Epigenetic loss of the RNA decapping enzyme NUDT16 mediates C-MYC activation in T-cell acute lymphoblastic leukemia

Altres ajuts: La Marató de TV3 Foundation #20131610, the Cellex Foundation, Obra Social 'La Caixa

Frequency and clinical impact of CDKN2A/ARF/CDKN2B gene deletions as assessed by in-depth genetic analyses in adult T cell acute lymphoblastic leukemia

Altres ajuts: This project was supported by the Asociación Española Contra el Cáncer, AECC (project ref.: GC16173697BIGA), Obra Social "La Caixa" and by Celgene Spain. A. Gonzalez-Perez is supported by a Ramon y Cajal fellowship (RYC-2013-14554) of the Educational Ministry (Madrid, Spain). This work was also partially supported by FEDER funds from CIBERONC (CB16/12/00284 and CB16/12/00400), Madrid, Spain).Recurrent deletions of the CDKN2A/ARF/CDKN2B genes encoded at chromosome 9p21 have been described in both pediatric and adult acute lymphoblastic leukemia (ALL), but their prognostic value remains controversial, with limited data on adult T-ALL. Here, we investigated the presence of homozygous and heterozygous deletions of the CDKN2A/ARF and CDKN2B genes in 64 adult T-ALL patients enrolled in two consecutive trials from the Spanish PETHEMA group. Alterations in CDKN2A/ARF/CDKN2B were detected in 35/64 patients (55%). Most of them consisted of 9p21 losses involving homozygous deletions of the CDKNA/ARF gene (26/64), as confirmed by single nucleotide polymorphism (SNP) arrays and interphase fluorescence in situ hybridization (iFISH). Deletions involving the CDKN2A/ARF/CDKN2B locus correlated with a higher frequency of cortical T cell phenotype and a better clearance of minimal residual disease (MRD) after induction therapy. Moreover, the combination of an altered copy-number-value (CNV) involving the CDKN2A/ARF/CDKN2B gene locus and undetectable MRD (≤ 0.01%) values allowed the identification of a subset of T-ALL with better overall survival in the absence of hematopoietic stem cell transplantation

Engraftment characterization of risk-stratified AML in NSGS mice

The authors thank Paola Romecin and Virginia Rodriguez-Cortez for technical assistance. This work was supported by the Spanish Ministry of Economy and Competitiveness (SAF2016-80481R, PID2019-108160RBI00), the Obra Social La Caixa (LCF/PR/HR19/52160011), Interreg V-A programme (POCTEFA) 2014-2020 (grant PROTEOblood EFA360/19), Health Canada (H4080-144541), and Deutsche Josep Carreras Leukämie Stiftung (P.M.). Additional funding was provided by Consejería de Salud y Familia (PI- 0119-2019) (R.D.d.l.G.), Health Institute Carlos III (ISCIII/FEDER, PI17/01028) and Asociación Española Contra el Cáncer (C.B.), Health Institute Carlos III/FEDER (CPII17/00032) (V.R.-M.), and Fundación Hay Esperanza (E.A.). CERCA/Generalitat de Catalunya and Fundación Josep Carreras-Obra Social la Caixa provided institutional support. B.L.-M. was supported by a Lady Tata Memorial Trust International Award and Asociación Española Contra el Cáncer (INVES20011LÓPE). O.M. and T.V.-H. were supported by Asociación Española Contra el Cáncer (INVES211226MOLI) and a Marie Sklodowska Curie Fellowship (792923), respectively. P.M. is an investigator in the Spanish Cell Therapy Network.Acute myeloid leukemia (AML) is the most common acute leukemia in adults. Disease heterogeneity is well documented, and patient stratification determines treatment decisions. Patient-derived xenografts (PDXs) from risk-stratified AML are crucial for studying AML biology and testing novel therapeutics. Despite recent advances in PDX modeling of AML, reproducible engraftment of human AML is primarily limited to high-risk (HR) cases, with inconsistent or very protracted engraftment observed for favorable-risk (FR) and intermediate-risk (IR) patients. We used NSGS mice to characterize the engraftment robustness/kinetics of 28 AML patient samples grouped according to molecular/ cytogenetic classification and assessed whether the orthotopic coadministration of patientmatched bone marrow mesenchymal stromal cells (BM MSCs) improves AML engraftment. PDX event-free survival correlated well with the predictable prognosis of risk-stratified AML patients. The majority (85-94%) of the mice were engrafted in bone marrow (BM) independently of the risk group, although HR AML patients showed engraftment levels that were significantly superior to those of FR or IR AML patients. Importantly, the engraftment levels observed in NSGS mice by week 6 remained stable over time. Serial transplantation and long-term culture-initiating cell (LTC-IC) assays revealed long-term engraftment limited to HR AML patients, fitter leukemia-initiating cells (LICs) in HR AML samples, and the presence of AML LICs in the CD342 leukemic fraction, regardless of the risk group. Finally, orthotopic coadministration of patient-matched BM MSCs and AML cells was dispensable for BM engraftment levels but favored peripheralization of engrafted AML cells. This comprehensive characterization of human AML engraftment in NSGS mice offers a valuable platform for in vivo testing of targeted therapies in risk-stratified AML patient samples.Spanish Ministry of Economy and Competitiveness (SAF2016-80481R, PID2019-108160RBI00)Obra Social La Caixa (LCF/PR/HR19/52160011)Interreg V-A programme (POCTEFA) 2014-2020 (grant PROTEOblood EFA360/19)Health Canada (H4080-144541)Deutsche Josep Carreras Leukämie StiftungConsejer ıa de Salud y Familia (PI- 0119-2019)Health Institute Carlos III (ISCIII/FEDER, PI17/01028)Asociación Española Contra el CáncerHealth Institute Carlos III/FEDER (CPII17/00032)Fundación Hay EsperanzaCERCA/Generalitat de CatalunyaFundació Josep Carreras-Obra Social la CaixaLady Tata Memorial Trust International AwardAsociación Española Contra el Cáncer (INVES20011LÓPE)Asociación Española Contra el Cáncer (INVES211226MOLI)Marie Sklodowska Curie Fellowship (792923

