Study gives promise to new treatment for appendix cancer
Appendix cancer is rare, with approximately 600 to 1,000 new patients diagnosed each year and an estimated 10,000 currently living with the disease. Because it is rare, few studies have been devoted to this cancer and standard treatment for appendix cancers relies upon the same chemotherapy drugs used for colorectal cancer. A new study by researchers at the University of California, San Diego School of Medicine has found that genetic mutations in appendix and colon cancers are, in fact, quite different, suggesting that new and different approaches to appendix cancer treatment should be explored.
The study was published in a recent issue of Genome Medicine.
Cancers are characterized by different gene mutations. Historically, genetic mutations in appendix cancer have been poorly characterized due to its low incidence. The cancer often remains undiagnosed until it is discovered during or after abdominal surgery or when an abnormal mass is detected  during a CT scan for an unrelated condition.
The primary treatment of localized appendix cancer is surgical but treatment for patients with inoperable appendix cancer has been limited to therapies developed for colorectal cancer. Although the chemotherapy drugs used for colorectal cancer dramatically improve patient outcomes, they have not proven to be as successful in patients with appendix cancer.
“We have been treating appendix cancer like colorectal cancer because it was thought to be the most similar tumor type, but this study identifies the signature differences between these two cancers,” said Andrew Lowy, MD, FACS, a senior author of the study and professor of Surgery at UC San Diego School of Medicine. “These findings suggest opportunities to develop novel therapies that specifically target appendix cancer.”  
The study initially evaluated 10 cases, nine with low-grade appendix cancers and one with high-grade cancer. The results from this group were then validated with 19 additional cases.
The results also identified a gene mutation in appendix cancer that is commonly found in a form of pancreatic cancer, which typically spreads rapidly and is seldom detected in its early stages.
“The study’s results are promising for patients. We now have a more in-depth knowledge of the biological make up of appendix cancers, which allow for a more customized approach,” said Lowy, who also serves as chief of the Division of Surgical Oncology at UC San Diego Health System. “The goal is to now conduct more studies that will test specific treatments targeted to these unique genetic mutations.”
To learn more about cancer treatments at UC San Diego Health System, visit cancer.ucsd.edu         Image: A histopathological photomicrograph depicting cancerous cells in the appendix.

Study gives promise to new treatment for appendix cancer

Appendix cancer is rare, with approximately 600 to 1,000 new patients diagnosed each year and an estimated 10,000 currently living with the disease. Because it is rare, few studies have been devoted to this cancer and standard treatment for appendix cancers relies upon the same chemotherapy drugs used for colorectal cancer. A new study by researchers at the University of California, San Diego School of Medicine has found that genetic mutations in appendix and colon cancers are, in fact, quite different, suggesting that new and different approaches to appendix cancer treatment should be explored.

The study was published in a recent issue of Genome Medicine.

Cancers are characterized by different gene mutations. Historically, genetic mutations in appendix cancer have been poorly characterized due to its low incidence. The cancer often remains undiagnosed until it is discovered during or after abdominal surgery or when an abnormal mass is detected  during a CT scan for an unrelated condition.

The primary treatment of localized appendix cancer is surgical but treatment for patients with inoperable appendix cancer has been limited to therapies developed for colorectal cancer. Although the chemotherapy drugs used for colorectal cancer dramatically improve patient outcomes, they have not proven to be as successful in patients with appendix cancer.

“We have been treating appendix cancer like colorectal cancer because it was thought to be the most similar tumor type, but this study identifies the signature differences between these two cancers,” said Andrew Lowy, MD, FACS, a senior author of the study and professor of Surgery at UC San Diego School of Medicine. “These findings suggest opportunities to develop novel therapies that specifically target appendix cancer.”  

The study initially evaluated 10 cases, nine with low-grade appendix cancers and one with high-grade cancer. The results from this group were then validated with 19 additional cases.

The results also identified a gene mutation in appendix cancer that is commonly found in a form of pancreatic cancer, which typically spreads rapidly and is seldom detected in its early stages.

“The study’s results are promising for patients. We now have a more in-depth knowledge of the biological make up of appendix cancers, which allow for a more customized approach,” said Lowy, who also serves as chief of the Division of Surgical Oncology at UC San Diego Health System. “The goal is to now conduct more studies that will test specific treatments targeted to these unique genetic mutations.”

To learn more about cancer treatments at UC San Diego Health System, visit cancer.ucsd.edu        

Image: A histopathological photomicrograph depicting cancerous cells in the appendix.

