Most human diseases can be traced to malfunctioning parts of a cell — a tumor is able to grow because a gene wasn’t accurately translated into a particular protein or a metabolic disease arises because mitochondria aren’t firing properly, for example. But to understand what parts of a cell can go wrong in a disease, scientists first need to have a complete list of parts.
By combining microscopy, biochemistry techniques and artificial intelligence, researchers at University of California San Diego School of Medicine and collaborators have taken what they think may turn out to be a significant leap forward in the understanding of human cells… Continue reading.
Only 4 percent of all cancer therapeutic drugs under development earn final approval by the U.S. Food and Drug Administration (FDA).
“That’s because right now we can’t match the right combination of drugs to the right patients in a smart way,” said Trey Ideker, PhD, professor at University of California San Diego School of Medicine and Moores Cancer Center. “And especially for cancer, where we can’t always predict which drugs will work best given the unique, complex inner workings of a person’s tumor cells… Continue reading.
The rarity of appendix cancer, accounting for less than 1 percent of tumors that originate in the gastrointestinal tract, and the lack of scientific data for this disease means that current treatment guidelines recommend applying therapies to people with appendix cancer that are intended for those with colon cancer.
To understand why some patients with appendix cancer respond to standard treatment while others do not, University of California San Diego School of Medicine and Moores Cancer Center researchers, in collaboration with Foundation Medicine, performed genetic profiling on 703 appendiceal tumors — the largest such study of this disease to date — to compare mutations present in both cancer types.
The findings, published online August 8 in JCO Precision Oncology, confirm that genetic mutations in appendix cancer are distinct from those found in colon cancer and that mutations in the genes TP53 and GNAS are good predictors of survival among people with appendix cancer.
“For tumors that are rare like appendix cancer, obtaining molecular profiles will help identify potential treatment options since we don’t have the clinical trial data to help guide treatments as we do in common tumors,” said lead author John Paul Shen, MD, a postdoctoral fellow in the lab of co-author Trey Ideker, PhD, UC San Diego School of Medicine professor of medicine… Continue reading.
“It seems like every time you turn around, someone is talking about the importance of artificial intelligence and machine learning,” said Trey Ideker, PhD, University of California San Diego School of Medicine and Moores Cancer Center professor. “But all of these systems are so-called ‘black boxes.’ They can be very predictive, but we don’t actually know all that much about how they work.”
Ideker gives an example: machine learning systems can analyze the online behaviors of millions of people to flag an individual as a potential “terrorist” or “suicide risk.” “Yet we have no idea how the machine reached that conclusion,” he said.
For machine learning to be useful and trustworthy in health care, Ideker said, practitioners need to open up the black box and understand how a system arrives at a decision… Continue reading.
The drive to map cells grew out of the successes – and the shortcomings – of the Human Genome Project. Completed in 2003, the project sequenced every gene in our genome, but fell short of hopes that the purpose and function of each gene would become immediately clear.
“What the Human Genome Project gave us is like the first page of an IKEA manual. It only lists the parts,” said Trey Ideker, PhD, a professor of medicine at UC San Diego and co-director with Krogan of the Cancer Cell Map Initiative (CCMI). “How these genes and gene products, the proteins, are tied together is the rest of the manual – except there’s about a million pages worth of it. You need to understand those pages if you’re really going to understand disease…. Read the Full Article.
In an effort to expand the number of cancer gene mutations that can be specifically targeted with personalized therapies, researchers at University of California San Diego School of Medicine and Moores Cancer Center looked for combinations of mutated genes and drugs that together kill cancer cells. Such combinations are expected to kill cancer cells, which have mutations, but not healthy cells, which do not. The study, published July 21 in Molecular Cell, uncovered 172 new combinations that could form the basis for future cancer therapies.
