Treatment Burden and Chronic Illness: Who is at Most Risk?

Background: There is a need to ascertain the type and level of treatment burden experienced by people with co-morbidities. This is important to identify the characteristics of participants who are at most risk of treatment burden.  Objective: The aim of this study is to identify the characteristics of participants who are at most risk of treatment burden.  Methods: This cross-sectional study was part of a larger project and recruitment was conducted across four Australian regions: rural, semi-rural and metropolitan. Participants were asked about their treatment burden using an adapted version of a measure, which included the following five dimensions: medication, time and administrative, lifestyle change, social life and financial burden.  Results: In total, 581 participants with various chronic health conditions reported a mean global treatment burden of 56.5 out of 150 (standard deviation = 34.5). Number of chronic conditions (β = .34, p < 0.01), age, (β = −.27, p < 0.01), the presence of an unpaid carer (β = .22, p < 0.001) and the presence of diabetes mellitus and other endocrine conditions (β = .13, p < 0.01) were significant predictors of overall treatment burden. For the five dimensions of treatment burden, social, medicine and administrative burden were predicted by the same cluster of variables: number of conditions, age, presence of an unpaid carer and diabetes. However, in addition to these variables, financial dimensions were also predicted by education level, ethnicity and health insurance. Educational level also influenced lifestyle burden.  Conclusion: A substantial proportion of community-dwelling adults with chronic conditions have considerable levels of treatment burden. Specifically, health professionals should provide greater focus on managing overall treatment burden for persons who are of young age, have an endocrine condition or an unpaid carer, or a combination of these factors

Characterizing the heterogeneity of triple-negative breast cancers using microdissected normal ductal epithelium and RNA-sequencing

Triple-negative breast cancers (TNBCs) are a heterogeneous set of tumors defined by an absence of actionable therapeutic targets (ER, PR, and HER-2). Microdissected normal ductal epithelium from healthy volunteers represents a novel comparator to reveal insights into TNBC heterogeneity and to inform drug development. Using RNA-sequencing data from our institution and The Cancer Genome Atlas (TCGA) we compared the transcriptomes of 94 TNBCs, 20 microdissected normal breast tissues from healthy volunteers from the Susan G. Komen for the Cure Tissue Bank, and 10 histologically normal tissues adjacent to tumor. Pathway analysis comparing TNBCs to optimized normal controls of microdissected normal epithelium versus classic controls composed of adjacent normal tissue revealed distinct molecular signatures. Differential gene expression of TNBC compared with normal comparators demonstrated important findings for TNBC-specific clinical trials testing targeted agents; lack of over-expression for negative studies and over-expression in studies with drug activity. Next, by comparing each individual TNBC to the set of microdissected normals, we demonstrate that TNBC heterogeneity is attributable to transcriptional chaos, is associated with non-silent DNA mutational load, and explains transcriptional heterogeneity in addition to known molecular subtypes. Finally, chaos analysis identified 146 core genes dysregulated in >90 % of TNBCs revealing an over-expressed central network. In conclusion, use of microdissected normal ductal epithelium from healthy volunteers enables an optimized approach for studying TNBC and uncovers biological heterogeneity mediated by transcriptional chaos

