Diffusion Tensor MRI is sensitive to fibrotic injury in a mouse model of oxalate induced chronic kidney disease.

Chronic kidney disease (CKD) is characterized by inflammation and fibrosis in the kidney. Renal biopsies and estimated glomerular filtration rate (eGFR) remain the standard of care, but these endpoints have limitations in detecting the stage, progression, and spatial distribution of fibrotic pathology in the kidney. MRI diffusion tensor imaging (DTI) has emerged as a promising non-invasive technology to evaluate renal fibrosis in vivo both in clinical and preclinical studies. However, these imaging studies have not systematically identified fibrosis particularly deeper in the kidney where biopsy sampling is limited, or completed an extensive analysis of whole organ histology, blood biomarkers, and gene expression to evaluate the relative strengths and weaknesses of MRI for evaluating renal fibrosis. In this study, we performed DTI in the sodium oxalate mouse model of CKD. The DTI parameters fractional anisotropy, apparent diffusion coefficient, and axial diffusivity were compared between the control and oxalate groups with region-of-interest (ROI) analysis to determine changes in the cortex and medulla. Additionally, voxel-based analysis (VBA) was implemented to systematically identify local regions of injury over the whole kidney. DTI parameters were found to be significantly different in the medulla by both ROI analysis and VBA, which also spatially matched with collagen III IHC. The DTI parameters in this medullary region exhibited moderate to strong correlations with histology, blood biomarkers, hydroxyproline and gene expression. Our results thus highlight the sensitivity of DTI to the heterogeneity of renal fibrosis and importance of whole kidney non-invasive imaging.

Opioid Switch Dosing in Chronic Cancer Pain: A Prospective Longitudinal Study.

Background: Opioid switching is common, however, conversion tables have limitations. Guidelines suggest postswitch dose reduction, yet, observations show opioid doses may increase postswitch. Objectives: To document the opioid conversion factor postswitch in cancer, and whether pain and adverse effect outcomes differ between switched opioid groups. Design/Setting: This multicenter prospective longitudinal study included people with advanced cancer in Australia. Clinical data (demographics, opioids) and validated instruments (pain, adverse effects) were collected twice, seven days apart. Results: Opioid switch resulted in dose increase (median oral morphine equivalent daily dose 90 mg [interquartile range {IQR} 45-184] to 150 mg [IQR 79-270]), reduced average pain (5.1 [standard deviation {SD} 1.7] to 3.8 [SD 1.6]), and reduced adverse effects. Hydromorphone dose increased 2.5 times (IQR 1.0-3.6) above the original conversion factor used. Conclusions: Opioid switching resulted in overall dose increase, particularly when switching to hydromorphone. Higher preswitch dosing may require higher dose conversion ratios. Dose reduction postswitch risks undertreatment and may not be always appropriate.

Effect of gene variants on opioid dose, pain and adverse effect outcomes in advanced cancer: an explorative study.

Aim: Associations between gene variants and opioid net effect are unclear. We conducted an exploratory pharmacogenetic analysis of 35 gene variants and opioid response in advanced cancer. Patients & methods: This multi-center prospective cohort study included clinical data, questionnaires (pain and adverse effects) and DNA (blood). Negative binomial regression and logistic regression were used. Results: Within 54 participants, eight statistically significant associations (p = 0.002-0.038) were observed between gene variants and opioid dose, pain scores or adverse effects, the majority being within the neuroimmune TLR4 pathway (IL1B [rs1143634], IL2 [rs2069762], IL6 [rs1800795], BDNF [rs6265]) and ARRB2 pathway (ARRB2 [rs3786047], DRD2 [rs6275]). Conclusion: Neuroimmune pathway genes may contribute to differences in opioid response in cancer and may be included in future similar studies.

Atlas of primary cell-type-specific sequence models of gene expression and variant effects.

