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Thursday, February 5, 2009

Phil Stephens, Sturctural Somatic Genomics of Cancer

Andy Futreal could not show up – he got snowed in in philladelphia.:
All work is from illumina platform.

Providing an overview of multistep model of cancer...

Precancer to in situ cancer, to invasive cancer, to metastatic cancer.

They believe cancer have 50-100 driver mutations plus 1000 passenger mutations. Some carry 10-100's of thousands of passengers.

The big question is how to identify the driver mutations. Today, the focus on is on structural variations. 200Bps to 10s of Mb. Can be seen as copy number variations or as copy number neutral (balanced translocations, etc)

For instance, the upregulation of ERBB2, by causing multi copies.

For the most part, we have no idea what the genomic instability does to the copy number at local points, and what they accomplish.

Balanced translocations are interesting because they tend to create fusion genes. There art at least 367 genes known to be implicated in human oncogenesis, 281 are known to be translocated. 90% are in leukemias, lymphomas... [missed the last one]

Use 2nd gen to study these phenomenon. For structural variation, it's always 400bp fragments, with PET sequencing. Align using MAQ. Basically, look for locations where the fragments align in wider than 400bp locations. Need high enough coverage to then check that these are real. You then need to verify using PCR – check if the germline had the mutation as well. Futreal's group is only interested in Somatic.

Now, that's the principle, so does it work? Yes, they published it last year.

NCI-H2171. Has 6 previously known structural changes for control. Very simple copy number variation identification. Solexa copy number data is at least as good as Affy Chip. They suggest Solexa has the ability to find the true copy number variation,whereas Affy chip tends to saturate.

For control, they found intra-chromosomal reads, and then verified with PCR. Two reads mapped to the breakpoint, and were able to work out the consequences of the break. Used a circos diagram to show most translocations are intra-chromosome, and only a small number of them are inter-chromosome.

Since publication, they've now worked on the same project to update the data. They're better at doing what they did the first time around. They redid it on 9 matched breast cancer cell lines, and got ~9x coverage.

HCC38 – no highly amplified regions. Found 289 somatic chromosomal translations. Most of the changes are due to Tandem Duplications, however it was not replicated in another cell line. So, structural variation is highly variable

distinct patters emerged: one has lots of Tandem Duplications, one with very little structural variations, and one with a more lymphoma like pattern.

“Sawtooth” pattern to CNV graph: lots of different things going on. Some are simple, some are difficult.

What are the Structural Variations doing?

Looked at examples of fusion proteins. In one cell line hCC38, found 4 S.V. Found smaller SV's as well.

Duplications of exons 14 and 15 in one particular gene: receptor tyrosine kinase, which seems to be in the ligand binding domain. Also evidence from many other observations of SNPs in the same domain.

What they didn't know was if it would reflect what's going on in breast cancer. 15 primary breast cancers were then sequenced. (65Gb total).

Huge diversity was found. Anywhere from 8-230+ structural variants per tumour. Same patterns as in the cell lines are found. 11 potential promotor fusions...

[The numbers are flying fast and furious, and I can't get close to keeping up with them.]

151 genes are found in 2 samples. 12 in 3, 5 in 4....

How do you assay for variants? FISH, cDNA pcr! Other mutations in rearranged genes. Whole exome seqeuncing, trancriptome sequencing, epigenetic changes are down the road.

Also can look at relationship between somatic break point positions in the geome.

Conclude: PET sequencing is useful for structural variation,
average breast cancer has ~100 mutations (somatic)
Average cancer has ~3.2 fusion genes

Question: Genome vs Transcriptome? Answer: Both!
Question: how many of the hits are false? Answer: at first it was 95%, now it's down to 10%.

My comments: very nice talk. Since this is basically similar to what I'm working, it's very cool. It's nice to know that PET makes such a huge difference. The paper referenced in the talk was a good read, but I'm giong to have to go back and reread it.



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