Photograph by Tsar Fedorsky/Getty ImagesLeif Ellisen says ‘smart drugs’ will one day change the way cancer is treated
This summer, Vancouver cancer researchers announced a medical first. Presented with an extremely rare case of tongue cancer—it was so unusual there were no standard treatments to use—they sequenced the DNA of the patient’s tumour, and discovered similarities with another cancer (renal cell carcinoma, a type of kidney cancer) for which there’s a known therapy. The patient received drugs tailored to these results, and the cancer stopped growing for several months. Steven Jones, a molecular biologist with the B.C. Cancer Agency Genome Sciences Centre and one of two lead researchers on the study, calls it a breakthrough. It isn’t standard in hospitals to genetically sequence a patient’s tumour, but “the goal would be, maybe in 10 years, this would be routine,” he says.
Dr. Leif Ellisen, an associate professor of medicine at Harvard Medical School, is working to bring tumour genotyping from the lab into the clinic. He and a team have designed a system that can screen relatively large numbers of patients for a variety of mutations across different cancer genes. These genetic mutations are a tumour’s “Achilles’ heel,” noted a recent editorial in the journal EMBO Molecular Medicine. “Every tumour has a flaw,” says Ellisen, who’ll be discussing his work as part of the Scienta Health Series in Toronto on Oct. 7, and his goal is to find it.
It’s the mantra of a growing number of researchers, who tout personalized medicine—treatments tailored to each individual—as the future of cancer care. Traditionally, cancer treatment “has been one-size-fits all,” Ellisen says. “If it’s breast cancer, you treat it one way; if it’s lung cancer, you treat it another.” The downside is that costly drugs are administered to patients, sometimes with harmful side effects and no real promise they’ll work. “The treatment needs to be tailored to the individual characteristics of the patient and, we’re learning now, the characteristics of the tumour,” he says. Cancers are typically classified by the organs where they arise, but it’s possible that a breast cancer and a lung cancer, for example, might share a genetic abnormality. As a result, they might even respond to the same treatment.
That’s the concept behind smart drugs, which are being developed to target specific molecular pathways activated by cancer gene mutations. “These drugs work in a very specific way, as opposed to chemotherapy, which works in a general way,” Ellisen says. One example is Herceptin, a drug that treats certain types of breast cancer. Herceptin inhibits a gene amplified in some women, but not others, that acts like a growth factor for cancer cells. Now, when breast cancer patients are diagnosed, “one of the first tests done on the tumour is to look for this gene,” called HER2, Ellisen says. “If it’s activated, the patient will get Herceptin. If not, they won’t, because it wouldn’t benefit them.”
A broader test might pick up on mutations we wouldn’t expect to see in a given tumour, but which might have a known treatment available. “We needed to develop a technology to look across the spectrum of cancer genes in the tumours,” Ellisen says. “That’s the only way we could personalize the therapy.” The genotyping system they developed can test for over 150 mutations in 18 cancer genes. In a journal, Ellison and his fellow researchers said they opted to scan for “cancer mutations most likely to have immediate clinical impact,” either because they’re already targeted by FDA-approved drugs, or new drugs in development.
Genotyping tumours in the lab is one thing, but in a hospital setting with actual patients, it’s quite different.
“One of the biggest challenges was purifying and analyzing genetic material” in a quick and reproducible fashion, Ellisen says. Tumour samples are generally “set in fixatives, embedded in wax blocks, and stored at room temperature,” he says, and the specimens themselves can have impurities, with tumour cells mixed in with normal ones. All this can, in some cases, make it much harder to detect mutations. Thanks to a series of robots the team designed, the lab can handle complete snapshot genotyping of up to 50 samples a week, “a substantial fraction of all the cancers diagnosed at Mass Gen,” says Ellisen, co-executive director of the hospital’s Cancer Center’s Translational Research Laboratory, which is focused on personalized cancer care.
Ellisen acknowledges we’ve still got a lot to learn. “We don’t know all the cancer genes that exist,” he says, and we need more and better drugs to treat them. (The sequence of the first cancer genome was published just two years ago.) But the field is advancing in leaps and bounds. In April, the International Cancer Genome Consortium, made up of several countries including Canada and the U.S., announced a plan to map the genomes of 25,000 cancer samples. (Canadian researchers are focused on prostate and pancreatic tumours, while the U.S. is working on brain, lung and others.) “There’s been a million-fold improvement in sequencing technology since 2001,” says Dr. Tom Hudson, president and scientific director of the Ontario Institute for Cancer Research. Even so, he expects it will take up to 10 years to complete it.
