Evidence map›Paper›PMID 36132155›Full record

ArticleFrontiers in oncology2022

Metabolic-imaging of human glioblastoma live tumors: A new precision-medicine approach to predict tumor treatment response early.

Mariangela Morelli, Francesca Lessi, Serena Barachini, Romano Liotti, Nicola Montemurro, Paolo Perrini, Orazio Santo Santonocito, Carlo Gambacciani, Matija Snuderl, Francesco Pieri and 16 more

Erratum issuedOpen access · goldAbstract read
In one paragraph

Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 24 papers.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed
4.6field-weighted citation impact, top 4% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

24 citing papers in PubMed, 30 citations in OpenAlex.

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  17. Cell-Free miRNAs as Non-Invasive Biomarkers in Brain Tumors.Diagnostics (Basel, Switzerland) · 2023
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

26 authors at 10 institutions in 3 countries.

Mariangela MorelliSection of Genomics and Transcriptomics, Fondazione Pisana per la Scienza, San Giuliano Terme, Pisa, Italy.
Francesca LessiSection of Genomics and Transcriptomics, Fondazione Pisana per la Scienza, San Giuliano Terme, Pisa, Italy.
Serena BarachiniSection of Genomics and Transcriptomics, Fondazione Pisana per la Scienza, San Giuliano Terme, Pisa, Italy.
Romano LiottiSection of Genomics and Transcriptomics, Fondazione Pisana per la Scienza, San Giuliano Terme, Pisa, Italy.
Nicola MontemurroDepartment of Neurosurgery, Azienda Ospedaliera Universitaria Pisana, Pisa, Italy.
Paolo PerriniDepartment of Neurosurgery, Azienda Ospedaliera Universitaria Pisana, Pisa, Italy.
Orazio Santo SantonocitoNeurosurgical Department of Spedali Riuniti di Livorno, Livorno, Italy.
Carlo GambaccianiNeurosurgical Department of Spedali Riuniti di Livorno, Livorno, Italy.
Matija SnuderlDepartment of Pathology, New York University (NYU) Langone Medical Center, New York City, NY, United States.
Francesco PieriNeurosurgical Department of Spedali Riuniti di Livorno, Livorno, Italy.
Filippo AquilaNeurosurgical Department of Spedali Riuniti di Livorno, Livorno, Italy.
Azzurra FarnesiNeurosurgical Department of Spedali Riuniti di Livorno, Livorno, Italy.
Antonio Giuseppe NaccaratoDepartment of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy.
Paolo ViacavaAnatomical Pathology Department, Azienda Ospedaliera Toscana Nord-ovest, Livorno, Italy.
Francesco CardarelliNational Enterprise for nanoScience and nanoTechnology (NEST), Scuola Normale Superiore and Istituto Nanoscienze-CNR, Pisa, Italy.
Gianmarco FerriNational Enterprise for nanoScience and nanoTechnology (NEST), Scuola Normale Superiore and Istituto Nanoscienze-CNR, Pisa, Italy.
Paul MulhollandDepartment of Oncology, University College London Hospitals, London, United Kingdom.
Diego OttavianiDepartment of Oncology, University College London Hospitals, London, United Kingdom.
Fabiola PaiarDepartment of Radiation Oncology, Azienda Ospedaliera Universitaria Pisana, University of Pisa, Pisa, Italy.
Gaetano LibertiDepartment of Neurosurgery, Azienda Ospedaliera Universitaria Pisana, Pisa, Italy.
Francesco PasqualettiDepartment of Radiation Oncology, Azienda Ospedaliera Universitaria Pisana, University of Pisa, Pisa, Italy.
Michele MenicagliSection of Genomics and Transcriptomics, Fondazione Pisana per la Scienza, San Giuliano Terme, Pisa, Italy.
Paolo AretiniSection of Bioinformatics, Fondazione Pisana per la Scienza, San Giuliano Terme, Pisa, Italy.
Giovanni SignoreSection of Nanomedicine, Fondazione Pisana per la Scienza, San Giuliano Terme, Pisa, Italy.
Sara FranceschiSection of Genomics and Transcriptomics, Fondazione Pisana per la Scienza, San Giuliano Terme, Pisa, Italy.
Chiara Maria MazzantiSection of Genomics and Transcriptomics, Fondazione Pisana per la Scienza, San Giuliano Terme, Pisa, Italy.
Fondazione Pisa · ITOspedale di Livorno · ITAzienda Ospedaliera Universitaria Pisana · ITUniversity of Pisa · ITIstituto Nanoscienze · ITNational Enterprise for NanoScience and NanoTechnology · ITNYU Langone Health · USRoyal London Hospital · GBUniversity College London · GBUniversity of Oxford · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Glioblastoma (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. Methods: GB 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. Results: Our 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. Conclusions: For 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.

Indexed as

drug response assayFLIM (fluorescence lifetime imaging microscopy)glioblastomametabolic imagingpredictive model

Identifiers

PMID36132155
PMCPMC9483168
OpenAlexW4294607214

What OpenQuestion holds

Textmetadata
LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.