Article Review: Accurate identification of A-to-I RNA editing in human by transcriptome sequencing

In this article, Bahn et al. develop a novel method to identify A-to-I RNA editing sites in next-generation sequencing data.

My favorite aspect of this paper was how the authors empirically estimated the false discovery rate of their algorithm using an ADAR siRNA knock-down in a cancer cell line that only showed normal expression levels for one member of the ADAR family (shown in Figure 2 of the paper).  Experimental validation with Sanger sequencing also shows a low false positive rate for the A-to-G events (although not necessarily for non-A-to-G events).

Supplemental Table 3 is also worth checking out: it provides a good review of genome-wide RNA editing studies, including the contentious study in Science by Li et al.  For example, only 34% of the RNA editing sites shared by Li et al. and this paper were A-to-G events, whereas 86-100% of the overlapping sites for all of the other studies were A-to-G events.  Likewise, the differences in the histograms for RNA editing sites (Figure 2A in this paper, and Figure 1A in Li et al.) emphasize how different the analysis in Li et al. is from other similar studies in the literature.

The supplemental table also shows how few RNA editing sites overlap between studies.  For example, the authors emphasize how their study recovers 854 A-to-G differences in the DARNED database, but I think it is worth keeping in mind that there were 42,045 sites in the DARNED database and 9636 predicted RNA editing sites (using the threshold for comparison with other studies).  This seems to be a common problem that isn't unique to this study (and the authors emphasize that the overlap between genes with RNA editing sites is greater than the overlap of individual RNA editing sties), but I think it is still an interesting observation that is worth keeping in mind for future analysis (which will hopefully have larger samples of paired DNA-Seq and RNA-Seq samples).

In general, I think this method does a good job of identifying and filtering likely causes of spurious RNA editing events (like those mentioned in Schrider et al. 2011).  For example, the authors use a "double-filtering" strategy to focus on reads with unique alignments (where a conservative threshold is used to define alignments to potential RNA editing sites but a more liberal criteria is used to search for homologous regions that could be causing inaccurate alignments).  I also liked that most of the in-depth analysis focused on sites with an editing ratio greater than 0.2.

This study focused on analysis of the grade IV glioma cell line U87MG (RNA-Seq: GSE28040, DNA-Seq: GSE19986) and a primary breast cancer sample (EGAS00000000054).  Although it probably allowed for more cost-effective analysis, I wonder if the results would have been even cleaner if the RNA-Seq and DNA-Seq data were both newly created for this study using similar technologies (for example, the RNA-Seq data is paired-end Illumina reads whereas the DNA-Seq data was from another study using SOLiD reads).  However, I think the results were clean enough that this probably didn't matter too much (based upon the ADAR knock-down data).

The novel motif discovery (Figure 5) was interesting, but I had a hard time imagining the relevance of this motif that isn't found at a consistent distance from the A-to-I site (like those shown in Figure 4).  That said, I would be interested in see any follow-up analysis that characterizes the mechanism by which this motif is involved with A-to-I editing.

I think this study only provides very limited analysis on A-to-I editing in cancer.  To be fair, the sample size (one sample at a time) is probably not sufficient to make many general claims about A-to-I editing in cancer.  However, I still think this aspect of the study was over-emphasized.  For example, Supplemental Table 13 shows how sensitive the hypergeometric test (comparing RNA editing sites in the two samples) will be when dealing with such a large background set; all of the RNA editing events except G-to-C were statistically significant with a p-value < 0.05, even though the A-to-G overlap was the only category with more than 5 overlapping sites.  In other words, I don't think statistical significance was a strong indicator of biological importance for this analysis.  Likewise, it was nice that the enrichment analysis of the NCI Cancer Gene Index genes provided some candidate genes, but I don't think this study is useful in identifying a gene where A-to-I editing is highly likely to play an important role in oncogenesis.

Overall, I would recommend this article to anyone interested in RNA editing and next-generation sequencing analysis.

Review of Biopunk

Biopunk is a book discussing biological research that isn't conducted in traditional research setting (like an academic lab or a pharmaceutical company).  The book covers a wide variety of topics such as a philosophical discussion about what motivates good scientists, how legal and political decisions affect scientific progress, and recent developments in the field of "DIY bio" (where the book mostly focuses on personalized medicine and synthetic biology).  Throughout the book, Wohlsen also provides several cool factoids, like the Bridges of Cherrapunji that are engineered from living tree roots.

