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A Novel Staging System for De Novo Metastatic Breast Cancer Refines Prognostic Estimates.

Publication ,  Journal Article
Plichta, JK; Thomas, SM; Sergesketter, AR; Greenup, RA; Rosenberger, LH; Fayanju, OM; Kimmick, G; Force, J; Hyslop, T; Hwang, ES
Published in: Ann Surg
April 1, 2022

OBJECTIVE: We aim to identify prognostic groups within a de novo metastatic cohort, incorporating both anatomic and biologic factors. BACKGROUND: Staging for breast cancer now includes anatomic and biologic factors, although the guidelines for stage IV disease do not account for how these factors may influence outcomes. METHODS: Adults with de novo metastatic breast cancer were selected from the National Cancer DataBase (2010-2013). Recursive partitioning analysis was used to group patients with similar overall survival (OS) based on clinical T/N stage, tumor grade, ER, PR, HER2, number of metastatic sites, and presence of bone-only metastases. Categories were created by amalgamating homogeneous groups based on 3-year OS rates (stage IVA: >50%, stage IVB: 30%-50%, stage IVC: <30%). RESULTS: 16,187 patients were identified; median follow-up was 32 months. 65.2% had 1 site of distant metastasis, and 42.9% had bone-only metastases. Recursive partitioning analysis identified the number of metastatic sites (1 vs >1) as the first stratification point, and ER status as the second stratification point for both resulting groups. Additional divisions were made based on HER2 status, PR status, cT stage, tumor grade, and presence of bone-only metastases. After bootstrapping, significant differences in 3-year OS were noted between the 3 groups [stage IVB vs IVA: HR 1.58 (95% confidence interval 1.50-1.67), stage IVC vs IVA: HR 3.54 (95% confidence interval 3.33-3.77)]. CONCLUSIONS: Both anatomic and biologic factors yielded reliable and reproducible prognostic estimates among patients with metastatic disease. These findings support formal stratification of de novo stage IV breast cancer into 3 distinct prognosis groups.

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Published In

Ann Surg

DOI

EISSN

1528-1140

Publication Date

April 1, 2022

Volume

275

Issue

4

Start / End Page

784 / 792

Location

United States

Related Subject Headings

  • Surgery
  • Retrospective Studies
  • Receptor, erbB-2
  • Receptor, ErbB-2
  • Prognosis
  • Neoplasm Staging
  • Humans
  • Female
  • Breast Neoplasms
  • Bone Neoplasms
 

Citation

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Plichta, J. K., Thomas, S. M., Sergesketter, A. R., Greenup, R. A., Rosenberger, L. H., Fayanju, O. M., … Hwang, E. S. (2022). A Novel Staging System for De Novo Metastatic Breast Cancer Refines Prognostic Estimates. Ann Surg, 275(4), 784–792. https://doi.org/10.1097/SLA.0000000000004231
Plichta, Jennifer K., Samantha M. Thomas, Amanda R. Sergesketter, Rachel A. Greenup, Laura H. Rosenberger, Oluwadamilola M. Fayanju, Gretchen Kimmick, Jeremy Force, Terry Hyslop, and E Shelley Hwang. “A Novel Staging System for De Novo Metastatic Breast Cancer Refines Prognostic Estimates.Ann Surg 275, no. 4 (April 1, 2022): 784–92. https://doi.org/10.1097/SLA.0000000000004231.
Plichta JK, Thomas SM, Sergesketter AR, Greenup RA, Rosenberger LH, Fayanju OM, et al. A Novel Staging System for De Novo Metastatic Breast Cancer Refines Prognostic Estimates. Ann Surg. 2022 Apr 1;275(4):784–92.
Plichta, Jennifer K., et al. “A Novel Staging System for De Novo Metastatic Breast Cancer Refines Prognostic Estimates.Ann Surg, vol. 275, no. 4, Apr. 2022, pp. 784–92. Pubmed, doi:10.1097/SLA.0000000000004231.
Plichta JK, Thomas SM, Sergesketter AR, Greenup RA, Rosenberger LH, Fayanju OM, Kimmick G, Force J, Hyslop T, Hwang ES. A Novel Staging System for De Novo Metastatic Breast Cancer Refines Prognostic Estimates. Ann Surg. 2022 Apr 1;275(4):784–792.

Published In

Ann Surg

DOI

EISSN

1528-1140

Publication Date

April 1, 2022

Volume

275

Issue

4

Start / End Page

784 / 792

Location

United States

Related Subject Headings

  • Surgery
  • Retrospective Studies
  • Receptor, erbB-2
  • Receptor, ErbB-2
  • Prognosis
  • Neoplasm Staging
  • Humans
  • Female
  • Breast Neoplasms
  • Bone Neoplasms