An enzyme found in the fluid around the brain and spine is giving researchers a snapshot of what happens inside the minds of Alzheimer’s patients and how that relates to cognitive decline.

Iowa State University researchers say higher levels of the enzyme, autotaxin, significantly predict memory impairment and Type 2 diabetes. Just a one-point difference in autotaxin levels – for example, going from a level of two to a three – is equal to a 3.5 to 5 times increase in the odds of being diagnosed with some form of memory loss, said Auriel Willette, an assistant professor of food science and human nutrition at Iowa State.

Autotaxin, often studied in cancer research, is an even stronger indicator of Type 2 diabetes. A single point increase reflects a 300 percent greater likelihood of having the disease or pre-diabetes. Willette and Kelsey McLimans, a graduate research assistant, say the discovery is important because of autotaxin’s proximity to the brain.

“We’ve been looking for metabolic biomarkers which are closer to the brain. We’re also looking for markers that reliably scale up with the disease and have consistently higher levels across the Alzheimer’s spectrum,” Willette said. “This is as directly inside of the brain as we can get without taking a tissue biopsy.”

Willette’s previous research found a strong association between insulin resistance and memory decline and detrimental brain outcomes, increasing the risk for Alzheimer’s disease. Insulin resistance is a good indicator, but Willette says it has limitations because what happens in the body does not consistently translate to what happens in the brain. That is why the correlation with this new enzyme found in the cerebrospinal fluid is so important.

“It has a higher predictive rate for having Alzheimer’s disease,” McLimans said. “We also found correlations with worse memory function, brain volume loss and the brain using less blood sugar, which have also been shown with insulin resistance, but autotaxin has a higher predictive value.”

The fact that autotaxin is a strong predictor of Type 2 diabetes and memory decline emphasizes the importance of good physical health. Researchers say people with higher levels of autotaxin are more likely to be obese, which often causes an increase in insulin resistance.

Willette says autotaxin levels can determine the amount of energy the brain is using in areas affected by Alzheimer’s disease. People with higher autotaxin levels had fewer and smaller brain cells in the frontal and temporal lobes, areas of the brain associated with memory and executive function. As a result, they had lower scores for memory and tests related to reasoning and multitasking.

“Autotaxin is related to less real estate in the brain, and smaller brain regions in Alzheimer’s disease mean they are less able to carry out their functions,” Willette said. “It’s the same thing with blood sugar. If the brain is using less blood sugar, neurons have less fuel and start making mistakes and in general do not process information as quickly.”

Iowa State University www.news.iastate.edu/news/2016/12/19/alzheimersautotaxin

An algorithm based on levels of metabolites found in a blood sample can accurately predict whether a child is on the Autism spectrum of disorder (ASD), based upon a recent study. The algorithm, developed by researchers at Rensselaer Polytechnic Institute, is the first physiological test for autism and opens the door to earlier diagnosis and potential future development of therapeutics.
 
“Instead of looking at individual metabolites, we investigated patterns of several metabolites and found significant differences between metabolites of children with ASD and those that are neurotypical. These differences allow us to categorize whether an individual is on the Autism spectrum,” said Juergen Hahn, lead author, systems biologist, professor, and head of the Rensselaer Department of Biomedical Engineering. “By measuring 24 metabolites from a blood sample, this algorithm can tell whether or not an individual is on the Autism spectrum, and even to some degree where on the spectrum they land.”
 
Big data techniques applied to biomedical data found different patterns in metabolites relevant to two connected cellular pathways that have been hypothesized to be linked to ASD: the methionine cycle and the transulfuration pathway. The methionine cycle is linked to several cellular functions, including DNA methylation and epigenetics, and the transulfuration pathway results in the production of the antioxidant glutathione, decreasing oxidative stress.
 
Autism Spectrum Disorder is estimated to affect approximately 1.5 percent of individuals and is characterized as “a developmental disability caused by differences in the brain,” according to the Centers for Disease Control and Prevention. The physiological basis for ASD is not known, and genetic and environmental factors are both believed to play a role. People with ASD “may communicate, interact, behave, and learn in ways that are different from most other people.” According to the CDC, the total economic costs per year for children with ASD in the United States are estimated between $11.5 billion and $60.9 billion. Research shows that early intervention can improve development, but diagnosis currently depends on clinical observation of behavior, an obstacle to early diagnosis and treatment. Most children are not diagnosed with ASD until after age 4 years.

