Stefan Enroth – Enabling precision detection and diagnostics of gynaecological cancers by harnessing the potential of self-sampling

Our research projects aim at finding biomarkers for gynecological cancer, mainly ovarian cancer in multiple settings; from early detection to detection of relapse after treatment. To facilitate future clinical use of the results, one of our clear goals is to use sample types that are suitable for self-sampling, even at home.

Today, detection of ovarian cancer is largely symptom-driven, leading to that a majority of cancers are detected late. This results in a low five-year survival.

In order not to miss any cancers among symptomatic women, surgery is often used as a final diagnostic method. In Sweden, 80% of those who undergo surgery after suspicion of cancer have benign diagnoses. The surgery itself is not risk-free, and a significant proportion of women with benign diagnoses experience complications.

In case of a malignant tumour, radical surgery in combination with chemotherapy is currently the most effective treatment. However, still about 50% of women relapse within two years of completed treatment. New, accurate ways to detect the cancer are therefore needed; for earlier detection, for diagnosis and for detection of relapse.

Computer-based analyses for biomarker discovery

Our research has a clear data-driven focus and focuses on computer-based analyses of so-called multi-omics data. Through collaboration with clinicians, primarily in Uppsala and Gothenburg, several well-documented sample collections are underway. They often contain several different sample types from the same patient ranging from plasma, tissue samples, dried self-sampled blood from a finger-prick to dried self-sampled vaginal fluid.

Our data analyses include DNA sequencing, RNA sequencing, proteomics and microbiome analyses. A related area of our research is mapping variance in biomarkers that are not directly linked to disease. In these projects, we look at, for example, how genetics or lifestyle variables affect biomarker levels in healthy individuals. The aim is to be able to adjust for that variation also in patients, or discard markers that we know a priori vary greatly with, for example, age or weight, or that are not stable in a certain type of sample handling.

Group members

Research leader: Stefan Enroth
Group members: Emmy Borgmästars, Mikaela Moskov

Last modified:

FOLLOW UPPSALA UNIVERSITY ON

Uppsala University on Facebook
Uppsala University on Instagram
Uppsala University on Youtube
Uppsala University on Linkedin