Major Setback in Fight Against Breast Cancer

November 17, 2009 by Dan Koboldt

On the front page of this morning’s USA Today, the headline reads “Report: Mammograms may not be needed until age 50.” The story covers the recent findings of the U.S. Preventive Services Task Force (USPSTF), published this month in Annals of Internal Medicine. In it, the “independent panel of experts” makes some rather startling recommendations. I’ll list three here with the preliminary note that I disagree with each of them. The USPSTF concluded that:

Women under the age of 50 should not undergo routine screening (mammograms). Exceptions are women with a family history of breast cancer or known risk-elevating mutations.
Women aged 50-74 should undergo mammograms every two years, rather than every year.
Clinicians should not teach women to perform breast self-exams.

I’m just shocked at these recommendations. I’d like to give them my own sarcastic headlines, starting with…

Saving A Few Lives: Inconvenient

To justify the first of them, that women under 50 shouldn’t get mammograms, they conclude “For biennial screening mammography in women aged 40 to 49 years, there is moderate certainty that the net benefit is small.” In other words, they’re somewhat certain that only a few lives will be saved. “The harms resulting from screening for breast cancer include psychological harms, unnecessary imaging tests and biopsies in women without cancer, and inconvenience due to false-positive screening results,” they wrote. It seems that a little anxiety and inconvenience outweigh the 1 out of every 1,904 women who will die each year because of this guideline (their numbers).

Give Cancer Another Year

The second recommendation, that women aged 50-74 should have mammograms every 2 years instead of every year, has a similarly ridiculous justification. “A decision analysis performed for the USPSTF projected that biennial screening produced 70% to 99% of the benefit of annual screening, with a significant reduction in the number of mammograms required and therefore a decreased risk for harms.” So, a computer model that the authors themselves didn’t even perform told them that screening every 2 years was at least 70% as effective as screening every year. I’m not an oncologist, but I do know one thing that’s universally agreed-upon by cancer clinicians: early detection is the best protection. This recommendation might save some women the “harms” of anxiety and inconvenience. It might prevent the “harm” of insurance companies paying for more mammograms. It will also give cancer one more year to appear and do its work, 1 more year to grow and metastasize to other parts of the body.

Why Should We Teach Anything?

The third recommendation that I list seems the most inane of all. Apparently, clinicians should not teach women to perform breast exams because “Adequate evidence suggests that teaching BSE does not reduce breast cancer mortality.” Great idea, guys, let’s tell the clinicians to go ahead and withold information that might save a woman’s life. Why? Because the woman who finds a lump during her self-exam is just going to die anyway. Never mind that encouraging women to self-exam, and teaching them how to do so correctly, is one of the most important exercises in breast cancer awareness, an intangible thing that probably encourages numerous women to take care of their bodies and live healthier lives.

If You Ever Want to Screw Something Up, Just Form A Task Force

As you might guess, I’m very upset by this report, by the fact that the press is picking it up, and the detrimental effect it will almost certainly have in our war against breast cancer. This is not science, it’s pseudoscience. The general recommendation of this so-called task force: let’s do less screening, because it might save some anxiety, money, and inconvenience. Seriously? Now might be a good time for a congressman to form another task force – one that would look for connections between the USPSTF and the insurance industry. Because now, you know what’s going to happen? Insurance companies are going to balk at paying for annual mammograms, refuse to cover tests for women under 50. And if a woman tell them she wants the test, because she has a family history of breast cancer, you know they’ll take that new, private information and use it to raise her rates or drop her coverage altogether.

Maybe I’m overreacting. But personally, for my friends and family, I would recommend mammograms annually for any woman over the age of 35. Because I’m happy to comfort said women in case they get anxious. I’m happy to talk to them, in the event that they’re stressed. I’m not too worried about the inconvenience of what might be unnecessary testing. Why? Because it just might save their life.

References

U.S. Preventive Services Task Force (2009). Screening for Breast Cancer: U.S. Preventive Services Task Force Recommendation Statement Annals of Internal medicine, 151 (10), 716-726

Short Read Aligners and Variant Detection

November 6, 2009 by Dan Koboldt

In recent weeks I’ve had conversations with many people in the NGS community who are attempting to call variants, accurately, in Illumina/Solexa data. Part of it stems from VarScan, my SNP and indel caller for next-gen sequencing data that works with Bowtie, Novoalign, cross_match, and other aligners.

