Novel profiling methodologies are redefining the diagnostic capabilities and therapeutic approaches towards more precise and personalized healthcare. Complementary information can be obtained from different omic approaches in combination with the traditional macro- and microscopic analysis of the tissue, providing a more complete assessment of the disease. Mass spectrometry imaging, as a tissue typing approach, provides information on the molecular level directly measured from the tissue. Lipids, metabolites, glycans, and proteins can be used for better understanding imbalances in the DNA to RNA to protein translation, which leads to aberrant cellular behavior. Several studies have explored the capabilities of this technology to be applied to tumor subtyping, patient prognosis, and tissue profiling for intraoperative tissue evaluation. In the future, intercenter studies may provide the needed confirmation on the reproducibility, robustness, and applicability of the developed classification models for tissue characterization to assist in disease management.

Currently, pathological evaluation of stage I/II colon cancer, following the Union Internationale Contre Le Cancer (UICC) guidelines, is insufficient to identify patients that would benefit from adjuvant treatment. In our study, we analyzed tissue samples from 276 patients with colon cancer utilizing mass spectrometry imaging. Two distinct approaches are herein presented for data processing and analysis. In one approach, four different machine learning algorithms were applied to predict the tendency to develop metastasis, which yielded accuracies over 90% for three of the models. In the other approach, 1007 m/z features were evaluated with regards to their prognostic capabilities, yielding two m/z features as promising prognostic markers. One feature was identified as a fragment from collagen (collagen 3A1), hinting that a higher collagen content within the tumor is associated with poorer outcomes. Identification of proteins that reflect changes in the tumor and its microenvironment could give a very much-needed prediction of a patient’s prognosis, and subsequently assist in the choice of a more adequate treatment.

The differentiation between the atypical cartilaginous tumor (ACT) and the enchondromas is crucial as ACTs require a curettage and clinical as well as imaging follow-ups, whereas in the majority of cases enchondromas require neither a treatment nor follow-ups. Differentiating enchondromas from ACTs radiologically remains challenging. Therefore, this study evaluated imaging criteria in a combination of computed tomography (CT) and magnetic resonance (MR) imaging for the differentiation between enchondromas and ACTs in long bones. A total of 82 patients who presented consecutively at our institution with either an ACT (23, age 52.7 ±18.8 years; 14 women) or an enchondroma (59, age 46.0 ± 11.1 years; 37 women) over a period of 10 years, who had undergone preoperative MR and CT imaging and subsequent biopsy or/and surgical removal, were included in this study. A histopathological diagnosis was available in all cases. Two experienced radiologists evaluated several imaging criteria on CT and MR images. Likelihood of an ACT was significantly increased if either edema within the bone (p = 0.049), within the adjacent soft tissue (p = 0.006) or continuous growth pattern (p = 0.077) were present or if the fat entrapment (p = 0.027) was absent on MR images. Analyzing imaging features on CT, the likelihood of the diagnosis of an ACT was significantly increased if endosteal scalloping >2/3 (p < 0.001), cortical penetration (p < 0.001) and expansion of bone (p = 0.002) were present and if matrix calcifications were observed in less than 1/3 of the tumor (p = 0.013). All other imaging criteria evaluated showed no significant influence on likelihood of ACT or enchondroma (p > 0.05). In conclusion, both CT and MR imaging show suggestive signs which can help to adequately differentiate enchondromas from ACTs in long bones and therefore can improve diagnostics and consequently patient management. Nevertheless, these features are rare and a combination of CT and MR imaging features did not improve the diagnostic performance substantially.

Cancer-related deaths are very commonly attributed to complications from metastases to neighboring as well as distant organs. Dissociate response in the treatment of pancreatic adenocarcinoma is one of the main causes of low treatment success and low survival rates. This behavior could not be explained by transcriptomics or genomics; however, differences in the composition at the protein level could be observed. We have characterized the proteomic composition of primary pancreatic adenocarcinoma and distant metastasis directly in human tissue samples, utilizing mass spectrometry imaging. The mass spectrometry data was used to train and validate machine learning models that could distinguish both tissue entities with an accuracy above 90%. Model validation on samples from another collection yielded a correct classification of both entities. Tentative identification of the discriminative molecular features showed that collagen fragments (COL1A1, COL1A2, and COL3A1) play a fundamental role in tumor development. From the analysis of the receiver operating characteristic, we could further advance some potential targets, such as histone and histone variations, that could provide a better understanding of tumor development, and consequently, more effective treatments.

Pancreatic ductal adenocarcinoma and cholangiocarcinoma constitute two aggressive tumor types that originate from the epithelial lining of the excretory ducts of the pancreatobiliary tract. Given their close histomorphological resemblance, a correct diagnosis can be challenging and almost impossible without clinical information. In this study, we investigated whether mass spectrometric peptide features could be employed to distinguish pancreatic ductal adenocarcinoma from cholangiocarcinoma. Three tissue microarrays of formalin-fixed and paraffin-embedded material (FFPE) comprising 41 cases of pancreatic ductal adenocarcinoma and 41 cases of cholangiocarcinoma were analyzed by matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI). The derived peptide features and respective intensities were used to build different supervised classification algorithms: gradient boosting (GB), support vector machine (SVM), and k-nearest neighbors (KNN). On a pixel-by-pixel level, a classification accuracy of up to 95% could be achieved. The tentative identification of discriminative tryptic peptide signatures revealed proteins that are involved in the epigenetic regulation of the genome and tumor microenvironment. Despite their histomorphological similarities, mass spectrometry imaging represents an efficient and reliable approach for the distinction of PDAC from CC, offering a promising complementary or alternative approach to the existing tools used in diagnostics such as immunohistochemistry.

Immunohistochemical evaluation of estrogen receptor, progesterone receptor, and human epidermal growth factor receptor-2 status stratify the different subtypes of breast cancer and define the treatment course. Triple-negative breast cancer (TNBC), which does not register receptor overexpression, is often associated with worse patient prognosis. Mass spectrometry imaging transcribes the molecular content of tissue specimens without requiring additional tags or preliminary analysis of the samples, being therefore an excellent methodology for an unbiased determination of tissue constituents, in particular tumor markers. In this study, the proteomic content of 1191 human breast cancer samples was characterized by mass spectrometry imaging and the epithelial regions were employed to train and test machine-learning models to characterize the individual receptor status and to classify TNBC. The classification models presented yielded high accuracies for estrogen and progesterone receptors and over 95% accuracy for classification of TNBC. Analysis of the molecular features revealed that vimentin overexpression is associated with TNBC, supported by immunohistochemistry validation, revealing a new potential target for diagnosis and treatment.