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Friday, February 26, 2010

AGBT 2010 - Elaine Mardis - Washington University School of Medicine

Single Molecule Sequencing to Detect and Characterize Somatic Mutations in Cancer Genomes

[Disclaimer Statement - she is a Pac Bio board member]

Why Sequence Whole Genomes?
* [same as always - nothing new]

Focus on talk today is on point mutations

How Current NGS (eg, Illumina) works:
* Sequence tumour & normal to 30x,
* Compare to reference, then compare tumour to normal, and remove known dbnps sites, etc etc...
* Validate SNVs.

4 Tier levels.
* focus validation on Tier 1 results.

Why Validate?
* Pipeline is tuned to have a slightly elevated false positive mutation rate so things aren't missed.
* Orthogonal validation is important.
* Validation is expensive and time consuming, however.

Why check for prevalence of mutations?
* Each tumour gNA sample consists of the contributions of many tumour cells
* digital nature of NGS data allows an estimation of how common each validated mutation is in the tumor cell population
* more prevalent mutations are likely "older" - happen earlier in progression.

Recurrent SNVs
* why? Adding evidence. The ones that happen more often are likely to be earlier in progression and are thus more likely to be drivers. [Not sure I buy that logic, however.]

* Faster Sequence data generation (analysis is not getting cheaper)
* iNcreased validation/prealece data demand (need to decrease cost)
* Recurrent mutation screening (site specific vs whole gene)

Medical impact:
* always want our results to be useful. [Kind of ignoring this part... selling us on the use of sequencing for medical use.]

Discussion of AML project, as discussed in last talk.
* prognostic IDH1 mutations.

[Dr. Mardis' talks always remind me of an infomercial... It has the feel of a commercial presentation, but with data to back it up. It's glossy, the slides are clean, and the presentations feel well rehearsed - something we just don't get much of in science talks.]

Insert sales pitch for Pac Bio systems here.

[5 slides later... ]

three experiments:
* first for accuracy
* second for sensitivity
* third for detection of mutational prevalence

* 32 directed PCr products from glioblastoma tumor normal pair
* 77% neoplastic cellularity
* SMRT sequencing (alpha prototype detector)
* Wrote software for SNP detection
* 94% of 86 known sites were found
* 6 FP and 6FN results

* 5 LOH sites were detected properly
* All mutations were detected at different confidence levels

* used AML genome
* 95% population purity
* All variants detected at each cellularity...

Detection of Mutational Prevalence:
* Concordance with Illumina is good - but not great in tier 3 mutations. C to T mutations were slightly biased against.

* Platform is Ramping up quickly



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