A group of researchers from Massachusetts General Hospital has developed a method using either BEAMing RT-PCR or digital droplet-based PCR to detect mutations associated with glioblastoma brain tumors in RNA extracted from extracellular vesicles released from these tumors into patients' cerebrospinal fluid.
In a paper published last week in Molecular Therapy—Nucleic Acids, the team presented results using both BEAMing technology from Germany's Inostics and digital droplet PCR from RainDance Technologies to detect mutant IDH1 mRNA in samples from patients with different tumor grades. According to the authors, both PCR methods yielded the same sensitivity and specificity, reliably detecting and quantifying the presence of mutant and wild-type IDH1 in CSF from patients with gliomas.
Xandra Breakefield, a researcher at MGH and Harvard Medical School and one of the study's lead investigators, told PCR Insider that though the results are preliminary, the group intends to continue to develop the approach — potentially also exploring other droplet-based digital PCR platforms like Bio-Rad's as well as directed deep sequencing approaches — with the hope that the method could become the backbone of a clinical test to detect mutations with prognostic significance or associations with drug response in either CSF or serum.
"If this turns out to be robust enough it's tempting to say that you could use this to test patients with MRI results showing a tumor to diagnose or track it without needing to biopsy" she said. "It would be especially important because we know that there are some low-grade tumors that might not need any treatment at all, so the further we can back off from rushing into treatment the better."
According to the group, mutations in IDH1 could be use to track tumors, and also inform treatment decisions with drugs being developed to target such mutations. But the team is also planning to expand its research to other potentially important mutations for classifying these tumors or making other treatment decisions.
Breakefield said the group has applied for IP on the use of BEAMing PCR to analyze RNA from extracellular vesicles, and that this IP will most likely be licensed to Exosome Diagnostics, an MGH spinout company that has been advancing technology originally developed at the hospital to isolate nucleic acids from extracellular vesicles for use in molecular diagnostics.
Clinically-directed work to further develop a BEAMing or other digital PCR-based method for detecting IDH1 and other gliblastoma-associated mutations would then take place under the umbrella of Exosome's CLIA lab, Breakefield said.
"We have a national repository that has been collecting CSF and serum samples from glioma patients, so [Exosome] can then, with these much larger cadres, take it over and test these findings with hundreds of samples," Breakefield said.
Exosome Diagnostics did not comment on future possibilities for the discovery but said in a statement that it has “an exclusive license agreement with [MGH] for exosome technology dating from 2008.”
Exosome last week announced a partnership with Qiagen to develop and commercialize co-branded kits to capture and process RNA and DNA from biofluid exosomes and other microvesicles (PCR 7/25/2013), saying the move may hopefully help it accelerate its own internal clinical assay development.
In the study published last week, the researchers first turned to BEAMing — and then later digital droplet PCR — after realizing that standard RT-qPCR would not be sensitive enough to detect their mutated IDH1 target.
While the group was successful using RT-qPCR to track larger and more frequent mutations in vesicle RNA in previous experiments, they realized they would need more sensitive technology to pick up single nucleotide mutations in IDH1 against a large background of wild-type sequence.
"When we tried to pick up single nucleotide changes, RT-qPCR just wasn’t sensitive enough, because if you are lucky, with something like IDH1 mutations, you have one mutant copy in a background of 10,000 wild types," Breakefield said.
The BEAMing method, which is an acronym for "beads, emulsion, amplification, and magnets," was originally developed in the laboratory of Johns Hopkins University researcher Bert Vogelstein to work with RNA instead of DNA.
Inostics, which Vogelstein co-founded, is using BEAMing to create assays for cancer mutation detection and companion diagnostics (PCR 6/28/2012).
Adapting the approach for mRNA from extracellular vesicles involved optimizing the reverse transcriptase step and the PCR pre-amplification steps, along with a number of other tweaks, said Leonora Balaj, an MGH researcher and co-author on the study.
Initially the MGH team tried to analyze vesicles from patients' serum using their adapted BEAMing protocol, but had little luck, so decided to work with cerebrospinal fluid instead.
By this point in the study commercial platforms for digital PCR were becoming available, Breakefield said, so the group decided to also test one of these — RainDance's RainDrop PCR.
"We were basically done with the paper — but by that time in 2012, 2013, these companies, including RainDance and Bio-Rad had instruments out and almost ready for market," said Balaj.
"BEAMing is very time-consuming, and very manual, so we thought we should try to see if we got the same results or better results with a digital droplet approach," she said. "RainDance was local, and was offering an early access program, so it was easy to work with them."
Studying a group of retrospective CSF samples from patients with IDH1-mutated gliomas as well as non-mutant controls, the researchers found that both BEAMing and the droplet platform were reliably able to identify mutant IDH1 in isolated vesicle mRNA.
The two methods both detected mutant IDH1 with the same specificity and sensitivity, accurately identifying the mutation in five out of eight known-mutant samples, and in neither of two wild-type controls.
According to the authors, two of the three IDH1 mutants that were not detected by either digital PCR method were from grade II tumors, while a third was from an unusually small grade III tumor, suggesting a possible correlation between mutation detection and tumor grade and size.
Moving forward, Balaj said the MGH group is working to increase the sensitivity of the method and planning to test Bio-Rad's droplet digital PCR system while continuing to work with the RainDance platform. Bio-Rad has been selling its platform, the QX100 Droplet Digital system, for nearly two years, and this week launched a next-generation version, the QX200.
"One problem we had was that there is a step where you have to add tags to your molecule of interest and that meant having a few PCR preamplification cycles. For us, with BEAMing, it wasn’t such a problem, but for RainDance it was a disadvantage for their instrument," Balaj said.
"So now we definitely want to go back with freshly isolated RNA and see if we can tweak that with RainDance and also test it with Bio-Rad," she added.
Breakefield, meantime, noted that the researchers would still like to detect mutations in serum samples. In the study, the authors suggested that using larger serum volumes might increase the sensitivity of the assay. Also, it might be possible to improve vesicle BEAMing or droplet PCR assays by enriching tumor vesicle fractions using immunoaffinity methods, the team wrote.
Additionally, Breakefield said, the group plans to look at other single nucleotide mutations in other genes of interest using the same approach, and is also looking at targeted deep sequencing services from outside companies as a potential alternative detection method for vesicle RNA markers.
"There is still much more expense involved in that right now, but I would guess that would probably be the most sensitive overall," she said.
According to Breakefield and Balaj, it would be unlikely that an eventual clinical assay would rely on BEAMing over a droplet-based system because of the manual and time-consuming nature of the process.
"With what we've been doing with BEAMing, you can only do six samples and it takes days," Balaj said, so for a clinical test scenario, an automated droplet-based platform would more likely be the way to go.