23 research outputs found
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
The PML-RARA fusion is not detectable in historical blood samples of acute promyelocytic leukaemia patients.
Favorable Outcome of Older Patients with AML and a Favorable Genotype NPM1mut FLT3-ITD Treated with Intensive Chemotherapy: A Subgroup Analysis of Cetlam Protocol 2003 & 2012
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
Minimal Residual Disease (MRD) Analysis in Acute Myeloid Leukemia (AML) with Favorable Cytogenetics [t(8;21) and inv(16)] by Real Time PCR(RT-PCR) and Flow Cytometry (FC).
Multilineage Dysplasia Confers Poor Prognosis to Patients with De Novo Acute Myeloid Leukemia with Intermediate-Risk Cytognetics and Wild-Type NPM1.
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