“Oncologists here at Moores Cancer Center at UC San Diego Health and elsewhere can often personalize cancer therapy based on an individual patient’s unique cancer mutations,” said senior author Trey Ideker, PhD, professor of genetics and bioengineering at UC San Diego School of Medicine and Jacobs School of Engineering. “But the vast majority of mutations are not actionable — that is, knowing a patient has a particular mutation doesn’t mean there’s an available therapy that targets it. The goal of this study was to expand the number of mutations we can pair with a precision therapy.”
Most cancers have gene mutations that do one of two things — promote cell growth or prevent cell death. The first type is the target of many therapies, which inhibit cell growth. But it’s much harder to develop therapies that restore malfunctioning genes that should be triggering cell death in abnormal cells, known as tumor-suppressor genes.
Rather than target a tumor-suppressor gene directly, Ideker and team took the approach of identifying genetic interactions between a tumor suppressor gene and another gene, such that simultaneous disruption of both genes selectively kills cancer cells.
Researchers from the University of California, San Diego School of Medicine and University of California, San Francisco, with support from a diverse team of collaborators, have launched an ambitious new project – dubbed the Cancer Cell Map Initiative or CCMI – to determine how all of the components of a cancer cell interact.
“We’re going to draw the complete wiring diagram of a cancer cell,” said Nevan Krogan, PhD, director of the UC San Francisco division of QB3, a life science research institute and accelerator, an investigator at Gladstone Institutes and co-director of CCMI with Trey Ideker, PhD, chief of medical genetics in the UC San Diego Department of Medicine and founder of the UC San Diego Center for Computational Biology & Bioinformatics.
In recent years, progress in genome sequencing has made it possible to decipher hundreds of mutations found in a patient’s tumor. But in only a few cases do scientists understand how these mutations give rise to cancer or indicate what treatments to pursue. More puzzling still, the mutations found in each patient are almost always different – even though they can lead to the same type of cancer.
It has long been thought that, while these mutations are unique to individuals, they hijack the same hallmark cancer pathways or genetic circuits. To interpret genomic data, researchers say the complete wiring diagram of the cell is needed, one that details all of the connections between normal and mutated genes and proteins.
“We have the genomic information already. The bottleneck is how to interpret the cancer genomes,” said Ideker. A comprehensive map of cancer cells would help – and accelerate the development of personalized therapy, the central aim of “precision medicine.”
Trey Ideker, a professor at UC San Diego’s School of Medicine, was cited by AAAS for “distinguished contributions to the fields of bioinformatics and computational biology, particularly in pioneering network research.” His research seeks to comprehensively map connections between the many genes and proteins in a cell and how these connections trigger or prevent disease. His current work focuses on DNA mutations that cause cancer. Although each person’s cancer tumor may be caused by a nearly unique set of mutations, Ideker has shown that different sets of mutations often alter and hijack the same gene networks. The long-term goal of his research is to build a whole working model of a cancer cell that can be used in the clinical setting to interpret patients’ genomic data—both their inherited DNA and the mutations associated with their particular malignancy—to refine and tailor cancer diagnoses and treatments. Such analyses are in the early stages of being used to screen people who are unlikely to respond to certain types of chemotherapy.
Although mutations in a gene dubbed “the guardian of the genome” are widely recognized as being associated with more aggressive forms of cancer, researchers at the University of California, San Diego School of Medicine have found evidence suggesting that the deleterious health effects of the mutated gene may in large part be due to other genetic abnormalities, at least in squamous cell head and neck cancers.
Trey Ideker, PhD, professor and chief in the Division of Medical Genetics and professor of bioengineering, is one of the study’s co-senior authors.
Other Department of Medicine faculty authors include Hannah Carter, PhD, assistant professor; and Scott M. Lippman, MD, professor of medicine and director of the UC San Diego Moores Cancer Center.
Researchers at the University of California, San Diego School of Medicine, with colleagues in The Netherlands and United Kingdom, have produced the first map detailing the network of genetic interactions underlying the cellular response to ultraviolet (UV) radiation.
The researchers say their study establishes a new method and resource for exploring in greater detail how cells are damaged by UV radiation and how they repair themselves. UV damage is one route to malignancy, especially in skin cancer, and understanding the underlying repair pathways will better help scientists to understand what goes wrong in such cancers.
