Using the Epitranscriptome to Diagnose and Treat Gliomas (EPIGLIO)

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.

Study Overview

• Status: Recruiting
• Conditions: Glioma
• Intervention / Treatment: Diagnostic test: Blood, urine and tumoral tissue samples; Diagnostic test: Tumoral tissue samples

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 Type: Interventional
• Enrollment (Estimated): 228
• Phase: Not Applicable

Contacts and Locations

• Study Contact: Name: Aurore MOUSSION; Phone Number: +33 467612446; Email: aurore.moussion@icm.unicancer.fr
• Study Contact Backup: Name: Emmanuelle TEXIER; Phone Number: +33 467613102; Email: emmanuelle.texier@icm.unicancer.fr

Study Locations

• France
  • Montpellier, France, 34090
    • Recruiting
    • CHU Montpellier - Hôpital St Eloi
    • Contact: Luc Bauchet, MD; Phone Number: +33 4 67 33 66 12; Email: l-bauchet@chu-montpellier.fr
  • Hérault
    • Montpellier, Hérault, France, 34298
      • Recruiting
      • Insitut Régional du Cancer de Montpellier
      • Contact: Amélie Darlix, MD; Phone Number: +33 4-67-61-25-57; Email: amelie.darlix@icm.unicancer.fr
      • Principal Investigator: Amélie Darlix, MD

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: Yes

Description

Inclusion Criteria:

• 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.

Exclusion Criteria:

• 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).

Study Plan

How is the study designed?

Design Details

• Primary Purpose: Diagnostic
• Allocation: Non-Randomized
• Interventional Model: Parallel Assignment
• Masking: None (Open Label)

Number of Arms

3

Arms and Interventions

Participant Group / Arm	Intervention / Treatment
Other: Cohort 1; 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	Diagnostic test: Blood, urine and tumoral tissue samples; Blood, urine and tumoral tissue samples
Other: Cohort 2; 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	Diagnostic test: Tumoral tissue samples; tumoral tissue samples
Other: Cohort 3; Spatial epitranscriptomic cohort: 8 patients (grade 2 mutated Isocitrate Dehydrogenase (IDH ) glioma with grade 3 or grade 4 focus	Diagnostic test: Tumoral tissue samples; tumoral tissue samples

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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.	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.	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.	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.	At baseline, 3 months, 9 months and 18 months

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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.	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.	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.	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.	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.	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 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.	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.	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).	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	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%.	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.	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.	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.	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).	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	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	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.	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	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).	At baseline
Existence of contrast (gadolinium) zone determined on magnetic resonance imaging (MRI)	Tumor zone appearing dark on imaging versus lighter healthy zone	At baseline

Collaborators and Investigators

• Sponsor: Institut du Cancer de Montpellier - Val d'Aurelle
• Collaborators: National Cancer Institute, France
• Investigators: Study Chair: Amélie DARLIX, MD, Institut régional du Cancer de Montpellier (ICM)

Publications and helpful links

General Publications

• Bland JM, Altman DG. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet. 1986 Feb 8;1(8476):307-10.
• Moons 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.
• Louis 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.
• 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.
• Posti 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.
• Darlix 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.
• Pedeutour-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.
• Barbieri 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.
• Macari 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.
• Relier 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.
• Relier 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.
• Amalric 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.
• Wen 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.

Study record dates

Study Major Dates

• Study Start (Actual): June 1, 2026
• Primary Completion (Estimated): May 1, 2028
• Study Completion (Estimated): May 1, 2031

Study Registration Dates

• First Submitted: August 23, 2024
• First Submitted That Met QC Criteria: August 26, 2024
• First Posted (Actual): August 28, 2024

Study Record Updates

• Last Update Posted (Actual): June 11, 2026
• Last Update Submitted That Met QC Criteria: June 9, 2026
• Last Verified: June 1, 2026

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Neoplasms; Neoplasms by Histologic Type; Neoplasms, Glandular and Epithelial; Neoplasms, Neuroepithelial; Neuroectodermal Tumors; Neoplasms, Germ Cell and Embryonal; Neoplasms, Nerve Tissue; Glioma; Investigative Techniques; Specimen Handling; Clinical Laboratory Techniques; Diagnostic Techniques and Procedures; Diagnosis; Punctures; Surgical Procedures, Operative; Reproductive and Urinary Physiological Phenomena; Urinary Tract Physiological Phenomena; Blood Specimen Collection; Urination
• Other Study ID Numbers: PROICM 2023-05 EPI; ID-RCB : 2023-A02360-45 (Other Identifier: ANSM)

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: YES
• IPD Plan Description: Participant data will be made available on request and with the completion of a contract between the sponsor and the requester.
• IPD Sharing Time Frame: Access to study data upon written detailed request sent to the institute of Montpellier Cancer (ICM), following publication and until 5 years after publication of summary data.
• IPD Sharing Access Criteria: The data shared will be limited to that required for independent mandated verification of the published results, the applicant will need authorization from ICM for personal access, and data will only be transferred after signing of a data access agreement.
• IPD Sharing Supporting Information Type: STUDY_PROTOCOL; SAP; ANALYTIC_CODE

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No