Human biology is rooted in highly specialized cell types programmed by a common genome, 98% of which is outside of genes. Genetic variation in the enormous noncoding space is linked to the majority of disease risk. To address the problem of linking these variants to expression changes in primary human cells, we introduce ExPectoSC, an atlas of modular deep-learning-based models for predicting cell-type-specific gene expression directly from sequence. We provide models for 105 primary human cell types covering 7 organ systems, demonstrate their accuracy, and then apply them to prioritize relevant cell types for complex human diseases. The resulting atlas of sequence-based gene expression and variant effects is publicly available in a user-friendly interface and readily extensible to any primary cell types. We demonstrate the accuracy of our approach through systematic evaluations and apply the models to prioritize ClinVar clinical variants of uncertain significance, verifying our top predictions experimentally.

Defining research priorities and needs in cancer symptoms for adults diagnosed with cancer: an Australian/New Zealand modified Delphi study.

PURPOSE: This study asked consumers (patients, carers) and healthcare professionals (HCPs) to identify the most important symptoms for adults with cancer and potential treatment interventions. METHODS: A modified Delphi study was conducted involving two rounds of electronic surveys based on prevalent cancer symptoms identified from the literature. Round 1 gathered information on participant demographics, opinions and/or experience on cancer symptom frequency and impact, and suggestions for interventions and/or service delivery models for further research to improve management of cancer symptoms. In Round 2, respondents ranked the importance of the top ten interventions identified in Round 1. In Round 3, separate expert panels of consumers and healthcare professionals (HCPs) attempted to reach consensus on the symptoms and interventions previously identified. RESULTS: Consensus was reached for six symptoms across both groups: fatigue, constipation, diarrhoea, incontinence, and difficulty with urination. Notably, fatigue was the only symptom to reach consensus across both groups in Round 1. Similarly, consensus was reached for six interventions across both groups. These were the following: medicinal cannabis, physical activity, psychological therapies, non-opioid interventions for pain, opioids for breathlessness and cough, and other pharmacological interventions. CONCLUSIONS: Consumers and HCPs prioritise differently; however, the symptoms and interventions that reached consensus provide a basis for future research. Fatigue should be considered a high priority given its prevalence and its influence on other symptoms. The lack of consumer consensus indicates the uniqueness of their experience and the need for a patient-centred approach. Understanding individual consumer experience is important when planning research into better symptom management.

Effectiveness of Opioid Switching in Advanced Cancer Pain: A Prospective Observational Cohort Study.

Opioid switching is a common practice of substituting one opioid for another to improve analgesia or adverse effects; however, it has limited evidence. This study aimed to examine the effectiveness of opioid switching in advanced cancer. This multi-center prospective cohort study recruited patients assessed to switch opioids (opioid switch group) or to continue ongoing opioid treatment (control group). Clinical data (demographics, opioids) and validated instruments (pain and adverse effects) were collected over two timepoints seven days apart. Descriptive analyses were utilized. Non-parametric tests were used to determine differences. Fifty-four participants were recruited (23 control group, 31 switch group). At the follow-up, opioid switching reduced pain (worst, average, and now) (p < 0.05), uncontrolled breakthrough pain (3-fold reduction, p = 0.008), and psychological distress (48% to 16%, p < 0.005). The switch group had a ≥25% reduction in the reported frequency of seven moderate-to-severe adverse effects (score ≥ 4), compared to a reduction in only one adverse effect in the control group. The control group experienced no significant pain differences at the follow-up. Opioid switching is effective at reducing pain, adverse effects, and psychological distress in a population with advanced cancer pain, to levels of satisfactory symptom control in most patients within 1 week.

Amino acid homeostasis is a target of metformin therapy.

OBJECTIVE: Unexplained changes in regulation of branched chain amino acids (BCAA) during diabetes therapy with metformin have been known for years. Here we have investigated mechanisms underlying this effect. METHODS: We used cellular approaches, including single gene/protein measurements, as well as systems-level proteomics. Findings were then cross-validated with electronic health records and other data from human material. RESULTS: In cell studies, we observed diminished uptake/incorporation of amino acids following metformin treatment of liver cells and cardiac myocytes. Supplementation of media with amino acids attenuated known effects of the drug, including on glucose production, providing a possible explanation for discrepancies between effective doses in vivo and in vitro observed in most studies. Data-Independent Acquisition proteomics identified that SNAT2, which mediates tertiary control of BCAA uptake, was the most strongly suppressed amino acid transporter in liver cells following metformin treatment. Other transporters were affected to a lesser extent. In humans, metformin attenuated increased risk of left ventricular hypertrophy due to the AA allele of KLF15, which is an inducer of BCAA catabolism. In plasma from a double-blind placebo-controlled trial in nondiabetic heart failure (trial registration: NCT00473876), metformin caused selective accumulation of plasma BCAA and glutamine, consistent with the effects in cells. CONCLUSIONS: Metformin restricts tertiary control of BCAA cellular uptake. We conclude that modulation of amino acid homeostasis contributes to therapeutic actions of the drug.

