Comparison of respiratory health-related quality of life in patients with intractable breathlessness due to advanced cancer or advanced COPD.

OBJECTIVES: Breathlessness is common in patients with advanced cancer and almost universal in advanced chronic obstructive pulmonary disease (COPD), but studies suggest their experiences of breathlessness vary. This report builds on these studies by providing quantitative evidence of differences in respiratory health-related quality of life (HRQoL) between these groups. Further, it explores the validity of the Chronic Respiratory Questionnaire (CRQ) in patients with cancer. METHODS: The CRQ-Original was completed within baseline interviews for a randomised controlled trial of a palliative intervention for intractable breathlessness due to advanced disease. Independent samples Mann-Whitney U tests were performed to identify significant differences in median scores for the four CRQ domains (mastery, dyspnoea, emotional function, fatigue) in patients with advanced COPD (n=73) or advanced cancer (n=67). The Minimally Clinically Important Difference of 0.5 was applied to determine clinical significance. RESULTS: Patients with advanced COPD scored lower across all four CRQ domains. This was statistically significant for dyspnoea, mastery and emotional function (p<0.05), and clinically significant for the latter two, suggesting poorer respiratory HRQoL. CONCLUSIONS: Patients with breathlessness due to advanced COPD have worse respiratory HRQoL than those with advanced cancer. This may result from greater burden of breathlessness in COPD due to condition longevity, lesser burden of breathlessness in cancer due to its episodic nature, or variance in palliative referral thresholds by disease group. Our results suggest that greater access to palliative care is needed in advanced COPD, and that formal psychometric testing of the CRQ may be warranted in cancer. TRIAL REGISTRATION NUMBER: NCT00678405.This paper presents independent research commissioned by the (NIHR under its Research for Patient Benefit (RfPB) programme (Grant Reference Number PB-PG-0107-11134). The views expressed are those of the author and not necessarily those of the NHS, the NIHR or the Department of Health. MF’s role in the Phase III RCT of CBIS was funded through a Macmillan Cancer Support post-doctoral fellowship.This is the author accepted manuscript. The final version is available from the BMJ Group via http://dx.doi.org/10.1136/bmjspcare-2015-00094

Association of Descriptors of Breathlessness With Diagnosis and Self-Reported Severity of Breathlessness in Patients With Advanced Chronic Obstructive Pulmonary Disease or Cancer.

CONTEXT: Verbal descriptors are important in understanding patients' experience of breathlessness. OBJECTIVES: The aim of this study was to examine the association between selection of breathlessness descriptors, diagnosis, self-reported severity of breathlessness and self-reported distress due to breathlessness. METHODS: We studied 132 patients grouped according to their diagnosis of advanced chronic obstructive pulmonary disease (n = 69) or advanced cancer (n = 63), self-reported severity of breathlessness as mild breathlessness (Numerical Rating Scale [NRS] ≤ 3, n = 53), moderate breathlessness (4 ≤ NRS ≥ 6, n = 59) or severe breathlessness (NRS ≥ 7, n = 20), and distress due to breathlessness as mild distress (NRS ≤ 3, n = 31), moderate distress (4 ≤ NRS ≥ 6, n = 44), or severe distress (NRS ≥ 7, n = 57). Patients selected three breathlessness descriptors. The relationship between descriptors selected and patient groups was evaluated by cluster analysis. RESULTS: Different combinations of clusters were associated with each diagnostic group; the cluster chest tightness was associated with cancer patients. The association of clusters with patient groups differed depending on their severity of breathlessness and their distress due to breathlessness. The air hunger cluster was associated with patients with moderate or severe breathlessness, and the chest tightness cluster was associated with patients with mild breathlessness. The air hunger cluster was associated with patients with severe distress due to breathlessness. CONCLUSION: The relationship between clusters and diagnosis is not robust enough to use the descriptors to identify the primary cause of breathlessness. Further work exploring how use of breathlessness descriptors reflects the severity of breathlessness and distress due to breathlessness could enable the descriptors to evaluate patient status and target interventions.This paper presents independent research commissioned by the (NIHR under its Research for Patient Benefit (RfPB) programme (Grant Reference Number PB-PG-0107-11134). The views expressed are those of the author and not necessarily those of the NHS, the NIHR or the Department of Health. MF’s role in the Phase III RCT of CBIS was funded through a Macmillan Cancer Support Post-Doctoral Fellowship. The funders had no involvement in the study design, the collection, analysis and interpretation of data, the writing of the report or the decision to submit.This is the author accepted manuscript. The final version is available from Elsevier via http://dx.doi.org/10.1016/j.jpainsymman.2016.01.01

