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Archive for the tag “personal-genomics”

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Link roundup

Here are some interesting links from the past few weeks (or in some cases, months). I’m toying with the idea of just tweeting most of the links I find in the future and reserving the blog for more in-depth ruminations. We’ll see how it turns out. Anyway … here are some links!

Open Data

The collaborative filtering news site Reddit has introduced a new Open Data category.

Following the example of New York and San Francisco (among others), London will launch an open data platform, the London Data Store.

Personal informatics and medicine

Quantified Self has a growing (and open/editable) list of self-tracking and related resources. Notable among those is Personal Informatics, which itself tracks a number of resources – I like the term personal informatics and the site looks slick.

Nicholas Felton’s Annual Report 2009. “Each day in 2009, I asked every person with whom I had a meaningful encounter to submit a record of this meeting through an online survey. These reports form the heart of the 2009 Annual Report.” Amazing guy.

What can I do with my personal genome? A slide show by LaBlogga of Broader Perspectives.

David Ewing Duncan, “the experimental man“, has read Francis Collins’ new book about the future of personalized medicine (Language of Life: DNA and the Revolution in Personalized Medicine­) and written a rather lukewarm review about it.

Duncan himself is involved in a very cool experiment (again) – the company Cellular Dynamics International has promised to grow him some personalized heart cells. Say what? Well, basically, they are going to take blood cells from him, “re-program” them back to stem-cell like cells (induced pluripotent cells), and make those differentiate into heart cells. These will of course be a perfect genetic match for him.

Duncan has also put information about his SNPs (single-nucleotide polymorphisms; basically variable DNA positions that  differ from person to person) online for anyone to view, and promises to make 2010 the year when he tries to make sense of all the data, including SNP information, that he obtained about his body when he was writing his book Experimental Man. As he puts it, “Producing huge piles of DNA for less money is exciting, but it’s time to move to the next step: to discover what all of this means.”

HolGenTech – a smartphone based system for scanning barcodes of products and matching them to your genome (!) – that is, it can tell you to avoid some products if you have had a genome scan which found you have a genetic predisposition to react badly to certain substances. I don’t think that the marketing video done in a very responsible way (it says that the system: “makes all the optimal choices for your health and well being every time you shop for your genome“, but this is simply not true – we know too little about genomic risk factors to be able to make any kind of “optimal” choices), but I had to mention it.

The genome they use in the above presentation belongs to the journalist Boonsri Dickinson. Here are some interviews she recently did with Esther Dyson and Leroy Hood, on personalized medicine and systems biology, respectively, at the Personalized Medicine World Conference in January.

Online calculators for cancer outcome and general lifestyle advice. These are very much in the spirit of The Decision Tree blog, through which I in fact found these calculators.

Data mining

Microsoft has patented a system for “Personal Data Mining”. It is pretty heavy reading and I know too little about patents to able to tell how much this would actually prevent anyone from doing various types of recommendation systems and personal data mining tools in the future; probably not to any significant extent?

OKCupid has a fun analysis about various characteristics of profile pictures and how they correlate to online dating success. They mined over 7000 user profiles and associated images. Of course there are numerous caveats in the data interpretation and these are discussed in the comments; still good fun.

A microgaming network has tried to curb data mining of their poker data. Among other things, bulk downloading of hand histories will be made impossible.

Individualized cancer research

I have been intrigued for some time by Jay Tenenbaum‘s idea to forget about clinical cancer trials and focus on deep DNA and RNA (and perhaps protein) profiling of individual patients in order to optimize a treatment especially for the given patient. (See e.g. this earlier blog post about his company, CollabRx.)

Tenenbaum and Leroy Hood of the Institute for Systems Biology recently wrote about their ideas in an editorial called A Smarter War on Cancer:

One alternative to this conventional approach would be to treat a small number of highly motivated cancer patients as individual experiments, in scientific parlance an “N of 1.” Vast amounts of data could be analyzed from each patient’s tumor to predict which proteins are the most effective targets to destroy the cancer. Each patient would then receive a drug regimen specifically tailored for their tumor. The lack of “control patients” would require that each patient serve as his or her own control, using single subject research designs to track the tumor’s molecular response to treatment through repeated biopsies, a requirement that may eventually be replaced by sampling blood.

