Under the banner “GeT Perfect”, Greiner Bio-One has kicked off the European promotion of the Greiner eHealth Technologies Solution – GeT.  For this purpose, a modern microsite in German and English has been created.  The one page site provides all essential information at a glance. GeT utilizes a flexible modular based software solution whilst applying the advantages of pre-barcoded VACUETTE tubes. The aim is to increase the efficiency of routine procedures in and around the laboratory. This page provides information on events, reference customers as well as study material. Testimonials from reference customers report on their experiences. Advantages of the system and working procedures can be seen in the form of videos and animation. All in all, the user has here a compact version of all initial information required for initiating any further steps.

Following a three-year study of the Arizona State University football program, researchers at the Translational Genomics Research Institute (TGen) have created the largest dataset to date of extracellular small RNAs, which are potential biomarkers for diagnosing medical conditions, including concussions.
 
The study amassed a collection of biomarkers from the ASU student-athletes’ biofluids: blood, urine and saliva. A portion of that information will be used with data from helmet sensors that recorded the number, intensity and direction of head impacts during games and practices from the 2013-16 football teams. TGen researchers are using that combined data to potentially develop new diagnostic and therapeutic tools.
 
"Large datasets – examining different biofluids, isolation methods, detection platforms and analysis tools – are important to further our understanding of the extent and types of extracellular materials present when someone is injured or develops disease," said Dr. Kendall Van Keuren-Jensen, TGen Associate Professor of Neurogenomics and Co-Director of TGen’s Center for Noninvasive Diagnostics, and one of the study’s senior authors.
 
"Concussion safety, protocol and diagnostics are key components of Sun Devil Athletics’ student-athlete welfare program," said Ray Anderson, ASU Vice President for University Athletics. "Our partnership with TGen and the research conducted with these biomarkers will ideally provide doctors, trainers and administrators with a mechanism to proactively safeguard the health of our student-athletes. We are proud and excited to be a part of this ground-breaking study that will significantly expand research in this important area of scientific discovery."
 
Because the data is being published in an open access journal, they are available to aid other researchers studying how to develop tests for the detection and extent of injuries involving everything from automobile accidents to battlefield explosions.
 
Sensors in the ASU student-athlete football helmets were wirelessly connected to a field-level computer as part of the Sideline Response System – a head impact monitoring and research tool developed and deployed by Riddell, a leading provider of helmets to the NFL and major college football teams. 
 
TGen researchers used advanced genomic sequencing to identify the biomarkers of extracellular RNA (exRNA), strands of genetic material that are released from cells, and which can be detected in biofluids. TGen sequenced these biomarkers from among 183 blood samples, 204 urine samples and 46 saliva samples derived from among 55 consenting student-athletes, ages 18-25.
 
"The small RNA profile of each biofluid is distinct," the study said. "These data significantly contribute to the current number of sequenced exRNA samples from young healthy individuals."  
 
By identifying biofluids associated with healthy individuals, researchers hope to use these as standards for assessing disease and injury: "Establishing a baseline for individuals when they are healthy may provide the most meaningful comparisons when exploring early indicators of disease, severity or outcome," the study said.

TGen
www.tgen.org/home/news/2017-media-releases/tgen-asu-riddell-concussion-study-results.aspx#.WM1FeGTyv5Y
 

