Subtopic Deep Dive
Genomic and Epigenomic Landscapes of De Novo AML
Research Guide
What is Genomic and Epigenomic Landscapes of De Novo AML?
Genomic and epigenomic landscapes of de novo AML describe the mutational profiles (DNMT3A, FLT3, NPM1) and epigenetic alterations identified through comprehensive sequencing in newly diagnosed adult acute myeloid leukemia cases without prior myelodysplastic syndromes or cytotoxic exposure.
Whole-genome sequencing reveals driver mutations in nearly all de novo AML samples, with complex genetic interplay driving pathogenesis (Ley, 2013, 5005 citations). Studies classify AML into molecular subgroups based on genomic lesions for prognostic stratification (Papaemmanuil et al., 2016, 4182 citations). Over 20 key papers from 1985-2022 define these landscapes, enabling subtype-specific therapies.
Why It Matters
Genomic profiling identifies actionable mutations like FLT3 and NPM1, guiding targeted inhibitors in precision medicine (Ley, 2013). Epigenomic data inform classification updates, improving risk stratification and outcomes in clinical trials (Papaemmanuil et al., 2016; Döhner et al., 2016). These insights distinguish de novo AML from secondary types, optimizing therapy for 20,000+ annual US cases (de Kouchkovsky and Abdul-Hay, 2016).
Key Research Challenges
Clonal Heterogeneity Profiling
De novo AML exhibits complex clonal evolution with multiple driver mutations, complicating longitudinal tracking (Ley, 2013). Sequencing must resolve intratumor heterogeneity to predict relapse risks (Papaemmanuil et al., 2016).
Epigenomic Integration
Combining genomic mutations with epigenomic alterations like DNA methylation patterns remains challenging for functional interpretation (Ley, 2013). Limited assays hinder subtype-specific epigenetic therapies.
Prognostic Subgroup Validation
Genomic classifications require validation across diverse cohorts for clinical adoption (Papaemmanuil et al., 2016). Distinguishing de novo from secondary AML mutations impacts treatment decisions (Lindsley et al., 2014).
Essential Papers
The 2016 revision to the World Health Organization classification of myeloid neoplasms and acute leukemia
Daniel A. Arber, Attilio Orazi, Robert P. Hasserjian et al. · 2016 · Blood · 10.0K citations
Abstract The World Health Organization (WHO) classification of tumors of the hematopoietic and lymphoid tissues was last updated in 2008. Since then, there have been numerous advances in the identi...
Diagnosis and management of AML in adults: 2017 ELN recommendations from an international expert panel
Hartmut Döhner, Elihu H. Estey, David Grimwade et al. · 2016 · Blood · 5.7K citations
Abstract The first edition of the European LeukemiaNet (ELN) recommendations for diagnosis and management of acute myeloid leukemia (AML) in adults, published in 2010, has found broad acceptance by...
Genomic and Epigenomic Landscapes of Adult De Novo Acute Myeloid Leukemia
T J Ley · 2013 · New England Journal of Medicine · 5.0K citations
We identified at least one potential driver mutation in nearly all AML samples and found that a complex interplay of genetic events contributes to AML pathogenesis in individual patients. The datab...
Genomic Classification and Prognosis in Acute Myeloid Leukemia
Elli Papaemmanuil, Moritz Gerstung, Lars Bullinger et al. · 2016 · New England Journal of Medicine · 4.2K citations
The driver landscape in AML reveals distinct molecular subgroups that reflect discrete paths in the evolution of AML, informing disease classification and prognostic stratification. (Funded by the ...
The 5th edition of the World Health Organization Classification of Haematolymphoid Tumours: Myeloid and Histiocytic/Dendritic Neoplasms
Joseph D. Khoury, Éric Solary, Oussama Abla et al. · 2022 · Leukemia · 3.5K citations
Proposed Revised Criteria for the Classification of Acute Myeloid Leukemia
John M. Bennett, DANIEL CATOVSKY, MARIE T. DANIEL et al. · 1985 · Annals of Internal Medicine · 2.9K citations
Position Papers1 October 1985Proposed Revised Criteria for the Classification of Acute Myeloid LeukemiaA Report of the French-American-British Cooperative GroupJOHN M. BENNETT, M.D., DANIEL CATOVSK...
Clonal hematopoiesis of indeterminate potential and its distinction from myelodysplastic syndromes
David P. Steensma, Rafael Bejar, Siddhartha Jaiswal et al. · 2015 · Blood · 1.9K citations
Abstract Recent genetic analyses of large populations have revealed that somatic mutations in hematopoietic cells leading to clonal expansion are commonly acquired during human aging. Clonally rest...
