Breaking News
December 10, 2018 - A Tale of Two Labels
December 10, 2018 - Triple combination cancer immunotherapy improves outcomes in preclinical melanoma model
December 10, 2018 - A 14-year-old explains what it’s like to get a new heart
December 10, 2018 - Team Players Honored with 2018 Baton Awards
December 10, 2018 - Global report highlights how the changing world is affecting children’s physical activity levels
December 10, 2018 - Genes play a role in physical activity and sleep
December 10, 2018 - DDT in Alaskan fish shown to increase risk of cancer
December 10, 2018 - Laws to curb use of cell phones have greatly reduced fatalities for motorcyclists
December 10, 2018 - Argenx Provides Detailed Data from Phase 2 Clinical Trial of Efgartigimod in Immune Thrombocytopenia and Phase 1/2 Clinical Trial of Cusatuzumab in Acute Myeloid Leukemia
December 10, 2018 - University of Maryland doctors treat first breast cancer patients with GammaPod radiotherapy
December 10, 2018 - The heartbeat seat: Demoing new well-being technologies in a car
December 10, 2018 - Leading Cancer Researcher to Direct Herbert Irving Comprehensive Cancer Center
December 10, 2018 - Study compares pain-related diagnoses in First Nations and non-First Nations children, youth
December 10, 2018 - Scientists find answers to how cancer spreads
December 10, 2018 - Study explores why older people read more slowly
December 10, 2018 - Smart life-collar could save lives of young children
December 10, 2018 - Asbestos found in most NHS hospitals finds BBC inquiry
December 10, 2018 - Researchers use new technique to probe hydrogen bonds
December 10, 2018 - Music improves social communication in autistic children
December 10, 2018 - Some Brain Tumors May Respond to Immunotherapy, New Study Suggests
December 10, 2018 - Banning junk food ads to combat childhood obesity
December 10, 2018 - Skin Autofluorescence Predicts T2DM, Heart Disease, Mortality
December 10, 2018 - Largest autism sequencing study to date yields 102 genes associated with ASD
December 10, 2018 - Statins associated with low risk of side effects
December 10, 2018 - Episodic memory tests help in predicting brain atrophy and Alzheimer’s disease
December 10, 2018 - Study explores how schools address adolescent self-harming practices
December 10, 2018 - Pregnancy in adolescence linked to increased risks of complications in young mothers
December 10, 2018 - Risk Analysis publishes special issue on communicating about Zika virus
December 10, 2018 - Botox May Help Prevent Post-Op A-Fib
December 10, 2018 - African-American mothers rate boys higher for ADHD
December 10, 2018 - Graphic warning labels cancel out cigarettes’ appeal to young people
December 10, 2018 - Australian researchers to study gas inhalational anaesthetic and likelihood of cancer return
December 10, 2018 - Individual neurons located within the brain have implications for psychiatric diseases
December 10, 2018 - Researchers improve bariatric surgery scoring system to extend prediction time for diabetic remission
December 10, 2018 - HPV type 16 or 18 associated with cervical cancer risk in young women
December 10, 2018 - Cervical cancer risk is higher in women with positive HPV, but no cellular abnormalities
December 10, 2018 - Combo therapy not needed if low RA disease activity achieved
December 10, 2018 - Novel therapeutic targets based on biology of aging show promise for Alzheimer’s disease
December 10, 2018 - UC San Diego professor receives NCI Outstanding Investigator Award for cancer research
December 10, 2018 - Study evaluates placental mesenchymal stem cell sheets for myocardial repair and regeneration
December 10, 2018 - Blueprint Medicines Announces Updated Results from Ongoing EXPLORER Clinical Trial of Avapritinib Demonstrating Broad Clinical Activity and Significant Symptom Reductions in Patients with Systemic Mastocytosis
December 10, 2018 - Study clarifies ApoE4’s role in dementia
December 10, 2018 - Eating disorders now a top priority with Australian Government
December 10, 2018 - Neuronal activity in the brain allows prediction of risky or safe decisions
December 10, 2018 - FDA Alerts Health Care Professionals and Patients Not to Use Drug Products Intended to be Sterile from Promise Pharmacy
December 10, 2018 - Improving dementia care and treatment saves thousands of pounds in care homes
December 10, 2018 - Heroin-assisted treatment can offer benefits, reduce harms
December 10, 2018 - People covered by Michigan’s expanded Medicaid program report improvements in health, finds study
December 10, 2018 - Hazelnuts improve micronutrient levels in older adults
December 9, 2018 - History of Partner Violence Tied to Menopause Symptoms
December 9, 2018 - Clean Up Safely After a Disaster|Natural Disasters and Severe Weather
December 9, 2018 - Drug wholesalers drove fentanyl’s deadly rise, report concludes
December 9, 2018 - Deprescribing could help manage polypharmacy in older adults
December 9, 2018 - Retraction of article “Joy of cooking too much” from journal
December 9, 2018 - FDA Warns of Rare Stroke Risk With MS Drug Lemtrada (Alemtuzumab)
December 9, 2018 - Feds say heroin, fentanyl remain biggest drug threat to US
December 9, 2018 - Eliminating microglia can reverse some aspects of stress sensitization, study shows
December 9, 2018 - New genetic insight could help treat rare debilitating heart and lung condition
December 9, 2018 - MiRagen Therapeutics Announces Final Safety, Biodistribution and Clinical Efficacy Data From Phase 1 Cobomarsen Clinical Trial in Patients With Mycosis Fungoides
December 9, 2018 - Work with your doctor to weigh pros, cons of treatment options for hyperthyroidism
December 9, 2018 - CWRU researcher secures $14.6 million funding for genetic study into Alzheimer’s disease
December 9, 2018 - High intensity statin treatment and adherence could save more lives
December 9, 2018 - Surgery patients use only 1/4 of prescribed opioids, and prescription size matters
December 9, 2018 - AXT offers Phi Optics upgrade to QPI systems for inverted light microscopes
December 9, 2018 - New booklet could help improve conditions of young pupils with albinism
December 9, 2018 - Few Physicians Work in Practices That Use Telemedicine
December 9, 2018 - Older Adults and Oral Health
December 9, 2018 - Health utility values improve after septorhinoplasty
December 9, 2018 - New EU-funded project provides insight into how the brain develops
December 9, 2018 - Expanded use of tele-emergency services can help strengthen rural hospitals
December 9, 2018 - Infections in the Young May Be Tied to Risk for Mental Illness: Study
December 9, 2018 - Profile: Native Hawaiians and Pacific Islanders
December 9, 2018 - Snoring poses greater cardiac risk to women
December 9, 2018 - Researcher takes further steps in understanding how and why cute aggression occurs
December 9, 2018 - Researchers create new light-activated tools for controlling neurons
December 9, 2018 - Spinal cord injury disrupts the body’s internal clock, study shows
December 9, 2018 - Babies recognize nested structures similar to our grammar
December 9, 2018 - UT Austin researcher receives $2.5 million CZI grant for neurodegenerative disease research
December 9, 2018 - Sleep problems found to be prevalent and increasing among college students
December 9, 2018 - Study reveals why some children are susceptible to the effects of maltreatment
Novel informatics tool helps predict genomic features associated with specific drug responses

