My contribution to the Genomics Law Report’s What ELSI is New? series appears today, Personalized medicine, leave U.S. behind. The piece discusses the challenges our current health care system presents to genetic research and proposed reforms that would improve the situation.

For the record, while this opinion piece was just published, I did write it before this similar piece appeared.

Since the Biology of Genomes meeting in early May, a tempest has been brewing. It is only in this last week that this tempest has gathered enough strength that it could no longer be contained by those who have chosen to stir it up. The esteemed Daniel MacArthur blogged and tweeted from the conference. This apparently caught the attention of the conference organizers and GenomeWeb. As journalists, the folks at GenomeWeb are required to follow CSHL’s media rules which require that journalists get the permission of a speaker before publishing any information from her talk. GenomeWeb saw a double standard when comparing what Daniel was allowed to do and what they were allowed to do. They then contacted CSHL. The initial write-up of the gathering storm in Science Insider characterized this contact as complaining. GenomeWeb characterized it as asking CSHL for clarification of their policy (in a comment on a response posted by Daniel in his blog, Genetic Future). Of course this attempt to, in effect, censor has only served to bring more attention to Daniel’s blog (the so-called Streisand effect), and has resulted in a number of responses from other bloggers like Anthony Fejes, DrugMonkey, and even GenomeWeb’s Daily Scan, comments (some quite passionate) on the Science Insider story, Daniel’s response, and FriendFeed, as well as a couple well-reasoned pieces on where the policy should head from here by Ed Yong and Andrew Maynard. Daniel himself provides a nice summary of it all in a follow-up post. With all that sound and fury, there is not much to add on the subject other than to say I suppose I am lucky that the 500 or so emails I had to pore through each night after the meeting ended at 10:30 or 11 p.m. prevented me from posting any commentary during the meeting (well, the emails plus the fact that I knew Daniel would do a better job than me).

Taking a step back, there is a larger double standard at play here than the distinction between professional journalists and peddlers of new media. Many of the conclusions around whether CSHL is right in restricting any type of journalist focus on the type of conference and the expectations that type of conference creates in the minds of the presenters. At a private, invitation-only conference, no publishing. At a breaking results conference like Biology of Genomes, get permission. At an open conference, anything goes. So then one might ask: why aren’t all conferences open? The whole notion that presenting something at a conference that has some understanding of respecting others’ unpublished work is a bit ridiculous (this point has been made by others, along with the fact that Biology of Genomes is over-subscribed every year; getting people in the door is not a problem). But I am not even going to debate that point. The more interesting question is: why aren’t all data and research released rapidly and freely available? Since the Bermuda Principles were agreed to in 1996, all genome sequencing centers have submitted their data, from raw sequence data to finished sequence to assemblies to annotation, to public repositories as quickly after generation as possible. These principles were reinforced by the Fort Lauderdale agreement in 2003 which added a provision that protected the production centers’ right to first publication. But as we have seen recently, that provision of the Fort Lauderdale agreement is not always enforced. As sequencing has moved into medical applications, the sequencing centers have taken great pains to release human sequence data in a responsible manner, but still rapidly. What’s more, they now also release the detected variants fully annotated and correlated with phenotypic information in protected access databases available to any researcher. As data that requires more and more analysis and significant human curation are made rapidly available well before publication, the production centers become ever more vulnerable to getting “scooped” on their hard won findings.

As Church and Hillier properly conclude in the above referenced article

Sequence data are now easier to produce, but decisions about timelines for data release, publication, and ownership and standards for assembly comparison and quality assessment, as well as the tools for managing and displaying these data, need considerable attention in order to best serve the entire community. (Emphasis mine)

This conclusion begets many questions. If the rapid release described in the Bermuda Principles still holds true, why does it only apply to large-scale sequencing centers? Many researchers are generating more sequence in a month than the Human Genome Project was able to produce in a year. As they continue to be allowed to perform pre-publication (as opposed to post-generation) data submission, why are they not being held to the same standard as the large-scale sequencing centers?

Stepping back further, does dumping all of those data, literally terabytes and terabytes, into public nucleotide repositories like the SRA and ERA as soon as it is generated still make sense? Who has the bandwidth to download and use it all? Mainly only those centers that are submitting it. For human data, a single instrument run contains enough data to identify an individual. Should there not be at least some provisions in place to allow data generators to properly assess and quality control their data?

