Assuming that ideally what you want is obtaining a single number being representative of tumor genomic instability, I would answer that, to me, it is a hard problem to deal with and that it is almost unfeasible. Simply because genomic instability has several sources which are difficult to merge in a single measure. The links given in David's answer (Fridlyand & al, Chin & al) give a good idea of this diversity in the case of breast cancer.
In the past, I was working on 300 breast cancer samples all hybridized on aCGH Agilent 244K platform. That was a pretty huge set and I was trying to answer the question you posed. I would distinguish 3 components in genomic instability :
- percentage of altered genome
- number of breakpoints (links to the number of events)
- nature of found alterations (amplifications...)
I don't think that those diverse components could be combined in a single measure and they rather represent different kind of instability not necessarily directly comparable. It certainly means that involved biological mechanisms are different. I would rather try to group tumors by kind of instability like it is done in those publications.
These components are difficult to merge, but you also can not omit one of them. Because taken individually these measure are not informative enough. Let's consider the proportion of altered genome : (unrealistic example) if you have a tumor losing chromosomes 1,2,3 entirely resulting in a proportion of ~20% of altered genome and another having many focal gene amplifications, losses and gains with all chromosomes involved and resulting in a total proportion of 15%, would you say that the first tumor is more unstable ? Certainly not.
Number of breakpoints is very interesting, and I found that this was a better 'single' measure. But my experience is that it was difficult to have a reliable measure of it. This number depends too much on the quality of your hybridization and on the sensitivity of your segmentation and "calling gains & losses" algorithms. I used DNAcopy with default parameters which is very sensitive and (with Agilent 244K) it catches almost all local trends in the data thus generating many false positive breakpoints. I did not find at that time a 'merging segments' algorithm that suited my needs. A suggestion could be to exclude tumors of poor hybridization quality or re-hybridize them, but depending on the size of your set and on the money you can invest, it is often not a conceivable solution !
Good luck !