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structural variation

MassGenomics

Medical genomics in the post-genome era

TCGA: The billion-dollar cancer project

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Earlier this month, Science’s News of the Week had a feature about the progress of The Cancer Genome Atlas (TCGA) pilot project. The catchy title: Billion-Dollar Cancer Project Moves Forward. It was a summary of the presentation that Eric Lander gave to NCI’s Board of Scientific Advisors at the end of June in which he described the accomplishments of TCGA pilot project on a certain type of brain tumor, glioblastoma multiforme (GBM). I didn’t know that when TCGA was proposed in 2005 as a long-term (10 year) effort to identify the common mutations underlying major human cancers, the estimated price tag was something like $1.5 billion. Many scientists balked at this; a huge investment in the “genomic” approach to cancer seemed to ignore other areas, like cancer prevention, nutrition, etc. Thus NCI/NCGRI proceeded with a three-year TCGA pilot project that would evaluate three major cancers: brain, lung, and ovarian.

I read some of the open letters in which some researchers questioned the TCGA approach to cancer research. The burning questions seemed to be: how will this help advance the field of cancer research, and is it the best way to spend research money. These are not uncommon protests in recent years, which have seen a major paradigm shift in publicly-funded research from individual PI-driven projects to what some people call “Big Science.” You know, the large, international, multi-year, multimillion dollar projects. There are many arguments for and against such a funding model, and I’ve seen it from both sides: from the view of a small lab competing for ever-shrinking NIH funds, and as part of a major collaboration funded by “big science” awards.

Perspective 1: Big Science Puts All the Eggs In One Basket

With each cycle, NIH receives numerous proposals, both solicited and unsolicited, for research from US investigators. Some of these are small proposals, some are large. There’s only so much money in the budget, however, so only a fraction of them will be funded; when I last checked, it was somewhere around 8%. Recently, NIH has favored large, multi-investigator proposals in a certain research area over several smaller projects. Case in point: structural variation. A consortium headed by Evan Eichler won $40 million or so to perform massive BAC-end sequencing of human genomes. Many smaller proposals went unfunded, including one that I helped put together: a computational effort to mine existing sequence trace archives for reads harboring structural variants. Will a large-scale survey of SV using a single approach capture the bulk of such variation in the human genome? Probably not. Even champions of this method admit a low-end threshold of 1-2 kb. Were other promising approaches passed over in favor of this project? Almost certainly. How much they would have contributed to or detracted from what Eichler and colleagues have published may never be known.

Perspective 2: Large-Scale Collaborations Yield Better Results

For those not embittered by unscored NIH proposals, there are numerous obvious benefits to large scale, long-term research projects. In the field of genomics, this funding model makes it possible to undertake bigger, more expensive efforts with more samples and deeper coverage – these are things that statisticians love. The big-name teams that usually put together such projects not only offer impressive combined expertise, but have the scientific clout to make the research happen (and get published). Case in point: the International HapMap Project (you knew that was coming, didn’t you?). How many genome-wide association studies in the past two years were made possible by the human haplotype map? Top genomics researchers in the US and several countries played a major role, not only driving the science behind the project but producing impressive deliverables as well – multiple publications in Nature, and a database that’s perhaps more widely used than any other by the community. That’s an undeniable strength of big science funding – just about every project makes a Nature paper.

Back to TCGA and its Future

From the Science blurb, it sounds like Eric Lander’s presentation was convincing, even to some skeptics. The genomic-centered approach yielded findings that changed the way we think about glioblastoma. And that’s from looking at just the exons of only a few hundred candidate genes! With the advent of next-generation sequencing, there’s no better time to demonstrate the potential of genome science to advance cancer research. Imagine what we’ll find from deep resequencing of entire cancer genomes. I don’t know if I look forward to the analysis, but I certainly am optimistic about what we might learn.

Landmark Study in Structural Variation

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At last, some results from Evan Eichler’s SV project! The results of the first phase of the “Human Genome Structural Variation Project” were presented in today’s issue of Nature. I’ve been cognizant of this project for a couple of years and eager for the results, as it is really the first large-scale, sequence-based study of copy number and structural variants. As it happens, our Genome Center played a big role in the sequencing, and two of our researchers (Tina Graves and Rick Wilson) are among the authors.

In fairness, however, I should disclose another thing about the Evan Eichler project.

In 2006, just after three simultaneous papers in Nature Genetics brought structural variation to the forefront, my former lab began working on a grant proposal. In it, we proposed to mine existing trace data (from NCBI’s Trace Archive) for putative structural variants in the human genome. We were developing a sequence-based approach to identify reads spanning insertions, deletions, duplications, inversions, and translocations. It was an ambitious project and the timing was perfect, but, unfortunately, NIH sent our proposal back twice. Unscored. Later, I learned that a group headed by Evan Eichler pretty much locked up U.S. funding for this research in the form of a $40 million grant.  With all of the NIH eggs in a single basket, I thought to myself, they had better deliver.

It looks like I won’t be disappointed.  Dr. Eichler and colleagues constructed whole-genome libraries of ~1 million fosmids for each of 8 individuals whose samples were used in the HapMap Project.  Four were African, two were CEPH European, one was Japanese, and one Han Chinese.  They used the fosmid end sequencing approach (described by Tuzun et al, 2004) in which you sequence the ends of each fosmid and map the sequences to the reference genome.  Altogether, about 6.1 million end-sequence pairs (ESPs) were uniquely mapped to the genome.  Of these some 76,767 (~1.26%) were discordant by alignment distance or orientation, indicating a possible underlying structural variant.  It’s a big paper (the Supplemental File alone was 57 pages) so I’ll hit you with the take-homes:

  1. The human genome is still incomplete.  The fosmid approach yielded a number of novel sequences not present in the current human genome assembly.  The sequences range in size from 2-130 kbp, were randomly distributed among genic/nongenic regions, and were often (40% of the time) copy-number variable.  There’s still more human genome out there, folks.
  2. Current CNV databases are inflated. The higher resolution of sequence-based approaches served to refine the location of many previously-reported CNVs.  Generally speaking, CNVs on a haplotype are smaller (in bp) than copy-number-variable regions as a whole, meaning that fewer genes are affected.  Array-cGH platforms, which to-date are among the most widely-used approaches to assess CNV, greatly exaggerate the size of these variants.  And less than half of the SVs in this study cannot adequately be genotyped with existing platforms.
  3. NAHR is the driving force behind most structural variation.  About half of the SVs detected in this study were flanked by segmental duplications.  This is nothing new, but it somewhat contradicts the study by Korbel and colleagues last year, which used 454 paired-end sequencing and reported that NAHR-mediated events were rare.  Eichler et. al.  suggest that the association of NAHR with segmental duplications and the difficulties of short sequence reads to resolve such regions might together mean that next-generation ESP approaches are missing a lot of variation.  I’m not sure I agree, but that’s what they said.

Overall it seems like an impressive publication.  They planned and executed a very careful study, and as a result, we learned quite a bit about the landscape of structural variation in the human genome.  Not bad, Dr. Eichler.