This sounds cool, but my gut feeling has been that it’s probably not a realistic concept yet. However, I came across a blogged conference report that suggests there may be some value in this approach already. MassGenomics writes about researchers in Canada who decided to try to help an 80-year-old patient with a rare type of tumor (an adenocarcinoma of the tongue). This tumor was surgically removed but metastasized to the lungs and did not respond to the prescribed drug. The researchers then sequenced the genome (DNA) and transcriptome ([messenger] RNA) of the tumor and a non-tumor control sample. They found four mutations that had occurred in the tumor, and also identified a gene that had been amplified in the tumor and against which there happened to be a drug available in the drug bank. Upon treatment with this drug, all metastases vanished – but unfortunately came back in a resistant form several months later. Still, it is encouraging to see that this type of genome studies can be used to delay the spread of tumors, even if just for a couple of months.

A while back, MIT Technology Review wrote about a microfluidic chip which is being used in a clinical trial for prostate cancer. This chip from Fluidigm is meant to analyze gene expression patterns in rare tumor cells captured from blood samples. It is hoped that the expression signatures will be predictive of how different patients respond to different medications. Another microfluidic device from Nanosphere has been approved by the U.S. Food and Drug Administration to be used to “…detect genetic variations in blood that modulate the effectiveness of some drugs.” This would take pharmacogenomics – the use of genome information to predict how individuals will respond to drugs – into the doctor’s office.

“You could have a version of our system in a molecular diagnostics lab running genetic assays, like those for cystic fibrosis and warfarin, or in a microbiology lab running virus assays, or in a stat lab for ER running tests, like the cardiac troponin test, a biomarker to diagnose heart attack, and pharmacogenomic testing for [Plavix metabolism],” says [Nanosphere CEO] Moffitt.

Update 10 Dec:

(a) Rick Anderson commented on this post and pointed to Exicon, a company that offers, among other things, personalized cancer diagnostics based on micro-RNA biomarkers.

(b) Via H+ magazine,  I learned about the Pink Army Cooperative, who do “open source personal drug development for breast cancer.” They want to use synthetic biology to make “N=1 medicines”, that is, drugs developed for one person only. They “…design our drugs computationally using public scientific knowledge and diagnostic data collected from the individual to be treated.”

Link roundup

A roundup of some interesting links from the past few weeks.
Brian Mossop of the Decision Tree blog is embarking on a project to find out how much personal data is needed to stay healthy. He will use devices like the Zeo sleep coach and the Nike+ sportband to record his personal data and post updates about what he has found. He’s also promised a longer blog post after 30 days summarizing his experiences.

dnaSnips is a site that compares reports received by the same person from three different direct-to-consumer (DTC) genetic analysis services:  23andme, deCODEme and Navigenics. Summarizing the experiment, the author feels that all three services give pretty accurate results. (link found via Anthony Fejes’ blog)

By now, there are probably few people who haven’t heard about the scientist who turned out to be call girl blogger Belle de Jour. I was intrigued to find that her Amazon wishlist contains hardcore statistical data analysis books like Chris Bishop’s Neural Networks for Pattern Recognition and An Introduction to Support Vector Machines and Other Kernel-based Learning Methods. In fact, she’s previously blogged about her Bayesian theory of relationships, so she’s clearly no slouch when it comes to statistics and data mining.

What do you do with a personal genome?

Now that the full sequencing of a person’s genome can be done for well below USD10,000 – Complete Genomics recently announced having sequenced three genomes for consumables costs between $1,726 and $8,005 – the question is what you would be able to do, today, with information about your genome.

Personalized Medicine recently published an article, Living with my personal genome by Jim Watson (co-discoverer of the structure of DNA.) The article is very short but it does tell us that Watson has changed his behavior in at least one way: he now takes beta-blockers only once a week instead of every day, because he discovered that he has an enzyme variant which causes him to metabolize the drug slowly, making him “…constantly fall asleep at inappropriate moments.” Apparently it took a whole-genome scan to realize that was abnormal!

Quantified Self has reported on its third New York Show & Tell session, where Esther Dyson, who also has had her genome sequenced, discussed what she had found out (video here). However, rather than the full genome sequence (which she calls “disappointing” in the beginning of the talk, saying that “it tells me nothing, I can’t interpret it” – if you think you could interpret it better, it’s online here), she focuses on her report from 23andme, which records information about a million SNPs (single-letter variations in the DNA) in each individual. She shows some rather nifty tools like the Relative Finder, which can be used to identify potential cousins.

