I would try out two approaches.

The first approach would be combining your disease subtypes and mutation factor levels for each sample. Your sample sheet would look as follows.

         condition
sample_1    A_mut1
sample_2    A_mut2
sample_3    A_mut3
...            ...

The regression formula would then just be ~ condition.

The next approach uses interaction terms in the regression, so would model both the main effects of each mutation and each disease subtype, as well as the differences in effect for each disease subtype based on the mutation. Your sample sheet would now look like the following.

         subtype mutation
sample_1       A     mut1
sample_2       A     mut2
sample_3       A     mut3
...          ...      ...

For this analysis the regression formula would be ~ subtype + mutation + subtype:mutation.

More information on multi-factor design can be found in the DESeq2 documentation, and a detailed explanation for working with interaction terms can be found in the help documentation for the results function, help("results", package="DESeq2").