Journal of Pain and Symptom Management
Volume 39, Issue 5 , Pages 859-871, May 2010

Cancer-Related Symptom Clusters, Eosinophils, and Survival in Hepatobiliary Cancer: An Exploratory Study

  • Jennifer L. Steel, PhD

      Affiliations

    • Department of Surgery, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
    • Corresponding Author InformationAddress correspondence to: Jennifer L. Steel, PhD, University of Pittsburgh School of Medicine; 3459 Fifth Avenue; Pittsburgh, PA 15213, USA.
    • ,
  • Kevin H. Kim, PhD

      Affiliations

    • Department of Psychology in Education, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
    • ,
  • Mary Amanda Dew, PhD

      Affiliations

    • Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
    • ,
  • Mark L. Unruh, MD

      Affiliations

    • Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
    • ,
  • Michael H. Antoni, PhD

      Affiliations

    • Department of Psychology, University of Miami, Miami, Florida, USA
    • ,
  • Marion C. Olek, MS, MPH

      Affiliations

    • University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania, USA
    • ,
  • David A. Geller, MD

      Affiliations

    • Department of Surgery, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
    • ,
  • Brian I. Carr, MD, PhD, FRCP

      Affiliations

    • Department of Surgery, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
    • ,
  • Lisa H. Butterfield, PhD

      Affiliations

    • Department of Surgery, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
    • Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
    • Department of Immunology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
    • ,
  • T. Clark Gamblin, MD

      Affiliations

    • Department of Surgery, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA

Accepted 16 October 2009.

Article Outline

Abstract 

Context

The study of symptom clusters is gaining increased attention in the field of oncology in an attempt to improve the quality of life of patients diagnosed with cancer.

Objectives

The aims of the present study were to 1) determine the prevalence and distribution of pain, fatigue, and symptoms of depression and their covariation as a cluster in people with hepatobiliary carcinoma (HBC), 2) characterize how variation in each individual symptom and/or their covariation as a cluster are associated with changes in immunity, and 3) determine if the symptom clusters, and associated biomarkers, are related to survival in people diagnosed with HBC.

Methods

Two hundred six participants diagnosed with HBC completed a battery of standardized questionnaires measuring cancer-related symptoms. Peripheral blood leukocytes were measured at diagnosis and at three- and six-month follow-ups. Survival was measured from the date of diagnosis to death.

Results

Cancer-related symptoms were prevalent and two-step hierarchical cluster analyses yielded three symptom clusters. High levels of pain, fatigue, and depression were found to be associated with elevated eosinophil percentages (F[1,78]=3.1, P=0.05) at three- and six-month follow-up using repeated-measures analysis of variance. Using multivariate latent growth curve modeling, pain was the primary symptom associated with elevated eosinophil percentages between diagnosis and six months (z=2.24, P=0.05). Using Cox regression, vascular invasion and age were negatively associated with survival (Chi-square=21.6, P=0.03). While stratifying for vascular invasion, Kaplan-Meier survival analysis was performed, and eosinophil levels above the median for the sample were found to be related to increased survival in patients with and without vascular invasion (Breslow Chi-square=4.9, P=0.03). Symptom clusters did not mediate the relationship between eosinophils and survival.

Conclusion

Cancer-related symptoms, particularly pain and depression, were associated with increased percentages of eosinophils. The presence of symptoms may reflect tumor cell death and be indicative of response to treatment, or other processes, in patients with HBC.

Key Words: Symptom cluster, immunity, cancer, pain, depression, fatigue, hepatobiliary cancer

 

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Introduction 

The National Cancer Institute (NCI) State-of-the-Science Consensus Statement reported that the three most prevalent and undertreated cancer-related symptoms are pain, fatigue, and depression.1 In a clinical context, it is well established that these symptoms co-occur but are treated independently. Although the concept of “symptom clusters” has been used in other areas of medicine, this conceptual framework has only been recently introduced to the field of oncology.2, 3, 4, 5, 6 Dodd and colleagues2 defined a symptom cluster as “three or more concurrent symptoms that are related to each other.” In the present study, we examined the prevalence and distribution of the three most common cancer-related symptoms in patients with hepatobiliary carcinoma (HBC). A growing body of research suggests that these cancer-related symptoms, at a molecular level, may have shared underlying biological mechanisms—the cytokine immunological model.5, 6

