Keynote Talk, Morning Session, 2 September (11th MGED Meeting, 1-4 September, 2008)
Metastatic breast cancer remains essentially incurable, but subtype specific drugs do show promise (survival after three years is very low). Would like to shift the survival curves to the right. Would like to develop molecular markers that will help identify the poor-outcome breast cancers early, so they can be treated early. This integrates omics, mathematical models and functional cancer biology.
Identify and model poor-outcome breast cancers
What are the poor-outcome subgroups? There are a number of ways to calculate these subtypes: there are relatively common PIK3CA pathway mutations (dna sequence), luminal / amplifier subtype (copy number, including ERB2), and the basal subtype (expression).
Identify these subtype-specific markers, and then select specific drugs for each subtype from within ~100 FDA-approved drugs and about 400 experimental drugs. They need a tractable model to accomplish marker and drug discovery efforts using 50 breast cancer cell lines selected to model the molecular subtypes of primary tumors. 50 cell lines isn't really enough – they'd like 250 cell lines, but just can't do that today.
Within these 50 cell lines, multiple instances of each poor-outcome subtype must be represented. Molecular abnormalities that influence drug response must be functioning. The cells have been characterized as much as possible. They've done high-res genome copy number and allelotype (BAC, SNP6, MIP array CGH). Further, interested in expression and alternate splicing using Affy arrays. Further, look at the secreted proteome (cytokine array) and ~140 protein and phosphoproteins using westerns and RPPL arrays. Also look at mutational status of candidate cancer genes from large-scale sequencing efforts.
To what extent do these cell lines look like primary tumors? Not always a good representation of the "average" breast cancer, but then no single breast cancer is close to average: there is large-scale heterogeneity. In terms of the genome, the cell lines are a pretty good match. At the transcriptome level, it's OK but not as good. The transcriptome results can get out the luminal/basal split in general, but the details are not so well matched.
Also been interested in looking at alternative splicing for the subtypes. For instance, isoforms that are spliced in a subtype-specific manner. Aspects of cell-surface signalling tend to be strongly-influenced by variable splicing.
Expression profiles of cell lines in 2D and 3D culture cluster together, but environment matters: 96 genes show strong environment-dependent expression (influence on TGF-Beta)
Develop molecular markers for early detection of poor-outcome subtypes
They are trying to identify subtype-specific, alternately-spliced proteins at an early stage. One example of the loads they are looking at is CD44 – seems from the exon arrays to be pretty interesting. It seems to be expressed almost totally in the basal subtypes. Further, it seems to divide the basals into the more and less virulent forms, in that splicing is very different in these two types. It's predominately spliced out in the Basal B group, and retained in Basal A.
There are about 30 candidates they're looking at more closely, and want to look at what version is in the blood. To do this they're using a new technology which requires a small amount of material: essentially, a small-capillary western, and is called Firefly (CellBio Sciences). All these differently-spliced isoforms are separated out and can be detected with antibodies. It's quite sensitive. Then you can use splice-isoform-specific antibodies in anatomic imaging using an engineered bacteriophage MS2 capsid.
Select/develop candidate therapeutics against the poor-outcome subtypes
Treat cell lines with therapeutic agents and identify milecular features. They're mostly focusing on pathway-targeting drugs, as well as some of the "classical" chemotherapy agents. Pathway-targed drugs tend to show strong subtype specificity. He talks about amounts in terms of concentration of the drug that can inhibit growth of the cell line by 50%. For instance, Lapatinib are most effective in the luminal cell lines, but still quite variable from cell line to cell line.
The drugs that tend to have the greatest luminal specificity are the ones that affect the P13-kinase pathway. For Basal subtype, the drugs that work best are affect the mitotic pathway. Bayesian network analysis reveals AKT-dependent signalling in luminal lines. There is strong connectivity in luminal subtype cells, and weaker connectivity in the basal subtypes.
There aren't all that many regions of recurrent amplification: maybe 5 different regions account for 30% of all breast cancers (8p11, 8q24, 11q13, 17q21, 20q130). Expression levels of 66 genes are deregulated by high-level amplification. siFGF3, siOOFIA1, siNEU3 induce cell apoptosis in 11q13 highly-amplified cell lines. Attacking these types of genes will be optimal for treating patients with this sort of luminal/amplified subtype. a similar story is present at 8q24: PVT1 but not MYC knockdown produces apoptosis-specific.
What do we do about the fact that we can't get drugs to work against some of these gene targets? Try to use isRNA therapeutics to produce in vivo delivery of PVT1 siRNA-DOPC. The PVT1 siRNA Rx is effective against PVT1-amplified xenografts.
In terms of the Basal subtype, they've been focussing on using MEK inhibitors, which shows some basal-subtype specificity, but it isn't very "durable" specificity. Therefore, also want to look at trascriptional features associated with response. The reason for the non-durable response is because the response is moderated by a MEK to EGFR feedback loop (via protein analysis). A negative inhibitor pathway – will just instead go to an alternative route. If you block the other route as well with P13K inhibitors, then you enhance the response in the basal subtype. Mitotic apparatus drugs also show promise for basal subtype tumors, but they'll be persuing that in the future.
Do these things have anything to do with the clinic?
They've started doing this with lapatinib. ERBB2+ is the strongest in vitro predictor of response. Can we stratify response within the ERBB2+ patients? They've focused on the transcriptome, and discounted markers that vary, and specifically focused genes that have a high response. They've ended up with a 6-gene assay. Can the patients be stratified? Indeed, those they predicted would be sensitive did have their survival curve shifted to the right. Transcriptional markers developed in vitro show promise in clinical studies. Subtype-specific drugs include: AKT-pathwya inhibitors for PI3-kinase mutants, MEK+P13-kinase drugs for the basal subtype, one more I missed…
These are just my notes and are not guaranteed to be correct.
Please feel free to let me know about any errors, which are all my
fault and not the fault of the speaker. 🙂