Torch-eCpG: a fast and scalable eQTM mapper for thousands of molecular phenotypes with graphical processing units.

BACKGROUND: Gene expression may be regulated by the DNA methylation of regulatory elements in cis, distal, and trans regions. One method to evaluate the relationship between DNA methylation and gene expression is the mapping of expression quantitative trait methylation (eQTM) loci (also called expression associated CpG loci, eCpG). However, no open-source tools are available to provide eQTM mapping. In addition, eQTM mapping can involve a large number of comparisons which may prevent the analyses due to limitations of computational resources. Here, we describe Torch-eCpG, an open-source tool to perform eQTM mapping that includes an optimized implementation that can use the graphical processing unit (GPU) to reduce runtime. RESULTS: We demonstrate the analyses using the tool are reproducible, up to 18 × faster using the GPU, and scale linearly with increasing methylation loci. CONCLUSIONS: Torch-eCpG is a fast, reliable, and scalable tool to perform eQTM mapping. Source code for Torch-eCpG is available at https://github.com/kordk/torch-ecpg .

Neurodegenerative disease pathways are perturbed in patients with cancer who self-report cognitive changes and anxiety: A pathway impact analysis.

BACKGROUND: Cancer-related cognitive impairment (CRCI) and anxiety co-occur in patients with cancer. Little is known about mechanisms for the co-occurrence of these two symptoms. The purposes of this secondary analysis were to evaluate for perturbed pathways associated with the co-occurrence of self-reported CRCI and anxiety in patients with low versus high levels of these two symptoms and to identify potential mechanisms for the co-occurrence of CRCI and anxiety using biological processes common across any perturbed neurodegenerative disease pathways. METHODS: Patients completed the Attentional Function Index and the Spielberger State-Trait Anxiety Inventory six times over two cycles of chemotherapy. Based on findings from a previous latent profile analysis, patients were grouped into none versus both high levels of these symptoms. Gene expression was quantified, and pathway impact analyses were performed. Signaling pathways for evaluation were defined with the Kyoto Encyclopedia of Genes and Genomes database. RESULTS: A total of 451 patients had data available for analysis. Approximately 85.0% of patients were in the none class and 15.0% were in the both high class. Pathway impact analyses identified five perturbed pathways related to neurodegenerative diseases (i.e., amyotrophic lateral sclerosis, Huntington disease, Parkinson disease, prion disease, and pathways of neurodegeneration-multiple diseases). Apoptosis, mitochondrial dysfunction, oxidative stress, and endoplasmic reticulum stress were common biological processes across these pathways. CONCLUSIONS: This study is the first to describe perturbations in neurodegenerative disease pathways associated with CRCI and anxiety in patients receiving chemotherapy. These findings provide new insights into potential targets for the development of mechanistically based interventions.

Stability and consistency of symptom clusters in younger versus older patients receiving chemotherapy.

BACKGROUND: By 2035, the number of newly diagnosed cancer cases will double and over 50% will be in older adults. Given this rapidly growing demographic, a need exists to understand how age influences oncology patients' symptom burden. The study purposes were to evaluate for differences in the occurrence, severity, and distress of 38 symptoms in younger (< 60 years) versus older (≥ 60 years) oncology patients undergoing chemotherapy and to evaluate for differences in the stability and consistency of symptom clusters across the two age groups. METHODS: A total of 1329 patients were dichotomized into the younger and older groups. Patients completed demographic and clinical questionnaires prior to the initiation of their second or third cycle of chemotherapy. A modified version of Memorial Symptom Assessment Scale was used to evaluate the occurrence, severity, and distress of 38 common symptoms associated with cancer and its treatment. Differences between the two age groups in demographic and clinical characteristics and ratings of occurrence, severity, and distress for the 38 symptoms were evaluated using parametric and nonparametric tests. Exploratory factor analyses were done within each age group to identify symptom clusters using symptom occurrence rates. RESULTS: Compared to the younger group (14.8 (± 7.0)), older adults reported a lower mean number of symptoms (12.9 (± 7.2)). Older patients experienced lower occurrence rates for almost 50% of the symptoms. Regarding symptom clusters, an eight-factor solution was selected for both age groups. Across the two age groups, the eight symptom clusters (i.e., physical and cognitive fatigue, respiratory, psychological, hormonal, chemotherapy-related toxicity, weight gain, gastrointestinal, epithelial) were stable. However, symptoms within the physical and cognitive, chemotherapy-related toxicity, and gastrointestinal clusters were not consistent across the age groups. CONCLUSIONS: To be able to provide tailored and effective symptom management interventions to older oncology patients, routine assessments of the core symptoms unique to the symptom clusters identified for this group warrants consideration. The underlying mechanism(s) for these inconsistencies in symptom burden is an important focus for future studies.

