Apparent Incompleteness of Subtypes

I find that subtype information can have a large amount of incompleteness. For example, in Genomic Classification of Cutaneous Melanoma, Cell, 2015

BRAF Subtype
The largest genomic subtype is defined by the presence of BRAF hot-spot mutations (n = 166).
RAS Subtype
The second major subtype is defined by the presence of RAS hot-spot mutations (n = 95), including known amino acid changes with functional consequences, in all three RAS family members (N-, K- and H-RAS).
NF1 Subtype
The third most frequently observed SMG in the MAPK pathway was NF1, which was mutated in 14% (n = 28) of samples.
Triple Wild-Type Subtype
We defined the Triple-WT subtype (n = 46) as a heterogeneous subgroup characterized by a lack of hot-spot BRAF, N/H/K-RAS, or NF1 mutations.

but the the R package is

> table(colData(cutaneousMelanoma)[, "MUTATIONSUBTYPES"])
BRAF_Hotspot_Mutants      NF1_Any_Mutants  RAS_Hotspot_Mutants            Triple_WT 
                  32                    5                   11                    8

The R package has 343 patients and the journal article has 331, so it is unclear why there are so few assigned to a subtype.

Start with the cutaneousMelanoma object and its colData(cutaneousMelanoma)[, "MUTATIONSUBTYPES"] table, then compare those assignments with the subtype counts in the cited Cell article. Trace why the package contains 343 patients but only 56 subtype assignments. Done means explaining the discrepancy and, if the data are incomplete, correcting or documenting the affected subtype information.