New Reprogramming Method Makes Better Stem Cells
A team of researchers from the University of California, San Diego School of Medicine, Oregon Health & Science University (OHSU) and Salk Institute for Biological Studies has shown for the first time that stem cells created using different methods produce differing cells. The findings, published in the July 2, 2014 online issue of Nature, provide new insights into the basic biology of stem cells and could ultimately lead to improved stem cell therapies.
Capable of developing into any cell type, pluripotent stem cells offer great promise as the basis for emerging cell transplantation therapies that address a wide array of diseases and conditions, from diabetes and Alzheimer’s disease to cancer and spinal cord injuries. In theory, stem cells could be created and programmed to replace ailing or absent cells for every organ in the human body.
The gold standard is human embryonic stem cells (ES cells) cultured from discarded embryos generated by in vitro fertilization, but their use has long been limited by ethical and logistical considerations. Scientists have instead turned to two other methods to create stem cells: Somatic cell nuclear transfer (SCNT), in which genetic material from an adult cell is transferred into an empty egg cell, and induced pluripotent stem cells (iPS cells), in which adult cells are reverted back to a stem cell state by artificially turning on targeted genes.
Until now, no one had directly and closely compared the stem cells acquired using these two methods. The scientists found they produced measurably different results. “The nuclear transfer ES cells are much more similar to real ES cells than the iPS cells,” said co-senior author Louise Laurent, PhD, assistant professor in the Department of Reproductive Medicine at UC San Diego. “They are more completely reprogrammed and have fewer alterations in gene expression and DNA methylation levels that are attributable to the reprogramming process itself.”
Read more here
Pictured: Scanning electron micrograph of cultured human neuron from induced pluripotent stem cell. Image courtesy of Mark Ellisman and Thomas Deerinck, National Center for Microscopy and Imaging Research, UC San Diego

New Reprogramming Method Makes Better Stem Cells

A team of researchers from the University of California, San Diego School of Medicine, Oregon Health & Science University (OHSU) and Salk Institute for Biological Studies has shown for the first time that stem cells created using different methods produce differing cells. The findings, published in the July 2, 2014 online issue of Nature, provide new insights into the basic biology of stem cells and could ultimately lead to improved stem cell therapies.

Capable of developing into any cell type, pluripotent stem cells offer great promise as the basis for emerging cell transplantation therapies that address a wide array of diseases and conditions, from diabetes and Alzheimer’s disease to cancer and spinal cord injuries. In theory, stem cells could be created and programmed to replace ailing or absent cells for every organ in the human body.

The gold standard is human embryonic stem cells (ES cells) cultured from discarded embryos generated by in vitro fertilization, but their use has long been limited by ethical and logistical considerations. Scientists have instead turned to two other methods to create stem cells: Somatic cell nuclear transfer (SCNT), in which genetic material from an adult cell is transferred into an empty egg cell, and induced pluripotent stem cells (iPS cells), in which adult cells are reverted back to a stem cell state by artificially turning on targeted genes.

Until now, no one had directly and closely compared the stem cells acquired using these two methods. The scientists found they produced measurably different results. “The nuclear transfer ES cells are much more similar to real ES cells than the iPS cells,” said co-senior author Louise Laurent, PhD, assistant professor in the Department of Reproductive Medicine at UC San Diego. “They are more completely reprogrammed and have fewer alterations in gene expression and DNA methylation levels that are attributable to the reprogramming process itself.”

Read more here

Pictured: Scanning electron micrograph of cultured human neuron from induced pluripotent stem cell. Image courtesy of Mark Ellisman and Thomas Deerinck, National Center for Microscopy and Imaging Research, UC San Diego

Cancer Avatars for Personalized MedicineTumor modeling predicts most effective drugs targeting brain cancer
Researchers at University of California, San Diego School of Medicine and Moores Cancer Center have used computer simulations of cancer cells – cancer avatars – to identify drugs most likely to kill cancer cells isolated from patients’ brain tumors.
The findings, published in May 21 online issue of the Journal of Translational Medicine, may help researchers stratify cancer patients for clinical trials according to their cancers’ genomic signatures and predicted sensitivities to different cancer drugs.
Such an approach would allow scientists to selectively test cancer drugs on those who would be most likely to respond to them, while simultaneously reducing patients’ exposures to toxic drugs that would likely be ineffective.
“Genomics tells us that cancers are a lot like snowflakes. No two cancers are alike so it does not make sense to give all patients the same drugs. This is the idea behind personalizing therapies for cancer,” said lead author Sandeep Pingle, MD, PhD, a project scientist in the laboratory of Santosh Kesari, MD, PhD, chief of the division of Neuro-Oncology, professor in the department of neurosciences, director of Neuro-Oncology at UC San Diego Moores Cancer Center and the study’s senior author.
“With the virtual cell model, we can take into account all the complexity of cellular processes to predict which drugs will be the most effective against a particular tumor based on its genomic profile,” Pingle said. “This is a first step toward personalized medicine.”
Researchers developed a virtual cell that represents the internal workings of a normal, healthy cell, depicting them as a complex collection of signaling pathways and metabolic networks. The virtual healthy cell can be made cancerous. Indeed, it can be turned into any kind of cancer cell by distorting specific points and pathways in the system. These cellular distortions represent a person’s so-called cancer avatar. Once the avatar is generated, a computer model predicts which drugs, based upon their known functions, are most likely to kill a real cancer cell.
For the study, researchers generated cancer avatars for cells obtained from patients with glioblastoma, a highly aggressive cancer of the brain’s glial cells. The condition has a five-year survival rate of about 10 percent. The computer generated predictions were then “truth-checked” against standard, cultured cells in drug-sensitivity experiments.
“The advantage of computational modeling is the ability to incorporate the wealth of genomic and proteomic information on cancer cells and to screen drugs and combinations of drugs much faster and cost effectively,” said Kesari. “Our ultimate goal is to take this technology to the clinic to identify the best drugs for treating each individual cancer patient.”