Another part of it stems from my involvement in 1,000 Genomes Pilot 3, for which several participants have applied their own variant detection pipelines to the same dataset. Last month, Goncalo Abecasis, with input from David Craig, Heng Li, Gerton Lunter, and Fiona Hyland, proposed an exercise comparing several read mappers on real and simulated ABI SOLiD and Illumina/Solexa data. The initial list of aligners – Maq, BWA, Stampy, BFAST, BioScope, and KARMA – demonstrated just how rapidly the field has grown since my aligner comparison last year at AGBT. I’d looked at Maq and BFAST, and knew about (but hadn’t tried) BWA, but the others on the list (Stampy, BioScope, and KARMA) were ones I’d never heard of.

I proposed adding three aligners to the list: Bowtie and Novoalign for Illumina data, and SHRiMP for SOLiD data. My suggestions were politely declined by Richard Durbin (WTSI), who said “In our hands Bowtie doesn’t seem accurate enough for variant calling. It is a great tool for fast assignment of reads for some other purposes. Novoalign is accurate and good, but perhaps a little slow. SHRiMP is also I think very slow.”

Personally, I think that Bowtie works very well for variant calling, I know of several groups who are using it for that exact purpose. And while Novoalign *is* a bit slow, in my experience it’s just as fast as Maq, one of the two aligners out of Durbin’s lab that were already on the list. Of course, Maq remains the most widely used tool for Illumina data (for now), and that’s an important consideration. Most NGS analysts know and love Maq as much as I do.

Balancing Speed and Sensitivity

However, these assessments bring into focus the key issue surrounding short read alignment for variant detection – finding the balance between speed and sensitivity. Bowtie and Novoalign exemplify this well. Bowtie is ultrafast – the fastest short read aligner I’ve used – and maps an entire lane (~15m reads) in just 1-2 hours. Yet in my experience, it places slightly fewer reads than BWA/Maq. And it performs only ungapped alignments, so indels won’t be detected. In contrast, Novoalign typically maps more reads than Maq and BWA, seems very accurate, and remains one of the few aligners to allow gaps in fragment-end reads. In general, my comparisons demonstrated that Novoalign speed is comparable to Maq on typical datasets. However, longer reads and lower-quality data can make Novoalign very slow indeed. The ultimate short read aligner, in my opinion, would have Bowtie-like speed, Novoalign-like sensitivity, and the widespread community support that Maq enjoys.

Ask the Guru: Heng Li

Heng Li, who led development of both Maq and BWA, told me that he’s not worried about sensitivity. “Most aligners nowadays are sensitive enough,” Heng wrote to me in an e-mail this week. “For detecting variations, specificity is of more importance. Nonetheless, how much wrong alignments may contribute to wrong SNPs is an open question. As long as alignment errors are random, more wrong alignments may not necessarily lead to worse SNP calls.” Clearly, he has already given some thought to these issues. If we’re lucky, Heng Li may begin to address these open questions in his new post at the Broad Institute.

Underlying Causes of False Positives

Read mis-alignment would not be a serious problem if it occurred randomly across the genome. The trouble is that wrong alignments don’t seem to be random, at least in my experience. In projects like TCGA Ovarian, we see numerous false positives (particularly in tumors) that seem to arise from read mis-alignment. These typically manifest as clusters of variants, often present in each of a subset of reads whose true alignment is probably a paralogous region of the genome. It’s also possible that they’re caused by an indel, which (as Kiran Garimella of the Broad Institute recently showed) sometimes manifest as clusters of substitutions at several positions near one another. We can aggressively filter these by looking for clusters of predicted SNPs, but even better would be to remove the mis-alignments before variant calling even begins.

Read Mis-Alignment and Paired-End Sequencing

Here at WashU, we have a growing concern that the alignment scores for short reads are continually over-estimated. Often our manual reviewers find that reads supporting false-positives have mate pairs that align to a different chromosome altogether. In the absence of translocation events, when this occurs, one of the two reads is incorrectly placed, and any variant it supports is probably not real. Personally, I’d rather remove both reads in such situations, and rely on correctly mapped read pairs for detection of small variants.

