The Progress of Next Generation Sequencing in the Assessment of Myeloid Malignancies

Puyan, Fulya Öz; Alkan, Serhan

doi:10.4274/balkanmedj.galenos.2018.2018.1195

Cited by 3 publications

(3 citation statements)

References 99 publications

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“…On the other side, due to inclusion of actionable biomarkers, quick turnaround times, and lower cost compared to WES, targeted gene panels are best suited for routine clinical applications and especially ideal for identifying mutations and translocations in specific genes. [58][59][60] WGS at 60× coverage gives good results but costs at 1300$ or 1900$ per sample 61 depending on the laboratory throughput while exome sequencing will also cost 500$ at 300× coverage, whereas targeted panels can give around 1000× coverage at 200 to 250$. Currently, many targeted panels are commercially available, which comprehensively cover all the variants, fusions, indels, CNVs, etc.…”

Section: Ngs Panels For Amlmentioning

confidence: 99%

“…Although the importance of WGS and WES in studying hematological malignancies is recognized, applying these methods for routine clinical testing is impractical due to costs, long turnaround time to results (weeks), and the complexities of data storage, analysis, and interpretation. On the other side, due to inclusion of actionable biomarkers, quick turnaround times, and lower cost compared to WES, targeted gene panels are best suited for routine clinical applications and especially ideal for identifying mutations and translocations in specific genes 58–60 . WGS at 60× coverage gives good results but costs at 1300$ or 1900$ per sample 61 depending on the laboratory throughput while exome sequencing will also cost 500$ at 300× coverage, whereas targeted panels can give around 1000× coverage at 200 to 250$.…”

Section: Ngs Panels For Amlmentioning

confidence: 99%

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Acute Myeloid Leukemia and Next-Generation Sequencing Panels for Diagnosis: A Comprehensive Review

Chaudhary,

Ahmad

et al. 2024

Journal of Pediatric Hematology/Oncology

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Acute myeloid leukemia (AML) is a genetically heterogeneous clonal disorder characterized by the accumulation of acquired somatic genetic alterations in hematopoietic progenitor cells, which alter the normal mechanisms of self-renewal, proliferation, and differentiation. Due to significant technological advancements in sequencing technologies in the last 2 decades, classification and prognostic scoring of AML has been refined, and multiple guidelines are now available for the same. The authors have tried to summarize, latest guidelines for AML diagnosis, important markers associated, epigenetics markers, various AML fusions and their importance, etc. Review of literature suggests lack of study or comprehensive information about current NGS panels for AML diagnosis, genes and fusions covered, their technical know-how, etc. To solve this issue, the authors have tried to present detailed review about currently in use next-generation sequencing myeloid panels and their offerings.

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Section: Ngs Panels For Amlmentioning

confidence: 99%

Section: Ngs Panels For Amlmentioning

confidence: 99%

Acute Myeloid Leukemia and Next-Generation Sequencing Panels for Diagnosis: A Comprehensive Review

Chaudhary,

Ahmad

et al. 2024

Journal of Pediatric Hematology/Oncology

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“…Our understanding of the clonal composition of cancer has substantially advanced in the last decade but we are increasingly facing the limitations of genomics for the prediction of treatment response and the identification of suitable targets of resistance. Initiated by the advent of high-throughput next-generation sequencing (NGS), considerable effort has been devoted to investigate the genomes and transcriptomes of various cancers, including myeloid malignancies [1][2][3]. These initiatives aimed for a better understanding of individual's disease biology, identification of prognostic as well as predictive biomarkers and lead to the development of targeted treatments according to the patients' molecular profiles (precision medicine) [4,5].…”

