Siyuan Zheng: So, I'm glad to have this opportunity to present
our work on gene expression using RNA-seq data.
So I think the first question about gene fusion is why gene fusion is important.
So back to three decades ago, the first fused gene was discovered from the Philadelphia
chromosome, which is a BCR-ABL fusion gene.
And in 2005, this paper Ara Janey's [spelled phonetically] group discovered the TMPRSS2
gene fusion in prostate cancer using high super sequencing data.
This is probably the first data -- the first study to use sequencing data to identify fusion
genes.
So in TCGA, we have quality RNA-seq data for so many samples, so which allows us to identify
gene fusions with confidence, and even at very low frequency.
So, to do this, we developed a tool called PRADA, Pipeline for RNA-seq Data Analysis.
So PRADA is used to identify gene fusions, but PRADA is more than fusion detection.
PRADA is composed of four modules: a processing module, expression and QC module, fusion module,
and GUESS module.
So the processing module performs some basic operations on RNA-seq, including realignment,
recalibration, and duplication removal, and et al.
The expression and QC module calculates the RPKM values and generates the quality control
metrics for RNA-seq dataset.
Fusion module detects all candidate gene fusions using all genes in the genome.
And while the GUESS model is a supervised search module which can detect if a fusion
is presenting in a sample, it's a very quick script to detecting fusions.
So...
Can it go back to the third slide?
No, it doesn't -- okay, it doesn't work.
It's pretty slow.
Male Speaker: It's trying to go back?
Siyuan Zheng: Yeah, it's -- doesn't work.
Probably I scrolled too quick?
Male Speaker: Yeah.
So they can control that over there.
Siyuan Zheng: The third one.
Male Speaker: The first one.
Siyuan Zheng: Okay.
So a central step in PRADA is the alignment strategy.
We use a multiple-tiered alignment strategy in which we align reads to both transcriptome
and genome.
By mapping the reads to transcriptome, we capture all the transcript variants.
And by mapping to genome, we capture all the -- we capture the unannotated transcripts
for RNA-seq data.
So as a -- RNA-seq data analysis pipeline --
PRADA uses many tools, the established tools and functions as infrastructure.
This shows a detailed diagram of PRADA, and we use
Samtools -- BWA for alignment -- we use Samtools, Picard, GATK and RNA-SeQC for the RNA-seq
quality controls.
So as an RNA-seq data analysis pipeline it's important to output RPKM values from PRADA.
So RPKM value is an estimate for gene expressions from RNA-seq data.
So a little memory refresh.
So back to 2010, we published a cancer cell paper in which we divided glioblastomas into
four subtypes, which we named proneuro, neuro, classical, and mesenchymal.
So we made this classification based on array-based data.
And right now, since we have the -- our RPKM values from RNA-seq, what is to compare the
RNA-seq based classifications with array-based classifications, and to see how consistent
these two metrics give us.
So we did this to around 160 samples, and this table shows a comparison of results for
these 160 samples.
So overall, we get a concordance rate of more than 80 percent, and consider we divided the
samples into call and non-call.
For those non-call samples, we cannot ambiguously classify into any of these four subtypes.
So we have 25 percent of the samples to be non-call samples, while considering that,
this concordance rate is pretty high, actually.
So for fusion detection, this is the rationale for PRADA to detect fusions.
PRADA uses two sources of -- two lines of evidence.
First is discordant read pair, and second is fusion spanning reads.
A discordant read pair is a pair of reads in which one end of the read mapped to gene
A, and while the other end of the read is mapped to gene B. So if gene A and gene B
has a fusion, then we expect to see their summaries mapping to the junctions, and while
the other end of the junction spanning reads map to either gene A or gene B. So in PRADA,
we require both evidences to call a fusion candidate, and like many other fusion detection
tools.
So an important issue in fusion detection is false positives.
So in PRADA we have many filters to filter out those false positives.
And the number one filter is, we require that the gene partner in a fusion cannot have a
significant sequence similarity.
So we call this homology filter.
We also require the ratio of fusion spanning reads and the discordant read pairs, the number
of the ratio cannot -- should be within a limit, and which is determined by the library
size and the read lengths.
We also look at gene patterns and junction patterns and other things from the fusion
identification.
So we applied PRADA to kidney cancer and glioblastoma, and we identified recurrent fusions which
includes SFPQ-TFE3 fusion in kidney cancer; FGFR attack gene fusion in glioblastoma.
Also, TFG and GPR128 fusion in both cancer types, which I will discuss in the next coming
slides.
So in kidney cancer, we identified 80 fusions in about 15 percent of the cohort.
So the most frequent fusion is SFPQ and TFE3 gene fusion, which occurs in five samples.
The second recurring fusion is TFG and GPR128, which occurs in four samples.
So interestingly, TFE3 translocation has been reported to associate with a rare subtype
of adult kidney cancer.
So to evaluate the accuracy of PRADA, so we selected 13 fusions, and we used RT-PCR to
validate at least 11 out of these 13.
