Self-Reported Cancer-Related Cognitive Impairment in Patients With Breast Cancer Is Associated With Potassium Channel Gene Polymorphisms.

OBJECTIVES: To evaluate for associations of polymorphisms for potassium channel genes in patients with breast cancer who were classified as having high or low-moderate levels of cancer-related cognitive impairment (CRCI). SAMPLE & SETTING: 397 women who were scheduled to undergo surgery for breast cancer on one breast were recruited from breast care centers located in a comprehensive cancer center, two public hospitals, and four community practices. METHODS & VARIABLES: CRCI was assessed using the Attentional Function Index prior to and for six months after surgery. The attentional function classes were identified using growth mixture modeling. RESULTS: Differences between patients in the high versus low-moderate attentional function classes were evaluated. Six single nucleotide polymorphisms for potassium channel genes were associated with low-moderate class membership. IMPLICATIONS FOR NURSING: The results contribute to knowledge of the mechanisms for CRCI. These findings may lead to the identification of high-risk patients and the development of novel therapeutics.

Stress and Coping in Patients With Cancer With Depression and Sleep Disturbance.

OBJECTIVES: To evaluate for differences in global, cancer-specific, and cumulative life stress, as well as resilience and use of various coping strategies among five groups (no depression or sleep disturbance, no depression and moderate sleep disturbance, subsyndromal depression and very high sleep disturbance, moderate depression and moderate sleep disturbance [Both Moderate]; and high depression and very high sleep disturbance [Both High]). SAMPLE & SETTING: Patients (N = 1,331) receiving chemotherapy were recruited from outpatient oncology clinics. METHODS & VARIABLES: Measures of global, cancer-specific, and cumulative life stress, resilience, and coping were obtained. Differences were evaluated using parametric and nonparametric tests. RESULTS: Global and cancer-specific stress scores increased as joint profiles worsened. Both Moderate and Both High classes had cancer-specific stress scores suggestive of post-traumatic stress. Both Moderate and Both High classes reported higher occurrence rates for several stressful life events and higher use of disengagement coping. Both Moderate and Both High classes had resilience scores below the normative score for the United States. IMPLICATIONS FOR NURSING: Clinicians need to screen vulnerable patients for post-traumatic stress disorder and implement interventions to reduce stress.

Big Data in Oncology Nursing Research: State of the Science.

OBJECTIVE: To review the state of oncology nursing science as it pertains to big data. The authors aim to define and characterize big data, describe key considerations for accessing and analyzing big data, provide examples of analyses of big data in oncology nursing science, and highlight ethical considerations related to the collection and analysis of big data. DATA SOURCES: Peer-reviewed articles published by investigators specializing in oncology, nursing, and related disciplines. CONCLUSION: Big data is defined as data that are high in volume, velocity, and variety. To date, oncology nurse scientists have used big data to predict patient outcomes from clinician notes, identify distinct symptom phenotypes, and identify predictors of chemotherapy toxicity, among other applications. Although the emergence of big data and advances in computational methods provide new and exciting opportunities to advance oncology nursing science, several challenges are associated with accessing and using big data. Data security, research participant privacy, and the underrepresentation of minoritized individuals in big data are important concerns. IMPLICATIONS FOR NURSING PRACTICE: With their unique focus on the interplay between the whole person, the environment, and health, nurses bring an indispensable perspective to the interpretation and application of big data research findings. Given the increasing ubiquity of passive data collection, all nurses should be taught the definition, characteristics, applications, and limitations of big data. Nurses who are trained in big data and advanced computational methods will be poised to contribute to guidelines and policies that preserve the rights of human research participants.

Epigenetic Regulation of Inflammatory Mechanisms and a Psychological Symptom Cluster in Patients Receiving Chemotherapy.

