mGluR5 mediates post-radiotherapy fatigue development in cancer patients

Mechanistic insights into behavioral clusters associated with cancer-related systemic inflammatory response

PURPOSE OF REVIEW

This focused, narrative review mostly describes our team's investigations into the potential inflammatory mechanisms that contribute to the development of cancer-related gastrointestinal (GI) mucositis and its associated symptoms. This review summarizes details of our clinical and preclinical findings to test the role of inflammation in the development and occurrence of these cancer-related conditions.

RECENT FINDINGS

GI mucositis (GIM) is a common, distressing condition reported by cancer patients. GIM is often clustered with other behaviors including fatigue, pain, anorexia, depression, and diarrhea. It is hypothesized that there is a common biologic mechanism underpinning this symptom cluster. Our multi-platform investigations revealed that GIM and its associated cluster of behaviors may be triggered by local inflammation spreading systemically causing pro-inflammatory-mediated toxicities, leading to alterations in immune, metabolic, and nervous system functions and activities. For example, behavioral toxicities related to local irradiation for non-metastatic cancer may be triggered by mGluR5 activation influencing prolonged T cell as well as NF-κB transcription factor activities. Thus, interventions targeting inflammation and associated pathways may be a reasonable strategy to alleviate GIM and its symptom cluster.

SUMMARY

GIM may be a sign of a broader systemic inflammatory response triggered by cancer or its treatment. Addressing GIM and its associated symptoms primarily involves supportive care strategies focused on relieving symptoms, promoting healing, and preventing complications.

Multidimensional Predictors of Cancer-Related Fatigue Based on the Predisposing, Precipitating, and Perpetuating (3P) Model: A Systematic Review

Cancer-related fatigue (CRF) is a widespread symptom with high prevalence in cancer patients, seriously affecting their quality of life. In the context of precision care, constructing machine learning-based prediction models for early screening and assessment of CRF is beneficial to this situation. To further understand the predictors of CRF for model construction, we conducted a comprehensive search in PubMed, Web of Science, Embase, and Scopus databases, combining CRF with predictor-related terms. A total of 27 papers met the inclusion criteria. We evaluated the above studies into three subgroups following the predisposing, precipitating, and perpetuating (3P) factor model. (1) Predisposing factors-baseline fatigue, demographic characteristics, clinical characteristics, psychosocial traits and physical symptoms. (2) Precipitating factors-type and stage of chemotherapy, inflammatory factors, laboratory indicators and metabolic changes. (3) Perpetuating factors-a low level of physical activity and poorer nutritional status. Future research should prioritize large-scale prospective studies with emerging technologies to identify accurate predictors of CRF. The assessment and management of CRF should also focus on the above factors, especially the controllable precipitating factors, to improve the quality of life of cancer survivors.

Recent evidence and progress for developing precision nursing in symptomatology: A scoping review

AIM

To summarize the omics results of symptomatic research that can help nurses identify intervention targets and design precision interventions for pain, mental health, cognitive impairment, sleep disorder, fatigue, lymphedema, and quality of life, so as to provide a comprehensive summary of help and inspire to precision nursing.

METHODS

CINAHL, PubMed, Web of Science, and ScienceDirect databases were searched. Retrieval time was from January 2012 to December 2021. Symptomatology research applying omics that can be used to guide nurses in designing targeted interventions was included.

RESULTS

Forty-six studies were included in the final review. Symptomatology research that can be integrated with nursing science to develop precision nursing focused on pain, mental health, cognitive impairment, sleep disorder, fatigue, lymphedema, and quality of life. Most studies were related to cognitive impairment (n = 10; 21.74%), pain (n = 9; 19.57%), and mental health (n = 8; 17.39%). Moreover, the included studies involved various omics technologies, such as whole genome, epigenome, transcriptome, proteome, and metabolome.

CONCLUSION

The rapid development of various omic technologies promotes symptomatology research, which can help nurses fully understand the information of patients. Phenotypic characteristics and biomarkers shown in symptomatology research help nurses identify intervention targets and develop individualization interventions, so as to prevent and reduce symptoms and improve the quality of life.

IMPLICATION FOR NURSING AND HEALTH POLICY

This scoping review is the first synthesis of all peer-reviewed literature to summarize and provide important information and references from the omic results of symptomatology studies to develop precision nursing, highlighting the status and development of precision nursing. Nursing education policies should introduce the development and importance of precision nursing. Further research could consider investing more attention in precision nursing. Nursing researchers can carry out some studies applying omics technology to explore more biomarkers, helping guide the formulation of clinical intervention for symptoms.

