I've got records from patients with different cancers. Naturally, common cancers were registered often, and the cases in this group are then overrepresented and vice versa for the rare cancers.

Let's say that I have:

 - 20 patients cancer 1  
 - 160 patients cancer 2 
 - 80 patients cancer 3

-> are these strata uneven?

I would like to iteratively sample a set with an equal amount of sufficient cases from each cancer to test on.

However, if I pick 20 cases from each cancer, this won't cover cancer 2, and I will include the same patients from cancer 1 in each iteration. 
Is this a problem?
If so, how would I solve this problem? 

If I use sampling with replacement, I do not necessarily pick the same 20 cases from cancer 1, but wouldn't I need more iterations ensure cases from the larger strata are covered enough?

**- edit -**

I actually need to find features from one particular cancer that are different from the other cancers. I've got thousands of features, couple hundreds of samples from different cancers in different proportions, this is all public data. I'm worried that a standard t-test or some kind of linear model for each feature between cancer of interest vs the rest will be biased because of the distribution of cancer types in 'the rest'. 

-> That's why, following the idea described in the methods [here][1], I wanted to subsample the cancers. But this means I would need to iterate over selections of samples, I guess - And that's where the authors of the paper stop providing information.

Then I will need to validate these results in our own samples, and finally we'll need to try and detect these features in specific human samples before we create any kind of model.

So now that this is clear:
I think I need a t-test and I think this requires to subsample the cancers. Is this indeed necessary, is this a problem, and if so, how do I solve this?

  [1]: https://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-1143-5#Sec14