The human reference has been published (with a recent update to GRCh37). The blueprint exists. Thus, many of the reasons underlying the conclusions of the Bermuda Principles are no longer applicable. So should those open access principles be applied more widely to other areas of biology and science at large or should they no longer apply to sequence data from a genome for which a reference exists? It is time to rethink the current policies and begin to apply them to all sequence generators. And people are doing just that. The double standard must end.

The third paper, DNA sequencing of a cytogenetically normal acute myeloid leukaemia genome, details research done at The Genome Center at Washington University in St. Louis on the first whole-genome sequencing of a cancer genome and its matched normal genome. The sequencing was done on tumor and skin samples from a deceased, female patient (it is also the first female genome sequenced) who had acute myeloid leukemia (AML). AML is a very deadly form of leukemia which has not seen much improvement in patient outcome over the last twenty years. This is largely due to the difficulty in finding recurrent, causative mutations that lead to tumorigenesis. Genome-wide studies have not found recurrent large-scale genomic events across a large cohort of AML patients and targeted studies have not found recurrent initiating mutations in the genes they have sequenced. Thus, a non-directed, whole-genome sequencing approach was used in this study in the hope that by avoiding the low-resolution of whole-genome array techniques and the bias of targeted techniques that sequence only previously implicated genes or genomic regions, novel mutations could be found that may unlock the clues needed to better understand the onset of AML. Our approach involved the sequencing of the genomes of both the tumor and normal tissues and then looking for differences between the two. Using this approach we were able to find several mutations in genes never before implicated in AML but whose pathways and involvement in other types of cancer indicates they are promising leads to follow. Further research is being done on those variants and we have started sequencing a second AML patient’s tumor and normal genomes. Much more to come.

Due to privacy concerns, the sequencing and mutation data will be made available through a protected access portal at NCBI dbGaP. For more information on the study of cancer through genome sequencing, see Towards a cure for cancer.

Today, [Orwell]‘s name is invoked to describe anything involving surveillance, paranoia or even books about animals. Orwell’s ideas have been bastardized and simplified over time, so that “Big Brother,” the totalitarian, state-run citizen-control mechanism of “1984,” is now the name of a reality-TV show that bears little resemblance to the book, except for the fact that contestants are watched by cameras. “When writers use the word ‘Orwellian,’ you can be pretty sure they’ve read very little of him,” says Packer. Rather than describing surveillance devices, or pig farms, a more accurate application of the adjective would mean something that aspires to the lucidity and integrity of Orwell’s writing. In that case, it would be the highest praise.

The complete works of George Orwell are available at george-orwell.org and you can read his diary published as a blog with a seventy year delay at Orwell Diaries. If you have not read 1984, you should stop whatever you are doing and read it immediately.

Cancer is a disease of the genome. Mutations occur over time in the DNA of every cell. Most of these mutations are benign; they have no affect on the normal operation of the cell. Some mutations or combinations of mutations can be deleterious. If the mutation causes the cell to die, the result is that a single cell dies and there is likely little affect on the organism as a whole. Some mutations, however, may affect the cell cycle (cell replication) in such a way that the cell replicates uncontrollably. Since all of the uncontrollably replicating cell’s progeny have the same mutations, they and their progeny also replicate uncontrollably. Since these cells grow and divide much more quickly than the surrounding cells, these tumor cells quickly come to dominate the tissue in which they originated, starving other cells in the tissue of nutrients. The rapid replication of cells often leads to more mutations being accumulated until the tumor metastasizes and the cancer spreads throughout the body. Over the past few decades, cancer care has improved for many types of cancer as early detection has increased and various new chemotherapeutics have been developed. More recently, drugs that target specific cell processes, or pathways, whose increase or decrease in function due to mutations play a role in cancer have been developed and used effectively in the clinic. Unfortunately, the many diseases that fall under the name cancer are widely varied and extremely complex. Breakthroughs in understanding and treating one subtype of cancer may not be applicable to other cancers or even other subtypes of the same cancer. Indeed, even within a tumor there can be heterogeneity; all cells will have the mutations that led to tumorigenesis but different subpopulations within the tumor may appear, each accumulating its own specific mutations. Thus we need a better molecular, DNA-level understanding of the many diseases collectively called cancer. In other words, for each tumor we need to understand what mutations lead to tumorigenesis, how those mutations affected cell replication, and how to reverse that change of function.

