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| ID | Type | Description | Link |
|---|---|---|---|
| No. D.D. 931 | Other Grant/Funding Number | Italian National Complementary Plan PNC |
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| Name | Class |
|---|---|
| Catholic University of the Sacred Heart | OTHER |
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The goal of this single-arm interventional study is to learn whether integrating a polygenic risk score (PRS) into the CanRisk model can help improve breast cancer risk prediction and prevention in adult women with or without a family history of breast cancer and in women diagnosed with unilateral breast cancer. The main questions it aims to answer are:
Participants will:
Because this is a single-arm study, there is no separate comparison group. The study team will use the results to see how well PRS can be integrated into clinical care and whether it offers any improvements in prevention strategies for breast cancer.
This single-arm feasibility study will integrate polygenic risk scores (PRS) into the CanRisk breast cancer risk model and evaluate the logistical and organizational aspects of its use in an established clinical setting. By embedding PRS testing into routine patient visits at the Fondazione Policlinico Universitario Agostino Gemelli, the study aims to examine how well these genomic data can be incorporated into existing workflows for breast cancer risk assessment.
Enrolled participants (both healthy individuals with a familial predisposition and those with unilateral breast cancer) will receive standard genetic counseling, including testing for known high-penetrance mutations if not already completed. In addition, they will be offered PRS testing using a SNP-based assay, which aggregates multiple low-penetrance genetic variants to refine the risk estimate provided by CanRisk. All molecular analyses will be performed under standardized laboratory protocols to ensure consistent quality control (QC), including genotyping and imputation steps.
Key technical procedures include:
Blood sample collection (≥0.5 mL) for DNA extraction and SNP genotyping using a commercially available array.
Genetic data management and QC, encompassing alignment to reference panels, imputation of missing genotypes, and filtering out low-frequency variants or those failing QC thresholds.
Integration of PRS results into the patient's risk profile alongside clinical, familial, and lifestyle factors already captured by CanRisk.
Study staff will document any changes (such as shifts in risk category) that occur once PRS results are factored in, as well as any modifications to the care pathway. Feasibility will be assessed using process metrics (e.g., number of participants offered PRS and acceptance rates, time from sample collection to result communication) and through structured questionnaires to both patients and healthcare professionals. These questionnaires capture impressions of risk communication clarity, perceived utility of the PRS, and any challenges or facilitators identified when introducing this genomic tool into routine practice.
Questionnaire to patients:
Communication with Patients and Families:
Collaboration with Territorial Services:
4. General practitioners are informed about the implementation of PRS. 5. There is good coordination between the genetics clinic and primary care. 6. There are clear communication channels between specialists and general practitioners.
Questionnaire to healthcare professionals.
- Patient-centred organization:
There is a patient-centered vision for genetic risk assessment within the organization.
The quality of genetic counseling and PRS testing is a priority within the organization.
The genetic counseling coordinator has a patient-centered vision.
The communication of genetic test results and PRS scores is considered important.
The organizational structure supports integrated genetic testing services.
There is a clear vision of the genetic testing policy throughout the hospital.
- Care process coordination:
The agreements regarding the PRS test workflow are respected.
All team members understand the stages of genetic testing and PRS evaluation.
There is an optimal timeline between genetic testing and PRS analysis.
There are clear protocols for the management of biological samples for PRS testing.
Team members are involved in the coordination of genetic and PRS testing.
Patients receive clear information about the results of both the genetic tests and the PRS.
Follow-up appointments (if applicable) are scheduled appropriately after the communication of results.
- Monitoring and follow-up
The quality indicators for PRS implementation are clearly defined.
Patients' needs are systematically monitored during the testing process.
Patient satisfaction with combined genetic and PRS testing is monitored.
The objectives of the integrated risk assessment are explicitly described.
There is a monitoring system in place to verify the completion of all testing phases.
The results of the combined risk assessment are systematically tracked.
Variations in PRS results are monitored and documented.
Risk communication processes are systematically evaluated.
