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. 2019 Jul 17;21(5):458–465. doi: 10.1177/1099800419863160

Influence of Inflammatory and Oxidative Stress Pathways on Longitudinal Symptom Experiences in Children With Leukemia

Marilyn J Hockenberry 1,✉, Wei Pan 1, Michael E Scheurer 2,3, Mary C Hooke 4, Olga Taylor 2,3, Kari Koerner 5, David Montgomery 5, Susan Whitman 5, Pauline Mitby 6, Ida Moore 5
PMCID: PMC6854429  PMID: 31315444

Abstract

Purpose:

The purpose of this study was to explore the influence of oxidative stress (F2-isoprostanes) and inflammatory (interleukin [IL]-8) biomarkers on symptom trajectories during the first 18 months of childhood leukemia treatment.

Method:

A repeated-measures design was used to evaluate symptoms experienced by 218 children during treatment. A symptom cluster (fatigue, pain, and nausea) was explored over four time periods: initiation of post-induction therapy, 4 and 8 months into post-induction therapy, and the beginning of maintenance therapy (12 months postinduction). F2-isoprostanes and IL-8 were evaluated in cerebrospinal fluid (CSF) samples collected at baseline (diagnosis) and then at the four time periods. The longitudinal relationships of these biomarkers with the symptom cluster were examined using the longitudinal parallel process.

Results:

Pain and fatigue levels were highest during the post-induction phases of treatment and decreased slightly during maintenance therapy, while nausea scores were relatively stable. Even in the later phases of treatment, children continued to experience symptoms. CSF levels of the biomarkers increased during the post-induction phases of treatment. Early increases in the biomarkers were associated with more severe symptoms during the same period; patients who had increased biomarkers over time also experienced more severe symptoms over time.

Conclusions:

Findings reveal that children experienced symptoms throughout the course of leukemia treatment and support hypothesized longitudinal relationships of oxidative stress and inflammatory biomarkers with symptom severity. Activation of the biomarker pathways during treatment may explain underlying mechanisms of symptom experiences and identify which children are at risk for severe symptoms.

Keywords: childhood leukemia, symptom trajectory, oxidative stress biomarker, inflammatory biomarker, longitudinal parallel process

Childhood leukemia treatment has resulted in survival rates exceeding 80% over the last three decades (Hunger et al., 2012; Tasian, Loh, & Hunger, 2015). The treatment intensity that is required for cure, however, comes with numerous side effects that are often overlooked. Fatigue, pain, nausea, depression, sleep disturbances, mental exhaustion, and changes in activity are the most common treatment-related symptoms and can profoundly impact children’s lives. Up to 80% of children with cancer experience at least one symptom during treatment, but on average, children experience more than 10 symptoms during therapy (Baggott, Cooper, Marina, Matthay, & Miaskowski, 2012). Treatment-related pain, nausea, and fatigue (Hedstrom, Haglund, Skolin, & von Essen, 2003) were the most common symptoms in a group of 121 children with cancer. Physical symptoms were common (prevalence >35%) in a group of 160 children with cancer and included lack of energy, pain, drowsiness, nausea, cough, and lack of appetite (Collins et al., 2000). Parents of cancer patients and health-care professionals reported by proxy that cancer-related fatigue was a frequent symptom that children experienced during treatment (Gibson, Garnett, Richardson, Edwards, & Sepion, 2005). In a study of health-related quality of life (HRQOL) in 61 children following myelosuppressive chemotherapy, children experienced an average of 10.6 separate symptoms, the most common of which were lack of energy, pain, feeling drowsy, nausea, and feeling sad and irritable (Baggott et al., 2010). Those with poorer functional status and higher symptom burden had significant decreases in HRQOL. In a recent published study by the present research team, children with leukemia, aged 3–18 years, experienced sleep disturbance and nausea that exhibited limited change over time. Fatigue, pain, and depression decreased over four time periods; however, none of the symptoms completely resolved over time (Hockenberry et al., 2017).