Novel informatics tool helps predict genomic features associated with specific drug responses

image_pdfDownload PDFimage_print

The rise of genomics, the shift from considering genes singly to collectively, is adding a new dimension to medical care; biomedical researchers hope to use the information contained in human genomes to make better predictions about individual health, including responses to therapeutic drugs. A new computational tool developed through a collaboration between the University of Illinois and the Mayo Clinic combines multiple types of genomic information to make stronger predictions about what genomic features are associated with specific drug responses.

The tool, described in Genome Research, was developed by members of KnowEnG, a Center of Excellence established by an NIH Big Data to Knowledge (BD2K) Initiative award to the University of Illinois in partnership with the Mayo Clinic. KnowEnG stands for Knowledge Engine for Genomics, representing the center’s mission to develop analytical resources for biomedical work with genomic data. The Center is housed within the Carl R. Woese Institute for Genomic Biology at the University of Illinois.

“We all know treatment outcomes for complex diseases like cancers vary dramatically among individuals, from lacking of efficacy resulting in disease recurring to severe toxicity resulting in noncompliance in patients who cannot tolerate these life-saving drugs,” said Leiwei Wang, a professor of pharmacology at the Mayo Clinic. “Therefore, it is extremely important for us to understand better of how and why patients respond differently, so that we can truly individualize their therapies by choosing the right drug at the right dose.”