BACKGROUND: A psychological symptom cluster is the most common cluster identified in oncology patients. Although inflammatory mechanisms are hypothesized to underlie this cluster, epigenetic contributions are unknown. OBJECTIVES: This study's purpose was to evaluate associations between the occurrence of a psychological symptom cluster and levels of DNA methylation for inflammatory genes in a heterogeneous sample of patients with cancer receiving chemotherapy. METHODS: Prior to their second or third cycle of chemotherapy, 1,071 patients reported the occurrence of 38 symptoms using the Memorial Symptom Assessment Scale. A psychological cluster was identified using exploratory factor analysis. Differential methylation analyses were performed in two independent samples using Illumina Infinium 450K and EPIC microarrays. Expression-associated CpG (eCpG) loci in the promoter region of 114 inflammatory genes on the 450K and 112 genes on the EPIC microarray were evaluated for associations with the psychological cluster. Robust rank aggregation was used to identify differentially methylated genes across both samples. Significance was assessed using a false discovery rate of 0.05 under the Benjamini-Hochberg procedure. RESULTS: Cluster of differentiation 40 ( CD40 ) was differentially methylated across both samples. All six promoter eCpGs for CD40 that were identified across both samples were hypomethylated in the psychological cluster group. CONCLUSIONS: This study is the first to suggest associations between a psychological symptom cluster and differential DNA methylation of a gene involved in tissue inflammation and cell-mediated immunity. Our findings suggest that increased CD40 expression through hypomethylation of promoter eCpG loci is involved in the occurrence of a psychological symptom cluster in patients receiving chemotherapy. These findings suggest a direction for mechanistic studies.

Gastrointestinal Symptom Cluster is Associated With Epigenetic Regulation of Lymphotoxin Beta in Oncology Patients Receiving Chemotherapy.

OBJECTIVES: While the gastrointestinal symptom cluster (GISC) is common in patients receiving chemotherapy, limited information is available on its underlying mechanism(s). Emerging evidence suggests a role for inflammatory processes through the actions of the nuclear factor kappa B (NF-κB) signaling pathway. This study evaluated for associations between a GISC and levels of DNA methylation for genes within this pathway. METHODS: Prior to their second or third cycle of chemotherapy, 1071 outpatients reported symptom occurrence using the Memorial Symptom Assessment Scale. A GISC was identified using exploratory factor analysis. Differential methylation analyses were performed in two independent samples using EPIC (n = 925) and 450K (n = 146) microarrays. Trans expression-associated CpG (eCpG) loci for 56 NF-κB signaling pathway genes were evaluated. Loci significance were assessed using an exploratory false discovery rate (FDR) of 25% for the EPIC sample. For the validation assessment using the 450K sample, significance was assessed at an unadjusted p-value of 0.05. RESULTS: For the EPIC sample, the GISC was associated with increased expression of lymphotoxin beta (LTB) at one differentially methylated trans eCpG locus (cg03171795; FDR = 0.168). This association was not validated in the 450K sample. CONCLUSIONS: This study is the first to identify an association between a GISC and epigenetic regulation of a gene that is involved in the initiation of gastrointestinal immune responses. Findings suggest that increased LTB expression by hypermethylation of a trans eCpG locus is involved in the occurrence of this cluster in patients receiving chemotherapy. LTB may be a potential therapeutic target for this common cluster.

Symptom clusters in oncology outpatients: stability and consistency across a cycle of chemotherapy.

OBJECTIVES: Improved understanding of the stability and consistency of symptom clusters across time, symptom dimensions and cancer diagnoses will lead to refinements in symptom assessments and management, and provide direction for mechanistic studies. Study purposes were to describe the occurrence, severity and distress of 38 symptoms; evaluate the stability and consistency of symptom clusters across a cycle of chemotherapy, three symptom dimensions and four distinct cancer types; and identify common and distinct symptom clusters. METHODS: Oncology outpatients (n=1329) completed the Memorial Symptom Assessment Scale prior to their next cycle of chemotherapy (T1), 1 week after chemotherapy (T2) and 2 weeks after chemotherapy (T3). Symptom clusters were identified using exploratory factor analysis using unweighted least squares. GEOMIN rotated factor loadings with absolute values ≥0.40 were considered meaningful. Clusters were stable if they were identified across each time point and/or dimension. Clusters were consistent if the same two or three symptoms with the highest factor loadings were identified across each time point and/or dimension. RESULTS: Patients reported 13.9 (±7.2) symptoms at T1, 14.0 (±7.0) at T2 and 12.2 (±6.8) at T3. Psychological, weight gain, gastrointestinal and respiratory clusters were stable across time and dimensions. Only the psychological, weight gain and respiratory clusters were consistent across time and dimensions. CONCLUSION: Given the stability of the psychological, weight gain and gastrointestinal clusters across cancer diagnoses, symptoms within these clusters need to be routinely assessed. However, respiratory and hormonal clusters are unique to specific cancer types and the symptoms within these clusters are variable.