As more biomarker-based studies open, such testing will increase opportunities to match patients with clinical trials.
Nearly three-quarters of patients with advanced cancer could be referred to a potential targeted treatment based on the results of a comprehensive analysis of their tumour’s genetic landscape, a new analysis finds.
The study suggests the value of so-called next generation sequencing, a sophisticated method of evaluating the DNA and RNA of a tumour to help direct treatment.
In a report on the first 500 patients with advanced solid tumours to go through the University of Michigan Comprehensive Cancer Center’s sequencing program, 72 percent qualified for a clinical trial based on a genetic marker in their tumour.
Although not all of those patients were able to enroll in a trial based on other eligibility factors and trial location, the number who did enroll doubled from about 5 percent of patients in 2012 to 11 percent in 2016. Increased trial enrollment occurred as several major national biomarker-based studies opened.
“Availability of biomarker trials is crucial for being able to act on these results. Over time, we became better at matching patients to clinical trials as more of these basket trials opened,” says Erin Cobain, M.D., clinical lecturer of haematology/oncology at the University of Michigan Medical School.
As part of the sequencing program, patients with stage 4 cancer undergo a biopsy and provide a blood sample to test their normal DNA. Patients also receive genetic counselling.
Results of the sequencing are discussed by a team of oncologists, genetics specialists, pathologists, bioinformatics specialists and genetic counsellors, among others, at a precision medicine tumour board. This group discusses all results and assesses the feasibility of pursuing treatment options based on the genomic findings.
Genetic sequencing involves looking at all of the DNA and RNA expressed within a tumor. Scientists comb through this enormous amount of data to identify anomalies that may prove to be targets for existing approved or experimental therapies.
In addition, the program sequences patients’ normal genome. This means it’s able to identify hereditary genetic variations, or those inherited from a mother or father and potentially passed down to children.
Researchers found these hereditary variations in 11 percent of patients, none of which had been previously identified through family history.
“That was a major surprise — that 11 percent of patients had a genetic change that increases cancer risk is much higher than we would expect. This has significant impact not only on the patients, but also on their families, who may carry a genetic susceptibility to cancer,” Cobain says.
MI-ONCOSEQ requires a fresh biopsy, whereas many commercial sequencing tools can use frozen tissue samples. This ensures the U-M researchers can perform a more comprehensive analysis. Commercial tests analyse only about 350 genes while MI-ONCOSEQ’s more thorough analysis of both DNA and RNA covers at least 1,700 genes.
This means many anomalies were identified that would not have been found on panel-based tests. Because MI-ONCOSEQ is run as a research study, patients do not pay for sequencing.
Cobain cites an example of a patient with cholangiocarcinoma, a cancer of the bile duct. Sequencing revealed a novel gene fusion that would not have been identified through panel-based tests. The patient was able to enroll on a clinical trial targeting the gene fusion and had an excellent response to that therapy.
“This would not have been found by a commercial assay,” Cobain says. “Sequencing is beginning to have a real impact on treatment recommendations. It’s important to consider this testing early in the patient’s clinical course in order to improve our ability to act on the results and impact the patient’s course.”

Michigan University
labblog.uofmhealth.org/rounds/genetic-sequencing-can-influence-treatment-for-advanced-cancer

Most of us would be lost without Google maps or similar route-guidance technologies. And when those mapping tools include additional data about traffic or weather, we can navigate even more effectively. For scientists who navigate the mammalian genome to better understand genetic causes of disease, combining various types of data sets makes finding their way easier, too.

A team at the Salk Institute has developed a computational algorithm that integrates two different data types to make locating key regions within the genome more precise and accurate than other tools. The method could help researchers conduct vastly more targeted searches for disease-causing genetic variants in the human genome, such as ones that promote cancer or cause metabolic disorders.

“Most of the variation between individuals is in noncoding regions of the genome,” says senior author Joseph Ecker, a Howard Hughes Medical Institute investigator and director of Salk’s Genomic Analysis Laboratory. “These regions don’t code for proteins, but they still contain genetic variants that cause disease. We just haven’t had very effective tools to locate these areas in a variety of tissues and cell types—until now.”

Only about two percent of our DNA is made up of genes, which code for proteins that keep us healthy and functional. For many years, the other 98 percent was thought to be extraneous “junk.” But, as science has developed ever more sophisticated tools to probe the genome, it has become clear that much of that so-called junk has vital regulatory roles. For example, sections of DNA called “enhancers” dictate where and when the gene information is read out.

Increasingly, mutations or disruption in enhancers have been tied to major causes of human disease, but enhancers have been hard to locate within the genome. Clues about them can be found in certain types of experimental data, such as in the binding of proteins that regulate gene activity, chemical modifications of proteins (called histones) that DNA wraps around, or in the presence of chemical compounds called methyl groups in DNA that turn genes on or off (an epigenetic factor called DNA methylation). Typically, computational methods for finding enhancers have relied on histone modification data. But Ecker’s new system, called REPTILE (for “regulatory-element prediction based on tissue-specific local epigenomic signatures”), combines histone modification and methylation data to predict which regions of the genome contain enhancers. In the team’s experiments, REPTILE proved more accurate at finding enhancers than algorithms that rely on histone modification alone.

 “The novelty of this method is that it uses DNA methylation to really narrow down the candidate regulatory sequences suggested by histone modification data,” says Yupeng He, a Salk graduate student and first author of the paper. “We were then able to test REPTILE’S predictions in the lab and validate them with experimental data, which gave us a high degree of confidence in the algorithm’s ability to find enhancers.”

Salk Institute www.salk.edu/news-release/finding-way-around-dna/