Blogging the biotechnology revolution

Systems Biology is changing the way biology is done. Is it a fad or is it effective? This blog tracks current happenings and helps you stay on top of the field. You can find a list of relevant papers at systems biology paper watch Have you heard a talk or read a paper in bioinformatics / systems biology you would like to tell other people about? Email: and get the word out!

Tuesday, January 24, 2006

Oncogenic pathway signatures in human cancers as a guide to targeted therapies in this month's Nature by Bild et al. describes a fairly comprehensive report on how the signature of expression of a group of genes (rather than genes acting individually as has been done in the past) can predict outcomes and subtypes of various cancers. Really here 'pathways' seem to be groups of genes whose expression is most correlated with a cancer outcome and then they are reduced to their dominant principal components. The principal components are used to classify and predict cancer outcomes. To test this approach they move from human to mouse models for these types of cancer and find that the same group of genes in mouse is diagnostic of cancer subtypes. Moving forward they use this approach for identifying metagenes (pathways) involved in cancer survival time, and drug sensitivity.
Overall the paper was very interesting and really showed a great in-depth validation of a bioinformatic approach that was previously published.
The mouse work was a very interesting method of validation that was particularly convincing.


Blogger The Bioinformatics Blog said...

What has always bugged me about principal component analysis was the lack of methods for assessing the statistical significance of the derived components. For example, in this paper they take the top three components for further analysis. Who is to say that there aren’t more components that are significant or that only the first one is significant? Am I wrong here? Are there ways to asses this?

3:54 PM  
Blogger Francis Crick said...

I don't know, but Han-Yu does. Ask her.

10:10 AM  
Anonymous Anonymous said...

Maybe I'm missing something, but I really didn't see anything great about this paper. Granted, its probably the most rigorous in terms of biological validation in this line of work, its nothing really new. Other groups have tried looking at microarrays phenotypes, contary to what the original reviewer thinks, and they have always at the end led to dead ends.

The reason is simple, what genes are used to make up a particular "phenotype" or "pathway?" When you see things like collagen and actin you really begin to wonder what the data is actually suggesting to you.

Regarding this paper, I don't see why the same information couldn't have been derived from running westerns against the same set of oncogenes. Any first year graduate student could have designed a simplier set of experiments that would have given the same information content. Everyone knows Ras, Myc, etc... are common oncogenes, and if you were to pubmed any of them, you would hit upon thousands of papers. The idea that a biologist studying a particular cancer wouldn't have tried assaying common oncogenes is rather humorous.

In addition, its not clear how this approach is extensible. Would you try to overexpress every oncogene and map their "pathways?" Only if you cared about "treating" a disease without understanding the molecular mechanism underlying the disease. In otherwords, if you're in industry and don't care whats going on, this is great. If you want any biological insight, stay away.

10:54 AM  
Blogger The Bioinformatics Blog said...

Thats an interesting point of view, but I would argue that microarray based classification/analysis has lead to changes in the targeting of drugs and early diagnosis. I cant find the link now, but I think ucsf runs a program where cancer patients tumors are arrayed defacto to diagnose the type of tumor they have.

As far as doing westerns to find the same things as this paper, thats probably not possible. The ras, myc pathways mentioned in the paper arent just monitoring one gene, but rather groups of related genes. There would be no way that a graduate student running westerns could a) identify the gene sets that were important b) assay the levels of all those genes in so many samples. Thats where the power of the array comes in.

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