Symptom clusters in outpatients with cancer using different dimensions of the symptom experience.

PURPOSE: Relatively few studies have evaluated for symptom clusters across multiple dimensions. It is unknown whether the symptom dimension used to create symptom clusters influences the number and types of clusters that are identified. Study purposes were to describe ratings of occurrence, severity, and distress for 38 symptoms in a heterogeneous sample of oncology patients (n = 1329) undergoing chemotherapy; identify and compare the number and types of symptom clusters based on three dimensions (i.e., occurrence, severity, and distress); and identify common and distinct clusters. METHODS: A modified version of the Memorial Symptom Assessment Scale was used to assess the occurrence, severity, and distress ratings of 38 symptoms in the week prior to patients' next cycle of chemotherapy. Symptom clusters for each dimension were identified using exploratory factor analysis. RESULTS: Patients reported an average of 13.9 (±7.2) concurrent symptoms. Lack of energy was both the most common and severe symptom while "I don't look like myself" was the most distressing. Psychological, gastrointestinal, weight gain, respiratory, and hormonal clusters were identified across all three dimensions. Findings suggest that psychological, gastrointestinal, and weight gain clusters are common while respiratory and hormonal clusters are distinct. CONCLUSIONS: Psychological, gastrointestinal, weight gain, hormonal, and respiratory clusters are stable across occurrence, severity, and distress in oncology patients receiving chemotherapy. Given the stability of these clusters and the consistency of the symptoms across dimensions, the use of a single dimension to identify these clusters may be sufficient. However, comprehensive and disease-specific inventories need to be used to identify distinct clusters.

Advances in Conceptual and Methodological Issues in Symptom Cluster Research: A 20-Year Perspective.

Two conceptual approaches are used to evaluate symptom clusters: "clustering" symptoms (ie, variable-centered analytic approach) and "clustering" patients (ie, person-centered analytic approach). However, these methods are not used consistently and conceptual clarity is needed. Given the emergence of novel methods to evaluate symptom clusters, a review of the conceptual basis for older and newer analytic methods is warranted. Therefore, this article will review the conceptual basis for symptom cluster research; compare and contrast the conceptual basis for using variable-centered versus patient-centered analytic approaches in symptom cluster research; review their strengths and weaknesses; and compare their applications in symptom cluster research.

Higher stress and symptom severity are associated with worse depressive symptom profiles in patients receiving chemotherapy.

PURPOSE: In a sample of oncology patients, identify subgroups of patients with distinct depressive symptom profiles and evaluate for differences in demographic and clinical characteristics, levels of stress and resilience, and the severity of common co-occurring symptoms. METHODS: Patients (n = 1327) had a diagnosis of breast, gastrointestinal, gynecological, or lung cancer; had received chemotherapy within the preceding four weeks; and were scheduled to receive at least two additional cycles of chemotherapy. Demographic and clinical characteristics, stress, resilience, and co-occurring symptoms were evaluated at enrollment. Depressive symptoms were evaluated using the Center for Epidemiological Studies-Depression (CES-D) scale a total of six times over two cycles of chemotherapy. Latent profile analysis (LPA) was used to identify subgroups of patients (i.e., latent classes) with distinct depressive symptom profiles using the six CES-D scores. RESULTS: Based on the findings from the LPA, 47.3% of the patients were classified as "None"; 33.6% as "Subsyndromal"; 13.8% as "Moderate"; and 5.3% as "High". Compared to None class, patients in the Subsyndromal, Moderate, and High classes had a lower functional status, a higher comorbidity burden, and a self-reported diagnosis of depression or back pain. Those patients with higher levels of depressive symptoms reported higher levels of stress, lower levels of resilience, and increased severity of co-occurring symptoms. CONCLUSIONS: Inter-individual variability in depressive symptoms was associated with demographic and clinical characteristics, multiple types of stress and levels of resilience, as well as with the increased severity of multiple co-occurring symptoms. The risk factors associated with worse depressive symptom profiles can assist clinicians to identify high risk patients and initiate more timely supportive care interventions.

