AGBT 2010 - Ogan Abaan - NIH/NCI

Identification of novel cancer mutations in sarcomas

Sarcomas: two categories

* simple genetic changes (eg. Ewings)

* Complex genetic changes (eg, osteosarcomas)

Soft tissue sarcomas in general:

* rare

* high mastastasis.

* connective tissue origin.

* 50 subgroups - most have unknown biology

Tumour samples from 24 soft tissue sarcoma patents

* matched normals will be sequenced when available at some point in the future.

Target:

* 15k exons from 1334 genes

* used "in-solution" capture method.

* 33.5k -150mers

* no repeat masking.

* biotinylated baits

Used Eland - and used GAII or GAIIx, as available - mixed read lengths

Custom python scripts - wrote them himself. Still a work in progress.

Variant Calling is VERY simple. Uses Phred score based approach, adjusted by error rate at that position.

Did the standard: filter on dbsnp130, annotate on UCSC refGene and Visual confirmation (IGV Browser)

Shows stats - they don't look great, but they seem similar to those published in Tewhey et al (Genome Biol 2009). [Shown to justify low rates?]

Some optimization could be done to get more coverage.

* gets 23-46% at greater than or equal to 10x, paper gets 88% or more at 7x

6 of variants are known in COSMIC db.

KEGG pathway: Many mismatch repair... [actually, this is the usual set you'd see with any cancer sample. Nothing sticks out.]

Conclusion:

* 305 variants, no common variants.

Future:

* increase sample size.

* pathway analysis

* Understand biology

[Not the most impressive talk - I could give the same talk on my cell lines, and would have roughly the same results.... nothing particularly interesting.]