One gene, one disease??

by Beyond Lab

Obviously not. Life is so complicated that it is impossible for one gene to be solely responsible for one function and therefore one disease. Almost all genes identified have multiple domains (function units) with different "potential" function. So, mess up one gene would certainly have more than one consequences. This has been confirmed in many species, from bakers yeast to human.

So, it is almost impossible to predict the occurrence of one disease just by analyzing the function/structure of one gene or its product. It has to be a combination of various information. With that being said, is it helpful to analyze one gene if it is know that this particular gene (or its mutant formats) always associates with certain disease. Absolutely!. But how accurate is it? No one knows when it comes to one individual -- yes or no. Probability means nothing for one individual.

Why am I saying this? Because one company Orion Genomics just licensed JHU technology to analyze IGF2 gene (insulin-like growth factor 2) with the hope to be able to predict the outcome (progression) of colorectal cancer. -- Beyond Lab personally thinks this is not very wise. I don't know how they will promote their future product to analyze one single gene. --Most current genetic or genomic detection products for breast cancer all involve many genes. One gene? Unlikely mean anything.

smoking leads to lung cancer??

Have you been told or been thinking that smoking causes lung cancer? Well, this is not exactly clear from recent studies.

An association between a genetic variation on chromosome 15 (15q24/15q25.1) and risk of lung cancer was found by three independent studies. But the link to smoking, or more scientifically nicotine dependence, is not clear. Some of the studies are still preliminary. So it is still possible that people with 'abnormal' chromosome 15 tend to be affected by environment - smoking - more easily and therefore are more likely to develop cancer in the long run.

We'll see if further studies with more - up to millions of - people can make it clear.

The papers mentioned here are:

1. Hung, R. J. et al. Nature 452, 633–637 (2008).
2. Thorgeirsson, T. E. et al. Nature 452, 638–641 (2008).
3. Amos, C. I. et al. Nature Genet. doi:doi: 10.1038/ng.109 (2008).

Dynamic "cold" genetic material

Two recent papers changed the concept of "cold" DNA, the so called heterochromatin.

1. Proliferation-dependent and cell cycle–regulated transcription of mouse pericentric heterochromatin
The Journal of Cell Biology, 2007; Vol. 179, No. 3, 411-421

2. Cell cycle control of centromeric repeat transcription and heterochromatin assembly
Nature, 2008;451(7179):734-7

Genetic information resides in DNA in most organisms. DNA is packed into chromatin and stays in nucleus of a cell. There are two different package status: one loose and one tight. These are the original concepts of euchromatin and heterochromatin coined by German botanist Heitz 80 years ago. He proposed that heterochromatin reflects a functionally inactive state of the genome (all DNA information of an organism). Decades of research have generally been supportive to this idea. Heterochromatin is generally gene poor, highly packed, late replicating, and has a very low recombination rate. Modern molecular hallmarks of heterochromatin generally include heterochromatin protein 1 (HP1, swi6 in fission yeast) and methylation of histone H3 lysine 9 (H3K9) by histone methyltransferase (HMTase) (suv39, clr4). On the whole, Heitz’s original “inactive state” hypothesis still holds until recently.

The first paper demonstrated cell-cycle-specific transient disruption and transcription of mouse pericentric heterochromatin. It shows that mammalian pericentric heterochromatin is transcribed by RNA polymerase II twice during the cell cycle. A heterogeneous population of short RNAs (about 150 bp) is generated during mitosis, while a longer (mostly >1 kb) population is produced in late G1 and early S phase. Cell cycle regulation of pericentric transcription does not require Suv39h1,2-dependent chromatin modification, but it does require passage through "Start" in G1 phase. Future studies will determine whether these mammalian pericentric transcripts are important for heterochromatin formation as they are in fission yeast, as reported in the second paper. in this paper, at regions serving as RNAi-dependent heterochromatin nucleation centers in fission yeast (in the pericentric, mating-type, and telomere regions), heterochromatin is abundant during G2 but greatly reduced during M, G1 and S phases. Heterochromatin reduction in M, G1 and S is correlated with phosphorylation of histone H3 on serine 10 (H3S10) and with binding of condensins. Genetic analyses show that condensin binding (in M and G1) and methylations of H3K36 (in S) and H3K9 (in G2) all contribute toward proper heterochromatin formation in G2 and toward proper regulation of transcription of the RNAi-dependent nucleation centers during S phase.

Because centromere /heterochromatin defect is almost the most common feature of cancers, this transcription could be the most basic factor during cancer formation. It might be that this transcription leads centromere structure problem, which leads to chromosome segregation defect which cause gene mutation etc. and cancer eventually.

Combining Genomic and Clinical Data for Cancer Therapy

This just came out today. So check it out.
http://jama.ama-assn.org/cgi/content/short/299/13/1574

An article entitled " Gene Expression Signatures, Clinicopathological Features, and Individualized Therapy in Breast Cancer" in the Journal of the American Medical Association, retrospectively studied traditional diagnostic standards of breast cancer outcomes — such as patient age, tumor size, and so on — and information about gene expression by modern genomic technology in a thousand breast cancer tumors. The conclusion is appealing: Gene expression patterns can, indeed, define subgroups of women with different prognoses and treatment responses.

“The combination of these two methods, one of which uses the clinical description of a patient’s breast cancer and the other which looks at gene expression at the molecular level in a patient’s tumor, may allow us to [match drugs with patients] with unprecedented accuracy,” senior author Anil Potti, an investigator at Duke University, said in a statement.

Traditionally, breast cancer evaluation is based on factors the so called TNM classification system, such as the patient’s age, tumor size, the level of lymph node involvement, and the degree of metastasis. These clinicopathological features could be employed to make predictions about clinical outcomes and help doctor’s to determine whether adjuvant cancer therapies such as chemotherapy or radiation therapy are warranted or necessary for different patients. However the estimation simply based on these factors are not always meaningful, e.g. it tends to overestimate cancer recurrence in younger patients.

To determine whether genomic data can provide additional information, the researchers studied women with early-stage breast cancer who had been followed for on average 11 years after initial assessment.

Indeed, the researchers did find that "molecular traits of patients in the poor prognostic clusters were highly specific and distinct from those of the good prognostic carriers”.

As the authors pointed out, identifying these subgroups may not only refine predictions about patient outcomes, it also provides information about patients’ underlying biology and the tumor microenvironment. That’s because gene expression patterns reveal different genetic pathways that are activated or silenced in different tumors during the long tumor formation progress.

For instance, low expression of cancer risk genes, chromosomal instability, and so on predict good outcome. However, high expression of genes associated with oncogenic pathway activation and wound healing etc. tend to be associated with poor outcome. Some genetic signatures also might indicate different responses to chemotherapy.

As wrote in an accompanying editorial in the same issue of JAMA, by Northwestern University researchers Chiang-Ching Huang and Markus Bredel, “This is one of the largest studies in human cancer showing the ability of gene expression profiles to improve risk stratification beyond established risk assessment algorithms that take into account clinicopathological variables”. This study “demonstrates the potential value of using microarray-based gene signatures to refine outcome predictions.”