Neutrophil/lymphocyte ratio predicts chemotherapy outcomes in patients with advanced colorectal cancer

Advances in the treatment of metastatic colorectal cancer (mCRC) in the last decade have significantly improved survival; however, simple biomarkers to predict response or toxicity have not

been identified, which are applicable to all community oncology settings worldwide. The use of inflammatory markers based on differential white-cell counts, such as the neutrophil/lymphocyte

ratio (NLR), may be simple and readily available biomarkers.

Clinical information and baseline laboratory parameters were available for 349 patients, from two independent cohorts, with unresectable mCRC receiving first-line palliative chemotherapy.

Associations between baseline prognostic variables, including inflammatory markers such as the NLR and tumour response, progression and survival were investigated.

In the training cohort, combination-agent chemotherapy (P=0.001) and NLR⩽5 (P=0.003) were associated with improved clinical benefit. The ECOG performance status ⩾1 (P=0.002), NLR>5 (P=0.01),

hypoalbuminaemia (P=0.03) and single-agent chemotherapy (P5 (P=0.002) predicted worse overall survival (OS). The NLR was confirmed to independently predict OS in the validation cohort (P10%

missing data were not included in the analysis. Differential white-cell counts (neutrophils and lymphocytes) were also collected for patients before cycle 2 of chemotherapy. Response rates,

dates of progression and survival were available for patients in the training set; however, only survival data were available for patients in the validation cohort. Dates of death were

followed up by the investigators through hospital records, local Cancer Registries or phone contact through patient relatives, local medical practitioners and palliative-care services.

Patients were consented to undergo analyses before commencing chemotherapy, and the study was approved by institutional research ethics committees in both Sydney and Edmonton.

Statistical analyses were performed using SPSS Graduate Version 17.0 (IBM Corporation 2010, Somers, NY, USA). Response rates were determined according to criteria determined by individual

clinical trials, RECIST criteria. Clinical response was defined as either complete or partial response and non-response as either stable or progressive disease. Clinical benefit was defined

as complete response, partial response and stable disease and no benefit as progressive disease alone. Progression-free survival (PFS) was defined as the date of commencing protocol

treatment to the date of first progression or death from any cause without progression. Overall survival (OS) was defined as the date from the date of commencing protocol treatment to the

date of death from any cause. The χ2-tests were used to test associations between variables of interest (grouped using standard thresholds) and clinical response or benefit. Multivariate

modelling was used for calculation of hazard ratios and clinical response and benefit. The follow-up period commenced at the start of chemotherapy with the censor date of January 2010.

Survival analysis was performed using the Kaplan–Meier method with log-rank test in univariate analyses. Cox regression analysis was used for multivariate survival analysis and for

calculation of hazard ratios.

Baseline clinical demographics and laboratory values for both training and validation sets are presented in Table 1. There were no differences in age and gender between the two cohorts.

However, a significantly higher proportion of patients in the validation cohort had rectal cancer as the primary tumour site and had ECOG PS⩾1. The majority of patients in both cohorts

received combination chemotherapy±a biological agent.

Table 2 shows the univariate analyses between prognostic variables of interest and clinical benefit, PFS and OS in the training set. At the time of analysis, all patients had progressed on

chemotherapy and 169 patients were deceased. The overall clinical response (complete response and partial response) was 55% (93 out of 168 evaluable patients) and clinical benefit (complete

response, partial response and stable disease) was 75% (128 out of 168 evaluable patients). The median PFS was 6.7 months (95% CI 5.6–7.8 months) and OS was 15.3 months (95% CI 12.4–18.2).

Younger age (⩽65 years old), ECOG performance status 0, absence of hypoalbuminaemia, normal alkaline phosphatase, low or normal neutrophil counts and NLR⩽5 were associated with improved

clinical benefit (Table 2). Similarly, younger age (⩽65 years old), ECOG performance status 0 and NLR⩽5 were associated with improved clinical response. In addition, combination-agent

chemotherapy was also associated with improved clinical response.

