Dataset Information

Cancer tissue classification using machine learning applied to MALDI MSI and high resolution H&E stained images

ABSTRACT: Aim is to identify a panel of m/z markers for the early detection of colorectal cancer (CRC). Identification of a molecular pattern that can distinguish the primary tumours of colorectal cancer with lymph node metastasis compared to those without. Materials and Methods: Using MALDI MSI data, we developed and validated a machine learning model that can be used for early screening of CRC. Our model yields high sensitivity and specificity in distinguishing normal tissue from the cancerous. Model described here, can be a used in clinical labs for early diagnosis of colorectal cancer

INSTRUMENT(S): ultraflex

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Epithelial Cell, Colon

DISEASE(S): Colon Cancer

SUBMITTER: Parul Mittal

LAB HEAD: Prof Peter Hoffmann

PROVIDER: PXD019662 | Pride | 2022-02-15

REPOSITORIES: Pride

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Publications

Cancer Tissue Classification Using Supervised Machine Learning Applied to MALDI Mass Spectrometry Imaging.

Mittal Paul P Condina Mark R MR Klingler-Hoffmann Manuela M Kaur Gurjeet G Oehler Martin K MK Sieber Oliver M OM Palmieri Michelle M Kommoss Stefan S Brucker Sara S McDonnell Mark D MD Hoffmann Peter P

Cancers 20211027 21

Matrix assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) can determine the spatial distribution of analytes such as protein distributions in a tissue section according to their mass-to-charge ratio. Here, we explored the clinical potential of machine learning (ML) applied to MALDI MSI data for cancer diagnostic classification using tissue microarrays (TMAs) on 302 colorectal (CRC) and 257 endometrial cancer (EC)) patients. ML based on deep neural networks discriminated c ...[more]

PMID: 34771551

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Project description:It has been recognised for a long time that cell surface glycans plays a vital role in the biological process and their altered form can lead to cancer. However, the molecular details and regulatory mechanism between the glycans and cancer is yet not clear. Over the past decade mass spectrometry-based techniques has become a prominent method for analysing glycans especially N-glycan matrix assisted laser desorption/ionisation mass spectrometry imaging (MALDI MSI). It is a powerful technique that combines mass spectrometry with histology, enabling the visualisation and label free detection of N-linked glycans on a single tissue section. Here, we carried out N-glycan MALDI MSI on six endometrial cancer (EC) formalin fixed paraffin embedded (FFPE) tissue sections including the adjacent normal endometrium, and tissue microarrays (TMA) consisting of 8 EC patients with lymph node metastasis (LNM) and 20 without LNM. By doing that several m/z values were detected that can significantly distinguish normal endometrium from cancerous. Also, detected a m/z value that can discriminate the primary tumour with LNM from those without. Identification of those discriminative m/z values was performed using porous graphitized carbon liquid chromatography tandem mass spectrometry (PGC-LC-MS/MS). Overall, we observed higher abundance of oligomannose in tumour regions compared to normal with AUC ranges from 0.85-0.99. Whereas, complex N-glycans were detected in lower abundance with AUC ranges from 0.03-0.28. Comparison of N-glycans between the primary tumours with LNM and without LNM indicates reduced abundance of a complex core-fucosylated N-glycan in patients with metastasis relative to without. In summary, N-glycan MALDI MSI can be used to characterize the cancerous endometrium from the normal, also patients with LNM from those without. Identification of those discriminative m/z values was performed using porous graphitized carbon liquid chromatography tandem mass spectrometry (PGC-LC-MS/MS).

Project description:Changes in IgG glycosylation have been reported to be associated with colorectal cancer (CRC), but the alteration in specific subclass of IgG is unknown. Herein, we optimized five common IgG glycopeptides enrichment methods to acquire comprehensive profiling of IgG glycoproteomics in CRC. Notably, incomplete tryptic digestion of IgG occurred when using ordinary ratio of protease to protein, which significantly impacted the final statistical analysis of association between IgG glycosylation and CRC. We introduced a method based on two-step enzymatic digestion, enabling the complete digestion of IgG glycopeptides and further improving the detection intensity of the target glycopeptides. By this approach, total 47 IgG glycopeptides can be identified by nanoLC-ESI-MS/MS. Following rapid and automatic data processing through the self-developed program, we observed that IgG1_H3N4F1 and IgG1_H3N4 were substantially increased in CRC, while IgG1_H5N5F1, IgG1_H5N4F1S1and IgG2_H5N4F1 were markedly decreased. Strikingly, these four glycopeptides were continually significantly changed in early and late stages of CRC. Further evaluation of the diagnostic performance showed they all obtained a fair performance in discriminating the patients from the normal. In terms of the glycan features, it demonstrated that CRC progression associates with the increased agalactosylation, and the decreased digalactosylation and galactosylation per antenna on diantennay glycans of IgG1 and IgG2. Concurrently, the decreased sialylation of IgG1 was strongly correlated with CRC. Moreover, tumor-specific glycosylation analysis showed that the alterations of subclass-specific IgG glycosylation were more significant in colon cancer, and no obvious difference between colon and rectal cancer was found. More importantly, all these findings were validated in an independent cohort. This two-step enzymatic approach is crucial to explore the relationship between the disease progression and IgG glycosylation.

Dataset Information

Cancer tissue classification using machine learning applied to MALDI MSI and high resolution H&E stained images

Publications

Cancer Tissue Classification Using Supervised Machine Learning Applied to MALDI Mass Spectrometry Imaging.

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