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Plasma nanoDSF Denaturation Profile at Baseline Is Predictive of Glioblastoma EGFR Status
Glioblastoma (GBM) is the most frequent and aggressive primary brain tumor in adults. Recently, we demonstrated that plasma denaturation profiles of glioblastoma patients obtained using Differential Scanning Fluorimetry can be automatically distinguished from healthy controls with the help of Artifi...
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| Published in: | Cancers 2023-01, Vol.15 (3), p.760 |
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| creator | Eyraud, Rémi Ayache, Stéphane Tsvetkov, Philipp O Kalidindi, Shanmugha Sri Baksheeva, Viktoriia E Boissonneau, Sébastien Jiguet-Jiglaire, Carine Appay, Romain Nanni-Metellus, Isabelle Chinot, Olivier Devred, François Tabouret, Emeline |
| description | Glioblastoma (GBM) is the most frequent and aggressive primary brain tumor in adults. Recently, we demonstrated that plasma denaturation profiles of glioblastoma patients obtained using Differential Scanning Fluorimetry can be automatically distinguished from healthy controls with the help of Artificial Intelligence (AI). Here, we used a set of machine-learning algorithms to automatically classify plasma denaturation profiles of glioblastoma patients according to their EGFR status. We found that Adaboost AI is able to discriminate
alterations in GBM with an 81.5% accuracy. Our study shows that the use of these plasma denaturation profiles could answer the unmet neuro-oncology need for diagnostic predictive biomarker in combination with brain MRI and clinical data, in order to allow for a rapid orientation of patients for a definitive pathological diagnosis and then treatment. We complete this study by showing that discriminating another mutation, MGMT, seems harder, and that post-surgery monitoring using our approach is not conclusive in the 48 h that follow the surgery. |
| doi_str_mv | 10.3390/cancers15030760 |
| format | article |
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alterations in GBM with an 81.5% accuracy. Our study shows that the use of these plasma denaturation profiles could answer the unmet neuro-oncology need for diagnostic predictive biomarker in combination with brain MRI and clinical data, in order to allow for a rapid orientation of patients for a definitive pathological diagnosis and then treatment. We complete this study by showing that discriminating another mutation, MGMT, seems harder, and that post-surgery monitoring using our approach is not conclusive in the 48 h that follow the surgery.</description><identifier>ISSN: 2072-6694</identifier><identifier>EISSN: 2072-6694</identifier><identifier>DOI: 10.3390/cancers15030760</identifier><identifier>PMID: 36765718</identifier><language>eng</language><publisher>Switzerland: MDPI AG</publisher><subject>Accuracy ; Algorithms ; Aneurysms ; Artificial intelligence ; Biochemistry, Molecular Biology ; Biomarkers ; Biophysics ; Brain cancer ; Brain tumors ; Cancer ; Care and treatment ; Chemotherapy ; Denaturation ; Diagnosis ; DNA methylation ; Epidermal growth factor receptors ; Fluorimetry ; Genetic aspects ; Glioblastoma ; Glioblastoma multiforme ; Life Sciences ; Lung cancer ; Magnetic resonance imaging ; Medical prognosis ; Neurobiology ; Neurons and Cognition ; Oncology ; Open source software ; Patients ; Plasma ; Support vector machines ; Surgery</subject><ispartof>Cancers, 2023-01, Vol.15 (3), p.760</ispartof><rights>COPYRIGHT 2023 MDPI AG</rights><rights>2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>Distributed under a Creative Commons Attribution 4.0 International License</rights><rights>2023 by the authors. 2023</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c522t-6283e36dde37d2048092bde5e4c23e18c836810565f28f5b51e107062583855c3</citedby><cites>FETCH-LOGICAL-c522t-6283e36dde37d2048092bde5e4c23e18c836810565f28f5b51e107062583855c3</cites><orcidid>0000-0002-5728-4759 ; 0000-0003-4545-6063 ; 0000-0001-5990-8898 ; 0000-0002-4031-2178 ; 0000-0002-8622-8836 ; 0000-0003-2982-7127</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/2774874387/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2774874387?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>230,314,724,777,781,882,25734,27905,27906,36993,36994,44571,53772,53774,74875</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/36765718$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink><backlink>$$Uhttps://amu.hal.science/hal-03957563$$DView record in HAL$$Hfree_for_read</backlink></links><search><creatorcontrib>Eyraud, Rémi</creatorcontrib><creatorcontrib>Ayache, Stéphane</creatorcontrib><creatorcontrib>Tsvetkov, Philipp O</creatorcontrib><creatorcontrib>Kalidindi, Shanmugha Sri</creatorcontrib><creatorcontrib>Baksheeva, Viktoriia E</creatorcontrib><creatorcontrib>Boissonneau, Sébastien</creatorcontrib><creatorcontrib>Jiguet-Jiglaire, Carine</creatorcontrib><creatorcontrib>Appay, Romain</creatorcontrib><creatorcontrib>Nanni-Metellus, Isabelle</creatorcontrib><creatorcontrib>Chinot, Olivier</creatorcontrib><creatorcontrib>Devred, François</creatorcontrib><creatorcontrib>Tabouret, Emeline</creatorcontrib><title>Plasma nanoDSF Denaturation Profile at Baseline Is Predictive of Glioblastoma EGFR Status</title><title>Cancers</title><addtitle>Cancers (Basel)</addtitle><description>Glioblastoma (GBM) is the most frequent and aggressive primary brain tumor in adults. Recently, we demonstrated that plasma denaturation profiles of glioblastoma patients obtained using Differential Scanning Fluorimetry can be automatically distinguished from healthy controls with the help of Artificial Intelligence (AI). Here, we used a set of machine-learning algorithms to automatically classify plasma denaturation profiles of glioblastoma patients according to their EGFR status. We found that Adaboost AI is able to discriminate