One chapter focuses on DTC genetic testing, where Wohlesen provides both an overview of this industry as well as accounts of individuals who have utilized DTC testing.  For example, Raymond McCauley conducted his own DIY bio research on metabolites in his own blood in order to try and better understand his 23andMe result indicating an increased risk for macular degeneration.  Although Wohlesen acknowledges "McCauley did not hesitate to concede that the results do not show anything conclusive," I think this is a very cool example of how DIY Bio can help inquisitive scientists try to learn more about themselves outside a formal research setting.

My subsequent research on Raymond McCauley also led me to learn more about DIYgenomics.org, which provides tools to help users further analyze their 23andMe data for health risk, drug response, and athletic performance for individual SNPs.  In some ways, this reminded me of the new, free Interpretome tool, but Interpretome can load my 23andMe data more quickly and with a more streamlined interface.  Nevertheless, I think it's good to know that this option is out there.

There were also a few aspects of the book that disappointed me.  For example, many accounts of biopunk research seem to focus more on buying used lab equipment off craigslist or eBay than new technological developments that can help democratize research.  It also seemed like a lot of the "biopunks" were pretty well-educated and not necessarily good examples of what I would consider amateur scientific research.  Also, I was somewhat disappointed at how difficult it was to additional information on some of the start-ups / organizations that were mentioned in the book (which has only been out for a few months).

For example, the chapter "Cancer Kitchen" discusses how John Schloendorn and Eri Gentry studied the role that the immune system played in cancer using Schloendorn's own cancer cells, which led the creation of DIY nonprofit called Livly to develop cancer immunotherapies (and Gentry later co-founded BioCurious, another DIY nonprofit).  However, the Livly website described in the book is no longer hosted on the internet (the old url, provided on the Livly facebook page, now links to an unrelated website).  Likewise, BioCurious only seems to have a facebook page with limited information.  Even with limited funding, the company can at least create a free Google Sites website (like my personal website) in order to more effectively convey information about the company.

I was also very interested in learning more about the Pink Army Cooperative (a DIY drug company attempting to deliver personalized treatments for breast cancer).  This time, I was able to find a generally well-designed and informative website, but I couldn't find much information about concrete research accomplishments (to be fair though, Wohlsen does warn readers that "so far, Pink Army is more a concept than an actual co-op").

Although it was frustrating that I couldn't learn much more about these specific non-profits, Biopunk has successfully encouraged me to learn more about the DIY bio movement.  Who knows, maybe I'll even stop by a meeting for my  local DIYbio chapter!

Article Recommendation: Glioblastoma Subtypes Defined Using Data from TCGA

After reading Verhaak et al. 2010 in Cancer Cell, and I was impressed by this very good study analyzing data from an important resource for genomics research.

The authors were able to define gene signatures to define 4 subtypes of glioblastoma.  The experimental design was pretty straightforward, and the results were quite clear.  Most importantly, their predictive model was trained an a relatively large set of 173 patient samples and validated on an even larger set of 260 patient samples (from 5 independent studies).

The study focused mostly on data provided by The Cancer Genome Atlas (TCGA).  TCGA is a database that contains various types of genomic data (gene/miRNA expression, gene/miRNA copy number, DNA sequence/polymorphism, and DNA methylation), and most or all types of genomic data are available for each patient in the database.  This provides a unique opportunity to integrate many different types of data, usually for a large number of clinical samples.  For anyone not aware of this resource, I would strongly recommend checking out the links provides above as well as the original TCGA paper (also on glioblastoma) published in Nature.

Is it worth the effort to develop personalized cancer treatments?

Robert Langreth has published a series of articles about personalized cancer treatments in Forbes Magazine (see Part I, Part II, Part III, and/or this summary in GenomeWeb).  In a nutshell, the author's main point (in the first article) is that it's very difficult to develop new drugs, and the costs to produce drugs that only help a small proportion of patients may outweigh the benefits for that drug.

I agree with the author in that I don't think it's reasonable to expect to produce an individualized drug for every possible mutation that can cause a disease (such as cancer).  However, I think genetic studies can still help improve treatments for several reasons.

First, there are a wide variety of tools that physicians can use to help patients, and I think it is wrong to view this argument from an "all-or-nothing" point of view.  Pfizer's response in the third article also criticizes the "all-or-nothing" thinking, although they cite the need for "accumulated modest advances" while I am saying it is good to have more options.  For example, the first article mentions the need to develop better surgical methods.  I'd like to see more personalized drug treatments as well as new surgical technologies.  I imagine there will be certain circumstances where a personalized drug therapy is ideal and certian circumstances where surgery will be necessary.  If we reach the point where even 30% of patients can receive personalized drug treatments, then I think that is pretty good.

