On the Scene at AMIA: Clinical Promise and Informatics Opportunities for Whole-Genome Sequencing

I recently got to attend the American Medical Informatics Association’s (AMIA) Joint Summits on Translational Science, held in nearby San Francisco. The event had tracks for both translational bioinformatics as well as clinical research informatics and served as a unique opportunity to hear about the informatics challenges that are being faced in the clinical realm right now.

Here are a few highlights from the talks I found particularly interesting:

Howard Jacob, director of the Human and Molecular Genetics Center at the Medical College of Wisconsin, gave a very charismatic talk on genome sequencing in the clinic. He told attendees the most compelling reason to use genome sequence with patients is that it’s family history with data. Jacob described an ongoing pilot whole-genome sequencing program at the college, noting that for a patient to be considered for the program, two physicians must nominate him or her and show that the sequencing would be actionable and “end a diagnostic odyssey.” Accepted patients must determine at the outset how much information they want to receive when returning results; some choose to know just the minimum, while others want to know about genetic variations for which we don’t yet understand the disease implications. Enrolled patients receive six to 10 hours of genetic counseling as part of the process.

Jacob said that with an 20% patient acceptance rate (16 out of 80) for the pilot program, clinicians are now eager to eliminate the approval process altogether to get their patients sequenced. To my surprise, four of the 10 cases submitted for insurance approval have gone through. Jacob added that as the cost continues to drop, it will be hard for clinicians not to use this approach for patients, especially for rare diseases and for pharmacogenomics decisions. (He noted that one lesson learned already is that clinical sequencing is not ready for common diseases, with the exception of cancer.) Jacob said the challenges in scaling up sequencing operations in the clinic will be: delivering adequate genetic counseling; selecting the most appropriate analytical tools for clinical sequencing; and determining who can order whole genome sequencing tests.

In another talk, Peter Tonalleto, a professor at the Center for Biomedical Informatics at Harvard Medical School, highlighted the medical opportunities associated with a patient’s genomic variation landscape. His research group is working on analysis pipelines for DNA sequencing, RNA-seq, microRNAs, and methylation. Those pipelines, coupled with preclinical and clinical variant annotation, yield application in areas such as risk prediction, tumor classification, and pathway analysis for alternative treatment approaches. He also noted a cost comparison his team did for a breast cancer project, in which standard practice cost about $19K and next-gen sequencing practice cost about $25K. With these costs finally being on par, Tonalleto told the audience that more clinicians at Harvard Medical School are eager to adopt whole genome sequencing for breast cancer patients.

In all, the conference offered valuable insight into how next-gen sequencing is being adopted in the clinic. It was clear from the talks I saw that clinicians are eager to embrace this new technology, despite the number of hurdles — including insurance reimbursement, approval processes, and technology learning curve — standing in the way. While there are a number of challenges to consider, I walked away from this conference feeling optimistic about the uptake of whole-genome sequencing in healthcare already.

ABRF: A Quick Meeting Recap

Here at DNAnexus, we’re lucky to have a terrific team supporting our goals. In this blog post, we wanted to share highlights from the recent ABRF meeting from the perspective of our marketing manager, Cristin Smith. Here’s her recap.

Just when we thought the Marco Island resort couldn’t be beaten for location, here comes the annual Association for Biomolecular Resource Facilities (ABRF) conference, held at the lakeside Disney Contemporary Resort right in the heart of Disney World, complete with a view of Space Mountain. I’m pretty sure the team back home in Mountain View was a little concerned that we weren’t going to come back.

The meeting’s opening keynote came from Trisha Davis, who runs the Yeast Resource Center at the University of Washington. Her work has focused on using yeast as a proving ground for various technologies, noting that as her center has evolved, so too has her team’s ability to really drill down into targeted interrogations of the organism. During her talk, entitled “Technology Development in a Multidisciplinary Center,” she noted how important it is to integrate multiple complex analyses in an attempt to relate genotype to phenotype.