Upregulation of Parkinson's disease-associated protein alpha-synuclein suppresses tumorigenesis via interaction with mGluR5 and gamma-synuclein in liver cancer

Numerous epidemiological studies suggest a link between Parkinson's disease (PD) and cancer, indicating that PD-associated proteins may mediate the development of cancer. Here, we investigated a potential role of PD-associated protein α-synuclein in regulating liver cancer progression in vivo and in vitro. We found the negative correlation of α-synuclein with metabotropic glutamate receptor 5 (mGluR5) and γ-synuclein by analyzing the data from The Cancer Genome Atlas database, liver cancer patients and hepatoma cells with overexpressed α-synuclein. Moreover, upregulated α-synuclein suppressed the growth, migration, and invasion. α-synuclein was found to associate with mGluR5 and γ-synuclein, and the truncated N-terminal of α-synuclein was essential for the interaction. Furthermore, overexpressed α-synuclein exerted the inhibitory effect on hepatoma cells through the degradation of mGluR5 and γ-synuclein via α-synuclein-dependent autophagy-lysosomal pathway (ALP). Consistently, in vivo experiments with rotenone-induced rat model of PD also confirmed that, upregulated α-synuclein in liver cancer tissues through targeting on mGluR5/α-synuclein/γ-synuclein complex inhibited tumorigenesis involving in ALP-dependent degradation of mGluR5 and γ-synuclein. These findings give an insight into an important role of PD-associated protein α-synuclein accompanied by the complex of mGluR5/α-synuclein/γ-synuclein in distant communications between PD and liver cancer, and provide a new strategy in therapeutics for the treatment of liver cancer.

MPEP Attenuates Intrahepatic Fat Accumulation in Obese Mice

The blockade of metabotropic glutamate receptor type 5 (mGluR5) was previously found to reduce fat accumulation in HEPG2 cells. Here, we evaluated the effects of mGluR5 blockade in a mouse model of steatosis. Male ob/ob mice fed a high-fat diet were treated with MPEP or vehicle. After 7 weeks, liver biopsies were collected, and nuclei were isolated from fresh tissue. Lipid droplet area and collagen deposition were evaluated on tissue slices; total lipids, lipid peroxidation, and ROS were evaluated on tissue homogenates; PPARα, SREBP-1, mTOR, and NF-κB were assayed on isolated nuclei by Western Blot. Target genes of the above-mentioned factors were assayed by RT-PCR. Reduced steatosis and hepatocyte ballooning were observed in the MPEP group with respect to the vehicle group. Concomitantly, increased nuclear PPARα and reduced nuclear SREBP-1 levels were observed in the MPEP group. Similar trends were obtained in target genes of PPARα and SREBP-1, Acox1 and Acc1, respectively. MPEP administration also reduced oxidative stress and NF-κB activation, probably via NF-κB inhibition. Levels of common markers of inflammation (Il-6, Il1β and Tnf-α) and oxidative stress (Nrf2) were significantly reduced. mTOR, as well as collagen deposition, were unchanged. Concluding, MPEP, a selective mGluR5 negative allosteric modulator, reduces both fat accumulation and oxidative stress in a 7-week murine model of steatosis. Although underlying mechanisms need to be further investigated, this is the first in vivo study showing the beneficial effects of MPEP in a murine model of steatosis.

Metabotropic glutamate receptor 5 knockout rescues obesity phenotype in a mouse model of Huntington's disease

Obesity represents a global health problem and is characterized by metabolic dysfunctions and a low-grade chronic inflammatory state, which can increase the risk of comorbidities, such as atherosclerosis, diabetes and insulin resistance. Here we tested the hypothesis that the genetic deletion of metabotropic glutamate receptor 5 (mGluR5) may rescue metabolic and inflammatory features present in BACHD mice, a mouse model of Huntington's disease (HD) with an obese phenotype. For that, we crossed BACHD and mGluR5 knockout mice (mGluR5-/-) in order to obtain the following groups: Wild type (WT), mGluR5-/-, BACHD and BACHD/mGluR5-/- (double mutant mice). Our results showed that the double mutant mice present decreased body weight as compared to BACHD mice in all tested ages and reduced visceral adiposity as compared to BACHD at 6 months of age. Additionally, 12-month-old double mutant mice present increased adipose tissue levels of adiponectin, decreased leptin levels, and increased IL-10/TNF ratio as compared to BACHD mice. Taken together, our preliminary data propose that the absence of mGluR5 reduce weight gain and visceral adiposity in BACHD mice, along with a decrease in the inflammatory state in the visceral adipose tissue (VAT), which may indicate that mGluR5 may play a role in adiposity modulation.

