Clonal evolution characteristics and reduced dimension prognostic model for non-metastatic metachronous bilateral breast cancer

Abstract

BackgroundHow to evaluate the prognosis and develop overall treatment strategies of metachronous bilateral breast cancer (MBBC) remains confused in clinical. Here, we investigated the correlation between clonal evolution and clinical characteristics of MBBC; we aim to establish a novel prognostic model in these patients.MethodsThe data from Surveillance, Epidemiology, and End Results (SEER) database and the First Hospital of Jilin University were analyzed for breast cancer–specific cumulative mortality (BCCM) by competing risk model. Meanwhile, whole-exome sequencing was applied for 10 lesions acquired at spatial–temporal distinct regions of five patients from our own hospital to reconstruct clonal evolutionary characteristics of MBBC. Then, dimensional-reduction (DR) cumulative incidence function (CIF) curves of MBBC features were established on different point in diagnostic interval time, to build a novel DR nomogram.ResultsSignificant heterogeneity in genome and clinical features of MBBC was widespread. The mutational diversity of contralateral BC (CBC) was significantly higher than that in primary BC (PBC), and the most effective prognostic MATH ratio was significantly correlated with interval time (R2 = 0.85, p< 0.05). In SEER cohort study (n = 13,304), the interval time was not only significantly affected the BCCM by multivariate analysis (p< 0.000) but determined the weight of clinical features (T/N stage, grade and ER status) on PBC and CBC in prognostic evaluation. Thus, clinical parameters after DR based on interval time were incorporated into the nomogram for prognostic predicting BCCM. Concordance index was 0.773 (95% CI, 0.769–0.776) in training cohort (n = 8,869), and 0.819 (95% CI, 0.813–0.826) in validation cohort (n = 4,435).ConclusionsBilateral heterogeneous characteristics and interval time were determinant prognostic factors of MBBC. The DR prognostic nomogram may help clinicians in prognostic evaluation and decision making.