MALDI-MSI: A Powerful Approach to Understand Primary Pancreatic Ductal Adenocarcinoma and Metastases

Abstract

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.

Obesity promotes pancreatic cancer through the ECM-PI3K-AKT signaling axis

“…COL1A2 is also one of the diagnostic targets for malignant pleural mesothelioma (MPM), and its high expression somewhat promotes the development of MPM and has prognostic value for the assessment of MPM. In contrast, in PC, it has also been mentioned that upregulation of COL1A2 expression has an important role in promoting the development of PC, and MiR-25-3p can inhibit the proliferation, migration, and invasive process of PC cells by suppressing the expression level of COL1A2 [29,30].…”

The journey towards clinical adoption of MALDI‐MS‐based imaging proteomics: from current challenges to future expectations

“…As a result, FFPE tissue specimens are employed for routine diagnostic assessment and also represent a valuable source of molecular information that can be exploited for tissue typing. Despite the high potential for application in clinical research and histopathological tissue classification being highlighted in multiple clinical studies [13][14][15], it is also imperative to demonstrate its robustness in large patient cohorts, across different centres, and throughout a large timescale, especially if the technique is to be implemented within a centralised unit [16]. Accordingly, Buck et al [17] performed a roundrobin MSI study on FFPE tissues to investigate the consequences of inter-and intra-centre technical variation on masking biological effects.…”

Advancements in Oncoproteomics Technologies: Treading toward Translation into Clinical Practice

“…MALDI-MSI is a label-free technique that allows multiplex analysis of numerous molecules in the same tissue section and has been extensively employed in clinical proteomics in cancer [ 91 , 92 , 93 ]. It has been used to understand primary pancreatic ductal adenocarcinoma and metastases [ 92 ], molecular signatures of medullary thyroid carcinoma [ 93 ], tumor-stroma interrelationships [ 94 ], de novo discovery of phenotypic intratumor heterogeneity [ 95 ], and as a complementary diagnostic tool in cytopathology for thyroid nodules characterization [ 96 ]. Recently, MALDI imaging along with deep learning has been employed in multi-class cancer subtyping in salivary gland carcinomas [ 97 ].…”