A data-integrated multi-omics approach to study cancer related fatigue
Creators: name: Kober, Kord M
Publisher: name: F1000 Research Limited
Year: 2021

description: Cancer-related fatigue (CRF) is a highly prevalent symptom reported that occurs during and persists following cancer treatment. CRF has significant negative outcomes on patients’ mood and ability to function. Research is needed to identify mechanisms and develop prediction models for CRF severity. In a sample of oncology patients (n=1343) undergoing chemotherapy (CTX), the first aim of our NCI R37 (CA233774) is to evaluate underlying mechanisms for CRF, by comparing patients with high versus low levels of CRF in terms of gene expression (GE), genetic, and epigenetic changes (i.e., “omics”). Given that no treatments are available for fatigue, this approach may identify potential therapeutic interventions. Our second aim is to develop a risk prediction model that could assist with the identification of patients at greatest risk for high levels of CRF. The approach that is being used in this R37 will be applied to future investigations of other patient reported outcomes (e.g., chemotherapy-induced peripheral neuropathy, sleep disturbance, and cognitive impairments).<br> <br> CRF is a complex phenotype, which is the result of environmental (e.g., demographic, clinical, social) and genetic factors. By integrating data from multiple sources, we will have increased power to identify and better interpret the omics-phenotype relationships relative to an analysis that uses only a single source of omics data. We will use this strategy to obtain a more complete biological model of the common and distinct molecular factors associated with the severity of morning and evening CRF. In systems genomics, a multi-staged data integrated analysis can be used, which follows a step-wise or hierarchical approach. In this study, we will perform a multi-staged analysis to identify potential mechanisms for CRF.<br> <br> First, we will use a subset of ~60% of the patients to identify associations between functional gene products (i.e., GE) and the severity of morning and evening CRF. Using this list of functional candidate genes, we will evaluate the relative contributions of heritability (i.e., genetics) and environment (i.e., epigenetics) to the CRF phenotypes in the remaining subset of patients (Figure 2). Identifying these candidate genes and pathways will improve our understanding of the mechanism(s) of CRF and provide new therapeutic targets.<br> <br> This multi-staged data-integrated multi-omics approach will dramatically reduce the number of tests performed, and increase the power to detect significant and clinically meaningful common and distinct associations with morning and evening CRF. In addition, by performing the discovery DE analysis on a different set of patients than the genetic and epigenetic association analyses we limit the bias inherit in the potential correlations between GE and methylation and genetic variations. Patients completed questionnaires during the week prior to their chemotherapy treatment. Severity of morning fatigue was evaluated using the Lee Fatigue Scale. Patients were classified as having low versus high morning fatigue using clinically meaningful cutoff scores. Gene expression analyses were performed in two independent samples using RNA-sequencing (S1, n=349) and Illumina HumanHT-12 v4.0 microarray (S2, n=349) methodologies. Here, we report on the current gene expression and pilot epigenetic findings.<br> <br> High levels of evening CRF was reported by 52.1% of the patients in S1 and by 54.7% in S2. Across the two samples, 29 pathways were found to be perturbed between patients with low and high levels of evening CRF. Examples of the perturbed KEGG signaling pathways associated with evening fatigue include: antigen processing and presentation, cytokine-cytokine receptor interaction, chemokine, and intestinal immune network for IgA production. This is the first study to identify perturbations in inflammatory pathways are involved in evening fatigue in patients undergoing CTX across multiple diagnoses.<br> <br> High levels of morning fatigue was reported by 53.9% of the patients in S1 and by 46.7% in S2. Across both samples, 39 pathways were found to be perturbed between patients with low and high levels of morning fatigue. Examples of the perturbed KEGG signaling pathways associated with morning fatigue include: PPAR, relaxin, oxytocin, serotonergic synapse, cytokine-cytokine receptor interaction and chemokine. This is the first study to identify perturbations in neuroendocrine and inflammatory pathways that are associated with morning fatigue among patients receiving CTX.<br> <br> In a pilot study, we evaluate for differences in regulatory regions of genes in the most significantly perturbed neurotransmitter signaling pathway (i.e., PPAR), exploratory tests for differentially methylated probes (DMPs) between the morning fatigue groups. Methylation levels were measured using the Illumina HumanMethylation450 microarray. In this exploratory analysis of a subset of patients diagnosed with breast cancer in S2 (n=116) for which DNA methylation data were available, 52.6% reported high morning fatigue. Morning fatigue group membership was significantly associated with methylation levels of one of the eCpG loci for the Sterol carrier protein (SCP2) gene (cg01802117; p-value = 0.048).<br> <br> The use of predictive models that include phenotypic and molecular characteristics has led to improvements in risk profiling and treatment for a several cancers. We will use machine learning methods, that incorporate the rich demographic, clinical, and omic data, to develop a valid predictive model of CRF. This prediction modeling may determine not only which phenotypic characteristics are the most salient in predicting CRF severity, but the relative contribution of omic characteristics to this outcome. Here, we report on the current prediction models we have developed for predicting morning and evening fatigue severity.<br> <br> Prediction models were created using multivariable linear regression, RPART, random forest (RF), and support vector machine. RF models provided the best fit across all models. For the RF model using individual LFS item scores, two of the 13 individual LFS items (i.e., “worn out”, “exhausted”) were the strongest predictors. An important clinical finding is the ability of the RF model using single items from evening LFS scores obtained prior to chemotherapy to predict the severity of evening fatigue post-chemotherapy as accurately as the model using total evening LFS scores. This finding suggests that clinicians can ask patients to rate (0-10) their level of feeling “worn out” or “exhausted” prior to chemotherapy to estimate their evening fatigue in the week following chemotherapy. The use of these descriptive single items may facilitate the assessment of evening fatigue in a busy oncology clinic.<br> <br> Prediction models were created using multivariable linear regression, RPART, RF, support vector machine, LASSO, and ElasticNet (EN). EN models provided the best fit across all models. For the EN model using individual LFS item scores, Karnofsky Performance Status score, two of the 13 individual LFS items (i.e., “worn out”, “exhausted”), quality of sleep, and excessive daytime sleepiness were the strongest predictors. This finding highlights the predictive value of the two LFS item terms and supports the hypothesis that morning and evening fatigue are similar but distinct symptoms.<br> <br> This approach has the potential to assist clinicians to identify high-risk patients and potentially modifiable risk factors (e.g., high levels of anxiety), as well as novel therapeutic targets. Future studies will include ‘omics data (i.e., genetic, transcriptomic, and epigenomic) predictors. These findings will guide the development of future clinical (e.g., risk reduction strategies, pre-emptive treatment of co-occurring symptoms) and preclinical (e.g., novel genomic markers) studies to decrease the severity of CRF.