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Whole genome sequencing; not ready for prime time
By gdpawel at 2014-03-14 06:06
Whole genome sequencing; not ready for prime time
A test of the utility of whole-genome sequencing (WGS) on 12 genetically healthy adults reveals several shortcomings, according to a study published in the March 12 issue of JAMA.

The report describes "the process that a team at an academic medical center went through to conduct sequencing from beginning to end. They raise a lot of interesting practical issues that have been circling for a while and provide preliminary numbers to quantify the process," Cinnamon S. Bloss, PhD, director of social sciences and bioethics at Scripps Translational Science Institute, La Jolla, California, told Medscape Medical News.

As costs plunge and time shrinks for sequencing human genomes, clinicians are considering how to use DNA data in their practices. Thus far, exome sequencing and limited WGS have performed well for individuals with atypical presentations or undiagnosed diseases, as well as for specific groups, such as children with developmental delay or admission to a neonatal intensive care unit. However, little is known about the technique's value in healthy adults.

Probing a Dozen Average Genomes

Therefore, Frederick E. Dewey, MD, from the Stanford Center for Inherited Cardiovascular Disease, Stanford Cardiovascular Institute, Stanford Center for Genomics and Personalized Medicine, and Division of Cardiovascular Medicine, Stanford University, California, and colleagues evaluated genomes from healthy adult volunteers at their center. The investigation considered coverage of clinically relevant gene variants, concordance using different sequencing platforms, measurement of risks for diseases and drug responses, resources required to evaluate data, and clinical follow-up.

They used an Illumina platform for sequencing, with confirmatory sequencing for 9 participants on the Complete Genomics platform. The researchers used appropriate reference genomes for the 5 white European and 7 East Asian participants and screened genes from many sources, including the Human Gene Mutation Database (HGMD), 56 genes that the American College of Medical Genetics and Genomics recommended for prioritization in testing because of actionability, 118 gene variants associated with diabetes mellitus type 2 or coronary artery disease, and 555 genetic variants associated with drug responses.

A team of 3 genetic counselors, 3 physician informaticists, and 1 molecular pathologist curated sequence variants from literature reviews and phenotype identification software that uses allele frequencies, functional classes, and evolutionary conservation to predict pathogenicity. Team members determined which variants to report to 5 physicians, who then suggested clinical follow-up solely on the basis of the DNA data. Three were academic primary care practitioners, 2 of whom had no experience with genetics or genomics, and the other 2 were academic medical geneticists.

Omissions and Uncertainties

Sequencing a genome requires randomly cutting many copies of the genome and then piecing back together overlapping sequences. The more copies of the genome that are sequenced (referred to as coverage depth), the more of the genome appears in the completed WGS. For these dozen patients, sequencing missed 10% to 19% of disease genes, depending on database and platform.

However, with the addition of targeted sequencing, detection of known variants in disease-causing genes and drug response variants was 99% to 100%, indicating that it is much easier to find known targets. The platforms were also better at detecting single-nucleotide variants (99% - 100%) than small insertions and deletions (53% - 59%).

The software identified 90 to 127 "novel and rare" genetic variants and up to 4 large structural variants per participant. Database and literature review for evidence of pathogenicity took a median of 54 minutes (5 - 223 minutes) per variant, at an estimated median cost for sequencing plus interpretation of $14,815 ($14,050 - $15,715).

Interpretation Varies, Even Among Experts

The investigators randomly selected 18 genetic variants to assess how likely 6 genomics professionals were to agree on potential personal risk or carrier status. The results indicated moderate interrater agreement on pathogenicity (Gross κ, 0.52; 95% confidence interval [CI], 0.40 - 0.64) and fair interrater agreement on suitability for reporting (Gross κ, 0.83; 95% CI, 0.73 - 0.93).

The researchers downgraded disease associations for some mutations based on context, such as whether a variant is pathogenic in a particular population. The team found 47 of 68 variants (69%) listed in HGMD as "disease-causing" to be less severe or variants of uncertain significance.

Take-Home Information

For each participant, WGS revealed 2 to 6 "personal disease-risk findings," most for adult-onset conditions, from the HGMD. Each participant carried 8 to 18 disease-causing recessive alleles. For the American College of Medical Genetics and Genomics–reportable genes, each participant had 12 to 20 variants, which fell to 1 to 7 variants after eliminating sequencing artifacts or common polymorphisms.

