MassGenomics

A company in Malaysia is offering to map whole-genome sequencing data and call variants in one week’s time for $4,000.

I readily admit that I have not taken sequencing-as-a-service companies very seriously. The idea of sending precious samples off to a third party and getting back the sequence and variants doesn’t appeal to me for a number of reasons. Outsourcing just the analysis of sequence data is even more anathema. Why would anyone want to do that? Analysis is the best part! Then again, I’m fairly biased in this matter because (1) I work at a major genome center with significant in-house sequencing resources, and (2) sequence analysis and variant detection are among my job responsibilities. Obviously I don’t want those to go away.

That said, there seems to be a growing interest in outsourcing sequencing and/or analysis in the wider research community. Complete Genomics had a strong presence at Marco Island this year, and has a growing customer list that includes (perhaps surprisingly) at least two genome centers. Beijing Genomics Institute (BGI) announced a purchase of 128 Illumina HiSeq2000 instruments in January; a month later in Science magazine I saw a full-page ad indicating that they’re open for business as a sequencing provider. No big deal, they’re half a world away, right? So I thought, until I heard whispers of a BGI facility in San Francisco.

Second and third-generation sequencing technologies are bringing about volatile changes in the fields of genetics and genomics. Throughput continues to skyrocket, while the costs of sequencing plummet.  It’s now possible to sequence a complete human or mammalian genome to high coverage on a single instrument run at ~$20,000. This has had two effects on the research community:

1. Genomes abound. At least a dozen individual human genomes have been published, but NGS technologies are being applied to a wide range of studies – exomes, transcriptomes, model organisms, you name it.
2. Everyone wants to sequence. Thanks to a lot of press and some high-profile publications, massively parallel sequencing is known to every corner of the biomedical research world. Suddenly every clinician with a patient cohort wants in, because if they don’t find the disease-causing genes, someone else will.
3. Not everyone can buy an NGS instrument. Commercially-available sequencers currently cost a quarter to a half million dollars or more each, which is a significant purchase even for labs flush with ARRA funding. This means that a lot of small labs will not be looking to buy a machine, but rather to rent space from someone who has one. Music, no doubt, to the ears of BGI and Complete Genomics.

One thing is clear. These new sequencers and service providers are going to put high-throughput sequencing into the hands of many investigators. Investigators, I might add, who likely have never dealt with NGS data. I think that’s potentially very exciting, and I hope that the experiences of major genome centers will help newcomers address the challenges of massively parallel sequencing.

The Genome Center at Washington University is currently hosting a remarkable two-day event focused on the study of cancer genomics.  Yesterday there was a symposium on the School of Medicine campus, featuring speakers from major genome centers around the world, who delivered an excellent series of talks on recent advances in cancer genome research.  Here were the highlights.

Mutational Signatures in Lung Cancer (Peter Campbell, WTSI)

First up was Peter Campbell from Wellcome Trust Sanger Institute, who presented their first cancer genome – a small lung cancer cell line called NCI-H209.  Using the ABI SOLiD platform, they sequenced NCI-H209 and a matched B-cell sample from the same individual to 30-40x coverage.  Extensive PCR-based validation yielded almost 23,000 somatic substitutions and over a hundred structural events (indels and rearrangements).  As expected, the mutational spectrum was enriched for G->T and C->A changes associated with adduct formation on guanine nucleotides induced by benzopyrene, the chemical mutagen found in tobacco smoke.  Dr. Campbell also described some of the complex rearrangements observed in the paired-end sequencing data, which were particularly convincing when overlaid with spectral karyotyping images.

