The big picture

Multiple approaches—and ultimately, more comprehensive ones—are critical to understanding and treating cancer

Jeffrey Bouley
The drug discovery and development world, along withdiagnostic research and development, has a tendency to produce alphabet soup,and oncology research is no different, with some of the notable acronyms beingSNP, CNV, miRNA and FISH, along with full words like gene expression,sequencing and resequencing.
 
Some arrive and may dominate the spotlight for a while, butin the end, each plays important role, and all of them share one things incommon: they increasingly revolve around genetic technologies and molecularapproaches.


It makes sense, of course, as the Cancer Genome Projectbased at the Wellcome Trust Sanger Institute in the United Kingdom notes that "All cancers occur due to abnormalities in DNA sequence," and thus, "theidentification of genes that are mutated and hence drive oncogenesis has been acentral aim of cancer research since the advent of recombinant DNA technology."
 
 
The Cancer Genome Project's Genomics of Drug Sensitivityinitiative, for example, is a five-year effort launched in late 2008 involvinginvestigators at the Sanger Institute and other researchers, notably those atthe Massachusetts General Hospital Cancer Center, and they plan to look at howsome 1,000 genetically characterized cancer cell lines respond to treatmentwith 400 anti-cancer treatments, alone and in combination. Along with drugsensitivity information, the team is providing genetic data on the cancer celllines tested, including information on mutations, copy number variants (CNVs)and gene expression patterns.
 
Findings from Genomics of Drug Sensitivity studies lookingat the effects of 18 anti-cancer drugs on 350 genetically characterized cancersamples are already being made available to other researchers because of thepromising insights offered by the various gene-oriented technologies. Manytreatment-related genetic patterns are already coming to the fore, includingactivating mutations in the BRAFgene in melanoma that correspond to BRAF-targetingtreatment response.
 
"It is very encouraging that we are able to clearly identifydrug–gene interactions that are known to have clinical impact at an early stagein the study," says Ultan McDermott, co-project leader and a medical oncologistand human genetics researcher at the Sanger Institute. "It suggests that wewill discover many novel interactions even before we have the full complementof cancer cell lines and drugs screened."
 
 
But there is no hard-and-fast rule about which technology is"best" because each has its own strengths and weaknesses, and the researchculture of any given pharma or biotech might work better with some approachesthan others, even aside from issues like cost or time.
 
For example, FISH (fluorescence in situ hybridization) was shown in a February 2004 journalarticle in Urology to be 92percent effective in detecting bladder cancer markers when testing urinespecimens, compared with a sensitivity of only 64 percent for traditionalcytology screenings.
 
But now, six years later, is FISH as relevant?
 
 
"FISH, it seems to me, is one of the technologies that willbe replaced by newer ones with higher resolution; for example, arrays," notesDr. Ulrich Schwoerer, head of global marketing for 454 Life Sciences Corp., thesequencing subsidiary of Roche.
But much also depends on the area of focus and not simplywhich technologies might be fading in prominence.
 
 
"Broadly speaking, if you are looking at things like diseaseproclivity or disease risk and changes that might be inherited, those thingsare more concerned with the single nucleotide polymorphisms [SNPs] andmutations—things that are passed from generation to generation," notes Dr.Nandan Padukone, president and CEO of Nuvera Biosciences, a company focused ondeveloping novel molecular diagnostics that make a significant impact on cancercare. "But if you're looking at more complex issues, like responsiveness totherapy or lifestyles that lead to cancer, you need to look at things like geneexpression, methylation or proteomics, for example."
 
 
Smaller companies, he points out, will often have to focuson just one platform because of staff and budget limitations, although as theygrow or need better characterization of the oncology data, they will invest inother platforms.
 
 
"Then they have to think, do we go after SNPs or CNV or geneexpression or interactions," Padukone says. "But size is a key, as is themarket demand for what you have, and that drives how many different platformsyou need. If you're looking at a couple biomarkers, your work might go finejust on one platform, versus the more extreme side at a larger company whereyou need two or more platforms, multiplexing and are dealing with 50 biomarkersat once. The endpoint and the marketplace determine a lot of this for you, andyour endpoint as well—pharmas may go one direction on approaches while adiagnostic company in a similar oncology area may need to go another."
 
 
Also at issue is how comprehensive a given approach ortechnology is. Short-read technologies are are very effective in identifyingSNPs, Schwoerer notes, but you have to have long reads in order to detect anyof the larger mutations, like insertions, deletions, inversions andtranslocations.
 
 
"With cancer, the whole genome is messed up—parts areduplicated, other parts are relocated, others again deleted," Schwoerer says."Also, the three-dimensional structure often looks differently. It is veryimportant to get the full picture in order to understand this disease better."
 
 
"Also, you have to de novo sequence genomes, especially when it comes to disease, but also ingeneral for human genome sequencing," he adds. "Multiple recent publicationshave shown that you miss parts of the genome if you only resequence. In orderto effectively de novo sequence,you have to have long reads; short reads are not capable to de novo sequence genomes."
 
 
Most of the short-read technologies currently do between 75and 100 base pairs, Schwoerer says, and although some of the companiescompeting with 454 claim longer reads than that, he maintains that the errorrate can often become a real problem.
454 Sequencing, he says, is already at a mode of 500 basepairs, with Q20 quality at the 400th base. His company is about to launch aplatform that can read up to 800 base pairs, and IBM is working with Roche and454 to develop a DND Transistor technology that promises reads in the severalthousands of base pairs.
 
"Ultimately,sequencing is the technology with the highest resolution and is geared tobecome the gold standard for many genomic research areas," Schwoerer says. "Theblessing of bioinformatics as well as the challenge is to deal with all thisgenomic data; with longer reads, the assembly becomes easier andbioinformaticians and researchers can focus more on the biological questions.There are plenty of challenges left when you get the whole picture, and webelieve that this is where the really interesting discoveries are."
 

Jeffrey Bouley

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