Title:

Evaluation of the red cell distribution width (RDW) as a biomarker of early mortality in

hepatocellular carcinoma

Authors:

Carlo Smirne,1 M.D., Ph.D., Glenda Grossi,1 M.D., David J. Pinato,2 MRes MRCP Ph.D., Michela

E. Burlone,1 M.D., Francesco A. Mauri,3 M.D. MRCPath, Adam Januszewski,4 B.Sc. MBBS MRCP

., Alberto Oldani,5 M.D., Rosalba Minisini,1 Ph.D., Rohini Sharma,2 MBBS FRACP Ph.D, Mario

Pirisi,1 M.D.

Affiliations:

1. Department of Translational Medicine, Università del Piemonte Orientale, via Solaroli 17, 28100

Novara, Italy

2. Division of Experimental Medicine, Hammersmith Campus of Imperial College London, Du

Cane Road, W120HS London, UK.

3. Department of Histopathology, Hammersmith Campus of Imperial College London, Du Cane

Road, W120HS London, UK.

4. Department of Oncology, Hammersmith Campus of Imperial College London, Du Cane Road,

W120HS London, UK.

5. Department of Health Sciences, Università del Piemonte Orientale, via Solaroli 17, 28100

Novara, Italy

Address for correspondence:

Prof. Mario Pirisi, Dipartimento di Medicina Traslazionale, Via Solaroli 17, 28100 Novara, Italy. e-

mail: mario.pirisi@med.unipmn.it, tel. +3903213733847, fax +3903213733600

1

Electronic word count: 2620

Abstract word count: 250

Source(s) of support in the form of grants, equipment, drugs: none

2

Abstract

Background: The red cell distribution width (RDW) is a biomarker of early mortality across

various disease states.

Aims: To verify whether the RDW may improve our ability to estimate survival of patients with

hepatocellular carcinoma (HCC).

Methods: The red cell distribution width measured at diagnosis was analyzed in relationship to

mortality by any cause both in a retrospective training cohort (N.=208; 97% with cirrhosis), and in

an independent prospectively collected validation cohort (N.= 106) of patients with HCC. Based on

Cox proportional hazards modelling, a prognostic index was constructed and validated.

Results: Median survival in the training cohort was 1,026 days in patients with RDW 14.6% vs.

282 days in patients with RDW >14.6%, hazard ratio 0.43 (95%CI: 0.31-0-60), p<0.0001. At

multivariate analysis, the RDW remained an independent predictor of survival (p=0.0003), together

with age, tumor size, serum alpha-fetoprotein, Child-Pugh-Turcotte score and Barcelona Clinic

Liver Cancer (BCLC) stage. In the validation cohort, median survival was 868 days in patients with

RDW 14.6%, while it was 340 days in patients with RDW >14.6%, hazard ratio 0.28 (95%CI:

0.17-0.47), p<0.0001. Applying to the validation dataset the prognostic index derived from the

training dataset, the ability of the model to discriminate fairly the survival probabilities of patients

was confirmed (Harrell’s C = 0.769). RDW permitted sub-stratification of patients through BCLC

stages A to C, but not D.

Conclusions: The RDW is a novel, reproducible, prospectively validated predictor of survival in

patients with HCC. Consideration should be given for its use in clinical practice.

3

Key Words: liver cancer, inflammation, prognosis, red cell distribution width

4

Introduction

Liver cancer, the major histologic subtype of which is Hhepatocellular carcinoma (HCC), the most

frequent primary liver malignancy, ranks fifth in men among the most frequentlycommonly

diagnosed types of cancer, being however the second most frequent cause of death attributable to

cancermost lethal solid tumor.; Iin females, the corresponding figures areHCC ranks seventh by

incidence and sixth by mortality, respectively [1]. The usual precursors of HCC are fibrosis and

cirrhosis [2], both the result of chronic inflammation in the liver parenchyma [3]. In industrialized

countries, therefore, the prognosis of HCC is largely affected by the degree of alteration of liver

functionliver dysfunction due secondary to cirrhosis. On the other hand,Equally, there is substantial

evidence to provenow suggests that cancer progression is dependent on the complex interaction

between the tumor and the host inflammatory response [4], and HCC makes no exception to this

rule [5-8].

