Studies of 2023

GENEROOS - The role of genetics in weight loss: a randomized diet intervention study in individuals with high or low genetic risk for obesity

Andrea Ganna, FIMM, University of Helsinki

The prevalence of both overweight and obesity is increasing at an alarming rate worldwide. Among other risk factors (e.g., environmental, behavioral, and medical), genetics holds an important role. Genome-wide association studies have discovered hundreds of loci associated with body mass index (BMI). Polygenic scores are generated from a combination of millions of genetic variants that are associated with BMI. The correlation between BMI polygenic score and actual BMI was found to be 0.22. Several diet intervention studies aimed at reducing bodyweight in overweight and obese individuals. However, the effect of genetic background in the effectiveness of diet interventions is largely unknown.

This study aims to determine whether BMI polygenic score impacts the effectiveness of dietary intervention in reducing BMI among individuals with elevated BMI. In brief, we will invite overweight (25-35 BMI) individuals with very high (Top 5%;  n=600) and very low (Bottom 5%; n=600) polygenic score for BMI who have given biobank consent, have genetic data available through their biobank, and fulfill the study’s inclusion criteria. Half of the participants in each group will be subsequently randomized to enroll in a dietary/lifestyle coaching intervention program or the placebo group and not receive any dietary advice or information. All participants will be asked to answer an online questionnaire and provide a fasting blood sample at baseline and at the end of the study.

Impact: This study will determine whether BMI polygenic score can be used to identify overweight and obese individuals that are more likely to succeed in a reduced-energy dietary intervention, which will assist our efforts in curbing the overweight and obesity epidemic.

Role of genomics in periprosthetic joint infection

Aleksi Reito, Tays

Prediction of PJI has been intensively studied during recent decades. This has solely focused on clinical variables such demographical variables and morbidities. Large bulk of research has shown that prediction of PJI is very complex and clinical variables are not accurate enough to tell which patients are at the higher risk of having PJI. A well-performing prediction model would be valuable for clinical decision making and patient counseling. This project aims to investigate the role of genomics in the periprosthetic joint infection. Genetic information may have a predictive role in PJIs. No study to date have investigated this topic.

New markers and methods in prostate cancer diagnostics, treatment and monitoring (PROLIB)

LT Jussi Nikkola

This study aims to promote the treatment of prostate cancer by gaining a deeper understanding of the factors affecting the origin, clinical course and treatment resistance of the disease. In particular, we investigate the ability of liquid biopsies to detect prostate cancer and to guide and monitor the course of the disease during treatments. We study circulating tumor DNA (ctDNA) from blood samples of prostate cancer patients and perform genetic analyzes from cancer tissue (fresh or FFPE sample) to study the effect of genetic profile of the tumor on the course of the disease.

ANO7 - new susceptibility gene for aggressive prostate cancer

Johanna Schleutker, Turun Yliopisto

Prostate cancer (PrCa) has a wide spectrum of clinical behavior that ranges from decades of indolence to rapid metastatic progression and lethality. Previous observations of ancestral differences in PrCa risk, in conjunction with studies demonstrating the influence of family history, have shown PrCa being among the most heritable of human cancers with 57% of the inter-individual variation in risk attributed to genetic factors. Based on GWAS studies it has been estimated that > 1800 common SNPs independently contribute to PrCa risk among populations of European ancestry. The vast majority of identified GWAS SNPs reside within non-coding genomic regions often far from the nearest genes, making the molecular mechanisms underlying the causal actions and biological effects hard to uncover. Moreover, these SNPs most often show little or no ability to discriminate between indolent and fatal forms of the disease. Therefore, to date, few causative SNPs can used for predicting PrCa outcome.

Biomarker Validation - Progranulin as a Biomarker for Sepsis

Amelie Gaignaux, Codex4SMEs consortium

The Codex4SMEs consortium is composed by nine members and two sub-partners, each with different backgrounds, experiences and competences. The role of the LIH-IBBL Translational Biomarker Group, TBG for short, is to deliver a service of biomarker (BM) validation aimed at increasing the chances for a putative BM to ‘progess’ from bench to bedside. Biobank samples are used for clinical verification of a putative BM for sepsis.

MUCIN 13 in lung cancer

Outi Kuittinen, UEF

Lung cancer is a common cancer with poor survival. Fortunately, checkpoint inhibitors enhancing patient’s own immune response to attack cancer cells have shown efficacy also in lung cancer, but only in a minority of patients. These drugs are expensive and they have serious side effects. Thus, better patient selection for these treatments is necessary. Mucins are transmembrane glycoproteins expressed on epithelial membranes, including airways. Mucins promote immunosuppressive acidic tumor microenvironment favoring cancer cell survival. Mucin 13 is a potential diagnostic and therapeutic target for cancer. We propose that dysfunctional regulation and expression of mucin 13 provides an immunosuppressive environment and associates with poor response to checkpoint inhibition and poor survival. A large-scale analysis of lung cancer patient samples with advanced biomolecular techniques combined with clinical characteristics will reveal the mechanisms in more detail.

FinnGen EA3 study for Women's health

Aarno Palotie, FIMM, Helsingin yliopisto

Endometriosis, PCOS and HPV-related gynecological lesions are among the most prevalent gynecological conditions affecting health and the quality of life in a significant number of women of all ages. The phenotypical spectrum and the associated co-morbidities within each condition are highly heterogenous, an effect that is largely attributed to the genetic differences in subgroups of patients. Characterizing the genetic background of the various phenotypes of these disease states, provides a clinically valuable tool for the diagnostics and targeted treatment of the affected women.

Correlating RNA expression profiles to clinical outcomes in muscle invasive bladder cancer

Philips Electronics Nederland B.V

Philips has defined a gene signature-based risk scoring system wherein a subset of the RNA expression profile of the tissue is correlated with the clinical outcomes of interest to give a better characterization of the disease in that patient. The purpose of this study is to evaluate the prognostic and/or predictive power of this score in diseases that are known to have an immune system relation which is cancer in particular, but also infectious diseases, or diseases with a systemic immune system reaction like and neurodegenerative disorders. Philips intends to test the utility of this score for clinical characterization of patients with muscle-invasive bladder cancer, which can help the clinicians in prognosis, evaluation and/or therapy selection.

Development of deep learning algorithms in DLBCL

F. Hoffmann-La Roche Ltd

The study will focus on developing and validating algorithms for lymphoma. The objective is to develop algorithms for predicting the progression of the disease. The study will utilize digitalized slides from diagnostic samples. Samples are accessed through Finnish Hospital Biobanks.