Real-world Evidence of Diagnostic Testing and Treatment Patterns in US Patients With Breast Cancer With Implications for Treatment Biomarkers From RNA Sequencing Data.


Journal

Clinical breast cancer
ISSN: 1938-0666
Titre abrégé: Clin Breast Cancer
Pays: United States
ID NLM: 100898731

Informations de publication

Date de publication:
08 2021
Historique:
received: 28 08 2020
revised: 27 10 2020
accepted: 13 11 2020
pubmed: 16 1 2021
medline: 20 1 2022
entrez: 15 1 2021
Statut: ppublish

Résumé

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. 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. 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 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.

Sections du résumé

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
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.

Identifiants

pubmed: 33446413
pii: S1526-8209(20)30296-2
doi: 10.1016/j.clbc.2020.11.012
pii:
doi:

Substances chimiques

Receptors, Estrogen 0
Receptor, ErbB-2 EC 2.7.10.1

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

e340-e361

Informations de copyright

Copyright © 2020. Published by Elsevier Inc.

Auteurs

Louis E Fernandes (LE)

Tempus Labs, Chicago, IL.

Caroline G Epstein (CG)

Tempus Labs, Chicago, IL.

Alexandria M Bobe (AM)

Tempus Labs, Chicago, IL.

Joshua S K Bell (JSK)

Tempus Labs, Chicago, IL.

Martin C Stumpe (MC)

Tempus Labs, Chicago, IL.

Michael E Salazar (ME)

Tempus Labs, Chicago, IL.

Ameen A Salahudeen (AA)

Tempus Labs, Chicago, IL.

Ruth A Pe Benito (RA)

Tempus Labs, Chicago, IL.

Calvin McCarter (C)

Tempus Labs, Chicago, IL.

Benjamin D Leibowitz (BD)

Tempus Labs, Chicago, IL.

Matthew Kase (M)

Tempus Labs, Chicago, IL.

Catherine Igartua (C)

Tempus Labs, Chicago, IL.

Robert Huether (R)

Tempus Labs, Chicago, IL.

Ashraf Hafez (A)

Tempus Labs, Chicago, IL.

Nike Beaubier (N)

Tempus Labs, Chicago, IL.

Michael D Axelson (MD)

Tempus Labs, Chicago, IL.

Mark D Pegram (MD)

Stanford Comprehensive Cancer Institute, Stanford University School of Medicine, Stanford, CA.

Sarah L Sammons (SL)

Department of Medicine, Duke University Medical Center, Duke University, Durham, NC.

Joyce A O'Shaughnessy (JA)

Baylor University Medical Center, Texas Oncology and US Oncology, Dallas, TX.

Gary A Palmer (GA)

Tempus Labs, Chicago, IL. Electronic address: publications@Tempus.com.

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Classifications MeSH