Rensselaer Polytechnic Institute
news.rpi.edu/content/2017/03/16/blood-test-autism

The first analysis of nearly 19,000 de-identified genomic records from the American Association for Cancer Research (AACR) international data-sharing initiative known as AACR Project Genomics Evidence Neoplasia Information Exchange (GENIE) has been published.
In addition to the genomic analysis, the report includes examples of how the AACR Project GENIE genomic data can be used to facilitate clinical research, including:
Analysis showing that more than 30 percent of the samples had mutations that are clinically actionable, meaning that they are suggestive of a specific treatment that is either already approved by the U.S. Food and Drug Administration or is being tested in clinical trials.
Analysis showing that the rate at which patients with samples in the AACR Project GENIE registry would match with arms of the NCI-MATCH trial reflected the actual accrual rates for the trial.
Details of two additional studies underway that are linking certain genetic characteristics of metastatic breast cancer with clinical and pathological features of the tumors, as well as with patient outcomes.
“There has been a lot of discussion about the potential of data-sharing initiatives to accelerate the pace of progress against cancer,” said Charles L. Sawyers, MD, FAACR, who is the AACR Project GENIE Steering Committee chairperson and an author on the paper. “This paper shows that AACR Project GENIE has made the first steps to delivering on this promise.
“We are particularly excited by the clinical actionability analysis,” continued Sawyers, who is also chairperson of the Human Oncology and Pathogenesis Program at Memorial Sloan Kettering Cancer Center in New York, and a Howard Hughes Medical Institute investigator. “Prior studies looking at how often tumour genome sequencing identifies a clinically actionable mutation have yielded variable results, leading some to question its clinical utility. The huge number of samples in our study and the high rate of clinical actionability give us confidence that tumour genome sequencing can have an important role in clinical care.”
AACR Project GENIE is a multi-phase, multi-year, international data-sharing project that was launched by the AACR in partnership with eight global academic leaders in clinical cancer genomics in November 2015. Just over a year later, in January 2017, the AACR Project GENIE consortium made public nearly 19,000 de-identified genomic records collected from patients who were treated at the eight international institutions participating in the first phase of the project.
“This paper describes the AACR Project GENIE consortium and provides a landscape overview of the first public GENIE data release,” said Ethan Cerami, PhD, director of the Knowledge Systems Group and lead scientist in the Department of Biostatistics and Computational Biology at the Dana-Farber Cancer Institute in Boston, and an author on the paper. “By showing that we can share data across multiple institutions in the United States, Canada, and Europe to obtain results none of the institutions could have obtained alone, we have put AACR Project GENIE at the forefront of data-sharing efforts to accelerate scientific discovery and ultimately improve patient care.”

American Association for Cancer Research
www.aacr.org/Newsroom/Pages/News-Release-Detail.aspx?ItemID=1059#.WTSKEmSGP5Y

Casting one of the largest genomic nets to date for the rare tumours of the autonomic nervous system known as pheochromocytoma and paraganglioma (PCC/PGL) captured several new mutations driving the disease that could serve as potential drug targets, researchers from Penn Medicine and other institutions report.

Analysing genetic data of 173 patients from The Cancer Genome Atlas, researchers, including senior author Katherine Nathanson, MD, a professor in the division of Translational Medicine and Human Genetics at the Perelman School of Medicine at the University of Pennsylvania and associate director for Population Science at Penn’s Abramson Cancer Center, identified CSDE1 and fusion genes in MAML3 as drivers of the disease, both a first for any cancer type. The researchers also classified PCC/PGL into four distinct subtypes, each driven by mutations in distinct biological pathways, two of which are novel.
“What’s interesting about these tumours is that while they are astonishingly diverse genetically, with both inherited and somatic drivers influencing tumorigenesis, each has a single driver mutation, not multiple mutations,” Nathanson said. “This characteristic makes these tumours ideal candidates for targeted therapy.”  Other cancer types typically contain anywhere from two to eight of these driver mutations.

The discovery of these single drivers in PCC/PGL provides more opportunities for molecular diagnosis and prognosis in these patients, particularly those with more aggressive cancers, the authors said.

PGLs are rare tumours of nerve ganglia in the body, whereas PCCs form in the centre of the adrenal gland, which is responsible for producing adrenaline. The tumour causes the glands to overproduce adrenaline, leading to elevated blood pressure, severe headaches, and heart palpitations.  Both are found in about two out of every million people each year. An even smaller percentage of those tumours become malignant – and become very aggressive. For that group, the five-year survival rate is about 50 percent.

Matthew D. Wilkerson, MD, the Bioinformatics Director at the Collaborative Health Initiative Research Program at the Uniformed Services University, is the paper’s co-senior author.

To identify and characterize the genetic missteps, researchers analysed tumour specimens using whole-exome sequencing, mRNA and microRNA sequencing, DNA-methylation arrays, and reverse-phase protein arrays.  The four molecularly defined subgroups included: a kinase-signalling subtype, a pseudohypoxia subtype, a cortical admixture subtype, and a Wnt-altered subtype.  The last two have been newly classified.

The results also provided clinically actionable information by confirming and identifying several molecular markers associated with an increased risk of aggressive and metastatic disease, including germline mutations in SDBH, somatic mutations in ATRX (previously established in a Penn Medicine study), and new gene fusions – a genetic hybrid, of sorts – in MAML3.

Because the MAML3 fusion gene activates the Wnt-altered subtype, the authors said, existing targeted therapies that inhibit the beta-catenin and STAT3 pathways may also prove effective in certain PCC/PGL tumours.

Penn Medicine www.pennmedicine.org/news/news-releases/2017/february/in-depth-gene-search-reveals-new-mutations-drug-targets-in-rare-adrenal-tumors