Prevalence, Severity, and Predictors of Insomnia in Advanced Colorectal Cancer.

CONTEXT: Insomnia is an under-recognized and undertreated symptom in palliative care and advanced cancer cohorts. Insomnia in an advanced colorectal cancer cohort is yet to be investigated despite colorectal cancer being the third commonest cancer worldwide and one with a high symptom burden. OBJECTIVES: To examine the prevalence of insomnia and its associations in a large advanced colorectal cancer cohort. METHODS: A consecutive cohort study of 18,302 patients with colorectal cancer seen by palliative care services across various settings (inpatient, outpatient, and ambulatory) was conducted from an Australia-wide database (2013-2019). The Symptom Assessment Score (SAS) was used to assess the severity of insomnia. Clinically significant insomnia was defined as SAS score ≥3/10, and used to compare associations with other symptoms and functional scores from validated questionnaires. RESULTS: The prevalence of any insomnia was 50.5%, and clinically significant insomnia 35.6%, particularly affecting people who were younger (<45-years-old), more mobile (AKPS score ≥70), or physically capable (RUG-ADL score ≤5). Outpatients and patients living at home had higher prevalence of insomnia. Nausea, anorexia and psychological distress were the commonest concurrent symptoms in patients with clinically significant insomnia. CONCLUSIONS: To our knowledge, this study was the first to investigate the prevalence and associations of insomnia in an advanced colorectal cancer cohort. Our findings demonstrate several groups at greater risk of suffering from insomnia (younger, greater physical capacity, living at home, and those with greater psychological distress). This may guide earlier recognition and management of insomnia to improve overall quality of life in this population.

Does cancer type and adjuvant analgesic prescribing influence opioid dose?-a retrospective cross-sectional study.

Opioids are the backbone of cancer pain management. Minimal evidence exists examining the relationship between cancer type and opioid dose. Similarly, the use of adjuvant analgesics and its impact within an inpatient cancer setting is understudied. This study examined the influence of cancer type upon opioid dose, measured by oral morphine equivalent daily dose (oMEDD). The effect of adjuvant analgesics on patient oMEDD was also examined. This retrospective cross-sectional study examined records of 520 patients admitted to Royal Melbourne Hospital or Peter MacCallum Cancer Centre between 2016 and 2018 with advanced cancer. Number and dose of both opioid and adjuvant analgesics were collected along with demographic and cancer data. Comparisons of median oMEDD by cancer type [analysis of variance (ANOVA), non-parametric t-tests] and adjuvant analgesics (Kruskal-Wallis test) were performed. There were no statistically significant differences in oMEDD between the 12 cancer types (P=0.83; n=215). Patients co-prescribed pregabalin (n=102) and paracetamol (n=73) as adjuvant analgesics were on significantly higher daily oMEDD [60 mg (P=0.015), 90 mg (P<0.001), respectively]. Opioid dose did not differ significantly between cancer types. The observed use of adjuvant analgesics coincided with significantly higher oMEDD prescription which may relate to complex pain seen in this cohort of inpatients in a quarternary cancer centre. Future research should focus on pain type and aetiology, and pain scores in different cancer pain syndromes to determine the net effect of opioids and adjuvants in cancer pain prescribing.

Do Patient Demographics and Performance Status Influence Opioid Dose in Cancer Pain?