Variables predicting improved PFS included younger age, ECOG performance status 0, combination-agent chemotherapy, single site of metastasis, absence of neutrophilia or hypoalbuminaemia and

NLR⩽5 (Table 2 and Figure 2A). The following variables were associated with improved OS: younger age, ECOG PS 0, combination-agent chemotherapy, absence of neutrophilia or anaemia.

Hypoalbuminaemia, elevated alkaline phosphatase and NLR>5 were also significantly associated with worse OS (Table 2 and Figure 2B).

PFS according to NLR in (A) training cohort. OS according to NLR in B (training) and (C) validation cohorts of patients with mCRC treated with chemotherapy.

In multivariate analysis performed in the training set (Table 3), combination-agent chemotherapy and NLR⩽5 were associated with improved clinical benefit. The ECOG performance status ⩾1,

NLR>5, hypoalbuminaemia and single-agent chemotherapy were associated with increased risk of progression. The ECOG performance status ⩾1 and NLR>5 predicted worse OS.

Table 4 summarises analysis of baseline characteristics and prognostic variables according to NLR groups. Patients with NLR>5 were more likely to suffer from hypoalbuminaemia (P-level 5 at

baseline and before cycle 2 of chemotherapy (n=21; cohort 2) and (3) NLR>5 at baseline with normalisation of NLR⩽5 before cycle 2 of chemotherapy (n=21; cohort 3). Patients with

normalisation of NLR before cycle 2 of chemotherapy (cohort 3) had an improved PFS of 5.8 months (95% CI 4.1–7.5) compared with patients without normalisation of NLR pre-cycle 2 (cohort 2;

median PFS 3.7 months; 95% CI 0.6–6.8 months; P-level 0.012; Figure 3A). Normalisation of NLR improved median OS from 9.4 months (cohort 2; 95% CI 3.2–15.5) to 12.1 months (cohort 3; 95% CI

7.3–16.8) in patients with a persistently elevated NLR, although this did not reach statistical significance (P-level 0.053; Figure 3B). Patients with normalised NLR before cycle 2 of

chemotherapy (cohort 3) did not have significantly different median PFS (5.8 months (95% CI 4.1–7.5) vs 8.0 months (95% CI 6.9–9.0); P-level 0.37) or OS (12.1 months (95% CI 7.3–16.8) vs

18.3 months (95% CI 16.2–20.4); P-level 0.77) in comparison with patients with NLR⩽5 before chemotherapy commencement (cohort 1; Figures 3A and B). Normalisation of NLR before cycle 2 of

chemotherapy was not performed in the validation cohort, as there was >10% of missing data for this patient group.

Changes in PFS (A) and OS (B) with normalisation of NLR in training cohort of mCRC treated with chemotherapy.

This is the first study, to our knowledge, to describe the use of NLR in a non-selected unresectable mCRC setting for patients receiving first-line palliative chemotherapy to provide useful

information regarding prognostication, and the data have been validated in an independent community-based cohort. These results support the use of NLR as a marker of systemic inflammatory

response and as an independent predictor of clinical benefit, progression and survival in patients receiving chemotherapy for mCRC. An NLR cutoff >5 was able to identify a subset of patients

least likely to respond to chemotherapy (40 vs 16%) and those at higher risk of progression and death (HR 1.6 and 1.7, respectively). A cutoff score of 5 was chosen on the basis of previous

studies (Halazun et al, 2008, 2009; Kishi et al, 2009) and this represents a simple measurement to use in clinical practice, although other cutoffs have been used (Duffy et al, 2006; Cho et

al, 2009). This identifies ∼30% of CRC patients with a raised NLR receiving first-line chemotherapy in both cohorts and associated with shorter survival of up to 8 months. These results are

highly clinically relevant in this increasingly common malignancy.