Pain has been previously described as an evolutionarily adaptive constellation of responses that enhance survival of the host.7 Proinflammatory cytokines have been found to facilitate inflammatory and neuropathic pain.7 Spinal glial cells (astrocytes and microglia) when activated not only lead to increased levels of pain but also release proinflammatory cytokines within the central nervous system.8, 9, 10 Furthermore, the exogenous administration of proinflammatory cytokines facilitates the induction of pain, and agents that antagonize proinflammatory responses have been shown to block pain.11

In regard to fatigue, cytokines act as autocrine or paracrine growth factor for neoplastic tissue that results in fatigue.12 In other disease states, such as multiple sclerosis, fatigue has been found to be associated with elevations in interferon-gamma (IFN-γ) and tumor necrosis factor-alpha (TNF-α). In patients with acute myelogenous leukemia or myelodysplastic syndrome, fatigue was found to be associated with elevations in interleukin (IL)-6, IL-1, and TNF-α levels in the serum. Bower and colleagues13 also recently found that fatigue was associated with higher levels of serum IL-1 receptor antagonist, soluble TNF receptor II, and neopterin in women diagnosed with breast cancer. Anemia, which is commonly associated with fatigue, has been shown to be associated with blunting of erythropoietin response and cytokine changes, including elevations in IL-1, IL-6, and TNF-α, which suppress erythropoiesis.14

Finally, abnormal secretion of IL-1 and IFN-γ, as well as IL-2 and IL-6, has been observed in people who report depressive symptoms.15, 16, 17, 18 According to Raison and Miller,19 changes in these cytokines may contribute to depressive symptoms in several ways, including 1) causing alterations in metabolism of monoamines, such as norepinephrine, serotonin, and dopamine, 2) activating the hypothalamic-pituitary-adrenal axis and stimulating corticotrophin-releasing hormone,20 3) leading to the resistance of nervous, endocrine, and immune system tissues to circulating glucocorticoid hormones through direct inhibitory effects on the expression or function of glucocorticoid receptors,21 and 4) reducing L-tryptophan and induction of enzyme indolamine 2,3 dioxygensase, which breaks down tryptophan into kynurenine and inhibits immunity.22, 23, 24, 25 Suarez and colleagues26 found that, even while controlling for age, race, alcohol use, and body mass index, mild to moderate depression was associated with monocyte-associated (CD14) expression of IL-1β, TNF-α, IL-8, and monocyte chemotactic protein-1 after in vitro lipopolysaccharide stimulation of undiluted whole blood. Finally, it is also well established that the introduction of exogenous IFN-α can lead to depression.27

Although the cytokine-immunological theory has not been empirically tested, a plethora of evidence has accumulated regarding the association between cancer-related symptom and cytokines from research investigating the underlying biological mechanisms of “sickness behavior;”5, 6 administration of exogenous cytokines, and subsequent induction of symptoms,27 and the reduction of these symptoms with cytokine antagonists across a variety of disease states.28 A separate but related literature regarding the role of tumor-associated tissue eosinophilia (TATE) and tumor-associated blood eosinophilia (TABE) has lead to important advances in the treatment of cancer,29, 30 namely, the development of kinase inhibitors (e.g., imatinib mesylate and sorafenib).31, 32 TABE followed by treatment with kinase inhibitors has been shown to be associated with a favorable prognosis in patients with solid tumors.31, 32, 33, 34, 35, 36, 37

Eosinophils and cytokines are closely wedded. The production of eosinophils require IL-3, IL-5, and granulocyte-macrophage colony stimulating factor.38, 39 Eosinophils also produce many proinflammatory cytokines, including TNF-α and transforming growth factor-β.38, 39 Conceivably, cancer-related symptom clusters may be not only associated with changes in cytokines but also related to changes in eosinophils. Eosinophils, in the setting of neoplasia, have two primary functions: a destructive effector function that limits the growth of the tumor (e.g., gastrointestinal cancers), and an immunoregulative activity to suppress immune response and promote proliferation of tumor cells (oral squamous cell carcinoma).29

