Multiple myeloma (MM) develops with the acquisition of genetic abnormalities in plasmacytes and changes in microenvironment cells (MECs). Despite the progress in understanding MM disease mechanism through omics studies, the genomic/transcriptomic profiling remains limited in Chinese MM patients. Here, we collected 277 newly diagnosed MM (NDMM) patients in the Shanghai MM Omics (SMMO) project. Analysis of 267 cases with whole-genome/whole-exome sequencing and RNA sequencing (RNA-seq) identified three genetic groups (MY, HRD, and MS/CD). Using single-cell RNA sequencing (scRNA-seq), we investigated 59 NDMM subjects from SMMO, 20 relapsed cases from public database and 30 normal controls. Eight subpopulations of plasmacytes from NDMM (mSP1-mSP8) were defined, each showing unique signatures while forming a differentiation trajectory. The mSP2 is worth noting due to its high proliferative property. Regarding MECs, we found T cell subsets including T-helpers, Tregs, and cytotoxic T cells all in dysfunctional status and increased myeloid-derived suppressor cells such as macrophages mainly in M2 polarization, both constituting a milieu in favor of MM cell growth and immune escape. Furthermore, we scrutinized the crosstalk between MM cells and MECs and that among distinct MECs. A dynamic, comprehensive MM pathogenesis network was revealed, with a number of ligand-receptor pairs. Importantly, the mSP2 signature can be projected to the MM cell RNA-seq data of 235 patients to generate a Score100 with prognostic value in SMMO. Via multivariate analysis of the International Staging System, Consensus Genomic Staging, and Score100, we propose a practical MM stratification model for evaluating aggressive myeloma.

BACKGROUND:Acute leukaemia represents a crucial health challenge. However, nationwide data delineating the incidence of acute leukaemia subtypes, as well as mortality and survival outcomes, remain scarce in China. We aimed to provide a comprehensive assessment of the epidemiology of acute leukaemia subtypes across China. METHODS:We conducted a population-based cancer registry analysis and cohort study in China, by integrating data from five national databases through unique national identification numbers. The main outcomes were age-standardised rates (ASRs) for incidence and mortality and overall survival for acute leukaemia subtypes. Acute leukaemia incidence and mortality data in 2019 were extracted from National Cancer Centre (NCC) registries linked to the Hospital Quality Monitoring System (HQMS), stratified by age, sex, and region. ASRs were calculated with Segi's world standard population with 95% CIs across the general population. For the survival analysis, we established a cohort from the Chinese Childhood Leukaemia Registry (33 530 children aged 0-14 years) and National Adult Acute Leukaemia Registry of China (71 477 adults aged ≥15 years) for 2016-20, integrated with the Cause of Death Reporting System and HQMS. Patients were stratified by subtype, age, sex, region, molecular characteristics, treatment modalities, and diagnosis period (2016-18 vs 2019-20). Overall survival and cause-specific survival were assessed with the Kaplan-Meier method at multiple timepoints (1 month, and year 1 to year 5) in our cohort. Multivariate Cox regression analysis was performed to identify prognostic factors. FINDINGS:Based on NCC registries covering a population of 628·4 million, we estimated 43 275 new acute leukaemia cases and 27 049 deaths in 2019 in China, with an ASR for incidence 2·83 (95% CI 2·78-2·88) per 100 000 population and an ASR for mortality of 1·51 (1·48-1·54) per 100 000 population. The ASR for the incidence of non-acute promyelocytic leukaemia-acute myeloid leukaemia was 1·24 (95% CI 1·21-1·26) per 100 000 population, that of acute lymphoblastic leukaemia was 0·92 (0·89-0·95) per 100 000 population, and that of acute promyelocytic leukaemia was 0·22 (0·21-0·23) per 100 000 population. The incidence of acute leukaemia spiked in children aged 1-4 years (4·54 per 100 000), then declined, and then rose markedly after age 60 years, peaking at 9·33 per 100 000 in people aged 75-79 years, before declining, while overall mortality remained relatively low across younger age groups (0-44 years), then increased progressively with advancing age, from 1·23 per 100 000 in adults aged 45-49 years to 8·77 per 100 000 in those aged 80-84 years. In children, 5-year overall survival was 66·5% (95% CI 65·3-67·9) for non-acute promyelocytic leukaemia acute myeloid leukaemia, 91·1% (89·6-92·6) for acute promyelocytic leukaemia, and 85·4% (84·9-85·8) for acute lymphoblastic leukaemia; in adults, 5-year overall survival was 23·9% (23·4-24·3) for non-acute promyelocytic leukaemia acute myeloid leukaemia, 82·5% (81·7-83·4) for acute promyelocytic leukaemia, and 30·1% (29·2-30·9) for acute lymphoblastic leukaemia. Survival improved in the more recent period (2019-20 vs 2016-18: hazard ratio 0·97 [95% CI 0·95-0·99]; p=0·0014), particularly among younger adults with non-acute promyelocytic leukaemia acute myeloid leukaemia (aged <60 years) and acute lymphoblastic leukaemia (aged <40 years), with improvements primarily attributable to expanded application of allogeneic haematopoietic stem-cell transplantation. However, prognosis remained poor in patients with acute leukaemia aged 60 years and older (5-year overall survival 14·9% [95% CI 14·3-15·5] in patients aged 60-74 years, and 4·8% [4·2-5·4] in patients aged ≥75 years). INTERPRETATION:This comprehensive nationwide study of acute leukaemia incidence, mortality and survival outcomes across China establishes age-specific epidemiological benchmarks, enabling ongoing risk factor monitoring, while supporting expanded transplantation access for eligible patients and highlighting the urgent need for novel, less toxic therapies for older patients who bear a disproportionately higher disease burden. FUNDING:State Key Laboratory of Medical Genomics, Double First-Class Project, Overseas Expertise Introduction Project for Discipline Innovation, National Natural Science Foundation of China, Innovative Research Team of High-level Local Universities in Shanghai, Shanghai Guangci Translational Medical Research Development Foundation, and CAMS Innovation Fund for Medical Sciences. TRANSLATIONS:For the Chinese translation of the abstract see Supplementary Materials section.

