Tuesday, April 24, 2012

Genetic methods and technologies with potentially important payoff

Last week a group of cancer researchers, largely in the UK, announced in Nature that after characterizing the genome and transcriptome of 2000 breast tumors, they have found that breast cancers can be divided into 10 subgroups, or even 'different diseases', each with characteristic aggressiveness and response to therapy.  The 'transcriptome'  is the set of the 20-25,000 human genes that the tumor cells are actually using (other genes are presumably quiescent and not needed by those cells).

To us, this demonstrates a welcome appropriate use of genetics in an important problem -- even if  not so welcome is the typical hype that's come with the announcement -- this will 'revolutionize' treatment, and this is 'breakthrough research', and so on.  It's a problem when every story is accompanied by such hype, a boy-crying-wolf kind of problem.  This study may well be very important work, but let's let it prove itself before shouting from the rooftops.   The human genome sequence, after all, was going to allow us all to live forever by 2020 if not earlier, leaders proclaimed in the '90s.

From the Nature paper:
We present an integrated analysis of copy number and gene expression in a discovery and validation set of 997 and 995 primary breast tumours, respectively, with long-term clinical follow-up. Inherited variants (copy number variants and single nucleotide polymorphisms) and acquired somatic copy number aberrations (CNAs) were associated with expression in ~40% of genes, with the landscape dominated by cis- and trans-acting CNAs. By delineating expression outlier genes driven in cis by CNAs, we identified putative cancer genes, including deletions in PPP2R2A, MTAP and MAP2K4. Unsupervised analysis of paired DNA–RNA profiles revealed novel subgroups with distinct clinical outcomes, which reproduced in the validation cohort.
('Unsupervised analysis' refers to a particular statistical method used.)

Both germline (transmitted in sperm or egg) and somatic (body cell) variation was found to contribute to tumor occurrence and architecture.  The terms are technical but essentially all sorts of variation was found to be involved:  CNA's (copy number aberrations), CNV's (copy number variants) and SNPs (single nucleotide polymorphisms), either on the same chromosome as the contributing gene (cis) or on a different chromosome (trans):  all these contributed to variation in expression of genes associated with tumors.

a, Venn diagrams depict the relative contribution of SNPs, CNVs and CNAs to genome-wide, cis and trans tumour expression variation for significant expression associations (Šidák adjusted P-value ≤0.0001). b, Histograms illustrate the proportion of variance explained by the most significantly associated predictor for each predictor type, where several of the top associations are indicated. [Figure and caption from the paper.]

If, as the paper suggests, integrating genetic information about germline as well as somatic aberrations, as well as tumor type helps to clarify decisions about treatment of breast cancer, this will prove to be a valuable application of genetic technologies and information.

This is just the the first step, however.  As the lead author, Carlos Caldas said, interviewed on the BBC Radio 4 program, Material World, on April 19, they've identified what he equated with continents, but the rivers, mountains, plains and other aspects of the landscape are yet to be determined. 

Indeed, this is a study of 'primary' tumors, and it is not clear what the story is if the tumor has spread to other parts of the  body ('metastasized').  Other recent studies have shown what cruder methods had previously shown, that new genetic changes occur that allow those secondary tumors to spread and grow.  Likewise treatment itself selects for cells that are by their good luck, and the patient's bad luck, resistant.  This study appears to show, assuming no post-study tumor recurrences, that the predictive methods can lead treatment to stay a step ahead of the tumor's evolution.

It's been clear for many years that somatic genetic changes may be important in disease, and cancer, which is a cascade of cells descendant from a founding aberrant misbehaving cell is the classic archetype.   In such instances, it makes sense to search for variation among cells within the individual.  Whether or not the pattern is too complex to be very useful, only time will tell.

If such work can reveal useful information as this paper claims, and treatment can be focused on patient-specific traits, there may indeed be something to shout from the rooftops.  Whether or not complexity again rises to bite, this study shows an appropriate use of high-throughput genetic technology.

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