HBC is an excellent model to test the associations among cancer-related symptom clusters, immunity, and survival. Suppression of nonadaptive immunity and changes in cytokines have been found to be associated with disease progression and survival in people diagnosed with hepatocellular carcinoma, which is the most prevalent of the HBCs.40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55 The aims of the study were to prospectively determine the prevalence and distribution of pain, fatigue, and symptoms of depression and their covariation as a cluster in people with HBC; characterize how variation in each individual symptom and/or their covariation as a cluster are associated with changes in immunity; and determine if the symptom clusters, and associated biomarkers, are related to survival in people diagnosed with HBC.

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Methods 

Design 

Patients were prospectively studied from diagnosis to death. For the purposes of this study, data concerning symptoms were collected at baseline (before treatment) and at three- and six-month follow-ups.

Participants 

Two hundred six patients were recruited from the University of Pittsburgh's Liver Cancer Center between April 2002 and April 2007. Inclusion criteria for participants in this phase of the study were 1) diagnosis of HBC, 2) fluency in English, and 3) age between 18 and 85 years. Exclusion criteria were 1) participants who are too medically ill to participate in the study or had a prognosis of less than three months, and 2) patients who reported psychiatric symptoms that included psychosis or thought disorder or report of suicidal or homicidal ideation.

Instruments/Assessment 

Sociodemographic Characteristics 

A 25-item questionnaire assessing the participant's gender, age, ethnic group, educational level, marital status, residence, number of children, occupation, income, religious preference, and health care insurance was administered.

The Functional Assessment of Cancer Therapy-Hepatobiliary (FACT-Hep)56, 57 

The FACT-Hep was used to assess changes in symptoms and side effects of treatment. The FACT-Hep is a combination of the FACT-General (FACT-G), a 27-item instrument that measures four dimensions of quality of life,56 and a module with 18 additional items specific for participants with hepatobiliary disease.57 The module includes questions that pertain to symptoms of the disease and side effects of the treatment. The FACT is one of the most widely used quality of life questionnaires in clinical trials for new cancer treatments, and the FACT-G and hepatobiliary module have been demonstrated to be valid and reliable.56, 57

Single items (“I have pain” and “I have fatigue”) and the emotional well-being (EWB) subscale of the FACT-Hep were used to measure pain, fatigue, and depressive symptoms, respectively. In a subsample of patients (n=40), the single items (i.e., pain and fatigue) and subscale (i.e., EWB subscale) were found to be significantly correlated with multi-item standardized measures of these symptoms, including the Brief Pain Inventory,58 FACT-Fatigue scale,59 and the Center for Epidemiological Studies-Depression scale.60

Liver Functioning Tests and Immune System Parameters 

Each patient who is evaluated and treated at the Liver Cancer Center has weekly blood draws as part of their routine work up and treatment. The panel of laboratory tests including total bilirubin, prothrombin time, partial thromboplastin time, albumin, alkaline phosphatase, gamma-glutamyl transpeptidase, hemoglobin, hematocrit, alpha-fetoprotein, and creatine was performed at each visit. Peripheral blood leukocyte counts and percentage of distinct cell types were assessed and included lymphocyte subsets, such as monocytes, eosinophils, neutrophils, and basophils. The lymphocyte subsets, in particular, were collected to assess neutropenia. Although these laboratories were not originally collected for the purposes of this study, because of the resurgence in interest in TATE and TABE and the link between eosinophils and cytokines, the study of these lymphocyte subsets and cancer-related symptoms was undertaken.

