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Category Archives: Genetic Medicine
Scientists pursue new genetic insights for health: Inside the world of deep mutational scanning – GeekWire
Jesse Bloom, left, and Lea Starita are genetic scientists pursuing advances with the technique known as Deep Mutational Scanning, which will be the subject of a symposium and workshop at the University of Washington in Seattle on Jan. 13 and 14. (GeekWire Photo / Todd Bishop)
It has been nearly two decades since scientists accomplished the first complete sequencing of the human genome. This historic moment gave us an unprecedented view of human DNA, the genetic code that determines everything from our eye color to our chance of disease, unlocking some of the biggest mysteries of human life.
Twenty years later, despite the prevalence of genetic sequencing, considerable work remains to fulfill the promise of these advances to alleviate and cure human illness and disease.
Scientists and researchers are actually extremely good at reading genomes, but were very, very bad at understanding what were reading, said Lea Starita, co-director of Brotman Baty Institute for Precision Medicines Advanced Technology Lab, and research assistant professor in the Department of Genome Sciences at the University of Washington.
But that is changing thanks to new tools and approaches, including one called Deep Mutational Scanning. This powerful technique for determining genetic variants is generating widespread interest in the field of genetics and personalized medicine, and its the subject of a symposium and workshop on Jan. 13 and 14 at the University of Washington.
I think approaches like Deep Mutational Scanning will eventually allow us to make better countermeasures, both vaccines and drugs that will help us combat even these viruses that are changing very rapidly said Jesse Bloom, an evolutionary and computational biologist at the Fred Hutchinson Cancer Research Center, the Howard Hughes Medical Institute and the University of Washington Department of Genome Sciences.
Bloom, who researches the evolution of viruses, will deliver the keynote at the symposium, held by the Brotman Baty Institute and the Center for the Multiplex Assessment of Phenotype.
On this episode of the GeekWire Health Tech Podcast, we get a preview and a deeper understanding of Deep Mutational Scanning from Bloom and Starita.
Listen to the episode above, or subscribe in your favorite podcast app, and continue reading for an edited transcript.
Todd Bishop: Lets start with the landscape for precision medicine and personalized medicine. Can you give us a laypersons understanding of how personalized medicine differs from the medicine that most of us have encountered in our lives?
Lea Starita: One of the goals of precision medicine is to use the genomic sequence, the DNA sequence of the human in front of the doctor, to inform the best course of action that would be tailored to that person given their set of genes and the mutations within them.
TB: Some people in general might respond to certain treatments in certain ways and others might not. Today we dont know necessarily why thats the case, but personalized medicine is a quest to tailor the treatment or
Starita: To the individual. Exactly. Thats kind of personalized medicine, but you could also extend that to infectious disease to make sure that youre actually treating the pathogen that the person has, not the general pathogen, if you would. How would you say that, Jesse?
Jesse Bloom: I would elaborate on what Lea said when it comes to infectious diseases and other diseases. Not everybody gets equally sick when they are afflicted with the same underlying thing, and people tend to respond very differently to treatments. That obviously goes for genetic diseases caused by changes in our own genes like cancer, and it also happens with infectious diseases. For instance, the flu virus. Different people will get flu in the same year and some of them will get sicker than others, and thats personalized variation. Obviously wed like to be able to understand what the basis of that variation is and why some people get more sick in some years than others.
TB: Where are we today as a society, as a world, in the evolution of personalized medicine?
Starita: Pretty close to the starting line still. Theres been revolutions in DNA sequencing, for example. Weve got a thousand dollar genome, right? So were actually extremely good at reading genomes, but were very, very bad at understanding what were reading. So you could imagine youve got a human genome, its three billion base pairs times two, because youve got two copies of your genome, one from your mother, one from your father, and within that theres going to be millions of changes, little spelling mistakes all over the genome. We are right now very, very, very I cant even use enough verys bad at predicting which ones of those spelling mistakes are going to either be associated with disease or predictive of disease, even for genes where we know a lot about it. Even if that spelling mistake is in a spot in the genome we know a lot about, say breast cancer genes or something like that, we are still extraordinarily bad at understanding or predicting what effects those changes might have on health.
Bloom: In our research, were obviously also interested in how the genetics of a person influences how sick they get with an infectious disease, but we especially focus on the fact that the viruses themselves are changing a lot, as well. So theres changes in the virus as well as the fact that were all genetically different and those will interact with each other. In both cases, it really comes back to what Lea is saying is that I think weve reached the point in a lot of these fields where we can now determine the sequences of a humans genome or we can determine the sequence of a virus genome relatively easily. But its still very hard to understand what those changes mean. And so, thats really the goal of what were trying to do.