The entire testing process is continuously monitored and adjusted.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Integrated PRS-enhanced breast cancer risk assessment arm | Experimental | Participants in this experimental (signle) arm will undergo an integrated breast cancer risk assessment combining the CanRisk model with polygenic risk score (PRS) testing. The intervention includes genetic counseling, blood collection for PRS analysis, and a comprehensive risk evaluation. Additionally, participants will complete a questionnaire to gather their feedback on the integrated PRS clinical pathway. |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Integrated PRS-Enhanced Breast Cancer Risk Assessment (CanRisk model) | Diagnostic Test | Standard genetic counseling followed by a blood draw (0.5 mL) for DNA extraction. The sample is processed using a high-throughput SNP genotyping platform, and the PRS, based on 313 SNPs, is calculated and integrated into the CanRisk model for refined breast cancer risk stratification. |
| Measure | Description | Time Frame |
|---|---|---|
| Number of women accessing the pathway | At the time of participant enrollment in the clinical pathway for risk assessment. | |
| Acceptance rate | Percentage of women accepting PRS testing among those offered | At the time of enrollment, when eligible participants are offered PRS testing. |
| Measure | Description | Time Frame |
|---|---|---|
| Qualitative assessment of PRS fesibility among healthcare professionals, using the CPSET questionnaire | This outcome will be measured using the Care Process Self-Evaluation Tool (CPSET), a validated 29-item instrument developed to assess how the process of care is organized. Three subscales will be used:
|
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Inclusion Criteria:
Ability to provide informed consent
Voluntary consent to participate
CanRisk score (without PRS) > 5% (calculated on www.canrisk.org)
Healthy women with:
Affected women with:
Exclusion Criteria:
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| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Sara Farina, MD | Contact | 0039 + 0630156808 | sarafarins96@gmail.com | |
| Francesco A Causio, MD | Contact | francescoandrea.causio@unicatt.it |
| Name | Affiliation | Role |
|---|---|---|
| Stefania Boccia, Phd | Dipartimento di Scienze della Vita e Sanità Pubblica, Università Cattolica del Sacro Cuore, Rome, Italy | Principal Investigator |
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| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 17683658 | Background | Vanhaecht K, De Witte K, Depreitere R, Van Zelm R, De Bleser L, Proost K, Sermeus W. Development and validation of a care process self-evaluation tool. Health Serv Manage Res. 2007 Aug;20(3):189-202. doi: 10.1258/095148407781395964. | |
| 33769540 | Background | Du Z, Gao G, Adedokun B, Ahearn T, Lunetta KL, Zirpoli G, Troester MA, Ruiz-Narvaez EA, Haddad SA, PalChoudhury P, Figueroa J, John EM, Bernstein L, Zheng W, Hu JJ, Ziegler RG, Nyante S, Bandera EV, Ingles SA, Mancuso N, Press MF, Deming SL, Rodriguez-Gil JL, Yao S, Ogundiran TO, Ojengbe O, Bolla MK, Dennis J, Dunning AM, Easton DF, Michailidou K, Pharoah PDP, Sandler DP, Taylor JA, Wang Q, Weinberg CR, Kitahara CM, Blot W, Nathanson KL, Hennis A, Nemesure B, Ambs S, Sucheston-Campbell LE, Bensen JT, Chanock SJ, Olshan AF, Ambrosone CB, Olopade OI, Yarney J, Awuah B, Wiafe-Addai B, Conti DV; GBHS Study Team; Palmer JR, Garcia-Closas M, Huo D, Haiman CA. Evaluating Polygenic Risk Scores for Breast Cancer in Women of African Ancestry. J Natl Cancer Inst. 2021 Sep 4;113(9):1168-1176. doi: 10.1093/jnci/djab050. |
| Label | URL |
|---|---|
| This website provides the calculator of the CanRisk score | View source |
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All de-identified individual participant data (IPD) that underlie the published results of the study will be shared. This includes baseline demographics, clinical assessments, PRS test results (based on 313 SNPs), and questionnaire responses. Data will be made available via a secure Figshare repository upon study completion, with access governed by standard data use agreements to ensure participant confidentiality.
