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Metabolites
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Artificial Intelligence in Cancer Metabolism and Metabolomics
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Artificial Intelligence in Cancer Metabolism and Metabolomics

A special issue of Metabolites (ISSN 2218-1989). This special issue belongs to the section "Advances in Metabolomics".

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 16793

Special Issue Editors

Prof. Dr. Andreas Stadlbauer

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Guest Editor
1. Department of Neurosurg, Friedrich-Alexander University of Erlangen Nürnberg, 91054 Erlangen, Germany
2. Institute of Medical Radiology, University Clinic St. Pölten, Karl Landsteiner University of Health Sciences, 3100 St. Pölten, Austria
Interests: metabolic imaging; brain tumors; Warburg effect; reverse Warburg effect; tumor microinvironment; metabolic coupling; energy metabolism; magentic resonance imaging; Förster resonance energy transfer imaging; artificial intelligence; deep learning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Anke Meyer-Baese

E-Mail Website
Guest Editor
Dept. of Scientific Computing, The Florida State University, 400 Dirac Science Library, Tallahassee, FL 32306-4120, USA
Interests: breast MRI; breast tumors
Special Issues, Collections and Topics in MDPI journals
Dr. Max Zimmermann

E-Mail Website
Guest Editor
Department of Preclinical Imaging and Radiopharmacy, University of Tübingen, Tübingen, Germany
Interests: breast cancer; glioblastoma
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Altered metabolism is recognized as a core hallmark of cancer and regarded as important as other features, such as sustained angiogenesis and avoiding immune destruction. Cancer cells can reprogram their metabolism to promote cellular growth and proliferation, adapt nutrient or oxygen depleted environments, and escape immune surveillance.

Artificial intelligence (AI) comprises a type of computer science that develops software programs for intelligent execution of tasks or decision making. These approaches allow for bridging the gap between the acquisition of data and its meaningful interpretation. Consequently, artificial intelligence has demonstrated outstanding capabilities for the resolution of a variety of biomedical problems, including cancer, over the past decade. Artificial intelligence can play an essential role in a wide variety of aspects of cancer metabolism and metabolomics: detection of metabolic alterations, metabolic classification and diagnosis, tracking tumor development, clinical decision-making as well as cancer therapy development and validation or prognosis prediction. This Special Issue will highlight several of the above issues. We welcome submissions of research and review articles addressing several facets of AI in both basic and clinical research of cancer metabolism and metabolomics.

Prof. Dr. Andreas Stadlbauer
Prof. Dr. Anke Meyer-Baese
Dr. Max Zimmermann
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Metabolites is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Metabolomics
  • Tumor microenvironment
  • Artificial Intelligence
  • Deep Learning
  • Machine Learning
  • Oncology
  • Personalized medicine

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Published Papers (6 papers)