Another early whole-genome sequencee, Steven Pinker, wrote a long and thoughtful article about his genome a while back in New York Times. Definitely worth a read.

Personal transcriptomics?

MIT’s Technology Review has an interesting blog post about Hugh Rienhoff, a clinical geneticist and entrepreneur, who is trying to apply personal genomics transcriptomics to find the causes of his daughter Beatrice’s unusual, Marfan’s syndrome-like symptoms. The blog post describes how Illumina (a leading company in DNA sequencing) has sequenced parts of the genomes of Rienhoff, his wife and his daughter, and how he has now spent about a year searching through these genome sequences for mutations that only Beatrice has.

In fact, looking at another blog post, it seems like they are actually sequencing RNA (mRNA, to be specific) rather than genomic DNA. This makes a lot of sense, because RNA sequencing (RNA-seq) gives information about genes that are actually being expressed – transcribed into mRNA and then presumably translated to proteins. This sort of “transcriptome profiling” should potentially be able to give a lot of information about disease states beyond what can be gleaned from a genome scan (although those are, of course, informative as well.)

From the sequencing data, Rienhoff has compiled a list of about 80 genes that are “less active” in Beatrice than in her parents. (I wonder what tissues or cell types they assayed?) According to the Nature blog post, Illumina will be doing similar transcriptome profiling on up to nine family trios (mum, dad, child) where the child has, for instance, autism or Loyes Dietz syndrome.

A quote from the Technology Review blog post:

One of the biggest challenges, Rienhoff says, is the software available to analyze the data. “To ask the questions I want to ask would take an army,” he says. “I’m trying to connect the dots between being a genomicist and a clinical geneticist. I don’t think anyone here realizes how difficult that is. I’m willing to take it on because it matters to me.”

Reading about this sort of literally personal genomics/medicine made me think of Jay Tenenbaum and his company CollabRx, which offers a “Personalized Oncology Research Program”, where they “…use state-of-the-art molecular and computational methods to profile the tumor and to identify potential treatments among thousands of approved and investigational drugs” So the approach here is presumably also to do some sort of individual-based transcriptional profiling, but this time on tumor material. After all, cancer is a heterogeneous disease (or a heterogeneous set of diseases) and tumors probably vary widely between patients. Echoing Rienhoff above, Tenenbaum said in an interesting interview a couple of months ago that biology is becoming an information science and that CollabRx are “heavily dependent on systems and computational biology” (=software, algorithms, data analysis, computing infrastructure).

I applaud the effforts of CollabRx, while simultaneously being sceptical about what can be achieved today using this approach in the way of clinical outcomes. But someone has to be the visionary and pave the way.

Personal genome glitch uncovered

As recounted in this New Scientist article and commented upon in Bio-IT World, journalist Peter Aldhous managed to uncover a bug in the deCODEme browser (Decode Genetics’ online tool for viewing parts of your own genome). deCODEme is one of a handful of services, including 23andme and Navigenics, that genotype small genetic variations called SNPs (snips; single-nucleotide polymorphisms) in DNA samples submitted by customers. The results are then used to calculate disease risks and other things, which are displayed to the customer in a personalized view of his or her genome.

Aldhous was comparing the output he got from two of these services – deCODEme and 23andme  – and discovered that they were sometimes very different. After patiently going to the bottom of the matter, he discovered that the reason for the discrepancy was that the deCODEme browser sometimes (but not always) displayed jumbled output for mitochondrial sequences. According to Bio-IT World, the bug seems to have been due to an inconsistency between 32-bit and 64-bit computing environments and has now been fixed.

Isn’t this a nice example of computational journalism, where a journalist is skilled or persistent enough to actually analyze the data that is being served up and detect inconsistencies?

I might as well sneak in another New Scientist article about personal genomes. This one urges you to make your genome public in the name of the public good. It mentions the Harvard Personal Genome Project, which aims to enroll 100,000 (!!) participants whose genomes will be sequenced. The first ten participants, some of which are pretty famous, have agreed to share their DNA sequence freely.

I have no idea whether the Personal Genome Project is related to the Coriell Personalized Medicine Collaborative which also wants to enroll 100,000 participants in a longitudinal study where the goal is to find out how much utility there is in using  personal genome information in health management and clinical decision-making

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