A Mass Spectrometry Imaging Based Approach for Prognosis Prediction in UICC Stage I/II Colon Cancer
by
, , and
Benedikt Martin
1,†, 
Juliana P. L. Gonçalves
2,†,
Christine Bollwein
2,
Florian Sommer
3,
Gerhard Schenkirsch
4,
Anne Jacob
2,
Armin Seibert
5,
Wilko Weichert
2,
Bruno Märkl
1
and
Kristina Schwamborn
2,* 
1
Institute of Pathology and Molecular Tumor Diagnostics, University Hospital of Augsburg, 86156 Augsburg, Germany
2
Institute of Pathology, School of Medicine, Technical University of Munich, 81675 Munich, Germany
3
Department of Visceral Surgery, University Hospital of Augsburg, 86156 Augsburg, Germany
4
Cancer Tumor Data Management, Comprehensive Cancer Center, University Hospital of Augsburg, 86156 Augsburg, Germany
5
Institute of Mathematics, Augsburg University, 86159 Augsburg, Germany
*
Author to whom correspondence should be addressed.
†
Both authors contributed equally.
Cancers 2021, 13(21), 5371; https://doi.org/10.3390/cancers13215371
Submission received: 1 September 2021
/
Revised: 15 October 2021
/
Accepted: 21 October 2021
/
Published: 26 October 2021
(This article belongs to the Section Molecular Cancer Biology)
Simple Summary
Tumor treatment is heavily dictated by the tumor progression status. However, in colon cancer, it is difficult to predict disease progression in the early stages. In this study, we have employed a proteomic analysis using matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI). MALDI-MSI is a technique that measures the molecular content of (tumor) tissue. We analyzed tumor samples of 276 patients. If the patients developed distant metastasis, they were considered to have a more aggressive tumor type than the patients that did not. In this comparative study, we have developed bioinformatics methods that can predict the tendency of tumor progression and advance a couple of molecules that could be used as prognostic markers of colon cancer. The prediction of tumor progression can help to choose a more adequate treatment for each individual patient.
Abstract
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.
Keywords:
colon cancer; mass spectrometry imaging; proteomics; MALDI; tumor prognosis
