Abstract
PurposeTo 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).MethodsReverse 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.ResultsSix 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).ConclusionWe 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.
Highlights
To inform decisions about therapy, it is necessary to have a better understanding of the molecular mechanisms underlying the heterogeneity of breast cancer
Identification of Prognostic Groups To develop a set of markers for breast cancer classification and outcomes prediction, we used a hypothesis-driven approach, selecting markers according to their functional assignments and subsequently performing supervised proteomic clustering analysis to optimize the selection of groups with the most distinct recurrence-free survival (RFS) outcomes
Proteins were chosen based on a literature search of important targets and proteomic processes in breast cancer for which robust antibodies binding to a single or dominant band on western blotting could be identified and validated for Reverse phase protein array (RPPA) as described [1,2,3,30,31,32]
Summary
To inform decisions about therapy, it is necessary to have a better understanding of the molecular mechanisms underlying the heterogeneity of breast cancer. Transcriptional profiling revealed that breast cancer represents at least six molecular subtypes associated with different clinical features [1,2,3]. Comprehensive analysis of breast cancer transcriptomes does not capture all levels of biological complexity; important additional information may reside in the proteome [4,5,6,7]. Many proteins have been studied as prognostic and predictive factors in breast cancer, only three alter current practice: estrogen receptor (ER), progesterone receptor (PR) and HER2. A systematic study of expression and activation of multiple proteins and signaling pathways may facilitate more accurate classification and prediction in breast cancer
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