Context: There is limited evidence on the role of objective parameters in influencing analgesic use in cancer pain management.Objective: To investigate the significance of objective parameters (age, male/female and performance status) in influencing opioid dose. Methods: We conducted a retrospective cross-sectional audit of adult inpatients with metastatic cancer at a major cancer centre from 1 January 2016 to 31 December 2018, who were prescribed slow release opioids for cancer pain on discharge. Main outcome measures were demographics (age, male/female and performance status), oral morphine equivalent daily dose (oMEDD) and adjuvant analgesic use. Results: Of the 7,747 eligible records, 215 patient records fulfilled inclusion criteria. Older patients (≥75 years) received half of the median oMEDD dose (30 mg) compared to their youngest counterparts (60 mg oMEDD in age ≤50 years) (P = .003). No significant differences were observed between oMEDD and male/female and performance status. Conclusion: Older patients are prescribed half the opioid dose compared to their younger counterparts. This highlights the importance of vigilance in opioid prescribing in the elderly in order to balance side effects with under treatment. Although no other significant relationships were observed, future studies comparing objective patient parameters with opioid prescription may uncover other at risk populations.

ENhANCE trial protocol: A multi-centre, randomised, phase IV trial comparing the efficacy of oxycodone/naloxone prolonged release (OXN PR) versus oxycodone prolonged release (Oxy PR) tablets in patients with advanced cancer.

Background: Oxycodone is a frequently used opioid in cancer. Opioid-induced constipation (OIC) is common. Oxycodone/Naloxone Prolonged Release (OXN PR) contains naloxone, which mitigates OIC. Trials have either focused on non-cancer pain, or conducted before significant experience of using OXN PR. This trial aims to: demonstrate (1) analgesic equivalence between OXN PR and Oxycodone Prolonged Release (Oxy PR), and (2) superiority of constipation outcomes in OXN PR compared to Oxy PR in cancer pain. Unlike other trials, it will only include patients with at least moderate pain scores (≥4/10), allow usual laxatives, and exclude potential liver dysfunction. Methods: This is a multi-centre, open-label, randomised, phase IV study of OXN PR vs Oxy PR in patients with cancer-related pain. The primary outcome is pain difference on Brief Pain Inventory-Short Form (BPI-SF) at 5 weeks. Secondary outcomes are comparison of other pain outcomes (BPI-SF) and neuropathic pain measures (Leeds Assessment of Neuropathic Symptoms & Signs (S-LANNS)), constipation (Bowel Function Index (BFI)), quality of life (EORTC-QLQ-C30), rescue analgesia use, total opioid dose, and total laxative dose over 5 weeks. Conclusion: The comparison of analgesic efficacy between both arms, and superiority of constipation in the OXN PR arm will add new knowledge on the comparisons of both agents, and oxycodone independently. This trial will extend knowledge of the effectiveness, safety, and adverse effect profiles of both drugs in terms of pain, constipation, quality of life outcomes for patients with cancer pain, and provide clinicians with high quality data to guide decision making. Trial registration: Name of the registry: ANZCTR. Trial registration number: ACTRN12619001282178. Date of registration: 17/09/2019. URL of trial registry record: https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=377673&isReview=trueProtocol version 2.1_28 August 2020.

The Role of Pharmacogenomics in Opioid Prescribing.

OPINION STATEMENT: Pharmacogenomics is increasingly important to guide objective, safe, and effective individualised prescribing. Personalised prescribing has revolutionised treatments in the past decade, allowing clinicians to maximise drug efficacy and minimise adverse effects based on a person's genetic profile. Opioids, the gold standard for cancer pain relief, are among the commonest medications prescribed in palliative care practice. This narrative review examines the literature surrounding opioid pharmacogenomics and its applicability to the palliative care cancer population. There is currently limited intersection between the fields of palliative care and pharmacogenomics, but growing evidence presents a need to build linkages between the two disciplines. Pharmacogenomic evidence guiding opioid prescribing is currently available for codeine and tramadol, which relates to CYP2D6 gene variants. However, these medications are prescribed less commonly for pain in palliative care. Research is accelerating with other opioids, where oxycodone (CYP2D6) and methadone (CYP2B6, ABCB1) already have moderate evidence of an association in terms of drug metabolism and downstream analgesic response and side effects. OPRM1 and COMT are receiving increasing attention and have implications for all opioids, with changes in opioid dosage requirements observed but they have not yet been studied widely enough to be considered clinically actionable. Current evidence indicates that incorporation of pharmacogenomic testing into opioid prescribing practice should focus on the CYP2D6 gene and its actionable variants. Although opioid pharmacogenomic tests are not widely used in clinical practice, the progressively reducing costs and rapid turnover means greater accessibility and affordability to patients, and thus, clinicians will be increasingly asked to provide guidance in this area. The upsurge in pharmacogenomic research will likely discover more actionable gene variants to expand international guidelines to impact opioid prescribing. This rapidly expanding area requires consideration and monitoring by clinicians in order for key findings with clinical implications to be accessible, meaningfully interpretable and communicated.