Sheep Updates 2008 - part 3

This session covers fiveteen papers from different authors: CONTROLLING FLY STRIKE 1. Breeding for Blowfly Resistance - Indicatoe Traits, LJE Karlsson, JC Greeff, L Slocombe, Department of Agriculture & Food, Western Australia 2.A practical method to select for breech strike resistance in non-pedigreed Merino flocks, LJE Karlsson, JC Greeff, L Slocombe, K. Jones, N. Underwood, Department of Agriculture & Food, Western Australia 3. Twice a year shearing - no mulesing, Fred Wilkinson, Producer, Brookton WA BEEF 4. Commercial testing of a new tool for prediction of fatness in beef cattle, WD HoffmanA, WA McKiernanA, VH OddyB, MJ McPheeA, Cooperative Research Centre for Beef Genetic Technologies, A N.S.W. Deptartment of Primary Industries, B University of New England 5. A new tool for the prediction of fatness in beef cattle, W.A. McKiernanA, V.H. OddyB and M.J. McPheeC; Cooperative Research Centre for Beef Genetic Technologies, A N.S.W. Dept of Primary Industries, B University of New England, C N.S.W. Dept of Primary Industries Beef Industry Centre of Excellence. 6. Effect of gene markers for tenderness on eating quality of beef, B.L. McIntyre, CRC for Beef Genetic Technologies, Department of Agriculture and Food WA 7. Accelerating beef industry innovation through Beef Profit Partnerships, Parnell PF1,2, Clark RA1,3, Timms J1,3, Griffith G1,2, Alford A1,2, Mulholland C1 and Hyland P1,4,1Co-operative Research Centre for Beef Genetic Technologies; 2NSW Department of Primary Industries; 3 Qld Department of Primary Industries and Fisheries; 4The University of Queensland. SUSTAINABILITY 8. The WA Sheep Industry - is it ethically and environmentally sustainable? Danielle England, Department of Agriculture and Food Western Australia 9. Overview of ruminant agriculture and greenhouse emissions, Fiona Jones, Department of Agriculture and Food Western Australia 10. Grazing for Nitrogen Efficiency, John Lucey, Martin Staines and Richard Morris, Department of Agriculture and Food Western Australia 11. Investigating potential adaptations to climate change for low rainfall farming system, Megan Abrahams, Caroline Peek, Dennis Van Gool, Daniel Gardiner, Kari-Lee Falconer, Department of Agriculture and Food Western Australia SHEEP 12. Benchmarking ewe productivity through on-farm genetic comparisons, Sandra Prosser, Mario D’Antuono and Johan Greeff; Department of Agriculture and Food Western Australia 13. Increasing profitability by pregnancy scanning ewes, John Young1, Andrew Thompson2 and Chris Oldham2; 1Farming Systems Analysis Service, Kojonup, WA, 2Department of Agriculture and Food Western Australia 14. Targeted treatment of worm-affected sheep - more efficient, more sustainable, Brown Besier, Department of Agriculture and Food Western Australia 15. Improving Weaner Sheep Survival, Angus Campbell and Ralph Behrendt, Cooperative Research Centre for Sheep Industry Innovatio

RNA-Seq Mapping and Detection of Gene Fusions with a Suffix Array Algorithm

High-throughput RNA sequencing enables quantification of transcripts (both known and novel), exon/exon junctions and fusions of exons from different genes. Discovery of gene fusions–particularly those expressed with low abundance– is a challenge with short- and medium-length sequencing reads. To address this challenge, we implemented an RNA-Seq mapping pipeline within the LifeScope software. We introduced new features including filter and junction mapping, annotation-aided pairing rescue and accurate mapping quality values. We combined this pipeline with a Suffix Array Spliced Read (SASR) aligner to detect chimeric transcripts. Performing paired-end RNA-Seq of the breast cancer cell line MCF-7 using the SOLiD system, we called 40 gene fusions among over 120,000 splicing junctions. We validated 36 of these 40 fusions with TaqMan assays, of which 25 were expressed in MCF-7 but not the Human Brain Reference. An intra-chromosomal gene fusion involving the estrogen receptor alpha gene ESR1, and another involving the RPS6KB1 (Ribosomal protein S6 kinase beta-1) were recurrently expressed in a number of breast tumor cell lines and a clinical tumor sample

Extensive sequencing of seven human genomes to characterize benchmark reference materials

The Genome in a Bottle Consortium, hosted by the National Institute of Standards and Technology (NIST) is creating reference materials and data for human genome sequencing, as well as methods for genome comparison and benchmarking. Here, we describe a large, diverse set of sequencing data for seven human genomes; five are current or candidate NIST Reference Materials. The pilot genome, NA12878, has been released as NIST RM 8398. We also describe data from two Personal Genome Project trios, one of Ashkenazim Jewish ancestry and one of Chinese ancestry. The data come from 12 technologies: BioNano Genomics, Complete Genomics paired-end and LFR, Ion Proton exome, Oxford Nanopore, Pacific Biosciences, SOLiD, 10X Genomics GemCode WGS, and Illumina exome and WGS paired-end, mate-pair, and synthetic long reads. Cell lines, DNA, and data from these individuals are publicly available. Therefore, we expect these data to be useful for revealing novel information about the human genome and improving sequencing technologies, SNP, indel, and structural variant calling, and de novo assembly

A second-generation combined linkage–physical map of the human genome

We have completed a second-generation linkage map that incorporates sequence-based positional information. This new map, the Rutgers Map v.2, includes 28,121 polymorphic markers with physical positions corroborated by recombination-based data. Sex-averaged and sex-specific linkage map distances, along with confidence intervals, have been estimated for all map intervals. In addition, a regression-based smoothed map is provided that facilitates interpolation of positions of unmapped markers on this map. With nearly twice as many markers as our first-generation map, the Rutgers Map continues to be a unique and comprehensive resource for obtaining genetic map information for large sets of polymorphic markers

