Search

Genetic changes in juvenile myelomonocytic leukemia (JMML) determine distinct subtypes, treatments, and outcomes. JMML with germline CBL mutation and somatic NRAS mutation possibly achieves spontaneous remission, but hematopoietic stem cell transplantation is indicated for other subtypes of JMML. We hereby report a child with JMML harboring a germline CBL mutation (c.1111T>C) and an NF1 variant (c.3352A>G) concurrently. After evaluation, we considered that the NF1 variant was not the major contributor. After one year of observation, this case had no signs of disease progression. This case highlights the importance of combining available evidence and clinical findings in caring for patients with unusual genomic variations.

Hodgkin lymphoma (HL) is a highly curable hematologic malignancy. Relapsed/refractory (R/R) HL is an important clinical challenge, despite advances in treatment. Brentuximab vedotin (BV) is highly effective in R/R HL in the western world. However, there are no real-world data on the use of BV in the Asian population. Our study aimed to evaluate the efficacy and safety of BV as salvage therapy in R/R HL patients in Taiwan. We recruited 20 R/R HL patients who received BV at National Taiwan University Hospital. BV was administered at 1.8 mg/kg once every 3 weeks. The median number of systemic treatment received before BV was three. The overall response rate was 73.7% with a complete remission rate of 21.1% in R/R HL. Overall survival was not reached and progression-free survival was 6.8 months. BV could strengthen disease control before transplantation and improve post-transplant outcomes, even among those heavily pretreated patients, without significant overlapping toxicities with prior therapies. Our data suggest that BV is well tolerated and effective in the treatment of Asian patients with R/R HL. BV may offer long-term disease control in selected patients. Keywords: Brentuximab vedotin, Hodgkin lymphoma, Relapse, Transplantation

Background: Expression of long non-coding RNAs (lncRNAs) has recently been recognized as a potential prognostic marker in acute myeloid leukemia (AML). However, it remains unclear whether incorporation of the lncRNAs expression in the 2017 European LeukemiaNet (ELN) risk classification can further improve the prognostic prediction. Methods: We enrolled 275 newly diagnosed non-M3 AML patients and randomly assigned them to the training (n = 183) and validation cohorts (n = 92). In the training cohort, we formulated a prognostic lncRNA scoring system composed of five lncRNAs with significant prognostic impact from the lncRNA expression profiling. Findings: Higher lncRNA scores were significantly associated with older age and adverse gene mutations. Further, the higher-score patients had shorter overall and disease-free survival than lower-score patients, which were also confirmed in both internal and external validation cohorts (TCGA database). The multivariate analyses revealed the lncRNA score was an independent prognosticator in AML, irrespective of the risk based on the 2017 ELN classification. Moreover, in the 2017 ELN intermediate-risk subgroup, lncRNA scoring system could well dichotomize the patients into two groups with distinct prognosis. Within the ELN intermediate-risk subgroup, we found that allogeneic hematopoietic stem cell transplantation could provide better outcome on patients with higher lncRNA scores. Through bioinformatics approach, we identified high lncRNA scores were correlated with leukemia/hematopoietic stem cell signatures. Interpretation: Incorporation of lncRNA scoring system in 2017 ELN classification can improve risk-stratification of AML patients and help clinical decision-making. Fund: This work was supported Ministry of Science and Technology, and Ministry of Health and Welfare of Taiwan. Keywords: lncRNA, AML, Risk stratification, Prognostication

Background: Multicolor flow cytometry (MFC) analysis is widely used to identify minimal residual disease (MRD) after treatment for acute myeloid leukemia (AML) and myelodysplastic syndrome (MDS). However, current manual interpretation suffers from drawbacks of time consuming and interpreter idiosyncrasy. Artificial intelligence (AI), with the expertise in assisting repetitive or complex analysis, represents a potential solution for these drawbacks. Methods: From 2009 to 2016, 5333 MFC data from 1742 AML or MDS patients were collected. The 287 MFC data at post-induction were selected as the outcome set for clinical outcome validation. The rest were 4:1 randomized into the training set (n = 4039) and the validation set (n = 1007). AI algorithm learned a multi-dimensional MFC phenotype from the training set and input it to support vector machine (SVM) classifier after Gaussian mixture model (GMM) modeling, and the performance was evaluated in The validation set. Findings: Promising accuracies (84·6% to 92·4%) and AUCs (0·921–0·950) were achieved by the developed algorithms. Interestingly, the algorithm from even one testing tube achieved similar performance. The clinical significance was validated in the outcome set, and normal MFC interpreted by the AI predicted better progression-free survival (10·9 vs 4·9 months, p