Introduction: B-cell acute lymphoblastic leukaemia (B-ALL) accounts for 25% of all childhood cancers. Despite remarkable advances in treatment, 10-20% of patients relapse and about half of these children do not survive. The search for new biological markers, combinations of already known predictive factors, less invasive diagnostic procedures, and less aggressive treatment strategies remains crucial for improving the management, survival, and long-term quality of life of these patients.
Methods: For the first part of the study, in which we comprehensively analysed genetic alterations in patients with B-cell acute lymphoblastic leukaemia, 99 consecutive unselected patients were included. RNA and DNA were isolated from bone marrow or peripheral blood samples taken at diagnosis. RNA sequencing was used for transcriptome analysis and multiplex ligation-dependent probe amplification (MLPA) was used to identify copy number changes. For the second part of the study, in which we monitored minimal residual disease in bone marrow and peripheral blood samples, we included 6 patients for whom samples were collected at diagnosis, at days 15 and 33 of treatment. DNA was isolated from the samples and libraries were prepared for monitoring immunoglobulin rearrangements by next-generation sequencing.
Results: Transcriptome analysis successfully identified B-ALL genetic subtypes in the majority of the samples studied, classifying patients into 13 of the 26 known subtypes. Among these, we identified rare, prognostically favourable as well as unfavourable subtypes that allow targeted therapy. We have shown that RNA sequencing can replace most of the methods currently used to identify the driving genetic alterations in B-ALL and improve diagnostic yield and patient management. Differential gene expression analysis revealed that patients with favourable subtypes that experience an event have lower expression of tumour suppressor genes at diagnosis, while patients with unfavourable subtypes that experience an event have higher expression of oncogenes at diagnosis. Gene copy number changes appeared to be a strong prognostic factor depending on the genetic subtype, while a higher number of genes with gene copy number changes did not translate into a worse prognosis. We have developed predictive models that integrate genetic and clinical data to accurately predict treatment outcome in patients with B-ALL. These machine learning-based models have proven to be effective in classifying patients into different prognostic groups. By monitoring the dynamics of immunoglobulin rearrangements in peripheral blood, we have validated the possibility of less invasive monitoring of minimal residual disease, which could reduce the number of bone marrow punctures needed and allow more frequent monitoring of response to treatment.
Conclusions: Our study highlights the importance of advanced genetic analyses and predictive models in the management of children with B-cell acute lymphoblastic leukaemia. The results confirm that accurate genetic subtyping can significantly improve the diagnosis and management of patients. Using the latest technologies such as RNA sequencing and immunoglobulin rearrangement sequencing, as well as advanced predictive models, more personalised and less invasive treatment can be expected, leading to improved short- and long-term outcomes for patients with B-cell acute lymphoblastic leukaemia.
|