As we learn more about cancer genes, Ellisen’s system can be updated. “This platform is very scalable, so we can add mutations as new discoveries are made,” he says. Because it’s relatively simple, it could be adopted in clinics elsewhere, he believes. The cost of a test is similar to an MRI scan. Meanwhile, other cancer centres are trying out different genotyping systems, and sharing information about what works best.
The day when cancer patients can be routinely treated based on the genetic makeup of their tumours is still a ways off.
As Ellisen notes, there’s “only a relatively small number of effective targeted therapies available for routine clinical use,” although that number is growing. As we continue to unravel the genetic mutations that lead to cancer, and develop smart drugs to target them, treatment will change drastically, he and others predict.
“There’s no question that this is going to be the way cancer is approached in the future,” Ellisen says.
Public release date: 6-Apr-2011
Contact: Neil Caporaso
Public Library of Science
Press release from PLoS Genetics
Two genes in which variation affects intake of caffeine, the most widely consumed stimulant in the world, have been discovered. A team of investigators from the National Cancer Institute, Harvard School of Public Health, Brigham and Women’s Hospital, and the University of North Carolina at Chapel Hill examined genetic variation across the entire genome of more than 47,000 individuals from the U.S., as described in the open-access journal PLoS Genetics.
The genes identified were CYP1A2, which has previously been implicated in the metabolism of caffeine, and AHR, involved in the regulation of CYP1A2. Individuals with the highest-consumption genotype for either gene consumed ~40 mg more caffeine than those with the lowest-consumption genotype, equivalent to the amount of 1/3 cup of caffeinated coffee, or 1 can of cola.
Caffeine is implicated in numerous physiological and medical conditions; it affects sleep patterns, energy levels, mood, and mental and physical performance. The identification of genes that have an impact on daily consumption offers opportunities to better understand these conditions. Further exploration of the identified genetic variants may provide insight into the speed of caffeine metabolism, how long caffeine circulates in the blood, or how strong the physiological effects of consuming a given amount of caffeine are.
Apart from smoking, genetic determinants of lifestyle behaviors have generally not been consistently described. This study is among the first to examine the entire genome for a relationship between genetics and caffeine intake, a lifestyle behavior relevant to over 90% of U.S. adults. The study’s success also suggests that additional genetic determinants of dietary and lifestyle behaviors may be identified in the future using a similar genomebased research strategy.
FINANCIAL DISCLOSURE: ARIC is carried out as a collaborative study supported by National Heart, Lung, and Blood Institute (NHLBI) contracts N01-HC-55015, N01-HC-55016, N01-HC-55018, N01-HC-55019, N01-HC-55020, N01-HC-55021, N01-HC-55022, R01HL087641, R01HL59367, and R01HL086694; National Human Genome Research Institute contract U01HG004402; and NIH contract HSN268200625226C. Infrastructure was partly supported by Grant Number UL1RR025005, a component of the NIH and NIH Roadmap for Medical Research. The NHS Breast Cancer GW scan was performed as part of the Cancer Genetic Markers of Susceptibility initiative of the NCI. We particularly acknowledge the contributions of R. Hoover, A. Hutchinson, K. Jacobs, and G. Thomas. The current research is supported by CA 40356 and U01-CA98233 from the NCI. The NHS/HPFS type 2 diabetes GWAS (U01HG004399) is a component of a collaborative project that includes 13 other GWAS funded as part of the Gene Environment-Association Studies (GENEVA) under the NIH Genes, Environment, and Health Initiative (GEI) (U01HG004738, U01HG004422, U01HG004402, U01HG004729, U01HG004726, 01HG004735, U01HG004415, U01HG004436, U01HG004423, U01HG004728, AHG006033) with additional support from individual NIH Institutes (NIDCR: U01DE018993, U01DE018903; NIAAA: U10AA008401; NIDA: P01CA089392, 01DA013423; NCI: CA63464, CA54281, CA136792, Z01CP010200). Assistance with genotype cleaning and general study coordination, was provided by the GENEVA Coordinating Center (U01HG004446). Assistance with data cleaning was provided by the NCBI. Genotyping was performed at the Broad Institute of MIT and Harvard, with funding support from the NIH GEI (U01HG04424), and Johns Hopkins University Center for Inherited Disease Research, with support from the NIH GEI (U01HG004438) and the NIH contract “High throughput genotyping for studying the genetic contributions to human disease” (HHSN268200782096C). Additional funding for the current research was provided by the NCI (P01CA087969, P01CA055075) and the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK, R01DK058845). The NHS/HPFS CHD GWAS was supported by HL35464 and CA55075 from the NIH with additional support for genotyping from Merck/Rosetta Research Laboratories, North Wales, PA. The NHS/HPFS Kidney GWAS was supported by NIDDK: 5P01DK070756. PLCO was supported the Intramural Research Program of the Division of Cancer Epidemiology and Genetics and by contracts from the Division of Cancer Prevention, National Cancer Institute, NIH, DHHS. The WGHS is supported by HL 043851 and HL69757 from the NHLBI and CA 047988 from the NCI, the Donald W. Reynolds Foundation, and the Fondation Leducq, with collaborative scientific support and funding for genotyping provided by Amgen. Marilyn C. Cornelis is a recipient of a Canadian Institutes of Health Research Fellowship. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
COMPETING INTERESTS: The authors have declared that no competing interests exist.
