Ignite Creation Date:
2024-05-06 @ 7:36 PM
Last Modification Date:
2024-10-26 @ 3:10 PM
Study NCT ID:
NCT06067347
Status:
RECRUITING
Last Update Posted:
2024-01-24
First Post:
2023-09-05
Brief Title:
A Global Study of the PETAL Consortium
Sponsor:
Massachusetts General Hospital
Organization:
Massachusetts General Hospital
Study Overview
Official Title:
Integration of Machine Learning and Genomics to Predict Outcomes for Newly Diagnosed Relapsed and Refractory Mature T-cell and NKT-cell Lymphomas a Global Study of the PETAL Consortium
Status:
RECRUITING
Status Verified Date:
2024-01
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
PETAL
Brief Summary:
The goal of this observational study is to correlate molecular alterations with outcomes including overall survival OS progression-free survival PFS for patients with a new diagnosis primary refractory or relapse of mature T-cell and NK-cell neoplasms TNKL We hypothesize that machine learning will uncover distinct genetic vulnerabilties that underlie treatment response and resistance for patient with TNKL
Detailed Description:
This study is a prospective longitudinal observational study of newly diagnosed and relapsedrefractory patients with T-cell and NK -cell neoplasm at participating institutions Patients will be enrolled in the study during the course of their first visit as a new patient at the participating institution and followed for up to 4 years through the course of their clinical management Routine demographics baseline clinical features including pathology molecular information related to the tumor radiology treatment characteristics and quality of life QoL related to their lymphoma care will be collected over the course of 4 years by clinical research teams at every participating institution This data will be de-identified data and then shared through a secure and password protected REDCap with other participating institutions under data usage agreements of the consortium agreement Next generation sequencing including but not limited to such as whole exome sequencing and bulk RNA-sequencing will be performed on archived lymphoma specimens and on mononuclear cells cfDNA and saliva when feasible for detailed molecular characterization of the tumor Molecular correlation with outcomes will be performed Deep learning algorithms will be utilized to predict response and survival of lymphoma subtypes and in heterogeneous clinical scenarios and to various potential therapeutic approaches that the patient has not been exposed to
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None