TB: What is deep mutational scanning in this context?
Lea Starita: A mutation is a change in the DNA sequence. DNA is just As, Cs, Ts and Gs. Some mutations which are called variants are harmless. You can think of a spelling mistake or a difference in spelling that wouldnt change the word, right? So the American gray, which is G-R-A-Y versus the British grey, G-R-E-Y. If you saw that in a sentence, its gray. Its the color.
But then it could be a spelling mistake that completely blows up the function of a protein, and then in that case, somebody could have a terrible genetic disease or could have an extremely high risk of cancer, or a flu virus could now be resistant to a drug or something like that, or resistant to your immune response. Or, mutations could also be beneficial, right? This is what allows evolution. This is how flu viruses of all the bacteria evolve to become drug resistant or gain some new enzymatic function that it needs to survive.
Bloom: For instance, in the case of mutations in the human genome, we know that everybody has mutations relative to the average human. Some of those mutations will have really major effects, some of them wont. The very traditional way or the way that people have first tried to understand what those mutations do is to sequence the genomes of a group of people and then compare them. Maybe here are people who got cancer and here are people who didnt get cancer and now you look to see which mutations are in the group that got cancer versus the group that didnt, and youll try to hypothesize that the mutations that are enriched in the group that did get cancer are associated with causing cancer.
This is a really powerful approach, but it comes with a shortcoming which is that theres a lot of mutations, and it gets very expensive to look across very, very large groups of people. And so the idea of a technique like deep mutational scanning is that we could simply do an experiment where we test all of the mutations on their own and we wouldnt have to do these sort of complicated population level comparisons to get at the answer. Because when youre comparing two people in the population, they tend to be different in a lot of ways, and its not a very well-controlled comparison. Whereas you can set up something in the lab where you have a gene that does have this mutation and does not have this mutation, and you can really directly see what the effect of that mutation is. Really, people have been doing that sort of experiment for many decades now. Whats new about deep mutational scanning is the idea that you can do that experiment on a lot of mutations all at once.
Starita: And its called deep because we try to make every possible spelling mistake. So every possible change in the amino acid sequence or the nucleotide sequence, which is the A, C, Ts and Gs, across the entire gene or the sequence were looking at.
Bloom: Lets say we were to compare me and Lea to figure out why one of us had some disease and other ones didnt. We could compare our genomes and theres going to be a lot of differences between them, and were not really going to know what difference is responsible. We dont even really know if it would be a change in their genomes thats responsible. It could be a change in something about our environment. So the idea behind deep mutational scanning is we would just take one gene. So in the case of Lea, she studies a particular gene thats related to breast cancer, and we would just make all of the individual changes in that gene and test what they do one by one. And then subsequently if we were to see that a mutation has some effect, if we were to then observe that mutation when we sequenced someones genome, we would have some idea of what it does.
Starita: The deep mutational scanning, the deep part is making all possible changes. We have all of that information at hand in an Excel file somewhere in the lab that says that this mutation is likely to cause damage to the function of the protein or the activity of the protein that it encodes. Making all of the possible mutations. Thats where the deep comes from.
TB: How exactly are you doing this? Is it because of advances in computer processing or is it because of a change in approach that has enabled this increase in volume of the different mutations you can look at?
Bloom: I would say that theres a number of technologies that have improved, but the really key one is the idea that the whole experiment can be done all at once. The traditional, if you were to go back a few decades way of doing an experiment like this, would be take one tube and put, lets say the normal or un-mutated gene variant in that, and then have another tube which has the mutant that you care about, and have somehow do an experiment on each of those two tubes and that works well.
But you can imagine if you had 10,000 tubes, it might start to become a little bit more difficult. And so the idea is that really the same way that people have gotten very good at sequencing all of these genomes, you can also use to make all of these measurements at once. The idea is you would now put all of different mutants together in the same tube and you would somehow set up the experiment, and this is really the crucial part of the whole thing, set up the experiment such that the cell or the virus or whatever youre looking at, how well it can grow in that tube depends on the effect of that mutation. And then you can just use the sequencing to read out how the frequencies of all of these mutations have changed. You would see that a good mutation that lets say helped the cell grow better would be more representative in the tube at the end, and a bad mutation would be less representative in the tube. And by doing this you could in principle group together tens of thousands or even hundreds of thousands or millions of mutations all at once and read it all out in one experiment.
