Recent research from US scientists has suggested that a gene-expression profile could predict response to aspirin in patients being prescribed the drug for the prevention of cardiovascular events. “Our study not only sheds light on novel mechanisms of action of aspirin but also provides a tool that may be potentially used to identify patients who might not receiving the cardiovascular benefits of the drug who may need an alternate therapy,” highlighted the study‘s senior author, Geoff Ginsburg, to Personalized Medicine. Ginsburg is the director of Genomic Medicine at Duke University‘s Institute for Genome Sciences & Policy (NC, USA) and executive director of the University‘s Center for Personalized Medicine. The study has been published online ahead of print in the Journal of the American College of Cardiology.
Aspirin is widely used for secondary prevention of major adverse cardiovascular events. However, some individuals (reportedly anywhere from 2 to >50%) may be resistant to its antiplatelet action. “Aspirin is one of the most commonly used drugs in the world, it‘s been around for more than 100 years, yet we still do not know the full extent to how it works and all of its potential effects,” explained Ginsburg. “More importantly, we have no easy way to assess whether aspirin is working in an individual,” he continued.
This recent study therefore performed whole-blood RNA microarray profiling and platelet function assessment following aspirin administration in three cohorts: one discovery cohort of healthy volunteers (n = 50) and two validation cohorts of volunteers (n = 53) or outpatient cardiology patients (n = 25).
Authors identified a set of 60 coexpressed genes (the ‘aspirin response signature; ARS‘) that were associated with progression-free survival. Seventeen of these were found to have corresponding proteins in the platelet proteome; and six of these were associated with progression-free survival. The ARS was also associated with death or myocardial infarction, independent of cardiovascular risk factors.
The study authors concluded that RNA profiles of platelet-specific genes are novel biomarkers for identifying those with a poor response to aspirin and who are at risk for death or myocardial infarction. “This is an important step in individualizing antiplatelet therapies in patients with cardiovascular disease. A molecular profile that identifies aspirin-resistant patients will allow tailoring therapies to prevent heart disease,” said Ginsburg.
However, despite validation of these results in several cohorts, continued validation in diverse populations is essential. Ginsburg also highlighted that we also need to explore the treatment options available for aspirin-resistant patients, and demonstrate that use of the ARS leads to improved health outcomes in this patient group.
Ginsburg also described to Personalized Medicine how the approaches used in this recent study could be applicable to other diseases and treatments in the future: “This study demonstrates the power of genomics to enable a ‘systems pharmacology‘ approach to understanding the diverse and broad impact of drugs on biology, as well as on novel biomarker discovery and development. The more we take this kind of approach when drugs are first being developed the more we will understand both their potential for diverse uses and indications as well as their potential for producing undesirable effects and the mechanisms underlying them.”
– Written by Sarah Miller
Sources: Voora D, Cyr D, Lucas J et al. Aspirin exposure reveals novel genes associated with platelet function and cardiovascular events. J. Am. Coll. Cardiol. doi:10.1016/j.jacc.2013.05.073 (2013) (Epub ahead of print); Duke Medicine news release: Click here for link
Researchers at the National Cancer Institute (NCI) have conducted whole-exome sequencing of the NCI60 human tumor cell lines, creating the world‘s largest data set of cancer-specific genetic variations. The database has been made publicly available for the benefit of the research community.
The NCI60 cancer cell lines are the most ubiquitously studied cells in cancer research and are representative of cancer tissue from the breast, ovary, prostate, colon, lung, kidney, brain, blood and skin. The cells have been subjected to thousands of screenings, testing compounds for anticancer potential as well as being extensively characterized by genome-wide expression and methylation studies. Indeed, Richard Simon, a lead author of this research believes that these cell lines now serve as a ‘lingua franca‘ for relating compound sensitivity to genomic and proteomic variation.
The team‘s goal to identify point mutations, and short insertions and deletions in all coding regions in the NCI60 panel was reached through the employment of next generation sequencing technology and computational pipelines, generating an impressive database with a total of 6 billion data points connecting drugs with genomic variants.
The researchers categorized the variants into type I and type II depending on whether they were variants found in the normal population or were cancer specific. Statistical models, such as the Super Learner algorithm, were subsequently used to predict the sensitivity of cells harboring type II variants to various compounds, including 103 US FDA-approved anticancer drugs and 207 investigational drugs.
Analyses were performed in order to relate genetic variants to compound sensitivity. Data validation was performed using proof of principle pharmacogenomic correlations, for example, between variants in genes such as TP53, BRAF, ERBB and ATAD5 and anticancer compounds such as nutlin, vemurafenib, erlotinib and bleomycin. These preliminary studies indicate how the database can be used to generate and test novel hypotheses regarding genetic variation and drug response. Thus the database will be an invaluable resource for allowing drugs to pass much faster through the development pipeline. This is crucial in the field of oncology where the recent and ongoing personalization of treatment has seen a rise in the number of drugs, which are targeted toward an individual‘s tumor particular genomic alteration.
With the database being available to all researchers via two portals; the CellMiner and the Ingenuity systems database, Simon told Personalized Medicine that he hopes that “these data will be of value to the many public and privately funded groups trying to develop improved treatments for patients with cancer.” If these hopes materialize, then we can expect an explosive growth in personalized cancer treatment, taking it closer to the needs of the invidual.
– Written by Katie Lockwood
Sources: Abaan OD, Polley EC, Meltzer PS et al. The exomes of the NCI-60 Panel: a genomic resource for cancer biology and systems pharmacology. Cancer Res. 73(14), 4372–4382 (2013); AACR news release: www.aacr.org/home/public--media/aacr-in-the-news.aspx?d=3132
A study conducted by a team at Duke University Hospital (NC, USA) has provided details of how pharmacometabolomics could be used to determine which type of antidepressants would be beneficial for individual patients. The study was published in a recent issue of PLoS ONE.