Steroid Hormone Biosynthesis Metabolism Is Associated With Fatigue Related to Androgen Deprivation Therapy for Prostate Cancer

Background

Androgen deprivation therapy (ADT) is a cornerstone treatment for prostate cancer. Despite the clinical benefits, ADT is associated with multiple adverse effects including fatigue. The goal of the study was to examine metabolomic changes to better understand cancer-related fatigue specific to ADT treatment.

Methods

A total of 160 plasma samples collected from participants with (+ADT, n = 58) or without neoadjuvant ADT (−ADT, n = 102) prior to radiation therapy for treatment of non-metastatic localized prostate cancer were included in the study. Fatigue and sleep-related impairment were measured using the Patient Reported Outcomes Measurement Information System. Plasma metabolites were identified and measured using untargeted ultrahigh-performance liquid chromatography/mass spectrometry metabolomics analyses. Partial least square discriminant analysis was used to identify discriminant metabolite features, and the diagnostic performance of selected classifiers was quantified using AUROC curve analysis. Pathway enrichment analysis was performed using metabolite sets enrichment analyses.

Findings

Steroid hormone biosynthesis pathways, including androstenedione metabolism as well as androgen and estrogen metabolism, were overrepresented by metabolites that significantly discriminated samples in the +ADT from the −ADT group. Additional overrepresented metabolic pathways included amino acid metabolism, glutathione metabolism, and carnitine synthesis. Of the metabolites that were significantly different between the groups, steroid hormone biosynthesis metabolites were most significantly correlated with fatigue severity. Sleep-related impairment was strongly correlated with fatigue severity and inversely correlated with ADT-induced reduction in androsterone sulfate.

Conclusions

Patients with non-metastatic prostate cancer receiving neoadjuvant ADT prior to radiation therapy reported relatively more severe fatigue. Increased fatigue in this population may be attributable to sleep-related impairment associated with alterations in steroid hormone biosynthesis. Findings in this study provide a basis for further research of changes in sleep patterns and their role in this specific subcategory of cancer-related fatigue caused by the treatment.

Plasma metabolomic profile associated with fatigue in cancer patients

BACKGROUND

Metabolomics is the newest -omics methodology and allows for a functional snapshot of the biochemical activity and cellular state. The goal of this study is to characterize metabolomic profiles associated with cancer-related fatigue, a debilitating symptom commonly reported by oncology patients.

METHODS

Untargeted ultrahigh performance liquid chromatography/mass spectrometry metabolomics approach was used to identify metabolites in plasma samples collected from a total of 197 participants with or without cancer. Partial least squares-discriminant analysis (PLS-DA) was used to identify discriminant metabolite features, and diagnostic performance of selected classifiers was quantified using area under the receiver operating characteristics (AUROC) curve analysis. Pathway enrichment analysis was performed using Fisher's exact test and the Kyoto Encyclopedia of Genes and Genomes (KEGG) metabolic pathway database.

FINDINGS

The global metabolomics approach yielded a total of 1120 compounds of known identity. Significant metabolic pathways unique to fatigued cancer versus control groups included sphingolipid metabolism, histidine metabolism, and cysteine and methionine metabolism. Significant pathways unique to non-fatigued cancer versus control groups included inositol phosphate metabolism, primary bile acid biosynthesis, ascorbate and aldarate metabolism, starch and sucrose metabolism, and pentose and glucuronate interconversions. Pathways shared between the two comparisons included caffeine metabolism, tyrosine metabolism, steroid hormone biosynthesis, sulfur metabolism, and phenylalanine metabolism.

CONCLUSIONS

We found significant metabolomic profile differences associated with cancer-related fatigue. By comparing metabolic signatures unique to fatigued cancer patients with metabolites associated with, but not unique to, fatigued cancer individuals (overlap pathways) and metabolites associated with cancer but not fatigue, we provided a broad view of the metabolic phenotype of cancer-related fatigue.

