Metabolites can be an important read-out of disease. The identification and validation of biomarkers in the cancer metabolome that can stratify high-risk patients is one of the main current research aspects. Mass spectrometry has become the technique of choice for metabolomics studies, and mass spectrometry imaging (MSI) enables their visualization in patient tissues. In this study, we used MSI to identify prognostic metabolite biomarkers in high grade sarcomas; 33 high grade sarcoma patients, comprising osteosarcoma, leiomyosarcoma, myxofibrosarcoma, and undifferentiated pleomorphic sarcoma were analyzed. Metabolite MSI data were obtained from sections of fresh frozen tissue specimens with matrix-assisted laser/desorption ionization (MALDI) MSI in negative polarity using 9-aminoarcridine as matrix. Subsequent annotation of tumor regions by expert pathologists resulted in tumor-specific metabolite signatures, which were then tested for association with patient survival. Metabolite signals with significant clinical value were further validated and identified by high mass resolution Fourier transform ion cyclotron resonance (FTICR) MSI. Three metabolite signals were found to correlate with overall survival (m/z 180.9436 and 241.0118) and metastasis-free survival (m/z 160.8417). FTICR-MSI identified m/z 241.0118 as inositol cyclic phosphate and m/z 160.8417 as carnitine. Graphical Abstractᅟ Electronic supplementary materialThe online version of this article (doi:10.1007/s13361-016-1544-4) contains supplementary material, which is available to authorized users.

Mass spectrometry imaging (MSI) is widely used for clinical research because when combined with histopathological analysis the molecular signatures of specific cells/regions can be extracted from the often-complex histologies of pathological tissues. The ability of MSI to stratify patients according to disease, prognosis, and response is directly attributable to this cellular specificity. MSI developments are increasingly focused on further improving specificity, through higher spatial resolution to better localize the signals or higher mass resolution to better resolve molecular ions. Higher spatial/mass resolution leads to increased data size and longer data acquisition times. For clinical applications, which analyze large series of patient tissues, this poses a challenge to keep data load and acquisition time manageable. Here we report a new tool to perform histology guided MSI; instead of analyzing large parts of each tissue section the histology from adjacent tissue sections is used to focus the analysis on the areas of interest, e.g., comparable cell types in different patient tissues, thereby minimizing data acquisition time and data load. The histology tissue section is annotated and then automatically registered to the MSI-prepared tissue section; the registration transformation is then applied to the annotations, enabling them to be used to define the MSI measurement regions. Using a series of formalin-fixed, paraffin-embedded human myxoid liposarcoma tissues, we demonstrate an 80% reduction of data load and acquisition time, thereby enabling high resolution (mass or spatial) to be more readily applied to clinical research. The software is freely available for download.

The combination of high heterogeneity, both intratumoral and intertumoral, with their rarity has made diagnosis, prognosis of high-grade sarcomas difficult. There is an urgent need for more objective molecular biomarkers, to differentiate between the many different subtypes, and to also provide new treatment targets. Mass spectrometry imaging (MSI) has amply demonstrated its ability to identify potential new markers for patient diagnosis, survival, metastasis and response to therapy in cancer research. In this study, we investigated the ability of MALDI-MSI of proteins to distinguish between high-grade osteosarcoma (OS), leiomyosarcoma (LMS), myxofibrosarcoma (MFS) and undifferentiated pleomorphic sarcoma (UPS) (Ntotal = 53). We also investigated if there are individual proteins or protein signatures that are statistically associated with patient survival. Twenty diagnostic protein signals were found characteristic for specific tumors (p ≤ 0.05), amongst them acyl-CoA-binding protein (m/z 11 162), macrophage migration inhibitory factor (m/z 12 350), thioredoxin (m/z 11 608) and galectin-1 (m/z 14 633) were assigned. Another nine protein signals were found to be associated with overall survival (p ≤ 0.05), including proteasome activator complex subunit 1 (m/z 9753), indicative for non-OS patients with poor survival; and two histone H4 variants (m/z 11 314 and 11 355), indicative of poor survival for LMS patients.