Integration of Machine Learning and Genomics to Predict Outcomes for Newly Diagnosed, Relapsed and Refractory Mature T-cell and NK/T-cell Lymphomas: a Global Study of the PETAL Consortium
Massachusetts General Hospital
Summary
The goal of this observational study is to correlate molecular alterations with outcomes including overall survival (OS), progression-free survival (PFS), response rates for patients with a new diagnosis, primary refractory or relapse, of mature T-cell and NK-cell neoplasms (TNKL). We hypothesize that machine learning can be leveraged to uncover distinct genetic vulnerabilities that underlie treatment response and resistance for patients with TNKL, thus moving towards personalized treatment solutions.
Description
This study is a prospective, longitudinal observational study of patients with newly diagnosed or relapsed/refractory T-cell and NK-cell neoplasms, conducted across multiple participating institutions globally. Patients will be enrolled during their initial visit as new patients and will be followed for up to four years through the course of their clinical management. Data for routine demographics, baseline clinical features, including pathology, molecular information related to the tumor, radiology, treatment characteristics and quality of life (QoL) associated with their lymphoma care will b…