Brain-derived neurotrophic factor polymorphism Val66Met protects against cancer-related fatigue

Cancer-related fatigue is an extremely common and debilitating psychiatric symptom that affects up to 80% of cancer patients. Despite its negative impact on the patient's quality of life, there is no well-established biomarker or mechanisms associated with this debilitating condition. The functional brain-derived neurotrophic factor (BDNF) Val66Met single nucleotide polymorphism (SNP) has been associated with a variety of psychiatric illnesses. We hypothesized that Val66Met may influence the risk for developing cancer-related fatigue. BDNF Val66Met was analyzed by polymerase chain reaction in 180 patients with confirmed cancer diagnoses. Fatigue was measured using the Functional Assessment of Cancer Therapy-Fatigue (FACIT-Fatigue) questionnaire. Depression was measured using the Hamilton Depression Scale (HAM-D). Data were transformed when necessary and regression models were constructed to access the association between genotype and symptom severity. Participants carrying the Met allele reported significantly less fatigue compared to the Val/Val genotype group. The presence of the Met allele did not influence depression levels. The results suggest that the BDNF Val66Met polymorphism confers protective advantage against cancer-related fatigue; whereas having the Val/Val genotype may be a genetic risk factor. Findings from this study not only provide clues to the neural basis of cancer-related fatigue, but also allow for symptom severity prediction and patient education with the goal to improve symptom management.

Immune dysregulation in depression: Evidence from genome-wide association

A strong body of evidence supports a role for immune dysregulation across many psychiatric disorders including depression, the leading cause of global disability. Recent progress in the search for genetic variants associated with depression provides the opportunity to strengthen our current understanding of etiological factors contributing to depression and generate novel hypotheses. Here, we provide an overview of the literature demonstrating a role for immune dysregulation in depression, followed by a detailed discussion of the immune-related genes identified by the most recent genome-wide meta-analysis of depression. These genes represent strong evidence-based targets for future basic and translational research which aims to understand the role of the immune system in depression pathology and identify novel points for therapeutic intervention.

Artificial intelligence in radiotherapy

Artificial intelligence (AI) has already been implemented widely in the medical field in the recent years. This paper first reviews the background of AI and radiotherapy. Then it explores the basic concepts of different AI algorithms and machine learning methods, such as neural networks, that are available to us today and how they are being implemented in radiotherapy and diagnostic processes, such as medical imaging, treatment planning, patient simulation, quality assurance and radiation dose delivery. It also explores the ongoing research on AI methods that are to be implemented in radiotherapy in the future. The review shows very promising progress and future for AI to be widely used in various areas of radiotherapy. However, basing on various concerns such as availability and security of using big data, and further work on polishing and testing AI algorithms, it is found that we may not ready to use AI primarily in radiotherapy at the moment.

Robust Sampling of Defective Pathways in Alzheimer's Disease. Implications in Drug Repositioning

We present the analysis of the defective genetic pathways of the Late-Onset Alzheimer’s Disease (LOAD) compared to the Mild Cognitive Impairment (MCI) and Healthy Controls (HC) using different sampling methodologies. These algorithms sample the uncertainty space that is intrinsic to any kind of highly underdetermined phenotype prediction problem, by looking for the minimum-scale signatures (header genes) corresponding to different random holdouts. The biological pathways can be identified performing posterior analysis of these signatures established via cross-validation holdouts and plugging the set of most frequently sampled genes into different ontological platforms. That way, the effect of helper genes, whose presence might be due to the high degree of under determinacy of these experiments and data noise, is reduced. Our results suggest that common pathways for Alzheimer’s disease and MCI are mainly related to viral mRNA translation, influenza viral RNA transcription and replication, gene expression, mitochondrial translation, and metabolism, with these results being highly consistent regardless of the comparative methods. The cross-validated predictive accuracies achieved for the LOAD and MCI discriminations were 84% and 81.5%, respectively. The difference between LOAD and MCI could not be clearly established (74% accuracy). The most discriminatory genes of the LOAD-MCI discrimination are associated with proteasome mediated degradation and G-protein signaling. Based on these findings we have also performed drug repositioning using Dr. Insight package, proposing the following different typologies of drugs: isoquinoline alkaloids, antitumor antibiotics, phosphoinositide 3-kinase PI3K, autophagy inhibitors, antagonists of the muscarinic acetylcholine receptor and histone deacetylase inhibitors. We believe that the potential clinical relevance of these findings should be further investigated and confirmed with other independent studies.

