Development of Malignancy-Risk Gene Signature Assay for Predicting Breast Cancer Risk.

BACKGROUND: Breast cancer (BC) risk assessment models are statistical estimates based on patient characteristics. We developed a gene expression assay to assess BC risk using benign breast biopsy tissue. METHODS: A NanoString-based malignancy risk (MR) gene signature was validated for formalin-fixed paraffin-embedded (FFPE) tissue. It was applied to FFPE benign and BC specimens obtained from women who underwent breast biopsy, some of whom developed BC during follow-up to evaluate diagnostic capability of the MR signature. BC risk was calculated with MR score, Gail risk score, and both tests combined. Logistic regression and receiver operating characteristic curves were used to evaluate these 3 models. RESULTS: NanoString MR demonstrated concordance between fresh frozen and FFPE malignant samples (r = 0.99). Within the validation set, 563 women with benign breast biopsies from 2007 to 2011 were identified and followed for at least 5 y; 50 women developed BC (affected) within 5 y from biopsy. Three groups were compared: benign tissue from unaffected and affected patients and malignant tissue from affected patients. Kruskal-Wallis test suggested difference between the groups (P = 0.09) with trend in higher predicted MR score for benign tissue from affected patients before development of BC. Neither the MR signature nor Gail risk score were statistically different between affected and unaffected patients; combining both tests demonstrated best predictive value (AUC = 0.71). CONCLUSIONS: FFPE gene expression assays can be used to develop a predictive test for BC. Further investigation of the combined MR signature and Gail Model is required. Our assay was limited by scant cellularity of archived breast tissue.

Managing a Large-Scale Multiomics Project: A Team Science Case Study in Proteogenomics.

Highly collaborative scientists are often called on to extend their expertise to different types of projects and to expand the scope and scale of projects well beyond their previous experience. For a large-scale project involving "big data" to be successful, several different aspects of the research plan need to be developed and tested, which include but are not limited to the experimental design, sample collection, sample preparation, metadata recording, technical capability, data acquisition, approaches for data analysis, methods for integration of different data types, recruitment of additional expertise as needed to guide the project, and strategies for clear communication throughout the project. To capture this process, we describe an example project in proteogenomics that built on our collective expertise and experience. Key steps included definition of hypotheses, identification of an appropriate clinical cohort, pilot projects to assess feasibility, refinement of experimental designs, and extensive discussions involving the research team throughout the process. The goal of this chapter is to provide the reader with a set of guidelines to support development of other large-scale multiomics projects.

Quantification of Breast Cancer Protein Biomarkers at Different Expression Levels in Human Tumors.

Liquid chromatography-selected reaction monitoring (LC-SRM) mass spectrometry has developed into a versatile tool for quantification of proteins with a wide range of applications in basic science, translational research, and clinical patient assessment. This strategy uniquely complements traditional pathology approaches, like hematoxylin and eosin (H&E) staining and immunohistochemistry (IHC). The multiplexing capabilities offered by mass spectrometry are currently unmatched by other techniques. However, quantification of biomarkers in tissue specimens without the other data obtained from H&E-stained slides or IHC, including tumor cellularity or percentage of positively stained cells inter alia, may not provide as much information that is needed to fully understand tumor biology or properly assess the patient. Therefore, additional characterization of the tissue proteome is needed, which in turn requires the ability to assess protein markers across a wide range of expression levels from a single sample. This protocol provides an example of multiplexed analysis in breast tumor tissue quantifying specific biomarkers, specifically estrogen receptor, progesterone receptor, and the HER2 receptor tyrosine kinase, in combination with other proteins that can report on tissue content and other aspects of tumor biology.

Activation of stat3 in primary tumors from high-risk breast cancer patients is associated with elevated levels of activated SRC and survivin expression.

PURPOSE: Constitutive activation of signal transducer and activator of transcription 3 (Stat3) protein has been observed in a wide variety of tumors, including breast cancer, and contributes to oncogenesis at least in part by prevention of apoptosis. In a study of 45 patients with high-risk breast cancer enrolled in a phase II neoadjuvant chemotherapy trial with docetaxel and doxorubicin, we evaluated the levels of Stat3 activation and potentially associated molecular biomarkers in invasive breast carcinoma compared with matched nonneoplastic tissues. EXPERIMENTAL DESIGN: Using immunohistochemistry and image analysis, we quantified the levels of phospho-Stat3 (pY-Stat3), phospho-Src (pY-Src), epidermal growth factor receptor, HER2/neu, Ki-67, estrogen receptor, Bcl-2, Bcl-xL, Survivin, and apoptosis in formalin-fixed, paraffin-embedded sections from invasive carcinomas and their paired nonneoplastic parenchyma. The levels of molecular biomarkers in nonneoplastic and tumor tissues were analyzed as continuous variables for statistically significant correlations. RESULTS: Levels of activated pY-Stat3 and pY-Src measured by immunohistochemistry were significantly higher in invasive carcinoma than in nonneoplastic tissue (P < 0.001). In tumors, elevated levels of pY-Stat3 correlated with those of pY-Src and Survivin. Levels of pY-Stat3 were higher in partial pathologic responders than in complete pathologic responders. In partial pathologic responders, pY-Stat3 levels correlated with Survivin expression. CONCLUSIONS: Our findings suggest important roles for elevated activities of Stat3 and Src, as well as Survivin expression, in malignant progression of breast cancer. Furthermore, elevated Stat3 activity correlates inversely with complete pathologic response. These findings suggest that specific Stat3 or Src inhibitors could offer clinical benefits to patients with breast cancer.