Next-Generation Sequencing-based Genetic Landscape and its Clinical Implications for Egyptian Patients with Acute Myeloid Leukemia and Myelodysplastic Syndrome

Abstract Background Myeloid malignancies are a heterogeneous group of clonal disorders characterized by disturbed hematopoietic stem cells self-renewal, proliferation, and differentiation capacity as a result of genetic and epigenetic changes. The molecular landscape of these diseases is complex involving many molecular alterations. Several techniques have been used to identify these genetic alterations including; FISH, RT-PCR (real time-PCR) and recently next generation sequencing (NGS) that is capable of detecting copy number variations (CNV) or translocations by a fast less expensive technique making it more practical in clinical practice. Objectives Designing a genetic map for Egyptian population with myeloid and detecting the prognostic impact of these genetic alterations. Patients and Methods Myeloid NGS Oncomine 79-gene panel was applied on 42 patients diagnosed with myeloid neoplasms (Mainly AML & MDS) recruited from the hematology unit, and their follow up till 12 months. Results Among the different studied groups, the highest NGS variants were found in AML compared to other myeloid neoplasms, while the percentage of patients with no NGS variants is higher in MDS (77.8%). The most frequently mutated genes detected in myeloid neoplasms were NPM1 mutation in 8 patients (19%), followed by NRAS mutation in 7 patients (16.7%). BCR-ABL1 and CALR mutations were significantly more common in the MPN group (P- value =0.000). TET2 was significantly more common in MDS/MPN patients (P-value =0.027). TP53 mutation was detected in all myeloid neoplasms (Mainly in MDS) except AML (P-value =0.027). NPM1 was significantly more frequent in AML group (P-value =0.042). There was a significant correlation between the presence of NGS variants and the clinical outcome, as the presence of one or more NGS variants is associated with poor clinical outcome & lower survival rate (p value =0.038). Conclusion Assessment of clinically important genetic alterations by NGS is essential for the diagnosis, prognosis, monitor measurable residual disease and detect predictive mutational markers/therapeutic targets in myeloid neoplasms. However, it cannot identify structural abnormalities, so simultaneous cytogenetic analysis to have a complete picture of the genomic profile is essential.

Authors

Institutions

Publication Details

Journal
QJM
Published
2024-10-01
DOI
https://doi.org/10.1093/qjmed/hcae175.190
Primary Topic
Acute Myeloid Leukemia Research
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Next-Generation Sequencing-based Genetic Landscape and its Clinical Implications for Egyptian Patients with Acute Myeloid Leukemia and Myelodysplastic Syndrome

Heba Samy Agamy, Nazneen Rahman, Mohamed Amin Mekawy, Mohamed Hamza et al.
1 links
QJM
Acute Myeloid Leukemia Research
article

Next-Generation Sequencing-based Genetic Landscape and its Clinical Implications for Egyptian Patients with Acute Myeloid Leukemia and Myelodysplastic Syndrome

Heba Samy Agamy, Nazneen Rahman, Mohamed Amin Mekawy, Mohamed Hamza, Gehan Moustafa Hamed Moustafa
article en

Abstract

Abstract Background Myeloid malignancies are a heterogeneous group of clonal disorders characterized by disturbed hematopoietic stem cells self-renewal, proliferation, and differentiation capacity as a result of genetic and epigenetic changes. The molecular landscape of these diseases is complex involving many molecular alterations. Several techniques have been used to identify these genetic alterations including; FISH, RT-PCR (real time-PCR) and recently next generation sequencing (NGS) that is capable of detecting copy number variations (CNV) or translocations by a fast less expensive technique making it more practical in clinical practice. Objectives Designing a genetic map for Egyptian population with myeloid and detecting the prognostic impact of these genetic alterations. Patients and Methods Myeloid NGS Oncomine 79-gene panel was applied on 42 patients diagnosed with myeloid neoplasms (Mainly AML & MDS) recruited from the hematology unit, and their follow up till 12 months. Results Among the different studied groups, the highest NGS variants were found in AML compared to other myeloid neoplasms, while the percentage of patients with no NGS variants is higher in MDS (77.8%). The most frequently mutated genes detected in myeloid neoplasms were NPM1 mutation in 8 patients (19%), followed by NRAS mutation in 7 patients (16.7%). BCR-ABL1 and CALR mutations were significantly more common in the MPN group (P- value =0.000). TET2 was significantly more common in MDS/MPN patients (P-value =0.027). TP53 mutation was detected in all myeloid neoplasms (Mainly in MDS) except AML (P-value =0.027). NPM1 was significantly more frequent in AML group (P-value =0.042). There was a significant correlation between the presence of NGS variants and the clinical outcome, as the presence of one or more NGS variants is associated with poor clinical outcome & lower survival rate (p value =0.038). Conclusion Assessment of clinically important genetic alterations by NGS is essential for the diagnosis, prognosis, monitor measurable residual disease and detect predictive mutational markers/therapeutic targets in myeloid neoplasms. However, it cannot identify structural abnormalities, so simultaneous cytogenetic analysis to have a complete picture of the genomic profile is essential.

QJMVol. 117(Supplement_2)
Ain Shams University (EG)
Openalex Percentile: Top 66%
Acute Myeloid Leukemia Research
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.