On the final day of the meeting, “Omics Technologies to Transform Research, Health & Daily Life” also resonated with me. This was Harvard professor George Church’s vision of a future where genome sequence information is widely used and readily available. He spoke about some current logistical limitations, such as the fact that a $100 blood draw is cost-prohibitive, and that the field will have to move toward buccal swabs and other technologies that may cost only $1 to process in order for ’omic testing to become affordable. Citing some 37 next-gen sequencing technologies as the driver for the rapid drop in sequence costs, he said that his own estimate of the current genome price — from sample to interpretation — is $4,000. In order for genome sequencing to become medically useful, Church noted a few factors that will have to be addressed: a focus on completeness and standards to give FDA confidence in these technologies; the need for significantly more genetic counselors than we have right now; and better interpretation software that makes genome analysis truly straightforward.

Overall, we were excited to see how eager the core lab community is to receive technology improvements that generate a higher quantity and quality of sequence data for their customers in support of their research. This enthusiasm was a great setting to unveil our newly redesigned booth at the exhibit hall. It’s hard to find a more tech-loving crowd than the people who run core facilities, and we were glad to meet so many of them last week.

SOT: Still Early Days for Next-Gen Sequencing in Molecular Toxicology

The Society of Toxicology’s 51st annual meeting was held this week right in our back yard. Since I am a longtime member, I headed up to the Moscone Convention Center in San Francisco to check it out. The Annual Meeting and ToxExpo were packed; almost 7,500 people and more than 350 exhibitors.

SOT isn’t like the sequencing-focused meetings I’ve been attending since I joined DNAnexus, but it’s actually home turf for my own research background in toxicogenomics. This year’s meeting sponsors included a number of pharmas and biotechs, from Novartis and Bristol-Myers Squibb to Amgen and Syngenta. Scientific themes at the conference ranged from environmental health to clinical toxicology to regulatory science and toxicogenomics. Next-gen sequencing is still in its infancy in the world of molecular toxicology, which is still dominated by microarray expression experiments. There were very few posters showing applications of NGS data in toxicogenomics — the ones that did tended to be centered around microRNAs — but a lot of the people I had conversations with have recently started running sequencing studies to eventually retire microarray type experiments.

I found Lee Hood’s opening presentation particularly interesting because he focused on the need to combine data from various technology platforms and institutions all over the world. He talked about his P4 vision, of course — the idea that medicine going forward will have to be predictive, personalized, preventive, and participatory. He also included great gems about fostering a cross-disciplinary culture, mentioning genome sequencing of families, the human proteome, and mining genomic data together with phenotypic and clinical data.

Lee Hood. Photo Copyright Chuck Fazio

Another exciting talk that was well received came from Joe DeRisi at the University of California, San Francisco. He presented work analyzing hundreds of honey bee samples with microarrays combined with DNA and RNA sequencing. Using an internally developed de novo assembler called PRICE (short for Paired-Read Iterative Contig Extension; freely available on his website), his team identified a number of different organisms associated with the sequence data of the honey bee samples, including different viruses, phorides, and parasites. At this moment it’s not clear what is causing the honey bee population decline; it appears that there are multiple factors contributing to the phenomenon. It is great to see that DeRisi and team will continue working in this area.

Last but not least, Scott Auerbach from the National Toxicology Program announced the release of the previously commercial toxicogenomics database DrugMatrix to the public for free (announced earlier this year, but now officially made public). With this release, DrugMatrix is now the largest scientific and freely available toxicogenomic reference database and informatics system. The data included is based on rat organ toxicogenomic profiles for 638 compounds; DrugMatrix allows an investigator to formulate a comprehensive picture of a compound’s potential for toxicity with greater efficiency than traditional methods. All of the molecular data stems from microarray experiments, but Auerbach and team are now investigating what it will take to move from microarrays to RNA-seq experiments and how to integrate the different types of data. They are currently performing a pilot on a subset of compounds with the same RNA used for the microarray experiments. Their challenge, as he sees it, lies in the interpretation and validation of the newly generated RNA-seq data: what qualifies one platform as superior to the other? Since they are interested in the biology and in generating drug classifiers, one way of looking at it is to assess which platform is the basis for better classifiers based on sensitivity and specificity thresholds. It will be interesting to see whether the RNA-seq data-based classifiers will be comparable or superior to microarray classifiers.