MALDI imaging mass spectrometry in clinical proteomics research of gastric cancer tissues

In the presented thesis, matrix-assisted laser
desorption/ionization (MALDI) imaging mass spectrometry was used
for the proteomic analysis of gastric cancer tissue samples, with
the aims of 1) identifying proteins that predict disease outcome of
patients with intestinal-type gastric cancer after surgical
resection, and 2) generating a proteomic classifier that determines
HER2-status in order to aid in therapy decision with regard to
trastuzumab (Herceptin) administration. In the first study, a
seven-protein signature was found to be associated with an
unfavorable overall survival independent of major clinical
covariates after analyzing 63 intestinal-type primary resected
gastric cancer samples by MALDI imaging. Of these seven proteins,
three could be identified as CRIP1, HNP-1, and S100-A6, and
validated immunohistochemically on tissue microarrays of an
independent validation cohort (n=118). While HNP-1 and S100-A6 were
found to further subdivide early (UICC-I) and late stage
(UICC-II-III) patients into different prognostic groups, CRIP1, a
protein previously unknown in gastric cancer, was confirmed as a
novel and independent prognostic factor for all patients in the
validation cohort. The protein pattern described here serves as a
new independent indicator of patient survival complementing the
previously known clinical parameters in terms of prognostic
relevance. In the second study, we hypothesized that MALDI imaging
mass spectrometry may be useful for generating a classifier that
may determine HER2-status in gastric cancer. This assumption was
based on previous results where HER2-status could be reliably
predicted in breast cancer patients. Here, 59 gastric cryo tissue
samples were analyzed by MALDI imaging and the obtained proteomic
profiles were used to create HER2 prediction models using different
classification algorithms. Astonishingly, the breast cancer
proteomic classifier from the previous study was able to correctly
predict HER2-status in gastric cancers with a sensitivity of 65%
and a specificity of 92%. In order to create a universal classifier
for HER2-status, breast and non-breast cancer samples were
combined, which increased sensitivity to 78%; specificity was 88%.
This study provides evidence that HER2-status can be identified on
a proteomic level across different cancer types suggesting that
HER2 overexpression may constitute a widely spread molecular event
independent of the tumor entity.