Search PubMedSearch

PubMed · 42640163

Consolidation Therapy Based on Mutation Clearance in Acute Myeloid Leukemia.

Abstract

BACKGROUND: Optimal consolidation therapy for patients with intermediate-risk acute myeloid leukemia (AML) in first complete remission (CR1) is controversial. Retrospective studies have suggested that the clearance of leukemia-associated mutations (LAMs) in CR1 may predict lower relapse risk and better outcomes with high-dose cytarabine (HiDAC) consolidation. We tested this hypothesis prospectively. METHODS: We performed a phase II, multicenter study of intermediate-risk, transplant-eligible, de novo AML in patients 18-60 years of age who achieved a complete remission (CR) or CR with incomplete count recovery (CRi) after induction therapy. Tumor and normal whole-exome sequencing was performed at presentation to identify somatic LAMs (median ∼30 LAMs/patient). In remission marrow samples, LAM variant allele frequencies (VAFs) were then remeasured using a VAF cutoff of less than 2.5% to define clearance. Patients who met this LAM clearance threshold received HiDAC consolidation, whereas those with persistent LAMs (VAF ≥2.5%) were recommended to undergo allogeneic hematopoietic cell transplantation. The primary endpoint compared relapse-free survival (RFS) of intermediate-risk patients with complete LAM clearance to historical cohorts with intermediate-risk AML who received HiDAC-based regimens in CR1. To account for an unplanned interim assessment, the significance threshold for the primary analysis was 0.01. RESULTS: Among 100 patients who were evaluated, intermediate-risk patients who cleared all LAMs in CR1 (n=33) had a median RFS of 33.1 months (95% confidence interval, 11.7-NA) compared to a median RFS of 11.7 months in the historical cohort (n=239; 95% confidence interval, 9.9-15.6, P=0.015). CONCLUSIONS: Among patients with intermediate-risk AML, clearance of LAMs after induction, followed by HiDAC consolidation in CR1, was associated with longer RFS compared with similarly treated historical controls. Although this result did not meet the prespecified threshold for statistical significance, the reported association sets the stage for a randomized trial to further evaluate this strategy. (ClinicalTrials.gov number, NCT02756962.).

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Meagan A Jacoby, David H Spencer, Feng Gao, Geoffrey L Uy, Tasha Burton, Sharon E Heath, Feiyu Du, Michelle O'Laughlin, Robert S Fulton, Christopher A Miller, Eric J Duncavage, Mark A Schroeder, Armin Ghobadi, Iskra Pusic, Keith E Stockerl-Goldstein, Brad S Kahl, Amanda F Cashen, Matthew J Christopher, Nathan Singh, Ravi Vij, Zachary D Crees, Lukas D Wartman, Ryan B Day, Todd A Fehniger, Karolyn A Oetjen, Dilan A Patel, Miriam Y Kim, Michael J Slade, Francesca Ferraro, Matthew J Walter, Camille N Abboud, Ramzi Abboud, Daniel C Link, Zeina A Al-Mansour, Christopher R Cogle, Eric J Huselton, John F DiPersio, Peter Westervelt, Timothy J Ley. 2026-08-25. Consolidation Therapy Based on Mutation Clearance in Acute Myeloid Leukemia.. https://doi.org/10.1056/evidoa2500352

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

Global Genomic Surveillance.

Global genomic surveillance has emerged as a foundational pillar of public health in the twenty-first century, enabling real-time tracking of pathogen evolution and informing outbreak response. This chapter examines the strategic architecture of global genomic surveillance, focusing on its application to arboviruses such as chikungunya virus (CHIKV). It explores the integration of genomic data with epidemiological, clinical, and environmental information within a One Health framework, while addressing critical challenges in governance, equity, and interoperability. The discussion covers the entire genomic surveillance workflow, from sample collection and sequencing to bioinformatic analysis and phylogenetic inference, and highlights the transformative role of artificial intelligence (AI) in predictive surveillance. By analyzing global initiatives, operational barriers, and emerging technologies, this chapter underscores the necessity of sustainable, equitable, and interoperable genomic systems to proactively address current and future infectious disease threats.

Humans

Systematic Dissection of Key Driver Perturbation Signatures in Single Cells via ECCITE-seq.

CRISPR screens, such as expanded CRISPR-compatible cellular indexing of transcriptomes and epitopes by sequencing (ECCITE-seq), enable the simultaneous measurement of transcriptomes, gRNA identity, and cell-surface protein expression at single-cell resolution to systematically interrogate gene function. This platform provides a powerful and scalable experimental approach for validating disease-associated regulators identified by large-scale association studies and other computational methods, including network-based analyses of multi-omics data. Here, as an example application, we describe an ECCITE-seq framework to characterize the transcriptomic consequences of perturbing multiple neuronal key driver genes associated with Alzheimer's disease (AD) in human-induced pluripotent stem cell (hiPSC)-derived neurons. More broadly, by integrating customized pooled gRNA libraries with different CRISPR effectors across multiple cell types, this approach allows for the assessment of the regulatory impact of candidate genes implicated in development and disease processes.

Humans

Identification of Genome-Wide Chromatin Structural Aberration in Cancer by Hi-C Analysis.

Aberrant three-dimensional genome organization is a hallmark of cancer, often driving oncogene activation through mechanisms such as enhancer hijacking. High-throughput chromosome conformation capture (Hi-C) maps these interactions on a genome-wide scale. Unlike earlier dilution-based methods, in situ Hi-C performs proximity ligation within intact nuclei, minimizing random ligation noise and enabling fine-scale structure detection. This chapter describes an optimized in situ Hi-C protocol tailored for cancer cell lines using MboI digestion and biotin-mediated pull-down to generate high-complexity libraries. We further outline a computational workflow that extends beyond standard topological mapping of compartments and topologically associating domains to identify cancer-specific aberrations. Specifically, we focus on detecting chromosomal rearrangements (structural variants) and characterizing the distinct circular topology of extrachromosomal DNA. This integrated experimental and analytical framework provides the necessary tools to dissect the spatial dysregulation underlying tumor evolution.

Humans