Machine Learning Improves Risk Stratification in Myelodysplastic Neoplasms : An Analysis of the Spanish Group of Myelodysplastic Syndromes

Myelodysplastic neoplasms (MDS) are a heterogeneous group of hematological stem cell disorders characterized by dysplasia, cytopenias, and increased risk of acute leukemia. As prognosis differs widely between patients, and treatment options vary from observation to allogeneic stem cell transplantation, accurate and precise disease risk prognostication is critical for decision making. With this aim, we retrieved registry data from MDS patients from 90 Spanish institutions. A total of 7202 patients were included, which were divided into a training (80%) and a test (20%) set. A machine learning technique (random survival forests) was used to model overall survival (OS) and leukemia-free survival (LFS). The optimal model was based on 8 variables (age, gender, hemoglobin, leukocyte count, platelet count, neutrophil percentage, bone marrow blast, and cytogenetic risk group). This model achieved high accuracy in predicting OS (c-indexes; 0.759 and 0.776) and LFS (c-indexes; 0.812 and 0.845). Importantly, the model was superior to the revised International Prognostic Scoring System (IPSS-R) and the age-adjusted IPSS-R. This difference persisted in different age ranges and in all evaluated disease subgroups. Finally, we validated our results in an external cohort, confirming the superiority of the Artificial Intelligence Prognostic Scoring System for MDS (AIPSS-MDS) over the IPSS-R, and achieving a similar performance as the molecular IPSS. In conclusion, the AIPSS-MDS score is a new prognostic model based exclusively on traditional clinical, hematological, and cytogenetic variables. AIPSS-MDS has a high prognostic accuracy in predicting survival in MDS patients, outperforming other well-established risk-scoring systems

Machine Learning Improves Risk Stratification in Myelodysplastic Neoplasms: An Analysis of the Spanish Group of Myelodysplastic Syndromes

Myelodysplastic neoplasms (MDS) are a heterogeneous group of hematological stem cell disorders characterized by dysplasia, cytopenias, and increased risk of acute leukemia. As prognosis differs widely between patients, and treatment options vary from observation to allogeneic stem cell transplantation, accurate and precise disease risk prognostication is critical for decision making. With this aim, we retrieved registry data from MDS patients from 90 Spanish institutions. A total of 7202 patients were included, which were divided into a training (80%) and a test (20%) set. A machine learning technique (random survival forests) was used to model overall survival (OS) and leukemia-free survival (LFS). The optimal model was based on 8 variables (age, gender, hemoglobin, leukocyte count, platelet count, neutrophil percentage, bone marrow blast, and cytogenetic risk group). This model achieved high accuracy in predicting OS (c-indexes; 0.759 and 0.776) and LFS (c-indexes; 0.812 and 0.845). Importantly, the model was superior to the revised International Prognostic Scoring System (IPSS-R) and the age-adjusted IPSS-R. This difference persisted in different age ranges and in all evaluated disease subgroups. Finally, we validated our results in an external cohort, confirming the superiority of the Artificial Intelligence Prognostic Scoring System for MDS (AIPSS-MDS) over the IPSS-R, and achieving a similar performance as the molecular IPSS. In conclusion, the AIPSS-MDS score is a new prognostic model based exclusively on traditional clinical, hematological, and cytogenetic variables. AIPSS-MDS has a high prognostic accuracy in predicting survival in MDS patients, outperforming other well-established risk-scoring systems