The Application of Imaging (MALDI-MSI) in Human Carcinomas for the Identification of New Diagnostic Markers

Intra-tumor heterogeneity (ITH) results from the coexistence of genetically distinct cancer cell (sub)populations, their phenotypic plasticity, and the presence of heterotypic components of the tumor microenvironment (TME). Here we addressed the potential association between phenotypic ITH revealed by mass spectrometry imaging (MSI) and the prognosis of breast cancer. Tissue specimens resected from 59 patients treated radically due to the locally advanced HER2-positive invasive ductal carcinoma were included in the study. After the on-tissue trypsin digestion of cellular proteins, peptide maps of all cancer regions (about 380,000 spectra in total) were segmented by an unsupervised approach to reveal their intrinsic heterogeneity. A high degree of similarity between spectra was observed, which indicated the relative homogeneity of cancer regions. However, when the number and diversity of the detected clusters of spectra were analyzed, differences between patient groups were observed. It is noteworthy that a higher degree of heterogeneity was found in tumors from patients who remained disease-free during a 5-year follow-up (n = 38) compared to tumors from patients with progressive disease (distant metastases detected during the follow-up, n = 21). Interestingly, such differences were not observed between patients with a different status of regional lymph nodes, cancer grade, or expression of estrogen receptor at the time of the primary treatment. Subsequently, spectral components with different abundance in cancer regions were detected in patients with different outcomes, and their hypothetical identity was established by assignment to measured masses of tryptic peptides identified in corresponding tissue lysates. Such differentiating components were associated with proteins involved in immune regulation and hemostasis. Further, a positive correlation between the level of tumor-infiltrating lymphocytes and heterogeneity revealed by MSI was observed. We postulate that a higher heterogeneity of tumors with a better prognosis could reflect the presence of heterotypic components including infiltrating immune cells, that facilitated the response to treatment. Full article

Risk classification plays a crucial role in clinical management and therapy decisions in children with neuroblastoma. Risk assessment is currently based on patient criteria and molecular factors in single tumor biopsies at diagnosis. Growing evidence of extensive neuroblastoma intratumor heterogeneity drives the need for novel diagnostics to assess molecular profiles more comprehensively in spatial resolution to better predict risk for tumor progression and therapy resistance. We present a pilot study investigating the feasibility and potential of matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) to identify spatial peptide heterogeneity in neuroblastoma tissues of divergent current risk classification: high versus low/intermediate risk. Univariate (receiver operating characteristic analysis) and multivariate (segmentation, principal component analysis) statistical strategies identified spatially discriminative risk-associated MALDI-based peptide signatures. The AHNAK nucleoprotein and collapsin response mediator protein 1 (CRMP1) were identified as proteins associated with these peptide signatures, and their differential expression in the neuroblastomas of divergent risk was immunohistochemically validated. This proof-of-concept study demonstrates that MALDI-MSI combined with univariate and multivariate analysis strategies can identify spatially discriminative risk-associated peptide signatures in neuroblastoma tissues. These results suggest a promising new analytical strategy improving risk classification and providing new biological insights into neuroblastoma intratumor heterogeneity. Full article

Despite the correlation of clinical outcome and molecular subtypes of high-grade serous ovarian cancer (HGSOC), contemporary gene expression signatures have not been implemented in clinical practice to stratify patients for targeted therapy. Hence, we aimed to examine the potential of unsupervised matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI-IMS) to stratify patients who might benefit from targeted therapeutic strategies. Molecular subtyping of paraffin-embedded tissue samples from 279 HGSOC patients was performed by NanoString analysis (ground truth labeling). Next, we applied MALDI-IMS paired with machine-learning algorithms to identify distinct mass profiles on the same paraffin-embedded tissue sections and distinguish HGSOC subtypes by proteomic signature. Finally, we devised a novel approach to annotate spectra of stromal origin. We elucidated a MALDI-derived proteomic signature (135 peptides) able to classify HGSOC subtypes. Random forest classifiers achieved an area under the curve (AUC) of 0.983. Furthermore, we demonstrated that the exclusion of stroma-associated spectra provides tangible improvements to classification quality (AUC = 0.988). Moreover, novel MALDI-based stroma annotation achieved near-perfect classifications (AUC = 0.999). Here, we present a concept integrating MALDI-IMS with machine-learning algorithms to classify patients according to distinct molecular subtypes of HGSOC. This has great potential to assign patients for personalized treatment. Full article