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Research

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13 pages, 1329 KiB  
Article
Application of Machine Learning to Metabolomic Profile Characterization in Glioblastoma Patients Undergoing Concurrent Chemoradiation
by Orwa Aboud, Yin Allison Liu, Oliver Fiehn, Christopher Brydges, Ruben Fragoso, Han Sung Lee, Jonathan Riess, Rawad Hodeify and Orin Bloch
Metabolites 2023, 13(2), 299; https://doi.org/10.3390/metabo13020299 - 17 Feb 2023
Cited by 8 | Viewed by 2516
Abstract
We here characterize changes in metabolite patterns in glioblastoma patients undergoing surgery and concurrent chemoradiation using machine learning (ML) algorithms to characterize metabolic changes during different stages of the treatment protocol. We examined 105 plasma specimens (before surgery, 2 days after surgical resection, [...] Read more.
We here characterize changes in metabolite patterns in glioblastoma patients undergoing surgery and concurrent chemoradiation using machine learning (ML) algorithms to characterize metabolic changes during different stages of the treatment protocol. We examined 105 plasma specimens (before surgery, 2 days after surgical resection, before starting concurrent chemoradiation, and immediately after chemoradiation) from 36 patients with isocitrate dehydrogenase (IDH) wildtype glioblastoma. Untargeted GC-TOF mass spectrometry-based metabolomics was used given its superiority in identifying and quantitating small metabolites; this yielded 157 structurally identified metabolites. Using Multinomial Logistic Regression (MLR) and GradientBoostingClassifier (GB Classifier), ML models classified specimens based on metabolic changes. The classification performance of these models was evaluated using performance metrics and area under the curve (AUC) scores. Comparing post-radiation to pre-radiation showed increased levels of 15 metabolites: glycine, serine, threonine, oxoproline, 6-deoxyglucose, gluconic acid, glycerol-alpha-phosphate, ethanolamine, propyleneglycol, triethanolamine, xylitol, succinic acid, arachidonic acid, linoleic acid, and fumaric acid. After chemoradiation, a significant decrease was detected in 3-aminopiperidine 2,6-dione. An MLR classification of the treatment phases was performed with 78% accuracy and 75% precision (AUC = 0.89). The alternative GB Classifier algorithm achieved 75% accuracy and 77% precision (AUC = 0.91). Finally, we investigated specific patterns for metabolite changes in highly correlated metabolites. We identified metabolites with characteristic changing patterns between pre-surgery and post-surgery and post-radiation samples. To the best of our knowledge, this is the first study to describe blood metabolic signatures using ML algorithms during different treatment phases in patients with glioblastoma. A larger study is needed to validate the results and the potential application of this algorithm for the characterization of treatment responses. Full article
(This article belongs to the Special Issue Artificial Intelligence in Cancer Metabolism and Metabolomics)
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17 pages, 4907 KiB  
Article
Differentiation of Glioblastoma and Brain Metastases by MRI-Based Oxygen Metabolomic Radiomics and Deep Learning
by Andreas Stadlbauer, Gertraud Heinz, Franz Marhold, Anke Meyer-Bäse, Oliver Ganslandt, Michael Buchfelder and Stefan Oberndorfer
Metabolites 2022, 12(12), 1264; https://doi.org/10.3390/metabo12121264 - 14 Dec 2022
Cited by 6 | Viewed by 2863
Abstract
Glioblastoma (GB) and brain metastasis (BM) are the most frequent types of brain tumors in adults. Their therapeutic management is quite different and a quick and reliable initial characterization has a significant impact on clinical outcomes. However, the differentiation of GB and BM [...] Read more.
Glioblastoma (GB) and brain metastasis (BM) are the most frequent types of brain tumors in adults. Their therapeutic management is quite different and a quick and reliable initial characterization has a significant impact on clinical outcomes. However, the differentiation of GB and BM remains a major challenge in today’s clinical neurooncology due to their very similar appearance in conventional magnetic resonance imaging (MRI). Novel metabolic neuroimaging has proven useful for improving diagnostic performance but requires artificial intelligence for implementation in clinical routines. Here; we investigated whether the combination of radiomic features from MR-based oxygen metabolism (“oxygen metabolic radiomics”) and deep convolutional neural networks (CNNs) can support reliably pre-therapeutic differentiation of GB and BM in a clinical setting. A self-developed one-dimensional CNN combined with radiomic features from the cerebral metabolic rate of oxygen (CMRO2) was clearly superior to human reading in all parameters for classification performance. The radiomic features for tissue oxygen saturation (mitoPO2; i.e., tissue hypoxia) also showed better diagnostic performance compared to the radiologists. Interestingly, both the mean and median values for quantitative CMRO2 and mitoPO2 values did not differ significantly between GB and BM. This demonstrates that the combination of radiomic features and DL algorithms is more efficient for class differentiation than the comparison of mean or median values. Oxygen metabolic radiomics and deep neural networks provide insights into brain tumor phenotype that may have important diagnostic implications and helpful in clinical routine diagnosis. Full article
(This article belongs to the Special Issue Artificial Intelligence in Cancer Metabolism and Metabolomics)
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12 pages, 1850 KiB  
Article
Can Persistent Homology Features Capture More Intrinsic Information about Tumors from 18F-Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography Images of Head and Neck Cancer Patients?
by Quoc Cuong Le, Hidetaka Arimura, Kenta Ninomiya, Takumi Kodama and Tetsuhiro Moriyama
Metabolites 2022, 12(10), 972; https://doi.org/10.3390/metabo12100972 - 14 Oct 2022
Cited by 3 | Viewed by 2023
Abstract
This study hypothesized that persistent homology (PH) features could capture more intrinsic information about the metabolism and morphology of tumors from 18F-fluorodeoxyglucose positron emission tomography (PET)/computed tomography (CT) images of patients with head and neck (HN) cancer than other conventional features. PET/CT [...] Read more.
This study hypothesized that persistent homology (PH) features could capture more intrinsic information about the metabolism and morphology of tumors from 18F-fluorodeoxyglucose positron emission tomography (PET)/computed tomography (CT) images of patients with head and neck (HN) cancer than other conventional features. PET/CT images and clinical variables of 207 patients were selected from the publicly available dataset of the Cancer Imaging Archive. PH images were generated from persistent diagrams obtained from PET/CT images. The PH features were derived from the PH PET/CT images. The signatures were constructed in a training cohort from features from CT, PET, PH-CT, and PH-PET images; clinical variables; and the combination of features and clinical variables. Signatures were evaluated using statistically significant differences (p-value, log-rank test) between survival curves for low- and high-risk groups and the C-index. In an independent test cohort, the signature consisting of PH-PET features and clinical variables exhibited the lowest log-rank p-value of 3.30 × 10−5 and C-index of 0.80, compared with log-rank p-values from 3.52 × 10−2 to 1.15 × 10−4 and C-indices from 0.34 to 0.79 for other signatures. This result suggests that PH features can capture the intrinsic information of tumors and predict prognosis in patients with HN cancer. Full article
(This article belongs to the Special Issue Artificial Intelligence in Cancer Metabolism and Metabolomics)
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17 pages, 2206 KiB  
Article
Weighted Gene Co-Expression Network Analysis and Support Vector Machine Learning in the Proteomic Profiling of Cerebrospinal Fluid from Extraventricular Drainage in Child Medulloblastoma
by Maurizio Bruschi, Xhuliana Kajana, Andrea Petretto, Martina Bartolucci, Marco Pavanello, Gian Marco Ghiggeri, Isabella Panfoli and Giovanni Candiano
Metabolites 2022, 12(8), 724; https://doi.org/10.3390/metabo12080724 - 5 Aug 2022
Cited by 3 | Viewed by 2433
Abstract
Medulloblastoma (MB) is the most common pediatric malignant central nervous system tumor. Overall survival in MB depends on treatment tuning. There is aneed for biomarkers of residual disease and recurrence. We analyzed the proteome of waste cerebrospinal fluid (CSF) from extraventricular drainage (EVD) [...] Read more.
Medulloblastoma (MB) is the most common pediatric malignant central nervous system tumor. Overall survival in MB depends on treatment tuning. There is aneed for biomarkers of residual disease and recurrence. We analyzed the proteome of waste cerebrospinal fluid (CSF) from extraventricular drainage (EVD) from six children bearing various subtypes of MB and six controls needing EVD insertion for unrelated causes. Samples included total CSF, microvesicles, exosomes, and proteins captured by combinatorial peptide ligand library (CPLL). Liquid chromatography-coupled tandem mass spectrometry proteomics identified 3560 proteins in CSF from control and MB patients, 2412 (67.7%) of which were overlapping, and 346 (9.7%) and 805 (22.6%) were exclusive. Multidimensional scaling analysis discriminated samples. The weighted gene co-expression network analysis (WGCNA) identified those modules functionally associated with the samples. A ranked core of 192 proteins allowed distinguishing between control and MB samples. Machine learning highlighted long-chain fatty acid transport protein 4 (SLC27A4) and laminin B-type (LMNB1) as proteins that maximized the discrimination between control and MB samples. Machine learning WGCNA and support vector machine learning were able to distinguish between MB versus non-tumor/hemorrhagic controls. The two potential protein biomarkers for the discrimination between control and MB may guide therapy and predict recurrences, improving the MB patients’ quality of life. Full article
(This article belongs to the Special Issue Artificial Intelligence in Cancer Metabolism and Metabolomics)
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Review

