Adult T-cell leukemia/lymphoma (ATL) is an aggressive T-cell neoplasia associated with human T-cell leukemia virus type 1 (HTLV-1) infection and has an extremely poor prognosis. Lenalidomide (LEN; a second-generation immunomodulatory drug [IMiD]) has been employed as an additional therapeutic option for ATL since 2017, but its mechanism of action has not been fully proven, and recent studies reported emerging concerns about the development of second primary malignancies in patients treated with long-term IMiD therapy. Our purpose in this study was to elucidate the IMiD-mediated anti-ATL mechanisms. Thirteen ATL-related cell lines were divided into LEN-sensitive or LEN-resistant groups. CRBN knockdown (KD) led to a loss of LEN efficacy and IKZF2-KD-induced LEN efficacy in resistant cells. DNA microarray analysis demonstrated distinct transcriptional alteration after LEN treatment between LEN-sensitive and LEN-resistant ATL cell lines. Oral treatment of LEN for ATL cell-transplanted severe combined immunodeficiency (SCID) mice also indicated clear suppressive effects on tumor growth. Finally, a novel cereblon modulator (CELMoD), iberdomide (IBE), exhibited a broader and deeper spectrum of growth suppression to ATL cells with efficient IKZF2 degradation, which was not observed in other IMiD treatments. Based on these findings, our study strongly supports the novel therapeutic advantages of IBE against aggressive and relapsed ATL.
Epstein-Barr virus (EBV) causes various diseases in the elderly, including B-cell lymphoma such as Hodgkin's lymphoma and diffuse large B-cell lymphoma. Here, we show that EBV acts in trans on noninfected macrophages in the tumor through exosome secretion and augments the development of lymphomas. In a humanized mouse model, the different formation of lymphoproliferative disease (LPD) between 2 EBV strains (Akata and B95-8) was evident. Furthermore, injection of Akata-derived exosomes affected LPD severity, possibly through the regulation of macrophage phenotype in vivo. Exosomes collected from Akata-lymphoblastoid cell lines reportedly contain EBV-derived noncoding RNAs such as BamHI fragment A rightward transcript (BART) micro-RNAs (miRNAs) and EBV-encoded RNA. We focused on the exosome-mediated delivery of BART miRNAs. In vitro, BART miRNAs could induce the immune regulatory phenotype in macrophages characterized by the gene expressions of interleukin 10, tumor necrosis factor-α, and arginase 1, suggesting the immune regulatory role of BART miRNAs. The expression level of an EBV-encoded miRNA was strongly linked to the clinical outcomes in elderly patients with diffuse large B-cell lymphoma. These results implicate BART miRNAs as 1 of the factors regulating the severity of lymphoproliferative disease and as a diagnostic marker for EBV+ B-cell lymphoma.
Many algorithms to detect copy number variations (CNVs) using exome sequencing (ES) data have been reported and evaluated on their sensitivity and specificity, reproducibility, and precision. However, operational optimization of such algorithms for a better performance has not been fully addressed. ES of 1199 samples including 763 patients with different disease profiles was performed. ES data were analyzed to detect CNVs by both the eXome Hidden Markov Model (XHMM) and modified Nord's method. To efficiently detect rare CNVs, we aimed to decrease sequencing biases by analyzing, at the same time, the data of all unrelated samples sequenced in the same flow cell as a batch, and to eliminate sex effects of X-linked CNVs by analyzing female and male sequences separately. We also applied several filtering steps for more efficient CNV selection. The average number of CNVs detected in one sample was <5. This optimization together with targeted CNV analysis by Nord's method identified pathogenic/likely pathogenic CNVs in 34 patients (4.5%, 34/763). In particular, among 142 patients with epilepsy, the current protocol detected clinically relevant CNVs in 19 (13.4%) patients, whereas the previous protocol identified them in only 14 (9.9%) patients. Thus, this batch-based XHMM analysis efficiently selected rare pathogenic CNVs in genetic diseases.