alterations in GBM with an 81.5% accuracy. Our study shows that the use of these plasma denaturation profiles could answer the unmet neuro-oncology need for diagnostic predictive biomarker in combination with brain MRI and clinical data, in order to allow for a rapid orientation of patients for a definitive pathological diagnosis and then treatment. We complete this study by showing that discriminating another mutation, MGMT, seems harder, and that post-surgery monitoring using our approach is not conclusive in the 48 h that follow the surgery.</description><subject>Accuracy</subject><subject>Algorithms</subject><subject>Aneurysms</subject><subject>Artificial intelligence</subject><subject>Biochemistry, Molecular Biology</subject><subject>Biomarkers</subject><subject>Biophysics</subject><subject>Brain cancer</subject><subject>Brain tumors</subject><subject>Cancer</subject><subject>Care and treatment</subject><subject>Chemotherapy</subject><subject>Denaturation</subject><subject>Diagnosis</subject><subject>DNA methylation</subject><subject>Epidermal growth factor receptors</subject><subject>Fluorimetry</subject><subject>Genetic aspects</subject><subject>Glioblastoma</subject><subject>Glioblastoma multiforme</subject><subject>Life Sciences</subject><subject>Lung cancer</subject><subject>Magnetic resonance 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nanoDSF Denaturation Profile at Baseline Is Predictive of Glioblastoma EGFR Status</title><author>Eyraud, Rémi ; Ayache, Stéphane ; Tsvetkov, Philipp O ; Kalidindi, Shanmugha Sri ; Baksheeva, Viktoriia E ; Boissonneau, Sébastien ; Jiguet-Jiglaire, Carine ; Appay, Romain ; Nanni-Metellus, Isabelle ; Chinot, Olivier ; Devred, François ; Tabouret, Emeline</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c522t-6283e36dde37d2048092bde5e4c23e18c836810565f28f5b51e107062583855c3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Accuracy</topic><topic>Algorithms</topic><topic>Aneurysms</topic><topic>Artificial intelligence</topic><topic>Biochemistry, Molecular Biology</topic><topic>Biomarkers</topic><topic>Biophysics</topic><topic>Brain cancer</topic><topic>Brain tumors</topic><topic>Cancer</topic><topic>Care and 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Recently, we demonstrated that plasma denaturation profiles of glioblastoma patients obtained using Differential Scanning Fluorimetry can be automatically distinguished from healthy controls with the help of Artificial Intelligence (AI). Here, we used a set of machine-learning algorithms to automatically classify plasma denaturation profiles of glioblastoma patients according to their EGFR status. We found that Adaboost AI is able to discriminate
alterations in GBM with an 81.5% accuracy. Our study shows that the use of these plasma denaturation profiles could answer the unmet neuro-oncology need for diagnostic predictive biomarker in combination with brain MRI and clinical data, in order to allow for a rapid orientation of patients for a definitive pathological diagnosis and then treatment. We complete this study by showing that discriminating another mutation, MGMT, seems harder, and that post-surgery monitoring using our approach is not conclusive in the 48 h that follow the surgery.</abstract><cop>Switzerland</cop><pub>MDPI AG</pub><pmid>36765718</pmid><doi>10.3390/cancers15030760</doi><orcidid>https://orcid.org/0000-0002-5728-4759</orcidid><orcidid>https://orcid.org/0000-0003-4545-6063</orcidid><orcidid>https://orcid.org/0000-0001-5990-8898</orcidid><orcidid>https://orcid.org/0000-0002-4031-2178</orcidid><orcidid>https://orcid.org/0000-0002-8622-8836</orcidid><orcidid>https://orcid.org/0000-0003-2982-7127</orcidid><oa>free_for_read</oa></addata></record> |
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| ispartof | Cancers, 2023-01, Vol.15 (3), p.760 |
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| subjects | Accuracy Algorithms Aneurysms Artificial intelligence Biochemistry, Molecular Biology Biomarkers Biophysics Brain cancer Brain tumors Cancer Care and treatment Chemotherapy Denaturation Diagnosis DNA methylation Epidermal growth factor receptors Fluorimetry Genetic aspects Glioblastoma Glioblastoma multiforme Life Sciences Lung cancer Magnetic resonance imaging Medical prognosis Neurobiology Neurons and Cognition Oncology Open source software Patients Plasma Support vector machines Surgery |
| title | Plasma nanoDSF Denaturation Profile at Baseline Is Predictive of Glioblastoma EGFR Status |
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