Introduction The lack of large panels of validated antibodies, tissue handling variability, and intratumoral heterogeneity potentially hamper comprehensive study of the functional proteome in non-microdissected solid tumors. The purpose of this study was to address these concerns and to demonstrate clinical utility for the functional analysis of proteins in non-microdissected breast tumors using reverse phase protein arrays (RPPA). Methods Herein, 82 antibodies that recognize kinase and steroid signaling proteins and effectors were validated for RPPA. Intraslide and interslide coefficients of variability were <15%. Multiple sites in non-microdissected breast tumors were analyzed using RPPA after intervals of up to 24 h on the benchtop at room temperature following surgical resection. Results Twenty-one of 82 total and phosphoproteins demonstrated time-dependent instability at room temperature with most variability occurring at later time points between 6 and 24 h. However, the 82-protein functional proteomic “fingerprint” was robust in most tumors even when maintained at room temperature for 24 h before freezing. In repeat samples from each tumor, intratumoral protein levels were markedly less variable than intertumoral levels. Indeed, an independent analysis of prognostic biomarkers in tissue from multiple tumor sites accurately and reproducibly predicted patient outcomes. Significant correlations were observed between RPPA and immunohistochemistry. However, RPPA demonstrated a superior dynamic range. Classification of 128 breast cancers using RPPA identified six subgroups with markedly different patient outcomes that demonstrated a significant correlation with breast cancer subtypes identified by transcriptional profiling. Conclusion Thus, the robustness of RPPA and stability of the functional proteomic “fingerprint” facilitate the study of the functional proteome in non-microdissected breast tumors.

The Second GO is publicly available through the GO Annotation Toolbox (GOAT.no): http://www.goat.no.

Purpose: We studied the expression levels of cyclins B1, D1, and E1 and the implications of cyclin overexpression for patient outcomes in distinct breast cancer subtypes defined by clinical variables and transcriptional profiling. Experimental Design: The expression levels of cyclins B1, D1, and E1 were quantified in 779 breast tumors and 53 cell lines using reverse phase protein arrays and/or transcriptional profiling. Results: Whereas cyclin E1 overexpression was a specific marker of triple-negative and basal-like tumors, cyclin B1 overexpression occurred in poor prognosis hormone receptor-positive, luminal B and basal-like breast cancers. Cyclin D1 overexpression occurred in luminal and normal-like cancers. Breast cancer subgroups defined by integrated expression of cyclins B1, D1, and E1 correlated significantly (P < 0.000001) with tumor subtypes defined by transcriptional profiling and clinical criteria. Across three hormone receptor-positive data sets, cyclin B1 was the dominant cyclin associated with poor prognosis in univariate and multivariate analyses. Although CCNE1 was present in significantly higher copy numbers in basal-like versus other subtypes (AN-OVA P < 0.001), CCNB1 gene copy number did not show gain in breast cancer. Instead, cyclin B1 expression was increased in tumors with co-occurrence of TP53 mutations and MYC amplification, a combination that seems to characterize basal-like and luminal B tumors. CCNB1 gene expression was significantly correlated with PLK, CENPE, and AURKB gene expression. Conclusion: Cyclins B1, D1, and E1 have distinct expressions in different breast cancer subtypes. Novel PLK, CENPE, and AURKB inhibitors should be assessed for therapeutic utility in poor prognosis cyclin B1-overexpressing breast cancers.Cyclins are critical to cell cycle progression. Cyclins D1 and E1 are well studied in breast cancer (1-10). There is a strong association between cyclin D1 expression and estrogen receptor (ER) positivity and between cyclin E1 overexpression and ER negativity. Whereas the relationship between cyclin D1 expression and breast cancer outcomes is somewhat controversial, cyclin E1 overexpression has been consistently associated with an increased risk of cancer relapse and death (8, 9). However, cyclin B1 is not nearly as extensively studied in breast cancer as either cyclin D1 or cyclin E1 (11).Breast cancer represents at least three clinical subtypes based on the expression of the hormone receptors ER and progesterone

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.

