Because the clinical behavior of cancer cannot be completely accounted for using morphology alone, it is important to develop molecular techniques for diagnosis, prognosis, and guiding treatment. The Bernard Lab is a “translational” research lab committed to the discovery and clinical implementation of biomarkers for classification, detection, and monitoring of cancer in humans.
The Bernard Lab at Huntsman Cancer Institute is developing new strategies for the classification, detection, and treatment of cancer. The Bernard Lab uses high-throughput molecular methods to comprehensively profile cancers and classify tumors based on biology and clinical correlations. Our areas of research include:
Over 200,000 new cases of breast cancer are diagnosed each year in the United States. Breast cancer is a spectrum of diseases comprised of different tumor types, each with a distinct biology and clinical behavior. Differences in breast cancer biology can result from inherited (e.g., familial breast cancer) mutations or somatic alterations that spontaneously occur during an individual's life. While a family history of breast cancer places an individual at higher risk, the majority (>90%) of breast cancers are not inherited and all women are at risk since 1 in 8 will develop breast cancer during their lifetime.
Currently, medical management of breast cancer is based on histopathological features (e.g., grade), anatomic staging (i.e., tumor-node-metastasis) and the expression of a few molecular markers (i.e., ER, PgR, HER2). These criteria have prognostic significance but provide little information for guiding therapy. In addition, these methods can be subjective due to different interpretations by pathologists. The Bernard lab strives to develop a comprehensive and objective molecular taxonomy for classifying breast cancer using methods in gene expression and mutation analyses.
One of the main projects in the lab is to use gene expression analyses to stratify breast cancer patients for risk of recurrence and to match molecular tumor subtypes with appropriate drug regimens. Using microarray, we have found that breast cancer can be reproducibly grouped into different subtypes defined by robust gene expression patterns. The molecular profile of each breast cancer describes the biology of the tumor and predicts its clinical behavior.
The Bernard Lab has successfully used the strategy of going from microarray for gene discovery to real-time PCR for biomarker validation and clinical implementation. In order to validate the biological classifications of breast cancer on large cohorts of patients and move this new taxonomy into clinical diagnostics, we are using real-time quantitative (q)RT-PCR to recapitulate the microarray classifications. Real-time PCR is a versatile technology that can be applied to any tumor (or tissue) type for gene quantification (DNA and RNA copy number) and mutation analyses (translocations and small base changes). The assays are well-suited for research and the clinical laboratory because they are accurate, rapid, automatable, cost-effective, and fit into the framework of specimen processing in pathology. The lab uses real-time qRT-PCR to profile tumors from formalin-fixed, paraffin-embedded (FFPE) tissue. The ability to accurately quantify transcripts from RNA extracted from FFPE tissues allows biomarkers to be interrogated using archived tumor samples from breast cancer patients with long-term follow-up.
The breast cancer projects in the Bernard Lab are funded by the NIH/NCI (U01 CA114722-01: Biological Classification of Breast Cancer by qRT-PCR) and the Breast Cancer Research Foundation in collaboration with the Cancer and Leukemia Group B clinical trial group. Over the next year, the consortium will be using newly developed techniques to validate hundreds of biomarkers for classification of breast cancer from FFPE tissues. These studies will ultimately result in molecular diagnostics that will guide personalized therapies for women with breast cancer.
Melanoma accounts for only 4% of skin cancers but is responsible for nearly 80% of skin cancer related deaths. Over the past 30 years, the incidence of melanoma has risen 50% and is continuing to increase at a rate of 3% per year.
The presence of a lymph node metastasis is the most important prognostic factor for patients diagnosed with localized melanoma. By histopathology (H&E/IHC), patients with node involvement have a significantly higher risk of relapse and death. Molecular methods are more sensitive in detecting occult lymph node metastases than standard histopathology and have utility in clinical diagnostics. We are using real-time qRT-PCR to assess the sensitivity and specificity of gene expression of molecular markers for melanoma. Our results will provide independent validation of several melanoma markers currently used in clinical trials and will help identify other markers that could improve the accuracy of RT-PCR assays used for molecular staging in melanoma.
Sarcomas represent a lethal subset of cancers within adult and pediatric populations. In contrast to many common cancers such as breast, prostate, colon, and lung, which are derived from epithelial tissues, sarcomas arise from mesenchymal tissues (bone, muscle, and connective tissues) and affect significantly younger individuals, especially children.
By light microcopy, sarcomas are often difficult to differentiate from each other and molecular methods are now being used to better define the disease. The Bernard Lab has developed real-time RT-PCR assays for the detection of disease-defining translocations in sarcomas. Based on this work there are now 3 clinically offered (ARUP Laboratories, Inc) molecular assays for Rhabdomyosarcoma (PAX3/7-FKHD), Synovial Sarcoma (SSX1/2-SYT), and Ewing's Sarcoma (EWS-FLI1/ERG). We are expanding the application of these assays, which are currently used to confirm the histological diagnosis, to detect minimal residual disease in the blood and bone marrow. These assays will be used in our mouse tumor models and in sarcoma patients.