Acute Erythroid Leukemia Post-Chemo-Radiotherapy and Autologous Stem Cell Transplantation Due to Multiple Myeloma: Tracing the Paths to Leukemic Transformation.

The clinical impact of therapy-related acute leukemias is increasing with the extension of cancer-related survival; however, the origins remain largely unknown. Acute erythroleukemia (AEL), a rare unfavorable type of myeloid neoplasia, may also develop secondary to cytotoxic therapy. The disorder is featured by specific genetic alterations, most importantly multi-allelic mutations of the TP53 gene. While AEL might appear as a part of the therapy-related MDS/AML, spectrum information regarding the genetic complexity and progression is largely missing. We present two AEL cases arising after cytotoxic therapy and melphalan-based myeloablation/autologous peripheral stem cell transplantation due to multiple myeloma (MM). As stated, multiple pathogenic TP53 variants were present unrelated to preexisting MM, in parallel with uninvolved/wild-type hemopoiesis. Potential mechanisms of leukemic transformation are discussed, which include (1) preexisting preneoplastic hemopoietic stem cells (HSC) serving as the common origin for both MM and AEL, (2) the generation and intramedullary survival of p53-deficient post-chemotherapy HSCs, (3) reinoculation of mobilized autologous TP53 mutated HSCs, and (4) melphalan treatment-related late-onset myelodysplasia/leukemia with newly acquired TP53 mutations.

Extracranial metastatic oligodendroglioma with molecular progression, case presentation.

BACKGROUND: Extraneural metastasis of central nervous system tumors is generally rare and most often reported in glioblastomas and medulloblastomas, whereas oligodendrogliomas seem to have the lowest risk of extracranial metastasis. Given its infrequent occurrence, both the diagnosis and therapy of metastatic oligodendroglioma is often challenging. CASE PRESENTATION: This case study presents an oligodendroglioma, the isocitrate dehydrogenase 1 (IDH1) mutant, 1p/19q-codeleted tumor with bone marrow metastasis. The significance of this case lies in the comprehensive molecular analysis conducted for both the primary tumor and the metastasis. Chromosome 7 trisomy and chromosome 10 monosomy (+ 7/-10) were detected in the metastasis indicating molecular progression, which, to the best of our knowledge, has not been previously documented in metastatic oligodendroglioma. CONCLUSIONS: This case study serves additional information for better understanding of the metastatic capabilities of CNS tumors.

Enhancing HLA-B27 antigen detection: Leveraging machine learning algorithms for flow cytometric analysis.

As the association of human leukocyte antigen B27 (HLA-B27) with spondylarthropathies is widely known, HLA-B27 antigen expression is frequently identified using flow cytometric or other techniques. Because of the possibility of cross-reaction with off target antigens, such as HLA-B7, each flow cytometric technique applies a "gray zone" reserved for equivocal findings. Our aim was to use machine learning (ML) methods to classify such equivocal data as positive or negative. Equivocal samples (n = 99) were selected from samples submitted to our institution for clinical evaluation by HLA-B27 antigen testing. Samples were analyzed by flow cytometry and polymerase chain reaction. Features of histograms generated by flow cytometry were used to train and validate ML methods for classification as logistic regression (LR), decision tree (DT), random forest (RF) and light gradient boost method (GBM). All evaluated ML algorithms performed well, with high accuracy, sensitivity, specificity, as well as negative and positive predictive values. Although, gradient boost approaches are proposed as high performance methods; nevertheless, their effectiveness may be lower for smaller sample sizes. On our relatively smaller sample set, the random forest algorithm performed best (AUC: 0.92), but there was no statistically significant difference between the ML algorithms used. AUC values for light GBM, DT, and LR were 0.88, 0.89, 0.89, respectively. Implementing these algorithms into the process of HLA-B27 testing can reduce the number of uncertain, false negative or false positive cases, especially in laboratories where no genetic testing is available.