Tumor Classification of Six Common Cancer Types Based on Proteomic Profiling by MALDI Imaging

In clinical diagnostics, it is of outmost importance
to correctly
identify the source of a metastatic tumor, especially if no apparent
primary tumor is present. Tissue-based proteomics might allow correct
tumor classification. As a result, we performed MALDI imaging to generate
proteomic signatures for different tumors. These signatures were used
to classify common cancer types. At first, a cohort comprised of tissue
samples from six adenocarcinoma entities located at different organ
sites (esophagus, breast, colon, liver, stomach, thyroid gland, n = 171) was classified using two algorithms for a training
and test set. For the test set, Support Vector Machine and Random
Forest yielded overall accuracies of 82.74 and 81.18%, respectively.
Then, colon cancer liver metastasis samples (n =
19) were introduced into the classification. The liver metastasis
samples could be discriminated with high accuracy from primary tumors
of colon cancer and hepatocellular carcinoma. Additionally, colon
cancer liver metastasis samples could be successfully classified by
using colon cancer primary tumor samples for the training of the classifier.
These findings demonstrate that MALDI imaging-derived proteomic classifiers
can discriminate between different tumor types at different organ
sites and in the same site.