Next-Gen Sequencing Strategies to Study Cancer Genomes (Elaine Mardis, WU)

Next was our own Elaine Mardis, who gave an excellent overview of the strategies developed here to apply NGS to cancer genomes.  She described five key elements to success in this arena:

1. Genomic characterization prior to sequencing. For example, at WashU we type tumor and normal samples on genome-wide SNP arrays, which yield tumor purity/ploidy estimates, LOH information, and a dense set of SNPs for tracking the coverage of genomes by Illumina sequencing.
2. Resource characterization. The tissue preservation method, DNA/RNA quality and quantity, and pathology information are all critical components.  Also important are high-quality clinical data (diagnosis, chemotherapy/radiation protocols, and outcome), informed consent, IRB approval, and additional cases of the same cancer subtype for recurrency screening.
3. Data production capacity.  US genome centers seem to have this, either in the form of Illumina (WashU and Broad) or ABI SOLiD (Baylor).  It’s not just the throughput of the machines, either – it’s the ability to construct sequencing libraries from ever-shrinking DNA inpus.  Tumor samples are precious, and the ability to use only a tiny amount of DNA or RNA while achieving informative results is one of the key areas of focus of tech development groups.
4. Informatics and bioinformatics.  We have entire groups devoted to LIMS, pipeline automation, medical genomics, and sequence data submission.  Other important elements of bioinformatics that Elaine touched on were data display interfaces for collaborators and high-end data storage and computational infrastructure.
5. Validation and recurrent site screening.  This the essential coup de grace for tumor genome characterization, in which we validate somatic mutations and identify those that are recurrent in other samples of the same subtype, the best indication that we currently have of pathological relevance.

Elaine also discussed the rapid scaling up of TCGA (which is adding 20 tumor types thanks to ARRA funds) and other projects, which will only exacerbate the challenges of scale that NGS platforms have already presented.

Integrating Genomics with Biology (Richard Gibbs, Baylor)

Richard Gibbs gave an action-packed talk of some relevant work going on at Baylor, both for cancer and inherited diseases.  They are applying an intriguing if controversial multiple-platform strategy for whole genome sequencing: deep (20-30x) coverage on ABI SOLiD and light (6-10x) coverage on 454. “We’re just telling people that if you do it twice, you’ll get it right,” Dr. Gibbs said.  One interesting project is an investigation of Charcot-Marie Tooth (CMT) syndrome, a recessive inherited disorder where the locus is unknown.  Whole-genome sequencing of an affected individual on ABI SOLiD identified a few dozen novel missense mutations; among them lurked the causal variant, which was found to segregate with the disease in a family cohort.

Dr. Gibbs also gave an overview of their investigations into heritable variants in pediatric cancers (in collaboration with MD Anderson).  There’s also a lot of work under way for TCGA, not just the 6K capture project, but also adjunct analyses of gene expression, DNA copy number, microRNA, and DNA methylation data being generated on TCGA samples.

Insights into Rare Tumors (Steven Jones, BC Cancer Agency)

Steven Jones from BC Cancer Agency retold the story of the rare tongue adenocarcinoma that I heard at AGBT 2009.  What I didn’t know about BCCA is that under the Canadian universal healthcare system, they see all of the cancer patients in the surrounding population of over 4 million citizens.  One of these was a rare one – an 80 year old man with adenocarcinoma of the tongue.  It was removed surgically, of course, but in a short time metastasized to the lungs.  The clinician prescribed erlotinib, an EGFR inhibitor, but unfortunately the patient did not respond.  To help the patient, and also make some advances in tech development, Jones and his colleagues did whole-genome and RNA-Seq of the tumor samples and matched normals.  There were just four somatic mutations: two in known cancer genes and two in zinc finger proteins (these remain unexplained).  Transcriptome and copy number analysis showed that the tumor had loss of PTEN and down-regulation of SMAD4.  Unfortunately, it had recently been shown that tumors lacking PTEN and TP53 don’t respond to TK inhibitors like erlotinib.  However, this particular tumor showed an amplification of Ret, and as it happened, the drug bank had a single drug, sunitinib, that was known to inhibit Ret.  The patient’s response, initially, was quite dramatic – all of the metastases vanished.  Sadly, several months later they turned up again, and this time were resistant even to sunitinib.  Still, the results of this effort were promising, because genomic information was used to keep cancer at bay, if only for a short time.

