Nuevos fármacos en hemopatía aguda maligna: antagonistas del receptor NK-1 en leucemia aguda mieloblástica

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High-Dimensional Analysis of Single-Cell Flow Cytometry Data Predicts Relapse in Childhood Acute Lymphoblastic Leukaemia

B-cell Acute Lymphoblastic Leukaemia is one of the most common cancers in childhood, with 20% of patients eventually relapsing. Flow cytometry is routinely used for diagnosis and follow-up, but it currently does not provide prognostic value at diagnosis. The volume and the high-dimensional character of this data makes it ideal for its exploitation by means of Artificial Intelligence methods. We collected flow cytometry data from 56 patients from two hospitals. We analysed differences in intensity of marker expression in order to predict relapse at the moment of diagnosis. We finally correlated this data with biomolecular information, constructing a classifier based on CD38 expression. Artificial intelligence methods may help in unveiling information that is hidden in high-dimensional oncological data. Flow cytometry studies of haematological malignancies provide quantitative data with the potential to be used for the construction of response biomarkers. Many computational methods from the bioinformatics toolbox can be applied to these data, but they have not been exploited in their full potential in leukaemias, specifically for the case of childhood B-cell Acute Lymphoblastic Leukaemia. In this paper, we analysed flow cytometry data that were obtained at diagnosis from 56 paediatric B-cell Acute Lymphoblastic Leukaemia patients from two local institutions. Our aim was to assess the prognostic potential of immunophenotypical marker expression intensity. We constructed classifiers that are based on the Fisher's Ratio to quantify differences between patients with relapsing and non-relapsing disease. We also correlated this with genetic information. The main result that arises from the data was the association between subexpression of marker CD38 and the probability of relapse

The shape of cancer relapse: Topological data analysis predicts recurrence in paediatric acute lymphoblastic leukaemia.

Although children and adolescents with acute lymphoblastic leukaemia (ALL) have high survival rates, approximately 15-20% of patients relapse. Risk of relapse is routinely estimated at diagnosis by biological factors, including flow cytometry data. This high-dimensional data is typically manually assessed by projecting it onto a subset of biomarkers. Cell density and "empty spaces" in 2D projections of the data, i.e. regions devoid of cells, are then used for qualitative assessment. Here, we use topological data analysis (TDA), which quantifies shapes, including empty spaces, in data, to analyse pre-treatment ALL datasets with known patient outcomes. We combine these fully unsupervised analyses with Machine Learning (ML) to identify significant shape characteristics and demonstrate that they accurately predict risk of relapse, particularly for patients previously classified as 'low risk'. We independently confirm the predictive power of CD10, CD20, CD38, and CD45 as biomarkers for ALL diagnosis. Based on our analyses, we propose three increasingly detailed prognostic pipelines for analysing flow cytometry data from ALL patients depending on technical and technological availability: 1. Visual inspection of specific biological features in biparametric projections of the data; 2. Computation of quantitative topological descriptors of such projections; 3. A combined analysis, using TDA and ML, in the four-parameter space defined by CD10, CD20, CD38 and CD45. Our analyses readily extend to other haematological malignancies

Measurable Residual Disease Assessed by Flow-Cytometry Is a Stable Prognostic Factor for Pediatric T-Cell Acute Lymphoblastic Leukemia in Consecutive SEHOP Protocols Whereas the Impact of Oncogenetics Depends on Treatment.

Robust and applicable risk-stratifying genetic factors at diagnosis in pediatric T-cell acute lymphoblastic leukemia (T-ALL) are still lacking, and most protocols rely on measurable residual disease (MRD) assessment. In our study, we aimed to analyze the impact of NOTCH1, FBXW7, PTEN, and RAS mutations, the measurable residual disease (MRD) levels assessed by flow cytometry (FCM-MRD) and other reported risk factors in a Spanish cohort of pediatric T-ALL patients. We included 199 patients treated with SEHOP and PETHEMA consecutive protocols from 1998 to 2019. We observed a better outcome of patients included in the newest SEHOP-PETHEMA-2013 protocol compared to the previous SHOP-2005 cohort. FCM-MRD significantly predicted outcome in both protocols, but the impact at early and late time points differed between protocols. The impact of FCM-MRD at late time points was more evident in SEHOP-PETHEMA 2013, whereas in SHOP-2005 FCM-MRD was predictive of outcome at early time points. Genetics impact was different in SHOP-2005 and SEHOP-PETHEMA-2013 cohorts: NOTCH1 mutations impacted on overall survival only in the SEHOP-PETHEMA-2013 cohort, whereas homozygous deletions of CDKN2A/B had a significantly higher CIR in SHOP-2005 patients. We applied the clinical classification combining oncogenetics, WBC count and MRD levels at the end of induction as previously reported by the FRALLE group. Using this score, we identified different subgroups of patients with statistically different outcome in both Spanish cohorts. In SHOP-2005, the FRALLE classifier identified a subgroup of high-risk patients with poorer survival. In the newest protocol SEHOP-PETHEMA-2013, a very low-risk group of patients with excellent outcome and no relapses was detected, with borderline significance. Overall, FCM-MRD, WBC count and oncogenetics may refine the risk-stratification, helping to design tailored approaches for pediatric T-ALL patients