Studies of 2026

Evaluation patient-derived serum samples for breast cancer biomarker detection

Nonappa Nonappa, Tampere University

The project aims to develop a rapid and reliable method for detecting biomarkers associated with the early-stage metastasis of primary breast cancer. By identifying and validating key molecular indicators of metastatic potential, we seek to enhance early detection strategies, which are critical for improving patient prognosis and guiding personalized treatment approaches. The overarching goal is to evaluate and validate the expression, relevance, and potential clinical utility of breast cancer biomarkers in patient-derived samples. The objectives are outlined based on our lab-based experimental validation using various breast cancer cell lines.

Prospective validation of machine learning model predicting clinical laboratory measurements (VALID)

Andrea Ganna ja Leena Viiri, Helsinki University

This medical research study aims to validate a machine learning model we have developed to predict the values of commonly used clinical laboratory measurements. In this study, we focus on clinical laboratory markers that can be easily measured from blood, have a well-established role in clinical diagnostics, and have a clearly characterized genetic background. In this recall-by-biobank study conducted through Finnish biobanks, approximately 2,000 individuals aged 30–70 years will be invited to provide blood samples. The clinical laboratory measurements obtained from these samples will be used to validate (i.e., test the performance of) the machine learning model. Participants will be invited across different predicted levels of the laboratory measurements (within and outside reference ranges) in order to evaluate the performance of the model. Including individuals with a wide range of predicted values allows us to assess how well the model performs in different situations, such as detecting abnormal values and confirming predictions for individuals whose values fall within reference ranges. This comprehensive approach will provide a more reliable assessment of the model’s accuracy and its potential usefulness in practical applications.

ROSTE-ruokaostotiedot terveystutkimuksessa

Anna-Leena Vuorinen, Tampereen yliopisto

The aim of this study is to use food purchase data as a novel tool to investigate onset and progression of chronic lifestyle-related conditions. We will conduct a biobank recall study that links food purchase data with genomic data, health register data, and a lifestyle questionnaire. Participants will be recruited among adult Tampere Biobank donors who have provided the biobank consent and the consent for recontacting and who have given birth between 2020–2026 defined through ICD-10 diagnostic codes O80.*–I84.* The outcomes of this study include lifestyle-related chronic conditions and their risk factors that are expected to be affected by food purchase behaviors such as gestational diabetes, pregnant hypertension, cholesterol levels, blood pressure levels, and child’s birth weight and growth trajectories. By developing statistical and machine learning models, we will study how food purchase behaviors relate to different chronic conditions. This study will advance our understanding of the genetic and food related behaviors drivers behind the development of cardiometabolic conditions, and pave the way for new prevention strategies.