Metabolic-imaging of human glioblastoma live tumors: A new precision-medicine approach to predict tumor treatment response early
BackgroundGlioblastoma (GB) is the most severe form of brain cancer, with a 12-15 month median survival. Surgical resection, temozolomide (TMZ) treatment, and radiotherapy remain the primary therapeutic options for GB, and no new therapies have been introduced in recent years. This therapeutic stand...
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Frontiers Media S.A.
2022-09-01
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| Series: | Frontiers in Oncology |
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| author | Mariangela Morelli Francesca Lessi Serena Barachini Serena Barachini Romano Liotti Romano Liotti Nicola Montemurro Paolo Perrini Orazio Santo Santonocito Carlo Gambacciani Matija Snuderl Francesco Pieri Filippo Aquila Azzurra Farnesi Antonio Giuseppe Naccarato Paolo Viacava Francesco Cardarelli Gianmarco Ferri Gianmarco Ferri Paul Mulholland Diego Ottaviani Fabiola Paiar Gaetano Liberti Francesco Pasqualetti Francesco Pasqualetti Michele Menicagli Paolo Aretini Giovanni Signore Sara Franceschi Chiara Maria Mazzanti |
| author_facet | Mariangela Morelli Francesca Lessi Serena Barachini Serena Barachini Romano Liotti Romano Liotti Nicola Montemurro Paolo Perrini Orazio Santo Santonocito Carlo Gambacciani Matija Snuderl Francesco Pieri Filippo Aquila Azzurra Farnesi Antonio Giuseppe Naccarato Paolo Viacava Francesco Cardarelli Gianmarco Ferri Gianmarco Ferri Paul Mulholland Diego Ottaviani Fabiola Paiar Gaetano Liberti Francesco Pasqualetti Francesco Pasqualetti Michele Menicagli Paolo Aretini Giovanni Signore Sara Franceschi Chiara Maria Mazzanti |
| author_sort | Mariangela Morelli |
| collection | DOAJ |
| description | BackgroundGlioblastoma (GB) is the most severe form of brain cancer, with a 12-15 month median survival. Surgical resection, temozolomide (TMZ) treatment, and radiotherapy remain the primary therapeutic options for GB, and no new therapies have been introduced in recent years. This therapeutic standstill is primarily due to preclinical approaches that do not fully respect the complexity of GB cell biology and fail to test efficiently anti-cancer treatments. Therefore, better treatment screening approaches are needed. In this study, we have developed a novel functional precision medicine approach to test the response to anticancer treatments in organoids derived from the resected tumors of glioblastoma patients.MethodsGB organoids were grown for a short period of time to prevent any genetic and morphological evolution and divergence from the tumor of origin. We chose metabolic imaging by NAD(P)H fluorescence lifetime imaging microscopy (FLIM) to predict early and non-invasively ex-vivo anti-cancer treatment responses of GB organoids. TMZ was used as the benchmark drug to validate the approach. Whole-transcriptome and whole-exome analyses were performed to characterize tumor cases stratification.ResultsOur functional precision medicine approach was completed within one week after surgery and two groups of TMZ Responder and Non-Responder tumors were identified. FLIM-based metabolic tumor stratification was well reflected at the molecular level, confirming the validity of our approach, highlighting also new target genes associated with TMZ treatment and identifying a new 17-gene molecular signature associated with survival. The number of MGMT gene promoter methylated tumors was higher in the responsive group, as expected, however, some non-methylated tumor cases turned out to be nevertheless responsive to TMZ, suggesting that our procedure could be synergistic with the classical MGMT methylation biomarker.ConclusionsFor the first time, FLIM-based metabolic imaging was used on live glioblastoma organoids. Unlike other approaches, ex-vivo patient-tailored drug response is performed at an early stage of tumor culturing with no animal involvement and with minimal tampering with the original tumor cytoarchitecture. This functional precision medicine approach can be exploited in a range of clinical and laboratory settings to improve the clinical management of GB patients and implemented on other cancers as well. |