Data Analyses 

Using SPSS.v16 (Chicago, IL), the data were entered and verified. Variables were examined to assess the distribution, linearity, and where appropriate, reliability and internal consistency of scales. The response scale for these single items, and the EWB subscale, is as follows: 0=not at all; 1=a little bit; 2=somewhat; 3=quite a bit; and 4=very much. A higher score reflected a greater frequency of that symptom in the last week for the single item. However, a higher EWB subscale score reflects a higher level of EWB. To determine whether pain, fatigue, and depressive symptoms clustered in a unique pattern, a two-step hierarchical cluster analysis was performed on symptoms (pain, fatigue, and depression) to discover homogeneous subgroups in the data file using Schwarz's Bayesian Information Criterion on log-likelihood distance measures.61, 62, 63, 61 Multivariate latent growth curve modeling (MLGM), using structural equation modeling (SEM), was performed to examine the relationships among rate of change of symptoms or symptom clusters and immune system parameters over time (e.g., a correlation between rate of change of pain and eosinophils). Parameters from these models were estimated using maximum likelihood with Yuan-Bentler robust adjustments, which adjust for non-normal data with missing data.64 The models were evaluated using a Yuan-Bentler model Chi-square and fit indices.64 A model Chi-square in SEM is known to be biased against a sample size. Therefore, fit indices are often used to evaluate a model fit. Hu and Bentler65, 66 recommended that a model be evaluated using at least two different fit indices from two different classes. Comparative fit index (CFI)67 and root mean square error of approximation (RMSEA)68 were used. A model is considered “good” if CFI is greater than or equal to 0.95 and RMSEA is less than or equal to 0.06.

Cox regression analysis was used to test the relationship among cancer-related symptoms, immune system parameters, and survival. The variables were categorized to maintain adequate power and provide clinically meaningful groups; they are as follows: gender (male or female); age (≤50 or >50); ethnicity (Caucasian and non-Caucasian); presence or absence of hepatitis B and/or C; presence or absence of cirrhosis, size of lesion (<5 or ≥5cm); number of lesions (≤2 or >2); vascularity of lesion (hypervascular/mixed vascularity or hypovascular); and the presence or absence of vascular invasion. Kaplan-Meier survival analyses were used to test the differences between eosinophil levels in regard to survival while stratifying for vascular invasion. The data analyses were performed with the entire sample and with patients diagnosed with only hepatocellular carcinoma.

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Results 

Of the total sample (n=206), 72% were male, and the mean age was 64 years (range 22–90 years). The majority of participants were Caucasian (91%). The patients were diagnosed with hepatocellular carcinoma (84%), gallbladder carcinoma (6%), cholangiocarcinoma (5%), or neuroendocrine carcinoma (3%), and other primary tumors with liver metastases (2%). Table 1 provides details of sociodemographic and disease-specific characteristics of the sample. Table 2 provides laboratory values at diagnosis for all patients by symptom cluster.

Table 1. Sociodemographic and Disease-Specific Characteristics of Sample
VariableSymptom ClusterTotal
AsymptomaticSymptomaticFatigue
Gender (%)
Male71677572
Female29332528

Age (years)
Mean67.560.467.565
Range30–9022–8431–8822–90

Ethnicity (%)a
Caucasian91949091
African American5276
Asian/Pacific Islander3622
Hispanic1011

Diagnosis (%)
Hepatocellular88758784
Cholangio3965
Neuroendocrine1433
Gallbladder41046
Liver metastases4202

Hepatitis (%)b
B7.65.91.46
C24.117.619.228
B and C3.85.99.68

Cirrhosis (%)b57496045
Tumor size (cm)
Median56.267
Range0.9–211–220–180.5–22

Number of lesions
Median2323
Range1–61–61–61–6
Vascular invasion (%)25.327.535.624

Vascularity (%)
Hypervascular78747977
Hypovascular20151717
Mixed21046

Treatment (%)
Transarterial infusion chemotherapy80836977
90-Yttrium20173023

Survival (months)
Median9.98.87.58
Range0.4–520.9–63.50.8–91.20.4–91.2

aP<0.001.

bP<0.01.

