Functional proteomics can define prognosis and predict pathologic complete response in patients with breast cancer

被引：72

作者：

Gonzalez-Angulo A.M. ^{[1
]}

Hennessy B.T. ^{[2
]}

Meric-Bernstam F. ^{[3
]}

Sahin A. ^{[4
]}

Liu W. ^{[5
]}

Ju Z. ^{[5
]}

Carey M.S. ^{[6
]}

Myhre S. ^{[7
,8
]}

Speers C. ^{[9
]}

Deng L. ^{[4
]}

Broaddus R. ^{[4
]}

Lluch A. ^{[10
]}

Aparicio S. ^{[11
]}

Brown P. ^{[12
]}

Pusztai L. ^{[13
]}

Symmans W.F. ^{[4
]}

Alsner J. ^{[14
]}

Overgaard J. ^{[14
]}

Borresen-Dale A.-L. ^{[7
,8
]}

Hortobagyi G.N. ^{[13
]}

Coombes K.R. ^{[5
]}

Mills G.B. ^{[6
]}

机构：

[1] Department of Breast Medical Oncology and Systems Biology, University of Texas MD Anderson Cancer Center, Houston, TX 77030

[2] Department of Gynecology Medical Oncology and Systems Biology, University of Texas MD Anderson Cancer Center, Houston, TX 77030

[3] Department of Surgical Oncology, University of Texas MD Anderson Cancer Center, Houston, TX 77030

[4] Department of Pathology, University of Texas MD Anderson Cancer Center, Houston, TX 77030

[5] Department of Bioinformatics, University of Texas MD Anderson Cancer Center, Houston, TX 77030

[6] Department of Systems Biology, University of Texas MD Anderson Cancer Center, Houston, TX 77030

[7] Department of Genetics, Institute for Cancer Research, Norwegian Radium Hospital, Oslo 0027

[8] Faculty Division, University of Oslo, Norwegian Radium Hospital, Oslo 0027

[9] Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030

[10] Department of Hematology and Oncology, Hospital Clinico Universitario de Valencia, Valencia, 46010, Avenida Blasco Ibáñez

[11] Molecular Oncology and Breast Cancer Program, University of British Columbia, 2211 Wesbrook Mall, Vancouver

[12] Department of Cancer Prevention, University of Texas MD Anderson Cancer Center, Houston, TX 77030

[13] Department of Breast Medical Oncology, University of Texas MD Anderson Cancer Center, Houston, TX 77030

[14] Department of Experimental Clinical Oncology, Aarhus University Hospital, DK-8000, Aarhus

来源：

Clinical Proteomics | 2011年 / 8卷 / 1期

关键词：

Breast Cancer; Functional Proteomics; Prediction; Prognosis;

D O I：

10.1186/1559-0275-8-11

中图分类号：

学科分类号：

摘要：

Purpose. To determine whether functional proteomics improves breast cancer classification and prognostication and can predict pathological complete response (pCR) in patients receiving neoadjuvant taxane and anthracycline- taxane-based systemic therapy (NST). Methods. Reverse phase protein array (RPPA) using 146 antibodies to proteins relevant to breast cancer was applied to three independent tumor sets. Supervised clustering to identify subgroups and prognosis in surgical excision specimens from a training set (n = 712) was validated on a test set (n = 168) in two cohorts of patients with primary breast cancer. A score was constructed using ordinal logistic regression to quantify the probability of recurrence in the training set and tested in the test set. The score was then evaluated on 132 FNA biopsies of patients treated with NST to determine ability to predict pCR. Results: Six breast cancer subgroups were identified by a 10-protein biomarker panel in the 712 tumor training set. They were associated with different recurrence-free survival (RFS) (log-rank p = 8.8 E-10). The structure and ability of the six subgroups to predict RFS was confirmed in the test set (log-rank p = 0.0013). A prognosis score constructed using the 10 proteins in the training set was associated with RFS in both training and test sets (p = 3.2E-13, for test set). There was a significant association between the prognostic score and likelihood of pCR to NST in the FNA set (p = 0.0021). Conclusion: We developed a 10-protein biomarker panel that classifies breast cancer into prognostic groups that may have potential utility in the management of patients who receive anthracycline-taxane-based NST. © 2011 Gonzalez-Angulo et al; licensee BioMed Central Ltd.

引用

共 36 条

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