Cancer Avatars for Personalized Medicine
Tumor modeling predicts most effective drugs targeting brain cancer

Researchers at University of California, San Diego School of Medicine and Moores Cancer Center have used computer simulations of cancer cells – cancer avatars – to identify drugs most likely to kill cancer cells isolated from patients’ brain tumors.

The findings, published in May 21 online issue of the Journal of Translational Medicine, may help researchers stratify cancer patients for clinical trials according to their cancers’ genomic signatures and predicted sensitivities to different cancer drugs.

Such an approach would allow scientists to selectively test cancer drugs on those who would be most likely to respond to them, while simultaneously reducing patients’ exposures to toxic drugs that would likely be ineffective.

“Genomics tells us that cancers are a lot like snowflakes. No two cancers are alike so it does not make sense to give all patients the same drugs. This is the idea behind personalizing therapies for cancer,” said lead author Sandeep Pingle, MD, PhD, a project scientist in the laboratory of Santosh Kesari, MD, PhD, chief of the division of Neuro-Oncology, professor in the department of neurosciences, director of Neuro-Oncology at UC San Diego Moores Cancer Center and the study’s senior author.

“With the virtual cell model, we can take into account all the complexity of cellular processes to predict which drugs will be the most effective against a particular tumor based on its genomic profile,” Pingle said. “This is a first step toward personalized medicine.”

Researchers developed a virtual cell that represents the internal workings of a normal, healthy cell, depicting them as a complex collection of signaling pathways and metabolic networks. The virtual healthy cell can be made cancerous. Indeed, it can be turned into any kind of cancer cell by distorting specific points and pathways in the system. These cellular distortions represent a person’s so-called cancer avatar. Once the avatar is generated, a computer model predicts which drugs, based upon their known functions, are most likely to kill a real cancer cell.

For the study, researchers generated cancer avatars for cells obtained from patients with glioblastoma, a highly aggressive cancer of the brain’s glial cells. The condition has a five-year survival rate of about 10 percent. The computer generated predictions were then “truth-checked” against standard, cultured cells in drug-sensitivity experiments.

“The advantage of computational modeling is the ability to incorporate the wealth of genomic and proteomic information on cancer cells and to screen drugs and combinations of drugs much faster and cost effectively,” said Kesari. “Our ultimate goal is to take this technology to the clinic to identify the best drugs for treating each individual cancer patient.”

Detecting Fetal Chromosomal Defects Without Risk Noninvasive sequencing is faster, cheaper and safer for mother and fetus, say researchers
Chromosomal abnormalities that result in birth defects and genetic disorders like Down syndrome remain a significant health burden in the United States and throughout the world, with some current prenatal screening procedures invasive and a potential risk to mother and unborn child.
In a paper published online this week in the Early Edition of PNAS, a team of scientists at the University of California, San Diego School of Medicine and in China describe a new benchtop semiconductor sequencing procedure and newly developed bioinformatics software tools that are fast, accurate, portable, less expensive and can be completed without harm to mother or fetus.  
“We believe this approach could become the standard of care for screening of prenatal chromosomal abnormalities,” said Kang Zhang, MD, PhD, professor of ophthalmology, founding director of the Institute for Genomic Medicine at UC San Diego and a staff physician at the San Diego VA Healthcare System.
The incidence of chromosomal abnormalities – in numbers or structure – is one in 160 live births in the United States, higher in other countries. In China, for example, the rate is one in 60 live births. The effects of these abnormalities, known as aneuploidies, can be severe, from developmental delays and neurological disorders to infertility and death. The incidence rate rises with maternal age, most notably after age 35.
Current diagnoses of fetal aneuploidies often rely upon invasive tests that sample amniotic fluid or placental tissues for fetal DNA that can then be analyzed using a variety of complex and expensive methods, including full karyotyping in which the entire set of chromosomes is viewed microscopically. While highly reliable, these invasive tests may cause infections in the pregnant woman and pose as much as a 1 percent risk of miscarriage and fetal loss. Results are not available for one to two weeks, extending anxiety for families waiting for information.
The new method relies upon massively parallel sequencing of cell-free fetal DNA using a benchtop semiconductor sequencing platform (SSP) called an Ion Torrent sequencer developed by Life Technologies. Cell-free fetal DNA is genetic material from the fetus that circulates naturally and freely in the mother’s bloodstream. It can be obtained through an ordinary blood draw, with SSP analysis achieved in less than four days.
To assess the SSP method, researchers tested 2,275 pregnant women. More than 500 participated in a retrospective analysis, undergoing full karyotyping to establish known chromosomal abnormalities followed by SSP testing. The remainder participated in a prospective study without prior karyotyping, and SSP testing results were then compared to karyotyping results. The sequencing and automated bioinformatics analyses were performed at iGenomics in Guangzhou, China.
“We used the retrospective study to establish the method and the prospective study to validate it,” said Zhang.
In the retrospective study, the researchers found that SSP detected multiple types of chromosomal abnormality with virtually 100 percent sensitivity and specificity compared to full karyotyping.
“To our knowledge, this is the first large-scale clinical study to systematically identify chromosomal aneuploidies based on cell-free fetal DNA using SSP,” said Zhang. “It provides an effective strategy for large-scale, noninvasive screenings in a clinical setting. It can be done in hospitals and outpatient clinics, more quickly and cheaply.”

