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Introduction: Genetic risk modifier testing (GRMT), an emerging form of genetic testing based on common single nucleotide polymorphisms and polygenic risk scores, has the potential to refine estimates of BRCA1/2 mutation carriers' breast cancer risks. However, for women to benefit from GRMT, effective approaches for communicating this novel risk information are needed. Objective: To evaluate patient preferences regarding risk communication materials for GRMT. Methods: We developed four separate presentations (panel of genes, icon array, verbal risk estimate, graphical risk estimate) of hypothetical GRMT results, each using varying risk communication strategies to convey different information elements including number of risk modifier variants present, variant prevalence among BRCA1/2 carriers, and implications and uncertainties of test results for cancer risk. Thirty BRCA1/2 carriers evaluated these materials (randomized to low, moderate, or high breast cancer risk versions). Qualitative and quantitative data were obtained through inperson interviews. Results: Across risk versions, participants preferred the presentation of the graphical risk estimate, often in combination with the verbal risk estimate. Interest in GRMT was high; 76.7% of participants wanted their own GRMT. Participants valued the potential for GRMT to clarify their cancer susceptibility and provide actionable information. Many (65.5%) anticipated that GRMT would make risk management decisions easier. Conclusions: Women with BRCA1/2 mutations could be highly receptive to GRMT, and the minimal amount of necessary information to be included in result risk communication materials includes graphical and verbal estimates of future cancer risk. Findings will inform clinical translation of GRMT in a manner consistent with patients' preferences.

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Background Hereditary Diffuse Gastric Cancer (HDGC) syndrome is an autosomal dominant hereditary cancer predisposition associated with germline pathogenic/likely pathogenic variants in the CDH1 gene. Identifying early stage HDGC is difficult, and prophylactic measures can be effective in preventing incidence. Preimplantation Genetic Testing (PGT) can provide information about CDH1 variant status, HDGC risk, and limit familial transmission of CDH1 variants. To date, however, little is known about the attitudes of individuals with CDH1 variants towards PGT. Methods Given that little is known about the reproductive attitudes of individuals with HDGC, we recruited participants with CDH1 variants from a familial gastric cancer registry and administered a cross-sectional survey with open- and closed-ended response items. We assessed attitudes regarding PGT and the effect of HDGC on quality of life. Results Participants (n = 21) were predominantly partnered (61.9%), had a personal cancer history (71.4%), and had biological children (71.4%). Interest in learning about PGT was high; 66.7% of participants were interested in PGT and 90.5% approved of healthcare providers discussing PGT with individuals with CDH1 variants. Attitudes regarding personal use were varied. Among all participants, 35% would not, 25% were uncertain, and 40% would use PGT. Personal philosophy and preferences for family and reproduction were key factors related to PGT attitudes. HDGC had moderate effects on participants’ quality of life, including social relationships, health behaviors, and emotional experiences including worry about cancer risk and guilt regarding familial implications. Conclusion PGT was identified by participants as acceptable for use in a variety of contexts and benefits of reproductive counseling involving PGT may extend beyond CDH1 carriers to family members’ reproductive behaviors. Dispositions towards PGT are governed by personal philosophy or belief systems. These findings can help guide providers counseling individuals with CDH1 variants.

6513 Background: Oncology care is complex and often multimodal. With recent technological advances, only a fraction of data is structured feasibly for research. Here we present a step-by-step method of building a novel comprehensive pan-cancer oncology data model using standard data definitions and industry-standard benchmarks. Methods: A team of 133 members was assembled including a project manager, bioinformatic engineers, business analysts, biostatisticians, data stewardship experts, clinical curators, and quality assurance (QA) managers. We first identified data domains that capture a comprehensive patient journey, leveraging existing oncology data models as a starting point, including NAACCR, PRISSMM (Deb Schrag & Eva Lepisto), and mCODE (ASCO). A common data model was developed using standard terms plus 5-10 disease specific elements (DSE). REDCap was used as the database platform, as it is HIPAA compliant and allows customizations. The data was stored in AuroraDB using an architecture and products that provide scalability from both an integration and consumption perspective. Results: We identified 10 data domains, including 186 distinct elements: demographics (20), comorbidities (2); cancer diagnosis & staging (27), pathology (45), imaging (18), medications (11), oncology responses (11), radiation treatments (14), cancer surgeries (11), cancer genomic (19), tumor markers (8), and vitals (8). Standard ontologies were used, including ICD-0-3 histology codes, ICD-10 comorbidities codes, CPT cancer surgeries codes, and CTCAE 5.0 for toxicities. We identified a data steward for each tumor type across medical oncology, surgery, pathology, radiology, and radiation oncology domains who aided curator training and the identification of DSE. QA managers and analysts performed 20% source data verification. In addition, we built REDCap rules (applicable across a form), and complex queries (applicable across multiple forms). To support QA and clinical engagement, interactive Tableau dashboards were constructed. In addition, timing and quality errors were monitored via Tableau dashboards at the individual curator level to provide timely feedback, leading to improved data quality and curation efficiency in real time. The Medical oncology and radiology domains were the most time-consuming, whereas Cancer diagnosis was the most difficult to curate. Conclusions: We collected genomic and phenomic data for 15,579 patients across six tumor types to date. Collecting comprehensive oncology data across tumor types is possible but requires institutional support, collaboration between clinical & informatics teams, and a dedicated QA team. [Table: see text]