Breast and ovarian cancers are among the most common malignancies among women worldwide. Approximately 20% of breast cancer patients will develop recurrence within 5 years after initial diagnosis. In addition, approximately 70% of newly diagnosed ovarian cancer patients are in stage III or IV, resulting in poor prognosis at the time of detection. There is, therefore, a need to improve patient risk assessment and to personalize therapy according to a combination of patient-specific clinicopathological features and tumor characteristics. To substantially increase the power to subclassify breast cancer and early-stage ovarian cancer patients, we will study the genetic makeup of tumor samples retrieved from the biobank at the Department of Oncology, Sahlgrenska University Hospital, in conjunction with clinical information from the National Quality Registry at the Regional Cancer Center West and the Cancer Registry at the National Board of Health and Welfare. In addition to conventional molecular and cellular biology techniques, statistical models will be used to identify novel biomarkers which can predict aggressive tumor behavior and detect invasive tumors at an early stage. In previous studies, we showed that a four-marker panel (AZGP1, PIP, S100A8, UBE2C) used in conjunction with established clinical variables significantly improved outcome prediction of breast cancer. Furthermore, these biomarkers might be targetable by proteasome and mitosis inhibitors.
We hypothesize that tumor biology, e.g. aberrant genetic and epigenetic changes, may guide the selection of therapeutic targets and refine assessment of prognosis, which in turn could have a decisive impact on the outcome of future cancer treatment. It is therefore important to determine the genetic and epigenetic events involved in cancer development.
The focus of this project is to (1) assess the tumorigenic and therapeutic potential of the four biomarkers in breast cancer cell lines and patient-derived xenografts, (2) identify novel genetic and epigenetic biomarkers associated with ovarian cancer-specific survival, and (3) examine whether the identified candidate biomarkers are breast/ovarian cancer-specific or general key cancer genes. Subsequently, these biomarkers may be useful as targets for early detection, drug development, patient stratification, and improved therapy.
We use several molecular biology technologies to study tumor biology, including array-CGH, gene expression microarrays, RNA-Seq, DNA methylation, FISH, immunohistochemistry, cell transfection, and qPCR. In addition to clinical breast and ovarian tumor samples, we also use tumor cell lines, primary cells and mouse xenografts as model systems.
Khalil Helou, Associate Professor (Team Leader)
Per Karlsson, Professor, MD (breast cancer oncologist)
Zakaria Einbeigi, MD, PhD (breast cancer oncologist)
Anikó Kovács, Associate Professor, MD (Pathologist)
Toshima Parris, Postdoc (Molecular Biologist)
Szilárd Nemes, PhD (Statistician)
Hanna Engqvist, PhD Student
Jana Biermann, PhD Student
Elisabeth Werner Rönnerman, MD, PhD Student (Pathologist)
Luaay Aziz, MD, PhD Student (Surgeon, head and neck cancer)
May Semaan, (Technical Assistant)