Related Experiment Video
Updated: Jan 9, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
Published on: October 11, 2018
Emerging genomic biomarkers in diagnosis and classification of T-cell acute lymphoblastic leukemia
Jason Xu1,2, David T Teachey1,2
1Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA.
Abstract:
Contemporary chemotherapy protocols have improved cure rates for children, adolescents, and young adults (CAYA) with T-lineage acute lymphoblastic leukemia (T-ALL) to greater than 80%. Unfortunately, outcomes for CAYA with relapsed and refractory disease, as well as older adults, remain poor. A key goal in the treatment of T-ALL therapy is preventing relapse; however, it is challenging to identify high-risk patients. Recently, several genomic initiatives have identified distinct biological subtypes of T-ALL and have correlated disease biology, including mutational status, transcriptional phenotype, and clonal drivers, with therapy response and outcome. The integration of genomic profiling into clinical diagnosis and treatment has promise to guide risk stratification, targeted therapy, and clinical trial design for high-risk patients. This review highlights the recently mapped genomic landscape of T-ALL, with particular emphasis on recently identified genomic molecular signatures, their utility in risk stratification, and targeted therapy selection for refractory cases.
More Related Videos
09:01Flow Cytometry to Estimate Leukemia Stem Cells in Primary Acute Myeloid Leukemia and in Patient-derived-xenografts, at Diagnosis and Follow Up
Published on: March 26, 2018
10:18From a 2DE-Gel Spot to Protein Function: Lesson Learned From HS1 in Chronic Lymphocytic Leukemia
Published on: October 19, 2014