Detecting Fetal Chromosomal Defects Without Risk
Noninvasive sequencing is faster, cheaper and safer for mother and fetus, say researchers

Chromosomal abnormalities that result in birth defects and genetic disorders like Down syndrome remain a significant health burden in the United States and throughout the world, with some current prenatal screening procedures invasive and a potential risk to mother and unborn child.

In a paper published online this week in the Early Edition of PNAS, a team of scientists at the University of California, San Diego School of Medicine and in China describe a new benchtop semiconductor sequencing procedure and newly developed bioinformatics software tools that are fast, accurate, portable, less expensive and can be completed without harm to mother or fetus.  

“We believe this approach could become the standard of care for screening of prenatal chromosomal abnormalities,” said Kang Zhang, MD, PhD, professor of ophthalmology, founding director of the Institute for Genomic Medicine at UC San Diego and a staff physician at the San Diego VA Healthcare System.

The incidence of chromosomal abnormalities – in numbers or structure – is one in 160 live births in the United States, higher in other countries. In China, for example, the rate is one in 60 live births. The effects of these abnormalities, known as aneuploidies, can be severe, from developmental delays and neurological disorders to infertility and death. The incidence rate rises with maternal age, most notably after age 35.

Current diagnoses of fetal aneuploidies often rely upon invasive tests that sample amniotic fluid or placental tissues for fetal DNA that can then be analyzed using a variety of complex and expensive methods, including full karyotyping in which the entire set of chromosomes is viewed microscopically. While highly reliable, these invasive tests may cause infections in the pregnant woman and pose as much as a 1 percent risk of miscarriage and fetal loss. Results are not available for one to two weeks, extending anxiety for families waiting for information.

The new method relies upon massively parallel sequencing of cell-free fetal DNA using a benchtop semiconductor sequencing platform (SSP) called an Ion Torrent sequencer developed by Life Technologies. Cell-free fetal DNA is genetic material from the fetus that circulates naturally and freely in the mother’s bloodstream. It can be obtained through an ordinary blood draw, with SSP analysis achieved in less than four days.

To assess the SSP method, researchers tested 2,275 pregnant women. More than 500 participated in a retrospective analysis, undergoing full karyotyping to establish known chromosomal abnormalities followed by SSP testing. The remainder participated in a prospective study without prior karyotyping, and SSP testing results were then compared to karyotyping results. The sequencing and automated bioinformatics analyses were performed at iGenomics in Guangzhou, China.

“We used the retrospective study to establish the method and the prospective study to validate it,” said Zhang.

In the retrospective study, the researchers found that SSP detected multiple types of chromosomal abnormality with virtually 100 percent sensitivity and specificity compared to full karyotyping.

“To our knowledge, this is the first large-scale clinical study to systematically identify chromosomal aneuploidies based on cell-free fetal DNA using SSP,” said Zhang. “It provides an effective strategy for large-scale, noninvasive screenings in a clinical setting. It can be done in hospitals and outpatient clinics, more quickly and cheaply.”

Confocal image of an Alzheimer’s brain showing region of amyloid plaque. Courtesy of Wellcome Images. 
Understanding a Protein’s Role in Familial Alzheimer’s DiseaseNovel genomic approach reveals gene mutation isn’t simple answer
Researchers at the University of California, San Diego School of Medicine have used genetic engineering of human induced pluripotent stem cells to specifically and precisely parse the roles of a key mutated protein in causing familial Alzheimer’s disease (AD), discovering that simple loss-of-function does not contribute to the inherited form of the neurodegenerative disorder.
The findings, published online in the journal Cell Reports, could help elucidate the still-mysterious mechanisms of Alzheimer’s disease and better inform development of effective drugs, said principal investigator Lawrence Goldstein, PhD, professor in the Departments of Cellular and Molecular Medicine and Neurosciences and director of the UC San Diego Stem Cell Program.
“In some ways, this is a powerful technical demonstration of the promise of stem cells and genomics research in better understanding and ultimately treating AD,” said Goldstein, who is also director of the new Sanford Stem Cell Clinical Center at UC San Diego. “We were able to identify and assign precise limits on how a mutation works in familial AD. That’s an important step in advancing the science, in finding drugs and treatments that can slow, maybe reverse, the disease’s devastating effects.”
Familial AD is a subset of early-onset Alzheimer’s disease that is caused by inherited gene mutations. Most cases of Alzheimer’s disease – there are an estimated 5.2 million Americans with AD – are sporadic and do not have a precise known cause, though age is a primary risk factor.    
More here

Confocal image of an Alzheimer’s brain showing region of amyloid plaque. Courtesy of Wellcome Images.

Understanding a Protein’s Role in Familial Alzheimer’s Disease
Novel genomic approach reveals gene mutation isn’t simple answer

Researchers at the University of California, San Diego School of Medicine have used genetic engineering of human induced pluripotent stem cells to specifically and precisely parse the roles of a key mutated protein in causing familial Alzheimer’s disease (AD), discovering that simple loss-of-function does not contribute to the inherited form of the neurodegenerative disorder.

