>Most of this talk is from 2-3 years ago. Breast cancer is now more deadly for women than lung cancer. Lifetime risk for women is 1 in 9 women. Two most significant risk factors: being a woman, aging.
Treatment protocols include surgery, irradiation, hormonal therapy, chemotherapy, directed antibody therapy. Several clinical and molecular markers are now available to decide the treatment course. These also predict recurrence/survival well… but…
Many caveats: only 50% of Her2+ tumours respond to trastuzumab (Herceptin). No regime for (Her2-, ER-, PR-) “tripple negative” patients other than chemo/radiation. Many ER+ patients do not benefit from tamoxifen. 25% of lymph node negative patients (a less aggressive cancer) will develop micrometastatic disease and possibly recurrence (an example of under-treatment.) – Many other examples of undertreatment.
Microarray data caused a whole new perspective on breast cancer treatment. Created a taxonomy of breast cancer – Breast cancer is at least 5 different diseases. (Luminal Subtype A, Subtype B, ERBB2+, Basal Subtype, Normal Beast-like. Left to right, better prognosis to worst prognosis.)
[background into cellular origin of each type of cell. Classification, too.]
There are now gene expression biomarker panels for breast cancer. Most of them do very well in clinical trials. Point made that we almost never find biomarkers that are single gene. Most of the time you need to look at many many genes to figure out what’s going on. (“Good sign for bioinformatics”)
Microenvironment: Samples used on arrays, as above, include environment when run on arrays. We end up looking at averaging over the tumour. (Contribution of microenvironment is lost.) Epithelial gene expression signature “swamping out” signatures from other cell types. However, tumour cells interact successfully with it’s surrounding tissues.
Most therapies target epithelial cells. Genetic instability in epi cells lead to therapeutic resistance. Stromal cells (endothelial cells in particular) are genetically stable (eg, non-cancer.)
Therefore, If you target the stable microenvironment cells, it won’t become resistant.
Method: using invasive tumours, patient selection, laser capture microdiseaction, RNA isolation and amplification (Two rounds) -> microarray.
BIAS bioinformatics integrative application software. (Tool they’ve built)
LCM + Linear T7 amplification leads to 3′ Bias. Nearly 48% of probes are “bad”. Very hard to pick out the quality data.
Looking at just the tumour epitheila profiles (tumours themselves), confirmed that subtypes cluster as before. (Not new data. The breast cancer profiles we already have are basically epithelial driven.) When you look just at the stroma (the microenvironment), you find 6 different categories, and each one of them have distinct traits, which are not the same. There is almost no agreement between endothelial and epithelial cell categorization.. they are orthogonal.
Use both of these categorizations to predict even more accurate outcomes. Stroma are better at predicting outcome than the tumour type itself.
Found a “bad outcome cluster”, and then investigated each of the 163 genes that were differentially expressid between cluster and rest. Can use it to create a predictor. The subtypes are more difficult to work with, and become confounding effects. Used genes ordered by p-value from logistic regression. Apply to simple naive bayes’ classifier and cross validation using subsets. Identified 26 (of 163) as optimal classifier set.
“If you can’t explain it to a clinician, it won’t work.”
Stroma classifier is stroma specific.. It didn’t work on epithelial cells. But shows as well or better than other predictors (New, valuable information that wasn’t previously available.)
Cross validation of stromal targets against other data sets: worked on 8 datasets which were on bulk tumour. It was surprising that it worked that way, even though bulk tumour is usually just bulk tumour. You can also replicate this with blood vessels from a tumour.
Returning back to biology, you find the genes represent: angiogensis, hypoxic areas, immunosuppression.
[Skipping a few slides that say “on the verge of submission.”] Point: Linear Orderings are more informative than clustering! Things are not binary – it’s a real continuum with transitions between classic clusters. (Crosstalk between activated pathways?)
In a survey (2007, Breast Cancer Research 9-R61?), almost all things that breast cancer clinicians would like research done on is bioinformatic driven classification/organization,etc.
- define all relevant breast cancer signatures
- analysis of signatures
- focus on transcriptional signatures
- improve quality of signatures
- aims for better statistics/computation with signatures.
There are too many papers coming out with new signature. Understanding breast cancer data in the litterature involves a lot of grouping and teasing out information – and avoiding noise. Signatures are heavily dependent on tissues type, etc etc.
Traditional pathway analysis: Always need experiment and control and require rankings. If that’s just two patients, that’s fine, if it’s a broad panel of patients, you won’t know what’s going on- you’re now in an unsupervised setting.
There are more than 8000 patients who have had array data collected. Even outcome is difficult to interpret.
Instead, using “BreSAT” to do linear ranking instead of clustering, and try to tease out signatures.
There is an activity of a signature – clinicians have always been ordering patients, so that’s what they want.
What is the optimal ordering that matches with the ordering….[sorry missed that.] Many trends show up when you do this than with hierarchical clustering. (Wnt, Hypoxia) You can even order two things: (eg. BRCA and Interferon), you can see tremendously strong signals. Start to see dependencies between signatures.
Working on several major technologies (chip-chip, microarray, smallRNA) and more precise view of microenvironment.