Arquivo para Diagnóstico Molecular

Breast Cancer Sequencing Study Finds Many Metastatic Mutations are Undetectable in Primary Tumor

27/12/2009 · Arquivado em Diagnóstico Molecular, Medicina, Sequenciamento

This article, originally published Oct.7, has been updated with comments from an author of the study.

By Julia Karow

A primary breast cancer acquired several new mutations in its protein-coding regions as it progressed toward metastasis, according to a new report by Canadian researchers, who argue that studying these mutations might help scientists understand why cancers become treatment resistant.

The research team, of the BC Cancer Agency in Vancouver, sequenced the genome and transcriptome of a metastatic breast cancer at high depth, using the Illumina Genome Analyzer platform, and analyzed the data for somatic coding mutations.

The scientists then determined how many of these mutations they could already detect in the primary tumor of the same patient, which was removed nine years earlier. Only a fraction of them could be found at all, and even fewer were present at a high frequency.

In addition, by analyzing not only genome but also transcriptome data, they uncovered two new RNA-editing events that change the amino acid sequence of two proteins.

The study “offered the opportunity, for the first time as far as we are aware, to look at the evolution of mutational burden across a very long period of time within the same patient,” said Marco Marra, director of the BC Cancer Agency Genome Sciences Centre in Vancouver, and one of the authors of the report, which appeared online in Nature last week.

“Our results show the importance of sequencing samples of tumor cell populations early as well as late in the evolution of tumors, and of estimating allele frequency in tumor genomes,” the authors noted.

Being able to study how a metastatic cancer’s genome differs from that of a primary tumor “is important because it gets at the heart of how treatment shapes the genetic
constitution of a malignancy,” Marra said. “We need to know, with great precision, what the changes are that allow tumors to evade treatment.”

The project, which was completed about six months ago, according to Marra, was just the beginning of a large-scale effort to study treatment-resistant cancer. “We are going to continue with using large-scale high-throughput approaches, such as DNA sequencing, to get at the mutational spectrum of treatment-resistant [cancers],” he said, including breast cancer, hematologic malignancies, childhood cancers, and lung cancer.

For their study, the Canadian researchers chose lobular breast cancer, an estrogen-receptor positive subtype that makes up about 15 percent of all breast cancers.

One of the reasons for focusing on breast cancer as the first example is that the Vancouver researchers plan to study more breast cancer samples as part of the Molecular Taxonomy of Breast Cancer International Consortium, or METABRIC, project, a collaboration between five hospitals and research centers in the UK and Canada, Marra said.

According to the BC Cancer Foundation, the Vancouver team is now sequencing the genomes of several hundred so-called triple negative breast tumors as part of an effort to build “a comprehensive genomic map of breast cancer” from 2,000 samples.

For their published study, the researchers initially sequenced DNA from a metastatic lobular breast cancer sample, generating approximately 2.9 billion paired-end reads with a mean read length of 48 base pairs, or 141 gigabases of sequence data, on the Illumina Genome Analyzer. About 121 gigabases aligned to the human reference genome, equivalent to about 43-fold coverage.

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CDC/NIH Workshop Releases Personal Genomics Recommendations

07/09/2009 · Arquivado em Diagnóstico Molecular

August 14, 2009
By a GenomeWeb staff reporter
NEW YORK (GenomeWeb News) – A government-led panel has issued a list of recommendations related to the clinical utility and clinical validity of personal genomic tests, enhancing knowledge about these tests, and developing standards for these tests, according to a report in the journal Genetics in Medicine.

The report is the product of a December 2008 workshop led by the National Institutes of Health and the Centers for Disease Control and Prevention, and it included a number of other stakeholders in government, academia, and the business world.

The working group developed five broad recommendations for ways to “enhance the foundation” for using personal genomics in order to improve health.

It said that efforts to develop and implement standards for personal genomics should be expanded to include transparent criteria for analytic standards, clinical standards on the selection of genetic variants, data interpretation, and the calibration and evaluation of risk distributions.

A multidisciplinary research agenda also should be implemented, the group advised, that could help to develop the personal genomics field. Beyond biological studies that could point to therapeutic and preventive interventions, this research could include epidemiologic studies for risk characterization, particularly those on gene-gene and gene-environment interactions. Also needed are clinical and population studies to assess the effectiveness of genetic information for consumers and providers, as well as health services research that assesses the uptake of evidence-based practice into routine care, and outcomes research, the group said.

The working group also said that public health surveillance and the assessment of cost-effectiveness, as well as current federal genomic initiatives in translational research should be enhanced.