A sequence-based global map of regulatory activity for deciphering human genetics.

Epigenomic profiling has enabled large-scale identification of regulatory elements, yet we still lack a systematic mapping from any sequence or variant to regulatory activities. We address this challenge with Sei, a framework for integrating human genetics data with sequence information to discover the regulatory basis of traits and diseases. Sei learns a vocabulary of regulatory activities, called sequence classes, using a deep learning model that predicts 21,907 chromatin profiles across >1,300 cell lines and tissues. Sequence classes provide a global classification and quantification of sequence and variant effects based on diverse regulatory activities, such as cell type-specific enhancer functions. These predictions are supported by tissue-specific expression, expression quantitative trait loci and evolutionary constraint data. Furthermore, sequence classes enable characterization of the tissue-specific, regulatory architecture of complex traits and generate mechanistic hypotheses for individual regulatory pathogenic mutations. We provide Sei as a resource to elucidate the regulatory basis of human health and disease.

A Multi-Centre COVID-19 Study Examining Symptoms and Medication Use in the Final Week of Life.

CONTEXT: Guidelines exist to direct end-of-life symptom management in COVID-19 patients. However, the real-world symptom patterns, and degree of concordance with guidelines on medication use, and palliative care involvement has received limited attention. OBJECTIVES: To describe the evolution of COVID-19 symptoms, medication used to alleviate these, and degree of palliative care involvement in the final week of life. METHODS: This retrospective study reviewed all COVID-19 inpatient deaths across five metropolitan hospitals in Australia from January 1 to December 31, 2020. Outcome measures were collected at day of death, and days one, two, five and seven before death. These were COVID-19 symptom severity (measured by the Palliative Care Outcome Scale), and use of supportive pharmacological and non-pharmacological therapies. Palliative care referral timepoint was also collected. RESULTS: Within the sample of 230 patients, commonest symptoms were breathlessness, agitation, pain, and respiratory secretions. On day of death, 79% (n = 181) experienced at least one symptom, and 30% (n = 68) experienced severe/extreme symptoms. The use of midazolam, glycopyrrolate, and infusions for symptom management occurred late, less frequently, and at lower doses than suggested in guidelines and other studies. Palliative care referrals were made late, at median three days before death (IQR 1-6 days), and for only half of people dying from COVID-19 (51%; n = 118). CONCLUSION: Symptoms peaked in final three days of life. Earlier use of in fusional and breakthrough medications should be considered in anticipation of symptoms given high likelihood of dying in discomfort. Earlier palliative care referral for high-risk patients should be considered at hospital admission.

A multi-centre study on patients dying from COVID-19: communication between clinicians, patients and their families.

BACKGROUND: COVID-19 has led to challenges in providing effective and timely communication in healthcare. Services have been required to adapt and evolve as successful communication remains core to high-quality patient-centred care. AIM: To describe the communication between admitted patients, their families and clinicians (medical, nursing, allied health) during end-of-life care. METHODS: This retrospective review included all patients (n = 230) who died directly due to COVID-19 at five Melbourne hospitals between 1 January and 31 December 2020. Contacts and modality used (face to face, video, telephone) during the 8 days prior to death were recorded. RESULTS: Patients were predominantly elderly (median age 86 years) and from residential aged care facilities (62%; n = 141). Communication frequency increased the closer the patient was to death, where on day of death, contact between clinicians and patients was 93% (n = 213) clinicians and families 97% (n = 222) and between patients and families 50% (n = 115). Most contact between patients and families was facilitated by a clinician (91.3% (n = 105) day of death) with the most commonly used mode being video call (n = 30 day of death). CONCLUSION: This study is one of the first and largest Australian reports on how communication occurs at the end of life for patients dying of COVID-19. Contact rates were relatively low between patients and families, compared with other cohorts dying from non-COVID-19 related causes. The impact of this difference on bereavement outcomes requires surveillance and attention.