The red cell distribution width (RDW) is one of the parameters in the full blood count generated

automatically by cell counters to quantitate anisocytosis (i.e. the variability of the size of the

circulating erythrocytes). Recently, RDW has emerged as a consistent and strong predictor of

overall and disease-specific mortality in middle-age and older adults [9], probably due to the ability

of RDW to reflect the systemic release of cytokines, such as IL-6, TNF-alpha and hepcidin across a

wide range of disease states [10, 11]. A second major factor leading to significant variation in

erythrocyte size is oxidative stress [12].

In the Barcelona Clinic Liver Cancer (BCLC) staging system of HCC, that has been adopted by

major scientific societies clinical practice guidelines to stratify liver cancer patients [13, 14], the

prognostic value of systemic inflammation is reflected by constitutional symptoms, the evaluation

of which is unfortunately largely subjective and more apparent in advanced stage disease. Our aim

was therefore to evaluate whether adding RDW to factors of demonstrated prognostic significance 5

in HCC may objectively, reproducibly and inexpensively improve our ability to estimate survival of

these patients at the time of diagnosis.

Material and methods

Patients., By scanning an electronic database including all patients aged ≥18 years who were

consecutively diagnosed with HCC at an Academic Center in Northern Italy (Novara) from

November 15, 2003 to September 19, 2013 we retrieved data from N.=238 patient records. One-

hundred ninety-eight patients satisfied either a) the radiological or combined criteria for the

diagnosis of HCC as indicated in the document summarizing the conclusions of the Barcelona-2000

EASL conference [15], or b) the criteria later recommended by the European Association for the

Study of the Liver (EASL) / European Organisation for Research and Treatment of Cancer clinical

practice guideline [14]. For all these patients, biopsy confirmation was not required. Pathological

conformation of diagnosis, based on the definitions of the International Consensus Group for

Hepatocellular Neoplasia [16], was obtained in the remaining 40 patients. Thirty patients whose

RDW value was not available at tumor diagnosis were excluded. Therefore, the training cohort

consisted of 208 patients. The primary treatment modalities chosen for these patients were curative

for 81 patients (39%; N.=3 liver transplantation, N.=20 surgical resection and N.=58 radiofrequency

ablation), non-curative for 69 patients (33%; N.=34 transarterial chemoembolization, N. =4

transarterial radioembolization, and N.=31 sorafenib), best supportive care for 56 patients (27%),

and unknown in 2 patients (1%). Further 11 patients (5%) received a liver transplant after having

been treated initially with a different treatment modality. The status of all these patients was known

and documented up to (and including) the censor date of 22 February 2015.

For external validation, survival analyses were then performed in an independent cohort of 106

consecutive patients aged ≥18 years with similar clinicopathological characteristics prospectively

recruited with the same inclusion criteria by another Academic Center (Hammersmith Hospital,

6

London, UK) from December 10th 2010 to January 8th 2014. None of these patients received a liver

transplant. In this latter population, the censor date was February 15, 2015.

An informed consent was obtained by all participants to the study, that was conducted in strict

adherence to the principles of the Declaration of Helsinki.

Collection of data. In both studied cohorts, overall survival was calculated from the time of

radiological diagnosis to the time of death or last follow-up, with the date of HCC diagnosis

referring to the date of the diagnostic imaging procedure, even when pathological confirmation was

obtained at a later date. Radiologic tumor characteristics in either patient cohorts were derived from

the diagnostic CT or MRI scan, always evaluated by an experienced radiologist.

All the clinic-pathological variables derived from the full blood count recorded in this study,

including RDW, were performed within one month from the HCC diagnostic imaging in ISO-

certified central Laboratories.