The findings will be published in the December 26, 2013 issue of Cell Reports.
Principal investigator Trey Ideker, PhD, division chief of genetics in the UC San Diego School of Medicine and a professor in the UC San Diego Departments of Medicine and Bioengineering, and colleagues mapped 89 UV-induced functional interactions among 62 protein complexes. The interactions were culled from a larger measurement of more than 45,000 double mutants, the deletion of two separate genes, before and after different doses of UV radiation.
Specifically, they identified interactive links to the cell’s chromatin structure remodeling (RSC) complex, a grouping of protein subunits that remodel chromatin – the combination of DNA and proteins that make up a cell’s nucleus – during cell mitosis or division. “We show that RSC is recruited to places on genes or DNA sequences where UV damage has occurred and that it helps facilitate efficient repair by promoting nucleosome remodeling,” said Ideker.
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.”
Researchers at the University of California-San Diego have developed a novel strategy to identify cancer subtypes not by the single mutations of individual patients, but by how those mutations affect shared genetic networks or systems. They published their paper (“Network-based stratification of tumor mutations”) in the September 15 advanced online edition of Nature Methods.
“Somatic tumor genome sequences provide a rich new source of data for uncovering [cancer] subtypes but have proven difficult to compare, as two tumors rarely share the same mutations,” wrote the scientists in their article. “Here we introduce network-based stratification (NBS), a method to integrate somatic tumor genomes with gene networks. This approach allows for stratification of cancer into informative subtypes by clustering together patients with mutations in similar network regions.”
The most basic step toward achieving personalized medicine is subtyping, according to Trey Ideker, Ph.D., 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,” he explained. “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.”
However, Dr. Ideker calls genes “wildly heterogeneous.” 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.
Better cancer treatments can be found by studying the genetic networks they involve, according to a study published Sunday by UC San Diego researchers.
While individual cancer patients vary greatly in the precise mutations that drive tumors, they can be grouped into similar genetic networks that mesh with response to therapy, stated the study, published in Nature Methods. Its senior author is Trey Ideker, division chief of genetics in the UCSD School of Medicine. The first author is Matan Hofree, of UCSD’s department of computer science and engineering.
The authors call this approach “network-based stratification,” or NBS. It groups patients together who have mutations in similar networks, matching them with outcomes. The study examined ovarian, uterine and lung cancers in The Cancer Genome Atlas.
In many ways, cancer is simply a devastating natural mutagenesis experiment. Alterations to genes and their products, as well as additional downstream modifications, lead to dangerous and deadly consequences. From recent studies, we know there are a few key cancer drivers, genes such as p53 and Ras that have central roles within the genetic pathways causing these devastating effects. But interestingly, these mutations don’t show up in all cancers; in fact, they represent a small portion of the information that researchers and clinicians require to understand tumor biology and diagnose and treat disease.
The result is that cancer researchers are now focusing on the “long tail,” collecting and cataloging rare mutations occurring in 1% or fewer of cancer patients. These rarer mutations may underlie the critical functional changes within cells that characterize and define this collection of diseases. But there is a big challenge here, a double-edge sword for researchers: because of their rarity, it is actually much harder to distinguish these mutations from random mutations that don’t affect disease.
Building network context
So how do researchers go about locating these important rare variants? The functional consequences of mutations in the genome often can be seen in the molecules, such as proteins, they encode. Over time, bioinformatics researchers have learned how these biomolecules interact with each other in the cell, curating protein and metabolite connections into wiring diagrams with nodes for proteins or other molecules and edges that indicate an interaction with another molecule. This has led to the development of a landscape of bioinformatics methods for understanding the misfires that cause cancer and control the disease process according to Trey Ideker a bioinformatician at the University of California at San Diego.
Don’t look to online calculators of “biological age” for an answer. Those focus mainly on risk factors for diseases, and say little about normal aging, the slow, mysterious process that turns children to codgers.