Robust pathway sampling in phenotype prediction. Application to triple negative breast cancer

Background

Phenotype prediction problems are usually considered ill-posed, as the amount of samples is very limited with respect to the scrutinized genetic probes. This fact complicates the sampling of the defective genetic pathways due to the high number of possible discriminatory genetic networks involved. In this research, we outline three novel sampling algorithms utilized to identify, classify and characterize the defective pathways in phenotype prediction problems, such as the Fisher’s ratio sampler, the Holdout sampler and the Random sampler, and apply each one to the analysis of genetic pathways involved in tumor behavior and outcomes of triple negative breast cancers (TNBC). Altered biological pathways are identified using the most frequently sampled genes and are compared to those obtained via Bayesian Networks (BNs).

Results

Random, Fisher’s ratio and Holdout samplers were more accurate and robust than BNs, while providing comparable insights about disease genomics.

Conclusions

The three samplers tested are good alternatives to Bayesian Networks since they are less computationally demanding algorithms. Importantly, this analysis confirms the concept of “biological invariance” since the altered pathways should be independent of the sampling methodology and the classifier used for their inference. Nevertheless, still some modifications are needed in the Bayesian networks to be able to sample correctly the uncertainty space in phenotype prediction problems, since the probabilistic parameterization of the uncertainty space is not unique and the use of the optimum network might falsify the pathways analysis.

Precision health: A nursing perspective

Precision health refers to personalized healthcare based on a person's unique genetic, genomic, or omic composition within the context of lifestyle, social, economic, cultural and environmental influences to help individuals achieve well-being and optimal health. Precision health utilizes big data sets that combine omics (i.e. genomic sequence, protein, metabolite, and microbiome information) with clinical information and health outcomes to optimize disease diagnosis, treatment and prevention specific to each patient. Successful implementation of precision health requires interprofessional collaboration, community outreach efforts, and coordination of care, a mission that nurses are well-positioned to lead. Despite the surge of interest and attention to precision health, most nurses are not well-versed in precision health or its implications for the nursing profession. Based on a critical analysis of literature and expert opinions, this paper provides an overview of precision health and the importance of engaging the nursing profession for its implementation. Other topics reviewed in this paper include big data and omics, information science, integration of family health history in precision health, and nursing omics research in symptom science. The paper concludes with recommendations for nurse leaders in research, education, clinical practice, nursing administration and policy settings for which to develop strategic plans to implement precision health.

Mapping the Alterations of Glutamate Using Glu-Weighted CEST MRI in a Rat Model of Fatigue

Objective: Glutamate dysregulation may play an important role in the pathophysiology of fatigue. Glutamate weighted chemical exchange saturation transfer (Glu-weighted CEST) MRI is a recently developed technology which enables measuring glutamate in vivo with high sensitivity and spatial resolution. The purpose of this study is to map the alternations of brain glutamate in a rat model of fatigue. Methods: Rats were subjected to 10 days fatigue loading procedure (fatigue group) or reared without any fatigue loading (control group). Spontaneous activities of rats in the fatigue group were recorded from 3 days before fatigue loading to 4 days after the end of fatigue loading. Glu-weighted CEST were performed following 10-day fatigue loading. Results: Rats in the fatigue group exhibited significant reduced spontaneous activities after 10-day fatigue loading. The glutamate level in the whole brain increased significantly in the fatigue group compared to that in the control group. Further analysis of glutamate in the sub-regions of brain including the prefrontal cortex, hippocampus, and striatum revealed a trend of increment, although statistical significance was not reached. Significance: The increase of glutamate level in the brain may be a crucial process involved in the pathophysiology of fatigue.

Analysis of defective pathways and drug repositioning in Multiple Sclerosis via machine learning approaches

BACKGROUND

Although some studies show that there could be a genetic predisposition to develop Multiple Sclerosis (MS), attempts to find genetic signatures related to MS diagnosis and development are extremely rare.

METHOD

We carried out a retrospective analysis of two different microarray datasets, using machine learning techniques to understand the defective pathways involved in this disease. We have modeled two data sets that are publicly accessible. The first was used to establish the list of most discriminatory genes; whereas, the second one was utilized for validation purposes.