Sheep Updates 2008 - part 3

This session covers fiveteen papers from different authors: CONTROLLING FLY STRIKE 1. Breeding for Blowfly Resistance - Indicatoe Traits, LJE Karlsson, JC Greeff, L Slocombe, Department of Agriculture & Food, Western Australia 2.A practical method to select for breech strike resistance in non-pedigreed Merino flocks, LJE Karlsson, JC Greeff, L Slocombe, K. Jones, N. Underwood, Department of Agriculture & Food, Western Australia 3. Twice a year shearing - no mulesing, Fred Wilkinson, Producer, Brookton WA BEEF 4. Commercial testing of a new tool for prediction of fatness in beef cattle, WD HoffmanA, WA McKiernanA, VH OddyB, MJ McPheeA, Cooperative Research Centre for Beef Genetic Technologies, A N.S.W. Deptartment of Primary Industries, B University of New England 5. A new tool for the prediction of fatness in beef cattle, W.A. McKiernanA, V.H. OddyB and M.J. McPheeC; Cooperative Research Centre for Beef Genetic Technologies, A N.S.W. Dept of Primary Industries, B University of New England, C N.S.W. Dept of Primary Industries Beef Industry Centre of Excellence. 6. Effect of gene markers for tenderness on eating quality of beef, B.L. McIntyre, CRC for Beef Genetic Technologies, Department of Agriculture and Food WA 7. Accelerating beef industry innovation through Beef Profit Partnerships, Parnell PF1,2, Clark RA1,3, Timms J1,3, Griffith G1,2, Alford A1,2, Mulholland C1 and Hyland P1,4,1Co-operative Research Centre for Beef Genetic Technologies; 2NSW Department of Primary Industries; 3 Qld Department of Primary Industries and Fisheries; 4The University of Queensland. SUSTAINABILITY 8. The WA Sheep Industry - is it ethically and environmentally sustainable? Danielle England, Department of Agriculture and Food Western Australia 9. Overview of ruminant agriculture and greenhouse emissions, Fiona Jones, Department of Agriculture and Food Western Australia 10. Grazing for Nitrogen Efficiency, John Lucey, Martin Staines and Richard Morris, Department of Agriculture and Food Western Australia 11. Investigating potential adaptations to climate change for low rainfall farming system, Megan Abrahams, Caroline Peek, Dennis Van Gool, Daniel Gardiner, Kari-Lee Falconer, Department of Agriculture and Food Western Australia SHEEP 12. Benchmarking ewe productivity through on-farm genetic comparisons, Sandra Prosser, Mario D’Antuono and Johan Greeff; Department of Agriculture and Food Western Australia 13. Increasing profitability by pregnancy scanning ewes, John Young1, Andrew Thompson2 and Chris Oldham2; 1Farming Systems Analysis Service, Kojonup, WA, 2Department of Agriculture and Food Western Australia 14. Targeted treatment of worm-affected sheep - more efficient, more sustainable, Brown Besier, Department of Agriculture and Food Western Australia 15. Improving Weaner Sheep Survival, Angus Campbell and Ralph Behrendt, Cooperative Research Centre for Sheep Industry Innovatio

Validated MCF-7 gene fusions and TaqMan expression ratios.

<p><b>Notes:</b> Each exon name (gene name-dash-exon-order) was obtained from RefSeq database. Inverted fusions are on same chromosome but different strands. Last four columns show the Cycle Threshold (CT) value in TaqMan assays. Lower CT values indicate higher expression.</p

Fusion breakpoints are biased to 5′ end of the genes.

<p>Histogram of order of 5′ (yellow) and 3′ (green) intron breakpoints for <b>A.</b> MCF-7, <b>B.</b> UHR and HBR combined gene fusions. Breakpoint is inferred to happen at the intron (X axis) following the exon that is fused. Y axis shows the count of breakpoints that are inferred to happen at numbered intron. <b>C.</b> Boxplot of the distribution of simulated gene fusion locations for each of the 23 genes in which a fusion was observed. Magenta star marks the location of the observed fusion, relative to the 5′ exon. 23 fusions correspond to the gene fusions from <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002464#pcbi-1002464-t002" target="_blank">Table 2</a> (except for <i>ESR1- C6orf97</i>, and <i>ADAMTS19- SLC27A6</i> alternatively spliced fusions merged into single data points).</p