The findings, published online in the journal Cell Reports, could help elucidate the still-mysterious mechanisms of Alzheimer’s disease and better inform development of effective drugs, said principal investigator Lawrence Goldstein, PhD, professor in the Departments of Cellular and Molecular Medicine and Neurosciences and director of the UC San Diego Stem Cell Program.

“In some ways, this is a powerful technical demonstration of the promise of stem cells and genomics research in better understanding and ultimately treating AD,” said Goldstein, who is also director of the new Sanford Stem Cell Clinical Center at UC San Diego. “We were able to identify and assign precise limits on how a mutation works in familial AD. That’s an important step in advancing the science, in finding drugs and treatments that can slow, maybe reverse, the disease’s devastating effects.”

Familial AD is a subset of early-onset Alzheimer’s disease that is caused by inherited gene mutations. Most cases of Alzheimer’s disease – there are an estimated 5.2 million Americans with AD – are sporadic and do not have a precise known cause, though age is a primary risk factor.   

More here

A gelatinous mass of DNA precipitated out of solution. Courtesy of Wellcome Images. 
The Role of “Master Regulators” in Gene Mutations and DiseaseResearchers identify key proteins that help establish cell function
Researchers at the University of California, San Diego School of Medicine have developed a new way to parse and understand how special proteins called “master regulators” read the genome, and consequently turn genes on and off. 
Writing in the October 13, 2013 Advance Online Publication of Nature, the scientists say their approach could make it quicker and easier to identify specific gene mutations associated with increased disease risk – an essential step toward developing future targeted treatments, preventions and cures for conditions ranging from diabetes to neurodegenerative disease.   
“Given the emerging ability to sequence the genomes of individual patients, a major goal is to be able to interpret that DNA sequence with respect to disease risk. What diseases is a person genetically predisposed to?” said principal investigator Christopher Glass, MD, PhD, a professor in the departments of Medicine and Cellular and Molecular Medicine at UC San Diego.
“Mutations that occur in protein-coding regions of the genome are relatively straight forward, but most mutations associated with disease risk actually occur in regions of the genome that do not code for proteins,” said Glass. “A central challenge has been developing a strategy that assesses the potential functional impact of these non-coding mutations. This paper lays the foundation for doing so by examining how natural genetic variation alters the function of genomic regions controlling gene expression in a cell specific-manner.”
Cells use hundreds of different proteins called transcription factors to “read” the genome, employing those instructions to turn genes on and off. These factors tend to be bound close together on the genome, forming functional units called “enhancers.” Glass and colleagues hypothesized that while each cell has tens of thousands of enhancers consisting of myriad combinations of factors, most enhancers are established by just a handful of special transcription factors called “master regulators.” These master regulators play crucial, even disproportional, roles in defining each cell’s identity and function, such as whether it will be a muscle, skin or heart cell.
“Our main idea was that the binding of these master regulators is necessary for the co-binding of the other transcription factors that together enable enhancers to regulate the expression of nearby genes,” Glass said.
The scientists tested and validated their hypothesis by looking at the effects of approximately 4 million DNA sequence differences affecting master regulators in macrophage cells in two strains of mice. Macrophages are a type of immune response cell. They found that DNA sequence mutations deciphered by master regulators not only affected how they bound to the genome, but also impacted neighboring transcription factors needed to make functional enhancers.
The findings have practical importance for scientists and doctors investigating the genetic underpinnings of disease, said Glass. “Without actual knowledge of where the master regulator binds, there is relatively little predictive value of the DNA sequence for non-coding variants. Our work shows that by collecting a focused set of data for the master regulators of a particular cell type, one can greatly reduce the ‘search space’ of the genome in a particular cell type that would be susceptible to the effects of mutations. This allows prioritization of mutations for subsequent analysis, which can lead to new discoveries and real-world benefits.”

A gelatinous mass of DNA precipitated out of solution. Courtesy of Wellcome Images.

The Role of “Master Regulators” in Gene Mutations and Disease
Researchers identify key proteins that help establish cell function

Researchers at the University of California, San Diego School of Medicine have developed a new way to parse and understand how special proteins called “master regulators” read the genome, and consequently turn genes on and off. 

Writing in the October 13, 2013 Advance Online Publication of Nature, the scientists say their approach could make it quicker and easier to identify specific gene mutations associated with increased disease risk – an essential step toward developing future targeted treatments, preventions and cures for conditions ranging from diabetes to neurodegenerative disease.   

“Given the emerging ability to sequence the genomes of individual patients, a major goal is to be able to interpret that DNA sequence with respect to disease risk. What diseases is a person genetically predisposed to?” said principal investigator Christopher Glass, MD, PhD, a professor in the departments of Medicine and Cellular and Molecular Medicine at UC San Diego.

“Mutations that occur in protein-coding regions of the genome are relatively straight forward, but most mutations associated with disease risk actually occur in regions of the genome that do not code for proteins,” said Glass. “A central challenge has been developing a strategy that assesses the potential functional impact of these non-coding mutations. This paper lays the foundation for doing so by examining how natural genetic variation alters the function of genomic regions controlling gene expression in a cell specific-manner.”

Cells use hundreds of different proteins called transcription factors to “read” the genome, employing those instructions to turn genes on and off. These factors tend to be bound close together on the genome, forming functional units called “enhancers.” Glass and colleagues hypothesized that while each cell has tens of thousands of enhancers consisting of myriad combinations of factors, most enhancers are established by just a handful of special transcription factors called “master regulators.” These master regulators play crucial, even disproportional, roles in defining each cell’s identity and function, such as whether it will be a muscle, skin or heart cell.