So we also validated the fusion identification in glioblastoma which we had a comparable
validation rate.
So we used whole genome sequencing data in glioblastoma to validate our fusions.
So in glioblastoma, we identified a total of 232 fusions in about -- around 70 percent
of the samples.
So the recurring fusions includes FGFR-TACC gene fusion, TFG-GPR fusion, and EGFR-involved
fusions.
So these fusions are important because, for example, the FGFR and TACC gene fusion was
reported in Science by Singh et al in July.
And they show, by experiment, they show this fusion is transforming fusion in astrocytes.
And TFG-GPR fusion is interesting because we saw this fusion in both kidney cancer and
brain tumors.
EGFR fusion is important, certainly, because EGFR gene is important in glioblastoma.
So since we have the data from both kidney cancer and glioblastomas, so we think we -- maybe
we can do some, you know, pan-cancer analysis.
So the first thing we look at is the fusion distance, the distance between two fusion
genes, in both cancer types.
So this shows -- the first bar shows -- the first three bars show the intrachromosome
fusion distance.
And the last bar shows the interchromosome.
So we can see for both glioblastoma and kidney cancer, you actually -- we actually find more
short distance fusions where these two fusion partners has a distance of less than one megabase.
So TFG-GPR fusion, so we saw this fusion in both kidney cancer and brain tumors.
And then we went back to see the copy number profiles of these fusions, and this filler
shows a screenshot of the IGV, IGV screenshot of the copy number profiles.
And you can see each row here, each line is one patient.
And you can see almost all of the patients with this fusion, they have a very focal amplification
on this locus.
So this fusion has actually caused by a focal amplification and a inversion.
And this pattern is not only present in kidney cancer and brain tumors, it's also -- it was
also found in some other cancer types.
And most interestingly, it was found in healthy individuals, so suggesting this is a germline
event.
So this gives us a reason to look at this fusion in normal samples in TCGA.
So to do this, we developed a tool called GUESS-ft.
So GUESS-ft, like I mentioned, GUESS-ft is a message to supervised search fusion in RNA-seq
data.
So we use GUESS-ft to all cancer types, to all normal -- to all fusions and the matched
normals.
In all cases, if we found this fusion in a -- okay -- in a tumor, then we found this
fusion in the matched normal.
So this result shows this fusion is a germline event.
Looking at the gene expression pattern, this fusion can activate the expression of GPR128.
So I mentioned EGFR a few slides ago in glioblastoma, and the most common mutation in EGFR and glioblastoma
is the V3 mutation, where EGFR loses exon 227.
So consequently, there is a rearrangement between exon one and eight.
This kind of rearrangement is different from gene-gene fusion because this is an intergenic
event.
So -- and this kind of intergenic event cannot be detected by the common fusion detection
tools.
So to fund this, we developed a tool called GUESS-ig: GUESS for intragenic rearrangement.
So we apply this GUESS-ig to RNA-seq data in glioblastoma.
We found V3, we found C-terminal deletions, and most surprisingly, we found exon 12 to
13 deletion and exon 14 to 15 deletion.
So those two kinds of deletion has not been functionally characterized.
To summarize, PRADA has several highlights.
So it provides powerful functions in processing RNA-seq data, and it can be used as a standalone
version or it can be used within a PBS or LSF system.
It has a modular design, so we -- users can actually flexibly use PRADA.
You can pause or you can resume your analysis, or you can just select to run individual modules
among all of the four modules.
So we -- because, considering the number of samples that we are handling, so we actually
designed PRADA to run samples in batch.
For example, we run around 180 samples in two weeks.
So, right now, PRADA is available in SourceForge.
So finally, I want to thank all the people involved in this project.
PRADA was developed in Verhaak lab.
In particular, I would like to thank Wandaliz Torres-Garcia and Rahul Vegesna for their
enormous contribution to this project.
I also wanted to thank collaborators within MD Anderson and out of MD Anderson.
Michael Berger from MSKCC; Andrey Schivachenko and Gaddy Getz from Broad Institute.
Also, this project benefits from TCGA Kidney Working Group and GBM working group.
Thank you.
John Weinstein: We have time for one quick question and one
quick answer.
Matthew Meyerson: Just wondering if you can comment more on
the very interesting finding of the exon 12/13 and 14/15 deletions.
Do you see -- so you see both -- do you see both DNA-based and transcript-based evidence
for these deletions?
Siyuan Zheng: Well, from our data, we see this deletion
from, you know, in the transcripts because we use RNA-seq data.
But using, we -- I don't remember if we see this in DNA.
So we --
Matthew Meyerson: Okay, so --
Siyuan Zheng: This is too short, we could not --
Matthew Meyerson: So you -- we can see these in sequencing.
We've actually seen these in sequencing data.
We have some single cell sequencing data where we've seen --
Siyuan Zheng: Yeah, that's -- that would be great.
Matthew Meyerson: And I would love to follow up with you on
this.
Siyuan Zheng: Yeah, that would be great.