Starita: This has been enabled by that same revolution that has given us the thousand dollar genome. These DNA sequencers that were now using, not really to sequence human genomes, but were using them as very expensive counting machines. So, were identifying the mutation and were counting it. Thats basically what were using the sequencers for. Instead of sequencing human genomes, were using them as a tool to count all of these different pieces of DNA that are in these cells.
TB: At what stage of development is deep mutational scanning?
Starita: It started about 10 years ago. The first couple of papers came out in 2009 and 2010 actually from the Genome Sciences department at University of Washington. Those started with short sequences and very simplified experiments, and we have been working over the years to build mutational scanning into better and more accurate model systems, but that are increasing the complexity of these experiments. And so weve gone from almost, Hey, thats a cute experiment you guys did, to doing impactful work that people are using in clinical genetics and things like that.
TB: When youre at a holiday party and somebody asks you what you do and then they get really into it and they ask you, Wait, what are the implications of not only personalized medicine but this deep mutational scanning? Whats this going to mean for my life?
Starita: Right now it hasnt been systematically used in the clinic, but well get phone calls from UW pathology that says, Hey, I have a patient that has this variant. We found the sequence variant and this patient has this phenotype. What does this mutation look like in your assay? And were like, Well, it looks like its damaging. And then they put all of that information together and they can actually go back to that patient and say, You are at high risk of cancer. Were going to take medical action. That has happened multiple times. Were working right now to try to figure out how to use the information that we are creating. So these maps of the effect of mutations on these very important proteins and how to systematically use them as evidence for or against their pathogenicity. Right now for a decent percentage of these people who are telling them, Well, youve got changes but we dont know what they do. We want those tests to be more informative. So you go, you get the test, they say, That is a bad one. That ones fine. That mutation is good. That ones OK. That one, though. That ones going to cause you problems. We want more people to have more informative genetic testing because right now in a decent proportion of tests come back with an I have no idea, answer.
Bloom: You can also think about mutations that affect resistance to some sort of drug. For many, many types of drugs, these include drugs against viruses, drugs against cancers and so on, the viruses and the cancers can become resistant by giving mutations that allow them to escape from that drug. In many cases there are even multiple drugs out there and you might have options of which drug to administer, but you might not really know which one. Clinicians have sort of built up lore that this drug tends to work more often or you try this one and then you try this other one, but because how well the drug works is probably in general determined by either the genetic mutations in lets say the cancer or the person or the genetic mutations in the virus or pathogen, if you knew what the effects of those mutations were ahead of time, you could make much more intelligent decisions about which drugs to administer. And there really shouldnt be a drug that works only 50 percent of the time; youre probably just not giving it in the right condition 50 perfect of the time. Wed like to be able to pick the right drug for the right condition all the time.
TB: And thats what precision medicine is about.
TB: Deep mutational scanning as a tool.
Starita: To inform precision medicine.
Bloom: These deep mutational scanning techniques were really developed by people like Jay Shendure and Stan Fields, and Lea and Doug Fowler to look at these questions of precision medicine from the perspective of changes in our human genomes affecting our susceptibility to diseases. I actually work on mutations in a different context, which has mutations in the viruses that infect us and make us sick. These viruses evolve quite rapidly. In the case of flu virus, youre supposed to get the flu vaccine every year. The reason why you have to get it every year is the virus is always changing and we have to make the vaccine keep up with the virus. The same thing is true with drugs against viruses like flu or HIV. Sometimes the viruses will be resistant, sometimes the drugs will work. These again have to do with the very rapid genetic changes that are happening in the virus. So, were trying to use deep mutational scanning to understand how these mutations to these viruses will affect their ability to, lets say, escape someones immunity or escape a drug that might be used to treat that person.
TB: How far along are you on that path?
Bloom: Were making progress. One of the key things weve found is that the same mutation of the virus might have a different impact for different people. So we found using these approaches that the ways that you mutate a virus will allow the virus sometimes to escape from one persons immunity much better than from another persons immunity. And so were really right now trying to map out the heterogeneity across different people. And hopefully that could be used to understand what makes some people susceptible to a very specific viral strain versus other people.
TB: And so then would your research extend into the mutations in human genes in addition to the changes in the virus?