The pervasive spread of paired-end sequencing is beginning to reveal just how often short aligners can get it wrong. The corollary here is that taking read pair information into account during alignment is of critical importance, and those hopeful short read aligners that don’t do it yet (crossmatch, for example) are destined for inferiority.

High-Throughput Sequencing: Speed Matters

Yet what I’m learning from discussions with others in the community – particularly the growing surge of users making the leap from Maq to BWA – is that speed matters. With Illumina machines cranking out 20 gigabases in a single run, and projects like the 1,000 Genomes generating terabytes of sequence over the course of months, we can’t afford to be using the slower aligners, no matter their sensitivity. At worst, we might apply a two-stage approach to alignment that rapidly maps reads that precisely match the reference, and passes only the variant reads to a more sensitive aligner for mapping.

Of course, as a colleague of mine recently joked, by the time we write the perfect aligner, Pac Bio will have come along and sequenced the entire genome, kilobases at a time.

The Search for Somatic Changes

October 29, 2009 by Dan Koboldt

As cancer genome sequencing ramps up here and pretty much everywhere around the world, I got to thinking about strategies for identifying somatic changes, with confidence, from massively parallel sequencing data. As part of the Cancer Genome Atlas Project (TCGA), we’ve been applying both targeted (capture-based) and whole-genome sequencing approaches to tumor samples and matched normal controls. Ideally, the resulting data will yield high (>20x) coverage in both tumor and normal across our positions of interest. What happens next, at least at WashU, is the culmination of a multiple-year effort to develop a comprehensive pipeline for detecting somatic variants.

First up: Single Nucleotide Variants (SNVs)

With more than 15 million entries in dbSNP, single nucleotide polymorphisms (SNPs) remain the most common form of DNA sequence variation in humans. In cancer, most of the well-characterized somatic mutations are single nucleotide changes as well. Conceptually, SNVs should be the easiest things to find in next-gen sequencing data. They occur at a single position that can be directly compared between tumor and normal. They should have minimal effects on sequence alignments to the reference genome. For example, here’s a putative somatic variant in TP53:

What you see above is SAMtools “pileup” output at a single position (7518990 on chr17), for Normal and Tumor. The Normal shows 4 reads that all support the reference on the – strand (,,,,). The Tumor, however, shows 6 reads that all support a G variant, 2 on the + strand (GG) and 4 on the – strand (gggg). It seems reasonable that, given this output across the entire genome for Normal and Tumor, one can compare them at every position and look for differences such as these.

Yet we struggle to validate even high-confidence SNVs that look to be somatic. Some are real, but Germline (probably under-sampled or missed in the Normal); most are simply false positives in the tumor. These might arise from a number of causes – homopolymers, paralogs, repeats, sequencing error, alignment error, etc. Only a small fraction of variants that appear somatic in NGS data will validate as such.

Why is that? In general, it’s because by screening for somatic variants, we remove all of the variants that are most likely to be real. First, we exclude any variants that are present in the normal (germline) – which account for the majority of true sequence variations. We also exclude known variants from dbSNP and 1,000 Genomes databases, which are also likely to be real but almost certainly germline events. Then, we prioritize variants that are predicted to have functional effects – on protein coding, on splicing, in conserved regions, etc. Such regions are often under negative selection for damaging mutations, meaning that variants should be exceedingly rare. Every one of these filters selects for variants that are less likely to be valid.

Small Indels

With longer (>50 bp) fragment-end reads and/or paired-end libraries, it’s possible to detect small insertion/deletion variants (indels) in next-gen sequencing data. Here, detection and specificity are the challenges. In 454 data, the reads are [hopefully] of sufficient length (250 bp) for accurate gapped alignment to a reference sequence, and indeed, aligners commonly used with 454 data (Newbler, BLAT, cross_match, SSAHA2) do so. Unfortunately, indels are both the strength and the weakness of 454 data – due to the underlying pyrosequencing, homopolymeric regions are often under- or over-called, resulting in numerous false positives. Many can be filtered, but often homopolymer-associated errors cause mis-alignment of reads, yielding indels that might not look like homopolymer artifacts.

