Multivariable regression analysis of febrile neutropenia occurrence in early breast cancer patients receiving chemotherapy assessing patient-related, chemotherapy-related and genetic risk factors

Chemotherapy-induced neutropenia (CIN) and febrile neutropenia (FN) are serious and frequent complications in breast cancer patients receiving adjuvant chemotherapy, and they result in hospitalisations [1‐3] and chemotherapy dose reductions or delays that impact on treatment outcome and short-term mortality [4]. Adjuvant fluorouracil, epirubicin, and cyclophosphamide (FEC) chemotherapy has an FN risk of between 9% and 14% (low-intermediate risk) [5].

Antibacterial or antifungal prophylaxis has recently been recommended for neutropenic patients expected to have a prolonged low neutrophil count or with other risk factors that favour complications [6]. Prophylaxis with granulocyte colony-stimulating factor (GCSF) in patients at high risk of FN (>20%) is recommended in international guidelines [5, 7, 8]. For chemotherapy regimens with an intermediate FN risk (10-20%), the European Organisation for Research and Treatment of Cancer (EORTC) GCSF guideline recommends that patient risk factors should also be considered to determine individual risk of FN [5] and the likely benefit of prophylactic GCSF. Therefore, it is important to identify patients at high risk of FN before the initiation of chemotherapy to provide them with appropriate prophylactic measures.

Risk models for the occurrence of CIN [9] and FN [10] in patients with breast cancer have been published. The risk factors identified include older age, lower weight, higher planned dose of chemotherapy, higher number of planned chemotherapy cycles, vascular comorbidity, lower baseline white blood cell count (WBC), lower platelet and neutrophil count, and higher baseline bilirubin. Prior chemotherapy, abnormal liver or renal function, low WBC, higher chemotherapy intensity, and planned delivery were identified as risk factors for neutropenic complications in a prospective US study of patients with different types of cancer [11]. Poor performance status and low lymphocyte and neutrophil counts were risk factors in a European study of solid tumour patients [12], as were tumour stage and number of comorbidities in elderly patients with solid tumours [13].

These risk models of CIN or FN that included patient- or chemotherapy-related factors were reported to be predictive. However, more refined models are necessary to achieve satisfactory performance in independent patient populations that include existing and emerging types of data, including stable genetic factors that are easily measurable, objective, and potentially independent from the inherent viabilities of clinical decision-making. Several studies have assessed the impact of genetic factors on haematological toxicity, but these studies were small in size or limited to only a few candidate genetic factors [14‐16].

The objective of this study was to develop risk models for the occurrence of FN in breast cancer patients receiving FEC chemotherapy in any cycle and the first cycle based on a large set of patient-related, chemotherapy-related, and genetic characteristics.

In this population of early breast cancer patients seen in routine clinical practice at a tertiary referral centre, we identified a set of genetic factors, in addition to patient-related and chemotherapy-related factors, that predict occurrence of FN in any cycle or the first cycle of chemotherapy. Significant predictors of a higher risk of FN in any cycle and in cycle one were: lower baseline platelet count, lower baseline haemoglobin, and carriers of the rs4148350 T-allele variant in MRP1, especially homozygous T-allele carriers. Patients with lower ALT and homozygous carriers of the rs246221 variant T-allele in MRP1 and rs351855 variant T-allele in FGFR4 had a lower risk of FN occurrence. Although the predictive ability of the models was improved by including genetic factors, the overall predictive ability remained poor. Genetic effects were stable and FN occurrence was very high in patients with specific SNP allele variants.

The observed effects of lower baseline platelet count and haemoglobin are consistent with previous reports. Baseline platelet count has been shown to differ between cancer patients with mild and severe haematological toxicity [16], and low haemoglobin has been mentioned as possible risk factor for FN [27] and survival [28]. In the model of FN occurrence in any cycle, higher baseline ALT was significantly associated with FN but not baseline bilirubin [9, 29]. Both measures are indicators of liver function and since the liver detoxifies drugs like epirubicin [30], impaired liver function may be an important risk factor for FN occurrence in patients receiving chemotherapy with epirubicin. A predictive role for WBC or ANC in CIN and FN occurrence in cancer patients receiving chemotherapy has been described in other studies [9‐12], but could not be confirmed in our models. Most SNPs previously associated with FN occurrence [18] and reported to be involved in anthracycline-induced cardiotoxicity [31‐33] were confirmed in the multivariable analysis. The SNP rs45511401 was not included in the multivariable regression model as it was highly correlated with rs4148350, and the latter variant explained the model variability slightly better. There were no correlations between SNPs included in the final model and patient- or chemotherapy-related factors.

