NGS in CML - New standard diagnostic procedure?

Molecular subtypes predict therapeutic responses and identifying and validating diagnostic signatures based on machine learning in chronic myeloid leukemia

Chronic myeloid leukemia (CML) is a hematological tumor derived from hematopoietic stem cells. The aim of this study is to analyze the biological characteristics and identify the diagnostic markers of CML. We obtained the expression profiles from the Gene Expression Omnibus (GEO) database and identified 210 differentially expressed genes (DEGs) between CML and normal samples. These DEGs are mainly enriched in immune-related pathways such as Th1 and Th2 cell differentiation, primary immunodeficiency, T cell receptor signaling pathway, antigen processing and presentation pathways. Based on these DEGs, we identified two molecular subtypes using a consensus clustering algorithm. Cluster A was an immunosuppressive phenotype with reduced immune cell infiltration and significant activation of metabolism-related pathways such as reactive oxygen species, glycolysis and mTORC1; Cluster B was an immune activating phenotype with increased infiltration of CD4 + and CD8 + T cells and NK cells, and increased activation of signaling pathways such as interferon gamma (IFN-γ) response, IL6-JAK-STAT3 and inflammatory response. Drug prediction results showed that patients in Cluster B had a higher therapeutic response to anti-PD-1 and anti-CTLA4 and were more sensitive to imatinib, nilotinib and dasatinib. Support Vector Machine Recursive Feature Elimination (SVM-RFE), Least Absolute Shrinkage Selection Operator (LASSO) and Random Forest (RF) algorithms identified 4 CML diagnostic genes (HDC, SMPDL3A, IRF4 and AQP3), and the risk score model constructed by these genes improved the diagnostic accuracy. We further validated the diagnostic value of the 4 genes and the risk score model in a clinical cohort, and the risk score can be used in the differential diagnosis of CML and other hematological malignancies. The risk score can also be used to identify molecular subtypes and predict response to imatinib treatment. These results reveal the characteristics of immunosuppression and metabolic reprogramming in CML patients, and the identification of molecular subtypes and biomarkers provides new ideas and insights for the clinical diagnosis and treatment.

An interesting case of chronic myeloid leukemia with twists and turns

Additional cytogenetic abnormalities (ACA) are known to crop up in Ph⁺ cells of chronic myeloid leukemia (CML) patients due to cytogenetic evolution. But the frequency of molecular evolution and ACA is much less in Ph^- cells of CML patients and is poorly understood. We report an interesting and rare case of Ph⁺ CML, who progressed to B lymphoblastic crisis, achieved remission, and later developed Ph^- acute myeloid leukemia (AML) with KMT2A gene rearrangement and no detectable BCR- ABL transcripts.

Fusion Gene Detection and Quantification by Asymmetric Capture Sequencing (aCAP-Seq)

Several fusion genes such as BCR::ABL1, FIP1L1::PDGFRA, and PML::RARA are now efficiently targeted by specific therapies in patients with leukemia. Although these therapies have significantly improved patient outcomes, leukemia relapse and progression remain clinical concerns. Most myeloid next-generation sequencing (NGS) panels do not detect or quantify these fusions. It therefore remains difficult to decipher the clonal architecture and dynamics of myeloid malignancy patients, although these factors can affect clinical decisions and provide pathophysiologic insights. An asymmetric capture sequencing strategy (aCAP-Seq) and a bioinformatics algorithm (HmnFusion) were developed to detect and quantify MBCR::ABL1, μBCR::ABL1, PML::RARA, and FIP1L1::PDGFRA fusion genes in an NGS panel targeting 41 genes. One-hundred nineteen DNA samples derived from 106 patients were analyzed by conventional methods at diagnosis or on follow-up and were sequenced with this NGS myeloid panel. The specificity and sensitivity of fusion detection by aCAP-Seq were 100% and 98.1%, respectively, with a limit of detection estimated at 0.1%. Fusion quantifications were linear from 0.1% to 50%. Breakpoint locations and sequences identified by NGS were concordant with results obtained by Sanger sequencing. Finally, this new sensitive and cost-efficient NGS method allowed integrated analysis of resistant chronic myeloid leukemia patients and thus will be of interest to elucidate the mutational landscape and clonal architecture of myeloid malignancies driven by these fusion genes at diagnosis, relapse, or progression.

Chronic myeloid leukemia-from the Philadelphia chromosome to specific target drugs: A literature review

Chronic myeloid leukemia (CML) is a myeloproliferative neoplasm and was the first neoplastic disease associated with a well-defined genotypic anomaly - the presence of the Philadelphia chromosome. The advances in cytogenetic and molecular assays are of great importance to the diagnosis, prognosis, treatment, and monitoring of CML. The discovery of the breakpoint cluster region (BCR)-Abelson murine leukemia (ABL) 1 fusion oncogene has revolutionized the treatment of CML patients by allowing the development of targeted drugs that inhibit the tyrosine kinase activity of the BCR-ABL oncoprotein. Tyrosine kinase inhibitors (known as TKIs) are the standard therapy for CML and greatly increase the survival rates, despite adverse effects and the odds of residual disease after discontinuation of treatment. As therapeutic alternatives, the subsequent TKIs lead to faster and deeper molecular remissions; however, with the emergence of resistance to these drugs, immunotherapy appears as an alternative, which may have a cure potential in these patients. Against this background, this article aims at providing an overview on CML clinical management and a summary on the main targeted drugs available in that context.