Dataset Information

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

ABSTRACT: To identify a panel of m/z markers for the early detection of colorectal cancer (CRC). Identication of a molecular pattern that can distinguish the primary tumours of colorectal cancer with lymph node metastasis compared to those without. 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 specicity 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: PXD019653 | 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).

Dataset Information

Cancer tissue classication 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.

Similar Datasets

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