科研之友
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摘要
Breast cancer can metastasize at any time during its course, but timing of systemic relapse cannot be predicted by traditional TNM staging. Characteristics of distant recurrence within the first 3 years of diagnosis may identify a group of patients who could benefit from novel strategies to prevent systemic relapse. Of 1,089 patients with breast cancer treated at our institution between January 2007 and May 2011, we identified 76 with de novo metastases (on presentation) and 40 with systemic relapse after a median follow up of 2.2 years. Compared to relapse, de novo metastatic disease was more likely to be grade 1 or 2 (43.1 vs. 18.4 %, p = 0.032), estrogen receptor (ER) positive (69.7 vs. 47.5 %, p = 0.019), progesterone receptor (PgR) positive (56.6 vs. 32.5 %, p = 0.014), and HER2-positive (27.5 vs. 10.3 %, p = 0.035). In the 815 patients with stage I-III disease who were at risk of systemic relapse, univariate analyses were performed for age, tumor size, grade, ER, PgR, HER2, lymph nodes, and TNM stage. A multivariate Cox regression model was built using step-wise model selection and identified 4 covariates that were significantly associated with risk of early relapse: stage-III (p %26lt; 0.001), grade-III (p = 0.002), PgR-negative status (p = 0.014), and HER2-negative status (p = 0.033). A risk-score was developed based on the linear combination of these covariates and time-dependent predictive curves were used to evaluate the predictive accuracy of the proposed risk-score. The highest risk-score group had a 1, 2, and 3-year relapse probabilities of 11.5, 41.2, and 52.5 %, respectively. The corresponding 1, 2, and 3-year relapse probabilities for the overall population were 1.2, 4.4, and 6.3 %, respectively. Our proposed model can be used to select patients at high-risk of early relapse who could benefit from enrollment on clinical trials with novel therapies designed for this group.
- 出版日期2012-11
