AUTHOR=Li Dan , Weng Shanshan , Zhong Chenhan , Tang Xiujun , Zhu Ning , Cheng Yi , Xu Dong , Yuan Ying TITLE=Risk of Second Primary Cancers Among Long-Term Survivors of Breast Cancer JOURNAL=Frontiers in Oncology VOLUME=9 YEAR=2020 URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2019.01426 DOI=10.3389/fonc.2019.01426 ISSN=2234-943X ABSTRACT=

Purpose: The current study explored the risk of developing second primary cancers (SPCs) among long-term early-stage breast cancer survivors and identified risk factors to build an externally validated clinical prediction model.

Methods: The cumulative incidence of SPCs was calculated by Gray method among survivors of early-stage initial primary breast cancer (IPBC). Comparisons of treatment-related risk by selected organ sites were performed. A nomogram was established to estimate the individual risk of developing SPCs based on the multivariate Fine and Gray risk model. Decision curve analysis (DCA) was used to evaluate clinical usefulness of the model.

Results: The cumulative incidence of developing SPCs after early-stage IPBC was 7.43% at 10 years, 14.41% at 15 years, and 20.08% at 20 years. Radiotherapy was associated with elevated risks of any SPCs and with elevated risks of lung cancer (SHR: 1.109; P = 0.045), breast cancer (SHR: 1.389; P < 0.001), and AML (SHR: 1.298; P = 0.045). Chemotherapy was significantly associated with a declined risk of any SPCs, with decreased risks of lung (SHR: 0.895; P = 0.015) and breast cancers (SHR: 0.891; P < 0.001), as well as elevated risks of other leukemias (SHR: 1.408; P = 0.002). HR-positive status was associated with decreased risks of any SPCs; with decreased risks of breast (SHR: 0.842; P < 0.001) and ovarian cancers (SHR: 0.483; P < 0.001); and with elevated risks of urinary tract cancers (SHR: 1.214; P = 0.029).

Conclusion: We found that the cumulative incidence of developing SPCs increased over time and did not plateau. Risk factors for developing SPCs identified by our study were not consistent with those of previous studies. The prediction model can help identify individuals at higher risk of SPCs.