The majority of research in childhood cancer symptoms has been among heterogeneous groupings of children with different pediatric oncology diagnoses (Hinds et al., 2007). This heterogeneity limits the ability to tailor inventions, as specific diagnoses and treatment modalities are likely associated with differing symptom profiles. Understanding of the underlying mechanisms associated with symptom severity is limited; nor is it clear why significant variations in symptomology and severity occur across patients even when disease and treatment are similar. Based on our prior work, in the present study, we focused on selected inflammatory and oxidative stress responses during the most intensive phase of childhood leukemia treatment, which potentially influence symptom toxicities (Caron et al., 2009; Hockenberry et al., 2014, 2015; Moore et al., 2015, 2018; Rodgers et al., 2016; Stenzel et al., 2010).

Inflammatory pathways clear an organism of the cause of cell injury (e.g., microorganisms, toxins) and the consequences of injury (e.g., necrotic cells, tissues). Inflammation is controlled by a host of extracellular mediators and regulators, including cytokines, which act as key modulators of inflammation (Turner, Nedjai, Hurst, & Pennington, 2014). Interleukin (IL)-8, or chemokine (C-X-C motif) ligand 8, is a critical inflammatory mediator that is elevated in many chronic inflammatory conditions found in adults; important examples include psoriasis, adult respiratory syndrome, chronic obstructive pulmonary disease, diabetes, chronic liver disease, polyarthritis, and asthma. Investigators report that in various cancers, upregulation of IL-8 is often correlated with disease activity (Donnelly et al., 1993; Koch et al., 1991; Nakamura, Yoshimura, McElvaney, & Crystal, 1992; Qiao et al., 2016; Zimmermann et al., 2011). Increased levels of inflammatory biomarkers have also been observed in children with numerous illnesses such as juvenile idiopathic arthritis (Sznurkowska et al., 2018) and acute kidney injury (Greenberg et al., 2018) and in children who are maltreated (Coelho, Viola, Walss-Bass, Brietzke, & Grassi-Oliveira, 2014). IL-8 also recently showed promise as a biomarker for acute child and adolescent psychopathology (Gariup et al., 2015).

Oxidative stress refers to cellular injury and degeneration caused by reactive oxygen species (ROS). ROS are reactive because they act as an electron acceptor and “steal” electrons from other molecules. These compounds damage cell structures, especially lipids. Compared to other organs, the brain is particularly vulnerable to oxidative stress due to limited antioxidant capacity, greater energy requirements, and higher lipid content (Floyd, 1999). Methotrexate, a drug given by multiple routes (intravenous, oral, and intrathecal) and commonly used for acute lymphoblastic leukemia (ALL) treatment, decreases antioxidant defense, resulting in increased oxidative stress. In prior research, we reported evidence of methotrexate-induced oxidative stress in the brain (Hockenberry et al., 2014). ROS have been linked to the pathophysiology of numerous disorders including traumatic brain injury, ischemia-reperfusion injury, atherosclerosis, inflammatory diseases, cancer, adriamycin-induced cardiomyopathy, neurodegenerative conditions, and age-related functional decline (Hannun, 1996; Liou, Clark, Henshall, Yin, & Chen, 2003; Margaill, Plotkine, & Lerouet, 2005; Minuz, Fava, & Cominacini, 2006; Montine et al., 2005; Pellegrini-Giampietro, Cherici, Alesiani, Carla, & Moroni, 1990; Pratico, MY Lee, Trojanowski, Rokach, & Fitzgerald, 1998; Pyne-Geithman et al., 2005; Shohami, Beit-Yannai, Horowitz, & Kohen, 1997; Weber, 1994). In two published studies, researchers evaluated an oxidative stress measure, total antioxidant capacity (TAC), in children with cancer. Papageorgiou et al. (2005) evaluated TAC in 20 children with cancer at the time of diagnosis and during the first three cycles of chemotherapy and reported significant decreases in TAC over time, suggesting an oxidative stress additive effect that could impact response to treatment, disease course, and prognosis. In another study, of 13 children with leukemia or various solid tumors, Mazor, Abucoider, Meyerstein, and Kapelushnik (2008) found that TAC was impaired in both groups; however, they observed higher levels of oxidative stress in the leukemia group.