Prediction of morning fatigue severity in outpatients receiving chemotherapy: less may still be more.

INTRODUCTION: Fatigue is the most common and debilitating symptom experienced by cancer patients undergoing chemotherapy (CTX). Prediction of symptom severity can assist clinicians to identify high-risk patients and provide education to decrease symptom severity. The purpose of this study was to predict the severity of morning fatigue in the week following the administration of CTX. METHODS: Outpatients (n = 1217) completed questionnaires 1 week prior to and 1 week following administration of CTX. Morning fatigue was measured using the Lee Fatigue Scale (LFS). Separate prediction models for morning fatigue severity were created using 157 demographic, clinical, symptom, and psychosocial adjustment characteristics and either morning fatigue scores or individual fatigue item scores. Prediction models were created using two regression and five machine learning approaches. RESULTS: Elastic net models provided the best fit across all models. For the EN model using individual LFS item scores, two of the 13 individual LFS items (i.e., "worn out," "exhausted") were the strongest predictors. CONCLUSIONS: This study is the first to use machine learning techniques to accurately predict the severity of morning fatigue from prior to through the week following the administration of CTX using total and individual item scores from the Lee Fatigue Scale (LFS). Our findings suggest that the language used to assess clinical fatigue in oncology patients is important and that two simple questions may be used to predict morning fatigue severity.

Anxiety in oncology outpatients is associated with perturbations in pathways identified in anxiety focused network pharmacology research.

PURPOSE: Evaluate for perturbed signaling pathways associated with subgroups of patients with low versus high levels of state anxiety. These pathways were compared to the pathways identified across eight network pharmacology studies of the anxiolytic effect(s) of a variety of compounds. METHODS: Adult outpatients 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. Latent profile analysis was used to identify subgroups of patients with distinct anxiety profiles based on Spielberger State Anxiety Inventory scores that were obtained six times over two cycles of chemotherapy. Blood samples were processed using RNA sequencing (i.e., RNA-seq sample, n = 244) and microarray (i.e., microarray sample; n = 256) technologies. Pathway perturbations were assessed using pathway impact analysis. Fisher's combined probability method was used to combine test results using a false discovery rate of 0.01. RESULTS: In the RNA-seq sample, 62.3% and 37.7% of the patients were in the low- and high-anxiety classes, respectively. In the microarray sample, 61.3% and 38.7% were in the low and high-anxiety classes, respectively. Forty-one perturbed signaling pathways were identified. Eight of these pathways were common to those identified in the network pharmacology studies. CONCLUSIONS: Findings increase our knowledge of the molecular mechanisms that underlie anxiety in patients receiving chemotherapy. This study provides initial insights into how anxiety in patients with cancer may share common mechanisms with anxiety in patients with other clinical conditions.

Distinct Profiles of Morning and Evening Fatigue Co-Occurrence in Patients During Chemotherapy.