Reading Guide
Foundational Papers
Start with Ley (2013) for core genomic/epigenomic mutation profiles in de novo AML (5005 citations); follow with Bennett et al. (1985) for historical FAB classification basis (2944 citations); then Lindsley et al. (2014) to distinguish de novo from secondary mutations.
Recent Advances
Papaemmanuil et al. (2016) for genomic prognosis (4182 citations); Döhner et al. (2017 ELN, 5711 citations) for management; Khoury et al. (2022) for 5th WHO edition (3500 citations).
Core Methods
Whole-genome sequencing for mutations; DNA methylation arrays for epigenomics; bioinformatics for clonal phylogeny and subgroup clustering (Ley, 2013; Papaemmanuil et al., 2016).
How PapersFlow Helps You Research Genomic and Epigenomic Landscapes of De Novo AML
Discover & Search
Research Agent uses searchPapers and citationGraph to map 50+ papers from Ley (2013) central node, revealing clusters around DNMT3A/FLT3 mutations; exaSearch uncovers epigenomic extensions in de novo AML subtypes; findSimilarPapers links to Papaemmanuil et al. (2016) for prognostic groups.
Analyze & Verify
Analysis Agent applies readPaperContent on Ley (2013) to extract mutation frequencies, then runPythonAnalysis with pandas to quantify NPM1/FLT3 co-occurrences across datasets; verifyResponse via CoVe cross-checks claims against Döhner et al. (2016); GRADE grading scores evidence strength for driver mutation prognostic impact.
Synthesize & Write
Synthesis Agent detects gaps in epigenomic-clonal evolution links via contradiction flagging across Ley (2013) and Papaemmanuil et al. (2016); Writing Agent uses latexEditText for manuscript sections, latexSyncCitations to integrate 20+ references, and latexCompile for figures; exportMermaid visualizes mutation pathways.
Use Cases
"Analyze mutation co-occurrence frequencies in de novo AML from Ley 2013 dataset using Python."
Research Agent → searchPapers(Ley 2013) → Analysis Agent → readPaperContent → runPythonAnalysis(pandas crosstab on DNMT3A/FLT3/NPM1) → matplotlib survival plots → researcher gets CSV of co-mutation stats and p-values.
"Draft LaTeX review section on genomic landscapes of de novo AML with citations."
Synthesis Agent → gap detection → Writing Agent → latexEditText(draft text) → latexSyncCitations(Papaemmanuil 2016, Döhner 2016) → latexCompile → researcher gets compiled PDF with synced bibliography and mutation tables.
"Find GitHub repos analyzing genomic data from de novo AML papers."
Research Agent → paperExtractUrls(Ley 2013) → Code Discovery → paperFindGithubRepo → githubRepoInspect → researcher gets repo links with scripts for TCGA-AML mutation analysis and epigenomic pipelines.
Automated Workflows
Deep Research workflow scans 50+ papers via searchPapers → citationGraph on Ley (2013), generating structured report with mutation prevalence tables and WHO classification ties (Arber et al., 2016). DeepScan applies 7-step CoVe to verify epigenomic claims in Papaemmanuil et al. (2016), with GRADE checkpoints. Theorizer hypothesizes novel subtype interactions from genomic/epigenomic data across Döhner et al. (2016) and Lindsley et al. (2014).
Frequently Asked Questions
What defines the genomic landscape of de novo AML?
Comprehensive sequencing identifies driver mutations in DNMT3A, FLT3, NPM1 in nearly all cases, with databases publicly available (Ley, 2013).
What methods profile these landscapes?
Whole-genome sequencing and epigenetic assays reveal mutational interplay and subtypes (Ley, 2013; Papaemmanuil et al., 2016).
What are key papers?
Ley (2013, 5005 citations) maps landscapes; Papaemmanuil et al. (2016, 4182 citations) provides genomic classification; Arber et al. (2016, 9991 citations) updates WHO standards.
What open problems exist?
Integrating epigenomics with clonal tracking for relapse prediction; validating subgroups in diverse populations (Papaemmanuil et al., 2016; Lindsley et al., 2014).
Research Acute Myeloid Leukemia Research with AI
PapersFlow provides specialized AI tools for your field researchers. Here are the most relevant for this topic:
AI Literature Review
Automate paper discovery and synthesis across 474M+ papers
Deep Research Reports
Multi-source evidence synthesis with counter-evidence
Paper Summarizer
Get structured summaries of any paper in seconds
AI Academic Writing
Write research papers with AI assistance and LaTeX support
Start Researching Genomic and Epigenomic Landscapes of De Novo AML with AI
Search 474M+ papers, run AI-powered literature reviews, and write with integrated citations — all in one workspace.
Part of the Acute Myeloid Leukemia Research Research Guide