Studies of 2025

Emerging Technologies in the Enhanced Diagnosis and Treatment of Pancreatic Cancer

Heikki Huhta, University of Oulu

The focus of this research is the early detection of pancreatic cancer and the identification of novel predictive biomarkers that may inform treatment decisions and improve patient outcomes. This is of critical importance, as pancreatic cancer is projected to become the second leading cause of cancer-related mortality in Western countries by 2030. Currently, only 15% of patients are diagnosed at an early stage, despite evidence that early detection could increase the survival rate from 5% to as high as 40%. The lack of predictive biomarkers poses a significant challenge to the personalization of treatment strategies.

The project aims to investigate the potential of convolutional neural network (CNN) technology in detecting pancreatic cancer cells from fine-needle aspiration samples. Furthermore, it seeks to explore the feasibility of classifying pancreatic cancers into prognostically distinct subgroups and predicting treatment response based on cellular morphology using deep learning methodologies. Artificial intelligence (AI) and deep learning have demonstrated considerable promise in the early detection of tumors and disease classification. The objective is to identify distinctive features in digitized cytological and histological samples that are relevant to patient care, and to differentiate pathological features from healthy tissue. To enhance the specificity and sensitivity of cancer detection algorithms, it is essential to utilize high-quality data representing diverse cancer types and stages.

This is a retrospective study utilizing a national pancreatic cancer cohort. The cohort comprises clinical data and radiological imaging studies from patients who underwent surgical treatment for pancreatic cancer between 2000 and 2019.

Eye Disease Task Force, age-related macular degeneration (AMD)

Aarno Palotie, FIMM, University of Helsinki

FinnGen is a ten-year research initiative aimed at identifying genetic risk factors for thousands of diseases. The third phase of FinnGen focuses on in-depth analysis of diseases and genetic variants identified in earlier phases, without expanding the current dataset of 520,000 biobank participants. The project conducts longitudinal studies on disease progression and treatment responses and investigates the biological mechanisms underlying genetic risk factors in selected diseases. Newly acquired health data and molecular profiling information derived from biobank samples are integrated with previously generated research data. This approach enables a deeper understanding of the biological processes influencing disease development in individuals carrying the studied genetic risk variants.

The objective is to investigate the progression of age-related macular degeneration (AMD) by collecting and analyzing clinical and genetic data, OCT imaging, and protein biomarkers. Together, these data sources enhance the ability to identify patients at highest risk of disease progression, which could, in the future, contribute to shortening the duration of clinical trials.

FinnGen 3 Neurodegenerative Diseases Task Force

Aarno Palotie, FIMM, University of Helsinki

FinnGen is a ten-year research project aimed at uncovering genetic risk factors for thousands of diseases. The third phase of FinnGen focuses on a more in-depth analysis of diseases and genetic variants identified in earlier phases, without expanding the current dataset of 520,000 biobank participants. The project includes longitudinal studies on disease progression and treatment responses and investigates the biological mechanisms of genetic risk factors in selected diseases. New health data and molecular profiling information derived from biobank samples are integrated with previously collected research data. This enables a deeper understanding of the biological processes that influence disease development in individuals carrying the studied genetic risk variants.

The objective is to study the progression of Alzheimer’s disease by analyzing existing brain MRI scans alongside novel, highly sensitive plasma biomarkers. Imaging data may help refine disease staging and prognosis when combined with genetic and biomarker information. Such data could potentially be used in the future to identify individuals at high risk, for example, in clinical diagnostics and prognostic evaluations.

FinnGen 3 Pulmonology Task Force access request for spirometry and DLCO data

Aarno Palotie, FIMM, University of Helsinki

FinnGen is a ten-year research initiative aimed at identifying genetic risk factors for thousands of diseases. The third phase of FinnGen focuses on in-depth analysis of diseases and genetic variants identified in earlier phases, without expanding the current cohort of 520,000 biobank participants. This phase includes longitudinal studies on disease progression and treatment responses, as well as investigations into the biological mechanisms underlying genetic risk factors in selected diseases. New health data and molecular profiling information derived from biobank samples are integrated with previously collected research data. This approach enables a deeper understanding of the biological processes that contribute to disease development among carriers of the studied genetic risk factors.

