GliomaEPIGLIONCT06575452
Using the Epitranscriptome to Diagnose and Treat Gliomas
A study testing Blood, urine and tumoral tissue samples and Tumoral tissue samples in people with Glioma, run by Institut du Cancer de Montpellier - Val d'Aurelle at 2 sites in France.
Trial summary
Diffuse gliomas are among the most common tumors of the central nervous system, with high morbidity and mortality and very limited therapeutic possibilities. The diffuse glioma are characterized by significant variability in terms of age at diagnosis, histological and molecular features, classification, ability to… Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Glioma
- What is being tested?
- Blood, urine and tumoral tissue samples and Tumoral tissue samples
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 228 participants
- Where
- 2 sitesFrance
- Study dates
- 1 Jun 2026to May 2031
- Phase
- Interventional
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Who may be able to join
- Male / female over 18 years of age,
- Surgery (tumor resection) scheduled at Montpellier University Hospital for suspected, diffuse glioma, confirmed on tissue sample: IDH mutated grade 2 glioma (excluding tumors with a focus of grade 3 or 4 glioma), IDH mutated grade 3 glioma or GBM, IDH wild-type,
- No history of treatment (surgery, radiotherapy or chemotherapy) for glioma,
- Willingness and ability to comply with scheduled visits, treatment plan, laboratory tests and other study procedures,
- Patient has given express written informed consent prior to any study procedure,
- Patient affiliated to a French health insurance.
Who cannot join
- Patients whose regular follow-up is impossible for psychological, family, social or geographical reasons,
- Patients under guardianship, curatorship or safeguard of justice,
- Pregnant and/or breast-feeding patient (information gathered from the medical file, as part of the patient's standard medical care and follow-up),
- Histo-molecular diagnosis of grade 4 IDH-mutated astrocytoma,
- For grade 2 gliomas, presence within the tumor of one or more higher-grade sites (3 or 4).
Trial locations and contacts
2 sites in 1 country. Contact the study team or a site near you to ask about taking part.
France2 sites, 2 recruiting
- Recruiting
Insitut Régional du Cancer de Montpellier
Montpellier, Hérault, 34298
Amélie Darlix, MD, 4-67-61-25-57 ext. +33, amelie.darlix@icm.unicancer.fr
Amélie Darlix, MD
- Recruiting
CHU Montpellier - Hôpital St Eloi
Montpellier, 34090
Luc Bauchet, MD, 4 67 33 66 12 ext. +33, l-bauchet@chu-montpellier.fr
About this study
Diffuse gliomas are among the most common tumors of the central nervous system, with high morbidity and mortality and very limited therapeutic possibilities. The diffuse glioma are characterized by significant variability in terms of age at diagnosis, histological and molecular features, classification, ability to transform to a higher grade and/or to disseminate in the brain, response to treatment and patient outcome.
One of the main challenges in the management of diffuse gliomas is related to tumor heterogeneity within the same subgroup. Establishing an accurate tumor classification is of paramount importance for selecting personalized therapy or avoiding unnecessary treatment.
Read more
At present, the main diagnostic methods for detecting gliomas are based on histopathological features and mutation detection. Yet difficulties remain, due to tumor heterogeneity and sampling bias for tumors obtained from small biopsies. In particular, grade 2 (low-grade) and grade 3 (high-grade) gliomas cannot be easily distinguished, as intra-tumoral tumor grade heterogeneity is not uncommon in patients treated with extensive surgical resection. Another challenge in the field of gliomas is longitudinal monitoring of disease progression, which is currently mainly based on repeated brain Magnetic Resonance Imaging (MRI). New tools to detect tumor changes before the onset of imaging changes would be useful.
Several genetic, epigenetic, metabolic and immunological profiles have been established for gliomas. Recently, the world of RiboNucleic Acid (RNA) has emerged as a promising area to explore for cancer therapy, especially since the (re)discovery of RNA chemical modifications. To date, more than 150 types of post-transcriptional modifications have been reported on various RNA molecules. This complex landscape of chemical marks embodies a new, invisible code that governs the post-transcriptional fate of RNA: stability, splicing, storage, translation.
Read the detailed description
Diffuse gliomas are among the most common tumors of the central nervous system, with high morbidity and mortality and very limited therapeutic possibilities. Diffuse gliomas are characterized by great variability in terms of age at diagnosis, histological and molecular features, classification, ability to progress to a higher grade and/or to disseminate in the brain, response to treatment and patient outcome. One of the major challenges in the management of diffuse gliomas is related to the heterogeneity of tumor behavior within the same tumor subgroup. Although efforts have been made in recent decades to improve tumor characterization and classification, with the integration of molecular markers (e.g. Isocitrate DeHydrogenase (IDH) mutation), it remains difficult to predict treatment response and patient outcome at the individual level. Yet accurate tumor classification is of paramount importance in choosing personalized therapy or avoiding unnecessary treatments. At present, the main diagnostic methods for detecting gliomas are based on histopathological features, mutation detection or chromosome copy number variation.
However, difficulties remain, particularly with tumor classification, due to tumor heterogeneity and sampling bias for tumors obtained from small biopsies. In particular, grade 2 ("low-grade") and grade 3 ("high-grade") gliomas cannot be easily distinguished, as intratumoral tumor grade heterogeneity is not uncommon in patients treated with extensive surgical resection. Another challenge posed by gliomas is longitudinal monitoring of disease progression, which currently relies mainly on repeated brain MRI scans, with no return to the tumor itself due to the difficulty of obtaining new tumor samples in this setting. New tools to detect tumor changes in plasma, before imaging changes occur, would be useful. However, circulating markers present a real challenge, as the detection of markers readily used in other cancer types (e.g. circulating free DNA and circulating tumor cells) is hampered by a lack of sensitivity in gliomas.