Overall, the study identified only a single "very likely pathogenic" gene variant, a 19-base deletion in BRCA1. The woman, who did not have a family history of the associated cancers, had risk-reducing surgery. Eleven of the 12 participants had at least 1 gene variant of pharmacogenetic significance.

The physicians suggested 1 to 3 diagnostic tests and/or referrals per participant, with a median cost of $351 to $776, which include a "high-complexity established patient visit" and 2 hours of genetic counseling.

The researchers conclude that in their study, WGS "was associated with incomplete coverage of inherited disease genes, low reproducibility of detection of genetic variation with the highest potential clinical effects, and uncertainty about clinically reportable findings."

Limitations of the study include the early stage of applying sequencing data to clinical decision-making, lack of full clinical information on the participants, and applicability beyond academic medicine.

In an accompanying editorial, William Gregory Feero, MD, PhD, from the Maine Dartmouth Family Medicine Residency, Augusta, and associate editor, JAMA, acknowledges that although medical decision-making with incomplete understanding of underlying biology is ancient, clinicians should "recognize that determining the biological consequences of very rare and novel variations" in a genome "remains arduous, tenuous, and costly even in highly experienced hands."

Dr. Bloss noted some trade-offs in the economics of WGS. "You might order a more expensive test if it's the most cost-effective and shortest road to diagnosis," she said, mentioning the diagnostic odysseys common in the rare disease world. "In those cases, clinical sequencing has the potential to be very cost-effective and avoid downstream tests. But I think we are a long way from seeing this type of analysis be cost-effective for someone who is relatively healthy. We'd all like to see the utility of WGS for disease prevention, but I don't know that we're there yet."

Dr. Dewey is a stockholder and member of the scientific advisory board of Personalis Inc and receives royalties for patents related to genome sequencing. One coauthor has received speaker's fees from Illumina Inc. One coauthor is on the board of Coriell Inc. Three coauthors receive royalties for patents related to genome sequencing. Four coauthors are founders, stockholders, and members of the board of Personalis Inc and receive royalties for patents related to genome sequencing. One coauthor is a stockholder and member of the board of NuMedii Inc; consultant to Lilly, Regeneron, Johnson & Johnson, Roche, Geisinger, Verinata, Pfizer, and Samsung; has received speaker's fees from Pfizer, Lilly, Siemens, Bristol-Myers Squibb, and Genentech; and holds stock in Carmenta, Eceos, Assay Depot, and Genstruct/Selva. One coauthor is on the board of and owns stock in Genapsys Inc. One coauthor is a member of the board of Aviir Inc. Dr. Feero is a contributing editor for the JAMA. Dr. Bloss has disclosed no relevant financial relationships.

JAMA. 2014;311:1117-1119, 1035-1044.

3 comments | 18035 reads

by gdpawel on Fri, 2014-03-14 06:35
(Reuters Health) - Routine genome-wide screening of cancers is likely a long way off, a new paper says.

The technology, known as next-generation sequencing, promises to revolutionize doctors' understanding of cancer and underpins perhaps the biggest paradigm shift taking place in cancer research today: the growing emphasis on a cancer's genetic makeup, rather than its location within the body.

Understanding the genetic makeup of an individual patient's tumor may allow physicians to pick the drug that best targets that specific tumor, as well as recognize when a tumor has developed resistance to the drug through new genetic mutations.

"Next-generation sequencing is especially promising in cancer because in a single test, one can interrogate all clinically relevant cancer genes for all types of genomic alterations, including sequence mutations and chromosomal rearrangements," Dr. Michael Berger, a geneticist at Memorial Sloan-Kettering Cancer Center in New York City, told Reuters Health in an email.

There are several different specific screening technologies that are considered "next-generation" - but all share the ability to sequence entire human genomes in a matter of days. When applied to cancer, the technology is used to screen the entire genome of cancer cells.

By some measures, this promise is already being realized. For example, last year The Cancer Genome Atlas Research Network used genome-wide screening of breast cancer tumors to demonstrate that there are four main breast cancer types defined by differing genomic and epigenetic mutations. The study showed that individual breast cancers have many genetic differences from each other but that one subgroup of breast cancers, basal-like breast cancer, was similar genetically to serous ovarian cancer.

Cancer cells present unique and complex challenges, they note. Because they are genetically so different from normal human tissue, there is not always a 'reference sequence' against which to compare the tumor DNA. There are also frequent chromosome-scale as well as epigenetic changes, and even significant genetic differences among cells within the same tumor, an issue specific to cancer cells known as tumor heterogeneity.