After publication of the study results and will remain available indefinitely.
Researchers wishing to access the de-identified individual participant data (IPD) and supporting documentation must submit a formal request that includes a research proposal outlining the planned analyses and justification for data use. All requests will be reviewed by the study's Data Access Committee to ensure that the proposed research meets ethical and scientific criteria. A data sharing agreement, detailing the conditions for data use and protecting participant confidentiality, must be signed before access is granted. Detailed submission instructions and contact information will be provided on the secure repository platform.
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| ID | Term |
|---|---|
| D001943 | Breast Neoplasms |
| D000096442 | Genetic Risk Score |
| ID | Term |
|---|---|
| D009371 | Neoplasms by Site |
| D009369 | Neoplasms |
| D001941 | Breast Diseases |
| D012871 | Skin Diseases |
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|
| At the end of the 12-month study period. |
| Qualitative assessment of PRS feasibility among patients, using CPSET questionnaire | This outcome will be measured using the Care Process Self-Evaluation Tool (CPSET), a validated 29-item instrument developed to assess how the process of care is organized. The CPSET evaluates two subscales (for patients):
| Immediately after receiving their genetic counseling (on average 2-4 weeks after enrollment). |
| Risk reclassification rate | Percentage of women whose risk classification changes after PRS integration, (comparing risk category assessed at baseline and reassessed after PRS analysis is complete). | Through study completion, up to 12 months. |
| Distribution of participants across low, moderate, and high breast cancer risk categories before and after integration of PRS | This outcome measure assesses the distribution of participants across the three risk categories (low, moderate, high) prior to PRS integration, followed by the distribution after PRS results have been incorporated. The number and proportion of participants in each category will be determined, and any changes in distribution attributed to PRS will be evaluated. | Through study completion, up to 12 months (the final distribution is calculated once all participants have received their PRS results). |
| 32624571 | Background | Lakeman IMM, Rodriguez-Girondo M, Lee A, Ruiter R, Stricker BH, Wijnant SRA, Kavousi M, Antoniou AC, Schmidt MK, Uitterlinden AG, van Rooij J, Devilee P. Validation of the BOADICEA model and a 313-variant polygenic risk score for breast cancer risk prediction in a Dutch prospective cohort. Genet Med. 2020 Nov;22(11):1803-1811. doi: 10.1038/s41436-020-0884-4. Epub 2020 Jul 6. |
| 37308304 | Background | Archer S, Donoso FS, Carver T, Yue A, Cunningham AP, Ficorella L, Tischkowitz M, Easton DF, Antoniou AC, Emery J, Usher-Smith J, Walter FM. Exploring the barriers to and facilitators of implementing CanRisk in primary care: a qualitative thematic framework analysis. Br J Gen Pract. 2023 Jul 27;73(733):e586-e596. doi: 10.3399/BJGP.2022.0643. Print 2023 Aug. |
| 37922883 | Background | Vassy JL, Brunette CA, Lebo MS, MacIsaac K, Yi T, Danowski ME, Alexander NVJ, Cardellino MP, Christensen KD, Gala M, Green RC, Harris E, Jones NE, Kerman BJ, Kraft P, Kulkarni P, Lewis ACF, Lubitz SA, Natarajan P, Antwi AA. The GenoVA study: Equitable implementation of a pragmatic randomized trial of polygenic-risk scoring in primary care. Am J Hum Genet. 2023 Nov 2;110(11):1841-1852. doi: 10.1016/j.ajhg.2023.10.001. |
