Visualizing treatment patterns and survival in metastatic breast cancer.

Academic Article


  • 316 Background: Optimal treatment sequencing (i.e., the order in which drugs are given) for metastatic breast cancer (MBC) is unknown. We aimed to develop an approach to visualize treatment patterns and survival in MBC. Methods: This retrospective study utilized ASCO’s CancerLinQ Discovery® database generated from electronic health records. Subjects included 3,312 women aged ≥18 years who were diagnosed with and received treatment for MBC after 1980. Hormone receptor (HR) status was determined by concordant diagnosis and treatment records. Human epidermal growth factor (HER2) status was determined by delivery of HER2-targeted therapy. Ordered and administered treatments were included. We created spatiotemporal plots of treatment patterns for HR+/HER2-, HER2+, and triple negative (TN) MBC. Individuals were represented on the Y-axis, and time on the X-axis with development of MBC aligned at time 0. Treatment classes were identified by colors: hormone therapies in shades of red, chemotherapies in shades of blue, HER2-targeted therapies in shades of green, and novel therapies in shades of orange. Concurrent treatments were represented by split bars. An overlaid Kaplan-Meier curve allowed for observations about the relationship between survival and treatment. Results: We developed a novel visualization approach to simultaneously display heterogeneous, longitudinal treatments and survival. Median survival after first documentation of MBC was 3.1 (IQR 1.4-7.2), 1.3 (IQR 0.6-2.8), and 2.6 (IQR 1.0-5.2) years for HR+/HER2-, TN, and HER2+ MBC, respectively. Patients with longer survival often had long duration of initial therapy, suggesting a more indolent or responsive disease. Substantial heterogeneity in treatment sequencing was observed for HR+HER2- and TN cohorts. In the HER2+ cohort, HER2-targeted therapy was commonly administered for the duration of treatment with more homogeneous sequencing. Conclusions: This novel visualization approach allows for observing the relationship between treatment patterns and survival, which is challenging to demonstrate with traditional quantitative methods. This approach can generate hypotheses regarding impact of treatment patterns on survival.
  • Published In

    Digital Object Identifier (doi)

    Author List

  • Gilbert A; Williams C; Kandhare P; Nakhmani A; Meersman SC; Garrett-Mayer E; Kaltenbaugh M; Azuero A; Ingram SA; Burkard ME
  • Start Page

  • 316
  • End Page

  • 316
  • Volume

  • 37
  • Issue

  • 27_suppl