Bloom: You could imagine eventually wanting to look at all of those combinations together, and we are very interested in this, but the immediate research were focusing on right now actually probably is not so much driven by the genetics of the humans. In the case of influenza virus, like I was saying, we found that if theres a virus that has some particular mutation, it might, lets say, allow it to escape from your immunity but not allow it to escape from the immunity of me or Lea. That doesnt seem to be driven as much we think by our genetics, but rather our exposure histories. So in the case of influenza, were not born with any immunity to influenza virus. We build up that immunity over the course of our lifetime because we either get infected with flu or we get vaccinated with flu and then our body makes an immune response, which includes antibodies which block the virus. Each of us have our own personal history, not genetic history, but life history of which vaccinations and which infections weve gotten. And so, that will shape how our immune response sees the virus. As a result, we think that that doesnt really have so much of a genetic component as a historical component.
TB: Just going with the flu example, could this result in a future big picture where I go in to get my flu vaccine and its different than the one the next person might go in to get?
Bloom: What we would most like to do is use this knowledge to just design a vaccine that works for everybody. So that would just be the same vaccine that everyone could get. But its a very interesting I think at this point I would say its almost in the thought experiment stage to think about this. When you think of something like cancer, like Lea was saying, you can use these tools to understand when people have mutations that might make them at risk for a cancer, but thats actually often a very hard thing to intervene for, right? Its not so easy to prevent someone from getting cancer even if you know theyre at risk. But obviously if people are able to do that, theyre interested in spending a lot of money to do it, because cancer is a very severe thing and you often have a very long window to treat it.
Something like a flu virus is very much at the other end. If I had the omniscient capability to tell you that three days from now youre going to get infected with flu and youre going to get really sick, we could prevent that. We have the technology basically right now to prevent that, if its nothing else than just telling you to put on a bunch of Purell and dont leave your bedroom. But theres also actually some pretty good interventions including prophylactics to flu that work quite well. But the key thing is, right now we think of everyone in the world as being at risk all the time and you cant be treating everybody in the world all the time against flu. Theres just too many people and the risk that any person
Starita: Not that much Tamiflu on the market.
Bloom: Not that much, and the risk of it So I think to the extent that we could really identify whos at the most risk in any given year, that might allow us to use these interventions in a more targeted way. Thats the idea.
TB: And how does deep mutational scanning lead to that potentially?
Bloom: Yeah. So the idea, and at this point, this is really in the research phase, but the idea is if we could identify that say certain people or certain segments of the population, that because of the way their immunity, lets say, is working makes them very susceptible to the viral mutant that happens to have arisen in this particular year, we could then somehow either suggest that theyre more at risk or, as you suggested, design a vaccine thats specifically tailored to work for them. So thats the idea. I should make clear that that is not anywhere close to anybody even thinking of putting it into economic practice at this point because even the concepts behind it are really quite new. But I do think that theres a lot of potential if we think of these infectious diseases not so much as an act of God, where you just happened to someone sneezed on you as youre walking down the street, but actually a complex interaction between the mutations in the virus and your own either genetics or immune system, we can start to identify who might be more at risk for certain things in certain years, and that would at least open the door to using a lot of interventions we already have.
Starita: The first year was three years ago, and some very enthusiastic graduate students started it. Basically, it was almost like a giant lab meeting where everybody who is interested in this field came. Somebody tweeted it out and then all of a sudden people from UCSF were there and were like, What the heck? It was great and we all talked about the technology and how we were using it. The next year, the Brotman Baty Institute came in and were like, OK, well, maybe if we use some of this gift to support this, we can have a bigger meeting. And then it was 200 people in a big auditorium and that was great. And now this year, its a two-day symposium and workshop, and its also co-sponsored by a grant from the National Human Genome Research Institute. But now weve got hundreds of people, so about 200 people again, but now flying in from all over the world. Weve got invited speakers, and the workshop, which is Tuesday, is a more practical, If youre interested in this, how do you actually do these experiments?
TB: Whats driving the interest in deep mutational scanning?
Bloom: We are starting to have so much genetic information about really everything. It used to be, going back a couple of decades, a big deal to determine even the sequence of a single flu virus. It was totally unthinkable to determine the sequence of a human genome, right? If you dont know what mutations are there, you dont really care that much what they do. Now we can determine the sequence of tens of thousands of flu viruses. I mean, this is happening all the time, and we can determine the sequence of thousands, even tens of thousands of human genomes. So now it becomes, as Lea said, really important to go from just getting these sequences to understanding what the mutations that you observe in these sequences actually will mean for human health.
See this site for more on the Brotman Baty Institute for Precision Medicine and the Deep Mutational Scanning Symposium and Workshop, Jan. 13 and 14 in Seattle. The symposium is free to attend if youre in the Seattle area, and it will also be livestreamed, with archived video available afterward.
