Network Biology 2.0 part 4

The second day of talks at Network Biology 2.0 was yesterday and there were a lot of talks I found really insightful.

Andreas Califano gave a talk "Interrogating Cancer Interactomes to Optimize Therapy on an Individual Basis". He talked about how the initial great hope of cancer genetics was to go from genetics to science to distinguishing and curing human cancer. This paradigm has of course turned out to be completely wrong. He advocates that these things are all mediated by complex epigentic/cell dynamic/environmental/genetic factors wrapped up in dynamic regulatory logic.

He talked briefly about ARACNE a program for reconstructing regulatory networks from gene expression data that his group created and then MINDy a program for reverse engineering conditional transcription factor interactions and lastly MARINa: MAster Regulator INference Algorithm (you know if you keep making acronyms like that it's kind of meaningless) which seems to be looking for the most powerful transcriptional regulator nodes. He defined a master regulator as a gene that is necessary and or sufficient to induce a specific cellular transformation or differentiation event.

He made the argument that in cancer research it was compelling to either describe disease on the level of individual epigenetic alterations which are largely patient specific or via expressed phenotype which are largely homogenous and proposed that a better more useful way was to look at this master regulator model of abstraction.

Using ARACNE and MARINA he was able to reconstruct a regulatory network and identify 6 TF regulatory modules controlling ~80% of the MGES (mesenychmal?) genes.

He then introduced another program IDEA - Interactome-based Dysregulation Enrichment Analysis which looks at dysregulated interactions occuring by more than random chance as a ranking methodology.

He then summarized with 5 points on cancer medicine:

The current emphais on genes harboring epigenetic alterations is inappropriate
The current approach to biomarker discovery must be re-evaluated from GWAS to PWAS (Pathway Wide Association)
That statistical power cannot be sacrificed for coverage
We must fundamentally rethink clinical studies because we cannot use a sample of one
One drug for one disease paradigm needs to shift to a toolkit of target specific drugs

He also made the point that you don't need to fully silence a gene via shRNA for it to be an effective treatment but rather just partially, that in fact fully silencing a gene would probably be fatal.

I was really impressed by this talk, he seemed to really understand the algorithmic underpinnings of his work and I will be definitely investigating more about his methodology. His idea of pathway analysis I believe took some inspiration from GSEA and rightfully so.