The authors of the new paper, writing online July 25 in the British Journal of Cancer, pointed out that this complexity creates a number of problems that must be solved before next-generation sequencing is a common part of cancer care.

One of the first issues is developing the algorithms that are used to map the genome.

"The computational challenges involved in analyzing and storing clinical (next-generation sequencing) data cannot be overstated," said Dr. Berger, who wasn't involved in the new study. "Better algorithms must be developed to reliably and accurately detect mutations in heterogeneous tumors."

In genome-wide sequencing, a seemingly minuscule misstep in the analysis could have massive consequences. For example, say the authors of the new paper, led by Dr. Danny Ulahannan of the Wellcome Trust Center for Human Genetics in Oxford, UK, "the sheer quantity of data means that getting 0.01% of the human genome wrong would correspond to 300,000 errors scattered along the three billion base pairs."

Dr. Lynda Chin, the chair of Genomic Medicine and scientific director of the Institute for Applied Cancer Science at MD Anderson Cancer Center, told Reuters Health this is often an overlooked problem.

"One barrier that is often overlooked or underestimated from the clinical side is the technical challenge of generating high-quality (next-generation sequencing) data," Dr. Chin said. "There is a sense that generating (the data) is easy, and it is the analysis that is hard. I would disagree, as I believe that the technology is still unstable, for lack of a better word, not yet turn-key, and no matter how good the analytics-interpretation become, if the data is poor quality, the result will be poor."

And mapping the genome is really only the first step. The next step is figuring out which mutations are relevant to the development of cancer and whether they can be targeted with a drug.

"I agree with the obvious barriers of interpretation. Not just analytically that we need improved algorithms, (but) more importantly, more knowledge and understanding of what each alteration means and how each event impact on clinical decision," Dr. Chin said.

The advances will require a "cultural change" in cancer research, Dr. Chin said, that makes "patient-oriented genomic research a standard, rather than a heroic effort by a researcher."

by gdpawel on Fri, 2014-03-14 06:36
Next-generation sequencing (NGS) technologies have come a long way since 1977 when Frederick Sanger developed chain-termination sequencing, but are they ready for prime time in drug selection?

Researchers have realized that cancer biology is driven by signaling pathways. Cells speak to each other and the messages they send are interpreted via intracellular pathways known as signal transduction. Many of these pathways are activated or deactivated by phosphorylations on select cellular proteins.

Sequencing the genome of cancer cells is explicitly based upon the assumption that the pathways - network of genes - of tumor cells can be known in sufficient detail to control cancer. Each cancer cell can be different and the cancer cells that are present change and evolve with time.

Although the theory behind inhibitor targeted therapy is appealing, the reality is more complex. Cancer cells often have many mutations in many different pathways, so even if one route is shut down by a targted treatment, the cancer cell may be able to use other routes.

In other words, cancer cells have "backup systems" that allow them to survive. The result is that the drug does not affect the tumor as expected. The cancer state is typically characterized by a signaling process that is unregulated and in a continuous state of activation.

In chemotherapy selection, genotype analysis (genomic profiling) examines a single process within the cell or a relatively small number of processes. All a gene mutation study can tell is whether or not the cells are potentially susceptible to a mechanism of attack. The aim is to tell if there is a theoretical predisposition to drug response.

It doesn't tell you the effectiveness of one drug (or combination) or any other drug which may target this in the individual. There are many pathways to altered cellular function. Phenotype analysis (functional profiling) measures the end result of pathway activation or deactivation to predict whether patients will actually respond (clinical responders).

It measures what happens at the end, rather than the status of the individual pathway, by assessing the activity of a drug (or combinations) upon combined effect of all cellular processes, using combined metabolic and morphologic endpoints, at the cell population level, measuring the interaction of the entire genome.

The challenge involved in whole-genome sequencing, is the sequencing the billions of nucleotides that make up all of a person's DNA, and then translating the results into clinical care recommendations, is significant. And whole-genome sequencing does not eliminate the problem of variants of uncertain significance - when a gene sequence deviates from the consensus, but the clinical effect of that change is unknown.

Should oncologists begin using deep genome sequencing in their clinical practice? At the annual meeting of the European Society for Medical Oncology, two key opinion leaders battled it out over this topic in a debate.

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