Search

Summary Copy number aberrations (CNAs) represent cooperating events in B-lineage acute lymphoblastic leukaemia (B-ALL); however, their clinical relevance across different age cohorts is unclear. We analysed the recurrent CNAs in 157 age-stratified B-ALL negative cases for recurrent rearrangements (B-NEG ALL), and their association with patients’ clinico-biological features. We found that: (i) CDKN2A/RB1-deleted and EBF1-deleted adults had a shorter disease-free survival than those with wild-type, (ii) among the unfavourable markers, CDKN2A/RB1 deletions and K/NRAS mutations retained their impact in multivariate analysis, encouraging the evaluation of CDKN2A/RB1 deletions and RAS mutations in the diagnostic/prognostic workflow to refine ALL risk assessment.

BCR/ABL1-like acute lymphoblastic leukaemia (ALL) is a subgroup of B-lineage acute lymphoblastic leukaemia that occurs within cases without recurrent molecular rearrangements. Gene expression profiling (GEP) can identify these cases but it is expensive and not widely available. Using GEP, we identified 10 genes specifically overexpressed by BCR/ABL1-like ALL cases and used their expression values - assessed by quantitative real time-polymerase chain reaction (Q-RT-PCR) in 26 BCR/ABL1-like and 26 non-BCR/ABL1-like cases to build a statistical "BCR/ABL1-like predictor", for the identification of BCR/ABL1-like cases. By screening 142 B-lineage ALL patients with the "BCR/ABL1-like predictor", we identified 28/142 BCR/ABL1-like patients (19·7%). Overall, BCR/ABL1-like cases were enriched in JAK/STAT mutations (P < 0·001), IKZF1 deletions (P < 0·001) and rearrangements involving cytokine receptors and tyrosine kinases (P = 0·001), thus corroborating the validity of the prediction. Clinically, the BCR/ABL1-like cases identified by the BCR/ABL1-like predictor achieved a lower rate of complete remission (P = 0·014) and a worse event-free survival (P = 0·0009) compared to non-BCR/ABL1-like ALL. Consistently, primary cells from BCR/ABL1-like cases responded in vitro to ponatinib. We propose a simple tool based on Q-RT-PCR and a statistical model that is capable of easily, quickly and reliably identifying BCR/ABL1-like ALL cases at diagnosis.