The purpose of the present study was to explore the influence of inflammatory and oxidative stress biomarkers on specific symptom trajectories during the first 18 months of treatment for childhood leukemia. We measured inflammatory and oxidative stress biomarkers in cerebrospinal fluid (CSF) samples during diagnostic and therapeutic lumbar punctures at baseline and four subsequent time points. Our hypothesis was that increased levels of inflammatory and oxidative stress biomarkers would be associated with more severe symptom experiences during ALL treatment.

The conceptual framework for this study (Figure 1) proposed that the physiologic responses to childhood leukemia treatment involve activation of inflammatory and oxidative stress pathways (measured by CSF biomarkers), which influence the number and severity of symptoms patients experience during therapy. Chemotherapy agents commonly used for childhood leukemia (e.g., methotrexate) can increase ROS production as a result of altered folate metabolism and decreased antioxidant enzyme production. These chemotherapy agents are also associated with an inflammatory response that induces cytokine release. A key factor underlying the organizational framework for this study is that individual phenotypic susceptibility to oxidative stress and inflammatory responses can contribute to subsequent clinical outcomes and symptom severity.

Figure 1.

Figure 1.

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Conceptual framework. Physiologic responses to childhood leukemia treatment involve activation of inflammatory and oxidative stress pathways, which influence the number and severity of symptoms patients experience during therapy. IL-8 = interleukin-8.

Method

Design

We used a repeated-measures, longitudinal design to evaluate associations between CSF biomarkers of the oxidative stress and inflammatory pathways and treatment-related symptoms in 218 children and adolescents, 3−18 years of age, receiving treatment for ALL. Specifically, we explored symptom trajectories over four time periods: initiation of post-induction therapy, 4 and 8 months into post-induction therapy, and the beginning of maintenance/continuation therapy (12 months postinduction). Symptoms measured included fatigue, pain, and nausea. We measured oxidative stress (F2-isoprostanes[F2-IsoPs]) and inflammatory (IL-8) biomarkers that were in CSF samples obtained at diagnosis (baseline) and at four time points during ALL treatment (Times 1 through 4).

Settings and Sample

The settings for this study were four major childhood cancer treatment centers in the United States. Combined, the four centers diagnose more than 200 children and adolescents with leukemia who meet the study eligibility criteria each year. Parents and their children were initially asked to consent to our obtaining and saving a CSF sample from the diagnostic lumbar puncture and subsequently prior to the end of the first phase of treatment (induction) through full participation in the study. Children with a cognitive disability identified preleukemia diagnosis were ineligible for study enrollment. We obtained consent from a parent or legal guardian and assent from children (age 7 years and older) and adolescents.

The sample size for this study was 218. All participants provided complete information about sociodemographic variables (age, sex, and race/ethnicity) and leukemia risk category. Leukemia risk category is determined by specific factors (biologic and genetic features of the leukemia cells, age, gender, and initial response to therapy) known to predict survival or relapse.

Leukemia Treatment

Children were treated on an ALL protocol. Induction therapy (1 month) included weekly treatment with vincristine and daunomycin (for high-risk ALL), a corticosteroid and a dose of Peg-asparaginase, and two intrathecal methotrexate (IT MTX) treatments (Days 1 and 29). Post-induction therapy (6–8 months) involved several courses of treatment that included asparaginase, high- or intermediate-dose intravenous (IV) MTX (depending on ALL protocol assignment), vincristine, doxorubicin, corticosteroid, cytarabine, and mercaptopurine. During post-induction treatment, IT MTX was given on Day 1 of each 12-week cycle. Throughout therapy, study participants received central nervous system prophylaxis with standardized doses of IT MTX based on age. Maintenance/continuation therapy began 6–8 months after the end of induction therapy. Time intervals for each phase of treatment were influenced by the side effects patients experienced and their recovery from low blood counts.

Data Collection

We obtained symptom assessments using standardized instruments during routine follow-up clinic visits. We chose study time points to evaluate the most intensive phases of therapy postinduction and collected CSF samples in conjunction with the lumbar punctures for IT MTX treatment that coincided most closely with symptom data collection.

