This article reports an integrated genomic analysis of young subjects with T-lineage acute lymphoblastic leukemia that identifies previously unrecognized mutation targets and modalities of mutation. Nat Gen 2017; 49: 1211–1218. doi:10.1038/ng.3909

Bolouri H et al. Nat Med  2017; doi:10.1038/nm.4439

Jones et al. review results from >50 novel agents and combinations tested against the Pediatric Preclinical Testing Program to better determine which agents/combinations are likely to be ineffective in different pediatric acute lymphoblastic leukemia subpopulations. Jones L et al. Leukemia 2016; 30: 2133–2141. doi:10.1038/leu.2016.192

A review of the results of novel agents tested in the Pediatric Preclinical Testing Program (PPTP) against acute lymphoblastic leukemia to highlight comparisons between PPTP results and clinical data. Leukemia  2016; 30: 2133–2141. doi:10.1038/leu.2016.192

Rasche M et al. Leukemia 2018. doi:10.1038/s41375-018-0071-7

In this study, Richter-Pechańska et al. identify a series of potential prognostic biomarkers with utility in treatment stratification in a pediatric cohort of relapsed T-cell acute lymphoblastic leukemia patients. Richter-Pechańska R et al. Blood Cancer J  2017; 7: e523. doi:10.1038/bcj.2017.3

Cytogenetic analysis has become a useful tool in the prediction of survival and adaptation of acute myeloid leukemia therapy. Using data from two French studies, Alloin et al. measure the impact of cytogenetic risk stratification. Alloin A-L et al. Bone Marrow Transplant  2017; 52: 516–521. doi:10.1038/bmt.2016.293

Jenkinson et al. analyze the frequency of PTEN gene variations in pediatric T-cell acute lymphoblastic leukemia patients to determine whether a correlation exists between genetic abnormalities at this loci and short-to-long term therapeutic responses. Jenkinson S et al. Leukemia 2016; 30: 39–47. doi:10.1038/leu.2015.206

Mahadeo KM et al.  Nat Rev Clin Oncol  2018. doi:10.1038/s41571-018-0075-2.

Maruffi M et al. Leukemia  2018. doi:10.1038/s41375-018-0058-4