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of young children, whereas Spitzoid melanomas
(SM) are malignant tumors with Spitzoid
features. These two types of lesions can at times
be dif
was then applied to the independent validation
set with 29 out of 30 SN and 26 out of 29 SM
correctly classi
ed. Two of the peptides that
made up the signature were identi
cult to differentiate, and treatments for
the two disease types are quite different. To
provide a better molecular diagnoses, we
pro
ed by MS/
MS directly from the tissue sections. A peptide
at
m/z
976.5 was identi
ed as originating from
actin and one at
m/z
1428.6 as being from
vimentin.
In addition to traditional biopsies, tissue
microarrays (TMA) have been analyzed using
these techniques allowing for the analysis of
a hundred or more samples in a single experi-
ment. This was
led a total of 114 Spitzoid lesions. Areas of
tumor and surrounding stroma (dermis) were
targeted from each sample when suf
cient tissue
was available (
Figure 2
). These were subdivided
into 26 SN and 25 SM in a training set and 30 SN
and 29 SM in an independent validation set.
After analysis of tryptic peptides from the tumor
in the training set, a genetic algorithm model
was generated using ClinProTools, consisting
of
rst demonstrated by Grose-
close et al.
51
in the analysis of a lung
carcinoma TMA comprised of 112 cores from
50 different patients. The patient diagnoses
could be subdivided into adenocarcinoma (21),
five peptides that could correctly diagnose
all samples in the training set. This algorithm
FIGURE 2
Analysis of Spitzoid lesions. (A) Spitzoid melanoma (top) and Spitz nevi (bottom) were annotated for areas of
tumor (blue) and dermis (yellow) and targeted for on-tissue tryptic digestion and mass spectrometry. (B) Portion of average
spectra from tumor regions from Spitzoid melanoma (red) and Spitz nevi (green). Peptides that are part of the classi
er are
marked with an asterisk.
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