OBJECTIVE/ BACKGROUND: We performed a retrospective analysis of longitudinal real-world data (RWD) from patients with breast cancer to replicate results from clinical studies and demonstrate the feasibility of generating real-world evidence. We also assessed the value of transcriptome profiling as a complementary tool for determining molecular subtypes. METHODS: De-identified, longitudinal data were analyzed after abstraction from records of patients with breast cancer in the United States (US) structured and stored in the Tempus database. Demographics, clinical characteristics, molecular subtype, treatment history, and survival outcomes were assessed according to strict qualitative criteria. RNA sequencing and clinical data were used to predict molecular subtypes and signaling pathway enrichment. RESULTS: The clinical abstraction cohort (n = 4000) mirrored the demographics and clinical characteristics of patients with breast cancer in the US, indicating feasibility for RWE generation. Among patients who were human epidermal growth factor receptor 2-positive (HER2+), 74.2% received anti-HER2 therapy, with ∼70% starting within 3 months of a positive test result. Most non-treated patients were early stage. In this RWD set, 31.7% of patients with HER2+ immunohistochemistry (IHC) had discordant fluorescence in situ hybridization results recorded. Among patients with multiple HER2 IHC results at diagnosis, 18.6% exhibited intra-test discordance. Through development of a whole-transcriptome model to predict IHC receptor status in the molecular sequenced cohort (n = 400), molecular subtypes were resolved for all patients (n = 36) with equivocal HER2 statuses from abstracted test results. Receptor-related signaling pathways were differentially enriched between clinical molecular subtypes. CONCLUSIONS: RWD in the Tempus database mirrors the overall population of patients with breast cancer in the US. These results suggest that real-time, RWD analyses are feasible in a large, highly heterogeneous database. Furthermore, molecular data may aid deficiencies and discrepancies observed from breast cancer RWD.
OBJECTIVE/ BACKGROUND: We performed a retrospective analysis of longitudinal real-world data (RWD) from patients with breast cancer to replicate results from clinical studies and demonstrate the feasibility of generating real-world evidence. We also assessed the value of transcriptome profiling as a complementary tool for determining molecular subtypes. METHODS: De-identified, longitudinal data were analyzed after abstraction from records of patients with breast cancer in the United States (US) structured and stored in the Tempus database. Demographics, clinical characteristics, molecular subtype, treatment history, and survival outcomes were assessed according to strict qualitative criteria. RNA sequencing and clinical data were used to predict molecular subtypes and signaling pathway enrichment. RESULTS: The clinical abstraction cohort (n = 4000) mirrored the demographics and clinical characteristics of patients with breast cancer in the US, indicating feasibility for RWE generation. Among patients who were human epidermal growth factor receptor 2-positive (HER2+), 74.2% received anti-HER2 therapy, with ∼70% starting within 3 months of a positive test result. Most non-treated patients were early stage. In this RWD set, 31.7% of patients with HER2+ immunohistochemistry (IHC) had discordant fluorescence in situ hybridization results recorded. Among patients with multiple HER2 IHC results at diagnosis, 18.6% exhibited intra-test discordance. Through development of a whole-transcriptome model to predict IHC receptor status in the molecular sequenced cohort (n = 400), molecular subtypes were resolved for all patients (n = 36) with equivocal HER2 statuses from abstracted test results. Receptor-related signaling pathways were differentially enriched between clinical molecular subtypes. CONCLUSIONS: RWD in the Tempus database mirrors the overall population of patients with breast cancer in the US. These results suggest that real-time, RWD analyses are feasible in a large, highly heterogeneous database. Furthermore, molecular data may aid deficiencies and discrepancies observed from breast cancer RWD.
Authors: Benjamin D Leibowitz; Bonnie V Dougherty; Joshua S K Bell; Joshuah Kapilivsky; Jackson Michuda; Andrew J Sedgewick; Wesley A Munson; Tushar A Chandra; Jonathan R Dry; Nike Beaubier; Catherine Igartua; Timothy Taxter Journal: BMC Cancer Date: 2022-05-28 Impact factor: 4.638
Authors: Mohamed E Salem; Sherif M El-Refai; Wei Sha; Alberto Puccini; Axel Grothey; Thomas J George; Jimmy J Hwang; Bert O'Neil; Alexander S Barrett; Kunal C Kadakia; Laura W Musselwhite; Derek Raghavan; Eric Van Cutsem; Josep Tabernero; Jeanne Tie Journal: JCO Precis Oncol Date: 2022-03
Authors: Donna R Rivera; Henry J Henk; Elizabeth Garrett-Mayer; Jennifer B Christian; Andrew J Belli; Suanna S Bruinooge; Janet L Espirito; Connor Sweetnam; Monika A Izano; Yanina Natanzon; Nicholas J Robert; Mark S Walker; Aaron B Cohen; Marley Boyd; Lindsey Enewold; Eric Hansen; Rebecca Honnold; Lawrence Kushi; Pallavi S Mishra Kalyani; Ruth Pe Benito; Lori C Sakoda; Elad Sharon; Olga Tymejczyk; Emily Valice; Joseph Wagner; Laura Lasiter; Jeff D Allen Journal: Clin Pharmacol Ther Date: 2021-11-11 Impact factor: 6.903