The neutrophil|[ndash]|lymphocyte ratio and its utilisation for the management of cancer patients in early clinical trials

Phase 1 oncology clinical trials are dose- and toxicity-finding studies for novel compounds or combinations that will potentially be used for evaluation in future trials. These are generally tested in patients with advanced cancer who have exhausted standard care options. The likely benefit from these agents may be limited and the commitment from the patient is significant. Predicting which patients will benefit from a phase 1 clinical trial is challenging, as their general health may be declining with advancing disease, and they may experience toxicity in exchange for limited benefit (Roberts et al, 2004). To assist with clinical decision-making and patient selection, several prognostic models have been developed that can be applied at the bedside (Chau et al, 2011; Fussenich et al, 2011; Olmos et al, 2011; Ploquin et al, 2012). The Penel model for 90-day mortality (Penel et al, 2010), the Hammersmith score for OS (Stavraka et al, 2014), and the Royal Marsden Hospital (RMH) score for OS (Arkenau et al, 2009) are the only models that have been validated in the phase 1 population. The RMH score is currently used in the Drug Development Unit, RMH. This score comprises three components, each assigned 1 point: albumin <35 g l−1, lactate dehydrogenase (LDH) >upper limit of normal, and >2 sites of metastases. Patients scoring 0–1 have a median OS of 33.0 weeks, whereas those scoring 2–3 have an inferior median OS of 15.7 weeks. Cancer-related inflammation is the seventh hallmark of cancer (Hanahan and Weinberg, 2011), with inflammatory cells and mediators being an essential component of the tumour microenvironment. This inflammatory response is detectable in the peripheral blood, evidenced by neutrophilia and/or lymphopenia (Mantovani et al, 2008). Moreover, the neutrophil–lymphocyte ratio (NLR), derived from the quotient of the absolute neutrophil count and the absolute lymphocyte count, is prognostic for patient outcomes in a variety of tumours (Guthrie et al, 2013; Templeton et al, 2014). A high NLR has been shown to be an independent prognostic factor in many advanced cancers with varying thresholds of NLR defined as being significant, including colorectal cancer (NLR>5) (Walsh et al, 2005; Kishi et al, 2009), advanced gastric cancer (NLR2.5) (Yamanaka et al, 2007), advanced pancreatic cancer (NLR>5) (An et al, 2010), castration-resistant prostate cancer (NLR>3) (Keizman et al, 2012a), metastatic renal cell carcinoma (NLR3) (Keizman et al, 2012b; Pichler et al, 2013), nasopharyngeal carcinoma (NLR>2.5) (Chang et al, 2013), non-small cell lung cancer (Sarraf et al, 2009), malignant mesothelioma (NLR5) (Kao et al, 2010), advanced cervical cancer (NLR1.9) (Lee et al, 2012) and advanced ovarian cancer (NLR2.60) (Cho et al, 2009). This may be particularly relevant in the development of drugs targeting the immune checkpoint, such as CTLA-4 (cytotoxic T-lymphocyte-antigen-4) and PD-1 (programmed cell death 1)/PD-L1 (programmed death-ligand 1) targeting antibodies. The prognostic utility of the NLR, a marker of systemic inflammation, for patients with advanced cancer entering phase 1 trials have not been explored. In this study, we hypothesised that a high NLR is prognostic for an inferior OS in patients enrolled in a phase 1 trial. We aimed to integrate NLR into the RMH score in order to improve the discriminative ability of the model for OS. Receiver operator characteristic (ROC) curve analysis was used to test the discriminative ability of the models combining the RMH score and NLR-measure (Hanley and Mcneil, 1983). Where the NLR-measure was binary, it was given a score of 0 when 3 months, patients with an RMH score+NLR50 score of 0–1 can certainly be considered. However, caution should be exercised in patients with an RMH score+NLR50 score of 2–4, as some of these patients will have a survival measured between 3 and 6 months. Second, the discriminating ability of the NLR alone was the same as that of the RMH score alone, suggesting that the NLR could be used instead of the RMH score in assessing a new patient for consideration of a phase 1 clinical trial, particularly when an up-to-date computerised tomography scan is not available. Although the interaction test between NLR and RMH score was negative, biologically, it is conceivable that there may be a potential interaction, as suggested by the RMH score 2–3 having a significantly higher NLR compared with RMH score 0–1. Hypoalbuminaemia is an independent biomarker of tumour inflammation and poor prognosis (Mcmillan, 2013), as is a raised LDH (Agarwala et al, 2009), both being crucial components of the RMH score. It is noteworthy that C-reactive protein levels are prognostic in cancer, as demonstrated by the Glasgow Prognostic Score; however, this has not been evaluated in a