期刊
RADIOTHERAPY AND ONCOLOGY
卷 154, 期 -, 页码 187-193出版社
ELSEVIER IRELAND LTD
DOI: 10.1016/j.radonc.2020.09.002
关键词
Radiation-induced lymphopenia; Non-small cell lung cancer; XRCC1 rs25487
资金
- National Cancer Institute [P01 CA021230, P30 CA016672]
This study identified age, tumor size, lung V5 and mean dose, heart V5 and mean dose, and XRCC1 rs25487 AA genotype as factors associated with severe RIL in lung cancer patients. Age, lung V5, and XRCC1 rs25487 AA were independently associated with a higher risk of severe RIL.
Background & Purpose: We investigated clinical and genetic factors associated with severe radiation-induced lymphopenia (RIL) in a randomized clinical trial of photon vs. proton radiation, with chemotherapy, for non-small cell lung cancer. Methods: XRCC1 rs25487 was genotyped in lymphocytes from serial peripheral blood samples. Severe RIL was defined as absolute lymphocyte count (ALC) < 0.3 x 10(9) cells/L. Univariate and multivariate analyses were used to identify independent risk factors, which were then used to group patients for risk of severe RIL. Results: Univariate analysis of the 178 patients in this analysis showed that older age, larger tumors, higher lung V5 and mean lung dose, and higher heart V5 and mean heart dose were associated with severe RIL during treatment (P < 0.05). The XRCC1 rs25487 AA genotype was also associated with increased risk of severe RIL during treatment (AA vs. others: hazard ratio [HR] = 1.665, 95% confidence interval [CI] 1.089-2.500, P = 0.018). Multivariate analyses showed that older age (HR = 1.031, 95% CI 1.009-1.054, P = 0.005), lung V5 (HR = 1.039, 95% CI 1.023-1.055, P < 0.0001), and AA genotype (AA vs. others, HR = 1.768, 95% CI 1.165-2.684, P = 0.007) were independently associated with higher incidence of severe RIL. These three risk factors (age >= 56 years, lung V5 >= 51% and XRCC1 rs25487 AA) distinguished patients at different risk of developing severe RIL (P < 0.0001). Conclusions: Age, lung V5 and XRCC1 rs25487 AA were all linked with risk of severe RIL. Our predictive risk model may be helpful for identifying patients at high risk of severe RIL so that treatment can be modified. (C) 2020 Elsevier B.V. All rights reserved.
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