“Mutations are part of life. They are mistakes in a gene like typos in a text message,” said Watanabe-Smith, a postdoctoral fellow with the OHSU Knight Cancer Institute. “But which mutations cause cancer? That’s the real question. And this problem is impossible to understand without a strong model system to test those mutations.”

Watanabe-Smith’s research sought to better understand one “typo” in a standard leukaemia assay, or test. While studying cancer biology and completing his doctorate in the lab of Brian Druker, M.D., at the OHSU Knight Cancer Institute, Watanabe-Smith encountered a new problem: an issue with the model system itself.

“When I was sequencing the patient’s DNA to make sure the original, known mutation is there, I was finding additional, unexpected mutations in the gene that I didn’t put there. And I was getting different mutations every time,” said Watanabe-Smith.

He decided to formally study this phenomenon with his lab advisers, who included Druker; Cristina Tognon, Ph.D., scientific director, Druker lab; and Anupriya Agarwal, Ph.D., assistant professor of hematology & medical oncology, OHSU School of Medicine; researcher with the OHSU Knight Cancer Institute, all co-authors on the paper.  

His initial research, identifying and characterizing a growth-activating mutation in a patient with T-cell leukaemia and was first published last April. This research published was focused on better understanding the lab’s model system, to ensure that future researchers trying to identify cancer-causing mutations are using accurate and reproducible methods.

Their research investigates a common cell line assay, used since the 1980’s, to detect which mutations are important in driving leukaemia and other cancers. They found this assay is prone to a previously unreported flaw, where the cells, called Ba/F3 cells, can acquire additional mutations.

“The potential impact is that a non-functional mutation could appear functional, and a researcher could publish results that would not be reproducible,” Watanabe-Smith said. “Then we had the question: ‘Did the cells transform because of a mutation the patient had, or did they transform because these new mutations they managed to pick up somewhere?’”

Ultimately, he says, the research team recommends an additional step in the Ba/F3 assay (sequencing outgrown cell lines) to improve reproducibility of future results. While the results urge further research, the message to scientific community is clear: There seems to be more potential for problems than previously anticipated in this standard assay.

OHSU Knight Cancer Institutenews.ohsu.edu/2017/02/21/gene-mutations-cause-leukemia-but-which-ones

Royal Philips and LabPON, the first clinical laboratory to transition to 100% histopathology digital diagnosis, recently announced its plans to create a digital database of massive aggregated sets of annotated pathology images and big data utilizing Philips IntelliSite Pathology Solution. The database will provide pathologists with a wealth of clinical information for the development of image analytics algorithms for computational pathology and pathology education, while promoting research and discovery to develop new insights for disease assessment, including cancer.
Deep learning algorithms have the potential to improve the objectivity and efficiency in tumour tissue diagnosis. In recent years, ‘deep learning’ techniques for image analysis have quickly become the state of the art in computer vision and has surpassed human performance in a number of tasks. The challenge for executing deep learning techniques is having access to a database with sufficient high volume and high quality data from which to develop the algorithms. As one of the largest pathology laboratories in the Netherlands, LabPON will contribute its repository of approximately 300,000 whole slide images (WSI) they prospectively create each year to the database. This will contain de-identified datasets of annotated cases that are manually commented by the pathologist, and will comprise of a wide variety of tissue and disease types, as well as other pertinent diagnostic information to facilitate deep learning.
“Deep learning focuses on the development of advanced computer programs that automatically understand and digitally map tissue images in considerable detail: The more data available, the more refined the computer analysis will be.” Said Peter Hamilton, Group Leader Image Analytics at Philips Digital Pathology Solutions. “Together, LabPON and Philips have the competence and skills to realize this.”
During a time where the pathologist shortage is mounting and cancer caseloads are increasing, the accurate diagnosis and grading of cancer has become increasingly complex, placing significant pressures on pathology services. Technologies such as computational pathology, could help pathologists with tools to work in the most efficient way possible.
“The role of the pathologist remains important by making the definitive diagnosis, which has a high impact on the patient’s treatment. Software tools could help to relieve part of the pathologists’ work such as identifying tumour cells, counting mitotic cells or identifying perineural and vaso-invasive growth, as well as carrying out measurements in a more accurate and precise way,” said Alexi Baidoshvili, pathologist at LabPON. “This ultimately could help to improve the quality of diagnosis and make it more objective.”
Next to the development of computational algorithms for diagnostic use, Philips intends to make available the database to research institutions and other partners through its translational research platform. This could enable selected parties to interrogate and combine massive datasets with the goal to discover new insights that ultimately could be translated into new personalized treatment options for patients.
www.philips.com/digitalpathology

Specific genetic errors that trigger congenital heart disease (CHD) in humans can be reproduced reliably in Drosophila melanogaster – the common fruit fly – an initial step toward personalized therapies for patients in the future.

“Studying CHD in fruit flies provides a fast and simple first step in understanding the roles that individual genes play in disease progression,” says Zhe Han, Ph.D., a principal investigator and associate professor in the Center for Cancer & Immunology Research at Children’s National Health System and senior author of the paper. “Our research team is the first to describe a high-throughput in vivo validation system to screen candidate disease genes identified from patients. This approach has the potential to facilitate development of precision medicine approaches for CHD and other diseases associated with genetic factors,” Han says.