Several genetic, epigenetic, metabolic and immunological profiles have been established in gliomas, considerably expanding the knowledge of the biological characteristics of these tumors and helping to identify potential treatments. Recently, the world of RNA has emerged as a promising area to explore for cancer therapy, particularly since the (re)discovery of chemical modifications of RNA (epitranscriptomics). To date, over 150 types of post-transcriptional modification have been reported on various RNA molecules. This landscape complex of chemical marks embodies a new, invisible code that governs the post-transcriptional fate of RNA: stability, splicing, storage, translation. Importantly, RNA epigenetics has emerged as a new layer of gene expression regulation in healthy tissues as well as in other pathologies such as cancer.
Chemical markers are associated with cancer evolution and adaptation, as well as with response to conventional therapies. Based on these observations, it is envisaged that: (1) the RNA epigenetic landscape evolves with cancer progression, establishing a "chemical signature" that could be exploited for diagnostic, prognostic and treatment response prediction purposes; (2) several chemical marks are not mere "transient" alterations but rather "driving" alterations of the tumorigenic process; (3) unlike unmodified nucleosides, modified nucleosides are preferentially excreted as metabolic end products in urine after circulating in the blood. Consequently, altered RNA markers in cancerous tissues can be detected in urine and blood and exploited for diagnostic purposes. An original approach recently published combines multiplex analysis of RNA marks by mass spectrometry with bioinformatics and machine learning. Using total RNA samples extracted from an existing cohort of patients (59 grade 2, 3 and 4 gliomas; 19 non-cancerous control samples), a first "chemical signature" capable of predicting glioma grade with remarkable efficiency and accuracy has been established.
N6, 2'-O-dimethyladenosine (m6Am), the most up-regulated marker in glioblastoma (GBM), is a driver of colorectal cancer aggressiveness. Located at the 5' end of messenger RiboNucleic Acid (mRNA), m6Am can influence mRNA stability and translation efficiency. This chemical tag is deposited by the Phosphorylated Carboxyl terminal domain Interacting Factor 1 (PCIF1), also known as CAPAM (PCIF1/CAPAM) methyltransferase (writer) and removed by the Fat mass and Obesity-associated protein (FTO) demethylase (eraser). FTO is down-regulated in colorectal cancer stem cells (CSCs), consistent with m6Am accumulation. High levels of m6Am significantly enhance CSC properties such as in vivo tumor initiation and chemoresistance, without significant changes to the transcriptome. This aggressive phenotype can be reversed by inhibition of PCIF1, demonstrating the potential of targeting epigenetic RNA effectors. The preliminary data on patient-derived glioma cell lines suggest a similar mechanism in glioma, where down-regulation of FTO promotes sphere-forming capacity in suspension culture of GBM stem cells.
(3) A method has been established to detect RNA markers in plasma samples that yielded favorable results after analysis of plasma samples from a colorectal cancer cohort. The same process was used to obtain preliminary data by analyzing plasma samples from grade 2 glioma patients vs. healthy donors. This experiment confirmed the possibility of detecting and quantifying 20 circulating nucleosides in blood. Significant changes were demonstrated between healthy donors and glioma patient samples for some of the circulating nucleosides. Some were up-regulated (e.g. n6,2'-O-dimethyladenosine (m6Am), 1-methylguanosine (m1G)) while others were down-regulated (e.g. adenosine (A), 5-methoxycarbonylmethyl-2-thiouridine (mcm5s2U)). Importantly, not all the tagged RNAs detected were altered (e.g. N1-methyladenosine (m1A); 5-methylcytosine (m5C)). If confirmed by a larger cohort, these changes could constitute an epitranscriptomics-based circulating signature for early disease detection. This preliminary experience reinforces the interest in m6Am.
Finally, changes were also observed in the serum of the same patients compared to healthy donor subjects, but from other nucleosides. This underlines the importance of studying circulating markers in blood for the diagnosis of gliomas.
- Conditions
- Glioma
- Being tested
- Blood, urine and tumoral tissue samples, Tumoral tissue samples
- Medical subject headings
- Glioma
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Blood, urine and tumoral tissue samplesDiagnostic test
Blood, urine and tumoral tissue samples
Tumoral tissue samplesDiagnostic test
tumoral tissue samples
| Study group | Role | What they receive |
|---|---|---|
| Cohort 1 | Other | Prospective cohort: 80 patients and 20 healthy volunteers
* Grade 2 mutated Isocitrate Dehydrogenase (IDH) glioma: 20 patients
* IDH mutated grade 3 glioma: 20 patients
* Glioblastoma (GBM), IDH wild-type: 40 patients Receives: Diagnostic Test: Blood, urine and tumoral tissue samples |
| Cohort 2 | Other | Retrospective cohort: 120 patients
* Grade 2 mutated Isocitrate Dehydrogenase (IDH) glioma: 40 patients
* IDH mutated grade 3 glioma: 40 patients
* Glioblastoma, IDH wild-type: 40 patients Receives: Diagnostic Test: Tumoral tissue samples |
| Cohort 3 | Other | Spatial epitranscriptomic cohort: 8 patients (grade 2 mutated Isocitrate Dehydrogenase (IDH ) glioma with grade 3 or grade 4 focus Receives: Diagnostic Test: Tumoral tissue samples |
What is being measured
The primary outcome is the main question the study is designed to answer.
Sensitivity of RNA-modified nucleoside expression marks for glioma diagnosis vs controls in blood for patients in cohort 1.
Sensitivity of a test corresponds to its ability to give a positive result when the hypothesis is verified.
Measured: At baseline, 3 months, 9 months and 18 months
Specificity of RNA-modified nucleoside expression marks for glioma diagnosis vs controls in blood for patients in cohort 1.
