Journal Club: Strong Inference- Platt

[S.A.M.]

I think it's great Too bad your case does not have an easy exclusion tree like some of the examples in the article, allowing for quick progress. Platt seemed to overlook this.



Might be going on a tangent now, but hopefully it's of interest...I googled "vitamin D enzyme pathway" just now and saw an interesting article about Vitamin D in cancer treatment. It says that they recently discovered how either of it's two enzymatic pathways are controlled, by changing one amino acid in the hydroxylase enzyme. The two pathways breakdown Vitamin D at a different speed. http://www.physorg.com/news104511326.html

I was gonna explain the vitamin D part in detail, but I thought I could use a technical paper for that, lets just discuss reasoning and inference here.

I'm curious, when you do the high dose do you ever do anything about the enzymatic pathway? In humans when you increase your vitamin, i.e., coenzyme, levels with supplementation I believe it has a positive short term effect, but in the long term somewhat blocks the enzymatic pathway.

The reason I do the high dose is because many nutrients have different effects at low and high levels: apart from adaptations to deficiency or toxicity, there are concerns about differential dose dependent effects on enzymes, signaling pathways, regulation (as you said). So no, I just look at the results and use them to suggest the effects.

Of course, further work in the lab is required to pinpoint the exact pathways, the exact signaling mechanism etc. But thats a different story.

 

[S.A.M.]

Since we are discussing the importance of reasoning, here is a related review of a recent study:

http://blogs.nature.com/nature/journalclub/2007/04/kornelia_polyak.html

What do you learn if you sequence 13,000 genes in 11 breast and 11 colorectal cancer samples? The question taps into an intense debate about how best to identify genes relevant to human cancer.

Last year, researchers reported the results of a survey such as the one described (T. Sjöblom et al. Science 314, 268–274; 2006). They found that each tumour contains, on average, 90 mutant genes — an unexpectedly high number. They also defined mutation spectra that were specific to colon and breast tumours, including the intriguing observation that the DNA letter sequence CG was swapped for GC at high frequency in breast tumours. This could be due to an uncharacterized DNA repair defect or differential carcinogen exposure.

I consider this report a step towards answering key questions in cancer biology, such as how many genes are mutated in cancer, how many mutations are required for cancer, and whether accumulation of genetic alterations in cancer cells drives tumour progression.

But others disagree. Many labs see large-scale sequencing of cancer genomes as unfocused and expensive fishing experiments. I have been doing genomics experiments since the dawn of this era, and have often faced this criticism.

But just this one study has identified more genes mutated in human cancer than thousands of investigators have found over past decades. And another recent, large-scale sequencing project pinpointed close to 120 mutant kinase enzymes that may have a role in human cancers (C. Greenman et al. Nature 446, 153–158; 2007).

Both cases show that the outcome of unbiased, genome-wide studies may not be what we expect, which is exactly why they're worth doing.