Cirrhosis was either diagnosed histologically or - when a liver biopsy was not available- according

to clinical, laboratory, radiologic and/or endoscopic criteria. In cirrhotic patients, Child-Pugh-

Turcotte score was recorded to describe liver function.

Tumor staging at diagnosis followed the BCLC criteria; patients were thus stratified into four

classes: A, very early or early stage; B, intermediate stage; C, advanced stage; D, end-stage. [17].

Statistical analysis. Statistical analysis was performed using MedCalc version 14.12.0 (MedCalc

Software, Mariakerke, Belgium) and Stata version 13.1(StataCorp LP, College Station, Texas,

United States of America). For continuous variables, the variability of data around the central value

was presented as medians (interquartile range), whilst categorical variables were presented as

frequencies (percentage of the total).

7

A univariate screen to identify potentially significant predictors of overall survival in HCC patients

was conducted in the training cohort. To compare group differences in survival, the log-rank test or

(when appropriate) the log-rank test for trend were used; hazard ratios (HR) and 95% confidence

intervals were also calculated. Kaplan-Meier survival estimates were determined for each covariate.

The selection of the optimal cut-off value(s) to identify groups was based on biological plausibility

[18] and was preceded by inspection of the survival curves obtained by generating a group variable

with four groups based on quartiles. In the case of RDW, the cut-off value chosen (>14.6%)

coincided with the upper limit of normal reference range [19] and the median value observed in the

training cohort.

To analyze the effects of multiple covariates on survival, the Cox proportional hazard regression

model was used. For those retained in the model, regression coefficients, hazard ratios with their

95%CI and respective p values are presented. To avoid to incur in the multicollinearity

phenomenon, as expected being the Child-Pugh-Turcotte class part of the criteria on which the

BCLC staging system is based, the Child-Pugh-Turcotte score, not the Child-Pugh-Turcotte class,

was included among the predictor variables. Eight variables (three categorical: gender, BCLC stage,

and Child-Pugh-Turcotte score; five continuous: age at diagnosis, major tumor size, alpha-

fetoprotein, haemoglobin, and RDW) were entered in the model backwards, thus obtaining a

baseline cumulative hazard H0(t), with the cumulative hazard and survival at mean of all covariates

in the model. The baseline cumulative hazard was used to calculate the survival probability S(t) for

any case at time t, by applying the regression coefficient b obtained in the training cohort to

individual values the corresponding variable in the validation cohort:

S(t) = exp(-H0(t) × PI) (equation [1])

where PI is a prognostic index:

PI = x1b1 + x2b2 + x3b3 + … + xkbk (equation [2])

The prognostic index was applied without any adjustment of the coefficients to the derivation

dataset; since no missing data were allowed, N.=96/106 patients in the validation cohort were 8

included in the analysis. To evaluate the discriminatory power and the predictive accuracy of the

models, we calculated the Harrell’s C coefficients for a) the model on the derivation dataset in

which the independent variable was the prognostic index, and b) the models on the training plus the

derivation datasets in which the independent variables were either the BCLC stage alone, or the

BCLC stage plus RDW.

The level of statistical significance chosen was <0.05 and was two-tailed.

9

Results

Patient characteristics and survival

The main clinical and demographic characteristics of patients in the training and validation cohorts

are reported in Table 1. In panel A, Figure 1 presents the Kaplan-Meier estimates of survival in both

cohorts. Although the median length of follow-up was significantly shorter in the validation (400

days, interquartile range 189-652) in comparison to the training cohort (549 days, interquartile

range 236-1131) (p = 0.002), median survival was not statistically different among the two groups

(629 days, 95% CI 504-782 vs. 633 days, 95% CI 458-768; p=0.51 at the log-rank test). The HR

(training vs validation cohort) was 1.10, 95% CI 0.81-1.50.