Specificity measures the ability of a test to give a negative result when the hypothesis is not verified.
Measured: At baseline, 3 months, 9 months and 18 months
Positive Predictive Value (PPV) of modified nucleoside expression marks for the diagnosis of glioma vs. controls in blood for patients in cohort 1.
Predictive value of a test is the probability of a condition being present as a function of the test result.
Measured: At baseline, 3 months, 9 months and 18 months
Negative Predictive Value (NPV) of modified nucleoside expression marks for the diagnosis of glioma vs. controls in blood for patients in cohort 1.
Negative predictive value is the probability that the condition is not present when the test is negative.
Measured: At baseline, 3 months, 9 months and 18 months
Secondary outcomes (21)
Sensitivity of RNA-modified nucleoside expression marks for glioma diagnosis vs controls in urine for patients in cohort 1.
The sensitivity of a test corresponds to its ability to give a positive result when the hypothesis is verified.
Measured: At baseline, 3 months, 9 months and 18 months
Specificity of RNA-modified nucleoside expression marks for glioma diagnosis vs controls in urine for patients in cohort 1.
Specificity measures the ability of a test to give a negative result when the hypothesis is not verified.
Measured: At baseline, 3 months, 9 months and 18 months
Positive Predictive Value (PPV) of modified nucleoside expression marks for the diagnosis of glioma vs. controls in urine for patients in cohort 1.
Predictive value of a test is the probability of a condition being present as a function of the test result.
Measured: At baseline, 3 months, 9 months and 18 months
Negative Predictive Value (NPV) of modified nucleoside expression marks for the diagnosis of glioma vs. controls in urine for patients in cohort 1.
Negative predictive value is the probability that the condition is not present when the test is negative.
Measured: At baseline, 3 months, 9 months and 18 months
Progression-free survival
The progression is determined by RANO criteria. The RANO criteria divide radiological response into four types, based on imaging (MRI) and clinical features 1,2 :
- complete response,
- partial response,
- stable disease,
- Progression.
Measured: Time from histological diagnosis to date of progression according to the Response Assessment in Neuro-Oncology Criteria (RANO 2.0) or death from any cause, assessed up to 18 months.
Global survival
Measured: Time from histological diagnosis to the date of death, whatever the cause, assessed up to 18 months.
The best response according to RANO 2.0
The RANO criteria divide radiological response into four types, based on imaging (MRI) and clinical features 1,2 :
- complete response,
- partial response,
- stable disease,
- Progression.
Measured: Time from surgery to magnetic response imagery (MRI) showing best response, assessed up to 18 months.
Quantitative value obtained by Liquid Chromatography-Mass Spectrometry (LC-MS) for each post-transcriptional modification (mark) of RiboNucleic Acid (RNA) in cohort 3
Modified nucleoside expression marks in grade 2 tissue versus grade 3 or 4 focus. Nucleoside is a constituent element of nucleic acids, made up of a nitrogenous base associated with a sugar (ribose for RNA and deoxyribose for DNA).
Liquid chromatography-mass spectrometry (LC-MS) is an analytical method that combines the performance of liquid chromatography and mass spectrometry to precisely identify and/or quantify a wide range of substances.
An LC-MS unit comprises two main components: a liquid chromatograph and a mass spectrometer.
Measured: At baseline, 3 months, 9 months and 18 months
Immunohistochemical detection of the Alpha-thalassemia-X-linked intellectual disability (ATRX) protein
Anti-ATRX immunostaining was classified into four semi-quantitative categories:
- Conserved expression (nuclear labeling of more than 90% of tumor cells),
- Total loss of expression (loss of expression by more than 90% of tumor cells),
- Partial loss of expression (labeling of 10-90% of tumor cells),
- Uninterpretable immunostaining (due to small or unrepresentative material).
Measured: At surgery, Day 0
Tumor grading and classification
Grade 2 : low-grade tumor Grade 2 : low-grade tumor Grade 4 : high-grade tumor, the most agressive Grade 3 : high-grade tumor
Measured: At surgery, Day 0
Immunohistochemical detection of kiel 67 (KI67) protein
Ki-67 is routinely detected on paraffin-embedded sections with an antibody, and its level calculated by evaluating the nuclear labeling of 1000 tumor cells, i.e. 100 cells/10 large fields (GC), with a positivity threshold above 5%.
Measured: At surgery, Day 0 of our timeline
Sensitivity of RNA-modified nucleoside expression marks for glioma diagnosis vs controls in tumoral tissue for patients in cohort 1 and 2.
The sensitivity of a test corresponds to its ability to give a positive result when the hypothesis is verified.
Measured: At Surgery, day 0 of our timeline
Specificity of RNA-modified nucleoside expression marks for glioma diagnosis vs controls in tumoral tissue for patients in cohort 1 and 2.
Negative predictive value is the probability that the condition is not present when the test is negative.
Measured: At Surgery, day 0 of our timeline
Positive Predictive Value (PPV) of modified nucleoside expression marks for the diagnosis of glioma vs. controls in tumoral tissue for patients in cohort 1 and 2.
Predictive value of a test is the probability of a condition being present as a function of the test result.
Measured: At surgery, day 0 of our timeline
Immunohistochemical detection of the isocitrate DeHydorgenase (IDH) mutated protein
Anti-IDH immunostaining was classified into two qualitative categories: positive or negative. When the mutation is present, all tumor cells express the mutated protein. Cytoplasmic immunopositivity predicts the presence of the mutation at position R132 of isocitrate dehydrogenase 1 (IDH1).