Decoding disease: from genomes to networks to phenotypes.

Interpreting the effects of genetic variants is key to understanding individual susceptibility to disease and designing personalized therapeutic approaches. Modern experimental technologies are enabling the generation of massive compendia of human genome sequence data and associated molecular and phenotypic traits, together with genome-scale expression, epigenomics and other functional genomic data. Integrative computational models can leverage these data to understand variant impact, elucidate the effect of dysregulated genes on biological pathways in specific disease and tissue contexts, and interpret disease risk beyond what is feasible with experiments alone. In this Review, we discuss recent developments in machine learning algorithms for genome interpretation and for integrative molecular-level modelling of cells, tissues and organs relevant to disease. More specifically, we highlight existing methods and key challenges and opportunities in identifying specific disease-causing genetic variants and linking them to molecular pathways and, ultimately, to disease phenotypes.

Modeling transcriptional regulation of model species with deep learning.

To enable large-scale analyses of transcription regulation in model species, we developed DeepArk, a set of deep learning models of the cis-regulatory activities for four widely studied species: Caenorhabditis elegans, Danio rerio, Drosophila melanogaster, and Mus musculus DeepArk accurately predicts the presence of thousands of different context-specific regulatory features, including chromatin states, histone marks, and transcription factors. In vivo studies show that DeepArk can predict the regulatory impact of any genomic variant (including rare or not previously observed) and enables the regulatory annotation of understudied model species.

COVID-19 end-of-life care: symptoms and supportive therapy use in an Australian hospital.

BACKGROUND: Descriptions of symptoms and medication use at end of life in COVID-19 are limited to small cross-sectional studies, with no Australian longitudinal data. AIMS: To describe end-of-life symptoms and care needs of people dying of COVID-19. METHODS: This retrospective cohort study included consecutive admitted patients who died at a Victorian tertiary referral hospital from 1 January to 30 September directly due to COVID-19. Clinical characteristics, symptoms and use of supportive therapies, including medications and non-pharmacological interventions in the last 3 days of life were extracted. RESULTS: The cohort comprised 58 patients (median age 87 years, interquartile range (IQR) 81-90) predominantly admitted from home (n = 30), who died after a median of 11 days (IQR 6-28) in the acute medical (n = 31) or aged care (n = 27) wards of the hospital. The median Charlson Comorbidity Score was 7 (IQR 5-8). Breathlessness (n = 42), agitation (n = 36) and pain (n = 33) were the most frequent clinician-reported symptoms in the final 3 days of life, with most requiring opioids (n = 52), midazolam (n = 40), with dose escalation commonly being required. While oxygen therapy was commonly used (n = 47), few (n = 13) required an anti-secretory agent. CONCLUSIONS: This study presents one of the first and largest Australian report of the end of life and symptom experience of people dying of COVID-19. This information should help clinicians to anticipate palliative care needs of these patients, for example, recognising that higher starting doses of opioids and sedatives may help reduce prevalence and severity of breathlessness and agitation near death.

Tissue-specific enhancer functional networks for associating distal regulatory regions to disease.

Systematic study of tissue-specific function of enhancers and their disease associations is a major challenge. We present an integrative machine-learning framework, FENRIR, that integrates thousands of disparate epigenetic and functional genomics datasets to infer tissue-specific functional relationships between enhancers for 140 diverse human tissues and cell types, providing a regulatory-region-centric approach to systematically identify disease-associated enhancers. We demonstrated its power to accurately prioritize enhancers associated with 25 complex diseases. In a case study on autism, FENRIR-prioritized enhancers showed a significant proband-specific de novo mutation enrichment in a large, sibling-controlled cohort, indicating pathogenic signal. We experimentally validated transcriptional regulatory activities of eight enhancers, including enhancers not previously reported with autism, and demonstrated their differential regulatory potential between proband and sibling alleles. Thus, FENRIR is an accurate and effective framework for the study of tissue-specific enhancers and their role in disease. FENRIR can be accessed at fenrir.flatironinstitute.org/.