Asthma, chronic obstructive pulmonary disease (COPD), and pulmonary fibrosis (interstitial lung disease, ILD) are common conditions characterized by substantial heterogeneity in both clinical presentation and molecular genetic mechanisms. Genetic studies have identified dozens of risk variants associated with susceptibility to these diseases. The majority of these variants are located outside protein-coding regions, making it challenging to elucidate the biological events that trigger disease onset, let alone their impact on disease progression, severity, or treatment response. FinnGen aims to address this knowledge gap by leveraging decades of comprehensive disease and treatment history data, which is unparalleled among large-scale biobank projects. Genetic research is complemented by the analysis of protein biomarkers from blood samples collected at different stages of disease.

Extracellular vesicle based prediction of suicide (pilot project)

Edwin van den Oord, Virginia Commonwealth University

More than 700,000 people worldwide die each year due to suicide and each suicide has a profound impact on a large circle of relatives and friends. Current methods for predicting suicide attempts are not accurate, making it impossible to identify people who are at risk for attempting suicide. The main aim of this project is to discover new powerful markers released by brain cells into the blood stream that improve the prediction and can be used by clinicians to prevent suicide.

Genetic and Behavioral Factors for Smoking and Suicide

Lloyd Balbuena, University of Saskatchewan

This project will pursue methodological and substantive aims. The methodological aims will compare two types of mediation models to understand what happens if their assumptions are violated. We will then apply these models to FinnGen phenotypes and genomes to examine how smoking behaviors, mental health diagnoses, and suicide deaths are related. We will also use FinnGen data as a reference panel for the development of statistical genetics tools to better understand how ancestral differences impact traits, and to see if using individual-level data have an added benefit over summary statistics.

Precision medicine workflows in colorectal and prostate cancer (”iCAN Tampere”)

Toni Seppälä, Tampere University Hospital

The iCAN Tampere initiative focuses on precision medicine for colorectal and prostate cancers. It aims to enhance therapeutic decision-making and understand cancer biology through comprehensive molecular profiling. Key objectives include implementing Extended Molecular Profiling (EMP) for metastatic colorectal cancer, standardizing non-operative management of rectal cancer, and investigating tumor microenvironment in prostate cancer. The project involves multi-center clinical trials (EMOPRO, NORPPA1) and advanced genomic techniques (WGS, WTS, scRNA-seq). This collaborative effort seeks to revolutionize cancer treatment, improve patient outcomes, and contribute to precision oncology advancements.

Genetic and biological background and follow-up of different phenotypes of coeliac disease

Teea Salmi, Tampere University Hospital

Coeliac disease is an immune-mediated disease with a diverse clinical picture. Dermatitis herpetiformis (DH), cutaneous manifestation of coeliac disease, is the best described extraintestinal phenotype of the disease. Increasing evidence shows differences in disease prognosis across the phenotypes of coeliac disease. Especially DH patients have been associated with decreased mortality, and decreased risk for malignancies and comorbidities when compared with other phenotypes of coeliac disease. The reasons for these differences remain unclear. This study aims to elucidate the association between clinical symptoms and disease prognosis with genetic, serological, immunological, and microbiological factors. The objective is to enable more personalized care.

Identification of gene defects causing Lynch syndrome from biobank samples and returning the information to the sample donors (RETURN study)

Toni Seppälä, Tampere University

In this biobank study, carriers of gene defects (hundreds) causing hereditary cancer syndrome (Lynch syndrome) are sought, whose gene findings are validated and the information is returned to the biobanks without knowing the identity of the sample donors. The biobank provides the researchers' contact information and the research information to the sample donors, who, if they wish, can participate in the study, where they receive genetic counseling and are directed to healthcare services for confirming the diagnosis and further treatment. Instead of participating in the study, those who make contact have the option to directly access healthcare services with a referral. The purpose of the study is to determine the attitudes of biobank donors towards returning genetic information and the effects of returning it.