RUNX1 is mutated in ∼10% of adult acute myeloid leukemia (AML). Although most RUNX1 mutations in this disease are believed to be acquired, they can also be germline. Indeed, germline RUNX1 mutations result in the well-described autosomal-dominant familial platelet disorder with predisposition to hematologic malignancies (RUNX1-FPD, FPD/AML, FPDMM); ∼44% of affected individuals progress to AML or myelodysplastic syndromes. Using the Leucegene RUNX1 AML patient group, we sought to investigate the proportion of germline vs acquired RUNX1 mutations in this cohort. Our results showed that 30% of RUNX1 mutations in our AML cohort are germline. Molecular profiling revealed higher frequencies of NRAS mutations and other mutations known to activate various signaling pathways in these patients with RUNX1 germline–mutated AML. Moreover, 2 patients (mother and son) had co-occurrence of RUNX1 and CEBPA germline mutations, with variable AML disease onset at 59 and 27 years, respectively. Together, these data suggest a higher than anticipated frequency of germline RUNX1 mutations in the Leucegene cohort and further highlight the importance of testing for RUNX1 mutations in instances in which allogeneic stem cell transplantation using a related donor is envisioned.

Key Points• Through lncRNA profiling, we identified an MDS patient subset with distinct clinical and mutational patterns along with inferior outcomes.• A concise yet powerful 4-lncRNA risk-scoring system was devised with the potential to improve current MDS risk stratification.Long noncoding RNAs (lncRNAs) not only participate in normal hematopoiesis but also contribute to the pathogenesis of acute leukemia. However, their clinical and prognostic relevance in myelodysplastic syndromes (MDSs) remains unclear to date. In this study, we profiled lncRNA expressions in 176 adult patients with primary MDS, and identified 4 lncRNAs whose expression levels were significantly associated with overall survival (OS). We then constructed a risk-scoring system with the weighted sum of these 4 lncRNAs. Higher lncRNA scores were associated with higher marrow blast percentages, higher-risk subtypes of MDSs (based on both the Revised International Prognostic Scoring System [IPSS-R] and WorldHealth Organization classification), complex cytogenetic changes, and mutations in RUNX1, ASXL1, TP53, SRSF2, and ZRSR2, whereas they were inversely correlated with SF3B1 mutation. Patients with higher lncRNA scores had a significantly shorter OS and a higher 5-year leukemic transformation rate compared with those with lower scores. The prognostic significance of our 4-lncRNA risk score could be validated in an independent MDS cohort. In multivariate analysis, higher lncRNA scores remained an independent unfavorable risk factor for OS (relative risk, 4.783; P , .001) irrespective of age, cytogenetics, IPSS-R, and gene mutations. To our knowledge, this is the first report to provide a lncRNA platform for risk stratification of MDS patients. In conclusion, our integrated 4-lncRNA risk-scoring system is correlated with distinctive clinical and biological features in MDS patients, and serves as an independent prognostic factor for survival and leukemic transformation. This concise yet powerful lncRNA-based scoring system holds the potential to improve the current risk stratification of MDS patients.

Myelodysplastic syndrome (MDS) comprised a heterogeneous group of diseases. The prognosis of patients varies even in the same risk groups. Searching for novel prognostic markers is warranted. Leukemic stem cells (LSCs) are responsible for chemoresistance and relapse in leukemia. Recently, expressions of 17 genes related to stemness of LSCs were found to be associated with prognosis in acute myeloid leukemia patients. However, the clinical impact of LSC genes expressions in MDS, a disorder arising from hematopoietic stem cells, remains unclear. We analyzed expression profile of the 17 stemness-related genes in primary MDS patients and identified expression of 4 genes (LAPTM4B, NGFRAP1, EMP1, and CPXM1) were significantly correlated with overall survival (OS). We constructed an LSC4 scoring system based on the weighted sums of the expression of 4 genes and explored its clinical implications in MDS patients. Higher LSC4 scores were associated with higher revised International Prognostic Scoring System (IPSS-R) scores, complex cytogenetics, and mutations in RUNX1, ASXL1, and TP53. High-score patients had significantly shorter OS and leukemia-free survival (LFS), which was also confirmed in 2 independent validation cohorts. Subgroup analysis revealed the prognostic significance of LSC4 scores for OS remained valid across IPSS-R lower- and higher-risk groups. Furthermore, higher LSC4 score was an independent adverse risk factor for OS and LFS in multivariate analysis. In summary, LSC4 score can independently predict prognosis in MDS patients irrespective of IPSS-R risks and may be used to guide the treatment of MDS patients, especially lower-risk group in whom usually only supportive treatment is given.

