From the inbox last month:
Dear Dr. Dove:
Just curious. Do you really think Kodadek’s Cell paper is a good piece of work? The response of the science community to this seemingly amazing news is silence. This usually means that the results of the paper are odd.
Thanks for your note, Wang, and I’m sorry it’s taken me a month to get back to you. This question touches on some much broader issues in science and science journalism, and I wanted to take the time to put together a proper blog post about it. For readers who don’t know the background, here is my original post about Kodadek’s work, in which I unabashedly raved about a novel technique for discovering new disease biomarkers.
I stand by that assessment. The researchers performed an extremely clever experiment, it apparently worked, and they followed up on it pretty carefully. That’s what science is supposed to be about.
So why isn’t everyone using Kodadek’s strategy to find a slew of new biomarkers already? I can think of a few possibilities. One, of course, is that the technique might be a lot harder to perform than the paper suggested. Perhaps it yields inconsistent results for different diseases. Perhaps there just aren’t good biomarkers for some of the diseases we want to study. Or perhaps people are all over this technique, and we just haven’t seen the papers yet – it hasn’t quite been two years, and these studies would take some time. It’s also possible that the method just isn’t as useful as it first seemed. These sorts of problems trip up new ideas a lot more often than even most scientists realize.
Consider, for example, the tangled tale of another biomarker detection scheme: immuno-PCR. Developed by Charles Cantor’s group way back in 1992, immuno-PCR uses the highly effective signal amplification of polymerase chain reaction (PCR) to detect proteins. The technique was supposed to solve one of the biggest problems in biochemistry at the time, which was identifying and quantifying proteins that are present in vanishingly small quantities in a sample, which certainly describes many promising disease biomarkers. Immuno-PCR was much more sensitive than the best available competitor, a technique called enzyme-linked immunosorbent assay (ELISA), and more sensitive protein assays really sounded like just the thing. The new method was poised for greatness.
But it flopped, or at least went to sleep for awhile. In 2008, I wrote a feature article for Bioscience Technology magazine about protein detection methods, and pretty much everyone I talked to agreed that immuno-PCR was a disappointment. Indeed, several companies were trying to develop a replacement that would work better. It turned out that immuno-PCR was just too hard to do properly, so most users gave up on it.
Similar problems befell David Ward and his colleagues at Yale University, who developed an equally clever protein detection system called the rolling circle immunoassay in 2000. Instead of PCR, Ward’s assay relied on a type of amplification called rolling circle replication, which many viruses use. This elegant new technique blew the doors off ELISA; the investigators could detect proteins at single-molecule sensitivity. Rolling circles were supposed to succeed where immuno-PCR had failed, but instead they also landed in the bin.
Some of the techniques developed since then have fared slightly better. A company called Nanosphere, building on work by Chad Mirkin and colleagues at Northwestern University, now has FDA approval for high-sensitivity diagnostic tests based on a nanoparticle detection system that uses DNA “barcodes” to identify individual proteins in a sample. Nanosphere’s technology apparently offers the sensitivity of techniques like immuno-PCR and rolling circles, without the same technical headaches.
While successive groups of scientists were working on these assays, though, advances in completely different protein analysis techniques made antibody-based detection somewhat less relevant. The past decade has seen astonishing advances in protein mass spectrometry, which allows researchers to identify and quantify proteins without having to make antibodies against them first. Why bother with DNA-bound immunological probes when you can simply feed your sample into a box and read a list of the proteins in it on your computer screen?
Meanwhile, we’ve learned more about protein-based assays, especially in medical testing, and it turns out that greater sensitivity isn’t always a good thing. Barcoded nanoparticles can detect previously undetectable levels of prostate-specific antigen (PSA), for example, but a growing body of evidence suggests that PSA testing does more harm than good. Making a bad test more sensitive only makes it a worse test.
Anyone who’s been a science journalist for more than a few years has probably collected a whole slew of similar stories: results that just didn’t pan out. That’s why I always try to discuss the limitations of a new paper as frankly as possible, even in the midst of an unabashed rave.
So what happened to peptoids? It’s too early to tell. Even if the technique proves troublesome, it represents a fresh approach to a question that currently looks important: what bloodstream biomarkers can we measure to diagnose and monitor chronic diseases such as Alzheimer’s and cancer? 18 months ago, peptoids looked like they might be a really cool strategy for exploring that topic. Maybe they still are.
1: Sano T, Smith CL, Cantor CR, “Immuno-PCR: very sensitive antigen detection by means of specific antibody-DNA conjugates,” Science, 1992 Oct 2;258(5079):120-2.
2. Schweitzer B, et al., “Immunoassays with rolling circle DNA amplification: a versatile platform for ultrasensitive antigen detection,” Proc Natl Acad Sci U S A. 2000 Aug 29;97(18):10113-9
3. Hill HD, Mirkin CA, “The bio-barcode assay for the detection of protein and nucleic acid targets using DTT-induced ligand exchange,” Nat Protoc. 2006;1(1):324-36
4. Thaxton CS, et al., “Nanoparticle-based bio-barcode assay redefines “undetectable” PSA and biochemical recurrence after radical prostatectomy,” Proc Natl Acad Sci U S A. 2009 Nov 3;106(44):18437-42. Epub 2009 Oct 19