomic Test Correlates with Residual Cancer Burden after Preoperative Chemotherapy for Breast Cancer

Peintinger¹,

Anderson²,

Mazouni³

et al. 2007

Clinical Cancer Research

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Purpose: We examined whether the response predicted by a 30-gene pharmacogenomic test correlated with the residual cancer burden (RCB) after preoperative chemotherapy with paclitaxel, 5-fluorouracil, doxorubicin, and cyclophosphamide (T/FAC). Experimental Design: Gene expression profiling was done at diagnosis in 74 patients with stages I to III breast cancer and was used to calculate a pharmacogenomic score and predict response to chemotherapy [pathologic complete response (pCR) or residual disease (RD)]. All patients received 6 months of preoperativeT/FAC. Following pathologic review, a RCB score was calculated based on residual tumor and lymph node features. Four RCB classes were assigned; RCB-0 (pCR), RCB-I (near-PCR), RCB-II (moderate RD), and RCB-III (extensive RD). The correlations between the pharmacogenomic score, predicted pathologic response, RCB score, and RCB class were examined. Results: Thirty-three patients were predicted to have pCR, and 40 were predicted to have RD. Observed responses were RCB-0: n = 20 (27%); RCB-I: n = 5 (7%); RCB-II: n = 36 (49%); and RCB-III: n = 13 (16%) patients. Pharmacogenomic and RCB scores were correlated (Pearson's R = -0.501, P < 0.0001). There was no difference between the mean genomic predictor scores for RCB-0/I groups (P = 0.94), but these were different from the mean scores of the RCB-II/III groups (P < 0.001). Among the 25 patients with RCB-0/I response, 19 (76%) were predicted to achieve pCR.The pharmacogenomic test correctly predicted RD in 92% of the patients with RCB-

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“…Figure 1B illustrates the reproducibility of ER mRNA measurements in 31 between-and within-laboratory replicate experiments. Data are based on previously published results [21]. Inclusion of other genes that reflect ER activity, HER-2 expression, and proliferative status of the tumor can substantially improve the predictive value of ER (i.e., further stratify ER-positive patients by outcome).…”

Section: Oncotype Dx™ and Other Prediction Scoresmentioning

confidence: 99%

Molecular Classification of Breast Cancer: Limitations and Potential

Pusztai¹,

Mazouni²,

et al. 2006

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Reverse transcription polymerase chain reaction and DNA microarrays are increasingly used in the clinic and in clinical research as prognostic or predictive tests. Results from these tests led to novel risk stratification methods and to new molecular classification of breast cancer. Some of these tools already complement existing diagnostic tests and can aid medical decision making in some situations. Better understanding of the molecular classes of breast cancer, independent of their prognostic and predictive values, may also lead to new biological insights and eventually to better therapies that are directed toward particular molecular subsets. However, there is substantially less experience with these emerging technologies than with the more established methods, the accuracy of which is often overestimated. This review discusses some of the limitations and strengths of current gene expression-based molecular classification of breast cancer. To provide context for this discussion, we also briefly examine the performance of estrogen receptor immunohistochemistry, which represents an essential part of the routine diagnostic workup for all breast cancer patients.

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Reproducibility of Gene Expression Signature–Based Predictions in Replicate Experiments

Cited by 33 publications

References 24 publications

Clinical Validation of a Customized Multiple Signature Microarray for Breast Cancer

Clinical Validation of a Customized Multiple Signature Microarray for Breast Cancer

Thirty-Gene Pharmacogenomic Test Correlates with Residual Cancer Burden after Preoperative Chemotherapy for Breast Cancer

Molecular Classification of Breast Cancer: Limitations and Potential

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