Protein expression Molecular subtypes Correlation RPPA A B S T R A C TFor a panel of cancer related proteins, the aim was to shed light on which molecular level the expression of each protein was mainly regulated in breast tumors, and to investigate whether differences in regulation were reflected in different molecular subtypes. DNA, mRNA and protein lysates from 251 breast tumor specimens were analyzed using appropriate microarray technologies. Data from all three levels were available for 52 proteins selected for their known involvement in cancer, primarily through the PI3K/Akt pathway.For every protein, in cis Spearman rank correlations between the three molecular levels were calculated across all samples and within each intrinsic gene expression subtype, enabling 63 comparisons altogether due to multiple gene probes matching to single proteins. Subtype-specific relationships between the three molecular levels were studied by calculating the variance of subtype-specific correlation and differences between overall and average subtype-specific correlation. The findings were validated in an external dataset comprising 703 breast tumor specimens. The proteins were sorted into four groups based on the calculated rank correlation values between the three molecular levels. Group A consisted of eight proteins with significant correlation between DNA copy number levels Abbreviations: RPPA, reverse phase protein array; DBCG, Danish Breast Cancer Group; RT, radiotherapy; CMF, cyclophosphamide methotrexate fluorouracil; LRR, locoregional recurrence; DM, distant metastasis; CBC, contralateral breast cancer; TCGA, The Cancer Genome Atlas; HE, Hematoxylin and Eosin; BCA, bicinchoninic acid; PCF, piecewise constant fit; aCGH, array Comparative Genomic Hybridization; FE, Feature Extraction; KS, KolmogoroveSmirnov; VSC, variance of subtype-specific correlation.

Purpose: To identify genes predicting benefit of radiotherapy in patients with high-risk breast cancer treated with systemic therapy and randomized to receive or not receive postmastectomy radiotherapy (PMRT). Experimental Design: The study was based on the Danish Breast Cancer Cooperative Group (DBCG82bc) cohort. Gene-expression analysis was performed in a training set of frozen tumor tissue from 191 patients. Genes were identified through the Lasso method with the endpoint being locoregional recurrence (LRR). A weighted gene-expression index (DBCG-RT profile) was calculated and transferred to quantitative real-time PCR (qRT-PCR) in corresponding formalin-fixed, paraffin-embedded (FFPE) samples, before validation in FFPE from 112 additional patients. Results: Seven genes were identified, and the derived DBCG-RT profile divided the 191 patients into “high LRR risk” and “low LRR risk” groups. PMRT significantly reduced risk of LRR in “high LRR risk” patients, whereas “low LRR risk” patients showed no additional reduction in LRR rate. Technical transfer of the DBCG-RT profile to FFPE/qRT-PCR was successful, and the predictive impact was successfully validated in another 112 patients. Conclusions: A DBCG-RT gene profile was identified and validated, identifying patients with very low risk of LRR and no benefit from PMRT. The profile may provide a method to individualize treatment with PMRT. Clin Cancer Res; 20(20); 5272–80. ©2014 AACR.

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(2014) Relationship between the prognostic and predictive value of the intrinsic subtypes and a validated gene profile predictive of loco-regional control and benefit from post-mastectomy radiotherapy in patients with high-risk breast cancer, Acta Oncologica, 53:10, 1337-1346 ABSTRACTBackground. Breast cancer is characterized by great molecular heterogeneity demonstrated, e.g. by the intrinsic subtypes. Administration of post-mastectomy radiotherapy (PMRT) does, however, not refl ect this heterogeneity. A gene profi le (DBCG-RT profi le) has recently been developed and validated, and has shown prognostic impact in terms of loco-regional failure and predictive impact for PMRT. Reports have also shown predictive value in terms of benefi t of PMRT from intrinsic subtypes and derived approximations. The aim of this study was to examine: 1) the agreement between various methods for determining the intrinsic subtypes; and 2) the relationship between the prognostic and predictive impact of the DBCG-RT profi le and the intrinsic subtypes. Material and methods. Intrinsic subtypes and the DBCG-RT profi le was determined from microarray analysis based on fresh frozen tissue from 191 patients included in the Danish Breast Cancer Cooperative Group (DBCG) 82bc trial. Corresponding formalin-fi xed, paraffi n-embedded tissue was available from 146 of these patients and from another 890 DBCG82bc patients. Estrogen receptor, progesterone receptor, HER2, CK5/6, Ki-67 and EGFR were combined into immunohistochemical approximations of the intrinsic subtypes. Endpoint considered was loco-regional recurrence (LRR).Results. The DBCG-RT profi le identifi ed a group of patients with low risk of LRR and no additional benefi t from PMRT among all subtypes. Combining six immunohistochemical markers identifi ed a subgroup of triple negative patients with high risk of LRR and signifi cant benefi t from PMRT. Agreement in the different assignments of tumors to the subtypes was suboptimal, and the clinical outcome and predicted benefi t from PMRT varied according to the method used for assignment. Conclusion. The prognostic and predictive information obtained from the DBCG-RT profi le cannot be substituted by any approximation of the tumors intrinsic subtype. The predictive value of the intrinsic subtypes in terms of PMRT was infl uenced by the method used for assignment to the intrinsic subtypes.