Prognostic significance of RDW both in the training and the validation cohort

In the training cohort, the median RDW value was 14.6% (interquartile range, 13.7-15.8); 107/208

patients (51.4%) had a RDW 14.6%, and 101/208 (48.6%) a RDW >14.6%. In the validation

cohort, the median RDW value was 15.3% (interquartile range, 13.9-16.7); 42/106 patients (39.6%)

had a RDW 14.6% (group C), and 64/106 (60.4%) a RDW >14.6% (group D). A RDW >14.6%

predicted patients’ survival both in the training and the validation cohort (Figure 1, panels B-C).

The median survival in the training cohort was 1,026 days in the group with RDW 14.6%

(95%CI: 740-1,337) vs. 282 days (95%CI: 212-465) in the group with RDW >14.6%, HR = 0.43

(95%CI: 0.31-0.60), p <0.0001. Moreover, median survival declined progressively at each RDW

quartile, being 1,376 days (95%CI: 921-1,975; reference) for patients belonging to the first quartile,

671 days (95%CI: 504-1,127; HR = 1.73, 95%CI: 1.15-2.58) for patients belonging to the second

quartile, 381 days (95%CI: 264-782; HR = 2.25, 95%CI: 1.49-3.40) for patients belonging to the

third quartile, and 216 days (95%CI: 152-465; HR = 3.70, 95%CI: 2.27-6.05) for patients belonging

to the fourth quartile (p<0.0001 at the log-rank test for trend). In the validation cohort, the median

survival was 868 days in the group with RDW 14.6% (95%CI: 736-1,358), vs. 340 days (95%CI:

278-565) in the group with RDW >14.6%, HR = 0.28 (95%CI: 0.17-0.47), p <0.0001.10

Fourteen patients out of 208 in the training cohort (6.7%) underwent liver transplantation. Among

these patients, RDW at diagnosis was 14.3% (interquartile range, 13.5-15.6), not statistically

different from the median value observed among the 194 patients who did not receive a transplant

(14.6%; interquartile range, 13.7-15.9, p = 0.548). Excluding these patients from survival analysis,

the prognostic value of a RDW value ≤14.7% remained intact (median survival 919 days, 95%CI

629-1,188 vs. 264, 95%CI 204-430; HR = 0.42, 95%CI 0.30-0.60, p <0.0001).

Survival rates at 1, 2 and 3 years in the training cohort.

In the training cohort, the survival rates at 1-, 2- and 3-years were 64%, 43% and 30%, respectively.

The corresponding figures in patients with RDW 14.6% were 79%, 57%, and 42%; in contrast,

among patients with RDW >14.6% they were 48%, 29% and 18%. The odds ratio of dying within

the first year from diagnosis for those with RDW >14.6% vs. those with RDW 14.6% was 4.2

(95%CI = 2.3-7.8).

Univariate and multivariate analysis of factors associated with survival in the training cohort

Table 2 presents the univariate analysis of a selection of other factors associated with survival in the

training cohort. Age of HCC diagnosis (>70 years), haemoglobin concentration (>120 g/L), Child-

Pugh-Turcotte class, Barcelona Clinic Liver Cancer (BCLC) stage, initial treatment modality,

maximum diameter of the major nodule (>4 cm) and serum alpha-fetoprotein (AFP) concentration

(<20, 20-100 and >100 g/L) were all significantly associated with survival. The univariate analysis

of the same variables in the validation cohort is presented in Table 3.

Table 4 presents a summary of the Cox proportional hazard analysis in the subset of patients in the

training cohort who did not undergo liver transplantation (N.=194). RDW was confirmed to be an

independent predictor of survival, together with age at diagnosis, BCLC stage, Child-Pugh-Turcotte

score, tumor size and serum AFP. Haemoglobin and gender were not retained in the model,

meaning that it did not contribute significantly to it. The same analysis was repeated in the 11

validation cohort: RDW was confirmed to be an independent predictor of survival (HR 1.39, 95%CI

1.20-1.60; p<0.0001), together with BCLC stage, Child-Pugh-Turcotte class and tumor size. Age at

diagnosis, alpha-fetoprotein, haemoglobin and gender were not retained in this second model.