RESULTS

The analysis provided a list of high discriminatory genes with predictive cross-validation accuracy higher than 95%, both in learning and in blind validation. The results were confirmed via the holdout sampler. The most discriminatory genes were related to the production of Hemoglobin. The biological processes involved were related to T-cell Receptor Signaling and co-stimulation, Interferon-Gamma Signaling and Antigen Processing and Presentation. Drug repositioning via CMAP methodologies highlighted the importance of Trichostatin A and other HDAC inhibitors.

CONCLUSIONS

The defective pathways suggest viral or bacterial infections as plausible mechanisms involved in MS development. The pathway analysis also confirmed coincidences with Epstein-Barr virus, Influenza A, Toxoplasmosis, Tuberculosis and Staphylococcus Aureus infections. Th17 Cell differentiation, and CD28 co-stimulation seemed to be crucial in the development of this disease. Furthermore, the additional knowledge provided by this analysis helps to identify new therapeutic targets.

Cognitive and motor aspects of cancer-related fatigue

BACKGROUND

Cancer-related fatigue (CRF) is a debilitating symptom frequently reported by patients during and after treatment for cancer. CRF is a multidimensional experience and is often solely assessed by self-report measures. The goal of the study is to examine the physical and cognitive aspects of self-reported CRF using a cognitive function test and a physical fatigue index in order to provide objective measures that can characterize the CRF phenotype.

METHODS

A total of 59 subjects with nonmetastatic prostate cancer receiving external beam radiation therapy were included in the study. Fatigue was measured using the Functional Assessment of Cancer Therapy-Fatigue (FACT-F) questionnaire. Cognitive characteristics of CRF was measured using the Stroop Color-Word Interference computerized test and the motor aspect of fatigue was measured using the static fatigue test using a handgrip dynamometer.

FINDINGS

Functional Assessment of Cancer Therapy-Fatigue scores significantly correlated with the Stroop Interference score, but not performance accuracy in all test conditions. Fatigued subjects exhibited a more rapid decline to 50% of maximal strength and increased static fatigue index in the handgrip test, whereas maximal grip strength was not affected.

CONCLUSIONS

The results suggest that CRF exhibits both cognitive and physical characteristics. Subjective fatigue was associated with increased time required to overcome cognitive interference, but not cognitive performance accuracy. Fatigued patients exhibited decreased physical endurance and the ability to sustain maximal strength over time. These objective measures may serve as valuable tools for clinicians to detect cognitive and physical impairment associated with CRF.

Fatigue, Sleep, and Autoimmune and Related Disorders

Profound and debilitating fatigue is the most common complaint reported among individuals with autoimmune disease, such as systemic lupus erythematosus, multiple sclerosis, type 1 diabetes, celiac disease, chronic fatigue syndrome, and rheumatoid arthritis. Fatigue is multi-faceted and broadly defined, which makes understanding the cause of its manifestations especially difficult in conditions with diverse pathology including autoimmune diseases. In general, fatigue is defined by debilitating periods of exhaustion that interfere with normal activities. The severity and duration of fatigue episodes vary, but fatigue can cause difficulty for even simple tasks like climbing stairs or crossing the room. The exact mechanisms of fatigue are not well-understood, perhaps due to its broad definition. Nevertheless, physiological processes known to play a role in fatigue include oxygen/nutrient supply, metabolism, mood, motivation, and sleepiness-all which are affected by inflammation. Additionally, an important contributing element to fatigue is the central nervous system-a region impacted either directly or indirectly in numerous autoimmune and related disorders. This review describes how inflammation and the central nervous system contribute to fatigue and suggests potential mechanisms involved in fatigue that are likely exhibited in autoimmune and related diseases.

Assessment and predictors of fatigue in men with prostate cancer receiving radiotherapy and androgen deprivation therapy

INTRODUCTION

Fatigue is a commonly reported symptom in men receiving radiation therapy and androgen deprivation therapy (ADT) for prostate cancer. Despite this, the complex mechanisms remain unclear. This study aims to investigate factors which correlate with development of fatigue.

METHODS

Twenty-seven men with high-risk prostate cancer undergoing radiation therapy and 18 months of ADT were assessed for fatigue, haemoglobin (Hb), testosterone, magnetic resonance imaging (MRI) fat fraction (FF) and apparent diffusion coefficient (ADC), at baseline and at intervals after radiotherapy. Changes from baseline were analysed using paired t-tests. Linear time trends were assessed using linear mixed effect models.