Determination of Cutpoints for Symptom Burden in Oncology Patients Receiving Chemotherapy.

CONTEXT: Cutpoints can be used as a threshold for screening symptom(s) that warrant intervention(s) and for monitoring patients' responses to these interventions. OBJECTIVES: In a sample of oncology patients undergoing chemotherapy, study purposes were to determine the optimal cutpoints for low, moderate, and high symptom burden and determine if these cutpoints distinguished among the symptom groups in any demographic, clinical, and stress characteristics, as well as QOL outcomes. METHODS: Total of 1329 patients completed a modified version of the Memorial Symptom Assessment Scale (38 symptoms). Using the methodology of Serlin and colleagues, cutpoints were created using symptom occurrence rates and cancer-specific quality of life (QOL) scores. Cutpoints were validated using measures of stress and resilience and a generic measure of QOL (i.e., Medical Outcomes Study Short Form 12 (SF-12)). RESULTS: Of the 25 possible cutpoints evaluated, the optimal cutpoint, with the largest between category F statistic, was CP8,15 (Low = 0-8, Moderate = 9-15, High = 16-38 symptoms). Percentage of patients in the Low, Moderate, and High cutpoint groups were 25.3%, 36.3%, and 38.4%, respectively. Significant differences were found among the symptom burden groups in global, cancer-specific, and cumulative life stress (i.e., Low < Moderate < High) and resilience and SF-12 (i.e., Low > Moderate > High) scores. CONCLUSION: Our findings provide evidence for clinically meaningful cutpoints that can be used to guide symptom assessment and management. These cutpoints may be used to establish alert thresholds for electronic monitoring of symptoms in oncology patients.

Symptom clusters in patients receiving chemotherapy: A systematic review.

BACKGROUND AND PURPOSE: Since 2001, symptom cluster research has grown considerably. However, because multiple methodological considerations remain, ongoing synthesis of the literature is needed to identify gaps in this area of symptom science. This systematic review evaluated the progress in symptom clusters research in adults receiving primary or adjuvant chemotherapy since 2016. METHODS: Eligible studies were published in English between 1 January 2017 and 17 May 2021; evaluated for and identified symptom clusters 'de novo;' and included only adults being treated with primary or adjuvant chemotherapy. Studies were excluded if patients had advanced cancer or were receiving palliative chemotherapy; symptoms were measured after treatment; symptom clusters were pre-specified or a patient-centred analytic approach was used. For each study, symptom instrument(s); statistical methods and symptom dimension(s) used to create the clusters; whether symptoms were allowed to load on more than one factor; method used to assess for stability of symptom clusters and associations with secondary outcomes and biomarkers were extracted. RESULTS: Twenty-three studies were included. Memorial Symptom Assessment Scale was the most common instrument and exploratory factor analysis was the most common statistical method used to identify symptom clusters. Psychological, gastrointestinal, and nutritional clusters were the most commonly identified clusters. Only the psychological cluster remained relatively stable over time. Only five studies evaluated for secondary outcomes. DISCUSSION: While symptom cluster research has evolved, clear criteria to evaluate the stability of symptom clusters and standardised nomenclature for naming clusters are needed. Additional research is needed to evaluate the biological mechanism(s) for symptom clusters. PROSPERO REGISTRATION NUMBER: CRD42021240216.

Cancer-related cognitive impairment is associated with perturbations in inflammatory pathways.