Some 134 genes have been implicated in causing CHD, a birth defect that affects 8 in 1,000 newborns, according to the National Institutes of Health. The research team led by Han used high-throughput techniques to alter the activity of dozens of genes in flies’ hearts simultaneously in order to validate genes that cause heart disease.

“Our team was able to characterize the effect of these specific genetic alterations on heart development, structure and activity,” Han adds. “The development of the human heart is a complicated process in which a number of different cell types need to mature and differentiate to create all of the structures in this essential organ. The precise timing of those cellular activities is critical to normal heart development, with disruptions in the structure of proteins called histones linked to later heart problems.”.

Of 134 genes studied by the research team, 70 caused heart defects in fruit flies, and several of the altered genes are involved in modifying the structure of histones. Quantitative analyses of multiple cardiac phenotypes demonstrated essential structural, functional and developmental roles for these genes, including a subgroup encoding histone H3K4 modifying proteins. The scientists then corroborated their work by reliably reproducing in flies the effect of specific genetic errors identified in humans with CHD.

“This may allow researchers to replicate individual cases of CHD, study them closely in the laboratory and fashion treatments personalized to that patient specifically,” he adds. “Precise gene-editing techniques could be used to tailor-make flies that express a patient’s specific genetic mutation. Treating CHD at the level of DNA offers the potential of interrupting the current cycle of passing along genetic mutations to each successive generation.”

Children’s National Health System childrensnational.org/news-and-events/childrens-newsroom/2017/studying-chd-in-fruit-flies

Scientists at the Center for Infection and Immunity (CII) at Columbia University’s Mailman School of Public Health are the first to report immune signatures differentiating two subgroups of myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS): “classical” and “atypical.” This complex, debilitating disease is characterized by symptoms ranging from extreme fatigue after exertion to difficulty concentrating, headaches, and muscle pain.
Typically, symptoms of ME/CFS begin suddenly following a flu-like infection, but a subset of cases classified by the investigators as “atypical” follows a different disease course, either from triggers preceding symptoms by months or years, or accompanied by the later development of additional serious illnesses.
To uncover evidence of these disease types, first author Mady Hornig, MD, director of translational research at CII and associate professor of Epidemiology at Mailman, and colleagues used immunoassays to measure levels of 51 immune biomarkers in cerebrospinal fluid samples taken from 32 cases of classical and 27 cases of atypical ME/CFS. All study participants were diagnosed using the same standard criteria, but atypical cases either had prior histories of viral encephalitis, illness after foreign travel or blood transfusion, or later developed a concurrent malady—seizure disorders, multiple sclerosis-like demyelinating disorders, Gulf War Illness, or a range of cancers—at rates much higher than seen in the general population.
Their analysis revealed lower levels of immune molecules in individuals with atypical ME/CFS than those with a classical presentation and course of illness, including dramatically lower levels of interleukin 7 (IL7), a protein linked to viral infections, and interleukin 17A (IL 17A) and chemokine (C-X-C motif) ligand 9 (CXCL9), inflammatory molecules implicated in a variety of neurological disorders.
“We now have biological evidence that the triggers for ME/CFS may involve distinct pathways to disease, or, in some cases, predispose individuals to the later development of serious comorbidities,” says Hornig. “Importantly, our results suggest that these early biomarker profiles may be detectable soon after diagnosis of ME/CFS, laying a foundation for better understanding of and treatments for this complex and poorly understood illness.”
“Early identification of patients who meet the usual clinical criteria when first diagnosed but then go on to develop atypical features would help clinicians like myself identify and treat these complex cases and even prevent fatal outcomes,” says co-author Daniel L. Peterson, MD, principal clinician at Sierra Internal Medicine in Incline Village, NV.
The new study builds on earlier research by Hornig and collaborators that found robust evidence of distinct stages in ME/CFS. A pair of 2015 publications based on analyses of blood and cerebrospinal fluid showed differences in the immune signatures of ME/CFS patients who had the disease for three years or less as compared with those who had been ill for more than three years. The researchers reported that patients were flush with cytokines and chemokines until around the three-year mark—suggesting an over-activated immune response in that phase of the illness; thereafter the immune system showed evidence of “exhaustion,” and levels of immune molecules dropped.
In the new study, both subsets of ME/CFS patients showed signs of an unbalanced or dysregulated immune system within the central nervous system, with immune markers different than those seen in healthy individuals. However, the dampened immune profiles previously observed after the three-year mark were only observed in individuals with the classical form of the disease, not in those with atypical ME/CFS. Among subjects in the atypical group, levels of cytokines and chemokines were more likely to remain steady or increase.
According to Hornig, instead of the immune exhaustion seen in later phases of classical ME/CFS, atypical patients may be experiencing a “smouldering inflammatory process” in which the immune system is still working to recover, although she acknowledges that much work remains to be done to confirm this hypothesis. Alternatively, these findings could suggest a pathway to disease in atypical individuals that involves biomarkers not captured in the 51-molecule assay, potentially even involving non-immune-related processes. Atypical individuals may also have genetic susceptibilities that lead their immune systems to respond differently than in classical cases.

Columbia University Mailman School of Public Health]
www.mailman.columbia.edu/public-health-now/news/scientists-discover-biological-evidence-atypical-chronic-fatigue-syndrome