Table 2. Baseline Laboratory Values
Laboratory Test Symptom ClusterTotal (n=206)
Normal RangesaAsymptomatic (n=80)Symptomatic (n=52)Fatigue (n=74)
Bilirubin0.1–1.2
Median 0.80.650.91.2
Range 0.2–3.90.1–3.60.2–3.51–4.1

Alpha-fetoprotein (mg/mL)0–8.9
Median 104.544140.578
Range 2–113,3442–109,6001–98,1971–113,340

Hemoglobin (g/dL)14–18
Median 12.911.712.712.5
Range 8.7–15.88.1–14.49.4–158.1–15.8

Albumin (mg/dL)3.3–5.2
Median 3.53.53.43.4
Range 2.3–4.42.4–4.321–4.52.1–4.5

Alkaline phosphatase (U/L)40–125
Median 188219199215
Range 107–102296–67999–35296–1022

Gamma-glutamyl transferase (IU/L)0–51
Median 188201176.5190
Range 23–78042–74732–75023–780

Prothrombin time (seconds)9.8–13.8
Median 12.912.912.612.9
Range 10.4–18.610.4–15.80.7–15.610.4–18.6

White blood cell (mm3)4.8–10.8
Median 5.766.25.8
Range 1.6–19.42–12.82.1–14.71.6–19.4

Lymphocyte (K/μL)0.8–3.5
Median 181.116.51
Range 4–3.10.44–0.994–430.16–2.6

Monocytes (K/μL)0.2–0.8
Median 8.1888
Range 0.8–261.1–150.9–210.8–26

Basophils (K/μL)0.01–0.20
Median 0100.15
Range 0.8–50.3–30.5–20.3–5

Eosinophils (K/μL)0.04–0.5
Median 2222.3
Range 0–90–120–120–12

aPlease note values of laboratories vary across laboratories, race, age, and gender.

Prevalence and Distribution of Symptoms and Their Covariation as a Cluster in People with HBC 

Fifty-nine percent of the patients reported pain at diagnosis and 67% reported pain at the three-month follow-up; at the six-month follow-up, 62% reported pain. At diagnosis, approximately 85% of patients reported fatigue. At the three- and six-month follow-ups, 85% and 77% of patients, respectively, reported fatigue. At diagnosis, three- and six-month follow-ups, 70%, 65%, and 62% of patients, respectively, reported feelings of anxiety and depression.

The cluster analyses yielded three clusters of patients who reported symptoms. The first cluster of patients (called asymptomatic) reported low levels of pain, low levels of fatigue, and high levels of EWB (40%). The second cluster of patients (called symptomatic) reported high levels of pain, high levels of fatigue, and low levels of EWB (25%). The third cluster (called fatigue), about 35% of the participants, reported low levels of pain, high levels of fatigue, and moderate EWB. Post hoc analyses with only patients diagnosed with hepatocellular carcinoma were performed and the same three-cluster solution was yielded.

Using repeated-measures multivariate analysis of variance (ANOVA) with dependent variables, including lymphocytes, basophils, polymorphonuclear leukocytes, and eosinophils, a significant between-group difference was found between symptom clusters and eosinophils (F[1,78]=3.1, p=0.05). The symptomatic cluster had significantly higher eosinophils at three months (3.1 vs. 2.9 and 1.6) and six months (3.6 vs. 3.0 and 1.5) when compared with the asymptomatic and the fatigued symptom cluster, respectively (Fig. 1).

Post hoc analyses were performed to determine if comorbid diseases (e.g., allergies, asthma, and autoimmune diseases) were associated with percentage of eosinophils. Using ANOVA at each time point, no significant association was found between these medical conditions and eosinophils at diagnosis (F[1,125]=0.03, p=0.86), three months (F[1,84]=0.09, p=0.76), and six months (F[1,42]=0.09, p=0.77). When using repeated-measures ANOVA, no significant differences were found between patients with medical conditions known to be associated with eosinophils and those without such conditions (F[2,27]=0.18, p=0.84) (Table 3).

Table 3. Mean (Standard Deviation) of Eosinophils for Patients with and Without Comorbid Conditions Known to Be Associated with Eosinophilsa
Time PointNo Comorbid ConditionComorbid Condition
Diagnosis (n=126)2.3 (3.2)2.1 (1.8)
3 months (n=85)2.3 (2.4)2.0 (2.2)
6 months (n=43)2.9 (3.2)2.4 (2.7)

aFor example, allergies or asthma.