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Scientists pursue new genetic insights for health: Inside the world of deep mutational scanning - GeekWire
Physicians expect almost one-third of their jobs to be automated by 2040, Stanford Medicine report finds – FierceHealthcare
Doctors say digital technology and data are driving change that will create a different world of medicine in the next couple of decades, a new report from Stanford Medicine finds.
In a survey, physicians, residents and medical students say they expect almost a third of their current duties could be automated in 20 years. And doctors are preparing for that very different healthcare future now, according to the report (PDF).
Nearly half of physicians (73%) and most medical students (73%) are seeking additional training in areas such as advanced statistics, genetic counseling, population health and coding. One-third are studying artificial intelligence, according to the national survey of more than 700 physicians, residents and medical students commissioned by Stanford Medicine to understand how changing trends will reach the doctors office and shape patient care.
"We found that current and future physicians are not only open to new technologies but are actively seeking training in subjects such as data science to enhance care for their patients," saidLloyd Minor, M.D., dean of theStanford UniversitySchool of Medicine, in a statement.
Emergency and Hospital Medicine Integration: Reimagining Patients Care Journeys
An integrated EM/HM care model standardizes processes and boosts communication to streamline patients care journeys. See how EM/HM integration helped one facility reduce 30-day readmissions by 55 percent and prevent Medicare reimbursement penalties.
"We are encouraged by these findings and the opportunity they present to improve patient outcomes. At the same time, we must be clear-eyed about the challenges that may stymie progress, he said.
Key trends that are reshaping healthcare include a maturing digital health market, new health laws opening patient access to data and AI gaining regulatory traction for medical use.
And the jurys still out when it comes to whether the private industrys foray into healthcarein the form of companies such as Amazon, Google and Apple will solve problems.
Physicians, residents and students had mixed views about the impact these companies will have on healthcare, with 30% of students and residents and 21% of physicians still undecided. While patient outcomes are likely to improve, respondents are divided on whether physician effectiveness will improve and say physician job satisfaction will likely decrease, while healthcare costs likely increase.
Other findings include:
The value of data. The survey also showed that providers are heavy digital users and they believe patient data from wearables can be clinically valuable. Nearly half the survey respondents wear a health monitoring device, and most of them use the data to inform their personal healthcare decisions (71% of physicians, 60% of students and residents). A majority of students and residents (78%) and physicians (80%) say self-reported data from a patients health app would be clinically valuable in supporting their care. They also see value in data from consumer genetic testing reports.
Doctors arent prepared to implement innovations. However, most providers dont believe the current generation of practitioners is ready for the data-driven future, even current medical students and residents. When asked to rate the effectiveness of their education to prepare them for these developments, only 18% of current medical students and residents surveyed said that their education was very helpful. And 44% of physicians surveyed said their education was either not very helpful or not helpful at all.
The report pointed to the need to modernize curriculum and training programs so current and future physicians can make the most of new technologies.
The ongoing struggle with medical practice burdens. And, no surprise, physicians and residents say they are struggling under medical practice burdens. Nearly 1 in 5 would change their career path if given the opportunity, citing poor work-life balance and administrative burdens as the top reasons to reconsider their decision.
Ahmad Farajzadeh-Sheikh, 1, 2 Mohammad Savari, 1, 2 Khadijeh Ahmadi, 2, 3 Hossein Hosseini Nave, 4 Mojtaba Shahin, 5 Maryam Afzali 2
1Department of Microbiology, School of Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran; 2Infectious and Tropical Diseases Research Center, Health Research Institute, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran; 3Abadan Faculty of Medical Sciences, Abadan, Iran; 4Department of Microbiology and Virology, School of Medicine, Kerman University of Medical Sciences, Kerman, Iran; 5Department of Medical Laboratory Sciences, Faculty of Medical Sciences, Islamic Azad University, Arak, Iran
Correspondence: Maryam AfzaliDepartment of Microbiology, School of Medicine, Ahvaz Jundishapur University of Medical Sciences, Golestan Blvd 39345-61355, Ahvaz, IranTel +09156127753Fax +98-61-3333 2036Email firstname.lastname@example.org
Background: Entero-invasive E. coli (EIEC) is one of the causes of bacillary dysentery in adults and children. The ability of EIEC to invade and colonize the surface of epithelial cells is influenced by many virulence factors. This study aimed to investigate the distribution of virulence factor genes in EIEC strains isolated from patients with diarrhea in Ahvaz, Iran, as well as the genetic diversity between these isolates by Multilocus variable-number tandem repeat analysis (MLVA).Materials and Methods: A total of 581 diarrheic stool samples were collected from patients with diarrhea attending two hospitals, in Ahvaz, Iran. The E. coli strains were identified by biochemical methods. Subsequently, all E. coli isolates were identified as EIEC by polymerase chain reaction (PCR) for the ipaH gene. The EIEC isolates evaluated by PCR for the presence of 8 virulence genes (ial, sen, virF, invE, sat, sigA, pic, and sepA). All EIEC strains were genotyped by the MLVA typing method.Results: A total of 13 EIEC isolates were identified. The presence of ial, virF, invE, sen, sigA, pic, and sat genes was confirmed among 92.3%, 84.6%, 84.6%, 76.9%, 69.2%, and 15.3% of EIEC isolates, respectively. On the other hand, none of the isolates were positive for the sepA gene. The EIEC isolates were divided into 11 MLVA types.Conclusion: Our results showed a high distribution of virulence genes among EIEC isolates in our region. This study showed that MLVA is a promising typing technique for epidemiological studies. MLVA can supply data in the form of codes that can be saved in the database and easily shared among laboratories, research institutes, and even hospitals.