International guidelines [5, 7, 8] and the literature [9, 12] report age, planned dose intensity, and planned number of chemotherapy cycles to be important risk factors for CIN and FN during chemotherapy. These risk factors could not be confirmed in our models. Patient-specific approaches to clinical management were not recorded in detail in this study and might therefore have masked the effect of age on FN occurrence. In addition, the exact cycle of FN occurrence was not available after the first cycle. Factors previously reported to protect against CIN and FN in any cycle of chemotherapy, such as dose reductions, dose delays, or growth factor use before an event occurred, could not be investigated since the details, reasons, and timing information were not available and only 15 out of 994 patients received primary prophylaxis with GCSF, mainly due to reimbursement criteria.

The apparent predictive ability, i.e., the predictive ability assessed in the ‘training’ dataset used to develop the models, was lower than in previously published models of CIN or FN occurrence in other cancers [9, 11, 34]. In these models, sensitivity and specificity at the optimal predicted probability cut-off was about 70% or higher, but in this study it remained below 70%. As commonly seen in models of FN occurrence, the NPV (≥ 90%) was much higher than the PPV because FN incidence is often around 20%; this implies an NPV of around 80% for simply assuming that FN does not occur in any patient. The areas under the ROC curves were relatively low but significantly higher than 0.5, the value indicating no predictive ability. In other words, the models allowed partial discrimination of patients at low or high risk of FN. Including genetic risk factors improved the models but absolute predictive ability remained rather low. The effects of the SNPs were stable and FN occurrence was very high in patients with specific, sometimes rare, SNP allele variants. In terms of clinical implications, genetic testing might help to identify a small proportion of patients at very high risk of FN who can be targeted with prophylactic measures. For the majority of patients, the current models do not reliably identify patients that will develop FN, but they do delineate patients who are unlikely to develop FN. This is clinically relevant since patients at low risk of FN probably do not need primary GCSF prophylaxis or nadir assessment, while the high-risk group is unpredictable and might need more extensive preventive measures or follow-up.

The performance of any model tends to be highest in the training dataset. The results obtained with bootstrap resampling supported the internal validity of the FN in any cycle and the FN in first cycle models. The predictive ability of the models has yet to be tested in an entirely independent population, where model performance is usually lower. Before risk models are put to clinical use, true external validation is essential [35, 36]. Another limitation of this study is the retrospective design; no detailed information was available on patient management in clinical practice, which is known to influence the risk of FN occurrence, and the reasons and timing of dose reductions and dose delays were not available. FN occurrence was not assessed according to chemotherapy cycle beyond the first cycle. GCSF was only administered to 15 patients before an event occurred due to stringent reimbursement criteria. Hence, the impact of GCSF on FN occurrence was difficult to assess.

To the best of our knowledge, this is the first study of risk of FN in the first and any cycle of chemotherapy in patients with early breast cancer that combined a set of patient- and chemotherapy-related factors with a large set of SNPs. Further validation studies are needed to confirm our findings, which should ideally be prospectively designed, sufficiently powered, and measure all possible predictors of FN occurrence reported in the literature. Approaches to clinical management that are measurable and known to influence the risk of FN occurrence, such as dose modifications or growth factor use before an FN event occurred, should be included. Information on SNPs should be available for as many patients as possible and the frequencies of possible genotypes of one SNP should be similar. Validated genetic factors have the potential to become reliable predictors of FN occurrence. The specific SNPs that were assessed in this study are independent from clinical decision-making and therefore less likely to be confounded by clinical practice.

We have identified a set of chemotherapy-related, patient-related, and genetic risk factors that predict occurrence of FN in the first and any cycle of chemotherapy in a large cohort of early breast cancer patients. Genetic effects in the models improved the predictive ability, but the overall predictive ability of the models remained poor. FN occurrence was very high in patients with specific SNP allele variants. Up-front genetic testing might be helpful to identify a limited group of very high-risk patients. Further independent validation is required to develop risk models that include genetic predictors of FN occurrence and can be used to personalise care.