| format | Article |
| id | doaj-art-a0c643cddb5e4ccf92ca9ca6b88b0ff3 |
| institution | DOAJ |
| issn | 2234-943X |
| language | English |
| publishDate | 2022-09-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj-art-a0c643cddb5e4ccf92ca9ca6b88b0ff32025-08-20T03:15:04ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2022-09-011210.3389/fonc.2022.969812969812Metabolic-imaging of human glioblastoma live tumors: A new precision-medicine approach to predict tumor treatment response earlyMariangela Morelli0Francesca Lessi1Serena Barachini2Serena Barachini3Romano Liotti4Romano Liotti5Nicola Montemurro6Paolo Perrini7Orazio Santo Santonocito8Carlo Gambacciani9Matija Snuderl10Francesco Pieri11Filippo Aquila12Azzurra Farnesi13Antonio Giuseppe Naccarato14Paolo Viacava15Francesco Cardarelli16Gianmarco Ferri17Gianmarco Ferri18Paul Mulholland19Diego Ottaviani20Fabiola Paiar21Gaetano Liberti22Francesco Pasqualetti23Francesco Pasqualetti24Michele Menicagli25Paolo Aretini26Giovanni Signore27Sara Franceschi28Chiara Maria Mazzanti29Section of Genomics and Transcriptomics, Fondazione Pisana per la Scienza, San Giuliano Terme, Pisa, ItalySection of Genomics and Transcriptomics, Fondazione Pisana per la Scienza, San Giuliano Terme, Pisa, ItalySection of Genomics and Transcriptomics, Fondazione Pisana per la Scienza, San Giuliano Terme, Pisa, ItalyDepartment of Clinical and Experimental Medicine, University of Pisa, Pisa, ItalySection of Genomics and Transcriptomics, Fondazione Pisana per la Scienza, San Giuliano Terme, Pisa, ItalyDepartment of Biology, University of Pisa, Pisa, ItalyDepartment of Neurosurgery, Azienda Ospedaliera Universitaria Pisana, Pisa, ItalyDepartment of Neurosurgery, Azienda Ospedaliera Universitaria Pisana, Pisa, ItalyNeurosurgical Department of Spedali Riuniti di Livorno, Livorno, ItalyNeurosurgical Department of Spedali Riuniti di Livorno, Livorno, ItalyDepartment of Pathology, New York University (NYU) Langone Medical Center, New York City, NY, United StatesNeurosurgical Department of Spedali Riuniti di Livorno, Livorno, ItalyNeurosurgical Department of Spedali Riuniti di Livorno, Livorno, ItalyNeurosurgical Department of Spedali Riuniti di Livorno, Livorno, ItalyDepartment of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, ItalyAnatomical Pathology Department, Azienda Ospedaliera Toscana Nord-ovest, Livorno, ItalyNational Enterprise for nanoScience and nanoTechnology (NEST), Scuola Normale Superiore and Istituto Nanoscienze-CNR, Pisa, ItalyNational Enterprise for nanoScience and nanoTechnology (NEST), Scuola Normale Superiore and Istituto Nanoscienze-CNR, Pisa, Italy0Section of Nanomedicine, Fondazione Pisana per la Scienza, San Giuliano Terme, Pisa, Italy1Department of Oncology, University College London Hospitals, London, United Kingdom1Department of Oncology, University College London Hospitals, London, United Kingdom2Department of Radiation Oncology, Azienda Ospedaliera Universitaria Pisana, University of Pisa, Pisa, ItalyDepartment of Neurosurgery, Azienda Ospedaliera Universitaria Pisana, Pisa, Italy2Department of Radiation Oncology, Azienda Ospedaliera Universitaria Pisana, University of Pisa, Pisa, Italy3Department of Oncology, University of Oxford, Oxford, United KingdomSection of Genomics and Transcriptomics, Fondazione Pisana per la Scienza, San Giuliano Terme, Pisa, Italy4Section of Bioinformatics, Fondazione Pisana per la Scienza, San Giuliano Terme, Pisa, Italy0Section of Nanomedicine, Fondazione Pisana per la Scienza, San Giuliano Terme, Pisa, ItalySection of Genomics and Transcriptomics, Fondazione Pisana per la Scienza, San Giuliano Terme, Pisa, ItalySection of Genomics and Transcriptomics, Fondazione Pisana per la Scienza, San Giuliano Terme, Pisa, ItalyBackgroundGlioblastoma (GB) is the most severe form of brain cancer, with a 12-15 month median survival. Surgical resection, temozolomide (TMZ) treatment, and radiotherapy remain the primary therapeutic options for GB, and no new therapies have been introduced in recent years. This therapeutic standstill is primarily due to preclinical approaches that do not fully respect