Knowledge should be synthesized based on standardized formats and evidence-based processes in order to summarize and update information on genetic associations and to document their clinical validity and clinical utility, the group advised. They also said that this information should be translated in an accessible fashion and should be spread among consumers, providers, and policy makers.

The evidence threshold for using personal genomic information in clinical practice and disease prevention should be considered by independent panels, according to the group.

Setting the bar for evidence too low for clinical utility and clinical validity may allow a diffusion of genomic discoveries into practice, but there may not be adequate information on their effectiveness, the group said. But, placing the bar for evidence too high could result in tests with high validity and utility but with lower financial incentive for innovation by developers.

For these reasons, the group noted, “extra caution” is needed when developing how much evidence is necessary for clinical utility and validity.

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What can DNA tell us? Place your bets now

10/07/2009 · Arquivado em Diagnóstico Molecular, GWAS, Genotipagem de SNPs, Sequenciamento

* 08 July 2009 by Lewis Wolpert and Rupert Sheldrake
* Magazine issue 2716. Subscribe and get 4 free issues.
* For similar stories, visit the Essays and Genetics Topic Guides

Read full article

From Newton to Hawking, scientists love wagers. Now Lewis Wolpert has bet Rupert Sheldrake a case of fine port that: “By 1 May 2029, given the genome of a fertilised egg of an animal or plant, we will be able to predict in at least one case all the details of the organism that develops from it, including any abnormalities.” If the outcome isn’t obvious, then the Royal Society will be asked to adjudicate. Watch this space…

Competition: Challenge New Scientist to a scientific wager
Lewis Wolpert

I HAVE entered into this wager with Rupert Sheldrake because of my interest in the details of how embryos develop, and how our understanding of this process will progress. In my latest book, How We Live and Why We Die, I suggest that it will one day be possible to predict from an embryo’s genome how it will develop, and I believe it is possible for this to happen in the next 20 years.

I am, in fact, being a little over-keen because 40 years is a more likely time frame for such a breakthrough. Cells and embryos are extremely complicated: for their size, embryonic cells are the most complex structures in the universe.

Animals develop from a single cell, a fertilised egg, which divides to produce cells that will form the embryo. How that egg develops into an embryo and newborn animal is controlled by genes in the chromosomes. These genes are passive: they do nothing, just provide the code for proteins. It is proteins that determine how cells behave. While the DNA in every cell contains the code for all the proteins in all the cells, it is the particular proteins produced in particular cells that determine how those cells behave.

Every cell of the embryo contains many copies of several thousand different proteins. These proteins have a plethora of functions: acting as enzymes to break down and build other molecules, providing structures for the cell, interacting with each other, and many more. The complexity of the interactions between millions of molecules is amazing.

As the proteins determine how the cells behave, it is their activity that causes the embryo to develop. Underlying this process, though, are the genes, as they control which proteins are made – including some proteins that activate specific genes. It is essential that there is this control over which cells continue to divide, and of mechanisms to pattern the embryo so that different cells develop into different structures, such as the brain or limbs.

There is a huge incentive to understand these processes and so be able to work out the development of an embryo given only its genome. This ability could pave the way for regenerative medicine by allowing scientists to program stem cells to become structures that could replace damaged parts of the body.

To win the bet, we will have to be able to predict the behaviour of almost all the cells in the embryo. In a small worm, say the nematode Caenorhabditis elegans, there are 959 cells, making it the ideal model to solve this problem. It is a major challenge, but advances in cell biology, systems biology and computing will take us there.
One of the nematode worms, with just 959 cells, is the ideal model to solve this problem

Rupert Sheldrake

LEWIS WOLPERT’s faith in the predictive power of the genome is misplaced. Genes enable organisms to make proteins, but do not contain programs or blueprints, or explain the development of embryos.

The problems begin with proteins. Genes code for the linear sequences of amino acids in proteins, which then fold up into complex three-dimensional forms. Wolpert’s wager presupposes that the folding of proteins can be computed from first principles, given the sequence of amino acids specified by the genes. So far, this has proved impossible. As in all bottom-up calculations, there is a combinatorial explosion. For example, by random folding, the amino-acid chain of the enzyme ribonuclease, a small protein, could adopt more than 1040 different shapes, which would take billions of years to explore. In fact, it folds into its habitual form in 2 minutes.

Even if we could solve protein-folding, the next stage would be to predict the structure of cells on the basis of the interactions of millions of proteins and other molecules. This would unleash a far worse combinatorial explosion, with more possible arrangements than all the atoms in the universe.