Measured: At surgery, Day 0 of our timeline
Measurement of mean tumor diameter (MTD) spontaneous growth rate by magnetic resoance imaging (MRI)
Calculated in mm/year
Measured: Assessed during follow-up, up to 18 months
Determination of the quality of surgical resection by magnetic resonance imaging (MRI)
The radiologist will assess whether the tumour resection margins are healthy or invaded by tumour foci
Measured: After surgery, approximately 30 days
Determination of tumor volume in cm3 by magnetic resonance imaging
Tumor volume (cm3) determined by manual segmentation of tumor contours and mean tumor diameter MTD (calculated according to the formula MTD = (2x volume)1/3) at baseline and during follow-up for the prospective cohort.
Measured: At baseline and during follow-up, assessed up to 18 months
Magnetic resonance imaging (MRI) evaluation of tumor invasion of soft meninges (leptomeningeal)
On MRI, the radiologist will assess whether the soft meninges have been invaded by the tumour
Measured: At baseline
Magnetic resonance imaging (MRI) determination of the number of tumor foci in the brain
The radiologist will assess the number of tumor foci per patient. Two groups will be created: unifocal (1 single tumor site) versus plurifocal (several tumor sites).
Measured: At baseline
Existence of contrast (gadolinium) zone determined on magnetic resonance imaging (MRI)
Tumor zone appearing dark on imaging versus lighter healthy zone
Measured: At baseline
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted28 Aug 2024
- Study start1 Jun 2026
- Primary completionMay 2028 (estimated)
- Study completionMay 2031 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (13)Papers the sponsor has linked to this study.
- Other trials for GliomaStudies of the same condition, open and under way.
- More trials run by Institut du Cancer de Montpellier - Val d'AurelleThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Diagnosis
- Allocation
- Non-randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Enrolment
- 228 participants (estimated)
Sponsor and investigators
- Sponsor type
- University, hospital or other
- Collaborators
- National Cancer Institute, France
- Responsible party
- Sponsor
- Investigators
- Amélie DARLIX, MD, Study chair, Institut régional du Cancer de Montpellier (ICM)
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06575452
- Sponsor's protocol ID
- PROICM 2023-05 EPI
- Other IDs
- ID-RCB : 2023-A02360-45
- Acronym
- EPIGLIO
- Status last verified
- Jun 2026
- First submitted
- 23 Aug 2024
- Last updated
- 11 Jun 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes: Participant data will be made available on request and with the completion of a contract between the sponsor and the requester.
Publications
Jonkhout N, Tran J, Smith MA, Schonrock N, Mattick JS, Novoa EM. The RNA modification landscape in human disease. RNA. 2017 Dec;23(12):1754-1769. doi: 10.1261/rna.063503.117. Epub 2017 Aug 30.
PubMed 28855326Louis DN, Perry A, Reifenberger G, von Deimling A, Figarella-Branger D, Cavenee WK, Ohgaki H, Wiestler OD, Kleihues P, Ellison DW. The 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summary. Acta Neuropathol. 2016 Jun;131(6):803-20. doi: 10.1007/s00401-016-1545-1. Epub 2016 May 9.
PubMed 27157931Posti JP, Bori M, Kauko T, Sankinen M, Nordberg J, Rahi M, Frantzen J, Vuorinen V, Sipila JO. Presenting symptoms of glioma in adults. Acta Neurol Scand. 2015 Feb;131(2):88-93. doi: 10.1111/ane.12285. Epub 2014 Sep 28.
PubMed 25263022Darlix A, Rigau V, Fraisse J, Goze C, Fabbro M, Duffau H. Postoperative follow-up for selected diffuse low-grade gliomas with WHO grade III/IV foci. Neurology. 2020 Feb 25;94(8):e830-e841. doi: 10.1212/WNL.0000000000008877. Epub 2020 Jan 22.
PubMed 31969465Pedeutour-Braccini Z, Burel-Vandenbos F, Goze C, Roger C, Bazin A, Costes-Martineau V, Duffau H, Rigau V. Microfoci of malignant progression in diffuse low-grade gliomas: towards the creation of an intermediate grade in glioma classification? Virchows Arch. 2015 Apr;466(4):433-44. doi: 10.1007/s00428-014-1712-5. Epub 2015 Jan 21.
PubMed 25861023Barbieri I, Kouzarides T. Role of RNA modifications in cancer. Nat Rev Cancer. 2020 Jun;20(6):303-322. doi: 10.1038/s41568-020-0253-2. Epub 2020 Apr 16.
PubMed 32300195Macari F, El-Houfi Y, Boldina G, Xu H, Khoury-Hanna S, Ollier J, Yazdani L, Zheng G, Bieche I, Legrand N, Paulet D, Durrieu S, Bystrom A, Delbecq S, Lapeyre B, Bauchet L, Pannequin J, Hollande F, Pan T, Teichmann M, Vagner S, David A, Choquet A, Joubert D. TRM6/61 connects PKCalpha with translational control through tRNAi(Met) stabilization: impact on tumorigenesis. Oncogene. 2016 Apr 7;35(14):1785-96. doi: 10.1038/onc.2015.244. Epub 2015 Aug 3.
PubMed 26234676Relier S, Amalric A, Attina A, Koumare IB, Rigau V, Burel Vandenbos F, Fontaine D, Baroncini M, Hugnot JP, Duffau H, Bauchet L, Hirtz C, Rivals E, David A. Multivariate Analysis of RNA Chemistry Marks Uncovers Epitranscriptomics-Based Biomarker Signature for Adult Diffuse Glioma Diagnostics. Anal Chem. 2022 Sep 6;94(35):11967-11972. doi: 10.1021/acs.analchem.2c01526. Epub 2022 Aug 23.
