BACKGROUND: Patients with de novo metastatic breast cancer (MBC) constitute a heterogeneous group with different clinicopathologic characteristics and survival outcomes. Despite controversy regarding its prognostic value, primary tumor surgery may improve survival for selected patients. PATIENTS AND METHODS: Patients with de novo MBC were identified using the SEER database and were then divided randomly into training and validation sets. A Fine-Gray competing risks model was developed to identify the variables associated with increased cancer-specific mortality in the training set. The M1 subdivision system was established based on the independent prognostic factors. Cumulative incidence curves were estimated and compared using Gray's test. RESULTS: Involvement of brain or liver and number of metastatic sites were identified as independent prognostic factors in multivariate analysis. The M1 category was subdivided into 3 subcategories: M1a, single site involvement except brain and liver; M1b, liver involvement only, or multiple site involvement except brain and liver; and M1c, brain involvement regardless of number of metastatic sites, or liver and other sites involvement except brain (M1b vs M1a: subdistribution hazard ratio [SHR], 1.48; 95% CI, 1.29-1.68; M1c vs M1a: SHR, 2.45; 95% CI, 2.18-2.75). Patients with the M1a subtype benefited most from primary tumor surgery in the adjusted competing risks model (M1a: SHR, 0.57; 95% CI, 0.48-0.67, M1b: SHR, 0.62; 95% CI, 0.47-0.83, and M1c: SHR, 0.59; 95% CI, 0.44-0.80), whereas benefits conferred by treatment with chemotherapy alone increased with the upstaging of metastatic disease (M1a: SHR, 0.72; 95% CI, 0.62-0.83, M1b: SHR, 0.54; 95% CI, 0.44-0.68, and M1c: SHR, 0.53; 95% CI, 0.45-0.61). CONCLUSIONS: Subdivision of M1 stage facilitates prognosis prediction and treatment planning for patients with de novo MBC. Treatment offered should be decided in a coordinated multidisciplinary setting. Primary tumor surgery may play an important role in the management of selected patients.
BACKGROUND:Patients with de novo metastatic breast cancer (MBC) constitute a heterogeneous group with different clinicopathologic characteristics and survival outcomes. Despite controversy regarding its prognostic value, primary tumor surgery may improve survival for selected patients. PATIENTS AND METHODS: Patients with de novo MBC were identified using the SEER database and were then divided randomly into training and validation sets. A Fine-Gray competing risks model was developed to identify the variables associated with increased cancer-specific mortality in the training set. The M1 subdivision system was established based on the independent prognostic factors. Cumulative incidence curves were estimated and compared using Gray's test. RESULTS: Involvement of brain or liver and number of metastatic sites were identified as independent prognostic factors in multivariate analysis. The M1 category was subdivided into 3 subcategories: M1a, single site involvement except brain and liver; M1b, liver involvement only, or multiple site involvement except brain and liver; and M1c, brain involvement regardless of number of metastatic sites, or liver and other sites involvement except brain (M1b vs M1a: subdistribution hazard ratio [SHR], 1.48; 95% CI, 1.29-1.68; M1c vs M1a: SHR, 2.45; 95% CI, 2.18-2.75). Patients with the M1a subtype benefited most from primary tumor surgery in the adjusted competing risks model (M1a: SHR, 0.57; 95% CI, 0.48-0.67, M1b: SHR, 0.62; 95% CI, 0.47-0.83, and M1c: SHR, 0.59; 95% CI, 0.44-0.80), whereas benefits conferred by treatment with chemotherapy alone increased with the upstaging of metastatic disease (M1a: SHR, 0.72; 95% CI, 0.62-0.83, M1b: SHR, 0.54; 95% CI, 0.44-0.68, and M1c: SHR, 0.53; 95% CI, 0.45-0.61). CONCLUSIONS: Subdivision of M1 stage facilitates prognosis prediction and treatment planning for patients with de novo MBC. Treatment offered should be decided in a coordinated multidisciplinary setting. Primary tumor surgery may play an important role in the management of selected patients.
Authors: Caitlin E Marks; Samantha M Thomas; Oluwadamilola M Fayanju; Gayle DiLalla; Sarah Sammons; E Shelley Hwang; Jennifer K Plichta Journal: Am J Surg Date: 2021-07-22 Impact factor: 2.565
Authors: Carlos H Barcenas; Juhee Song; Rashmi K Murthy; Akshara S Raghavendra; Yisheng Li; Limin Hsu; Robert W Carlson; Debu Tripathy; Gabriel N Hortobagyi Journal: JCO Clin Cancer Inform Date: 2021-08