Amaral, Patricia;
Christie, Rhona;
Gresham, Daisy OF;
Lucas, Emma JM;
Xu, Luyao Kevin;
Behrmann, Lena;
Bond, Jonathan;
... Barata, Joao T; + view all
(2025)
Underlying biology, challenges and emergent concepts in the treatment of relapsed and refractory pediatric T-cell acute lymphoblastic leukemia.
Leukemia
10.1038/s41375-025-02723-2.
(In press).
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Final accepted_Amaral.pdf - Accepted Version Access restricted to UCL open access staff until 15 February 2026. Download (1MB) |
Abstract
Relapsed and refractory disease in children with T-cell acute lymphoblastic leukemia (R/R T-ALL) remains a major clinical challenge. Outcomes for children who relapse or exhibit resistance to initial treatments are dismal, with survival rates frequently below 25% despite aggressive therapy. To minimize toxicities and improve outcomes, individualized precision medicine approaches targeting the underlying biology of R/R T-ALL are especially important, considering that T-ALL is characterized by genetic, epigenetic and posttranscriptional heterogeneity, and organ and niche specificities (e.g. the central nervous system), all of which underlie disease progression and therapy resistance. Here, we summarize the current understanding of the complexity of pediatric T-ALL biology and how such knowledge may be clinically leveraged, emphasizing the need for innovative therapeutic routes to improve outcomes for children with R/R T-ALL. Emerging approaches that hold promise or show palpable results include proteasome inhibitors, BCL-2 antagonists, and JAK (for JAK- and IL-7R-driven cases), ABL and SRC family tyrosine kinase (for LCK-activated cases), MEK or PI3K-mTOR inhibitors. MYC-targeting agents, DNA demethylating agents, histone deacetylase inhibitors, splicing modulators, or drugs exploring T-ALL metabolic vulnerabilities, are other examples for potential pharmacological intervention. Immunotherapies, particularly CAR T-cell products targeting CD7 and other markers, but also biologics (e.g. targeting CD38), are under development and increasing interest. These agents should be rationally integrated into precision medicine combination therapies informed by genetic, epigenetic, and posttranscriptional insights that will be essential to refine risk stratification and minimize the risk of resistance. Novel strategies leveraging artificial intelligence and machine learning could accelerate discovery and optimize treatment frameworks.
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