Indel detection is also possible with Illumina data, though the shorter read lengths make this challenging. Few short read aligners can handle the throughput of Illumina data and allow for gaps in read alignments, because speed and gapped alignment are at odds with one another. Fortunately, paired-end sequencing on Illumina offers a solution implemented by Maq some time ago – first, map all reads that you can without gaps, and then, look for gapped alignments in unplaced reads whose mate is mapped nearby. This reduces the search space considerably for gapped alignment, and also limits the query space to reads that likely contain indels (gaps).

In cancer sequencing, small indels present one additional problem – determining whether they are present in the normal. Even the best aligners can’t always precisely define where an indel starts or stops. Thus, a germline indel might have different coordinates in the tumor than in its matched control; when comparing the samples, it might appear to be somatic.

Loss of Heterozygosity (LOH)

It is well known that the genomes of tumor show extensive loss of heterozygosity (LOH). Generally, this occurs because a position that is heterozygous in the germline is affected by some kind of structural event – deletion, gene conversion, chromosome loss, etc. – that results in the loss of one allele. Of course, to detect LOH, one needs a variant that’s heterozygous in the Normal, and to precisely define the region of LOH, one needs a dense set of heterozygotes. Even so, the maximum precision for the start and stop of an LOH region is the interSNP distance, since only SNPs can inform on LOH, and that can be hundreds or thousands of bases. But LOH calls do tend to cluster, and detection of LOH regions is not really the problem. Even lower-resolution array technologies identify recurrent LOH regions in tumor samples.

But what exactly does LOH mean in terms of cancer development and growth? It’s hard to say. Quite possibly, a tumor suppressor gene was deleted, or an oncogenic allele was duplicated. Unfortunately, LOH regions tend to be kilobases or megabases in size, containing dozens or hundreds of genes, and identifying which ones are truly affected in terms of cancer remains challenging. We see a lot of LOH in cancer, but sadly, it never seems to get anyone excited.

Structural and Copy Number Variation

Image Credit: Wikipedia

Last and most difficult to characterize are the sub-microscopic structural changes – insertions, deletions, inversions, translocations, duplications, etc. – that often occur in tumor genomes. These tend to be large, complex events that are tough to infer from NGS data. We run Ken Chen’s breakDancer, of course, and it predicts numerous SVs. But how do you validate a massive, complex variant spanning thousands of bases? We do our best with PCR and 3730/454 sequencing, but until read lengths get really really long (perhaps on single-molecule sequencing), validating such events and determining their breakpoints is tough.

There are well-characterized, recurrent copy number alterations in cancer, like EGFR amplification on chromosome 7. Here’s my question: where are all of those extra copies? Are they just tandem duplications of part of a chromosome, or are they duplications that get inserted elsewhere in the genome? In the absence of a complete, linear, high-confidence genome, I’m not sure we can tell.

Fruits of Our Labors

It occurs to me that this is a bit of a negative article – focusing entirely on the challenges and failures, without highlighting the successes. And there are many successes. Every cancer genome tells us something, and every new piece of knowledge goes into our arsenal in the war against cancer. As sequencing ramps up, we’ll see exponential growth in the number of known somatic mutations across a wide array of cancers. With the help of cancer biologists, these data will be leveraged to better understand the genes, proteins, and pathways underlying tumorigenesis. Greater understanding will undoubtedly improve the detection, diagnosis, prognosis, and treatment of cancer patients.

First Breast Cancer Genome in Nature

October 9, 2009 by Dan Koboldt

October is Breast Cancer Awareness Month, and the timing couldn’t be better. Our friends at the BC Cancer Agency published the whole genome sequencing of a breast cancer this week in a letter to Nature.

Nature Vol 461 | 8 October 2009

Using Illumina paired-end sequencing, Shah et al generated 141 Gbp of sequence to achieve 43x haploid coverage of a metastatic lobular breast cancer. Some 32 somatic, protein-altering (nonsynonymous) mutations were identified, of which 11 could be detected in the primary tumor sample from 9 years earlier. Deep RNA-Seq data from the metastatic sample also permitted transcriptome analysis, though its presentation was brief. Interestingly, the authors validated two novel RNA-editing events that change the amino acid sequences of SRP9 and COG3.

