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

Mol Cell Oncol. 2018 Sep 12;5(5):e1472057. doi: 10.1080/23723556.2018.1472057. eCollection 2018.

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

Grants and funding

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