Peeter Padrik

Background/Objectives: Polygenic risk scores (PRSs) have been extensively studied and are increasingly applied in healthcare. One of the most studied and developed areas is predictive medicine for breast cancer, but there is no wider consensus on the indications for the clinical use of PRSs for breast cancer. This current guidance endeavours to articulate the scientific evidence underpinning the clinical utility of PRSs in stratifying breast cancer risk, with a particular emphasis on clinical application. Methods: This guidance has been prepared by a group of experts who have been active in breast cancer PRS research and development, combining a review of the evidence base with expert opinion for indications for clinical use. Results: Based on data from various studies and existing breast cancer prevention and screening services, the indications for clinical use of breast cancer PRSs can be divided into the following scenarios: (1) Management of cancer-free women with a family history of cancer; (2) individual personalised breast cancer prevention and screening in healthcare services; and (3) breast cancer screening programs for more personalised screening. Conclusions: The integration of PRSs into clinical practice enables healthcare providers to deliver more accurate risk assessments, personalised prevention strategies, and optimised screening programmes, thereby improving patient outcomes and enhancing the effectiveness of breast cancer care. PRS testing represents a novel component in clinical breast cancer risk assessment, supporting a personalised, risk-based approach to breast cancer prevention and screening.

Background/Objectives: Polygenic risk scores (PRSs) have been extensively studied and are in-creasingly applied in healthcare. One of the most studied and developed areas is predictive med-icine for breast cancer, but there is no wider consensus on the indications for the clinical use of PRSs for breast cancer. Current guidance endeavours to articulate the scientific evidence under-pinning the clinical utility of PRS in stratifying breast cancer risk, with a particular emphasis on clinical application. Methods: The guidance has been prepared by a group of experts who have been active in breast cancer PRS research and development, combining a review of the evidence base with expert opin-ion for indications for clinical use. Results: Based on data from various studies and existing breast cancer prevention and screening services, the indications for clinical use of breast cancer PRS can be divided into the following scenarios: 1) Management of cancer-free women with a family history of cancer; 2) Individual personalised breast cancer prevention and screening in healthcare services; 3) Breast cancer screening programs for more personalised screening. Conclusions: The integration of PRS into clinical practice enables healthcare providers to deliver more accurate risk assessments, personalised prevention strategies, and optimised screening programmes, thereby improving patient outcomes and enhancing the effectiveness of breast cancer care. PRS testing represents a novel component in clinical breast cancer risk assessment, support-ing a personalised, risk-based approach to breast cancer prevention and screening.

Background: Statistical associations of numerous single nucleotide polymorphisms with breast cancer (BC) have been identified in genome-wide association studies (GWAS). Recent evidence suggests that a Polygenic Risk Score (PRS) can be a useful risk stratification instrument for a BC screening strategy, and a PRS test has been developed for clinical use. The performance of the PRS is yet unknown in the Norwegian population. Aim: To evaluate the performance of PRS models for BC in a Norwegian dataset. Methods: We investigated a sample of 1053 BC cases and 7094 controls from different regions of Norway. PRS values were calculated using four PRS models, and their performance was evaluated by the area under the curve (AUC) and the odds ratio (OR). The effect of the PRS on the age of onset of BC was determined by a Cox regression model, and the lifetime absolute risk of developing BC was calculated using the iCare tool. Results: The best performing PRS model included 3820 SNPs, which yielded an AUC = 0.625 and an OR = 1.567 per one standard deviation increase. The PRS values of the samples correlate with an increased risk of BC, with a hazard ratio of 1.494 per one standard deviation increase (95% confidence interval of 1.406–1.588). The individuals in the highest decile of the PRS have at least twice the risk of developing BC compared to the individuals with a median PRS. The results in this study with Norwegian samples are coherent with the findings in the study conducted using Estonian and UK Biobank samples. Conclusion: The previously validated PRS models have a similar observed accuracy in the Norwegian data as in the UK and Estonian populations. A PRS provides a meaningful association with the age of onset of BC and lifetime risk. Therefore, as suggested in Estonia, a PRS may also be integrated into the screening strategy for BC in Norway.

Background: Breast cancer (BC) screening with mammography reduces mortality but considers currently only age as a risk factor. Personalized risk-based screening has been proposed as a more efficient alternative. For that, risk prediction tools are necessary. Genome-wide association studies have identified numerous genetic variants (single-nucleotide polymorphisms [SNPs]) associated with BC. The effects of SNPs are combined into a polygenic risk score (PRS) as a risk prediction tool. Objectives: We aimed to develop a clinical-grade PRS test suitable for BC risk-stratified screening with clinical recommendations and implementation in clinical practice. Design and methods: In the first phase of our study, we gathered previously published PRS models for predicting BC risk from the literature and validated them using the Estonian Biobank and UK Biobank data sets. We selected the best performing model based on prevalent data and independently validated it in both incident data sets. We then conducted absolute risk simulations, developed risk-based recommendations, and implemented the PRS test in clinical practice. In the second phase, we carried out a retrospective analysis of the PRS test’s performance results in clinical practice. Results: The best performing PRS included 2803 SNPs. The C-index of the Cox regression model associating BC status with PRS was 0.656 (SE = 0.05) with a hazard ratio of 1.66. The PRS can stratify individuals with more than a 3-fold risk increase. A total of 2637 BC PRS tests have been performed for women between the ages 30 and 83. Results in clinical use overlap well with expected PRS performance with 5.7% of women with more than 2-fold and 1.4% with more than 3-fold higher risk than the population average. Conclusion: The PRS test separates different BC risk levels and is feasible to implement in clinical practice.