No WGS of Normal or Primary Tumor?

I realize that a letter to Nature must be brief, but even so, what struck me most about this paper is what’s missing. First of all, only the metastatic sample was whole-genome sequenced – the primary tumor and matched normal were not. Instead, the authors identified nonsynonymous coding variants in met WGS data, and validated them by PCR/3730 sequencing in the met, tumor, and normal samples. This seems laborious to me, since there were 1,120 nonsynonymous SNVs, of which 437 (39%) were valid and only 32 (<3%) were absent in the normal and therefore somatic. Another regrettable limitation of this approach is that it doesn’t offer a complete picture of the somatic mutations beyond nonsynonymous-coding events.

Missing Methods

My understanding of Nature journals is that there’s no limit on supplementary material that accompanies publications. Thus, I don’t understand why the methods are incomplete. For example, though the authors found and confirmed >60 germline indels, there’s no description anywhere of the indel-calling algorithm. There’s a lot of text describing their internally developed SNVmix algorithm to identify SNVs, but no link to download it that I could find. No mention of dbSNP or Affy SNP array concordance for SNVmix calls was offered, so one cannot evaluate the algorithm. Also, there’s no description of read de-duplication, which is alarming because it suggests that duplicate reads from the same molecule weren’t removed prior to analysis.

The Importance of RNA-Seq

I do like that the authors performed RNA-Seq of the transcriptome, which provides insights into mechanisms like alternative splicing (AS), allele-specific expression (ASE), and RNA editing. Sadly, only the last one received mention in the results section, suggesting that no significant AS or ASE events were found. Interestingly, not only did the authors validate two instances of high-frequency, protein-altering RNA editing (COG3 and SRP9), but they found that the ADAR RNA-editing enzyme was one of the most highly expressed genes in the metastasis. The authors note that “these observations emphasize the importance of integrating RNA-seq data with tumor genomes,” although this claim would have been far better supported if one did not have to dig through a massive/disorganized Excel file for most of the RNA-seq data.

“Evolution” of a Breast Cancer Tumor

Perhaps the most intriguing – and contentious – finding of the paper (as highlighted by GT’s In Sequence magazine and Keith Robison on Omics Omics) was that few of the somatic mutations in the metastasis were detected in the primary tumor sample from 9 years earlier. PCR and deep resequencing of mutation-containing amplicons in the metastasis and primary tumor allowed for a frequency analysis of the 32 somatic mutations. Five of these (in ABCB11, HAUS3, SLC24A4, SNX4, and PALB2) were present at high levels in the primary tumor, while another six (in KIF1C, USP28, MYH8, MORC1, KIAA1468, and RNASEH2A) were detectable at lower frequencies (1-3%). Of the remaining 21 mutations, 19 were not detected at all and 2 could not be determined.

I’m not an oncologist, but I still wonder how surprising it should be that many of the mutations in a metastatic tumor are absent from a primary tumor almost a decade earlier. Are these simply passenger mutations that arose from a surviving subclone from the original tumor, or are they key drivers of metastasis and tumor growth? Or was it the intervening radition and therapy that caused these mutations? There was zero discussion of the known functions of these genes in this paper, so it’s difficult to say. The authors contrast this result with our sequencing of AML1, though I’m not sure it is an appropriate comparison since (1) we had data from a relapse 3 years post-diagnosis, whereas theirs was from a metastasis 9 years post-diagnosis. Even so, the findings in the breast cancer study are interesting enough to merit further investigation.

References
Shah, S., Morin, R., Khattra, J., Prentice, L., Pugh, T., Burleigh, A., Delaney, A., Gelmon, K., Guliany, R., Senz, J., Steidl, C., Holt, R., Jones, S., Sun, M., Leung, G., Moore, R., Severson, T., Taylor, G., Teschendorff, A., Tse, K., Turashvili, G., Varhol, R., Warren, R., Watson, P., Zhao, Y., Caldas, C., Huntsman, D., Hirst, M., Marra, M., & Aparicio, S. (2009). Mutational evolution in a lobular breast tumour profiled at single nucleotide resolution Nature, 461 (7265), 809-813 DOI: 10.1038/nature08489

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