BACKGROUND: Morning and evening fatigue are distinct and distressing symptoms experienced during chemotherapy that demonstrate a large amount of interindividual variability. OBJECTIVES: The objectives of this study were to identify subgroups of patients with distinct morning and evening fatigue co-occurrence profiles and evaluate for differences among these subgroups in demographic, clinical, and symptom characteristics and quality of life. METHODS: Oncology patients ( n = 1,334) completed the Lee Fatigue Scale to self-report morning and evening fatigue, six times over two cycles of chemotherapy. Latent profile analysis was used to identify subgroups of patients with distinct morning and evening physical fatigue profiles. RESULTS: Four distinct morning and evening fatigue profiles were identified (i.e., Both Low, Low Morning + Moderate Evening, Both Moderate, and Both High). Compared to the Both Low profile, the Both High profile was significantly younger, less likely to be married or partnered, more likely to live alone, had a higher comorbidity burden, and lower functional status. The Both High profile had higher levels of anxiety, depressive symptoms, sleep disturbance, and pain and lower levels of quality of life. DISCUSSION: The variability in the morning and evening severity scores among the four profiles supports the hypothesis that morning and evening fatigue are distinct but related symptoms. Clinically meaningful levels of both morning and evening fatigue were reported by 50.4% of our sample, which suggests that the co-occurrence of these two symptoms is relatively common. Patients in Both Moderate and Both High profiles experienced an extremely high symptom burden that warrants ongoing assessments and aggressive symptom management interventions.

An Evaluation of the Multifactorial Model of Cancer-Related Cognitive Impairment.

BACKGROUND: Up to 45% of patients report cancer-related cognitive impairment (CRCI). A variety of characteristics are associated with the occurrence and/or severity of CRCI. However, an important gap in knowledge of risk factors for CRCI is the relative contribution of each factor. The multifactorial model of cancer-related cognitive impairment (MMCRCI) is a conceptual model of CRCI that can be used to evaluate the strength of relationships between various factors and CRCI. OBJECTIVES: The purpose of this study was to use structural regression methods to evaluate the MMCRCI using data from a large sample of outpatients receiving chemotherapy ( n = 1,343). Specifically, the relationships between self-reported CRCI and four MMCRCI concepts (i.e., social determinants of health, patient-specific factors, treatment factors, and co-occurring symptoms) were examined. The goals were to determine how well the four concepts predicted CRCI and determine the relative contribution of each concept to deficits in perceived cognitive function. METHODS: This study is part of a larger, longitudinal study that evaluated the symptom experience of oncology outpatients receiving chemotherapy. Adult patients were diagnosed with breast, gastrointestinal, gynecological, or lung cancer; had received chemotherapy within the preceding 4 weeks; were scheduled to receive at least two additional cycles of chemotherapy; were able to read, write, and understand English; and gave written informed consent. Self-reported CRCI was assessed using the attentional function index. Available study data were used to define the latent variables. RESULTS: On average, patients were 57 years of age, college educated, and with a mean Karnofsky Performance Status score of 80. Of the four concepts evaluated, whereas co-occurring symptoms explained the largest amount of variance in CRCI, treatment factors explained the smallest amount of variance. A simultaneous structural regression model that estimated the joint effect of the four exogenous latent variables on the CRCI latent variable was not significant. DISCUSSION: These findings suggest that testing individual components of the MMCRCI may provide useful information on the relationships among various risk factors, as well as refinements of the model. In terms of risk factors for CRCI, co-occurring symptoms may be more significant than treatment factors, patient-specific factors, and/or social determinants of health in patients receiving chemotherapy.

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.

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.

Impact of worst pain severity and morning fatigue profiles on oncology outpatients' symptom burden and quality of life.

PURPOSE: Pain and fatigue are common symptoms in oncology patients. In a sample of oncology outpatients receiving chemotherapy (n = 1342), the study purposes were to identify subgroups of patients with distinct worst pain and morning fatigue profiles and evaluate for differences among the subgroups in demographic and clinical characteristics, as well as the severity of common symptoms and quality of life (QOL) outcomes. METHODS: Oncology outpatients receiving chemotherapy (n = 1342) completed self-report questionnaires to assess pain and morning fatigue, a total of six times over two cycles of chemotherapy. Joint latent profile analysis was used to identify subgroups of patients with distinct pain and morning fatigue profiles. Differences among the classes were evaluated using parametric and non-parametric tests. RESULTS: Five distinct profiles were identified (no pain and low morning fatigue (27.6%), moderate pain and low morning fatigue (28.2%), moderate pain and morning fatigue (28.0%), moderate pain and increasing and decreasing morning fatigue (6.9%), severe pain and very high morning fatigue (9.3%)). Patients with the three worst profiles had clinically meaningful levels of depression and sleep disturbance and decrements in QOL. CONCLUSIONS: Over 44% of the sample had moderate to high levels of both pain and morning fatigue. Unrelieved pain may contribute to disturbed sleep which results in higher levels of morning fatigue. Clinicians need to assess for pain and fatigue, as well as sleep disturbance during chemotherapy.

