A Mass Spectrometry Imaging Based Approach for Prognosis Prediction in UICC Stage I/II Colon Cancer

Currently, pathological evaluation of stage I/II colon cancer, following the Union Internationale Contre Le Cancer (UICC) guidelines, is insufficient to identify patients that would benefit from adjuvant treatment. In our study, we analyzed tissue samples from 276 patients with colon cancer utilizin...

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Bibliographic Details
Published in:Cancers 2021-10, Vol.13 (21), p.5371
Main Authors: Martin, Benedikt, Gonçalves, Juliana P. L., Bollwein, Christine, Sommer, Florian, Schenkirsch, Gerhard, Jacob, Anne, Seibert, Armin, Weichert, Wilko, Märkl, Bruno, Schwamborn, Kristina
Format: Article
Language:English
Subjects:
Bioinformatics
Biomarkers
Cancer therapies
Chemotherapy
Collagen
Colon cancer
Colorectal cancer
Industrialized nations
Ions
Lasers
Learning algorithms
Machine learning
Mass spectrometry
Mass spectroscopy
Medical prognosis
Metastases
Microenvironments
Patients
Peptides
Predictions
Prognosis
Scientific imaging
Tumors
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Summary:Currently, pathological evaluation of stage I/II colon cancer, following the Union Internationale Contre Le Cancer (UICC) guidelines, is insufficient to identify patients that would benefit from adjuvant treatment. In our study, we analyzed tissue samples from 276 patients with colon cancer utilizing mass spectrometry imaging. Two distinct approaches are herein presented for data processing and analysis. In one approach, four different machine learning algorithms were applied to predict the tendency to develop metastasis, which yielded accuracies over 90% for three of the models. In the other approach, 1007 m/z features were evaluated with regards to their prognostic capabilities, yielding two m/z features as promising prognostic markers. One feature was identified as a fragment from collagen (collagen 3A1), hinting that a higher collagen content within the tumor is associated with poorer outcomes. Identification of proteins that reflect changes in the tumor and its microenvironment could give a very much-needed prediction of a patient’s prognosis, and subsequently assist in the choice of a more adequate treatment.
ISSN:2072-6694
2072-6694
DOI:10.3390/cancers13215371