To begin to discover more about the molecular nature of cancer, several years ago we began the Tumor Sequencing Project. In TSP, we use both array-based technologies and DNA sequencing to study tumor and normal tissues from a cohort of about 200 patients with lung adenocarcinoma, a form of lung cancer that affects a disproportionate amount of never-smokers. SNP arrays were used to determine copy-number variation (CNV). The CNV work was published last year in Nature, Characterizing the cancer genome in lung adenocarcinoma. While the CNV work provided valuable information about large-scale differences between the tumor and normal tissues over the entire genome, it and other array-based approaches cannot provided detailed information about specific mutations. To get a single base level resolution of the differences between an individual’s tumor and normal genomes, you have to sequence the DNA. At the time TSP started, the cost of sequencing an entire genome was much greater than running a SNP array. Therefore, the low-resolution information from our SNP array investigations and other, similar investigations in the literature were used to guide the selection of specific genes thought to play a role in cancer in general and lung adenocarcinoma in particular. For each patient, we sequenced these genes in both their tumor and normal tissues and looked for differences between the two sequences. Once you have those differences, genes annotations (definitions) can be used to determine if the mutation in the tumor tissue would lead to a change in the amino acid sequence of the protein that gene encodes. If the protein would be changed, other programs can be used to predict whether the change would lead to a change in protein conformation and a possible loss or gain of function. The cellular pathways in which the mutated protein participates can also be researched to see if any of the pathways control cell division or other important signaling paths in the cell cycle. Once the gene and its pathways are known, they can be correlated with known oncogenes and oncogene families. Once that is established, further experiments, e.g., induction of found mutations in disease model organisms, can be done to determine if the mutations and predicted changes do contribute to carcinogenesis. It is also interesting to see how changes that span several patients correlate with phenotypic/patient information, e.g., cancer subtype, patient outcome, age, sex, and smoking status. The initial sequencing results of the TSP will be in tomorrow’s issue of Nature, Somatic mutations affect key pathways in lung adenocarcinoma.

The Cancer Genome Atlas pilot project, which is studying brain, ovarian, and lung cancer, has followed much the same path as TSP, albeit on a larger scale. Compared to TSP, a wider variety of array platforms, more patients, and DNA sequencing of more genes have been employed. Initial gene lists for sequencing were obtained through a review of the literature with subsequent lists obtained through the results of the array-based studies. The initial glioblastoma paper from TCGA was published online by Nature last month and, like the TSP paper, will be in tomorrow’s issue, Comprehensive genomic characterization defines human glioblastoma genes and core pathways.

With both TSP and TCGA, we are able to look at the whole genome in a coarse-grained way and specific parts of the genome (genes) in a fine-grained way. So the question becomes, what are we missing by not studying the entire genome at single base resolution? The advent of next-generation/massively parallel sequencing has allowed us to begin to answer this question. About a year and a half ago, we began whole-genome sequencing of a single AML patient’s tumor and normal genomes using the Illumina/Solexa sequencing platform. Generating such a complete picture of a single human genome raised significant privacy issues that needed to be addressed before publishing and data release. The massive amount of data generated for each of these genomes presented significant challenges to our informatics infrastructure. All of the data had to be combed through to find the small variants (single nucleotide variations and small insertions and deletions of sequence) between the tumor and normal genomes (at the time, it was not possible to detect larger variations, e.g., structural rearrangements, with the Illumina/Solexa technology). With this approach, you are not only able to find mutations in genes that have not previously been implicated in cancer, but you are also able to find mutations in sequences conserved across species, microRNAs, regulatory regions, etc.; basically anything annotated in Ensembl or UCSC. Of course, the effect of mutations in non-genic regions are more difficult to interpret than those in genic regions that alter proteins, but the efforts of the ENCODE project, disease model organism studies, and sequencing more cancer genomes will greatly aid in the interpretation of these mutations.

In summary, these cancer sequencing efforts, especially whole-genome cancer sequencing, are increasing our understanding of cancer at a very rapid pace, laying the groundwork for more individualized approaches to treatment. As our knowledge of tumorigenesis increases, our ability to detect cancers early and treat cancers effectively will also increase. Ultimately, these sorts of studies will make the goal of ending suffering and death from cancer achievable.

You can read the original paper in PLoS Genetics (an open access journal): Resolving Individuals Contributing Trace Amounts of DNA to Highly Complex Mixtures Using High-Density SNP Genotyping Microarrays.