Measures

English and Spanish versions of the instruments were available on tablet PCs. We used standardized measures to evaluate fatigue, pain, and nausea. For consistency across symptoms, we used parent proxy for the 3−6-year-old age-group and self-report for children ≥7 years of age. Specifically, we measured fatigue with three instruments designed for different age ranges: the Child Fatigue Scale (Hinds et al., 2010) for ages 7−12 years, the Adolescent Fatigue Scale (Mandrell et al., 2011) for ages 13−18 years, and the Parent Fatigue Scale (Hockenberry et al., 2003) for ages 3−6 years. We measured nausea using a Visual Analogue Scale (VAS) with a metric of 0–100, with 100 indicating most nausea possible. To measure pain, we used the Wong-Baker Faces Scale, a 6-point VAS with higher scores indicating worse pain. Then, we converted all the scores from the three instruments to comparable, standardized T scores. All symptoms were reported over the past 2−4 weeks. Symptom data collection required less than 25 min to complete at each time point. For all symptom scales, a higher score indicates greater symptom severity. We used the scores to determine symptom severity and classify symptoms into different profiles labeled as mild, moderate, or severe.

Because the three symptom measures (fatigue, pain, and nausea) were highly correlated with each other at each time point (Hockenberry et al., 2017), we combined them into a symptom cluster through exploratory factor analysis with maximum likelihood estimation, which returned a one-factor solution with significant factor loadings from .35 to .88 and at least 54% of variance explained at each time point (Table 1).

Table 1.

Factor Loadings and Variance Explained in One-Factor Solution for Symptom Cluster at Each Time Point.

Symptom Factor Loadings—Symptom Cluster
Time 1 Time 2 Time 3 Time 4
Fatigue .76 .71 .73 .88
Pain .73 .64 .72 .49
Nausea .53 .59 .56 .35
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Note. The variances explained by the factor at Times 1, 2, 3, and 4 were 63.2%, 61.3%, 63.2%, and 54.0%, respectively.

CSF cytokine measures

We obtained 2 ml of CSF in conjunction with diagnostic and therapeutic lumbar punctures as part of clinical treatment. CSF samples were placed on ice and processed within 2 hr of collection to prevent reactions such as autooxidation. Samples were centrifuged at 4 °C to remove any particulates, aliquoted, frozen at −80 °C, and shipped on dry ice to the core research lab for this study. IL-8 was measured using a cytokine-specific AlphaLISA immunoassay kit (Perkin-Elmer) according to the manufacturer’s instructions. This kit is a luminescent, homogenous (no wash) proximity-based assay with extremely high sensitivity and a wide (4 logs) dynamic range. Each sample was assayed in triplicate wells of 96-well, half-area white plates. Briefly, each well contained 10 µl of blank (artificial CSF with Prionex), sample, or standard and 20 µl of acceptor bead solution. Once the acceptor bead solution was added to the plate, the plate was sealed and incubated at room temperature for 1 hr. After 1 hr and under low light, 20 µl of donor bead solution was added to each well and incubated at room temperature for 30 min. The plate seal was removed under low light, and plates were read at 615 nm wavelength using a Perkin-Elmer Envision multilabel plate reader equipped with the Alpha option. Cytokine concentrations were determined by comparison to a standard curve of recombinant cytokine, using a linear regression logistic equation, and expressed as pg/ml.

CSF oxidative stress measures

CSF samples for the oxidative stress biomarker were obtained at the same time as the cytokine samples, processed, and stored as described above. F2-IsoPs are a sensitive noninvasive index of oxidative stress in vivo. We measured them using a competitive enzyme-linked immunoassay kit according to instructions (Cayman Chemical). The kit was designed for extraction-free analysis of bodily fluids including CSF and has been validated for detection of 8-F2-IsoP in urine, plasma, and other sample matrices. Briefly, each well contained 50 μl of sample or standard, 50 μl of 8-F2-IsoP AChE tracer, and 50 μl of 8-F2-IsoP antiserum. Plates were incubated for 18 hr at 4 °C. Wells were then washed 5 times with 200 μl of wash buffer at room temperature. Ellman’s reagent (200 μl) was added to each well. A total activity well contained only Ellman’s reagent and tracer added just before the second incubation. A reaction blank contained only Ellman’s reagent, and a nonspecific binding well contained only tracer, added before incubation, and Ellman’s reagent. Plates were covered and placed on an orbital shaker at 400 rpm for 90−120 min at room temperature. The plate was then read on a Perkin Elmer Envision Multilabel plate reader at 405 nm. Average replicates of each standard (0.08−500 pg/ml) were used to generate the standard curve. CSF samples were analyzed in triplicate to confirm reproducibility.

