RESUMO
A multistage model instructed by a large dataset (knowledge bank [KB] algorithm) has recently been developed to improve outcome predictions and tailor therapeutic decisions, including hematopoietic stem cell transplantation (HSCT) in acute myeloid leukemia (AML). We assessed the performance of the KB in guiding HSCT decisions in first complete remission (CR1) in 656 AML patients younger than 60 years from the ALFA-0702 trial (NCT00932412). KB predictions of overall survival (OS) were superior to those of European LeukemiaNet (ELN) 2017 risk stratification (C-index, 68.9 vs 63.0). Among patients reaching CR1, HSCT in CR1, as a time-dependent covariate, was detrimental in those with favorable ELN 2017 risk and those with negative NPM1 minimal residual disease (MRD; interaction tests, P = .01 and P = .02, respectively). Using KB simulations of survival at 5 years in a scenario without HSCT in CR1 (KB score), we identified, in a similar time-dependent analysis, a significant interaction between KB score and HSCT, with HSCT in CR1 being detrimental only in patients with a good prognosis based on KB simulations (KB score ≥40; interaction test, P = .01). We could finally integrate ELN 2017, NPM1 MRD, and KB scores to sort 545 CR1 patients into 278 (51.0%) HSCT candidates and 267 (49.0%) chemotherapy-only candidates. In both time-dependent and 6-month landmark analyses, HSCT significantly improved OS in HSCT candidates, whereas it significantly shortened OS in chemotherapy-only candidates. Integrating KB predictions with ELN 2017 and MRD may thus represent a promising approach to optimize HSCT timing in younger AML patients.
Assuntos
Algoritmos , Transplante de Células-Tronco Hematopoéticas , Leucemia Mieloide Aguda/terapia , Medicina de Precisão , Adolescente , Adulto , Protocolos de Quimioterapia Combinada Antineoplásica/uso terapêutico , Tomada de Decisão Clínica , Ensaios Clínicos Fase II como Assunto/estatística & dados numéricos , Terapia Combinada , Conjuntos de Dados como Assunto , Feminino , Transplante de Células-Tronco Hematopoéticas/normas , Humanos , Leucemia Mieloide Aguda/tratamento farmacológico , Leucemia Mieloide Aguda/genética , Leucemia Mieloide Aguda/patologia , Masculino , Pessoa de Meia-Idade , Modelos Teóricos , Estudos Multicêntricos como Assunto/estatística & dados numéricos , Neoplasia Residual , Proteínas Nucleares/genética , Nucleofosmina , Prognóstico , Ensaios Clínicos Controlados Aleatórios como Assunto/estatística & dados numéricos , Indução de Remissão , Medição de Risco , Transplante Homólogo , Adulto JovemRESUMO
Recent advancements in shRNA and Cas protein technologies have enabled functional screening methods targeting genes or non-coding regions using single or pooled shRNA and sgRNA. CRISPR-based systems have also been developed for modulating DNA accessibility, resulting in CRISPR-mediated interference (CRISPRi) or activation (CRISPRa) of targeted genes or genomic DNA elements. However, there is still a lack of software tools for integrating diverse array of functional genomics screening outputs that could offer a cohesive framework for comprehensive data integration. Here, we developed PitViper, a flexible and interactive open-source software designed to fill this gap, providing reliable results for the type of elements being screened. It is an end-to-end automated and reproducible bioinformatics pipeline integrating gold-standard methods for functional screening analysis. Our sensitivity analyses demonstrate that PitViper is a useful tool for identifying potential super-enhancer liabilities in a leukemia cell line through genome-wide CRISPRi-based screening. It offers a robust, flexible, and interactive solution for integrating data analysis and reanalysis from functional screening methods, making it a valuable resource for researchers in the field.
RESUMO
Selinexor is a first-in-class inhibitor of the nuclear exportin XPO1 that was recently approved by the US Food and Drug Administration for the treatment of multiple myeloma and diffuse large B-cell lymphoma. In relapsed/refractory acute myeloid leukemia (AML), selinexor has shown promising activity, suggesting that selinexor-based combination therapies may have clinical potential. Here, motivated by the hypothesis that selinexor's nuclear sequestration of diverse substrates imposes pleiotropic fitness effects on AML cells, we systematically catalog the pro- and anti-fitness consequences of selinexor treatment. We discover that selinexor activates PI3Kγ-dependent AKT signaling in AML by upregulating the purinergic receptor P2RY2. Inhibiting this axis potentiates the anti-leukemic effects of selinexor in AML cell lines, patient-derived primary cultures and multiple mouse models of AML. In a syngeneic, MLL-AF9-driven mouse model of AML, treatment with selinexor and ipatasertib outperforms both standard-of-care chemotherapy and chemotherapy with selinexor. Together, these findings establish drug-induced P2RY2-AKT signaling as an actionable consequence of XPO1 inhibition in AML.