PubMed 35998076Relier S, Ripoll J, Guillorit H, Amalric A, Achour C, Boissiere F, Vialaret J, Attina A, Debart F, Choquet A, Macari F, Marchand V, Motorin Y, Samalin E, Vasseur JJ, Pannequin J, Aguilo F, Lopez-Crapez E, Hirtz C, Rivals E, Bastide A, David A. FTO-mediated cytoplasmic m6Am demethylation adjusts stem-like properties in colorectal cancer cell. Nat Commun. 2021 Mar 19;12(1):1716. doi: 10.1038/s41467-021-21758-4.
PubMed 33741917Amalric A, Bastide A, Attina A, Choquet A, Vialaret J, Lehmann S, David A, Hirtz C. Quantifying RNA modifications by mass spectrometry: a novel source of biomarkers in oncology. Crit Rev Clin Lab Sci. 2022 Jan;59(1):1-18. doi: 10.1080/10408363.2021.1958743. Epub 2021 Sep 2.
PubMed 34473579Wen PY, van den Bent M, Youssef G, Cloughesy TF, Ellingson BM, Weller M, Galanis E, Barboriak DP, de Groot J, Gilbert MR, Huang R, Lassman AB, Mehta M, Molinaro AM, Preusser M, Rahman R, Shankar LK, Stupp R, Villanueva-Meyer JE, Wick W, Macdonald DR, Reardon DA, Vogelbaum MA, Chang SM. RANO 2.0: Update to the Response Assessment in Neuro-Oncology Criteria for High- and Low-Grade Gliomas in Adults. J Clin Oncol. 2023 Nov 20;41(33):5187-5199. doi: 10.1200/JCO.23.01059. Epub 2023 Sep 29.
PubMed 37774317Moons KG, Altman DG, Reitsma JB, Ioannidis JP, Macaskill P, Steyerberg EW, Vickers AJ, Ransohoff DF, Collins GS. Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): explanation and elaboration. Ann Intern Med. 2015 Jan 6;162(1):W1-73. doi: 10.7326/M14-0698.
PubMed 25560730Bland JM, Altman DG. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet. 1986 Feb 8;1(8476):307-10.
PubMed 2868172
Study at a glance
From the registry record, last updated by the sponsor on 11 Jun 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Using the Epitranscriptome to Diagnose and Treat Gliomas
- ClinicalTrials.gov ID
- NCT06575452
- Acronym / sponsor ID
- EPIGLIO / PROICM 2023-05 EPI
- Conditions
- Glioma
- Study type
- Interventional
- Recruitment status
- Recruiting
- Enrolment
- 228 participants (estimated)
- Lead sponsor
- Institut du Cancer de Montpellier - Val d'AurelleUniversity, hospital or other
- Collaborators
- National Cancer Institute, France
- Study start
- 1 Jun 2026 (actual)
- Primary completion
- May 2028 (estimated)
- Study completion
- May 2031 (estimated)
- First posted
- 28 Aug 2024 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
Diffuse gliomas are among the most common tumors of the central nervous system, with high morbidity and mortality and very limited therapeutic possibilities. The diffuse glioma are characterized by significant variability in terms of age at diagnosis, histological and molecular features, classification, ability to transform to a higher grade and/or to disseminate in the brain, response to treatment and patient outcome.
One of the main challenges in the management of diffuse gliomas is related to tumor heterogeneity within the same subgroup. Establishing an accurate tumor classification is of paramount importance for selecting personalized therapy or avoiding unnecessary treatment.
At present, the main diagnostic methods for detecting gliomas are based on histopathological features and mutation detection. Yet difficulties remain, due to tumor heterogeneity and sampling bias for tumors obtained from small biopsies. In particular, grade 2 (low-grade) and grade 3 (high-grade) gliomas cannot be easily distinguished, as intra-tumoral tumor grade heterogeneity is not uncommon in patients treated with extensive surgical resection. Another challenge in the field of gliomas is longitudinal monitoring of disease progression, which is currently mainly based on repeated brain Magnetic Resonance Imaging (MRI). New tools to detect tumor changes before the onset of imaging changes would be useful.
Several genetic, epigenetic, metabolic and immunological profiles have been established for gliomas. Recently, the world of RiboNucleic Acid (RNA) has emerged as a promising area to explore for cancer therapy, especially since the (re)discovery of RNA chemical modifications. To date, more than 150 types of post-transcriptional modifications have been reported on various RNA molecules. This complex landscape of chemical marks embodies a new, invisible code that governs the post-transcriptional fate of RNA: stability, splicing, storage, translation.
Detailed description
Diffuse gliomas are among the most common tumors of the central nervous system, with high morbidity and mortality and very limited therapeutic possibilities. Diffuse gliomas are characterized by great variability in terms of age at diagnosis, histological and molecular features, classification, ability to progress to a higher grade and/or to disseminate in the brain, response to treatment and patient outcome. One of the major challenges in the management of diffuse gliomas is related to the heterogeneity of tumor behavior within the same tumor subgroup. Although efforts have been made in recent decades to improve tumor characterization and classification, with the integration of molecular markers (e.g. Isocitrate DeHydrogenase (IDH) mutation), it remains difficult to predict treatment response and patient outcome at the individual level. Yet accurate tumor classification is of paramount importance in choosing personalized therapy or avoiding unnecessary treatments. At present, the main diagnostic methods for detecting gliomas are based on histopathological features, mutation detection or chromosome copy number variation.
However, difficulties remain, particularly with tumor classification, due to tumor heterogeneity and sampling bias for tumors obtained from small biopsies. In particular, grade 2 ("low-grade") and grade 3 ("high-grade") gliomas cannot be easily distinguished, as intratumoral tumor grade heterogeneity is not uncommon in patients treated with extensive surgical resection. Another challenge posed by gliomas is longitudinal monitoring of disease progression, which currently relies mainly on repeated brain MRI scans, with no return to the tumor itself due to the difficulty of obtaining new tumor samples in this setting. New tools to detect tumor changes in plasma, before imaging changes occur, would be useful. However, circulating markers present a real challenge, as the detection of markers readily used in other cancer types (e.g. circulating free DNA and circulating tumor cells) is hampered by a lack of sensitivity in gliomas.
