The Comparison of Newly Diagnosed Invasive Breast Patient Cohorts in Genomics Evidence Neoplasia Information Exchange Biopharma Collaborative (GENIE-BPC) and Other Real-World Databases.

BACKGROUND: Oncology databases that integrate genomic and clinical data have become valuable resources for precision medicine. However, the generalizability of these databases has not been comprehensively assessed. OBJECTIVES: To describe the demographics, clinical characteristics, treatments, and overall survival of breast cancer cohorts in GENIE-BPC and three other databases. METHODS: This study utilized GENIE-BPC, SEER, SEER-Medicare, and Merative MarketScan Research Databases. Women with invasive breast cancer were identified through EHR, cancer registries or ICD-9/10-CM codes. The ages were 18+ years or per database requirement. Treatments were based on EHR or HCPCS/NDC codes in claims. Overall survival was estimated as time from diagnosis to death. RESULTS: Of female breast cancer patients in GENIE-BPC (n = 775), SEER (n = 548 336), SEER-Medicare (n = 68 914), and Marketscan (n = 109 499) databases, the median ages at initial diagnosis were 44, 62, 74, and 57 years, respectively. A greater proportion of patients in GENIE-BPC, compared to SEER/SEER-Medicare, had higher nuclear grades (%III-%IV: 57% vs. 26%/24%), advanced disease stage (%IV: 25.3% vs. 5%/3.6%), percent of triple negative breast cancer (19.7% vs. 10.2%/8.5%), and receipt of chemotherapy (85.0% vs. NA/22.3%). The 1-, 3-, and 5-year overall survival rates were lower in GENIE-BPC (78.5%, 60.5%, 55.5%) than in SEER (95.8%, 89.5%, 85.5%) and SEER-Medicare (91.6%, 81.4%, 75.0%). CONCLUSION: Breast cancer patients in GENIE-BPC were younger, had more advanced disease, had a higher proportion of triple negative breast cancer and recipients of chemotherapy, and had poorer overall survival. Researchers must use statistical adjustment when extrapolating results (e.g., biomarker prevalence) from GENIE-BPC to the larger breast cancer population.

Describing diversity of real world data sources in pharmacoepidemiologic studies: The DIVERSE scoping review.

PURPOSE: Real-world evidence (RWE) is increasingly used for medical regulatory decisions, yet concerns persist regarding its reproducibility and hence validity. This study addresses reproducibility challenges associated with diversity across real-world data sources (RWDS) repurposed for secondary use in pharmacoepidemiologic studies. Our aims were to identify, describe and characterize practices, recommendations and tools for collecting and reporting diversity across RWDSs, and explore how leveraging diversity could improve the quality of evidence. METHODS: In a preliminary phase, keywords for a literature search and selection tool were designed using a set of documents considered to be key by the coauthors. Next, a systematic search was conducted up to December 2021. The resulting documents were screened based on titles and abstracts, then based on full texts using the selection tool. Selected documents were reviewed to extract information on topics related to collecting and reporting RWDS diversity. A content analysis of the topics identified explicit and latent themes. RESULTS: Across the 91 selected documents, 12 topics were identified: 9 dimensions used to describe RWDS (organization accessing the data source, data originator, prompt, inclusion of population, content, data dictionary, time span, healthcare system and culture, and data quality), tools to summarize such dimensions, challenges, and opportunities arising from diversity. Thirty-six themes were identified within the dimensions. Opportunities arising from data diversity included multiple imputation and standardization. CONCLUSIONS: The dimensions identified across a large number of publications lay the foundation for formal guidance on reporting diversity of data sources to facilitate interpretation and enhance replicability and validity of RWE.

Real-world evidence to support regulatory submissions: A landscape review and assessment of use cases.

Real-world evidence (RWE) has an increasing role in preapproval settings to support the approval of new medicines and indications. The main objectives of this study were to identify and characterize regulatory use cases that utilized RWE and other related observational approaches through targeted review of publications and regulatory review documents. After screening and inclusion/exclusion, the review characterized 85 regulatory applications with RWE. A total of 31 were in oncology and 54 were in non-oncology therapeutic areas. Most were for indications in adults only (N = 42, 49.4%), while 13 were in pediatrics only (15.3%), and 30 were in both (35.3%). In terms of regulatory context, 59 cases (69.4%) were for an original marketing application, 24 (28.2%) were for label expansion, and 2 (2.4%) were for label modification. Most also received special regulatory designations (e.g., orphan indication, breakthrough therapy, fast track, conditional, and accelerated approvals). There were 42 cases that utilized RWE to support single-arm trials. External data to support single-arm trials were utilized in various ways across use cases, including direct matching, benchmarking, natural history studies as well as literature or previous trials. A variety of data sources were utilized, including electronic health records, claims, registries, site-based charts. Endpoints in oncology use cases commonly included overall survival, progression-free survival. In 13 use cases, RWE was not considered supportive/definitive in regulatory decision-making due to design issues (e.g., small sample size, selection bias, missing data). Overall, RWE is utilized in regulatory approval processes for new indications/label expansion across various therapeutic areas with wide range of approaches. Multifaceted cross-sector efforts are needed to further improve the quality and utility of RWE in regulatory decision-making.