Second, genetic tools can assist with the diagnosis of existing drugs (or drugs in clinical trails that were not originally designed for individuals with a specific mutation).  For example, a drug company may come very close to bringing a drug to the market, but then realize that the drug has severe side-effects for certain individuals.  If the individuals with the severe side-effects can be identified ahead of time, then the company can prevent total loss of their research costs by targeting individuals without a particular mutation.  Furthermore, scientists can discover new functions for drug candidates (as happened with Viagra), so genetic information may be able to provide researchers with alternative uses for existing drugs (or novel drug candidates).

Finally, it's important to keep in mind that cancer is the 2nd leading cause of death (in the US).  I'm sure there is a going to be a point where a mutation in a particular gene (or related pathway) is too rare to warrant developing a personalized treatment, but drugs that can decrease mortality in even 10-20% of patients may still be able to help a large number of people.

Why Do Genomic Cancer Diagnostics Cost So Much?

After reading the introduction to this PLoS ONE article, I started to wonder why there are there several published microarray expression profiles for cancer progression yet relatively few microarray-based diagnostics used in a clinical setting. Although this PLoS ONE paper focuses on analysis of ovarian cancer (and mentions the lack of a clinical microarray diagnostic for ovarian cancer), the paper also cites the current use of a breast cancer diagnostic called MammaPrint.

After reading the wikipedia entry on MammaPrint, I was surprised to learn that it took 5 years for the diagnostic to reach the market following the initial publication showing that the expression profiles for a set of 70 genes could successfully predict the cancer progression. This information is important because more aggressive treatments early in cancer progression may be able to help cancer patients who would otherwise have a high mortality rate (as predicted by their gene expression profile). I was also surprised to learn the high price of both MammaPrint and its competitor Oncotype DX. Although I do not think I can provide a complete answer to why these prices are so high, I would like to take a moment to first demonstrate that the price of these tests far exceeds the cost to conduct the test and then discuss how I think these costs can be offset by decreasing the amount of time and effort that it takes to bring a medical diagnostic tool to the market.

Based upon their wikipedia entries, the MammaPrint diagnostic costs $4,200 and the Oncotype DX test costs $3,978. To give you an idea about how much it actually costs to carry out this test, it costs $350 for a full service microarray analysis (including labor and data analysis) of an Agilent Whole Human Genome Microarray for on-campus customers at the UT-Southwestern Micoarray facility. This is comparable to the cost of most of the microarray facilites that I have worked with, and Agilent produces high quality microarrays. Now, most laboratory kits have a warning that they are “intended for research purposes only,” and this is probably true for the human Agilent array. However, I think this warning is mostly to avoid litigation and not due to a severe lack of technical accuracy, and I expect the actual cost for a clinical microarray test to be in the hundreds (not thousands) of dollars. I’m sure that this high cost is the product of a combination of factors, such as research costs, legal costs, patent law, and the US healthcare system. However, I’m going to focus on ways to potentially cut research costs because that is the area that I know most about.

Now, I want to make clear that the initial publication of a potential diagnostic test is not sufficient to prove the widespread effectiveness of that test. For example, the microarray test for ovarian cancer in the PLoS ONE article had substantially better predictive power on the training dataset than when applied to a new dataset. Therefore, I want to make clear that I do think follow-up studies were necessary to prove the effectiveness of MammaPrint.  However, I still don’t think it should have taken 5 years to test the effectiveness of this diagnostic and I think effectiveness can be determined without as much government regulation.

Before MammaPrint could be put into widespread use, it had to gain FDA approval. This required multiple verification studies, and this is the crucial event that defines the 5 year gap between initial publication and availability on the free market. First off, I don’t think FDA approval should be necessary for diagnostics. I do think physicians need some way to quickly access the effectiveness of a medical diagnostic and/or therapeutic, but I think there are more better ways to determine the effectiveness of a given treatment. For example, a relatively recently posted TED talk by Jamie Heywood discusses how his start-up Patients Like Me, developed by three MIT engineers, can diagnose medical treatments more quickly and effectively than clinical trails. This website analyses a database of information provided by patients, and therapeutic effectiveness can be assessed immediately based upon currently available data. In the very least, I think this company could be an excellent model for a more formal system using data from physicians that does not carry all the restrictions of a clinical trail. These changes should decrease the cost of medical care because companies claim that these price markups are necessary to recoup the costs of research and development, and a more streamlined process for accessing the effectiveness of treatments will decrease research costs.