Then, for each subject in the validation cohort we calculated a prognostic index by applying the

regression coefficients shown in Table 4 to each corresponding variable, according to equation [2],

as described in Methods. Figure 2 presents the Kaplan-Meier estimates for the validation cohort,

with patients grouped according to prognostic index tertiles. Compared to the first tertile

(reference), the HR was 3.18 (95% CI: 1.74-5.81) in the second and 7.27 (95%CI: 3.50-15.1) in the

third tertile (p<0.0001 at the log-rank test for trend). Figure 3 presents a comparison between the

observed (Kaplan-Meier) and predicted survival by each tertile of the prognostic index. The

Harrell’s C coefficient of the Cox proportional regression analysis conducted on the validation

cohort and including the prognostic index as the only independent variable was = 0.769. Finally,

after merging of the two datasets (i.e., those from the training and validation cohort) used to

develop the Cox model, the Harrell’s C coefficient increased from 0.709 using the BCLC stage as

the only independent variable to 0.754 using both the BCLC stage and RDW. At a cutoff value of

14.6%, the ability to stratify patients with different survival probabilities at the log-rank test

remained through BCLC stages A (p = 0.021), B (p=0.005) and C (p=0.029) but not D (p=0.637).

Discussion

Our study shows that RDW at the time of diagnosis is an independent and reproducible predictor of

patient survival in HCC. These results need to be interpreted in light of our current knowledge on

the RDW as a prognostic factor and the relationship between systemic inflammation and the clinical

progression of liver cancer.

12

In clinical practice, RDW has been proposed to differentiate iron deficiency anemia from a

thalassemic trait [18, 19], although it has been objected that, to this aim, RDW is not sufficiently

specific to obviate the need for more focused tests [20]. More recently, RDW elevation has been

reported to predict for patients’ prognosis across a wide spectrum of benign and malignant

conditions in which systemic inflammation plays a role, such as ischemic heart disease, acute and

chronic heart failure, acute pulmonary embolism, atherosclerosis, vascular occlusive disease,

hypertension, inflammatory bowel disease, rheumatoid arthritis [21-26], as well as several solid

cancers [27-32]. RDW may therefore represent a surrogate marker of inflammation (through

inflammation-associated erythroid maturation impairment) [33], although this remains to be proven.

Interestingly, in agreement with the data presented here, RDW remains strongly associated with

mortality after correction for anemia, and even when patients with extreme RDW values or

nutritional deficiencies are excluded [9].

Our data expand the prognostic role of RDW to the clinical context of HCC with a strong rationale,

providing further evidence that the clinical progression of HCC is at least in part influenced by the

presence of an underlying inflammatory diathesis. Of all solid tumors, HCC is in fact considered

amongst the most strongly inflammation-associated cancers [34], with the inflammation-fibrosis-

carcinoma axis being paradigmatic of the relationship occurring between the tumor

microenvironment and the selection of the neoplastic clone [35]. Within the liver parenchyma, a

pro-inflammatory milieu is undeniably fundamental in promoting tumor cell proliferation and

survival and stimulating angiogenesis and metastasis. Mechanistic studies have elucidated its

permissive effects on tumor growth through the local release of a number of immune mediators

such as, for instance, interleukin 17A [36]. Furthermore, in the pathogenesis of HCC, the presence

of a subclinical systemic inflammatory response seems to represent the molecular link between the

metabolic syndrome and the risk of cancer [37]. Besides its role as factor promoting

13

hepatocarcinogenesis, systemic inflammation represents an area of growing interest in the clinical

management and prognostic assessment of HCC.