Genomic Medicine in Pediatric Brain Tumors (Chinc C. Lau, Baylor)

Ching Lau of Baylor presented genomic studies of medulloblastoma (MBM), which accounts for 20% of all brain tumors and has a 60% survival rate.  Classification of MBM patients in the past was relatively crude – based on the amount of residual tumor post-surgery and metastatic status. Using gene expression profiling, Lau and colleagues identified 4-5 distinct clusters.  Two clusters were associated with known cancer pathways – SHH signaling and WNT activation. The same four clusters could also be isolated by unsupervised miRNA clustering. Also, gene expression analysis showed that ERBB2 expression correlates with outcome (higher expression = poor prognosis).

Finally, Dr. Lau mentioned some future directions for targeted cancer therapy.  One of these that I readily admit I don’t understand: cytotoxic T-cells with Chimeric TCRs. Evidently these are T-cells that recognize and attack cancer cells in the body.  There was a short movie, courtesy of Dr. Lau’s collaborators, in which we saw these specially programmed immune cells recognizing and attacking a tumor that was roughly four times their size.  It was like watching ants swarm a piece of fruit on the sidewalk, and very compelling.

Evolution of a Breast Cancer Tumor (Samuel Aparicio, BC Cancer Agency)

Dr. Aparicio presented a study recently published in Nature and already discussed on Massgenomics.  However, he did discuss the continuing challenge of mutation heterogeneity in tumors – we can no longer refer to mutations as present or absent, but instead should report their frequency, which represents the proportion of clones with each mutation.  The question of how deep we need to sequence to find the very rare variants has yet to be answered.

Breast Cancer Genomics (Matthew Ellis, Siteman Cancer Center)

Matthew Ellis, our collaborator from the Siteman Cancer Center, presented very recent work we’ve done on a basal subtype breast cancer.  A quartet of samples were sequenced in this study – the primary breast tumor, the matched normal tissue, the brain metastasis (from which the patient died), and finally, a mouse xenograft model developed in “humanized” NODSCID mice.  We validated some 50 tier 1 mutations, all of which were detected (at some level) in all four samples.  Deep read counts for these mutations in each sample revealed some interesting stories about the progression of the cancer from tumor to metastasis.

Genomic Signatures and Cancer  (Todd Golub, Broad / Dana Farber)

Todd Golub of the Broad Institute and Dana Farber Cancer Center presented his group’s work on Hepatocellular Carcinoma (liver cancer), which is the fifth most common cancer worldwide.  It’s a disease of growing concern on the African and Asian continents, and presents numerous challenges.  Molecular classification “is a mess,” Dr. Golub said, and recurrence is common.  The problem is that there are few frozen samples with long-term outcome information.  Thus, Dr. Golub and his group applied the Illumina DASL assay – which enables very small, highly multiplexed, locus-specific PCR – to perform expression profiling in formalin-fixed paraffin-embedded (FFPE) samples.  They achieved up to 90% success across 6,000 genes in samples that were 25 years old.  Doing so opened up a vast bank of viable samples for gene expression profiling, from which Dr. Golub and colleagues made some interesting findings.

The AML Genome (Tim Ley, WashU and Siteman Cancer Center)

Tim Ley gave the last talk, which highlighted the work that he and colleagues at WashU began around a decade ago on the disease acute myeloid leukemia (AML).  Our goal, he said, was to find 95% of the mutations that occur in at least 5% of AMLs.  To do so will require whole genome sequencing of at least 30 genomes, according to statistics from my colleague Mike Wendl.  Two of these (AML-1 and AML-2) are already done and published, and a number of others are currently under way.  One intriguing bit of work that Dr. Ley described was on the “Mouse APL” project, a knock-in mouse with the PML-RARA gene fusion backcrossed 10+ generations to CBL/BL6 mice.  This yielded inbred strains of mice, some of which developed AML after ~6 months, presumably after acquiring “cooperative” mutations.  One mouse was sequenced to 15x coverage, and among the handful of somatic nonsynonymous mutations found, one was recurrent, not only in the APL mice, but also in the same gene in human tumors.

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.