In addition, evidence for significantly improved outcomes with normalisation of NLR after the first cycle is promising for possible manipulation of the systemic inflammatory response through

targeted anti-inflammatory mediators such as IL-6 blocking antibodies. If the use of NLR and normalistion of NLR after cycle 1 are confirmed, this would provide additional prognostic

information for clinicians at an earlier time point before conventional staging with computed tomography scans and potentially identify a proportion of patients in whom further treatment may

be futile. For example, in the training cohort, there was NLR normalisation after one cycle of chemotherapy in 50% (21 out of 42) of evaluable patients, which resulted in a 2-month PFS

improvement (5.8 vs 3.7 months) compared with patients without NLR normalisation. These data will permit not only retrospective evaluations of established large cohorts with known outcome

data to corroborate these findings but also to undertake correlation with molecular characteristics, such as microsatellite instability and B-raf mutations, which are associated with worse

cancer outcomes.

The strengths in our training cohort were that patient data were retrospectively analysed from robust prospectively collected data through entry into clinical trials. As the patients were

eligible for enrolment in a clinical trial, it is highly unlikely that the elevated NLR was due to other active inflammatory diseases or infection or were requiring high doses of steroids;

however, these issues should be specifically assessed in future studies. Other independent predictive variables identified from the training cohort, such as performance status, use of

combination chemotherapy and hypoalbuminaemia, have also been reported from previous studies and strengthens the case for this cohort being representative of a palliative mCRC population.

The median OS in both cohorts (15.3 and 16.8 months in training and validation cohorts, respectively) are shorter than those reported using modern combination chemotherapy regimens, which

have median OS upwards of 24 months. However, a significant proportion of the patients in both cohorts received single-agent chemotherapy, with patients enrolled in chemotherapy trials from

as early as 1999. There were also significant baseline differences in the types of chemotherapy regimens between the Australian and Canadian cohorts. In the Canadian cohort, up to 29% of

patients did not have the type of chemotherapy specified, which may account for some of the survival difference between the two cohorts. The validation cohort in this study failed to

identify performance status as an independent prognosticator, which, although surprising, may reflect the community-based origins of this group. However, in both cohorts, the proportion of

patients with NLR>5 was surprisingly consistent between the two cohorts (29 and 31%). In spite of differences between the cohorts, NLR remained an independent prognosticator and may reflect

that it is an even more robust and accurate prognosticator than performance status alone. The heterogeneity of treatment regimens used could be criticised; however, this is probably more

reflective of day-to-day clinical practice.

The NLR is a simple, readily available and robust laboratory variable. Other authors have advocated the use of GPS or a modified GPS, based on albumin and CRP levels, and validated its use

as a prognostic variable particularly in the pre-operative setting. Two studies have reported the use of GPS in patients receiving chemotherapy for mCRC and gastro-oesophageal malignancies

(Crumley et al, 2008; Ishizuka et al, 2009). However, this assessment is complicated by the requirement for an additional blood test to measure CRP levels, which may not be readily available

as was in the case of both our training and validation sets. The NLR, as a continuous variable, may also be a more accurate and dynamic variable reflecting acute changes in the inflammatory

state of a patient rather than GPS, which is applied as a static, categorical variable. The NLR and GPS have not been compared in the same population in CRC patients, and this comparison

should be undertaken to discern whether these two indices are overlapping or additive as indicators of cancer-associated inflammation. In CRC, the use of NLR has previously been confirmed as

an independent prognostic factor in a cohort of patients with liver-only colorectal metastases, the majority of whom proceeded to hepatic resection post chemotherapy (Kishi et al, 2009).