Melanoma (MEL) is an aggressive form of skin cancer, causing over 60,000 deaths every year and it is considered one of the fastest-growing cancer forms. Genome-wide association studies have identified numerous genetic variants (SNPs) independently associated with MEL. The effects of such SNPs can be combined into a single polygenic risk score (PRS). Stratification of individuals according to PRS could be introduced to the primary prevention of melanoma. Our aim was to combine PRS with health behavior recommendations to develop a personalized recommendation for primary prevention of melanoma. Previously published PRS models for predicting the risk of melanoma were collected from the literature. Models were validated on the UK Biobank dataset consisting of a total of 487,410 quality-controlled genotypes with 3791 prevalent and 2345 incident cases. The best performing sex-specific models were selected based on the AUC in prevalent data and independently validated on an independent UKBB incident dataset for females and males separately. The best performing model included 28 SNPs. The C-index of the best performing model in the dataset was 0.59 (0.009) and hazard ratio (HR) per unit of PRS was 1.38 (standard error of log ( HR ) = 0.03) for both males and females. We performed absolute risk simulations on the Estonian population and developed individual risk-based clinical follow-up recommendations. Both models were able to identify individuals with more than a 2-fold risk increase. The observed 10-year risks of developing melanoma for individuals in the 99th percentile exceeded the risk of individuals in the 1 ^st percentile more than 4.5-fold. We have developed a PRS-based recommendations pipeline for individual health behavior suggestions to support melanoma prevention.

Prostate cancer (PC) is the second-most common type of cancer and the fifth-leading cause of cancer-related death in men worldwide. Genome-wide association studies have identified numerous genetic variants (SNPs) independently associated with PC. The effects of such SNPs can be combined into a single polygenic risk score (PRS). Stratification of men according to PRS could be applied in secondary prevention. We aimed to construct a PRS model and to develop a pipeline for personalized prostate cancer screening. Previously published PRS models for predicting the risk of prostate cancer were collected from the literature. These were validated on the Estonian Biobank (EGC) consisting of a total of 16,390 quality-controlled genotypes with 262 prevalent and 428 incident PC cases and on 209 634 samples in the UK Biobank with 3254 prevalent cases and 6959 incident cases. The best performing model was selected based on the AUC in prevalent data and independently validated in both incident datasets. Using Estonian PC background information, we performed absolute risk simulations and developed individual risk-based clinical follow-up recommendations. The best-performing PRS included 121 SNPs. The C-index of the Cox regression model associating PC status with PRS was 0.641 (SE = 0.015) with a hazard ratio of 1.65 (95% confidence interval 1.51 – 1.81) on the incident EGC dataset. The model is able to identify individuals with more than a 3-fold risk increase. The risk of an average 45-year old could be attained by individuals between the ages of 41 and 52. A 41-year old male on the 95th percentile has the same risk as an average 45-year old but by age 55, he has attained the same genetic risk as an average 68-year-old. PRS is a powerful predictor of prostate cancer risk that can be combined with current non-invasive practices of PC screening. We have developed PRS-based recommendations for personalized PSA testing. Our approach is easily adaptable to other nationalities by using population-specific background data of other genetically similar populations.

Colorectal cancer (CRC) is the second most common cancer in women and third most common cancer in men. Genome-wide association studies have identified numerous genetic variants (SNPs) independently associated with CRC. The effects of such SNPs can be combined into a single polygenic risk score (PRS). Stratification of individuals according to PRS could be introduced to primary and secondary prevention. Our aim was to combine risk stratification of a sex-specific PRS model with recommendations for individualized CRC screening. Previously published PRS models for predicting the risk of CRC were collected from the literature. These were validated on the UK Biobank (UKBB) consisting of a total of 458 696 quality-controlled genotypes with 1810 and 1348 prevalent male cases, and 2410 and 1810 incident male and female cases. The best performing sex-specific model was selected based on the AUC in prevalent data and independently validated in the incident dataset. Using Estonian CRC background information, we performed absolute risk simulations and examined the ability of PRS in risk stratifying individual screening recommendations. The best-performing model included 91 SNPs. The C-index of the best performing model in the dataset was 0.613 (SE = 0.007) and hazard ratio (HR) per unit of PRS was 1.53 (1.47 – 1.59) for males. Respective metrics for females were 0.617 (SE = 0.006) and 1.50 (1.44 – 1.58). PRS risk simulations showed that a genetically average 50-year-old female doubles her risk by age 58 (55 in males) and triples it by age 63 (59 in males). In addition, the best performing PRS model was able to identify individuals in one of seven groups proposed by Naber et al. for different coloscopy screening recommendation regimens. We have combined PRS-based recommendations for individual screening attendance. Our approach is easily adaptable to other nationalities by using population-specific background data of other genetically similar populations.