CONTACT: Neil Caporaso
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By Justin Petrone
Sequence first, genotype second. That is the message of a new study published last week in the American Journal of Human Genetics.
Led by David Goldstein, director of the Duke University Center for Human Genome Variation, the scientists sequenced the complete genomes of 29 people of European origin to assess the relationship between the functional properties of variants and their population allele frequencies.
The Duke researchers determined that the most common genetic variants in the human genome aren’t the ones most likely to cause disease and posited that, in fact, rare genetic variants are more likely linked to disease. The findings prompted Goldstein to argue that sequencing-based approaches, rather than array-based genome-wide association studies, are more likely to identify the rare variants behind common diseases.
“I am entirely convinced that sequencing, which is becoming less expensive every month, will unlock a lot of the causes of genetic disease,” Goldstein said. “What we can do clinically with that information will become the primary challenge,” he said. “It may take sequencing thousands of patient genomes to track down the responsible mutations, but they will be found.”
Lead author Qianqian Zhu told BioArray News this week that the study’s findings suggest it would be “more reasonable to sequence a group of cases and controls first to identify potential causal variants, then genotype a large cohort using arrays to verify those findings.”
According to Zhu, disease-causing variants are likely to be rare and not likely to be seen in many control samples. She noted that “most” genotyping arrays contain common variants. Therefore, causal variants are not likely to be on current chips. “You have to know the variants first,” said Zhu, a postdoc in Goldstein’s lab.
The Duke researchers’ recommended approach differs from others’ opinions on how such studies should be conducted. For instance, Stephen Chanock, chief of the Laboratory of Translational Genomics at the National Cancer Institute, suggested in an interview with BioArray News last month that targeted, sequencing-based studies were more likely to follow the current round of array-based association studies (BAN 3/1/2011).
A common argument from array vendors is that sequencing is too expensive and analytically burdensome to be adopted en masse by researchers seeking to identify causal variants in a statistically significant cohort of cases. This has prompted some, like Illumina CEO Jay Flatley, to declare that the next round of studies will be based on new chips containing rare variant content from sources like the 1000 Genomes Project.
The Duke researchers have long argued that array based approaches are limited by the fact that they do not represent rare variants very well and that many common diseases would require sequencing based approaches. In a PLoS Biology paper last year, they warned that such array-based studies would “hit a wall” because of their inability to identify rare causal variants (BAN 2/2/2010). The same group made similar arguments in a paper in the New England Journal of Medicine in 2009 (BAN 4/21/2009).
“Because the first round of association studies didn’t find many causal variants, we began to realize that the real variants may be rare,” Zhu said this week. “Overall, this latest study just gives more genome-wide evidence that rare variants are more likely to be in functional regions of the genome, that they are more likely to do something,” she said. “That is our major point.”
Zhu recognized that efforts like the 1000 Genomes Project have greatly expanded the number of rare variants that can be made available to researchers via current array platforms. But there is still risk that the causal variants of some diseases are not found by the 1000 Genome Project. She cited a recently identified rare variant that is very likely to be causal for sick sinus syndrome in Icelanders but was not in the 1000 Genomes database.
Despite the shortcomings of array-based approaches in identifying potential causal variants, Zhu acknowledged that sequencing is still expensive when compared to arrays, and that initial sequencing studies have to be done in a “small set” of patients and controls at the moment. Because of this, she advocates following on those initial findings with targeted, array-based genotyping in larger cohorts. “I think array technology still has value,” she said.
