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Investigation of Genetic Dependencies Using CRISPR-Cas9-based Competition Assays

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Published on: January 7, 2019

Using genomics to refine pediatric AML risk stratification.

Martina Pigazzi^1,2, Soheil Meshinchi³, Franco Locatelli^4,5

¹Department of Women's and Children's Health, Onco-hematology Lab and Clinic, University of Padova, Padova, Italy.

Hematology. American Society of Hematology. Education Program

|December 5, 2025

Summary

Genomic advances enhance understanding of pediatric acute myeloid leukemia (AML). Identifying specific genetic alterations and targeted therapies offers personalized treatment strategies for better outcomes.

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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

Area of Science:

Genomics
Pediatric Oncology
Molecular Biology

Background:

Genomic technologies have significantly advanced the understanding of pediatric acute myeloid leukemia (AML) over the past two decades.
Cytogenetic tests detect structural changes in ~75% of pediatric AML cases, aiding risk assessment.
Next-generation sequencing reveals cryptic fusions and mutations impacting disease biology and treatment response.

Purpose of the Study:

To review the evolving genetic landscape of pediatric AML.
To highlight novel genetic subtypes and their clinical implications.
To discuss the potential and challenges of targeted therapies in pediatric AML.

Main Methods:

Review of recent genomic technologies and their application in pediatric AML research.
Analysis of identified genetic alterations, including specific fusions (NUP98::NSD1, CBFA2T3::GLIS2, KMT2A) and mutations (NPM1, WT1, DNMT3A, TP53).
Discussion of emerging targeted therapies (FLT3, BCL2, menin inhibitors).

Main Results:

Specific genetic fusions define unique childhood AML subtypes with distinct clinical outcomes.
Certain NPM1 isoforms and mutations in WT1, DNMT3A, and TP53 influence patient prognosis.
Targeted therapies show promise for personalized treatment but face integration challenges due to AML's complexity.

Conclusions:

The genetic complexity of pediatric AML necessitates further research to identify prognostic biomarkers.
Personalized therapy requires accurate prediction of treatment response and effective monitoring strategies.
Collaborative efforts are crucial for validating biomarkers and optimizing novel drug combinations for pediatric AML.