Variation in Symptoms and Its Association with Changes in Immunity 

To investigate changes over time in symptom clusters and immune system parameters, MLGM was used. Three MLGMs were performed on the FACT-EWB subscale, FACT-Pain, and FACT-Fatigue. Yuan and Bentler64 adjustment for non-normal data is analogous to Satorra and Bentler. The initial values of EWB and FACT-Pain were negatively correlated (r=−0.909, z=−6.103). The rate of change of EWB and FACT-Pain was negatively correlated (r=−0.704; Yuan-Bentler χ2(11)=22.893, CFI=1.00, RMSEA=0.071, standardized root mean square residual [SRMR]=0.031). The variance of rate of change of FACT-Fatigue was not significant (i.e., within individuals, there was no change in fatigue; , z=0.021). Similarly, there was no significant variance of rate of change of FACT-Fatigue. The correlation between the rate of change of FACT-Fatigue and other variables (FACT-Pain and EWB) was, therefore, not tested. The initial values of FACT-Fatigue and FACT-Pain were positively correlated (r=0.764, z=5.129; Yuan-Bentler χ2 (13)=49.371, CFI=1.000, RMSEA=0.113, SRMR=0.042). The RMSEA is high, but it tends to provide a biased measure with a simple model. SRMR was added to assess fit, and a good fit is indicated by the fact that SRMR is below 0.08. The initial FACT-Fatigue and EWB were negatively correlated (r=−0.725, z=−3.503; Yuan-Bentler χ2 (13)=33.096, CFI=1.000, RMSEA=0.084, SRMR=0.037). The rate of change in the symptom clusters and immune system parameters was not significant, but the correlation between rate of change in symptom and immune system parameters was large, with a strong trend toward significance for the rate of change for FACT-Pain and eosinophils, z=1.914, P=0.056 (Table 4). Post hoc analyses that included only hepatocellular carcinoma patients yielded the same results, with eosinophils and pain being significantly associated over time and a trend toward significance in depressive symptoms and eosinophils.

Table 4. Rate of Change in Individual Symptoms and Immune System Parameters
ModelChi-square (degrees of freedom)CFIRMSEASRMRr (initial values)r (rate of change)
DepressionLymphocytes6.552 (11)1.0000.0000.020−0.204 (z=−0.824)0.347 (z=0.846)
Eosinophils6.441 (11)1.0000.0000.023−0.230 (z=−1.717)0.229 (z=0.907)
Basophils7.155 (11)1.0000.0000.025−0.004 (z=−0.033)0.088 (z=0.482)
Poly10.442 (11)1.0000.0000.0300.281 (z=1.586)0.008 (z=0.035)

PainLymphocytes13.232 (11)1.0000.0310.0260.107 (z=0.684)−0.047 (z=−0.142)
Eosinophils19.192 (11)1.0000.0580.0340.191 (z=2.239)a−0.211 (z=−1.914)
Basophils15.349 (11)1.0000.0420.026−0.042 (z=−0.386)0.127 (z=0.761)
Poly19.493 (11)1.0000.0590.029−0.035 (z=−0.439)−0.190 (z=−0.924)

FatigueLymphocytes8.726 (11)1.0000.0000.0180.043 (z=0.260)−0.148 (z=−0.385)
Eosinophils12.080 (11)1.0000.0210.030−0.034 (z=−0.330)0.005 (z=0.044)
Basophils9.404 (11)1.0000.0000.023−0.125 (z=−1.046)0.279 (z=1.520)
Poly11.542 (11)1.0000.0150.028−0.063 (z=−0.615)−0.071 (z=−0.234)

Poly=polymorphonuclear leukocytes.

aP<0.05.

