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Screening of important metabolites and KRAS genotypes in colon cancer using secondary ion mass spectrometry
Kookrae Cho1, Eun-Sook Choi2, Sung Young Lee3
1Division of Electronic Information System Research Daegu Gyeongbuk Institute of Science and Technology (DGIST) Daegu Republic of Korea.
Abstract:
Time-of-flight secondary ion mass spectrometry (TOF-SIMS) is an imaging-based analytical technique that can characterize the surfaces of biomaterials. We used TOF-SIMS to identify important metabolites and oncogenic KRAS mutation expressed in human colorectal cancer (CRC). We obtained 540 TOF-SIMS spectra from 180 tissue samples by scanning cryo-sections and selected discriminatory molecules using the support vector machine (SVM) algorithm. Each TOF-SIMS spectrum contained nearly 860,000 ion profiles and hundreds of spectra were analyzed; therefore, reducing the dimensionality of the original data was necessary. We performed principal component analysis after preprocessing the spectral data, and the principal components (20) of each spectrum were used as the inputs of the SVM algorithm using the R package. The performance of the algorithm was evaluated using the receiver operating characteristic (ROC) area under the curve (AUC) (0.9297). Spectral peaks (m/z) corresponding to discriminatory molecules used to classify normal and tumor samples were selected according to p-value and were assigned to arginine, α-tocopherol, and fragments of glycerophosphocholine. Pathway analysis using these discriminatory molecules showed that they were involved in gastrointestinal disease and organismal abnormalities. In addition, spectra were classified according to the expression of KRAS somatic mutation, with 0.9921 AUC. Taken together, TOF-SIMS efficiently and simultaneously screened metabolite biomarkers and performed KRAS genotyping. In addition, a machine learning algorithm was provided as a diagnostic tool applied to spectral data acquired from clinical samples prepared as frozen tissue slides, which are commonly used in a variety of biomedical tests.
Insights
Time-of-flight secondary ion mass spectrometry (TOF-SIMS) identified key metabolites and KRAS mutations in colorectal cancer (CRC). This technique offers a novel diagnostic tool for analyzing frozen tissue slides in biomedical tests.
Area of Science:
- Biomaterial surface characterization
- Analytical chemistry
- Molecular imaging
Background:
- Colorectal cancer (CRC) diagnosis relies on identifying specific molecular markers.
- Current diagnostic methods may require invasive procedures or extensive sample processing.
- Need for rapid, simultaneous detection of metabolic and genetic biomarkers in CRC.
Purpose of the Study:
- To utilize Time-of-flight secondary ion mass spectrometry (TOF-SIMS) for identifying diagnostic biomarkers in colorectal cancer.
- To develop a machine learning-based approach for analyzing TOF-SIMS spectral data for CRC classification and KRAS mutation detection.
- To assess the potential of TOF-SIMS as a diagnostic tool for clinical applications using frozen tissue slides.
Main Methods:
- Acquisition of 540 TOF-SIMS spectra from 180 human colorectal cancer cryo-sections.
- Dimensionality reduction using Principal Component Analysis (PCA) on spectral data.
- Classification of samples using Support Vector Machine (SVM) algorithm with PCA components, evaluated by Receiver Operating Characteristic (ROC) Area Under the Curve (AUC).
Main Results:
- Identification of discriminatory molecules including arginine, α-tocopherol, and glycerophosphocholine fragments differentiating normal and tumor tissues (AUC=0.9297).
- Pathway analysis indicated involvement of these molecules in gastrointestinal disease and organismal abnormalities.
- Successful classification of spectra based on KRAS somatic mutation expression with high accuracy (AUC=0.9921).
Conclusions:
- TOF-SIMS efficiently screens for both metabolite biomarkers and performs KRAS genotyping simultaneously in colorectal cancer.
- A machine learning algorithm applied to TOF-SIMS spectral data from frozen tissue slides shows promise as a diagnostic tool.
- This approach offers a potential advancement in the rapid and comprehensive molecular characterization of clinical samples.
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