The researchers’ first step toward this goal was a large-scale data collection effort. They assembled a panel of lab-reared tumor cells derived from a diverse set of individuals, and exposed samples of those cells to one of a set of common anticancer drugs. This allowed them to quantify the drug responses of different genetic backgrounds in a directly comparable way.

Using these data, Mayo Clinic researchers wanted to ask what characteristics of cells from each individual helped determine its unique set of responses to the drugs tested. They collected data on the “expression” of every gene in the genome–how often each gene was being read by the cell and used to create the corresponding protein that gene carries the instructions for.

The team also wanted to look at where those differences in gene expression might come from. DNA sequence surrounding genes in the genome influence when genes are expressed. So do the actions of special proteins called transcription factors, which bind to DNA and make it easier or harder for genes to be read by cellular machinery. Finally, how different regions of the long DNA strands of the genome are coiled up, the “epigenetic state” of genomic DNA also helps determine how likely a gene is to be expressed.

The team decided to collect data on all of these characteristics of their lines of cells. They had built a comprehensive dataset, but lacked something vital–an analytical tool that could use it to full advantage.

“There was no tool that would exploit all of these together,” said Professor of Computer Science and Willett Faculty Scholar Saurabh Sinha, who co-directs the BD2K Center. “From the question came the data . . . then came our part, what do you do with it?”

Sinha and graduate student Casey Hanson developed an algorithm that takes in data on gene expression, genomic factors that help control gene expression, and resulting traits (such as drug response) and uses these to predict which genes are most important in determining the latter. They based their work on a tool they had previously developed named “Gene Expression in the Middle,” or GENMi. Their new model, because of its ability to appropriately weight and integrate multiple sources of data, is named “probabilistic GENMi” or pGENMi.

“It’s a more rigorous tool; it should automatically handle how to weight different aspects of the data when it’s trying to look at many different types of data to reach a common conclusion,” Sinha said. “Methodologically, that was the most challenging part, the development of the probabilistic model.”

Because this tool is the first of its kind, team had to get creative to assess how well it was working–they had no prior standard of performance for comparison, and the results generated by pGENMi are the basis for further experimental work, not an endpoint.

“Our end result was testable predictions . . . a ranking of what experiments to do and verify that this transcription factor indeed has a role in regulating the response to that drug,” Sinha said.

“In a lot of computer science and bioinformatics papers, there is a gold standard database to validate predictions against – but we didn’t have the luxury of that,” Hanson said. “We had to search a vast literature to try to find, among the myriad ways of doing so and stating that one has done so, experiments that [could] confirm our hypothesis.” The team’s mix of computer science and biological knowledge was what made this task possible.

Hanson and his coauthors examined whether the predictions generated by the algorithm included associations that were already confirmed by the studies he identified. The literature searches revealed examples in which transcription factors highlighted by pGENMi had been experimentally manipulated, resulting in changes in drug responsiveness. Many of the predictions generated by pGENMi were supported by previous work, making it likely that those not supported by prior work are novel but real associations.

“For example . . . we found a paper in which rapamycin [an anticancer drug] decreased GATA1 [a transcription factor’s] binding with DNA. Another paper, we found that . . . rapamycin increased expression of a gene, ERCC1,” Hanson said. The same paper linked the transcription factor, GATA1, to ERCC1’s expression. Hanson noted that “our own experiments showed that knocking down GATA1 changed the sensitivity of cells to rapamycin,” in agreement with the previous work.

To test pGENMi’s results even further, the group selected transcription factors predicted to impact drug responsiveness, as well as several predicted to have little impact, and reduced their function in lab-grown cancer cells. For the majority of the TFs examined, these experimental results were consistent with pGENMi’s predictions.

Although in this initial project pGENMi was used to explore the factors that influence the response of cancer cells to therapeutic drugs, its flexibility would allow for a wide range of applications.

“We have generated tools that can be used broadly by the research community. These tools will be open to anyone who might have the right data sets to both help generate hypothesis and also to help refine the algorithms,” Wang said. “This is a perfect example of how expertise in complementary research areas, in this case, computational science and pharmacoproteomics, come together to make a difference.”

Source:

https://www.igb.illinois.edu/efi/article/new-informatics-tool-makes-most-genomic-data

Tagged with:

About author

Related Articles