Several genetic, epigenetic, metabolic and immunological profiles have been established in gliomas, considerably expanding the knowledge of the biological characteristics of these tumors and helping to identify potential treatments. Recently, the world of RNA has emerged as a promising area to explore for cancer therapy, particularly since the (re)discovery of chemical modifications of RNA (epitranscriptomics). To date, over 150 types of post-transcriptional modification have been reported on various RNA molecules. This landscape complex of chemical marks embodies a new, invisible code that governs the post-transcriptional fate of RNA: stability, splicing, storage, translation. Importantly, RNA epigenetics has emerged as a new layer of gene expression regulation in healthy tissues as well as in other pathologies such as cancer.
Chemical markers are associated with cancer evolution and adaptation, as well as with response to conventional therapies. Based on these observations, it is envisaged that: (1) the RNA epigenetic landscape evolves with cancer progression, establishing a "chemical signature" that could be exploited for diagnostic, prognostic and treatment response prediction purposes; (2) several chemical marks are not mere "transient" alterations but rather "driving" alterations of the tumorigenic process; (3) unlike unmodified nucleosides, modified nucleosides are preferentially excreted as metabolic end products in urine after circulating in the blood. Consequently, altered RNA markers in cancerous tissues can be detected in urine and blood and exploited for diagnostic purposes. An original approach recently published combines multiplex analysis of RNA marks by mass spectrometry with bioinformatics and machine learning. Using total RNA samples extracted from an existing cohort of patients (59 grade 2, 3 and 4 gliomas; 19 non-cancerous control samples), a first "chemical signature" capable of predicting glioma grade with remarkable efficiency and accuracy has been established.
N6, 2'-O-dimethyladenosine (m6Am), the most up-regulated marker in glioblastoma (GBM), is a driver of colorectal cancer aggressiveness. Located at the 5' end of messenger RiboNucleic Acid (mRNA), m6Am can influence mRNA stability and translation efficiency. This chemical tag is deposited by the Phosphorylated Carboxyl terminal domain Interacting Factor 1 (PCIF1), also known as CAPAM (PCIF1/CAPAM) methyltransferase (writer) and removed by the Fat mass and Obesity-associated protein (FTO) demethylase (eraser). FTO is down-regulated in colorectal cancer stem cells (CSCs), consistent with m6Am accumulation. High levels of m6Am significantly enhance CSC properties such as in vivo tumor initiation and chemoresistance, without significant changes to the transcriptome. This aggressive phenotype can be reversed by inhibition of PCIF1, demonstrating the potential of targeting epigenetic RNA effectors. The preliminary data on patient-derived glioma cell lines suggest a similar mechanism in glioma, where down-regulation of FTO promotes sphere-forming capacity in suspension culture of GBM stem cells.
(3) A method has been established to detect RNA markers in plasma samples that yielded favorable results after analysis of plasma samples from a colorectal cancer cohort. The same process was used to obtain preliminary data by analyzing plasma samples from grade 2 glioma patients vs. healthy donors. This experiment confirmed the possibility of detecting and quantifying 20 circulating nucleosides in blood. Significant changes were demonstrated between healthy donors and glioma patient samples for some of the circulating nucleosides. Some were up-regulated (e.g. n6,2'-O-dimethyladenosine (m6Am), 1-methylguanosine (m1G)) while others were down-regulated (e.g. adenosine (A), 5-methoxycarbonylmethyl-2-thiouridine (mcm5s2U)). Importantly, not all the tagged RNAs detected were altered (e.g. N1-methyladenosine (m1A); 5-methylcytosine (m5C)). If confirmed by a larger cohort, these changes could constitute an epitranscriptomics-based circulating signature for early disease detection. This preliminary experience reinforces the interest in m6Am.
Finally, changes were also observed in the serum of the same patients compared to healthy donor subjects, but from other nucleosides. This underlines the importance of studying circulating markers in blood for the diagnosis of gliomas.
Study design
- Primary purpose
- Diagnosis
- Allocation
- Non-randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Number of arms
- 3
| Arm | Type | Receives |
|---|---|---|
| Cohort 1 | Other | Diagnostic Test: Blood, urine and tumoral tissue samplesProspective cohort: 80 patients and 20 healthy volunteers * Grade 2 mutated Isocitrate Dehydrogenase (IDH) glioma: 20 patients * IDH mutated grade 3 glioma: 20 patients * Glioblastoma (GBM), IDH wild-type: 40 patients |
| Cohort 2 | Other | Diagnostic Test: Tumoral tissue samplesRetrospective cohort: 120 patients * Grade 2 mutated Isocitrate Dehydrogenase (IDH) glioma: 40 patients * IDH mutated grade 3 glioma: 40 patients * Glioblastoma, IDH wild-type: 40 patients |
| Cohort 3 | Other | Diagnostic Test: Tumoral tissue samplesSpatial epitranscriptomic cohort: 8 patients (grade 2 mutated Isocitrate Dehydrogenase (IDH ) glioma with grade 3 or grade 4 focus |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (6 listed)
- Male / female over 18 years of age,
- Surgery (tumor resection) scheduled at Montpellier University Hospital for suspected, diffuse glioma, confirmed on tissue sample: IDH mutated grade 2 glioma (excluding tumors with a focus of grade 3 or 4 glioma), IDH mutated grade 3 glioma or GBM, IDH wild-type,
- No history of treatment (surgery, radiotherapy or chemotherapy) for glioma,
- Willingness and ability to comply with scheduled visits, treatment plan, laboratory tests and other study procedures,
- Patient has given express written informed consent prior to any study procedure,
Key exclusion criteria (5 listed)
- Patients whose regular follow-up is impossible for psychological, family, social or geographical reasons,
- Patients under guardianship, curatorship or safeguard of justice,
- Pregnant and/or breast-feeding patient (information gathered from the medical file, as part of the patient's standard medical care and follow-up),
- Histo-molecular diagnosis of grade 4 IDH-mutated astrocytoma,
- For grade 2 gliomas, presence within the tumor of one or more higher-grade sites (3 or 4).