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Aside from the cell-intrinsic factors such as genetic alterations, immune dysregulation in the bone marrow (BM) microenvironment plays a role in the development and progression of myelodysplastic syndromes (MDS). However, the prognostic implications of various immune cells in MDS patients remain unclear. We adopted CIBERSORTx to estimate the relative fractions of 22 subtypes of immune cells in the BM of 316 MDS patients and correlated the results with clinical outcomes. A lower fraction of unpolarized M0 macrophages and higher fractions of M2 macrophages and eosinophils were significantly associated with inferior survival. An immune cell scoring system (ICSS) was constructed based on the proportion of these three immune cells in the BM. The ICSS high-risk patients had higher BM blast counts, higher frequencies of poor-risk cytogenetics, and NPM1, TP53, and WT1 mutations than intermediate- and low-risk patients. The ICSS could stratify MDS patients into three risk groups with distinct leukemia-free survival and overall survival among the total cohort and in the subgroups of patients with lower and higher disease risk based on the revised International Prognostic Scoring System (IPSS-R). The prognostic significance of ICSS was also validated in another independent cohort. Multivariable analysis revealed that ICSS independently predicted prognosis, irrespective of age, IPSS-R, and mutation status. Bioinformatic analysis demonstrated a significant correlation between high-risk ICSS and nuclear factor kappa B signaling, oxidative stress, and leukemic stem cell signature pathways. Further studies investigating the mechanistic insight into the crosstalk between stem cells and immune cells are warranted.

Myelodysplastic syndromes (MDS) are a heterogeneous group of hematologic malignancies. Although most MDS patients have normal or increased BM cellularity (NH-MDS), some have hypocellular BM (h-MDS). The reports concerning the differences in genetic alterations between h-MDS and NH-MDS patients are limited. In this study, 369 MDS patients diagnosed according to the WHO 2008 criteria were recruited. h-MDS patients had lower PB white blood cell and blast counts, and lower BM blast percentages, than those with NH-MDS. h-MDS was closely associated with lower-risk MDS, defined by the International Prognostic Scoring System (IPSS) and revised IPSS (IPSS-R). IPSS-R could properly predict the prognosis in h-MDS (P<0.001) as in NH-MDS patients. The h-MDS patients had lower incidences of RUNX1, ASXL1, DNMT3A, EZH2 and TP53 mutations than NH-MDS patients. The cumulated incidence of acute leukemic transformation at 5 years was 19.3% for h-MDS and 40.4% for NH-MDS patients (P= 0.001). Further, the patients with h-MDS had longer overall survival (OS) than those with NH-MDS (P= 0.001), and BM hypocellularity remains an independent favorable prognostic factor for OS irrespective of age, IPSS-R, and gene mutations. Our findings provide evidence that h-MDS indeed represent a distinct clinico-biological subgroup of MDS and can predict better leukemia-free survival and OS.