Keywords: entero-invasive Escherichia coli, diarrhea, virulence factor, MLVA
This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution - Non Commercial (unported, v3.0) License.By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms.
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Distribution of Genes Encoding Virulence Factors and the Genetic Diver | IDR - Dove Medical Press
IDEAYA Biosciences and Boston Children’s Hospital Collaborate on Preclinical Evaluation of IDE196 for Sturge Weber Syndrome – a Rare Disease…
SOUTH SAN FRANCISCO, Calif., Jan. 10, 2020 /PRNewswire/ -- IDEAYA Biosciences, Inc. (NASDAQ:IDYA), an oncology-focused precision medicine company committed to the discovery and development of targeted therapeutics, announced that the company has entered into a Sponsored Research Agreement with Boston Children's Hospital for preclinical evaluation of the role of protein kinase C (PKC) in Sturge Weber syndrome (SWS), a rare neurocutaneous disorder characterized by capillary malformations and associated with mutations in GNAQ.
Under the agreement, IDEAYA will collaborate with and support research at Boston Children's Hospital in the laboratory of Dr. Joyce Bischoff, Ph.D., Research Associate, Department of Surgery and Professor, Harvard Medical School, who is Principal Investigator of the research studies. The preclinical research will evaluate IDE196, a potent, selective PKC inhibitor, in vitro to assess whether pharmacological inhibition of PKC in endothelial cells having GNAQ mutations will restore normal cell function, as well as in vivo to assess whether pharmacological inhibition of PKC can regulate blood vessel size in murine models that recapitulate enlarged vessels seen in SWS capillary malformations.
SWS is a rare disease characterized by a facial birthmark, neurological abnormalities (e.g. seizures) and glaucoma, which occurs in 1 to 20,000 to 50,000 live births. The disease is believed to be mediated by a somatic GNAQ mutation in skin or brain tissue which enhances signaling in the PKC pathway in a reported 88% (n=26) of SWS patients. (NEJM Shirley et al., May 2019). "SWS is a rare disease that can present debilitating symptoms for patients, such as choroidal hemangiomas which may lead to glaucoma. There are no current FDA approved treatments specifically developed for SWS highlighting the high unmet medical need for these patients," noted Dr. Bischoff, Ph.D.
IDE196 is a potent, selective, small molecule inhibitor of protein kinase C (PKC), which IDEAYA is evaluating in a Phase 1/2 basket trial in patients with Metastatic Uveal Melanoma or other solid tumors, such as cutaneous melanoma, having GNAQ or GNA11 hotspot mutations which enhance signaling in the PKC pathway. "We are excited to work with Boston Children's Hospital to evaluate IDE196 activity in preclinical models relevant to Sturge Weber, a rare disease believed to be driven by genetic mutation of GNAQ. This important work is part of our broader strategy to deliver precision medicine therapies for patients with GNAQ or GNA11 mutations, by targeting the underlying biology of the disease," said Yujiro S. Hata,Chief Executive Officer and President at IDEAYA Biosciences.
About IDEAYA Biosciences
IDEAYA is an oncology-focused precision medicine company committed to the discovery and development of targeted therapeutics for patient populations selected using molecular diagnostics. IDEAYA's approach integrates capabilities in identifying and validating translational biomarkers with small molecule drug discovery to select patient populations most likely to benefit from the targeted therapies IDEAYA is developing. IDEAYA is applying these capabilities across multiple classes of precision medicine, including direct targeting of oncogenic pathways and synthetic lethality which represents an emerging class of precision medicine targets.