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Blood, urine and tumoral tissue samplesBlood, urine and tumoral tissue samples | Diagnostic test | Cohort 1 |
| Tumoral tissue samplestumoral tissue samples | Diagnostic test | Cohort 2, Cohort 3 |
Primary outcome
Sensitivity of RNA-modified nucleoside expression marks for glioma diagnosis vs controls in blood for patients in cohort 1.
Time frame: At baseline, 3 months, 9 months and 18 months
Sensitivity of a test corresponds to its ability to give a positive result when the hypothesis is verified.
Specificity of RNA-modified nucleoside expression marks for glioma diagnosis vs controls in blood for patients in cohort 1.
Time frame: At baseline, 3 months, 9 months and 18 months
Specificity measures the ability of a test to give a negative result when the hypothesis is not verified.
Positive Predictive Value (PPV) of modified nucleoside expression marks for the diagnosis of glioma vs. controls in blood for patients in cohort 1.
Time frame: At baseline, 3 months, 9 months and 18 months
Predictive value of a test is the probability of a condition being present as a function of the test result.
Negative Predictive Value (NPV) of modified nucleoside expression marks for the diagnosis of glioma vs. controls in blood for patients in cohort 1.
Time frame: At baseline, 3 months, 9 months and 18 months
Negative predictive value is the probability that the condition is not present when the test is negative.
21 secondary outcomes
Sensitivity of RNA-modified nucleoside expression marks for glioma diagnosis vs controls in urine for patients in cohort 1.
Time frame: At baseline, 3 months, 9 months and 18 months
Specificity of RNA-modified nucleoside expression marks for glioma diagnosis vs controls in urine for patients in cohort 1.
Time frame: At baseline, 3 months, 9 months and 18 months
Positive Predictive Value (PPV) of modified nucleoside expression marks for the diagnosis of glioma vs. controls in urine for patients in cohort 1.
Time frame: At baseline, 3 months, 9 months and 18 months
Negative Predictive Value (NPV) of modified nucleoside expression marks for the diagnosis of glioma vs. controls in urine for patients in cohort 1.
Time frame: At baseline, 3 months, 9 months and 18 months
Progression-free survival
Time frame: Time from histological diagnosis to date of progression according to the Response Assessment in Neuro-Oncology Criteria (RANO 2.0) or death from any cause, assessed up to 18 months.
Global survival
Time frame: Time from histological diagnosis to the date of death, whatever the cause, assessed up to 18 months.
The best response according to RANO 2.0
Time frame: Time from surgery to magnetic response imagery (MRI) showing best response, assessed up to 18 months.
Quantitative value obtained by Liquid Chromatography-Mass Spectrometry (LC-MS) for each post-transcriptional modification (mark) of RiboNucleic Acid (RNA) in cohort 3
Time frame: At baseline, 3 months, 9 months and 18 months
Immunohistochemical detection of the Alpha-thalassemia-X-linked intellectual disability (ATRX) protein
Time frame: At surgery, Day 0
Tumor grading and classification
Time frame: At surgery, Day 0
Immunohistochemical detection of kiel 67 (KI67) protein
Time frame: At surgery, Day 0 of our timeline
Sensitivity of RNA-modified nucleoside expression marks for glioma diagnosis vs controls in tumoral tissue for patients in cohort 1 and 2.
Time frame: At Surgery, day 0 of our timeline
Specificity of RNA-modified nucleoside expression marks for glioma diagnosis vs controls in tumoral tissue for patients in cohort 1 and 2.
Time frame: At Surgery, day 0 of our timeline
Positive Predictive Value (PPV) of modified nucleoside expression marks for the diagnosis of glioma vs. controls in tumoral tissue for patients in cohort 1 and 2.
Time frame: At surgery, day 0 of our timeline
Immunohistochemical detection of the isocitrate DeHydorgenase (IDH) mutated protein
Time frame: At surgery, Day 0 of our timeline
Measurement of mean tumor diameter (MTD) spontaneous growth rate by magnetic resoance imaging (MRI)
Time frame: Assessed during follow-up, up to 18 months
Determination of the quality of surgical resection by magnetic resonance imaging (MRI)
Time frame: After surgery, approximately 30 days
Determination of tumor volume in cm3 by magnetic resonance imaging
Time frame: At baseline and during follow-up, assessed up to 18 months
Magnetic resonance imaging (MRI) evaluation of tumor invasion of soft meninges (leptomeningeal)
Time frame: At baseline
Magnetic resonance imaging (MRI) determination of the number of tumor foci in the brain
Time frame: At baseline
Existence of contrast (gadolinium) zone determined on magnetic resonance imaging (MRI)
Time frame: At baseline
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 11 Jun 2026.
- Background: PubMed 28855326Jonkhout N, Tran J, Smith MA, Schonrock N, Mattick JS, Novoa EM. The RNA modification landscape in human disease. RNA. 2017 Dec;23(12):1754-1769. doi: 10.1261/rna.063503.117. Epub 2017 Aug 30.
- Background: PubMed 27157931Louis DN, Perry A, Reifenberger G, von Deimling A, Figarella-Branger D, Cavenee WK, Ohgaki H, Wiestler OD, Kleihues P, Ellison DW. The 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summary. Acta Neuropathol. 2016 Jun;131(6):803-20. doi: 10.1007/s00401-016-1545-1. Epub 2016 May 9.