Characteristics associated with inter-individual variability in financial distress in patients with breast cancer prior to and for 12 months following surgery.

PURPOSE: To evaluate for inter-individual differences in financial distress and identify demographic, clinical, and symptom characteristics associated with higher levels of financial distress. METHODS: Patients (n = 387) were enrolled prior to breast cancer surgery and followed for 12 months. Financial distress was measured using a 0 (no problem) to 10 (severe problem) numeric rating scale. Hierarchical linear modeling was used to evaluate for inter-individual differences in trajectories of financial distress and characteristics associated with financial distress at enrollment and over 12 months. RESULTS: Patients' mean age was 55.0 (± 11.7) years and the majority underwent breast conservation surgery (80.6%). Mean financial distress score prior to surgery was 3.3 (± 3.4; range 0 to 10). Unconditional model for financial distress demonstrated no significant changes over time (-0.006/month). Younger age, lower income, receipt of an axillary lymph node dissection and adjuvant chemotherapy, and lower attentional function were associated with higher preoperative levels of financial distress. CONCLUSION: Risk factors identified in this study can be used to inform clinicians regarding the need to initiate financial discussions and social work referrals for some patients. Additional clinical or system level interventions should be considered for vulnerable groups with these risk factors.

Distinct sleep disturbance and cognitive dysfunction profiles in oncology outpatients receiving chemotherapy.

PURPOSE: Sleep disturbance and cancer-related cognitive impairment (CRCI) are two of the most common symptoms reported by patients undergoing chemotherapy. Less is known about how these symptoms co-occur and their associated risk factors. Study purposes were to identify subgroups of patients with distinct sleep disturbance and CRCI profiles and evaluate for differences among the subgroups in demographic and clinical characteristics, symptom severity scores, and QOL outcomes. METHODS: A total of 1,333 oncology outpatients receiving chemotherapy completed self-report questionnaires on sleep disturbance and cognitive dysfunction six times over two cycles of chemotherapy. Latent profile analysis was used to identify distinct sleep disturbance AND cognitive dysfunction profiles. Parametric and non-parametric tests were used to evaluate for differences among the classes. RESULTS: Two distinct profiles were identified (i.e., Low = low levels of both sleep disturbance and cognitive dysfunction (53.5%); High = high levels of both sleep disturbance and cognitive dysfunction (45.5%)). Patients in the High class were younger, more likely to be female, had a lower functional status and a higher level of comorbidity. In addition, these patients had a higher symptom burden and a lower quality of life. CONCLUSION: Almost half of the patients undergoing chemotherapy experienced clinically meaningful levels of both symptoms. Of note, sleep disturbance is frequently overlooked by both clinicians and patients. Clinicians need to recommend cognitive rehabilitation and physical activity programs to decrease patients' symptom burden.

Loneliness and symptom burden in oncology patients during the COVID-19 pandemic.

BACKGROUND: Loneliness and social isolation are significant public health problems that are being exacerbated during the coronavirus disease 2019 pandemic. Little is known about the associations between loneliness and symptom burden in oncology patients before and during the pandemic. Study purposes include determining the prevalence of loneliness in a sample of oncology patients; evaluating for differences in demographic, clinical, and symptom characteristics between lonely and nonlonely patients; and determining which demographic, clinical, and symptom characteristics were associated with membership in the lonely group. METHODS: A convenience sample (n = 606) completed online surveys that evaluated the severity of loneliness, social isolation, and common symptoms (ie, anxiety, depression, fatigue, sleep disturbance, cognitive dysfunction, and pain) in oncology patients. Parametric and nonparametric tests were used to evaluate for differences in scores between the lonely and nonlonely groups. Logistic regression analysis was used to determine risk factors for membership in the loneliness group. RESULTS: Of the 606 patients, 53.0% were categorized in the lonely group. The lonely group reported higher levels of social isolation, as well as higher symptom severity scores for all of the symptoms evaluated. In the multivariate model, being unmarried, having higher levels of social isolation, as well as higher levels of anxiety and depressive symptoms were associated with membership in the lonely group. CONCLUSIONS: Study findings suggest that a significant number of oncology patients are experiencing loneliness, most likely as a result of mandate social distancing and isolation procedures. The symptom burden of these patients is extremely high and warrants clinical evaluation and interventions.

