Abstract
Abstract Over the last three decades, the incidence of early-stage breast cancer has doubled while the incidence of late stage breast cancer has slightly decreased; however, the standard treatment for both has not changed significantly in the last 10 years. Of those diagnosed with early-stage breast cancer, 30% will develop recurrent disease, though there is currently no diagnostic available to identify these cases. Conversely, identifying late stage patients with a low- risk for recurrence could spare them from post-surgical chemotherapy. We have developed a technology that allows in situ staining and analysis of up to 60 proteins on a single tissue slide. In this exploratory study we evaluated 25 known biomarkers on a cohort of more than 800 breast cancer cases assembled onto three tissue arrays. The cohort spans clinical grades and breast cancer subtypes, and is thus useful in novel associations of biomarker expression across a variety of clinical features. Twenty-five markers were chosen based on literature reports for marker association with disease recurrence and represent a range of biological features including signaling pathways and cell phenotypes. The slides were sequentially stained with antibodies that were directly conjugated to Cy3 and Cy5 dyes, imaged, evaluated for quality and subject inclusion, and subsequently analyzed for statistical correlations of expression values to clinical features. The images were then processed using a single cell analysis algorithm which allows for quantification of individual tumor cells and signal intensities for the markers to be extracted from sub-cellular domains (nucleus, cytoplasm, and membrane of each cell) allowing unique patterns of protein expression to be determined. Univariate Cox proportional hazards analysis was first applied to determine those features with strongest association with ‘death due to disease’. Of the 25 markers analyzed, several markers (e.g. API3, AMPH2, beta-catenin, CD44, CEACAM5, CK15, CK19, cMET, Her2, Ki67, TRIM29) were found to have strong association with poor outcome (FDR < 0.05). After adjusting for clinical variables (stage, number of positive nodes, grade), a number of markers (e.g. CD44, CEACAM5, cMET, TRIM29) still show a strong association with poor clinical outcome (FDR < 0.05, Cox proportional hazards model). CEACAM5 is part of the MammostratTM panel of markers used for determining prognosis in ER positive breast cancers after Tamoxifen® treatment. CD44, a cancer stem cell marker, and TRIM 29, a transcriptional regulatory factor involved in carcinogenesis, have been associated with basal-like breast cancers which have the worst prognosis of all the breast cancer subtypes. High levels of cMET have been associated with poor prognosis for all breast cancer subtypes and cMET is being explored as a therapeutic target. The MultiomyxTM methodology enabled us to verify a subset of biomarkers that are clinically relevant to breast cancer outcome. Using this technology could lead to identification of novel biosignatures that stratify patients and enable precision medicine that results in better treatment decisions and prognosis for patients. Citation Information: Cancer Res 2013;73(24 Suppl): Abstract nr P4-05-12.
Published Version
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