Gene mutation profile in patients with acquired pure red cell aplasia

Pure red cell aplasia: The second hundred years

Pure red cell aplasia (PRCA) is a rare hematologic syndrome, characterized by an isolated normocytic anemia with severe reticulocytopenia, and defined by absence or near absence of erythroid precursors in the bone marrow. First described in 1922, PRCA may be a primary autoimmune or clonal myeloid or lymphoid disorder, but may also be secondary to other disorders of immune dysregulation/autoimmunity, to infections, to neoplasms, or to drugs. Insights from the study of PRCA have helped illuminate the understanding of the regulation of erythropoiesis. This review summarizes the classification, diagnostic, and therapeutic approach to PRCA as it begins its second century, with a particular focus on opportunities and challenges provided by new developments in the role of T-cells and T-cell regulatory mutations; the role of clonal hematopoiesis; and new developments in therapy for refractory PRCA and PRCA associated with ABO incompatible stem cell transplantation.

Distinct mutational pattern of T-cell large granular lymphocyte leukemia combined with pure red cell aplasia: low mutational burden of STAT3

T-cell large granular lymphocyte leukemia (T-LGL) is often accompanied by pure red cell aplasia (PRCA). A high depth of next generation sequencing (NGS) was used for detection of the mutational profiles in T-LGL alone (n = 25) and T-LGL combined with PRCA (n = 16). Beside STAT3 mutation (41.5%), the frequently mutated genes included KMT2D (17.1%), TERT (12.2%), SUZ12 (9.8%), BCOR (7.3%), DNMT3A (7.3%), and RUNX1 (7.3%). Mutations of the TERT promoter showed a good response to treatment. 3 of 41 (7.3%) T-LGL patients with diverse gene mutations were revealed as T-LGL combined with myelodysplastic syndrome (MDS) after review of bone marrow slide. T-LGL combined with PRCA showed unique features (low VAF level of STAT3 mutation, low lymphocyte count, old age). Low ANC was detected in a STAT3 mutant with a low level of VAF, suggesting that even the low mutational burden of STAT3 is sufficient for reduction of ANC. In retrospective analysis of 591 patients without T-LGL, one MDS patient with STAT3 mutation was revealed to have subclinical T-LGL. T-LGL combined with PRCA may be classified as unique subtype of T-LGL. High depth NGS can enable sensitive detection of concomitant MDS in T-LGL. Mutation of the TERT promoter may indicate good response to treatment of T-LGL, thus, its addition to an NGS panel may be recommended.

Gene Mutation Classification through Text Evidence Facilitating Cancer Tumour Detection

A cancer tumour consists of thousands of genetic mutations. Even after advancement in technology, the task of distinguishing genetic mutations, which act as driver for the growth of tumour with passengers (Neutral Genetic Mutations), is still being done manually. This is a time-consuming process where pathologists interpret every genetic mutation from the clinical evidence manually. These clinical shreds of evidence belong to a total of nine classes, but the criterion of classification is still unknown. The main aim of this research is to propose a multiclass classifier to classify the genetic mutations based on clinical evidence (i.e., the text description of these genetic mutations) using Natural Language Processing (NLP) techniques. The dataset for this research is taken from Kaggle and is provided by the Memorial Sloan Kettering Cancer Center (MSKCC). The world-class researchers and oncologists contribute the dataset. Three text transformation models, namely, CountVectorizer, TfidfVectorizer, and Word2Vec, are utilized for the conversion of text to a matrix of token counts. Three machine learning classification models, namely, Logistic Regression (LR), Random Forest (RF), and XGBoost (XGB), along with the Recurrent Neural Network (RNN) model of deep learning, are applied to the sparse matrix (keywords count representation) of text descriptions. The accuracy score of all the proposed classifiers is evaluated by using the confusion matrix. Finally, the empirical results show that the RNN model of deep learning has performed better than other proposed classifiers with the highest accuracy of 70%.

BCOR gene alterations in hematological diseases

The BCL6 co-repressor (BCOR) is a transcription factor involved in the control of embryogenesis, mesenchymal stem cells function, hematopoiesis and lymphoid development. Recurrent somatic clonal mutations of the BCOR gene and its homologue BCORL1 have been detected in several hematological malignancies and aplastic anemia. They are scattered across the whole gene length and mostly represent frameshifts (deletions, insertions), nonsense and missence mutations. These disruptive events lead to the loss of full-length BCOR protein and to the lack or low expression of a truncated form of the protein, both consistent with the tumor suppressor role of BCOR. BCOR and BCORL1 mutations are similar to those causing two rare X-linked diseases: the oculo-facio-cardio-dental (OFCD) and the Shukla-Vernon syndromes, respectively. Here, we focus on the structure and function of normal BCOR and BCORL1 in normal hematopoietic and lymphoid tissues and review the frequency and clinical significance of the mutations of these genes in malignant and non-malignant hematological diseases. Moreover, we discuss the importance of mouse models to better understand the role of Bcor loss, alone and combined with alterations of other genes (e.g. Dnmt3a and Tet2), in promoting hematological malignancies and in providing a useful platform for the development of new targeted therapies.

How I manage acquired pure red cell aplasia in adults

Pure red cell aplasia (PRCA) is a rare hematological disorder with multiple etiologies. The multifaceted nature of this disease is emphasized by the variety of concomitant clinical features. Classic idiopathic presentation aside, prompt recognition of pathogenetic clues is important because of their diagnostic and therapeutic implications. As a consequence, treatment of PRCA is diverse and strictly dependent on the presented clinical scenario. Here, we propose a series of clinical vignettes that showcase instructive representative situations derived from our routine clinical practice. Using these illustrative clinical cases, we review the diagnostic workup needed for a precise diagnosis and the currently available therapeutic options, discussing their applications in regard to the various PRCA-associated conditions and individual patients' characteristics. Finally, we propose a treatment algorithm that may offer guidance for personalized therapeutic recommendations.