A new, high-density array could help Affymetrix regain momentum in the gene-expression market.
The company last week launched its GeneChip Human Transcriptome and Splice Junction Array, a 6.9-million-feature chip designed with researchers from Stanford University.
The Stanford researchers this month published a paper comparing the performance of the new array with RNA-seq. According to lead author Weihong Xu, "lower technical variance with the microarrays translated to increased power for detecting low-level expression changes" compared to RNA-seq.
Additionally, in one of the experiments comparing liver and muscle expression, "two times the number of differentially expressed exons was discovered using the array," Xu told BioArray News this week.
According to Stanford, the chip costs $400 per sample, including reagent and processing fees, and has a throughput of "hundreds of samples per week in an average core."
The university concluded the array "provides a high-throughput and cost effective method for clinical research."
With these and other points in mind, Affy is betting the paper will encourage others to adopt the new chip for large-scale clinical studies and even eventual clinical use. According to Chief Commercial Officer Andy Last, having the chip on the market could help to revive Affy's expression sales.
"The Stanford research clearly demonstrates the benefits of using a dual-technology strategy," Last told BioArray News this week. "Their results clearly demonstrated array performance that will drive the use of microarrays in concert with the growth of information" from next-generation sequencing. "Microarrays make applying RNA-seq discoveries in large clinical studies and, ultimately, the clinic a viable option."
Affy's new product launch comes at a time when the firm is facing pressure in the expression market from next-generation sequencing. Illumina CEO Jay Flatley recently boasted that more than 50 percent of expression studies are being conducted on sequencers, while analysts partially blamed Affy's disappointing second-quarter sales figures to loss of market share to NGS (BAN 8/9/2011, BAN 7/12/2011).
This week, Last maintained that Affy is committed to offering the "best, cutting-edge gene-expression tools available," and said that the GeneChip Human Transcriptome and Splice Junction array "is the next generation of these tools, offering an even better view of the transcriptome."
At the same time, he was keen to point out the ways in which the company's new chip could be used with sequencing.
"NGS is forcing customers to think about increasingly inclusive sets of transcripts, in particular alternative-splicing events as opposed to genes," said Last. "Because accomplishing this with RNA-seq comes with such high costs and data complexity, we believe the time is right to focus our expertise on the type of analysi?s tools that interrogate the whole transcriptome with better reproducibility and coverage than the equivalent amount of sequencing."
That is more in line with new CEO Frank Witney's vision of repositioning Affy's products to better fit customer workflows. During the firm's Q2 earnings call last month, he acknowledged that sequencing is "drawing a lot of attention and money" from the market, but argued that it is also creating opportunities for the Affy, particularly when it comes to gene-expression profiling (BAN 7/26/2011).
"We believe that we are well positioned with our array products to leverage discoveries from NGS," Witney said. "Data is being published to demonstrate that as a practical matter for large-scale clinical studies, array-based approaches are a perfect complement to the discovery of transcriptome elements."
According to Last, Affy's new array fits downstream of discovery projects driven by RNA-seq.
"Our technology follows the use of NGS or Sanger sequencing in discovery to take the workflow downstream with the validation performance of catalog or custom expression microarrays," he said.
[ pagebreak ]
He also said the firm "needs to do a better job of explaining the utility of our products in this workflow, as well as where RNA-seq’s strengths are synergistic with our microarrays and multiplex technologies" like the company's QuantiGene platform.
He said the company's platforms today in general are being used by Affy's core customer base, which comprises "clinical researchers looking for RNA signatures for patient diagnosis, and pharmaceutical and diagnostic companies involved in biomarker discovery and validation for drug and companion-diagnostic development."
Affy said the new array is now available through an early-access program to these customers. The Santa Clara, Calif.-based firm added that the chip has also attracted attention from customers seeking a tool "sensitive enough to reproducibly measure low-abundance transcripts in complex disease and reproducible enough to take their de novo research to the clinical level."
Because of its high density, the chip is available in Affy's traditional cartridge format rather than its newer peg-array format, Last said. He said Affy expects to fully commercialize the new chip in early 2012.
Last added that the firm is also "exploring avenues within our core labs and service-provider networks for those who wish to access the array but do not have the bioinformatics infrastructure or in-house analyses expertise."
At Stanford, Affy's new array is known as the Glue Grant Human Transcriptome Array. Together with Affy, the chip was developed at Stanford's Genome Technology Center with funding from the National Institutes of Health's Glue Grants program, a three-year, multicenter effort overseen by the National Institute of General Medical Sciences.
Noting that alternative splicing is a "major source of the diversity of proteins and their functions," Stanford's Xu said that the team, led by Ronald Davis, aimed to study the transcriptome response to injury, a project that "requires a high-throughput, reliable, and cost-effective genomic platform."
After evaluating available technologies in the market, the Stanford researchers determined that none was directly applicable to its need. At this point they reached out to Affy to develop the array in-house, Xu said.
Designing the array, the Stanford team set a goal to profile different aspects of the human transcriptome, such as gene- and exon-level expression, alternative splicing, SNP coding, drug-metabolism enzymes and transporters, and non-coding RNAs, Xu said.
They validated this design by comparing the array's results with RNA-seq over multiple independent replicates of liver and muscle samples. According to the study, the array detected the same number of genes and two times the number of exons; had lower variance over a wide range of expression levels; improved the percentage of true-positive detections in alternative splicing analysis; and measured more non-coding RNA than RNA-Seq.
In a statement, Davis said that "arrays of this type will be the platform of choice for patient profiling in clinical trials." This together with the "power of next-generation sequencing harnessed into a clinically viable platform … will be what changes the face of patient care."
According to the paper, the researchers conducted 5,000-sample studies to estimate it would take RNA-seq 10 times longer to analyze 1 percent of the number of genes processed by the new array, and 20 times longer to analyze 0.5 percent of exons.
The Stanford team also estimated that to achieve the same level of reproducibility as the new array, RNA-seq would require 150 million mappable reads for genes and 200 million for exons.
In light of these calculations, Davis suggested that researchers embarking on large-scale clinical genomic studies "first use RNA-Seq to the sufficient depth of 200 million or greater reads for the discovery of transcriptome elements relevant to the disease process," followed by high-throughput screening of these elements using custom designed arrays."
Xu noted that the array-based approach would result in "lower cost for large-scale clinical studies."
Have topics you'd like to see covered in BioArray News? Contact the editor at jpetrone [at] genomeweb [.] com.