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.

Distinct employment interference profiles in patients with breast cancer prior to and for 12 months following surgery.

PURPOSE: To identify subgroups of female breast cancer patients with distinct self-reported employment interference (EI) profiles and determine which demographic, clinical, and symptom characteristics, and quality of life outcomes were associated with subgroup membership. METHODS: Women with breast cancer (n = 385) were assessed for changes in EI over ten times, from prior to, through 12 months after breast cancer surgery. Latent profile analysis (LPA) was used to identify subgroups of patients with distinct EI profiles. RESULTS: Three distinct EI profiles (i.e., None - 26.2% (n = 101), Low - 42.6% (n = 164), High - 31.2% (n = 120)) were identified. Compared to the None and Low groups, patients in the High group were more likely to be younger. Higher proportions in the High group were non-White, pre-menopausal prior to surgery, had more advanced stage disease, had received an axillary lymph node dissection, had received neoadjuvant chemotherapy, had received adjuvant chemotherapy, and had a re-excision or mastectomy on the affected breast within 6 months after surgery. In addition, these patients had lower quality of life scores. Compared to the None group, the High group had higher levels of trait and state anxiety, depressive symptoms, fatigue and sleep disturbance and lower levels of cognitive function. CONCLUSIONS: This study provides new knowledge regarding EI profiles among women in the year following breast cancer surgery. The non-modifiable risk factors (e.g., younger age, being non-White, having more advanced stage disease) can inform current screening procedures. The potentially modifiable risk factors can be used to develop interventions to improve employment outcomes of breast cancer patients.

Distinct sleep disturbance profiles among patients with gynecologic cancer receiving chemotherapy.

OBJECTIVE: In a sample of patients with gynecologic cancer receiving chemotherapy, we sought to identify subgroups of patients with distinct sleep disturbance profiles and assess for differences in patient characteristics and the severity of co-occurring symptoms among these subgroups. METHODS: Adults with gynecologic cancer (n = 232) completed questionnaires six times over two chemotherapy cycles. Sleep disturbance was assessed using the General Sleep Disturbance Scale (GSDS). Clinically meaningful sleep disturbance was defined as a GSDS total score of ≥43. Subgroups of patients with distinct sleep disturbance profiles were identified using latent profile analysis. Differences in patient characteristics and co-occurring symptoms were assessed using Chi-square, Kruskal Wallis, and one-way analysis of variance. RESULTS: Four distinct sleep disturbance profiles were identified: Low (18.5%), Moderate (43.6%), High (29.3%), and Very High (8.6%). Compared to the Low class, patients in the other three classes had lower functional status scores and higher levels of depressive symptoms, trait anxiety, and morning and evening fatigue. Compared to the Low class, patients in the Very High class were younger, had a higher body mass index, and were more likely to report a diagnosis of depression or back pain. CONCLUSIONS: Over 80% of the patients with gynecologic cancer reported sleep disturbance that persisted over two cycles of chemotherapy. Patients in the Very High class experienced problems with both sleep initiation and maintenance. Clinicians should routinely assess sleep disturbance alongside depression, anxiety, and fatigue. Interventions that target the underlying mechanisms of these co-occurring symptoms are warranted.

Distinct diarrhea profiles during outpatient chemotherapy.