Cancer-related cognitive impairment (CRCI) is a significant problem for patients receiving chemotherapy. While a growing amount of pre-clinical and clinical evidence suggests that inflammatory mechanisms underlie CRCI, no clinical studies have evaluated for associations between CRCI and changes in gene expression. Therefore, the purpose of this study was to evaluate for differentially expressed genes and perturbed inflammatory pathways across two independent samples of patients with cancer who did and did not report CRCI. The Attentional Function Index (AFI) was the self-report measure used to assess CRCI. AFI scores of <5 and of >7.5 indicate low versus high levels of cognitive function, respectively. Of the 185 patients in Sample 1, 49.2% had an AFI score of <5 and 50.8% had an AFI score of >7.5. Of the 158 patients in Sample 2, 50.6% had an AFI score of <5 and 49.4% had an AFI score of >7.5. Data from 182 patients in Sample 1 were analyzed using RNA-seq. Data from 158 patients in Sample 2 were analyzed using microarray. Twelve KEGG signaling pathways were significantly perturbed between the AFI groups, five of which were signaling pathways related to inflammatory mechanisms (e.g., cytokine-cytokine receptor interaction, tumor necrosis factor signaling). This study is the first to describe perturbations in inflammatory pathways associated with CRCI. Findings highlight the role of cytokines both in terms of cytokine-specific pathways, as well as pathways involved in cytokine production and cytokine activation. These findings have the potential to identify new targets for therapeutics and lead to the development of interventions to improve cognition in patients with cancer.

Anxiety profiles are associated with stress, resilience and symptom severity in outpatients receiving chemotherapy.

PURPOSE: The purposes of this study, in a sample of oncology patients (n = 1326) receiving chemotherapy, were to identify subgroups of patients with distinct anxiety profiles and evaluate for differences in demographic and clinical characteristics, stress and resilience measures, and severity of co-occurring symptoms (i.e., depression, sleep disturbance, attentional function, fatigue, pain). METHODS: Patients completed self-report questionnaires a total of six times over two cycles of chemotherapy. Severity of state anxiety was evaluated using the Spielberger State Anxiety Inventory and resilience was assessed using the Connor-Davidson Resilience Scale. Symptoms were assessed using the Center for Epidemiologic Studies Depression Scale, General Sleep Disturbance Scale, Lee Fatigue Scale, Attentional Function Index and Brief Pain Inventory. RESULTS: Based on the findings from the latent profile analysis that utilized the six assessments of state anxiety, 47.7% of the patients were classified as "Low," 28.3% as "Moderate," 19.5% as "High," and 4.5.% as "Very High." Anxiety levels remained relatively stable across the six timepoints. Compared to the Low class, membership in the Moderate, High, and Very High classes was associated with a number of characteristics (e.g., younger age, female gender, lower functional status, more comorbidities). Those patients with higher levels of anxiety reported higher levels of stress, lower levels of resilience, and increased severity of co-occurring symptoms. CONCLUSION: Our findings suggest that a substantial number of oncology patients may warrant referral to psychological services. Clinicians need to perform systematic assessments of anxiety, stress, and common symptoms and initiate appropriate interventions to enhance resilience and coping.

Multi-Staged Data-Integrated Multi-Omics Analysis for Symptom Science Research.

The incorporation of omics approaches into symptom science research can provide researchers with information about the molecular mechanisms that underlie symptoms. Most of the omics analyses in symptom science have used a single omics approach. Therefore, these analyses are limited by the information contained within a specific omics domain (e.g., genomics and inherited variations, transcriptomics and gene function). A multi-staged data-integrated multi-omics (MS-DIMO) analysis integrates multiple types of omics data in a single study. With this integration, a MS-DIMO analysis can provide a more comprehensive picture of the complex biological mechanisms that underlie symptoms. The results of a MS-DIMO analysis can be used to refine mechanistic hypotheses and/or discover therapeutic targets for specific symptoms. The purposes of this paper are to: (1) describe a MS-DIMO analysis using "Symptom X" as an example; (2) discuss a number of challenges associated with specific omics analyses and how a MS-DIMO analysis can address them; (3) describe the various orders of omics data that can be used in a MS-DIMO analysis; (4) describe omics analysis tools; and (5) review case exemplars of MS-DIMO analyses in symptom science. This paper provides information on how a MS-DIMO analysis can strengthen symptom science research through the prioritization of functional genes and biological processes associated with a specific symptom.