“Our main idea was that the binding of these master regulators is necessary for the co-binding of the other transcription factors that together enable enhancers to regulate the expression of nearby genes,” Glass said.

The scientists tested and validated their hypothesis by looking at the effects of approximately 4 million DNA sequence differences affecting master regulators in macrophage cells in two strains of mice. Macrophages are a type of immune response cell. They found that DNA sequence mutations deciphered by master regulators not only affected how they bound to the genome, but also impacted neighboring transcription factors needed to make functional enhancers.

The findings have practical importance for scientists and doctors investigating the genetic underpinnings of disease, said Glass. “Without actual knowledge of where the master regulator binds, there is relatively little predictive value of the DNA sequence for non-coding variants. Our work shows that by collecting a focused set of data for the master regulators of a particular cell type, one can greatly reduce the ‘search space’ of the genome in a particular cell type that would be susceptible to the effects of mutations. This allows prioritization of mutations for subsequent analysis, which can lead to new discoveries and real-world benefits.”

The figure above depicts the complexity of genes with high network-smoothed mutation scores in a single subtype of ovarian cancer. Patients in this subtype exhibited the lowest survival and highest chemotherapy resistance rates in the cohort. Node size corresponds to smoothed mutation scores. Node color corresponds to the gene’s functional  category. Thickened node outlines indicate known cancer genes. An underlined gene symbol in the network indicates that somatic mutations were found for that gene in the examined subtype. Image courtesy of Trey Ideker, UC San Diego. 
“Wildly Heterogeneous Genes”New approach subtypes cancers by shared genetic effects; a step toward personalized medicine
Cancer tumors almost never share the exact same genetic mutations, a fact that has confounded scientific efforts to better categorize cancer types and develop more targeted, effective treatments.
In a paper published in the September 15 advanced online edition of Nature Methods, researchers at the University of California, San Diego propose a new approach called network-based stratification (NBS), which identifies cancer subtypes not by the singular mutations of individual patients, but by how those mutations affect shared genetic networks or systems.  
“Subtyping is the most basic step toward the goal of personalized medicine,” said principal investigator Trey Ideker, PhD, division chief of Genetics in the UC San Diego School of Medicine and a professor in the Departments of Medicine and Bioengineering at UC San Diego. “Based on patient data, patients are placed into subtypes with associated treatments. For example, one subtype of cancer is known to respond well to drug A, but not drug B. Without subtyping, every patient looks the same by definition, and you have no idea how to treat them differently.” 
Recent advances in knowledge and technology have made it easier (and less expensive) to sequence individual genomes, especially in the treatment of cancer, which is fundamentally a disease of genes. 
But genes are “wildly heterogeneous,” said Ideker. It is in combination, influenced by other factors, that mutated genes cause diseases like cancer. Every patient’s cancer is genetically unique, which can affect the efficacy and outcomes of clinical treatment.
More here

The figure above depicts the complexity of genes with high network-smoothed mutation scores in a single subtype of ovarian cancer. Patients in this subtype exhibited the lowest survival and highest chemotherapy resistance rates in the cohort. Node size corresponds to smoothed mutation scores. Node color corresponds to the gene’s functional  category. Thickened node outlines indicate known cancer genes. An underlined gene symbol in the network indicates that somatic mutations were found for that gene in the examined subtype. Image courtesy of Trey Ideker, UC San Diego.

“Wildly Heterogeneous Genes”
New approach subtypes cancers by shared genetic effects; a step toward personalized medicine

Cancer tumors almost never share the exact same genetic mutations, a fact that has confounded scientific efforts to better categorize cancer types and develop more targeted, effective treatments.

In a paper published in the September 15 advanced online edition of Nature Methods, researchers at the University of California, San Diego propose a new approach called network-based stratification (NBS), which identifies cancer subtypes not by the singular mutations of individual patients, but by how those mutations affect shared genetic networks or systems. 

“Subtyping is the most basic step toward the goal of personalized medicine,” said principal investigator Trey Ideker, PhD, division chief of Genetics in the UC San Diego School of Medicine and a professor in the Departments of Medicine and Bioengineering at UC San Diego. “Based on patient data, patients are placed into subtypes with associated treatments. For example, one subtype of cancer is known to respond well to drug A, but not drug B. Without subtyping, every patient looks the same by definition, and you have no idea how to treat them differently.”

Recent advances in knowledge and technology have made it easier (and less expensive) to sequence individual genomes, especially in the treatment of cancer, which is fundamentally a disease of genes.

But genes are “wildly heterogeneous,” said Ideker. It is in combination, influenced by other factors, that mutated genes cause diseases like cancer. Every patient’s cancer is genetically unique, which can affect the efficacy and outcomes of clinical treatment.