As for what vendors hope will be a second round of association studies based on new chips with expanded content, Zhu said that these arrays may do a better job of uncovering disease-associated variants than first-generation GWAS. “For now, I think it’s also a valid approach,” she said.
By Julia Karow
As the first example of a targeted assay for its sequencing platform that may be used clinically in the future, Roche’s 454 Life Sciences last week launched kits for genotyping HLA genes at high and medium resolution.
Independently, RainDance Technologies this week launched a kit to amplify a large panel of HLA genes using its microdroplet PCR platform and to analyze them by next-gen sequencing. It developed the assay in collaboration with Expression Analysis.
The human leukocyte antigen, or HLA, locus encodes more than 200 genes with key roles in the immune system. It is highly polymorphic, with approximately 6,000 known alleles. HLA genotypes are associated with autoimmune and infectious diseases, as well as some cancers, and HLA typing is important in transplant medicine for matching donors and recipients.
The two 454 kits — called GS GType HLA MR and HR — are available immediately for research use only. They are the first in a series of assays 454 plans to launch in other disease areas including immunogenetics, infectious disease, and cancer.
According to the company, the assays offer several advantages over HLA typing by Sanger sequencing including increased throughput — depending on the 454 platform and resolution, between 5 and 80 samples can be analyzed per run — and savings in time and labor because polymorphisms can be linked, or phased, unambiguously within exons.
According to 454 CEO Chris McLeod, there is a “huge use” of HLA typing just in disease research, and there is potential for its use in other areas, such as forensics or population genetics.
The current level of resolution is “great for a lot of research applications,” he said. The plan is “to build on this in the future to potentially launch a registered product” for clinical use, he added.
The company is not yet announcing when it plans to file an application with the US Food and Drug Administration, but McLeod said it aims to file for a sequencing platform, as well as an assay, both based on the current research versions. For many clinical applications of HLA typing, he said, the resolution of the assay needs to be higher than it is currently. The clinical sequencing platform is “not going to be totally new,” but similar to the existing one.
According to McLeod, clinical HLA typing is “a material market” for 454 that today is primarily served by Sanger sequencing, although no registered diagnostic product exists.
Last month, Life Technologies said it has started clinical trials for HLA typing on its 3500 Dx Genetic Analyzer — a Sanger capillary sequencer — and plans to apply for 510(k) clearance of the platform and assay kits from the FDA (CSN 3/22/2011).
McLeod said 454 sees clinical HLA testing as a market “where we would be able to have significant advantages over Sanger sequencing.” Compared to other next-gen sequencing platforms, 454′s relatively long reads help to determine the phase of alleles unambiguously. “A lot of the other [platforms] have to do that virtually because their read lengths are just not long enough,” he said.
454 and Roche have tested their HLA assay in collaboration with researchers at Stanford University, Technical University Dresden in Germany, Children’s Hospital of Philadelphia, the Red Cross Transfusion Service of Upper Austria, Children’s Hospital and Research Centers in Oakland, and the Institute of Immunology and Genetics in Kaiserslautern in Germany, and published the results in Tissue Antigens last month.
According to Elizabeth Trachtenberg, director of the HLA Immunogenetics Laboratory at the Children’s Hospital and Research Center Oakland, an author on the paper, one of the main advantages of the 454 platform is that it reduces the amount of ambiguity from heterozygous combinations of alleles. With Sanger sequencing, “we have to do a lot of secondary testing to resolve these ambiguous heterozygous combinations, and this new clonal sequencing drastically reduces that,” she told Clinical Sequencing News, “which is really incredibly helpful for an HLA clinical laboratory. We spend at least a quarter of our time resolving ambiguities.”
The cost per sample is also “significantly less than with Sanger sequencing,” she said, because many samples can be processed in parallel — her lab currently sequences 11 loci for 40 samples per run — and because ambiguity is reduced, though she has not done an exact cost calculation yet.
To be sure, though Trachtenberg’s 454 GS FLX runs in a CLIA laboratory, it is not used for clinical work at the moment but for disease research studies. “But as time goes on, we will use it for clinical trials and for patient samples,” she said, even before the FDA approves a 454 instrument. While she would not be able to use Roche’s reagent kits for clinical work, she could develop her own reagents for that, she said.
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