JE Abraham, L Hiller, L Dorling, A-L Vallier, J Dunn, S Bowden, S Ingle, L Jones, R Hardy, C Twelves, CJ Poole, PDP Pharoah, C Caldas, HM Earl
BACKGROUND: The relationship between chemotherapy-related toxicities and prognosis is unclear. Previous studies have examined the association of myelosuppression parameters or neuropathy with survival and reported conflicting results. This study aims to investigate 13 common chemotherapy toxicities and their association with relapse-free survival and breast cancer-specific survival. METHODS: Chemotherapy-related toxicities were collected prospectively for 6,248 women with early-stage breast cancer from four randomised controlled trials (NEAT; BR9601; tAnGo; Neo-tAnGo). Cox proportional-hazards modelling was used to analyse the association between chemotherapy-related toxicities and both breast cancer-specific survival and relapse-free survival. Models included important prognostic factors and stratified by variables violating the proportional hazards assumption. RESULTS: Multivariable analysis identified severe neutropenia (grades ≥3) as an independent predictor of relapse-free survival (hazard ratio (HR) = 0.86; 95% confidence interval (CI), 0.76-0.97; P = 0.02). A similar trend was seen for breast cancer-specific survival (HR = 0.87; 95% CI, 0.75-1.01; P = 0.06). Normal/low BMI patients experienced more severe neutropenia (P = 0.008) than patients with higher BMI. Patients with fatigue (grades ≥3) showed a trend towards reduced survival (breast cancer-specific survival: HR = 1.17; 95% CI, 0.99-1.37; P = 0.06). In the NEAT/BR9601 sub-group analysis by treatment component, this effect was statistically significant (HR = 1.61; 95% CI, 1.13-2.30; P = 0.009). CONCLUSIONS: This large study shows a significant association between chemotherapy-induced neutropenia and increased survival. It also identifies a strong relationship between low/normal BMI and increased incidence of severe neutropenia. It provides evidence to support the development of neutropenia-adapted clinical trials to investigate optimal dose calculation and its impact on clinical outcome. This is important in populations where obesity may lead to sub-optimal chemotherapy doses.