More here

Boosting the Powers of Genomic ScienceWith two new methods, UC San Diego scientists hope to improve genome-wide association studies
As scientists probe and parse the genetic bases of what makes a human a human (or one human different from another), and vigorously push for greater use of whole genome sequencing, they find themselves increasingly threatened by the unthinkable: Too much data to make full sense of. 
In a pair of papers published in the April 25, 2013 issue of PLOS Genetics, two diverse teams of scientists, both headed by researchers at the University of California, San Diego School of Medicine, describe novel statistical models that more broadly and deeply identify associations between bits of sequenced DNA called single nucleotide polymorphisms or SNPs and say lead to a more complete and accurate understanding of the genetic underpinnings of many diseases and how best to treat them.      
“It’s increasingly evident that highly heritable diseases and traits are influenced by a large number of genetic variants in different parts of the genome, each with small effects,” said Anders M. Dale, PhD, a professor in the departments of Radiology, Neurosciences and Psychiatry at the UC San Diego School of Medicine. “Unfortunately, it’s also increasingly evident that existing statistical methods, like genome-wide association studies (GWAS) that look for associations between SNPs and diseases, are severely underpowered and can’t adequately incorporate all of this new, exciting and exceedingly rich data.”
Dale cited, for example, a recent study published in Nature Genetics in which researchers used traditional GWAS to raise the number of SNPs associated with primary sclerosing cholangitis from four to 16. The scientists then applied the new statistical methods to identify 33 additional SNPs, more than tripling the number of genome locations associated with the life-threatening liver disease.
Generally speaking, the new methods boost researchers’ analytical powers by incorporating a priori or prior knowledge about the function of SNPs with their pleiotrophic relationships to  multiple phenotypes. Pleiotrophy occurs when one gene influences multiple sets of observed traits or phenotypes.
Dale and colleagues believe the new methods could lead to a paradigm shift in CWAS analysis, with profound implications across a broad range of complex traits and disorders.
“There is ever-greater emphasis being placed on expensive whole genome sequencing efforts,” he said, “but as the science advances, the challenges become larger. The needle in the haystack of traditional GWAS involves searching through about one million SNPs. This will increase 10- to 100-fold, to about 3 billion positions. We think these new methodologies allow us to more completely exploit our resources, to extract the most information possible, which we think has important implications for gene discovery, drug development and more accurately assessing a person’s overall genetic risk of developing a certain disease.”

Boosting the Powers of Genomic Science
With two new methods, UC San Diego scientists hope to improve genome-wide association studies

As scientists probe and parse the genetic bases of what makes a human a human (or one human different from another), and vigorously push for greater use of whole genome sequencing, they find themselves increasingly threatened by the unthinkable: Too much data to make full sense of. 

In a pair of papers published in the April 25, 2013 issue of PLOS Genetics, two diverse teams of scientists, both headed by researchers at the University of California, San Diego School of Medicine, describe novel statistical models that more broadly and deeply identify associations between bits of sequenced DNA called single nucleotide polymorphisms or SNPs and say lead to a more complete and accurate understanding of the genetic underpinnings of many diseases and how best to treat them.      

“It’s increasingly evident that highly heritable diseases and traits are influenced by a large number of genetic variants in different parts of the genome, each with small effects,” said Anders M. Dale, PhD, a professor in the departments of Radiology, Neurosciences and Psychiatry at the UC San Diego School of Medicine. “Unfortunately, it’s also increasingly evident that existing statistical methods, like genome-wide association studies (GWAS) that look for associations between SNPs and diseases, are severely underpowered and can’t adequately incorporate all of this new, exciting and exceedingly rich data.”

Dale cited, for example, a recent study published in Nature Genetics in which researchers used traditional GWAS to raise the number of SNPs associated with primary sclerosing cholangitis from four to 16. The scientists then applied the new statistical methods to identify 33 additional SNPs, more than tripling the number of genome locations associated with the life-threatening liver disease.

Generally speaking, the new methods boost researchers’ analytical powers by incorporating a priori or prior knowledge about the function of SNPs with their pleiotrophic relationships to  multiple phenotypes. Pleiotrophy occurs when one gene influences multiple sets of observed traits or phenotypes.

Dale and colleagues believe the new methods could lead to a paradigm shift in CWAS analysis, with profound implications across a broad range of complex traits and disorders.

“There is ever-greater emphasis being placed on expensive whole genome sequencing efforts,” he said, “but as the science advances, the challenges become larger. The needle in the haystack of traditional GWAS involves searching through about one million SNPs. This will increase 10- to 100-fold, to about 3 billion positions. We think these new methodologies allow us to more completely exploit our resources, to extract the most information possible, which we think has important implications for gene discovery, drug development and more accurately assessing a person’s overall genetic risk of developing a certain disease.”

