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    Prediction of Breast Cancer Outcome to Adjuvant Chemotherapy Using Expression Profiling

    Scientific Abstract:
    Title: Prediction of breast cancer outcome to adjuvant chemotherapy using expression profiling Background: Recent advances in gene expression profile analysis have provided highly sophisticated markers of tumor biology and clinical behavior. To date, studies in a small number of cancer types have shown that expression profile analysis can identify prognostic categories that may eventually guide treatment choices. Currently, the use of gene expression technologies to identify predictive signatures of clinical outcome in the setting of adjuvant therapy is limited by the use of archived frozen tissue samples. The existence of frozen breast tissue repositories with long-term clinical follow-up data is scarce. However, numerous pathology departments and institutions, worldwide, possess large formalin fixed paraffin-embedded (FFPE) breast tissue repositories that are linked with long-term clinical follow-up data. A technological disconnect between the standard use of formalin and paraffin to preserve breast tissue from clinical breast specimens and the supposed requirement of frozen tissue for any sort of global gene expression profiling (e.g., microarray analysis) exists. This technological disconnect is the single greatest impediment to the application of gene expression technologies to large, well defined, pre-existing breast cancer cohorts. Recently, a method for RNA extraction and mRNA amplification from FFPE tissue has been developed in our laboratories and is compatible with microarray profiling. Objective/Hypotheses: The objective of this study is to use innovative methods to generate gene expression profiles from FFPE breast tissue samples obtained from a well-defined cohort of breast cancer patients following treatment with a widely-used adjuvant polychemo-therapeutic regime of Adriamycin/Cytoxan/Taxol(AC/T). Specific Aims: (1) To generate and compare gene expression profiles from patients with poor clinical outcome to those from patients with favorable long-term outcome following adjuvant AC/T chemotherapy, and to identify and validate a gene expression signature that predicts for poor clinical outcome in this setting (2) To validate gene expression in the AC/T cohort through the use of real-time quantitative PCR and immunohistochemistry. Study Design: Using FFPE breast cancer tissues, we will laser capture microdissect malignant invasive carcinoma cells from patients with poor clinical outcome and from patients with favorable long-term outcome following adjuvant AC/T chemotherapy. The RNA will be extracted, subjected to T7-based linear amplification, and labeled probe hybridized to a custom 70-mer oligonucleotide microarray containing 22,000 human genes. Using linear discriminant and leave one out cross validation (LOOCV) analysis techniques, we will perform comparative gene expression analysis as a means to identify a gene expression signature that predicts clinical outcome following adjuvant AC/T chemotherapy. We will validate the gene expression signatures in this cohort using real-time quantitative PCR and immunohistochemistry. Potential Outcome and Benefits of Research: Our work will demonstrate, for the first time, the feasibility of performing FFPE tissue-based gene expression profiling and will dispel the notion that such profiling cannot be performed with FFPE tissues. Moreover, the use of FFPE specimens will now allow for the broad and expeditious evaluation of microarray-based expression technologies in the clinical setting of breast cancer treatment. It is anticipated that we will identify a predictive gene expression signature that will aid in determining which breast cancer patients will benefit from adjuvant AC/T chemotherapy and will assist in tailoring chemotherapy in a patient-specific and effective manner. Furthermore, it is anticipated that this study will generate novel hypotheses regarding the in situ mechanisms that are associated with adjuvant chemotherapy sensitivity and resistance.

    Lay Abstract:
    Title: Prediction of breast cancer outcome to adjuvant chemotherapy using expression profiling Breast cancer affects about 1 in 10 women in Western countries and is a major cause of illness and death. Patients with aggressive disease can benefit form adjuvant chemotherapy or hormonal therapy and are identified according to a combination of clinicopathological criteria that include age, tumor size and grade, tumor type, lymph node status, and tumor hormone receptor status. The ability of these criteria to predict who will fail a particular adjuvant chemotherapeutic regime is imperfect. Since there are multiple adjuvant therapeutic options, if one could predict outcome to a particular regime then one could tailor chemotherapy in a more patient-specific and effective manner. Recently, gene expression technologies have been utilized to predict clinical outcome in breast cancer patients who did not receive adjuvant chemotherapy. Excitingly, these technologies outperformed all currently utilized clinical and pathological parameters in predicting outcome, and promise some very exciting opportunities in the clinical management of breast cancer. The current use of these powerful technologies is confined to the use frozen clinical breast cancer specimens. Unfortunately, since the existence of large frozen breast tissue repositories with long-term clinical follow-up data is scarce worldwide, the expeditious evaluation of these promising technologies to multiple clinical settings in breast cancer has been significantly hindered. In order to overcome this impediment, we have recently developed a highly innovative methodological strategy to perform gene expression profiling with routine formalin fixed, paraffin-embedded (FFPE) pathological specimens. Given the fact that FFPE repositories containing well-defined, breast cancer cohorts with long-term clinical follow-up data are commonplace, we believe that extension of expression profiling capabilities to such samples will provide a major leap in our ability to assess the clinical utility of these powerful and exciting technologies. The goal of this project is to use our innovative methodological strategy to generate gene expression profiles (fingerprints) from FFPE breast cancer specimens obtained from a well-defined cohort of patients that demonstrate good and poor outcome following treatment with one of the the most widely-used adjuvant chemotherapeutic adjuvant regime of Adriamycin/Cytoxan/Taxol (AC/T). The first specific aim will generate gene expression profiles from FFPE specimens, and will identify and validate a gene expression signature (fingerprint) that will predict good or poor clinical outcome following adjuvant AC/T chemotherapy. The second specific aim will utilize complimentary molecular biological technologies, real-time quantitative PCR and immunohistochemistry, to validate the gene expression signature. This project takes an innovative approach to approach to studying gene expression profiling in clinical breast cancer specimens by using FFPE archival specimens instead of frozen tissue specimens. The use of FFPE specimens will now allow for the broad and expeditious application of these technologies to multiple clinical problems in human breast cancer. Furthermore, the use of routine FFPE specimens obviates the need for special processing, handling and expensive storage of frozen tissue samples. Most importantly, we anticipate that we will identify a gene expression signature that will predict long-term clinical outcome for breast cancer patients treated with adjuvant AC/T chemotherapy. More specifically, identification of a predictive signature will help stratify those patients most likely to benefit from AC/T chemotherapy from those who are not likely to benefit, and this signature may assist in tailoring chemotherapy in a more patient-specific manner. We believe that identification of such a predictive signature will provide novel opportunities for the clinical management of breast cancer and anticipate that the beneficial use of such a signature could be implemented within 5 years of its discovery.