- Background: PubMed 25263022Posti JP, Bori M, Kauko T, Sankinen M, Nordberg J, Rahi M, Frantzen J, Vuorinen V, Sipila JO. Presenting symptoms of glioma in adults. Acta Neurol Scand. 2015 Feb;131(2):88-93. doi: 10.1111/ane.12285. Epub 2014 Sep 28.
- Background: PubMed 31969465Darlix A, Rigau V, Fraisse J, Goze C, Fabbro M, Duffau H. Postoperative follow-up for selected diffuse low-grade gliomas with WHO grade III/IV foci. Neurology. 2020 Feb 25;94(8):e830-e841. doi: 10.1212/WNL.0000000000008877. Epub 2020 Jan 22.
- Background: PubMed 25861023Pedeutour-Braccini Z, Burel-Vandenbos F, Goze C, Roger C, Bazin A, Costes-Martineau V, Duffau H, Rigau V. Microfoci of malignant progression in diffuse low-grade gliomas: towards the creation of an intermediate grade in glioma classification? Virchows Arch. 2015 Apr;466(4):433-44. doi: 10.1007/s00428-014-1712-5. Epub 2015 Jan 21.
- Background: PubMed 32300195Barbieri I, Kouzarides T. Role of RNA modifications in cancer. Nat Rev Cancer. 2020 Jun;20(6):303-322. doi: 10.1038/s41568-020-0253-2. Epub 2020 Apr 16.
- Background: PubMed 26234676Macari F, El-Houfi Y, Boldina G, Xu H, Khoury-Hanna S, Ollier J, Yazdani L, Zheng G, Bieche I, Legrand N, Paulet D, Durrieu S, Bystrom A, Delbecq S, Lapeyre B, Bauchet L, Pannequin J, Hollande F, Pan T, Teichmann M, Vagner S, David A, Choquet A, Joubert D. TRM6/61 connects PKCalpha with translational control through tRNAi(Met) stabilization: impact on tumorigenesis. Oncogene. 2016 Apr 7;35(14):1785-96. doi: 10.1038/onc.2015.244. Epub 2015 Aug 3.
- Background: PubMed 35998076Relier S, Amalric A, Attina A, Koumare IB, Rigau V, Burel Vandenbos F, Fontaine D, Baroncini M, Hugnot JP, Duffau H, Bauchet L, Hirtz C, Rivals E, David A. Multivariate Analysis of RNA Chemistry Marks Uncovers Epitranscriptomics-Based Biomarker Signature for Adult Diffuse Glioma Diagnostics. Anal Chem. 2022 Sep 6;94(35):11967-11972. doi: 10.1021/acs.analchem.2c01526. Epub 2022 Aug 23.
- Background: PubMed 33741917Relier S, Ripoll J, Guillorit H, Amalric A, Achour C, Boissiere F, Vialaret J, Attina A, Debart F, Choquet A, Macari F, Marchand V, Motorin Y, Samalin E, Vasseur JJ, Pannequin J, Aguilo F, Lopez-Crapez E, Hirtz C, Rivals E, Bastide A, David A. FTO-mediated cytoplasmic m6Am demethylation adjusts stem-like properties in colorectal cancer cell. Nat Commun. 2021 Mar 19;12(1):1716. doi: 10.1038/s41467-021-21758-4.
- Background: PubMed 34473579Amalric A, Bastide A, Attina A, Choquet A, Vialaret J, Lehmann S, David A, Hirtz C. Quantifying RNA modifications by mass spectrometry: a novel source of biomarkers in oncology. Crit Rev Clin Lab Sci. 2022 Jan;59(1):1-18. doi: 10.1080/10408363.2021.1958743. Epub 2021 Sep 2.
- Background: PubMed 37774317Wen PY, van den Bent M, Youssef G, Cloughesy TF, Ellingson BM, Weller M, Galanis E, Barboriak DP, de Groot J, Gilbert MR, Huang R, Lassman AB, Mehta M, Molinaro AM, Preusser M, Rahman R, Shankar LK, Stupp R, Villanueva-Meyer JE, Wick W, Macdonald DR, Reardon DA, Vogelbaum MA, Chang SM. RANO 2.0: Update to the Response Assessment in Neuro-Oncology Criteria for High- and Low-Grade Gliomas in Adults. J Clin Oncol. 2023 Nov 20;41(33):5187-5199. doi: 10.1200/JCO.23.01059. Epub 2023 Sep 29.
- Background: PubMed 25560730Moons KG, Altman DG, Reitsma JB, Ioannidis JP, Macaskill P, Steyerberg EW, Vickers AJ, Ransohoff DF, Collins GS. Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): explanation and elaboration. Ann Intern Med. 2015 Jan 6;162(1):W1-73. doi: 10.7326/M14-0698.
- Individual participant dataSharing planned: Yes. Participant data will be made available on request and with the completion of a contract between the sponsor and the requester.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Amélie DARLIX, MD study chair
Institut régional du Cancer de Montpellier (ICM)
2 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Blood, urine and tumoral tissue samples and Tumoral tissue samples in people with Glioma, run by Institut du Cancer de Montpellier - Val d'Aurelle at 2 sites in France.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 6 inclusion and 5 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 11 Jun 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 2 sites in France. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Aurore MOUSSION (467612446 ext. +33, aurore.moussion@icm.unicancer.fr), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Sensitivity of RNA-modified nucleoside expression marks for glioma diagnosis vs controls in blood for patients in cohort 1. (time frame: At baseline, 3 months, 9 months and 18 months). The study lists 4 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT06575452, which also lists 13 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 11 Jun 2026; AzTrials last refreshed its copy on 2 Nov 2026.