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12 pages, 2419 KiB  
Review
Using Machine Vision of Glycolytic Elements to Predict Breast Cancer Recurrences: Design and Implementation
by Howard R. Petty
Metabolites 2023, 13(1), 41; https://doi.org/10.3390/metabo13010041 - 27 Dec 2022
Cited by 2 | Viewed by 2669
Abstract
A major goal of biomedical research has been the early and quantitative identification of patients who will subsequently experience a cancer recurrence. In this review, I discuss the ability of glycolytic enzyme and transporter patterns within tissues to detect sub-populations of cells within [...] Read more.
A major goal of biomedical research has been the early and quantitative identification of patients who will subsequently experience a cancer recurrence. In this review, I discuss the ability of glycolytic enzyme and transporter patterns within tissues to detect sub-populations of cells within ductal carcinoma in situ (DCIS) lesions that specifically precede cancer recurrences. The test uses conventional formalin fixed paraffin embedded tissue samples. The accuracy of this machine vision test rests on the identification of relevant glycolytic components that promote enhanced glycolysis (phospho-Ser226-glucose transporter type 1 (phospho-Ser226-GLUT1) and phosphofructokinase type L (PFKL)), their trafficking in tumor cells and tissues as judged by computer vision, and their high signal-to-noise levels. For each patient, machine vision stratifies micrographs from each lesion as the probability that the lesion originated from a recurrent sample. This stratification method removes overlap between the predicted recurrent and non-recurrent patients, which eliminates distribution-dependent false positives and false negatives. The method identifies computationally negative samples as non-recurrent and computationally positive samples are recurrent; computationally positive non-recurrent samples are likely due to mastectomies. The early phosphorylation and isoform switching events, spatial locations and clustering constitute important steps in metabolic reprogramming. This work also illuminates mechanistic steps occurring prior to a recurrence, which may contribute to the development of new drugs. Full article
(This article belongs to the Special Issue Artificial Intelligence in Cancer Metabolism and Metabolomics)
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Other