In fact, scientists are still hunting for biological markers of age that reliably register how fast the process is unfolding. Seemingly obvious candidates won’t do. Wrinkles, for example, often have more to do with sun exposure than aging. Markers like age-related increases in blood pressure are similarly problematic, often confounded by factors unrelated to aging.
But recently researchers have identified some particularly good indicators of time’s largely hidden toll on our bodies and how fast it’s increasing.
On April 22, 2013, computational biologists and computer scientists at UC San Diego released version 3 of Cytoscape for general availability. Cytoscape is the leading open source visualization software platform supporting systems biology; it enables researchers to visualize molecular interaction networks and biological pathways and integrate them with annotations, gene expression profiles and other state and process data.
Cytoscape was developed in the early 2000s to meet the need for an analytical tool that would allow researchers to organize, view and interpret large-scale biological data in a unified conceptual framework.
Approximately 1,600 scientific papers have cited the software to date, with approximately 300-400 new papers each year.
UC San Diego researchers have dashed the hopes of scientists looking for an easy way to determine how genes are turned off and on by regulatory chemicals, a field known as epigenetics.
The genes interact with histones, proteins that surround DNA, and also with other epigenetic factors. Where the genes are placed in the DNA changes how they’re regulated. So the effect of a gene varies depending on its neighborhood, even though the gene sequence itself is unchanged.
That means there isn’t one neat code that can predict gene regulation, said Trey Ideker, the study’s senior author and head of the Division of Genetics in the UCSD School of Medicine. The study was published Thursday in the journal Cell Reports. Its first author is Menzies Chen of UCSD’s Department of Bioengineering.
In a novel use of gene knockout technology, researchers at the University of California, San Diego School of Medicine tested the same gene inserted into 90 different locations in a yeast chromosome – and discovered that while the inserted gene never altered its surrounding chromatin landscape, differences in that immediate landscape measurably affected gene activity.
The findings, published online in the Jan. 3 issue of Cell Reports, demonstrate that regulation of chromatin – the combination of DNA and proteins that comprise a cell’s nucleus – is not governed by a uniform “histone code” but by specific interactions between chromatin and genetic factors.
“One of the main challenges of epigenetics has been to get a handle on how the position of a gene in chromatin affects its expression,” said senior author Trey Ideker, PhD, chief of the Division of Genetics in the School of Medicine and professor of bioengineering in UC San Diego’s Jacobs School of Engineering. “And one of the major elements of that research has been to look for a histone code, a general set of rules by which histones (proteins that fold and structure DNA inside the nucleus) bind to and affect genes.”
Turning vast amounts of genomic data into meaningful information about the cell is the great challenge of bioinformatics, with major implications for human biology and medicine. Researchers at the University of California, San Diego School of Medicine and colleagues have proposed a new method that creates a computational model of the cell from large networks of gene and protein interactions, discovering how genes and proteins connect to form higher-level cellular machinery.
The findings are published in the December 16 advance online publication of Nature Biotechnology.
Women live longer than men. Individuals can appear or feel years younger – or older – than their chronological age. Diseases can affect our aging process. When it comes to biology, our clocks clearly tick differently.
In a new study, researchers at the University of California, San Diego School of Medicine, with colleagues elsewhere, describe markers and a model that quantify how aging occurs at the level of genes and molecules, providing not just a more precise way to determine how old someone is, but also perhaps anticipate or treat ailments and diseases that come with the passage of time.
The findings are published in the November 21 online issue of the journal Molecular Cell.
In January, over 50 researchers from 30 academic and commercial organizations agreed on a standard for describing data sets. The BioSharing initiative, comprising both researchers and publishers, launched the Investigation-Study-Assay (ISA) Commons, which promises to streamline data sharing among different databases1. Life scientists have thousands of databases, over 300 terminologies and more than 120 exchange formats at their disposal, says BioSharing co-founder Susanna-Assunta Sansone of the University of Oxford. In this era of collaborative big science, researchers only move forward by “walking together.”
Although increased data sharing is central to scientific progress and is attracting attention from many quarters2, standards are only some of the stars that must align to make it possible.