An electrophoresis gel separates nucleic acid molecules into constituent DNA, RNA and proteins, based on size and electrical charge. 
More Links Found Between Schizophrenia and Cardiovascular Disease
A new study, to be published in the Feb. 7, 2013 issue of the American Journal of Human Genetics, expands and deepens the biological and genetic links between cardiovascular disease and schizophrenia. Cardiovascular disease (CVD) is the leading cause of premature death among schizophrenia patients, who die from heart and blood vessel disorders at a rate double that of persons without the mental disorder.
“These results have important clinical implications, adding to our growing awareness that cardiovascular disease is under-recognized and under-treated in mentally ill individuals,” said study first author Ole Andreassen, MD, PhD, an adjunct professor at the University of California, San Diego School of Medicine and professor of psychiatry at the University of Oslo. “Its presence in schizophrenia is not solely due to lifestyle or medication side effects. Clinicians must recognize that individuals with schizophrenia are at risk for cardiovascular disease independent of these factors.”
Led by principal investigator Anders M. Dale, PhD, professor of radiology, neurosciences, psychiatry and cognitive science at UC San Diego School of Medicine, an international team of researchers used a novel statistical model to magnify the analytical powers of genome-wide association studies or GWAS.
These are studies in which differing bits of sequential DNA – called single nucleotide polymorphisms or SNPs – in persons and groups are compared to find common genetic variants that might be linked to a trait or disease. The researchers boosted the power of GWAS by adding information based on genetic pleiotropy, the concept that at least some genes influence multiple traits or phenotypes.
“Our approach is different in that we use all available genetic information for multiple traits and diseases, not just SNPs below a given statistical threshold,” said Dale. “This significantly increases the power to discover new genes by leveraging the combined power across multiple GWAS of pleiotropic traits and diseases.”
The scientists confirmed nine SNPs linked to schizophrenia in prior studies, but also identified 16 new loci – some of which are also associated with CVD. Among these shared risk factors: triglyceride and lipoprotein levels, waist-hip ratio, systolic blood pressure and body mass index.
“Our findings suggest that shared biological and genetic mechanisms can help explain why schizophrenia patients have a greater risk of cardiovascular disease,” said study co-author Rahul S. Desikan, MD, PhD, research fellow and radiology resident at the UC San Diego School of Medicine.
“In addition to schizophrenia, this new analysis method can be used to examine the genetic overlap between a number of diseases and traits,” Desikan said. “Examining overlap in common variants can shed insight into disease mechanisms and help identify potential therapeutic targets for common diseases.”

An electrophoresis gel separates nucleic acid molecules into constituent DNA, RNA and proteins, based on size and electrical charge.

More Links Found Between Schizophrenia and Cardiovascular Disease

A new study, to be published in the Feb. 7, 2013 issue of the American Journal of Human Genetics, expands and deepens the biological and genetic links between cardiovascular disease and schizophrenia. Cardiovascular disease (CVD) is the leading cause of premature death among schizophrenia patients, who die from heart and blood vessel disorders at a rate double that of persons without the mental disorder.

“These results have important clinical implications, adding to our growing awareness that cardiovascular disease is under-recognized and under-treated in mentally ill individuals,” said study first author Ole Andreassen, MD, PhD, an adjunct professor at the University of California, San Diego School of Medicine and professor of psychiatry at the University of Oslo. “Its presence in schizophrenia is not solely due to lifestyle or medication side effects. Clinicians must recognize that individuals with schizophrenia are at risk for cardiovascular disease independent of these factors.”

Led by principal investigator Anders M. Dale, PhD, professor of radiology, neurosciences, psychiatry and cognitive science at UC San Diego School of Medicine, an international team of researchers used a novel statistical model to magnify the analytical powers of genome-wide association studies or GWAS.

These are studies in which differing bits of sequential DNA – called single nucleotide polymorphisms or SNPs – in persons and groups are compared to find common genetic variants that might be linked to a trait or disease. The researchers boosted the power of GWAS by adding information based on genetic pleiotropy, the concept that at least some genes influence multiple traits or phenotypes.

“Our approach is different in that we use all available genetic information for multiple traits and diseases, not just SNPs below a given statistical threshold,” said Dale. “This significantly increases the power to discover new genes by leveraging the combined power across multiple GWAS of pleiotropic traits and diseases.”

The scientists confirmed nine SNPs linked to schizophrenia in prior studies, but also identified 16 new loci – some of which are also associated with CVD. Among these shared risk factors: triglyceride and lipoprotein levels, waist-hip ratio, systolic blood pressure and body mass index.

“Our findings suggest that shared biological and genetic mechanisms can help explain why schizophrenia patients have a greater risk of cardiovascular disease,” said study co-author Rahul S. Desikan, MD, PhD, research fellow and radiology resident at the UC San Diego School of Medicine.

“In addition to schizophrenia, this new analysis method can be used to examine the genetic overlap between a number of diseases and traits,” Desikan said. “Examining overlap in common variants can shed insight into disease mechanisms and help identify potential therapeutic targets for common diseases.”

Genomic “Hotspots” Offer Clues to Causes of Autism, Other Disorders

An international team, led by researchers from the University of California, San Diego School of Medicine, has discovered that “random” mutations in the genome are not quite so random after all. Their study, to be published in the journal Cell on December 21, shows that the DNA sequence in some regions of the human genome is quite volatile and can mutate ten times more frequently than the rest of the genome. Genes that are linked to autism and a variety of other disorders have a particularly strong tendency to mutate.

Clusters of mutations or “hotspots” are not unique to the autism genome but instead are an intrinsic characteristic of the human genome, according to principal investigator Jonathan Sebat, PhD, professor of psychiatry and cellular and molecule medicine, and chief of the Beyster Center for Molecular Genomics of Neuropsychiatric Diseases at UC San Diego.

“Our findings provide some insights into the underlying basis of autism—that, surprisingly, the genome is not shy about tinkering with its important genes” said Sebat.  “To the contrary, disease-causing genes tend to be hypermutable.”

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