Babak Alaeimahabadi: Okay good morning everyone.
My name is Babak Alaeimahabadi.
I’m a PhD student in Eric Goshen Group [spelled phonetically] at University of Gothenburg.
First I would like to thank the organizer to give me the opportunity to talk to you
about my project.
Today I will talk about somatic structural alterations and their influence on gene expression.
Cancer is a disease of the genome, point mutations, chromosomal rearrangements, and copy number
change.
They all contribute to activating oncogenes and inactivating tumor suppressors.
Structural variations, or chromosomal rearrangements, they can result in formation of oncogenic
fusion genes.
And there is also recent data suggesting that they can alter the expression in some other
ways, such as shuffling regulatory region around the transcription start site.
Copy number changes and structural variation -- they have typically been studied in isolation,
but in fact they are tightly connected.
A copy number change arises as a consequence of a structural variation in the genome.
As you all know, there are now several pan-cancer studies thoroughly exploring mutations and
copy number, many of them using TCGA data sets.
You can easily explore them, for example, using cBioPortal as it is shown here for EGFR
gene.
However, when it comes to structural variations, there is a few systematic studies investigating
these events across multiple cancers.
This has motivated us to comprehensively map structural variations in cancer genome using
TCGA whole genome sequencing data.
We also wanted to investigate the relationship between copy number changes and structural
variations, as the structural basis of copy number is not well-explored.
Additionally, from central dogma of biology, we know that any genomic alteration -- to
be important in cancer -- it should have an effect on RNA produced by the tumor.
Because of that, we wanted to systematically explore the global effect of a structural
change on tumor transcriptome.
To do all of these things, we used 600 cancer patients in 18 different cancer types, and
set up a pipeline to integrate copy number data, expression data with structural variation
determined from whole genome sequencing.
As it turns out, detecting structural variation was not without challenges.
We did this using available tools, but on computational resources available in our lab
-- apart from being massive computational forged -- it is also a challenge to get clean,
good results, and not all the available tools will provide that.
The problem is that it’s difficult to know if the results are good or not.
However, by combining copy number data and structural variation data, we believe that
we’ve found a way to evaluate and increase the performance of the results.
By combining these two data types, we could investigate the structural basis of copy number
alterations.
As an important part of analysis, we looked into the structural alterations in regulatory
regions, and their effect on mRNA.
And last, we also combined DNA and RNA data to get a better fusion -- functional fusions
for cancer.
As I said earlier, whole genome-based structural variation detection was not so trivial, and
we didn’t like the fact that different tools were giving different results.
So we needed a way to benchmark it, and we didn’t have the answer.
So to find a way around this, we used the area based copy number data, and we know that
the copy number change is a result of structural alteration in the genome.
And because of that, we can learn about some of the structural breakpoints in the samples.
Of course, this is not perfect but still it provides a true positive set.
And we know that a perfect structural variation detection tool should basically be able to
identify all of these copy number breakpoints.
So one of the things we did was to calculate the sensitivity score based on the overlap
of copy number breakpoints and structural variation number breakpoints.
After applying this method on four different tools, we came to the conclusion that Meerkat
gives the highest sensitivity and works as the most specific tool, since it had the lowest
correlation with randomized copy number breakpoints.
We also could improve the performance by adding some post filters to our pipeline, such as
removing breakpoints in repeated regions.
So we applied this pipeline on 600 whole genome sequencing samples, and saw that around 35
percent of area-based copy number breakpoints can be explained structurally.
This number did vary between cancer types, and it appears that those cancer types that
have low scores have mostly arm-level or chromosomal-level copy number change.
Out of those copy number segments that had support from our structural variation data,
we had almost all the deleted regions classified as deletion in whole genome sequencing, which
increased the confidence in the result.
We could -- interestingly, we also saw that most of the copy number amplifications are
tandem duplications.
For those of you who don’t know what tandem duplication is -- tandem duplication is a
piece of DNA that is duplicated and inserted adjacent to itself.
Interestingly, when we compared the two data types, we find that some simple copy number
change are in fact more complicated than they seem.
Here in this example, we have two copy number deleted regions with a copy number neutral
region in the middle of it.