Breast cancer (BC) is the leading cause of cancer deaths in women in the world. Genome-wide association studies have identified numerous genetic variants (SNPs) independently associated with BC. The effects of such SNPs can be combined into a single polygenic risk score (PRS). Stratification of women according to PRS could be introduced to primary and secondary prevention. Our aim was to revalidate a PRS model and to develop a pipeline for individualizing breast cancer screening. Previously published PRS models for predicting the risk of breast cancer were collected from the literature. Models were validated on the Estonian Biobank (EGC) dataset consisting of 32,548 quality-controlled genotypes with 315 prevalent and 365 incident BC cases and on 249,062 samples in the UK Biobank dataset consisting of 8637 prevalent and 6825 incident cases. The best performing model was selected based on the AUC in prevalent data and independently validated in both incident datasets. Using Estonian BC background information, we performed absolute risk simulations and developed individual risk-based recommendations for prevention. The best-performing PRS included 2803 SNPs. The C-index of the Cox regression model associating BC status with PRS was 0.656 (SE = 0.05) with a hazard ratio of 1.66 (95% confidence interval 1.5 - 1.84) on the incident EGC dataset. The PRS is able to stratify individuals with more than a 3-fold risk increase. The observed 10-year risks of individuals in the 99th percentile exceeded the 1 ^st percentile more than 10-fold. PRS is a powerful predictor of breast cancer risk. Currently, PRS scores are not implemented in routine BC screening. We have developed PRS-based recommendations for personalized primary and secondary prevention and our approach is easily adaptable to other nationalities by using population-specific background data of other genetically similar populations.

Genotype-first approach allows to systematically identify carriers of pathogenic variants in BRCA1 / 2 genes conferring a high risk of familial breast and ovarian cancer. Participants of the Estonian biobank have expressed support for the disclosure of clinically significant findings. With an Estonian biobank cohort, we applied a genotype-first approach, contacted carriers and offered return of results with genetic counseling. We evaluated participants’ responses to and the clinical utility of the reporting of actionable genetic findings. Twenty-two of 40 contacted carriers of 17 pathogenic BRCA1 / 2 variants responded and chose to receive results. Eight of these 22 participants qualified for high-risk assessment based on National Comprehensive Cancer Network criteria. Twenty of 21 counseled participants appreciated being contacted. Relatives of 10 participants underwent cascade screening. Five of 16 eligible female BRCA1 / 2 variant carriers chose to undergo risk-reducing surgery, and 10 adhered to surveillance recommendations over the 30-month follow-up period. We recommend the return of results to population-based biobank participants; this approach could be viewed as a model for population-wide genetic testing. The genotype-first approach permits the identification of individuals at high risk who would not be identified by application of an approach based on personal and family histories only.

Background: Before the era of targeted therapies, cytokines were the main therapy for metastatic renal cell carcinoma (mRCC). Our aim was to analyze the changes in treatments and overall survival (OS) of all mRCC patients in Estonia in relation to the introduction of new medications. Methods: All patients with mRCC who started medical therapy in Estonia during the years 2004–2012 were identified using the database of the Estonian Health Insurance Fund. Tumor and treatment data were gathered from medical records. Vital status data were obtained from the Estonian Population Registry. The only available therapy before 2008 was interferon alpha-2A (INFa2A), targeted agents added from 2008. For survival analysis, patients were divided into 2 groups: INFa therapy only (group 1) and INFa followed by targeted agents or targeted agents therapy only (group 2). Results: Out of 416 identified patients, 380 were eligible for analysis. The most common 1st-line treatments were INFa (55%), sunitinib (32%) and INFa+bevacizumab (13%). 28% of patients received 2nd-line therapies and 15% 3rd-line treatments. Median survival of all patients was 13.7 months [95% confidence interval (CI) 11.3–16.2]; 7.6 months (CI 6.4–8.6) for group 1 and 19.8 months (CI 15.6–22.9) for group 2. In multivariate analysis, group 1 had nearly four times higher risk of dying than group 2 [hazard ration (HR) 3.88, 95% CI 2.64–5.72]. Conclusions: The implementation of targeted therapies significantly changed the outcomes of mRCC in Estonia: it prolonged median survival, reduced the risk of death and also enlarged the proportion of patients who received medical therapy.