Associations Between Symptom Clusters and Associated Biomarkers and Survival 

Kaplan-Meier analyses were performed to investigate the role of eosinophils on survival. Kaplan-Meier survival analyses were then performed after stratifying for vascular invasion, which is consistently found to be associated with poor prognosis. Peripheral blood eosinophils (above and below the median=2) were found to be significantly associated with survival (Breslow Chi-square=4.9, P=0.03). For patients who had vascular invasion and a median eosinophil count less than the median for the sample, a shorter survival (4.3 months median survival; 95% confidence interval [CI]=3.3-5.3) was found when compared with patients who had eosinophils greater than the median for the sample (7.5 median months; 95% CI=5, 10). The same pattern of results was found with patients without vascular invasion, in that patients who had eosinophil percentages less than the median for the sample, shorter survival (15 median months; 95% CI=0.1, 30.8) was found when compared with patients who had eosinophil percentages higher than the median for the sample (21.5 median months; 95% CI=11.4-31.6) (Fig. 2, Fig. 3).

Cox regression was used to adjust for and examine the contribution of sociodemographic, disease-specific, symptom clusters, and eosinophils in regard to overall survival of people diagnosed with HBC. The model remained significant (Chi-square=21.1, P=0.03) with vascular invasion (P=0.003), age (P=0.05), and eosinophils (P=0.03) significantly contributing to survival (P=0.01) (Table 5).

Table 5. Cox Regression Analysis of Predictors of Survival
VariableB (SE)WaldP-levelExp(B)95% CI
LowerUpper
Diagnosis 1.6410.65
0.675 (0.562)1.4410.231.9630.6535.905
0.559 (0.858)0.4240.521.7480.3259.388
1.084 (1.194)0.8250.362.9580.28530.685

Gender0.582 (0.322)3.2590.071.7890.9513.364

Age−0.833 (0.432)3.7250.050.4350.1871.013

Ethnicity−0.085 (0.463)0.0340.850.9180.3712.274

Cirrhosis−0.145 (0.341)0.1800.670.8650.4441.688

Tumor size0.295 (0.313)0.8900.351.3430.7282.479

Vascular invasion0.818 (0.273)8.9890.0032.2661.3273.868

Vascularity 3.6900.16
−0.878 (0.601)2.1350.140.4160.1281.349
−0.409 (0.665)0.3790.540.6640.1802.444
Eosinophils0.547 (0.249)4.8220.031.7271.0602.813

Symptom cluster 0.5290.77
0.190 (0.293)0.4190.521.2090.6812.146
0.208 (0.342)0.3720.541.2320.6302.408

SE=standard error; Exp=exponential; B = Beta.

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Discussion 

Consistent with the NCI consensus statement, pain, fatigue, and depression independently, as well as a symptom cluster, were found to be prevalent in people diagnosed with HBC, with approximately 25% of patients experiencing high levels of pain, depression, and fatigue, and another 35% of patients reporting persistent fatigue. Overall, the three symptoms were reported in 62%–85% of patients from diagnosis to six-month follow-up.

Although research has been conducted regarding the association of cancer-related symptoms and cytokines, no study has investigated the association between cancer-related symptoms and eosinophils, which may have a significant role in the context of cancer. The high level of cancer-related symptoms and association with eosinophils could not be accounted for by comorbid diseases that are established as being associated with eosinophilia (e.g., allergy and asthma).

Prospectively, pain was associated with higher percentages of eosinophils both at three- and six-month follow-ups. Eosinophils are multifunctional leukocytes that are involved in numerous inflammatory processes across disease types. Eosinophils, independent of other subtypes of leukocytes, can be recruited from the circulation into inflammatory areas of the body and modulate immune responses through various mechanisms, including antigen presentation and release of cytokines IL-2, IL-4, IL-5, IL-10, IL-12, IL-13, RANTES, and eotaxin-1. Activation and recruitment of eosinophils may (regulated upon activation, normal T cell expressed and secreted) regulate vascular permeability and smooth muscle constriction. In addition, eosinophils may serve as a major effector cell, inducing tissue damage by releasing toxic granule proteins.