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15 pages, 2430 KiB  
Systematic Review
Assessing Metabolic Markers in Glioblastoma Using Machine Learning: A Systematic Review
by Zachery D. Neil, Noah Pierzchajlo, Candler Boyett, Olivia Little, Cathleen C. Kuo, Nolan J. Brown and Julian Gendreau
Metabolites 2023, 13(2), 161; https://doi.org/10.3390/metabo13020161 - 21 Jan 2023
Cited by 5 | Viewed by 2584
Abstract
Glioblastoma (GBM) is a common and deadly brain tumor with late diagnoses and poor prognoses. Machine learning (ML) is an emerging tool that can create highly accurate diagnostic and prognostic prediction models. This paper aimed to systematically search the literature on ML for [...] Read more.
Glioblastoma (GBM) is a common and deadly brain tumor with late diagnoses and poor prognoses. Machine learning (ML) is an emerging tool that can create highly accurate diagnostic and prognostic prediction models. This paper aimed to systematically search the literature on ML for GBM metabolism and assess recent advancements. A literature search was performed using predetermined search terms. Articles describing the use of an ML algorithm for GBM metabolism were included. Ten studies met the inclusion criteria for analysis: diagnostic (n = 3, 30%), prognostic (n = 6, 60%), or both (n = 1, 10%). Most studies analyzed data from multiple databases, while 50% (n = 5) included additional original samples. At least 2536 data samples were run through an ML algorithm. Twenty-seven ML algorithms were recorded with a mean of 2.8 algorithms per study. Algorithms were supervised (n = 24, 89%), unsupervised (n = 3, 11%), continuous (n = 19, 70%), or categorical (n = 8, 30%). The mean reported accuracy and AUC of ROC were 95.63% and 0.779, respectively. One hundred six metabolic markers were identified, but only EMP3 was reported in multiple studies. Many studies have identified potential biomarkers for GBM diagnosis and prognostication. These algorithms show promise; however, a consensus on even a handful of biomarkers has not yet been made. Full article
(This article belongs to the Special Issue Artificial Intelligence in Cancer Metabolism and Metabolomics)
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