Poor prognosis of SRSF2 gene mutations in patients treated with VEN-AZA for newly diagnosed acute myeloid leukemia.

Mutations in spliceosome genes (SRSF2, SF3B1, U2AF1, ZRSR2) correlate with inferior outcomes in patients treated with intensive chemotherapy for Acute Myeloid Leukemia. However, their prognostic impact in patients treated with less intensive protocols is not well known. This study aimed to evaluate the impact of Spliceosome mutations in patients treated with Venetoclax and Azacitidine for newly diagnosed AML. 117 patients treated in 3 different hospitals were included in the analysis. 34 harbored a mutation in at least one of the spliceosome genes (splice-mut cohort). K/NRAS mutations were more frequent in the splice-mut cohort (47% vs 19%, p=0.0022). Response rates did not differ between splice-mut and splice-wt cohorts. With a median follow-up of 15 months, splice mutations were associated with a lower 18-month LFS (p=0.0045). When analyzing splice mutations separately, we found SRSF2 mutations to be associated with poorer outcomes (p=0.034 and p=0.037 for OS and LFS respectively). This negative prognostic impact remained true in our multivariate analysis. We believe this finding should warrant further studies aimed at overcoming this negative impact.

Impact of Next-Generation Sequencing in Diagnosis, Prognosis and Therapeutic Management of Acute Myeloid Leukemia/Myelodysplastic Neoplasms.

For decades, the diagnosis, prognosis and thus, the treatment of acute myeloblastic leukemias and myelodysplastic neoplasms has been mainly based on morphological aspects, as evidenced by the French-American-British classification. The morphological aspects correspond quite well, in a certain number of particular cases, to particular evolutionary properties, such as acute myelomonoblastic leukemias with eosinophils or acute promyelocytic leukemias. Advances in biology, particularly "classical" cytogenetics (karyotype) and molecular cytogenetics (in situ hybridization), have made it possible to associate certain morphological features with particular molecular abnormalities, such as the pericentric inversion of chromosome 16 and translocation t(15;17) in the two preceding examples. Polymerase chain reaction techniques have made it possible to go further in these analyses by associating these karyotype abnormalities with their molecular causes, CBFbeta fusion with MYH11 and PML-RAR fusion in the previous cases. In these two examples, the molecular abnormality allows us to better define the pathophysiology of leukemia, to adapt certain treatments (all-transretinoic acid, for example), and to follow up the residual disease of strong prognostic value beyond the simple threshold of less than 5% of marrow blasts, signaling the complete remission. However, the new sequencing techniques of the next generation open up broader perspectives by being able to analyze several dozens of molecular abnormalities, improving all levels of management, from diagnosis to prognosis and treatment, even if it means that morphological aspects are increasingly relegated to the background.

Extreme Gradient Boosting Tuned with Metaheuristic Algorithms for Predicting Myeloid NGS Onco-Somatic Variant Pathogenicity.

The advent of next-generation sequencing (NGS) technologies has revolutionized the field of bioinformatics and genomics, particularly in the area of onco-somatic genetics. NGS has provided a wealth of information about the genetic changes that underlie cancer and has considerably improved our ability to diagnose and treat cancer. However, the large amount of data generated by NGS makes it difficult to interpret the variants. To address this, machine learning algorithms such as Extreme Gradient Boosting (XGBoost) have become increasingly important tools in the analysis of NGS data. In this paper, we present a machine learning tool that uses XGBoost to predict the pathogenicity of a mutation in the myeloid panel. We optimized the performance of XGBoost using metaheuristic algorithms and compared our predictions with the decisions of biologists and other prediction tools. The myeloid panel is a critical component in the diagnosis and treatment of myeloid neoplasms, and the sequencing of this panel allows for the identification of specific genetic mutations, enabling more accurate diagnoses and tailored treatment plans. We used datasets collected from our myeloid panel NGS analysis to train the XGBoost algorithm. It represents a data collection of 15,977 mutations variants composed of a collection of 13,221 Single Nucleotide Variants (SNVs), 73 Multiple Nucleoid Variants (MNVs), and 2683 insertion deletions (INDELs). The optimal XGBoost hyperparameters were found with Differential Evolution (DE), with an accuracy of 99.35%, precision of 98.70%, specificity of 98.71%, and sensitivity of 1.

Cytological Diagnosis of Classic Myeloproliferative Neoplasms at the Age of Molecular Biology.

Myeloproliferative neoplasms (MPN) are clonal hematopoietic stem cell-derived disorders characterized by uncontrolled proliferation of differentiated myeloid cells. Two main groups of MPN, BCR::ABL1-positive (Chronic Myeloid Leukemia) and BCR::ABL1-negative (Polycythemia Vera, Essential Thrombocytosis, Primary Myelofibrosis) are distinguished. For many years, cytomorphologic and histologic features were the only proof of MPN and attempted to distinguish the different entities of the subgroup BCR::ABL1-negative MPN. World Health Organization (WHO) classification of myeloid neoplasms evolves over the years and increasingly considers molecular abnormalities to prove the clonal hematopoiesis. In addition to morphological clues, the detection of JAK2, MPL and CALR mutations are considered driver events belonging to the major diagnostic criteria of BCR::ABL1-negative MPN. This highlights the preponderant place of molecular features in the MPN diagnosis. Moreover, the advent of next-generation sequencing (NGS) allowed the identification of additional somatic mutations involved in clonal hematopoiesis and playing a role in the prognosis of MPN. Nowadays, careful cytomorphology and molecular biology are inseparable and complementary to provide a specific diagnosis and to permit the best follow-up of these diseases.

Impact of Molecular Biology in Diagnosis, Prognosis, and Therapeutic Management of BCR::ABL1-Negative Myeloproliferative Neoplasm.

BCR::ABL1-negative myeloproliferative neoplasms (MPNs) include three major subgroups-polycythemia vera (PV), essential thrombocythemia (ET), and primary myelofibrosis (PMF)-which are characterized by aberrant hematopoietic proliferation with an increased risk of leukemic transformation. Besides the driver mutations, which are JAK2, CALR, and MPL, more than twenty additional mutations have been identified through the use of next-generation sequencing (NGS), which can be involved with pathways that regulate epigenetic modifications, RNA splicing, or DNA repair. The aim of this short review is to highlight the impact of molecular biology on the diagnosis, prognosis, and therapeutic management of patients with PV, ET, and PMF.

Description of 22 new alpha-1 antitrypsin genetic variants.

Alpha-1 antitrypsin deficiency is an autosomal co-dominant disorder caused by mutations of the highly polymorphic SERPINA1 gene. This genetic disorder still remains largely under-recognized and can be associated with lung and/or liver injury. The laboratory testing for this deficiency typically comprises serum alpha-1 antitrypsin quantification, phenotyping according to the isoelectric focusing pattern and genotyping if necessary. To date, more than 100 SERPINA1 variants have been described and new genetic variants are frequently discovered. Over the past 10 years, 22 new genetic variants of the SERPINA1 gene were identified in the daily practice of the University Medical laboratories of Lille and Lyon (France). Among these 22 variants, seven were Null alleles and one with a M1 migration pattern (M1Cremeaux) was considered as deficient according to the clinical and biological data and to the American College of Medical Genetics and Genomics (ACMG) criteria. Three other variants were classified as likely pathogenic, three as variants of uncertain significance while the remaining ones were assumed to be neutral. Moreover, we also identified in this study two recently described SERPINA1 deficient variants: Trento (p.Glu99Val) and SDonosti (p.Ser38Phe). The current data, together with a recent published meta-analysis, represent the most up-to-date list of SERPINA1 variants available so far.