PURPOSE: Chemotherapy-induced diarrhea (CID) is a common symptom that occurs in 50 to 80% of patients. Given that the majority of the data on the occurrence and severity of CID is based on physician-rated toxicity criteria, this study's purposes were to identify subgroups of patients with distinct CID profiles and determine how these subgroups differ in terms of demographic and clinical characteristics; severity, frequency, and distress of CID; the co-occurrence of common GI symptoms; and QOL. METHODS: Patients (n = 1133) completed the Memorial Symptom Assessment Scale six times over two cycles of chemotherapy. Latent profile analysis was used to identify subgroups of patients with distinct diarrhea profiles. Differences among these subgroups were evaluated using parametric and nonparametric statistics. RESULTS: Four distinct diarrhea profiles were identified: none (58.3%), decreasing (22.0%), increasing (5.2%), and high (14.5%). Compared with the none class, patients in the high class had a lower functional status, a worse comorbidity profile, were more likely to have gastrointestinal cancer, and were more likely to receive chemotherapy on a 14-day cycle. No differences were found among the classes in the percentages of patients who received chemotherapy with a targeted therapy. CONCLUSION: Given that CID occurred in over 40% of the patients, clinicians should assess for this symptom and other common GI symptoms and initiate appropriate pharmacologic and dietary interventions.

Subgroups of patients undergoing chemotherapy with distinct cognitive fatigue and evening physical fatigue profiles.

PURPOSE: The purpose was to model cognitive fatigue and evening physical fatigue together to determine subgroups of patients with distinct cognitive fatigue AND evening physical fatigue profiles. Once these profiles were identified, differences among the subgroups in demographic and clinical characteristics, co-occurring symptoms, and quality of life outcomes were evaluated. METHODS: Oncology patients (n = 1332) completed self-report measures of cognitive fatigue and evening physical fatigue, six times over two cycles of chemotherapy. Latent profile analysis, which combined the two symptom scores, was done to identify subgroups of patients with distinct cognitive fatigue AND evening physical fatigue profiles. RESULTS: Three distinct profiles (i.e., Low [20.5%], Moderate [39.6%], and High [39.6%]) were identified. Compared to the Low class, patients in the High class were younger, female, and more likely to live alone and had a higher comorbidity burden and a lower functional status. In addition, these patients had a higher symptom burden and a poorer quality of life. CONCLUSION: Based on clinically meaningful cutoff scores, 80% of the patients in this study had moderate to high levels of both cognitive fatigue and evening physical fatigue. In addition, these patients experienced high levels of other common symptoms (e.g., anxiety, depression, sleep disturbance, and pain). These co-occurring symptoms and other modifiable characteristics associated with membership in the Moderate and High classes may be potential targets for individualized symptom management interventions.

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.

Distinct profiles of multiple co-occurring symptoms in patients with gastrointestinal cancers receiving chemotherapy.

PURPOSE: Identify subgroups of gastrointestinal (GI) cancer patients with distinct multiple co-occurring symptom profiles and evaluate for differences among these subgroups in demographic and clinical characteristics and quality of life (QOL) outcomes. METHODS: Patients with GI cancers (n = 399) completed the Memorial Symptom Assessment Scale (MSAS) that was used to assess for multiple co-occurring symptoms. Latent class analysis (LCA) was used to identify subgroups of patients with distinct symptom profiles using symptom occurrence ratings. Differences in demographic and clinical characteristics and QOL outcomes among the subgroups were evaluated using parametric and nonparametric tests. RESULTS: All Low (36.6%), Moderate (49.4%), and All High (14.0%) classes were identified. Compared to the All Low class, patients in the other two classes were significantly younger and were more likely to report depression and back pain. Compared to the other two classes, patients in the All High class had fewer years of education and a higher number of comorbidities. Significant differences were found among the three classes for comorbidity burden and total number of MSAS symptoms (i.e., All Low < Moderate < All High), as well as for performance status (i.e., All Low > Moderate > All High). A higher symptom burden was associated with poorer QOL outcomes. CONCLUSIONS: The first study to identify subgroups of patients with GI cancers based on distinct symptom profiles. LCA allowed for the identification of risk factors associated with a higher symptom burden. Clinicians can use this information to identify high-risk patients and develop personalized symptom management interventions.