phase 1 patient population (Mcmillan, 2013) and deserves further consideration. The NLR has potential application in drug development. The mapping of the human kinome has led to accelerated drug discovery and personalised medicine. This has been paralleled with biomarker development, in order to enrich trials with patients more likely to respond, including phase 1 trials. Current paradigms in trial design rely on genomic biomarkers, based on gene amplification or loss, or genetic mutations (Carden et al, 2010; Bauer et al, 2014). Biomarkers predictive of response to immunotherapies remain an area of unmet need. This work has validated the NLR as a prognostic biomarker in phase 1 trial patients, identifying patients whose tumours are generating an inflammatory response. There is scope for further investigation of NLR as a predictive biomarker of response to immunotherapies, particularly with immune checkpoint targeting drugs such as CTLA-4 and PD-1/PDL-1 targeting antibodies, and the utility of normalisation of the NLR with treatment (Pinato et al, 2014). The biology underlying the role of inflammation in cancer pathogenesis and progression is an area of intense research. A raised NLR is a result of a high absolute neutrophil count and/or a low absolute lymphocyte count. Our univariate analysis showed that a raised absolute neutrophil count was significantly associated with poor OS, compared with a low absolute lymphocyte count. Tumour-associated neutrophils, defined as having CD11b+/Gr-1+ expression, have been recognised as being a poor prognostic factor (Fridlender and Albelda, 2012). Patients with tumour-associated neutrophils have a raised absolute neutrophil count in the peripheral blood (Schmidt et al, 2005). This concept lends itself to two potential therapeutic opportunities. First, two phenotypes of tumour-associated neutrophils have been recognised; the N1-phenotype resulting from low TGFβ/high IFNβ, causing tumour growth retardation; and the N2-phenotype resulting from high TGFβ/low IFNβ, causing tumour growth. Depletion of TGFβ can shift the phenotype towards N1, causing growth retardation (Fridlender et al, 2009). Second, murine mammary adenocarcinoma models have shown that neutrophil depletion with anti-granulocyte receptor-1 antibody can result in tumour regression (Pekarek et al, 1995). Di Mitri et al (2014) have shown in PTEN-null prostate tumours in mice that CD11b+/Gr-1+ myeloid cells prevent tumour senescence through secretion of IL-1RA and that CD11b+/Gr-1+ myeloid cells can be reduced using a CXCR2 antagonist, encouraging tumour senescence following docetaxel. Several validated prognostic models have been developed for patients referred for phase 1 clinical trials. The work by Pinato et al (2014) is the only model to take inflammation into account. However, in contrast to this work, the merits of our data are that it has been validated in a large sample size. Moreover, the NLR was analysed as a continuous variable in order to maintain statistical power. We deliberately did not prespecify an NLR threshold but subdivided our population into quartiles in an attempt to optimise this statistical evaluation. Our results add to the established RMH score, improving on the prognostic model for patient selection onto phase 1 trials. This is the first publication to define the optimal NLR in a phase 1 patient population. Limitations of this study include that it is a single institution retrospective analysis. Further prospective multicenter validation should be now considered in an external data set. The results presented here are from patients treated in phase 1 trials with cytotoxic chemotherapy and/or small-molecule inhibitors, making the data difficult to extrapolate to patient being treated with immunotherapies. Validation in this specific subpopulation receiving immunotherapies is required. The NLR may be an objective measure of inflammation that can be easily derived from routine laboratory assessments, in addition to the RMH score. The NLR has been validated as a prognostic tool for OS in patients being treated in a phase 1 trial. Using the NLR of 3.0 in our 1000 patient validation cohort, the RMH score+NLR50 generated the most prognostic dichotomisation of the population for OS by 6.2 months. This robust prognostic biomarker must now be evaluated as a predictive and response biomarker for cancer immunotherapies. The authors declare no conflict of interest. Author contributions The literature search was performed by RK, EG, VM, MG, UN, DL, SK and JD. Figures were prepared by RK, EG, VM, MG, UN, DL and JD. The study was designed by RK, UN, DL, SK and JD. Data for this research was collected by RK, EG, VM, MG, DL and UN. All authors contributed equally to the preparation of the manuscript. Statistical analyses were performed by RK, DL and UN. Supplementary Information accompanies this paper on British Journal of Cancer website