This press release contains forward-looking statements, including, but not limited to, statements related to IDE196 activity in preclinical models relevant to Sturge Weberand IDEAYA's ability to deliver precision medicine therapies. Such forward-looking statements involve substantial risks and uncertainties that could cause IDEAYA's preclinical and clinical development programs, future results, performance or achievements to differ significantly from those expressed or implied by the forward-looking statements. Such risks and uncertainties include, among others, the uncertainties inherent in the drug development process, including IDEAYA's programs' early stage of development, the process of designing and conducting preclinical and clinical trials, the regulatory approval processes, the timing of regulatory filings, the challenges associated with manufacturing drug products, IDEAYA's ability to successfully establish, protect and defend its intellectual property and other matters that could affect the sufficiency of existing cash to fund operations. IDEAYA undertakes no obligation to update or revise any forward-looking statements. For a further description of the risks and uncertainties that could cause actual results to differ from those expressed in these forward-looking statements, as well as risks relating to the business of IDEAYA in general, see IDEAYA's recent Quarterly Report on Form 10-Q filed on November 13, 2019 and any current and periodic reports filed with the U.S. Securities and Exchange Commission.
SOURCE IDEAYA Biosciences, Inc.
In defence of imprecise medicine: the benefits of routine treatments for common diseases – The Conversation UK
The NHS states that it will be the world-leading healthcare system in its use of cutting-edge genomic technologies to predict and diagnose inherited and acquired disease, and to personalise treatments and interventions. As all diseases are either inherited or acquired, this is no modest claim.
This approach to medical care is known as precision medicine, and given the hype that surrounds the model, you might be forgiven for thinking that the usual practice of imprecise medicine is greatly inferior. And yet it has been the routine and, in many respects, indiscriminate use of effective treatments for a range of common diseases that has improved the health of large numbers of patients over the past few decades.
Precision medicine assumes that genes play a big role in causing diseases and that new treatments targeting genes and their processes can have significant benefits. The government is so enthusiastic about this new approach that in 2019 it offered gene sequencing to the entire UK population, albeit for a fee. In announcing this initiative, Health Secretary Matt Hancock said there are huge benefits to sequencing as many genomes as we can every genome sequenced moves us a step closer to unlocking life-saving treatments.
But just how big are the benefits likely to be? How relevant is precision medicine to preventing and treating the diseases responsible for most premature deaths and hospital admissions in the UK, such as heart disease, stroke, hip fracture and dementia diseases where genetic links are not clear.
In a study of half a million participants in the UK Biobank project, 1.7 million separate gene variants were shown to be associated with heart disease. Yet in combination, these variants accounted for less than 3% of heart disease after considering known causes such as smoking and high cholesterol.
Precision medicine seems likely to offer most promise for preventing and treating less common diseases, as they are more likely to have a major genetic cause. The poster child for precision medicine is the drug trastuzumab (also known as Herceptin), which was developed following the discovery of HER2, a genetic factor implicated in about 20% of breast cancer cases.
Trastuzumab targets a specific biological mechanism that is involved in HER2 positive cancer, and treatment with this drug improves survival and reduces cancer recurrence. But the effects are not quite as remarkable as has been sometimes suggested. A meta-analysis of clinical trials reported that after ten years, 74% of patients treated with trastuzumab remained alive and recurrence-free compared with 62% of those who did not receive trastuzumab. A worthwhile effect for sure, but only for about 10-15% of patients.
Comparing these important but small gains with the impact of an imprecise approach taken to other diseases offers a stark contrast. For example, HIV used to be a death sentence. Today, 94% of people with the disease are still alive after 30 years, thanks to antiretroviral drugs. Similarly, deaths in the five-year period following a heart attack declined by 70% between 1979 and 2013, largely due to the routine use of drugs such as aspirin, ACE inhibitors and statins.
Interestingly, for both heart attacks and HIV, when efforts have been made to personalise treatment, it has generally led to worse outcomes; in large part as a consequence of doctors withholding treatments they believe may not be beneficial or could be dangerous for a particular person. Unfortunately, such clinical insights are more often wrong than right.
Its hard not to conclude that the nations health would be better served by the NHS if it aspired to be a global leader in the standardisation of care for common serious diseases. Lets not let the current enthusiasm for precision medicine blind us to the benefits of the imprecise medicine we know saves millions of lives every year.