Sparse Binary Relation Representations for Genome Graph Annotation

High-throughput DNA sequencing data are accumulating in public repositories, and efficient approaches for storing and indexing such data are in high demand. In recent research, several graph data structures have been proposed to represent large sets of sequencing data and to allow for efficient querying of sequences. In particular, the concept of labeled de Bruijn graphs has been explored by several groups. Although there has been good progress toward representing the sequence graph in small space, methods for storing a set of labels on top of such graphs are still not sufficiently explored. It is also currently not clear how characteristics of the input data, such as the sparsity and correlations of labels, can help to inform the choice of method to compress the graph labeling. In this study, we present a new compression approach, Multi-binary relation wavelet tree (BRWT), which is adaptive to different kinds of input data. We show an up to 29% improvement in compression performance over the basic BRWT method, and up to a 68% improvement over the current state-of-the-art for de Bruijn graph label compression. To put our results into perspective, we present a systematic analysis of five different state-of-the-art annotation compression schemes, evaluate key metrics on both artificial and real-world data, and discuss how different data characteristics influence the compression performance. We show that the improvements of our new method can be robustly reproduced for different representative real-world data sets.ISSN:1066-5277ISSN:1557-866

Sparse Binary Relation Representations for Genome Graph Annotation

High-throughput DNA sequencing data is accumulating in public repositories, and efficient approaches for storing and indexing such data are in high demand. In recent research, several graph data structures have been proposed to represent large sets of sequencing data and allow for efficient query of sequences. In particular, the concept of colored de Bruijn graphs has been explored by several groups. While there has been good progress towards representing the sequence graph in small space, methods for storing a set of labels on top of such graphs are still not sufficiently explored. It is also currently not clear how characteristics of the input data, such as the sparsity and correlations of labels, can help to inform the choice of method to compress the labels. In this work, we present a systematic analysis of five different state-of-the-art annotation compression schemes that evaluates key metrics on both artificial and real-world data and discusses how different data characteristics influence the compression performance. In addition, we present a new approach, Multi-BRWT, that shows an up to 50% improvement in compression performance over the current state-of-the-art and is adaptive to different kinds of input data. Using our comprehensive test datasets, we show that this improvement can be robustly reproduced for different representative real-world datasets

LongTR: genome-wide profiling of genetic variation at tandem repeats from long reads.

Tandem repeats are frequent across the human genome, and variation in repeat length has been linked to a variety of traits. Recent improvements in long read sequencing technologies have the potential to greatly improve tandem repeat analysis, especially for long or complex repeats. Here, we introduce LongTR, which accurately genotypes tandem repeats from high-fidelity long reads available from both PacBio and Oxford Nanopore Technologies. LongTR is freely available at https://github.com/gymrek-lab/longtr and https://zenodo.org/doi/10.5281/zenodo.11403979

A deep population reference panel of tandem repeat variation.

Tandem repeats (TRs) represent one of the largest sources of genetic variation in humans and are implicated in a range of phenotypes. Here we present a deep characterization of TR variation based on high coverage whole genome sequencing from 3550 diverse individuals from the 1000 Genomes Project and H3Africa cohorts. We develop a method, EnsembleTR, to integrate genotypes from four separate methods resulting in high-quality genotypes at more than 1.7 million TR loci. Our catalog reveals novel sequence features influencing TR heterozygosity, identifies population-specific trinucleotide expansions, and finds hundreds of novel eQTL signals. Finally, we generate a phased haplotype panel which can be used to impute most TRs from nearby single nucleotide polymorphisms (SNPs) with high accuracy. Overall, the TR genotypes and reference haplotype panel generated here will serve as valuable resources for future genome-wide and population-wide studies of TRs and their role in human phenotypes