Purpose Differential counting of blood cells is the basis of diagnostic hematology. In many circumstances, identification of cells in bone marrow smears is the golden standard for diagnosis. Presently, methods for automatic differential counting of peripheral blood are readily available commercially. However, morphological assessment and differential counting of bone marrow smears are still performed manually. This procedure is tedious, time-consuming and laden with high inter-operator variation. In recent years, deep neural networks have proven useful in many medical image recognition tasks, such as diagnosis of diabetic retinopathy, and detection of cancer metastasis in lymph nodes. However, there has been no published work on using deep neural networks for complete differential counting of entire bone marrow smear. In this work, we present the results of using deep convolutional neural network for automatic differential counting of bone marrow nucleated cells. Materials & Methods The bone marrow smears from patients with either benign or malignant disorders in National Taiwan University Hospital were recruited in this study. The bone marrow smears are stained with Liu's stain, a modified Romanowsky stain. Digital images of the bone marrow smears were taken using 1000x oil immersion lens and 20MP color CCD camera on a single microscope with standard illumination and white-balance settings. The contour of each nucleated cell was artificially defined. These cells were then divided into a training/validation set and a test set. Each cell was then classified into 1 of the 11 categories (blast, promyelocyte, neutrophilic myelocyte, neutrophilic metamyelocyte, neutrophils, eosinophils and precursors, basophil, monocyte and precursors, lymphocyte, erythroid lineage cells, and invalid cell). In training/validation set, the classification of each cell was annotated once by experienced medical technician or hematologist. The annotated dataset was used to train a Path-Aggregation Network for instance segmentation task. In test set, cell classification was annotated by three medical technicians or hematologists; only over 2/3 consensus was regarded as valid. After the neural network model was fully trained, the ability of the model to classify and detect bone marrow nucleated cells was evaluated in terms of precision, recall and accuracy. During the model training, we used group normalization and stochastic gradient descent optimizer for training. Random noise, Gaussian blur, rotation, contrast and color shift were also used as means for data augmentation. Results The digital images of 150 bone marrow aspirate smears were taken for this study. They included 61 for acute leukemia, 39 for lymphoma, 2 for myelodysplastic syndrome (MDS), 2 for myeloproliferative neoplasm (MPN), 10 for MDS/MPN, 12 for multiple myeloma, 4 for hemolytic anemia, 9 for aplastic anemia, 8 for infectious etiology and 3 for solid cancers. The final data contained 5927 images and 187730 nucleated bone marrow cells, which were divided into 2 sets: 5630 images containing 170966 cells as the training/validation set, and 297 images containing 16764 cells as the test set. Among the 16764 cells annotated in test set, 15676 cells (93.6 %) reached over 2/3 consensus. The trained neural network achieved 0.832 recall and 0.736 precision for cell detection task, 0.79 mean intersection over union (IOU) for cell segmentation task, mean average precision of 0.659 and accuracy of 0.801 for cell classification. For individual cell categories, the model performs the best with "erythroid-lineage-cells" (0.971 recall, 0.935 precision) and the worst with "monocyte-and-precursors" (0.825 recall, 0.337 precision). Conclusions We have created the largest and the most comprehensive annotated bone marrow smear image dataset for deep neural network training. Compared with previous works, our approach is more practical for clinical application because it is able to take in an entire field of smear and generate differential counts without any other preprocessing steps. Current results are highly encouraging. With continued expansion of dataset, our model would be more precise and clinically useful. Figure Disclosures Yeh: aether AI: Other: CEO and co-founder. Yang:aether AI: Employment. Tien:Novartis: Honoraria; Daiichi Sankyo: Honoraria; Celgene: Research Funding; Roche: Honoraria; Johnson &Johnson: Honoraria; Alexion: Honoraria; BMS: Honoraria; Roche: Research Funding; Celgene: Honoraria; Pfizer: Honoraria; Abbvie: Honoraria. Hsu:aether AI: Employment.

BackgroundExpression 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.MethodsWe 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.FindingsHigher 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.InterpretationIncorporation of lncRNA scoring system in 2017 ELN classification can improve risk-stratification of AML patients and help clinical decision-making.FundThis work was supported Ministry of Science and Technology, and Ministry of Health and Welfare of Taiwan.