Section: Introductionmentioning

confidence: 99%

Inference of kinase-signaling networks in human myeloid cell line models by Phosphoproteomics using kinase activity enrichment analysis (KAEA)

et al. 2021

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Background Despite the introduction of targeted therapies, most patients with myeloid malignancies will not be cured and progress. Genomics is useful to elucidate the mutational landscape but remains limited in the prediction of therapeutic outcome and identification of targets for resistance. Dysregulation of phosphorylation-based signaling pathways is a hallmark of cancer, and therefore, kinase-inhibitors are playing an increasingly important role as targeted treatments. Untargeted phosphoproteomics analysis pipelines have been published but show limitations in inferring kinase-activities and identifying potential biomarkers of response and resistance. Methods We developed a phosphoproteomics workflow based on titanium dioxide phosphopeptide enrichment with subsequent analysis by liquid chromatography tandem mass spectrometry (LC-MS). We applied a novel Kinase-Activity Enrichment Analysis (KAEA) pipeline on differential phosphoproteomics profiles, which is based on the recently published SetRank enrichment algorithm with reduced false positive rates. Kinase activities were inferred by this algorithm using an extensive reference database comprising five experimentally validated kinase-substrate meta-databases complemented with the NetworKIN in-silico prediction tool. For the proof of concept, we used human myeloid cell lines (K562, NB4, THP1, OCI-AML3, MOLM13 and MV4–11) with known oncogenic drivers and exposed them to clinically established kinase-inhibitors. Results Biologically meaningful over- and under-active kinases were identified by KAEA in the unperturbed human myeloid cell lines (K562, NB4, THP1, OCI-AML3 and MOLM13). To increase the inhibition signal of the driving oncogenic kinases, we exposed the K562 (BCR-ABL1) and MOLM13/MV4–11 (FLT3-ITD) cell lines to either Nilotinib or Midostaurin kinase inhibitors, respectively. We observed correct detection of expected direct (ABL, KIT, SRC) and indirect (MAPK) targets of Nilotinib in K562 as well as indirect (PRKC, MAPK, AKT, RPS6K) targets of Midostaurin in MOLM13/MV4–11, respectively. Moreover, our pipeline was able to characterize unexplored kinase-activities within the corresponding signaling networks. Conclusions We developed and validated a novel KAEA pipeline for the analysis of differential phosphoproteomics MS profiling data. We provide translational researchers with an improved instrument to characterize the biological behavior of kinases in response or resistance to targeted treatment. Further investigations are warranted to determine the utility of KAEA to characterize mechanisms of disease progression and treatment failure using primary patient samples. Graphical abstract

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Next Generation Sequencing in MPNs. Lessons from the Past and Prospects for Use as Predictors of Prognosis and Treatment Responses

Skov

2020

Cancers

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The myeloproliferative neoplasms (MPNs) are acquired hematological stem cell neoplasms characterized by driver mutations in JAK2, CALR, or MPL. Additive mutations may appear in predominantly epigenetic regulator, RNA splicing and signaling pathway genes. These molecular mutations are a hallmark of diagnostic, prognostic, and therapeutic assessment in patients with MPNs. Over the past decade, next generation sequencing (NGS) has identified multiple somatic mutations in MPNs and has contributed substantially to our understanding of the disease pathogenesis highlighting the role of clonal evolution in disease progression. In addition, disease prognostication has expanded from encompassing only clinical decision making to include genomics in prognostic scoring systems. Taking into account the decreasing costs and increasing speed and availability of high throughput technologies, the integration of NGS into a diagnostic, prognostic and therapeutic pipeline is within reach. In this review, these aspects will be discussed highlighting their role regarding disease outcome and treatment modalities in patients with MPNs.

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The Progress of Next Generation Sequencing in the Assessment of Myeloid Malignancies

Cited by 3 publications

References 99 publications

Acute Myeloid Leukemia and Next-Generation Sequencing Panels for Diagnosis: A Comprehensive Review

Acute Myeloid Leukemia and Next-Generation Sequencing Panels for Diagnosis: A Comprehensive Review

Inference of kinase-signaling networks in human myeloid cell line models by Phosphoproteomics using kinase activity enrichment analysis (KAEA)

Next Generation Sequencing in MPNs. Lessons from the Past and Prospects for Use as Predictors of Prognosis and Treatment Responses

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