RESULTS

Overall, mean fatigue score increased from baseline to the 18-month time interval (difference 4.5, P = 0.0114). The mean value for Hb significantly decreased (P < 0.001) from baseline to 18 months. The mean value for testosterone significantly decreased (P < 0.001) from baseline to 12 months, and remained low. Mean for MRI FF showed a significant increase (P < 0.001) from baseline to 6 months. MRI ADC showed a non-significant decrease from baseline to 6 months (P = 0.4416).

CONCLUSION

Radiotherapy and ADT resulted in a significant increase in fatigue scores. Statistically significant changes were noted in Hb, testosterone and MRI FF and ADC, however, none were shown to have a strong association with worsening fatigue. Further investigation in a larger cohort is required to assess the interaction between fatigue and possible biological factors.

Co-Occurring Symptoms Contribute to Persistent Fatigue in Prostate Cancer

PURPOSE

Cancer-related fatigue is one of the most debilitating side effects of cancer and cancer therapy. We aimed to investigate co-occurring symptoms associated with persistent fatigue in men receiving external beam radiation therapy (EBRT) for nonmetastatic prostate cancer.

METHODS

A sample of 47 men with prostate cancer scheduled to receive radiotherapy (RT) were followed at baseline and 1 year after RT. Clinical and demographic data were obtained from chart review. Symptom measurements included urinary dysfunction (American Urological Association symptoms score), fatigue (Functional Assessment of Cancer Therapy - Fatigue questionnaire), sleep disturbance (Patient-Reported Outcomes Measurement Information System - Sleep Disturbance form), pain (physical well-being domain pain item of the Functional Assessment of Cancer Therapy - General), and depressive symptoms (Hamilton Depression Rating Scale). Paired t tests, correlations, general linear models, and logistic regressions were used to determine associations between fatigue and other symptom scores.

RESULTS

At 1 year after RT, 34% of subjects continued to experience fatigue. Urinary dysfunction was the best clinical predictor of persistent fatigue. Pain and depressive symptoms further improved the predictive power of the model. A multivariate linear regression model containing all these three clinical variables (urinary dysfunction, pain, and depressive symptoms) explained 74% of total variance associated with persistent fatigue after RT.

CONCLUSIONS

Persistent fatigue at 1 year after EBRT in prostate cancer survivors is likely related to a cluster of symptoms elicited by chronic inflammation. Therapies that target each of these symptoms will likely reduce fatigue in this patient population.

Encephalitis Associated to Metabotropic Glutamate Receptor 5 (mGluR5) Antibodies in Cerebrospinal Fluid

A 68-years-old Hispanic man, complained of night sweats, low grade fewer, unexplained weight loss, and memory problems over 3 months. Abdominal tomography showed multiple intra-abdominal adenopathy and biopsy confirmed classic Hodgkin's lymphoma. He commenced treatment with chemotherapy. Three months later, he had acute onset of inattention, auditory hallucinations and alterations of anterograde memory. The patient developed psychomotor agitation, unresponsive to a combination of neuroleptics and benzodiazepines. Brain MRI showed a small established cerebellar infarction. Electroencephalogram was normal. Tests for toxic metabolic encephalopathy were negative. One oligoclonal IgG bands was found in the Cerebrospinal fluid (CSF), which was not observed in corresponding serum, but cell count and protein were normal. Extensive testing for infectious encephalitis was unremarkable. CSF testing for commercially available neural and non-neural autoantibodies was negative. The patient fulfilled the Gultekin diagnostic criteria for paraneoplastic limbic encephalitis and methylprednisolone IV 1g/d for 5 days was given. He recovered rapidly, with progressive improvement in memory and psychomotor agitation. After treatment commenced, results for antibodies to mGluR5 in CSF taken prior to treatment were returned as positive. mGluR5 is found on post-synaptic terminals of neurons and microglia and is expressed primarily in the hippocampus and amygdala. This case highlights the difficulties in diagnosing this type of encephalitis: the CSF did not show pleocytosis, the MRI showed only chronic change and the electroencephalogram was normal. The dramatic recovery after methylprednisolone help to better characterized the clinical spectrum of auto-immune encephalitis. Diagnosing anti mGlutR5 encephalitis may lead to potentially highly effective treatment option and may anticipate the diagnostic of a cancer. A high index of suspicion is needed to avoid missed diagnosis. In patients with unexplained encephalitis, testing for antibodies to mGluR5 in CSF and serum should be considered. When there is a reasonable index of suspicion of auto-immune encephalitis, treatment should not be delayed for the antibody results.