Random molecular permutations simply cannot explain how organisms work. Instead, cells, tissues and organs develop in a modular manner, shaped by morphogenetic fields, first recognised by developmental biologists in the 1920s. Wolpert himself acknowledges the importance of such fields. Among biologists, he is best known for “positional information”, by which cells “know” where they are within the field of a developing organ, such as a limb. But he believes morphogenetic fields can be reduced to standard chemistry and physics. I disagree. I believe these fields have organising abilities, or systems properties, that involve new scientific principles.

Issue 2716 of New Scientist magazine

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MEDomics Announces MitoDx(TM), the First NextGen Mitochondrial Genome Diagnostic Test

09/07/2009 · Arquivado em Diagnóstico Molecular, Sequenciamento, Solid

LOS ANGELES, June 9 /PRNewswire/ — MEDomics, LLC (www.medomics.com) announces an innovative test for early diagnosis of mitochondrial diseases, a group of disorders that can result in neurological dysfunction, muscle weakness, gastrointestinal symptoms, migraine headaches, blindness, deafness, and diabetes. The MEDomics mitochondrial genome test, MitoDx(TM), uses the revolutionary NextGen sequencing technology to detect all mutations in any of the 37 mitochondrial DNA genes. The MEDomics team of experts provides interpretation of the functional significance of detected mutations. This comprehensive test offers exceptionally high diagnostic utility for suspected mitochondrial disease, enabling potentially lifesaving therapy and accurate risk counseling.
Disease from mutations in mitochondrial DNA is now thought to be common in both adults and children. In childhood, mitochondrial disease is more common than muscular dystrophy or cancer. Most mitochondrial disease may go undiagnosed because a primary care physician does not suspect the disease or because the causative mutation is missed by current methods.
“To my knowledge, MEDomics is the first laboratory to offer a whole genome clinical diagnostic test utilizing the powerful NextGen sequencing technique,” says Steve S Sommer, MD, PhD, Founder and President of MEDomics.
Mitochondria are the “power plants” of the cell, providing energy for cellular processes, including growth, and metabolism. Mutations in mitochondrial genes may decrease energy production and affect multiple organs. Since cells contain hundreds of mitochondrial DNA molecules, any particular tissue may contain mitochondrial DNA molecules that are all identical, or there may be a fraction that differs. When both normal and mutant molecules exist, the mitochondria are said to be “heteroplasmic.” The heteroplasmic fraction of mutations can differ substantially among tissues.
It is critical to detect heteroplasmy sensitively, since even low levels in blood, which is routinely tested, may reveal disease affecting other organs. Such low levels of heteroplasmy in blood are generally not detected by standard methods, but are detected by the MEDomics test utilizing NextGen sequencing technology. The error rate determines how small a mutant fraction is reliably detected. MEDomics uses the Applied Biosystems SOLiD(TM) 3 NextGen sequencing platform which has an exceptionally low error rate, allowing detection of heteroplasmy down to about 1%.
The MEDomics NextGen mitochondrial genome test can help diagnose mitochondrial disease, enabling life-saving therapy decisions and allowing for accurate family risk counseling.
About MEDomics
MEDomics is a molecular diagnostic laboratory founded in 2008 by Steve S. Sommer, MD, PhD, with the mission of providing Mutation Expert-based Diagnosis (“MED”) to support the physician in delivering personalized medicine based on analysis of the patient’s genome (“omics”). The mutation experts at MEDomics provide unparalleled quality interpretation to aid the practicing physician.
Dr. Sommer is a Founding Fellow of the American College of Medical Genetics with 25 years experience in Clinical Molecular Diagnosis and over 300 scientific publications and patents. During the past few years, his personalized cancer genetics research and clinical team, including Kelly Gonzalez, MS, and Bill Scaringe, MS, discovered mutation showers. Mutation showers may occasionally cause cancer in an instant. His neuropsychiatric genetics team, including Carolyn Buzin, PhD, also helped to define the first genes for which mutations strongly predispose to schizophrenia or autism. Carolyn Buzin, Kelly Gonzalez, and Bill Scaringe are currently Senior Scientist, Director of Genetic Counseling & Education, and Director of Bioinformatics at MEDomics, respectively.
Richard Boles, MD, Director of the Mitochondrial and Metabolic Disorders Clinic at Childrens Hospital, Los Angeles, is the distinguished clinical consultant for MEDomics in mitochondrial diseases.
SOURCE MEDomics, LLC

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