Our findings are consistent with reports of hypereosinophilia outside the context of cancer. Eosinophilia has been associated with upper quadrant pain and fatigue67 and is hypothesized to be associated with necrotic cell death, particularly during periods of nutrient, hypoxic, and oxidant stress.29 Although a trend toward significance was found in regard to depressive symptoms and eosinophilia over time, fatigue was not found to be associated with eosinophil percentage secondary to the lack of interindividual variability in the report of fatigue over time.

The link between pain and eosinophils is believed to result from a cascade of events, possibly including increased tissue temperature caused by the release of histamines, which stimulate pain-sensing neurons. This may be followed by an increase in capillary permeability, resulting in the migration of leukocytes and macrophages from the circulatory system to the damaged tissue. As a result of the increased leukocytes and macrophages migrating into the tissue, edema and white blood cell body remnants, which increase the cellular pressure, may result in the sensation of pain.

In the setting of cancer, pain is often thought to be associated with tumor burden, which may also be indicative of decreased survival. However, post hoc analyses were performed and found that pain at diagnosis, as well as at three- and six-month follow-ups, was not related to tumor size or survival. Prior studies have found that elevations in eosinophils were associated with cirrhosis.69, 70, 71 However, post hoc analyses revealed a lack of association between cirrhosis and eosinophil levels in the present study.

Tumor-associated blood eosinophilia in the present study, and previous studies of solid organ tumors, is consistent with a favorable prognosis for people diagnosed with HBC. Although the pathogenesis of eosinophilia is not well understood, necrosis of the tumor has been hypothesized as a possible etiology of eosinophilia.29 Although TATE and TABE often occur independently, TABE is more often observed in advanced disease or metastatic disease.31, 32, 33, 34, 35, 36, 37

Eosinophilia has been observed in a number of disease processes, most notably allergy and parasitic infection;29, 30 therefore, the elevations in eosinophils could be related to comorbid diseases rather than the tumor cell death. Although random assignment, which would control for the influence of comorbid medical conditions that may be correlated with eosinophilia, was not the design of this study, post hoc analyses found no association between eosinophil levels and comorbid disease processes.

Although this is a rather new area of investigation, we did not expect that other leukocyte subsets would necessarily be associated with symptoms or survival. Although basophils are associated with inflammatory reactions, these granulocytes are specifically associated with allergic reactions or exoparasitic infections and, therefore, did not expect elevations or associations in this subset. Although we did expect neutrophils, and in particular, neutropenia, to be associated with fatigue, secondary to the lack of variability over time and between clusters, an association could not be detected.

A limitation of the present study included the single-item measures of pain, fatigue, and depression used for the purposes of this study. However, in a subsample of patients, these single items were found to be highly correlated with multidimensional instruments, such as the Center for Epidemiological Studies-Depression scale, Brief Pain Inventory, and the FACT-Fatigue scale.

Although we did assess other symptoms and side effects of treatment (e.g., nausea and vomiting, itching, fevers), the frequency of these symptoms was low (<10%), and, thus, we did not include these symptoms in the analyses. Future research should include multidimensional instruments designed to measure each symptom to better understand the “multiplicative” or catalytic effects of symptoms on one another and the use of multilevel factor analysis.3

The present study provides preliminary support regarding the co-occurrence of cancer-related symptoms and the association among these symptoms, biomarkers, and disease progression. However, cellular infiltration of eosinophils (e.g., TATE) should also be studied in patients with HBC to provide further evidence of the role of tumor-associated tissue eosinophilia in disease progression. Furthermore, the cytokine-immunological model of cancer-related symptoms warrants testing based on the results of this study. Understanding the link between cancer-related symptoms, immunity, and disease progression may contribute to the development of pharmacological interventions to facilitate the management of cancer-related symptoms and potentially slow disease progression in solid tumor cancers.

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 This study was funded by grants from the American Cancer Society, the Pittsburgh Mind Body Center (National Institutes of Health grants HL065111, HL065112, HL076852, and HL076858), and the National Cancer Institute (5K07CA118576).

PII: S0885-3924(10)00142-9

doi:10.1016/j.jpainsymman.2009.09.019

Journal of Pain and Symptom Management
Volume 39, Issue 5 , Pages 859-871, May 2010

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