New year health kicks are great but your environment is also vital | Dr Robert Wright – The Guardian
Exercising and eating better as part of our new year health kicks are great, but we should also think more deeply about the role the environment plays on our health. As a professor of environmental medicine, I believe this is an exciting new area of study that will play a big part in the future of personalized medicine.
Consider this, every day we are bombarded with messages: genes that cause cancer, supplements that prevent Alzheimers disease, diets that prevent asthma, chemicals that make us gain weight. But while headlines frequently proclaim game changing new findings, over the last 20 years in the US and Europe our health status as a population has seriously deteriorated. Rates of obesity, diabetes, heart disease, cancer and learning disorders continue to rise. Genetic variation may be part of the puzzle that explains why we get sick, but clearly there are missing pieces.
After all, 20 years of increasing obesity and diabetes represents only a single generation. If our genes didnt change in the last 20 years, then our environment must have.
Genes never work in isolation. Instead, they determine how we react to our diet, social surroundings, physical environment, infections and chemical exposures. Environment is the missing piece of the puzzle.
The old 20th-century concept of nature v nurture needs to be redefined, as genetics and environment do not compete, they work hand in hand, sometimes to our benefit and sometimes to our detriment. The correct formula is really nature times nurture. Right now the nurture part of that equation is largely unknown, but that may soon change.
Recently, a new concept has arisen, the science of the exposome: the measurement of all the health-relevant environmental factors across the lifetime.
The exposome is to our environment what genomics is to our genetics. Most of what we know about environment and health is still a black box consisting of yet to be discovered risk factors we too often attribute to bad luck ie because we dont measure the environmental cause, the problem appears random.
But most of what we now understand about genetics was also a black box in the 20th century.
Physicians see the role of environment daily even if it is not clear to them that environment is the cause. For example, a child with autism develops more frequent combative oppositional behaviors and emotional outbursts. An adult with diabetes cant seem to control her blood sugar despite higher doses of insulin. A newborn is born with blue skin but a normal heart.
For each of these cases, sequencing the genome would not have identified the cause. The autistic child had lead poisoning because of pica brought on by autism, the diabetic adult used perfumes high in phthalates, chemicals that affect metabolism and the newborn baby drank formula mixed with well water contaminated by fertilizer runoff that reacted with his hemoglobin.
In each case, genomics would not have given us the correct answer, but if we had the tools to measure the exposome, we would have made the correct diagnosis. Just as importantly, because the underlying causes were environmental, we can treat the problem with interventions.
Furthermore, in most diseases, environment and genetics work in combination. Its very rare to have a genetic variant that causes Alzheimers disease, but it is fairly common to have a genetic variant that makes us susceptible to environments that can cause Alzheimers. The different between those with the genetic variant who get sick and those who dont is their different environments.
Imagine a visit to your physician in which you begin by handing over your smartwatch to have its data downloaded, followed by a blood draw to measure your chemical environment and nutritional status, then you update your lifetime home address and occupational history into a secure computer that houses your genomic data. This then computes your personalized risk score for heart disease, diabetes and other diseases. Or, if you already have one of these diseases, computes the ideal treatment regimen based on this big data. This is how we will be able to personalize medicine.
We are not there yet, but the technology to measure the exposome is far more advanced than the general public, and even many researchers, realize. There are now lab tests that can demonstrate the presence of thousands of chemicals in our bodies and satellites that record our daily weather, air pollution, light exposure and built environment. Public records have data on water quality, age of housing, local crime statistics, outdoor noise levels and even where disease clusters are occurring. Cellphones are ubiquitous and can link our daily behavior and movement patterns with the quality of the local air and water while simultaneously measuring our heart rate, physical activity and sleep quality.
Computational science has advanced to a point where storage of terabytes of data is routine and computer clusters are found in every major university and methods to bring these databases together are no longer science fiction. Artificial intelligence and other big data approaches to genomics also provide a roadmap for analyzing exposomic data.
Understanding how environment affects your health will empower people to make the changes in their lifestyle that will matter most. To understand what food to buy, which fragrances to avoid, where and when to exercise, etc. All the pieces to solve this puzzle are beginning to come together. What is needed is the grand vision to invest in and integrate exposomic science into public health and clinical medicine. This is the final piece of the puzzle. Once we understand our exposome and integrate it with our genome, we will finally understand why and how chronic diseases have become so common and how we can start to reverse their trends in society.
Dr Robert Wright is a pediatrician, medical toxicologist, environmental epidemiologist and director of the Institute for Exposomic Research at the Icahn School of Medicine at Mount Sinai