[Yay,we can blog Obi’s talk]
“Transcriptome and Exome Sequencing of Breast Cancer Cell Lines to Identigy Molecular Predictors of Response to Anti-Cancer Compounds.”
Using a panel of breast cancer cell lines.
Hypothesis: correlating drug response of the breast cancer cell lines with molecular characteristics will help identify drug treatments.
Combine drug response data with molecular data -> molecular signiture of drug response.
Panel: 72 cell lines. [I see a few normals in the cell line list, tho, so not all breast cancer cell lines… odd.]
after filtering : 82 drugs, 63 are publicly available, 19 are through private companies.
Methods: U133A, exon array, mehtylation, rppa, westerns, cnv . Also: Cosmic, RNAseq (Alexa-seq), Exome-seq. All data types can be combined and filtered (unsupervised) for variance.
Ideally, in the perfect world, all cell lines would be profiled with all of the techniques – but “we don’t live in that world”. [Speaks to a lot of issues in working with cell lines, IMHO.]
RNA-Seq: 55 cell lines. See Alexa-seq poster [74, I think.]
Description of pipeline. Looks relatively standard.
For each drug, cell lines separated into responders/non-responders. Random Forests used for classification. (Internal cross validation step)
Exome-seq identifies known and novel cancer variants. Concordant calls identify mutations. – Almost all discrepancies are false negatives in exome-seq. [interesting!]
RNA-seq recapitulates known subtypes with high accuracy. (unsupervised clustering map shown, looks very pretty, and cell lines are nearly all in the correct clusters.)
Drugs with best predictors listed – 58/82 are better than random, (AUC > 0.5), [gets better the higher you go, obviously.]
Example given with Lapatinib – predicted by Her2 amplicon. Over expression of her2 amplicon is visible across all of the methods, and thus, it’s possible to use that info for lapatinib response.
Example : BIBW2992 – drug response also associated with HER2, but also 3 other mutations. classification requires use of multiple genes for response prediction.
- Predictors can be found for many drugs
- modst important predictors come from a wide range of data types.
- Experiment with sensitivity parameters/thresholds
- control for subtype
- compare performatnce of individual data types.
[I really didn’t do justice with the notes – much of this talk was visual, and data was hard to summarize in text. Good talk, however, and nice to see that molecular classification is becoming more feasible.]