Consolidated evidence in the literature shows that an ongoing pro-inflammatory diathesis, measured

by common inflammatory markers like C-reactive protein (CRP), predicts for poorer survival

outcomes in both curative [38, 39] and non-curative [7] settings. These findings have led to the

validation of coherent prognostic models based on inflammation-related derangements in full blood

count parameters such as peripheral blood neutrophilia, lymphopaenia, hypoalbuminaemia and

elevated CRP, which have been diversely combined to generate inflammation based prognostic

scores such as the neutrophil-to-lymphocyte ratio [40, 41],the Prognostic Nutritional Index [42] and

the Inflammation Based Index, the latter having been shown to hold, amongst the others, the best

prognostic accuracy in HCC [8, 43]. Given the wide array of clinical conditions that can affect the

RDW, red blood cell anisocytosis seems well suited to both reflect the ongoing inflammatory

diathesis stemming from the underlying cirrhosis and the superimposed, cancer-driven cytokine

release. Furthermore, it has been shown that peripheral blood anisocytosis may reflect impaired red

cell generation secondary to iron, vitamin B12 or folate deficiency, among other causes. Thus, the

prognostic power of the RDW may extend beyond systemic inflammation, potentially reflecting the

presence of an overall sub-optimal health status indicating diminished capacity for systemic repair,

recovery, and defense [44]. Whether a reflection of an ongoing inflammatory diathesis or an overall

deficiency state, anisocytosis emerged in our study as a reproducible and validated prognostic

determinant in HCC, able to capture a significant piece of prognostic information not conveyed by

other, widely accepted factors including age, AFP levels, tumor size, Child-Pugh-Turcotte score,

initial treatment modality and BCLC class.

To examine the generalizability of the prediction model, external validation was conducted on a

completely independent patient cohort, collected prospectively in tertiary referral centre in UK. The

performance of the prediction model and the prognostic ability of RDW were confirmed to be good. 14

A limitation of the present study, however, is that it does not provide any new clue to the

identification of the (likely multiple) mechanisms leading to anisocytosis in patients with HCC.

Equally, our study focused only on one single RDW measurement at the time the diagnosis of HCC.

Whether RDW can reflect disease-modulating effects stemming from treatment and alter survival

probabilities as a consequence, it still unknown.

Moreover, it must be acknowledged that, to date, studies reporting RDW as a significant predictor

of survival have had little impact on clinical practice, probably because to replace or implement

existing prognostic biomarkers the RDW needs to fill gap perceived as clinically significant, and

this may not always have been the case. However, when examined across the different stages of the

BCLC system, the RDW maintained adequate discriminating ability in BCLC stages A through C,

being strongest in the intermediate stage B. Such finding is of major consequence for the clinical

applicability of the RDW as a prognostic marker, given that intermediate stage patients are a highly

heterogeneous patient population with predicted survival ranging from 14 to 45 months [45]. Being

objective, inexpensive and widely available, the RDW may qualify to implement the BCLC staging

system, although this hypothesis remains to be provend by appropriately powered studies.

Moreover, our study focused on the RDW measured at the time the diagnosis of HCC was made;

whether its change in time (for example, when a second or third line of treatment is used) may still

reflect different survival probabilities is unknown.

In conclusion, the present study suggests that the inclusion of the RDW can optimise our ability to

predict survival outcomes and refine treatment allocation in HCC, which may be of particular value

in a context such that of intermediate stage disease, where alternative treatment strategies are

available and expected survival can be widely variable.

15

Acknowledgments

The Authors are grateful to Minhaz Rahman for his contribution in data mining.

16

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Figure legends

Figure 1. Kaplan-Meier estimates of survival probabilities are presented in panel A for the entire

training (continuous line) and validation cohorts (dashed line); in panel B, for the training cohort,

dichotomized according to individual values of red cell distribution width coefficient of variation

(RDW) 14.6% (continuous line) or >14.6% (dashed line) measured at diagnosis; and in panel

C, for the validation cohort, dichotomized according to individual values of red cell distribution

width coefficient of variation (RDW) 14.6% (continuous line) or >14.6% (dashed line)

measured at diagnosis.

Figure 2. Kaplan-Meier estimates for the validation cohort, with patients grouped according to

prognostic index tertiles. The first tertile is indicated by continuous line, the second tertile: by a

dashed line and the third tertile by a dotted line.

Figure 3. Comparison between the observed and predicted survival by each tertile of the prognostic

index applied to the validation cohort.

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