As increasing numbers of protein–protein interactions are identified, researchers are finding ways to interrogate these data and understand the interactions in a relevant context.
Around the time that scientists celebrated the completion of the draft sequence of the human genome, papers from two separate groups described results of another project that tested all the possible pairings of thousands of yeast proteins to see whether they interact1, 2.
The importance of protein–protein interactions is beyond dispute. Little happens in a cell without one protein ‘touching’ another. Whether a cell divides, secretes a hormone or triggers its own death, protein–protein interactions make the event happen. Consequently, comprehensive maps showing which proteins came together in a yeast cell were much anticipated.
But the results took scientists aback. Although the two research groups had explored the full collection of proteins in the same organism using the same yeast two-hybrid (Y2H) assay, the two papers found fewer than 150 interactions in common — only 10% of the findings that either team dubbed high quality. Most scientists regarded the results as so riddled with artefacts that they were useless.
Using a new technology called “differential epistasis maps,” an international team of scientists, led by researchers at the University of California, San Diego School of Medicine, has documented for the first time how a cellular genetic network completely rewires itself in response to stress by DNA-damaging agents.
The research – to be published in the December 3 issue of Science – is significant because it represents a major technological leap forward from simply compiling lists of genes in an organism to actually describing how these genes actively work together.
“Cell behavior is dynamic, but the genetic networks that govern these behaviors have been studied mostly only under normal, benign laboratory conditions,” said Trey Ideker, PhD, professor of medicine and bioengineering, and chief of the Division of Medical Genetics in the UCSD School of Medicine. “This work is the next milestone. It shows that we can map how genetic networks in cells are reprogrammed in response to stimuli, thus revealing functional relationships that would go undetected using other approaches.”
A new center called the National Resource for Network Biology (NRNB), based at the University of California, San Diego School of Medicine, will help clinicians analyze an ever-growing wealth of complex biological data and apply that knowledge to real problems and diseases.
In recent years, the study of biological networks has exploded, with scientists shifting much of their focus from single cells to complex systems, producing novel maps of interactive networks of genes and proteins that help define and describe a functioning human being. But the exponential growth in data has created a new challenge: How do you effectively use it?
The NRNB is part of the answer, said Trey Ideker, PhD, associate professor of bioengineering in UC San Diego’s Jacobs School of Engineering, chief of the Division of Genetics at the School of Medicine and principal investigator of the new center., which is funded by a five-year, $6.5 million grant from the National Institute of Health’s National Center for Research Resources (NCRR), and the only center of its type to be funded this year.
Thanks to combination antiretroviral therapies, many people with HIV can expect to live decades after being infected. Yet doctors have observed these patients often show signs of premature aging. Researchers at University of California San Diego School of Medicine and the University of Nebraska Medical Center have applied a highly accurate biomarker to measure just how much HIV infection ages people at the cellular level — an average of almost five years.
The study is published April 21 in Molecular Cell.
“The medical issues in treating people with HIV have changed,” said Howard Fox, MD, PhD, professor at the University of Nebraska Medical Center and one of the authors of the new study. “We’re no longer as worried about infections that come from being immunocompromised. Now we worry about diseases related to aging, like cardiovascular disease, neurocognitive impairment and liver problems.”
The tool used in the new study looks at epigenetic changes in people’s cells. Epigenetic changes affect DNA structure, but not DNA sequence. Once epigenetic changes occur, they are passed down from one generation of cell to the next, influencing how genes are expressed. The particular epigenetic change used as a biomarker in this research was methylation, the process by which small chemical groups are attached to DNA. Methylation of DNA can impact how genes get translated into proteins.
“What we’ve seen in previous studies is that as we age, methylation across the entire genome changes,” said co-corresponding author Trey Ideker, PhD, professor of genetics in UC San Diego School of Medicine. “Some people call it entropy or genetic drift. Although we’re not sure of the exact mechanism by which these epigenetic changes lead to symptoms of aging, it’s a trend that we can measure inside people’s cells.”