Background
Breast cancer is a heterogeneous disease with complex underlying biology and very different prognosis for patients, and it is often difficult for the clinician to assess the risk of recurrent disease. Classical clinical and pathologic markers (tumor size, nodal status, ER status, Her2 status and Ki-67) are useful markers to calculate the prognosis of patients, but they are far from perfect. PAM50 classification is based on the expression of 50 genes that divide breast cancer into five subgroups: Luminal A, Luminal B, Her2-enriched, Basal-like and Normal-like. Patients have different survival depending on which subgroup their tumor belongs, but there is still a need for an even better subclassification of patients.
Overtreatment of breast cancer patients is major problem. Many patients will have a good prognosis even without adjuvant chemotherapy, and this type of treatment can reduce the quality of life and cause long-term side effects. Therefore, it is not only important to identify high risk patients and develop medicines for them, but also to identify patients at low risk, so that they can receive less aggressive treatment.
Non-invasive biomarkers are samples taken without surgery or other procedures that involve severe discomfort or risk of complications. A further advantage of the non-invasive diagnosis is that it requires fewer resources from the hospital. In the cancer context the biopsy will be taken from tissues that are not in direct contact with the tumor itself, but may contain DNA that the tumor releases into the bloodstream.
DNA methylation is a modification of the DNA molecule. Increased methylation in promoter regions of genes prevents gene expression, and changes in methylation are associated with both silencing of tumor suppressor genes and activation of oncogenes. DNA methylation is affected by the environment, and may be a link between lifestyle and development of cancer. In our research group we want to use DNA methylation as a biomarker for breast cancer patients. Results show that patients with Luminal A tumors can be divided into two subgroups based on DNA methylation, and patients have varying prognosis depending on which subgroup they belong to. This finding may help to avoid overtreatment of patients with very good prognosis, and may identify patients that should get more aggressive treatment than they currently get.
Aims
- Develop, refine and validate biomarkers from tumor tissue. Preliminary data from our laboratory show that it is possible to divide breast cancer patients with Luminal A tumors in two groups with different prognosis, and such classification may help to avoid treatment of patients with very good prognosis. These biomarkers have to be further developed so that they can be applied to individual patients.
- Develop biomarkers based on DNA methylation in blood samples. Identification of disease specific properties in blood samples is challenging because the sample is not taken directly from the tumor. To develop clinical tests for blood samples it is necessary to improve methods in the laboratory, and to identify genomic regions where aberrations caused by the tumor can be identified in the blood.
Methods and implementation
- Patient cohorts with clinical and molecular data are available for the student.
- DNA methylation profiles for patient samples have been generated using Illumina Infinium HumanMethylation450 BeadChip which determines the methylation level of more than 450,000 CpGs in our genome.
- DNA from blood samples will be isolated by help of QIAGEN QIAamp Circulating Nucleic Acid Kit.
- DNA methylation data will be analyzed using bioinformatics and statistics in the R software: a very powerful and flexible software that allows large-scale analysis of genomic data.
The student’s assignments
The student's tasks will be adapted according to their wishes, and will include
- Bioinformatic analysis of DNA methylation data using the R software.
- Analysis and assessment of the clinical validity and utility of biomarkers.
- DNA isolation from blood samples.
- DNA methylation analysis using pyrosequencing and Illumina infinium HumanMethylation450 BeadChip.
About the research environment
The student will be part of an interdisciplinary environment that includes collaboration with other biologists, bioinformaticians, statisticians, clinicians and engineers, and the student will have the company of fellow Master students and PhD students. Department of Cancer Genetics is based in science building at the Radium Hospital. The student will be affiliated with Professor Vessela N. Kristensen’s group called Cancer Genome Variation. The department includes research groups working with breast, lung and colon cancer, and there is a good tradition of collaboration between the groups. There are weekly meetings with project presentations and literature discussions that will give the student valuable insight into the other aspects of cancer research. Supervisors will be postdoc Thomas Fleischer and Professor Vessela N. Kristensen.
Contact
Thomas Fleischer
Email: thomas.fleischer@rr-research.no
Phone: 98861883
Oslo University Hospital, Radiumhospitalet
Institute for Cancer Research, Department of Cancer Genetics
Group: Cancer Genome Variation (led by Professor Vessela N. Kristensen)