Statistical Analyses

Descriptive statistics were computed for sample characteristics (sociodemographics and leukemia risk category) and the baseline biomarker information as well as the longitudinal variables were measured from Time 1 to Time 4.

There were intermittent missing data (3.7−28.4%) across all the time points among the longitudinal variables. Fortunately, Little’s (1988) missing completely at random (MCAR) test showed that the missing data were MCAR, χ2(1236) = 1,223.82, p = .592, and did not have a negative impact on parameter estimation (Little & Rubin, 2002). Thus, we performed an expectation-maximization algorithm for missing data imputation on the two baseline biomarker measures. We controlled for baseline biomarker measurements, sociodemographic variables, and leukemia risk category as covariates in the main analysis. Meanwhile, the missing longitudinal data on the biomarkers and the symptom measures were automatically handled in multilevel modeling within the two-step longitudinal parallel-process technique described below.

To test the hypothesis that participants who had increased F2-IsoPs and IL-8 biomarkers during the post-induction phase of treatment would experience higher severity of symptoms, longitudinal relationships were examined from the initiation of post-induction therapy (Time 1) to 4 and 8 months into post-induction therapy (Times 2 and 3) to the beginning of maintenance/continuation therapy (Time 4), controlling for sociodemographic variables, leukemia risk category, and the baseline biomarkers. The longitudinal nature was expressed by the growth parameters—the initial status (or intercept) and the rate of change (or slope)—of the longitudinal variables.

More specifically, the longitudinal parallel process (LPP; Cheong, Mackinnon, & Khoo, 2003), a process of two-step modeling, was used to test hypothesized longitudinal relationships. In the first step, the intercept and slope of each of the longitudinal variables were estimated separately by using univariate growth models with multilevel modeling in SAS Proc Mixed (SAS Institute, 2011). Such separate modeling helps to manage the unbalanced, intermittent longitudinal missing data across the different longitudinal variables and to control for covariates, such as the sociodemographic and other baseline variables, in the estimation of the intercepts and slopes of the longitudinal variables so that they do not need to be controlled again in the second step. Due to its modeling flexibility, researchers are increasingly reporting the use of LPP for studies with longitudinal measurements (Dowling, Johnson, Gleason, & Jagust, 2015; Petrou, Demerouti, & Schaufeli, 2018; Sousa, Kwok, Schmiege, & West, 2014; Winkler et al., 2017; Yiotaldiotariotam et al., 2013).

In the second step, structural equation modeling was employed for testing the longitudinal relationships between the biomarkers and symptom cluster using IBM SPSS Amos (Arbuckle, 2010). The structural equation model fit was evaluated using the following model-fit indices: χ2 of the estimated model (χ2), goodness of fit index (GFI), normed fit index (NFI), incremental fit index (IFI), relative fit index (RFI), comparative fit index (CFI), and root mean square error of approximation (RMSEA). A nonsignificant χ2 value (p > .05) suggests a good overall model fit to the data. For GFI, NFI, IFI, RFI, and CFI, values larger than .90 indicate that the model provides a good fit to the data, whereas RMSEA should be below .06. We used fit indices and criteria that are commonly recommended in the literature (Hu & Bentler, 1999; Kline, 2005).

Results

Descriptive Statistics

Table 2 shows that, among 218 participants in the study sample, the average age was 8.38 years (SD = 4.48). Sex was not evenly distributed across boys and girls, and race/ethnicity was even more unbalanced, with nearly half of the participants being Hispanic (43.6%) and the other half being other race/ethnicity groups. As for leukemia risk category, the distribution was not even or in any ranking order, with more participants at very high risk or average/standard risk than at high risk or low risk. The means of the two biomarkers at baseline were 8.38 pg/ml (SD = 10.42) for F2-IsoPs and 142.77 pg/ml (SD = 112.20) for IL-8.