| 38834743 | Background | Tsoulaki O, Tischkowitz M, Antoniou AC, Musgrave H, Rea G, Gandhi A, Cox K, Irvine T, Holcombe S, Eccles D, Turnbull C, Cutress R; Meeting Attendees; Archer S, Hanson H. Joint ABS-UKCGG-CanGene-CanVar consensus regarding the use of CanRisk in clinical practice. Br J Cancer. 2024 Jun;130(12):2027-2036. doi: 10.1038/s41416-024-02733-4. Epub 2024 Jun 4. |
| 38001640 | Background | Mbuya-Bienge C, Pashayan N, Kazemali CD, Lapointe J, Simard J, Nabi H. A Systematic Review and Critical Assessment of Breast Cancer Risk Prediction Tools Incorporating a Polygenic Risk Score for the General Population. Cancers (Basel). 2023 Nov 12;15(22):5380. doi: 10.3390/cancers15225380. |
| 38718029 | Background | Hovhannisyan M, Zemankova P, Nehasil P, Matejkova K, Borecka M, Cerna M, Dolezalova T, Dvorakova L, Foretova L, Horackova K, Jelinkova S, Just P, Kalousova M, Kral J, Machackova E, Nemcova B, Safarikova M, Springer D, Stastna B, Tavandzis S, Vocka M, Zima T, Soukupova J, Kleiblova P, Ernst C, Kleibl Z, Janatova M. Population-specific validation and comparison of the performance of 77- and 313-variant polygenic risk scores for breast cancer risk prediction. Cancer. 2024 Sep 1;130(17):2978-2987. doi: 10.1002/cncr.35337. Epub 2024 May 8. |
| 36162852 | Background | Yang X, Eriksson M, Czene K, Lee A, Leslie G, Lush M, Wang J, Dennis J, Dorling L, Carvalho S, Mavaddat N, Simard J, Schmidt MK, Easton DF, Hall P, Antoniou AC. Prospective validation of the BOADICEA multifactorial breast cancer risk prediction model in a large prospective cohort study. J Med Genet. 2022 Dec;59(12):1196-1205. doi: 10.1136/jmg-2022-108806. Epub 2022 Sep 26. |
| 30643217 | Background | Lee A, Mavaddat N, Wilcox AN, Cunningham AP, Carver T, Hartley S, Babb de Villiers C, Izquierdo A, Simard J, Schmidt MK, Walter FM, Chatterjee N, Garcia-Closas M, Tischkowitz M, Pharoah P, Easton DF, Antoniou AC. BOADICEA: a comprehensive breast cancer risk prediction model incorporating genetic and nongenetic risk factors. Genet Med. 2019 Aug;21(8):1708-1718. doi: 10.1038/s41436-018-0406-9. Epub 2019 Jan 15. |
| 30959525 | Background | Qaseem A, Lin JS, Mustafa RA, Horwitch CA, Wilt TJ; Clinical Guidelines Committee of the American College of Physicians; Forciea MA, Fitterman N, Iorio A, Kansagara D, Maroto M, McLean RM, Tufte JE, Vijan S. Screening for Breast Cancer in Average-Risk Women: A Guidance Statement From the American College of Physicians. Ann Intern Med. 2019 Apr 16;170(8):547-560. doi: 10.7326/M18-2147. Epub 2019 Apr 9. |
| 34837076 | Background | Canelo-Aybar C, Posso M, Montero N, Sola I, Saz-Parkinson Z, Duffy SW, Follmann M, Grawingholt A, Giorgi Rossi P, Alonso-Coello P. Benefits and harms of annual, biennial, or triennial breast cancer mammography screening for women at average risk of breast cancer: a systematic review for the European Commission Initiative on Breast Cancer (ECIBC). Br J Cancer. 2022 Mar;126(4):673-688. doi: 10.1038/s41416-021-01521-8. Epub 2021 Nov 26. |
| 33538338 | Background | Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021 May;71(3):209-249. doi: 10.3322/caac.21660. Epub 2021 Feb 4. |
| 39348703 | Background | Visvanathan K. USPSTF recommends biennial mammography for breast cancer screening in women aged 40 to 74 y. Ann Intern Med. 2024 Oct;177(10):JC110. doi: 10.7326/ANNALS-24-02229-JC. Epub 2024 Oct 1. |
| D017437 |
| Skin and Connective Tissue Diseases |
| D020022 | Genetic Predisposition to Disease |
| D004198 | Disease Susceptibility |
| D020969 | Disease Attributes |
| D010335 | Pathologic Processes |
| D013568 | Pathological Conditions, Signs and Symptoms |