the complexity of GB cell biology and fail to test efficiently anti-cancer treatments. Therefore, better treatment screening approaches are needed. In this study, we have developed a novel functional precision medicine approach to test the response to anticancer treatments in organoids derived from the resected tumors of glioblastoma patients.MethodsGB organoids were grown for a short period of time to prevent any genetic and morphological evolution and divergence from the tumor of origin. We chose metabolic imaging by NAD(P)H fluorescence lifetime imaging microscopy (FLIM) to predict early and non-invasively ex-vivo anti-cancer treatment responses of GB organoids. TMZ was used as the benchmark drug to validate the approach. Whole-transcriptome and whole-exome analyses were performed to characterize tumor cases stratification.ResultsOur functional precision medicine approach was completed within one week after surgery and two groups of TMZ Responder and Non-Responder tumors were identified. FLIM-based metabolic tumor stratification was well reflected at the molecular level, confirming the validity of our approach, highlighting also new target genes associated with TMZ treatment and identifying a new 17-gene molecular signature associated with survival. The number of MGMT gene promoter methylated tumors was higher in the responsive group, as expected, however, some non-methylated tumor cases turned out to be nevertheless responsive to TMZ, suggesting that our procedure could be synergistic with the classical MGMT methylation biomarker.ConclusionsFor the first time, FLIM-based metabolic imaging was used on live glioblastoma organoids. Unlike other approaches, ex-vivo patient-tailored drug response is performed at an early stage of tumor culturing with no animal involvement and with minimal tampering with the original tumor cytoarchitecture. This functional precision medicine approach can be exploited in a range of clinical and laboratory settings to improve the clinical management of GB patients and implemented on other cancers as well.https://www.frontiersin.org/articles/10.3389/fonc.2022.969812/fullglioblastomametabolic imagingdrug response assaypredictive modelFLIM (fluorescence lifetime imaging microscopy) |
| spellingShingle | Mariangela Morelli Francesca Lessi Serena Barachini Serena Barachini Romano Liotti Romano Liotti Nicola Montemurro Paolo Perrini Orazio Santo Santonocito Carlo Gambacciani Matija Snuderl Francesco Pieri Filippo Aquila Azzurra Farnesi Antonio Giuseppe Naccarato Paolo Viacava Francesco Cardarelli Gianmarco Ferri Gianmarco Ferri Paul Mulholland Diego Ottaviani Fabiola Paiar Gaetano Liberti Francesco Pasqualetti Francesco Pasqualetti Michele Menicagli Paolo Aretini Giovanni Signore Sara Franceschi Chiara Maria Mazzanti Metabolic-imaging of human glioblastoma live tumors: A new precision-medicine approach to predict tumor treatment response early Frontiers in Oncology glioblastoma metabolic imaging drug response assay predictive model FLIM (fluorescence lifetime imaging microscopy) |
| title | Metabolic-imaging of human glioblastoma live tumors: A new precision-medicine approach to predict tumor treatment response early |
| title_full | Metabolic-imaging of human glioblastoma live tumors: A new precision-medicine approach to predict tumor treatment response early |
| title_fullStr | Metabolic-imaging of human glioblastoma live tumors: A new precision-medicine approach to predict tumor treatment response early |
| title_full_unstemmed | Metabolic-imaging of human glioblastoma live tumors: A new precision-medicine approach to predict tumor treatment response early |
| title_short | Metabolic-imaging of human glioblastoma live tumors: A new precision-medicine approach to predict tumor treatment response early |
| title_sort | metabolic imaging of human glioblastoma live tumors a new precision medicine approach to predict tumor treatment response early |
| topic | glioblastoma metabolic imaging drug response assay predictive model FLIM (fluorescence lifetime imaging microscopy) |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2022.969812/full |
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