Rima Kaddurah-Daouk, associate professor of psychiatry and behavioral sciences at Duke University Hospital lead the recent study‘s research team. They used pharmacometabolomics to analyze the chemicals in the blood revealing the mechanisms that play a role in disease and in turn could develop new treatment strategies by taking a patient‘s metabolic profile into consideration.
In the USA, one of the most prevalent mental disorders is major depressive disorder (MDD), which affects up to 6.7% of the population. Selective serotonin reuptake inhibitors (SSRIs) are prescribed to patients suffering from MDD, although the response rate to this therapy varies broadly.
Rima explained that pharmacometabolomics “could help us to better target the right therapies for patients suffering from depression who can benefit from treatment with certain antidepressants, and identify, early on, patients who are resistant to treatment and should be placed on different therapies.”
The investigators used pharmacometabolomics to map out the serotonin pathway, which is implicated in depression, and, in turn, provided information regarding an individual‘s response to SSRIs or placebo. The tryptophan pathway was analyzed using the methoxyindole and kynurenine (KYN) branches. The research team wanted to examine if varied regulation of these pathways could contribute to the variation in response to treatment. The team had analyzed outpatients who were suffering from MDD and were randomly assigned to sertraline (SSRI; n = 35) or placebo (n = 40) in a double-blind 4-week trial. The treatment outcomes were measured by using the 17-item Hamilton Rating Scale for Depression. To profile serum samples from the MDD patients, a targeted electrochemistry-based metabolomic platform was used.
The results of the study showed that that there was a higher response rate for sertraline when compared with the placebo. Patients who showed a promising response to sertraline, had higher pretreatment levels of 5-methoxytryptamine (5-MTPM), but a greater reduction in 5-MTPM levels after treatment. There was also an increase in 5-methoxytryptophol (5-MTPOL) and melatonin (MEL) levels, as well as decreases in the KYN/MEL and 3-hydroxykynurenine/MEL ratios post-treatment compared with pretreatment. These reported changes were not observed in patients who had shown a poor response to the SSRI.
It was also observed that in the placebo group, the more successful treatment outcome was associated with increases in 5-MTPOL and MEL levels as well as significant decreases in the KYN/MEL and 3-hydroxykynurenine/MEL. It was found that changes in 5-MTPM levels did not include the 4-week response.
The results suggested that recovery from depression, using SSRIs or a placebo, could depend on the varied utilization of serotonin leading to the end product of 5-MTPOL or melatonin.
In the future, further research would need to be undertaken where samples should be taken from patients at different times of the day so that the effect of the circadian cycle can be taken into consideration with analysis between responders and nonresponders to SSRIs and to placebo.
– Written by Simi Thankaraj
Source: Zhu H, Bogdanov MB, Boyle SH et al. Pharmacometabolomics research network. Pharmacometabolomics of response to sertraline and to placebo in major depressive disorder – possible role for methoxyindole pathway. PLoS ONE 8(7), e68283 (2013).
The first whole-genome sequencing (WGS) study in individuals with autism spectrum disorder (ASD) has recently been published, providing the most detailed information so far regarding the genetic etiology of this lifelong developmental condition. “Previous genetic-testing technologies (microarrays and exome sequencing) only provide an underlying genetic explanation in approximately 20% of cases, but it is known that ASD has even more of a genetic composition,” explained senior author Stephen Scherer, Director of the Centre for Applied Genomics at The Hospital for Sick Children (ON, Canada), when speaking to Personalized Medicine. “The WGS technology allows more genetic variants to be found and using it we detected something we think to be clinically relevant in 50% of families.”
This recent study performed WGS on 32 unrelated Canadian individuals with ASD and their families in order to detect de novo or rare inherited genetic variants predicted to be deleterious. In order to do this, the researchers faced bioinformatics challenges, as Scherer explained: “WGS yields approximately 3 million genetic variants per sample, so we had to develop new informatics approaches and tools to sort through all this data to find the genetic variants we thought had a medical relevance.”
WGS identified deleterious de novo mutations in six families (19%) and X-linked or autosomal inherited alterations in ten families (31%). Some had combinations of mutations. Variants of interest were found in four unrecognized, nine known and eight candidate ASD-risk genes. This is a higher proportion of detected mutations than has ever previously been reported, demonstrating the comprehensive and uniform coverage of WGS.
“Since we found some specific changes in genes that cause medical complications we can now target specific drugs to treat these symptoms (e.g., the seizures in some families),” explained Scherer. He continued, “There are also some other genes found in some families whereby drugs are being developed to treat the autism itself. Also, in many cases we now have genetic markers that will inform towards early identification of autism such that perhaps early behavioral intervention protocols can be applied.”
Scherer also informed Personalized Medicine that in the future he hopes every newly diagnosed child with ASD will undergo WGS in order to guide their clinical management, but he also cautioned that the data must be communicated back to the family only by a highly trained professional.
– Written by Sarah Miller
Sources: Jiang YH, Yuen RK, Jin X et al. Detection of clinically relevant genetic variants in autism spectrum disorder by whole-genome sequencing. Am. J. Hum. Genet. doi:10.1016/j.ajhg.2013.06.012 (2013) (Epub ahead of print); SickKids news release: www.sickkids.ca/AboutSickKids/Newsroom/Past-News/2013/First-autism-study-to-apply-whole-genome-sequencing.html