556 Background: Bladder cancer has one of the highest rates of human epidermal growth factor receptor 2 (HER2) alteration. Novel HER2-directed agents are being investigated in metastatic BC. We sought to define the incidence and clinical characteristics of HER2-altered BC across disease states. Methods: We retrospectively analyzed our single-institution, clinically annotated cohort of urothelial BC pts with available genomic profiling data (MSK-IMPACT). We quantified the prevalence of HER2 alterations, defined as driver mutation (based on OncoKB), and/or amplification, across BC disease states. We examined the association between HER2 alteration and disease progression and survival. The Kaplan-Meier method was used for time-to-event analyses. Results: A total of 1073 BC pts underwent MSK IMPACT profiling of tumor tissue derived from the following disease states: 36% (n = 380) non-muscle invasive (NMI)BC, 41% (n = 443) muscle invasive (MI)BC, and 23% (n = 250) (met)BC. At initial diagnosis, the median age was 67 years, 77% (n = 822) were male, 86% (n = 928) were white, and 66% (n = 710) were smokers. Overall, 16% (n = 177) of pts had any oncogenic HER2 alteration (Table), including 11% with a HER2 driver mutation and 7% with HER2 amplification The most frequent mutations were S310F (40%, n = 53) and S310Y (14%, n = 19). The rate of HER2 amplification was different among the three groups (p = 0.002), 9% in MIBC and metBC compared to 3% in NMIBC. Among 514 pts with NMIBC, the median time to progression (TTP) to MIBC or metBC was 111.6 months (95% Cl: 85.7-NR). Among NMIBC pts, TTP was significantly shorter for HER2-amplified (n = 17) vs. non-amplified (n = 497) (HR = 1.99, 95%CI: 1.05-3.76, p = 0.034, median 26 vs. 114 months). Among pts with metBC receiving frontline platinum-based chemotherapy (n = 143), the median overall survival (OS) was 25.3 months (95%CI: 18.5-33.9). OS was numerically higher in pts with any oncogenic HER2 alteration (n = 26) compared to wild-type (n = 117) (HR = 0.59, 95% Cl: 0.33-1.07, p = 0.082), though this difference was not statistically significant. The median OS for platinum-refractory metBC pts receiving 2nd line immunotherapy (n = 63) was 10.3 months (95%CI: 7.2-31.6), and the association between OS and HER2 alteration was not significant (HR = 0.57, 95%CI: 0.24-1.37, p = 0.2). Conclusions: HER2 amplification is more frequent in MIBC and metBC than in NMIBC. In NMIBC, HER2 amplification is associated with shorter TTP to MIBC or metBC. HER2 alteration in metBC is associated with a non-significant trend towards improved OS in frontline platinum-treated pts, though this analysis is limited by small sample size.[Table: see text]
e18755 Background: The 2016 21st Century Cures Act supports the use of Real-World Data (RWD) for regulatory decision/approval. Due to technological advances, a vast amount of health-related data are now available, but most are not standardized nor readily useable for research. Also, currently available standardized RWD models are not applicable across cancer types or oncology specialties (surgery, medical oncology, radiation oncology, pathology, radiology, etc.). To address these deficiencies Memorial Sloan Kettering Cancer Center (MSKCC) built a comprehensive, pan-cancer, pan-specialty RWD model. Methods: The Core Clinical Data Element (CCDE) data model incorporates aspects of existing academic and biopharma data models, including PRISSMM framework, ASCO’s mCODE, and NAACCR tumor registry model. The data model encompasses 11 domains that are critical to the understanding of the patient’s cancer journey, including: demographic, comorbidities, diagnosis, pathology, imaging, genomics, cancer surgeries, radiation oncology treatments, medical oncology treatments, cancer status/progression, and additional health information. To align with current standards, we are using ICD-10, ICDO3, CTACE V5.0, HL7, SNOMED and LOINC code sets. Further, this adaptable model allows for 5-10 disease specific elements to accommodate for disease heterogenicity and capture the differences among cancer types. Results: The CCDE database includes 1,126 of total data elements. MSKCC has 52,704 patients with MSK-IMPACT (Next-Generation sequencing platform with 505 genes panel) testing of which, we have identified 1,132 bladder cancer patients with at-least one year of cancer care follow-up for the initial curation cohort. Patients were identified as having an OncoTree bladder tumor type code that is assigned by a pathologist who attests the diagnosis by reviewing results from clinical tests on tumor specimens. To the date, 641 patients including 46,415 curated forms have been curated (Table). Conclusions: The comprehensive MSKCC’s CCDE data model standardizes the common and critical pan-cancer and pan-specialty elements for RWD. The dataset resulting from this curation efforts will provide robust structured and unified genomic and phenomic data across tumor types for future research enabling greater collaboration across various cancer types as well as oncology specialties.[Table: see text]
e18775 Background: The production of high-quality real-world data requires comprehensive and meticulous data quality assurance (QA) methods to guarantee that adequate standards of accuracy, completeness, and consistency are met. Memorial Sloan Kettering Cancer Center (MSKCC) synthesizes manually curated Electronic Health Record (EHR) data to collect and harmonize the fundamental data elements across all cancer types. Centralized real-time analysis of curated data quality can allow for rigorous review to identify areas of strength and opportunities for improvement in the curation process. Methods: MSKCC built the Core Clinical Data Element (CCDE) data model, which encompasses aspects of PRISSMM, ASCO’s mCODE, and NAACCR tumor registry frameworks, to capture standardized real-world, pan-cancer, pan-specialty data across 11 modules, including cancer genomics, imaging, pathology, surgery, and radiation. A key component within the QA process is source data verification (SDV), the comparison of curated data against source documents to identify inconsistencies. Any discrepancies detected are classified into major and minor violations. Major violations are errors or omissions on core data elements that would impact time interval calculations, such as an incorrect procedure date. Minor violations are errors or omissions on less critical data elements, such as a missing radiation therapy dose. Identifying these inconsistences allows the QA team to recognize patterns in curation errors and distinguish areas for curator retraining. Results: With limited functionality in basic standard data quality checks that exist across various data storage platforms, an interactive application was developed using the R Shiny package to access data as cases are recorded and summarize findings from SDV in real time. The app has two panels, each stratified by CCDE module. The first panel details the total number of forms curated and percentage of forms that underwent SDV, with each form representing one of the 11 modules. The other panel consists of a set of tables that summarize specific major and minor violations based on user selection of a denominator of either patients (e.g. how many patients had a violation on at least one imaging report) or forms (e.g. how many imaging reports had a violation). We will demonstrate the utility of the app and discuss benefits of real time evaluation in large-scale, real-world EHR curation efforts. Conclusions: We recommend automated, user-friendly tools to assess data quality of such efforts. With real-time analysis, the tool allows for ongoing and regular data checks, enabling clarification of directives and retraining of curators as necessary early in the curation process. As the data curation efforts expand to more cancer cohorts, the app examines data quality of each cohort to ensure consistent evaluation. This offers transparency of data quality to ensure usability in real-world data for rigorous research.
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]
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