Table 2.

Sample Characteristics and Mean Baseline Biomarker Levels.

Characteristic Mean (SD) or n (%)
Age (years), mean (SD) 8.38 (4.48)
Sex (male), n (%) 122 (56.0)
Race/ethnicity, n (%)
 Hispanic 95 (43.6)
 Non-Hispanic White 87 (39.9)
 Non-Hispanic Black 18 (8.3)
 Non-Hispanic Other 18 (8.3)
Leukemia risk category, n (%)
 Very high 71 (32.6)
 High 47 (21.6)
 Average/standard 76 (34.9)
 Low 24 (11.0)
Biomarker at baseline (pg/ml), mean (SD)
 F2-isoprostanes 8.38 (10.42)
 IL-8 142.77 (112.20)
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Note. N = 218. IL = interleukin. SD = standard deviation.

The means and SDs of the longitudinal variables across the four time points (Table 3) demonstrate that fatigue and pain decreased over time, whereas nausea remained almost the same from Time 1 to Time 3 before it decreased from Time 3 to Time 4. Both biomarkers decreased greatly from Time 1 to Time 2 and then remained almost the same for the remaining time points. The trend of the decrease in biomarkers coinciding with that of symptoms can be attributed to the hypothesized positive relationships between biomarkers and symptoms.

Table 3.

Means (M) and Standard Deviation (SDs) of the Longitudinal Variables by Time.

Variable (Scale Range) Time 1 Time 2 Time 3 Time 4
n M (SD) n M (SD) n M (SD) n M (SD)
Symptoms
 Fatigue (20−80)a 195 53.92 (9.98) 210 49.97 (10.36) 196 48.20 (9.10) 193 46.70 (8.09)
 Pain (1−6)b 193 2.57 (2.22) 207 1.66 (2.15) 194 1.32 (1.60) 191 1.29 (1.80)
 Nausea (0−100)c 192 14.33 (21.53) 206 14.06 (21.43) 192 15.52 (21.35) 192 12.05 (19.14)
Biomarkers (pg/ml)
 F2-isoprostanes 195 7.49 (8.77) 209 6.36 (5.17) 196 6.58 (7.71) 194 6.66 (8.42)
 IL-8 193 113.54 (103.85) 206 84.85 (74.74) 196 73.15 (53.75) 193 87.22 (90.37)
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Note. N = 218. IL = interleukin.

aMeasured using the Child Fatigue Scale (Hinds et al., 2010), the Adolescent Fatigue Scale (Mandrell et al., 2011), and the Parent Fatigue Scale (Hockenberry et al., 2003). All the scores from the three instruments were converted to comparable, standardized T scores.

bMeasured using the Wong-Baker Faces Scale (Wong & Baker, 1988).

cMeasured using a Visual Analog Scale (Scott & Huskisson, 1979; Revill, Robinson, Rosen, & Hogg, 1976).

Longitudinal Relationships

The model-fit indices for the initially hypothesized longitudinal relationships were satisfactory, χ2(6) = 6.12, p = .409; GFI = .99, NFI = .99, IFI = 1.00, RFI = .97, CFI = 1.00; and RMSEA = .010, but one path coefficient (i.e., from the intercept of F2-IsoPs to the slope of symptom cluster) was not significant at the α = .10 level. To obtain a parsimonious model, we removed the nonsignificant path coefficient in the final model. Figure 2 shows the final parsimonious model with the standardized estimates of path coefficients. The model-fit indices for the parsimonious model improved and all remained satisfactory, χ2(7) = 6.13, p = .525; GFI = .99, NFI = .99, IFI = 1.00, RFI = .97, CFI = 1.00; and RMSEA < .001.

Figure 2.

Figure 2.

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The final parsimonious model of the longitudinal relationships between biomarkers and symptom cluster in terms of their growth parameters (intercepts and slopes) with standardized estimates. IL = interleukin. † p < .10. *p < .05. **p < .01. ***p < .001.