But when we include the structural variation data, we found that there was only one tandem
duplication in this region.
We could explain this by an arm-level deletion in one allele and the tandem duplication in
the other allele, which neutralized the amplitude for that region.
Here is another example, but instead the deletion is the whole chromosome.
Again the tandem duplications neutralized the amplitude of those regions.
So for structural alterations to be important in cancer, they need to have an influence
on transcription in some way, by altering mRNA level or structure.
What we did next was to systematically investigate the influence of the structural change in
regulatory regions near the transcription start site and mRNA.
This is still work in progress and we are digging into the data at the moment.
But to give you a couple of examples, we found structural alteration in third promoter region
in kidney chromophobe cancer, and these alterations were associated with strong transcriptional
activity.
This has been described before -- recently in a paper -- in Anchor Science Journal [spelled
phonetically].
Another example is FUBP1 in breast cancer.
Again we saw strong association with the mRNA level in the sample with the structure alteration
the promoter, regulatory region.
Another way of affecting mRNA via structural alterations is to swap the strong and weak
promoters in context of gene fusions.
We did a systematic screen for these cases, and to see the effect on expression.
We found that in many cases, these structural alterations changed the transcription activity.
Here we used quantile plots to show the observed fault change value for the gene expression
with the structural change relative to the other samples against the expected full change
using randomly picked sample.
The blue line and dot line shows the median and 90 percent confidence interval for the
null distribution, respectively.
As you see here, for many cases these structural changes alter the expression level.
A well-known example of this would be RET to CDCC6 fusion in thyroid carcinoma.
This has been described before, but we can see that this fusion happens when inversion
and RETS gene is being activated by hijacking the strong promoter of CCDC6 gene.
So knowing about structural basis of gene fusions, gave us a reason to look into the
gene fusions using our structural variation data.
So lately we’ve seen a lot of studies using RNA-Seq to identify gene fusions.
We did this too.
We applied fusion catcher on TCGA RNA-Seq data.
But looking at the data from RNA-Seq, we came to the conclusion that the data is noisy and
the specificity is so low, so we used the intersection of DNA and RNA to identify the
cancer -relevant genes -- gene fusions.
Again, we needed a way to benchmark our approach.
And for that we used cancer gene sensors -- cosmic cancer genes, as a metric to benchmark the
results.
Using only the DNA data gave us eight percent overlap with the known cancer genes.
But when we used in-frame fusions from RNA-Seq, we had again same specificity.
However, when we used the intersection of these two data types, we had a much higher
specificity with more cancer relevant fusions.
Using this approach, we could identify several known fusions in different cancers, but also
some novel functional fusions.
To give you an example, we found packs eight 2Nrf2 [spelled phonetically] fusions in thyroid
carcinoma.
This happens by tandem duplication.
And -- Nrf2 gene is being activated by losing its key point winding sites [spelled phonetically]
in this fusion.
Yeah, as it is shown here.
So to summarize, we used -- area based copy number data is useful to optimize structure
variation detection.
Most copy number amplifications are due to tandem number duplications.
Shuffling regulatory regions, such as promoters and enhancers, impact expression level globally,
and detection of fusion can be improved by combining whole genome sequencing and RNA-Seq.
And I would like to thank my lab members and colleagues.
Yeah.
Thank you for listening.
[applause]
Male Speaker: Hi.
Thanks, that was really good.
So can you comment on the use of cosmic to assess specificity?
Because it seems like the hypothesis there is that you’re more likely to get fusions
in cancer genes, which -- I guess I’m not sure why that would be.
Babak Alaeimahabadi: Using only RNA-Seq data, we will find a lot
of different fusions.
But we don’t -- a lot of them are happening randomly, or they are not functional in cancer.
So we need a metric to see if these fusions -- the ones that are functional -- most likely
they are in the genes that are relevant to cancer.
So if we use that to get rid of the noise in the data.
Male Speaker: Okay, so specificity with respect to functional
--
Babak Alaeimahabadi: Yes.
Male Speaker: -- likely functional.
Babak Alaeimahabadi: Yes, exactly.
Male Speaker: Thank you.
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