Although this is an important subset of patients with mCRC, these patients would have been highly selected for surgical intervention and not representative of the majority of patients with

mCRC. The findings in our study are not only consistent with this earlier report but also supports the use of NLR in a more generalised patient population receiving first-line chemotherapy

both in a clinical trial and community setting. Although elevated NLR was correlated with the presence of hypoalbuminaemia and elevated alkaline phosphatase in this study (Table 4), other

prognostic variables, such as performance status, site or extent of disease, were relatively well-balanced between the high- and low-NLR groups, suggesting that NLR provides additional

information than these other variables. The association of both raised NLR and hypoalbuminaemia is likely because of its role as a marker of systemic inflammation. The reasons for the

correlation between alkaline phosphatase and NLR are unclear. Alkaline phosphatase may be a more accurate marker of the extent of liver involvement or indirectly related to systemic

inflammation. The NLR has also been previously shown to independently predict outcomes in non-malignant disease, such as post-ST-segment elevation myocardial infarction (Núñez et al, 2008)

and percutaneous coronary intervention (Duffy et al, 2006) in which the systemic inflammation response has been implicated as a major contributing factor. This adds credibility for the use

of NLR as a potential biomarker of the systemic inflammatory response.

In recent years, there have been significant developments and discoveries in cancer genomics. The development of gene-expression-based arrays or examining germline single-nucleotide

polymorphisms for defining prognosis or predicting response to therapy has limited clinical application even in the two most common malignancies, lung and breast cancers (Hartman et al,

2010; Subramanian and Simon, 2010). For example, Wacholder et al (2010) discovered that the inclusion of 10 common breast cancer genetic variants only modestly improved the performance of

existing risk-assessment models in >11 000 patients, with little change in the predicted breast-cancer risk among most women, using currently available genetic information. These tests are

also expensive and confined to use in developed countries, with limited application in under-resourced communities. A useful biomarker needs to be not only accurate and reproducible but also

easily accessible. The prognostic importance of the systemic reaction to tumours has been relatively ignored in the quest for tumour-based molecular assessments of outcome. These data will

encourage a re-evaluation of that approach.

These results have highlighted the use of a potential clinical biomarker of systemic inflammatory response in predicting clinically meaningful outcomes in two independent cohorts. In

addition, results of the study have also confirmed the importance of a chronic systemic inflammatory response influencing clinical outcomes in patients with mCRC. Validation of these results

in larger patient populations will allow many potential applications in the treatment of mCRC, a major cause of morbidity worldwide. Clinical applications include (1) prognostication and

in-patient stratification in clinical trials, (2) as a marker of response to chemotherapy treatment and, more excitingly, (3) in identifying patients for possible interventions with

anti-inflammatory mediators. The results of this study, we believe, strongly support the use of NLR in these settings, and more importantly, as a dynamic marker of interactions among tumour,

host and the systemic inflammatory response.

This paper was modified 12 months after initial publication to switch to Creative Commons licence terms, as noted at publication

Arieta O, Michel Ortega RM, Villaneuva-Rodriguez G, Serna-Thome MG, Flores-Estrada D, Diaz-Romero C, Rodriguez CM, Martinez L, Sanchez-Lara K (2010) Association of nutritional status and

serum albumin levels with development of toxicity in patients with advanced non-small cell lung cancer treated with paclitaxel-cisplatin chemotherapy: a prospective study. BMC Cancer 10: 50

We thank Jenny Peat for assistance with statistical analysis. Wei Chua was supported by a NSW Cancer Institute Fellowship (Australia) and a Pfizer Australia Cancer Research Grant. The

research was facilitated by a Translational Colorectal Cancer Research Grant from the NSW Cancer Institute. This study was funded by the Canadian Institute of Health Research.

Sydney Cancer Centre, Concord Repatriation General Hospital, Hospital Road, Concord, 2139, New South Wales, Australia

Faculty of Medicine, University of Sydney, Sydney, New South Wales, Australia

School of Medical Sciences (Pharmacology) and Bosch Institute, University of Sydney, Sydney, New South Wales, Australia

Department of Oncology, University of Alberta, Edmonton, Alberta, Canada

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