From Figure 2, we can conclude that positive, longitudinal relationships did exist between biomarkers and the symptom cluster in the data. Specifically, patients with an initially high level of both biomarkers also experienced higher severity of symptoms initially (β = .24, p < .001 for F2-IsoPs and β = .21, p < .001 for IL-8). Patients who had increased levels of biomarkers from baseline to the post-induction phase of treatment also experienced higher severity of symptoms over time (β = .14, p = .035 for F2-IsoPs and β = .30, p = .015 for IL-8). In addition, patients with an initially high level of IL-8 experienced marginally higher severity of symptoms over time (β = .22, p = .082).

Discussion

Pain and fatigue levels were highest during the post-induction phase of treatment (Times 1 and 2) and then decreased slightly during maintenance therapy. This finding suggests that children experience the most intense symptoms during the first few months of leukemia therapy. However, children in the present study continued to experience symptoms even at the later phase of treatment. Nausea scores were in the mild range and relatively stable at all four measurement time points, which was consistent with the type of chemotherapy the children were receiving.

We found higher levels of IL-8 and F2-IsoPs in the participants’ CSF samples during the most intensive phase of therapy, which suggests that inflammatory and oxidative stress pathways are activated in response to ALL treatment. Previous studies specifically evaluating biomarkers for oxidative stress also confirmed activation of the oxidative stress pathway during childhood cancer treatment (Mazor, Abucoider, Meyerstein, & Kapelushnik, 2008; Papageorgiou et al., 2005). Evidence suggests that the oxidative stress pathway is interactive and may result in significant cellular toxicity. Our findings thus support the influence of pediatric leukemia chemotherapy drugs on ROS production as by-products of cellular toxicity. As hypothesized, we also found that higher levels of these biomarkers were associated with more severe symptoms, adding to the existing evidence that activation of the oxidative stress pathway can induce numerous somatic symptoms (Mazor et al., 2008; Papageorgiou et al., 2005).

These collective findings suggest that early increases in inflammatory and oxidative stress biomarkers may help to identify which children are at greater risk of experiencing treatment-related symptoms and to develop novel interventions to mitigate symptom severity. As research continues to identify and describe biomarkers of oxidative stress biological, it may eventually be possible to determine individual susceptibility to oxidative stress and its influence on clinical outcomes and symptom severity. Findings from this work have the potential to allow individualized treatment focused on curing disease. Nursing interventions to minimize symptom toxicities can then be focused on the specific trajectories unique to each child with cancer.

Our findings describe only two biomarkers and three symptom measures. Future research should be conducted that examines biomarkers of other pathways and symptom measures among children undergoing treatment for ALL. In addition, our findings were based on a convenience sample from only four specific health systems. A larger randomized-cluster multisite study on the topic in future research would be desirable to provide more comprehensive evidence for informing future nursing research and practice.

Footnotes

Author Contributions: Marilyn J. Hockenberry contributed to conception, design, and interpretation; drafted the article critically; revised the article; gave final approval; and agreed to be accountable for all aspects of work ensuring integrity and accuracy. Wei Pan contributed to design, analysis, and interpretation; drafted the article; critically revised the article; gave final approval; and agreed to be accountable for all aspects of work ensuring integrity and accuracy. Michael E. Scheurer contributed to conception, design, and interpretation; critically revised the article; gave final approval; and agreed to be accountable for all aspects of work ensuring integrity and accuracy. Mary C. Hooke contributed to conception, design, and interpretation; critically revised the article; gave final approval; and agreed to be accountable for all aspects of work ensuring integrity and accuracy. Olga Taylor contributed to conception, acquisition, and interpretation; critically revised the article; gave final approval; and agreed to be accountable for all aspects of work ensuring integrity and accuracy. David Montgomery, Susan Whitman, and Pauline Mitby contributed to acquisition and interpretation, critically revised the article, gave final approval, and agreed to be accountable for all aspects of work ensuring integrity and accuracy. Ida Moore contributed to conception, design, and interpretation; drafted the article; critically revised the article; gave final approval; and agreed to be accountable for all aspects of work ensuring integrity and accuracy.

Declaration of Conflicting Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding: The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the National Institutes of Health (R01CA1693398).

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