Single-cell mass cytometry and machine learning predict relapse in childhood leukemia

ABSTRACT

Improved insight into cancer cell populations responsible for treatment failure will lead to better outcomes for patients. We herein highlight a single-cell study of B-cell precursor acute lymphoblastic leukemia (BCP-ALL) at diagnosis that revealed hidden developmentally dependent cell signaling states uniquely associated with relapse.

Keywords:

• Childhood leukemia
• mass cytometry
• cell signaling
• relapse prediction
• machine learning
• innovative methods in molecular and cellular oncology
• mechanisms of oncogenesis and tumor progression
• mechanisms of resistance to therapy
• novel therapeutic targets
• prognostic and predictive biomarkers

Additional information

Funding

This work was supported by the Associazione Italiana per la Ricerca sul Cancro (AIRC) [19488];CureSearch Young Investigator Award; “St. Baldrick’s Foundation”;