Estimation of the impact of chemotherapy-induced biological age acceleration on breast cancer survivorship using multi-scale simulation modeling.

Abstract

12121 Background: Treatment of breast cancer with chemotherapy improves breast cancer survival. But chemotherapy also increases the accumulation of senescent cells in breast cancer survivors, which induces more senescent cells through paracrine signaling. The increased accumulation of senescent cells increases inflammation and tissue damage, which leads to earlier onset of aging-related diseases in survivors. We developed a simulation model connecting the biology of aging with breast cancer epidemiology to estimate the impact of chemotherapy-induced biological age acceleration on breast cancer survivorship. Methods: We integrated a systems biology model of accumulation of senescent cells with chronological age and an established Cancer Intervention and Surveillance Modeling Network (CISNET) breast cancer simulation model to estimate the impact of biological age-acceleration on breast cancer survivorship outcomes. We used published clinical data on senescence biomarker expression level ( p16 INK4a mRNA expression) in chemotherapy recipient breast cancer survivors to simulate the elevated senescence expression level in the remaining lifetime of breast cancer survivors. The difference in the senescence expression level in the chemotherapy group was evaluated against the senescence expression level in the general female population to quantify the excess biological age, or the biological age acceleration, after chemotherapy. The modified biological age induced by chemotherapy was used to determine the hazard of non-breast cancer mortality in breast cancer survivors. We used the CISNET breast cancer simulation model to simulate multi-birth cohorts of US females diagnosed with breast cancer to estimate survivorship outcomes after chemotherapy. Outcomes included remaining life years after breast cancer diagnosis, absolute number of non-breast cancer deaths, and the time-point at which the risk non-breast cancer mortality starts to dominate breast cancer mortality. Results: Biological age acceleration after anthracycline-based regimes caused a greater loss of life years than anthracycline-free regimens for women diagnosed at ages 30-39 years (median of 11.7 years vs. 2.1 years lost). This loss in life years diminished with increasing age at diagnosis (median of 6.0 years vs. 1.4 years lost for 70-79 year old women). The risk of non-breast cancer mortality exceeded breast cancer mortality up to 10-15 years earlier than expected based on chronological age after anthracycline-based regimens. Conclusions: Integration of computational systems biology modeling with breast cancer population-level modeling helps to identify subgroups of breast cancer survivors who are likely to experience survivorship loss due to biological age acceleration and may need survivorship care targeting non-breast cancer mortality.