CONTEXT.— Conventional karyotype analysis, which provides comprehensive cytogenetic information, plays a significant role in the diagnosis and risk stratification of hematologic neoplasms. The main limitations of this approach include long turnaround time and laboriousness. Therefore, we developed an integral R-banded karyotype analysis system for bone marrow metaphases, based on deep learning. OBJECTIVE.— To evaluate the performance of the internal models and the entire karyotype analysis system for R-banded bone marrow metaphase. DESIGN.— A total of 4442 sets of R-banded normal bone marrow metaphases and karyograms were collected. Accordingly, 4 deep learning-based models for different analytic stages of karyotyping, including denoising, segmentation, classification, and polarity recognition, were developed and integrated as an R-banded bone marrow karyotype analysis system. Five-fold cross validation was performed on each model. The whole system was implemented by 2 strategies of automatic and semiautomatic workflows. A test set of 885 metaphases was used to assess the entire system. RESULTS.— The denoising model achieved an intersection-over-union (IoU) of 99.20% and a Dice similarity coefficient (DSC) of 99.58% for metaphase acquisition. The segmentation model achieved an IoU of 91.95% and a DSC of 95.79% for chromosome segmentation. The accuracies of the segmentation, classification, and polarity recognition models were 96.77%, 98.77%, and 99.93%, respectively. The whole system achieved an accuracy of 93.33% with the automatic strategy and an accuracy of 99.06% with the semiautomatic strategy. CONCLUSIONS.— The performance of both the internal models and the entire system is desirable. This deep learning-based karyotype analysis system has potential in clinical application.

Chromosome karyotyping is a critical way to diagnose various hematological malignancies and genetic diseases, of which chromosome detection in raw metaphase cell images is the most critical and challenging step. In this work, focusing on the joint optimization of chromosome localization and classification, we propose ChromTR to accurately detect and classify 24 classes of chromosomes in raw metaphase cell images. ChromTR incorporates semantic feature learning and class distribution learning into a unified DETR-based detection framework. Specifically, we first propose a Semantic Feature Learning Network (SFLN) for semantic feature extraction and chromosome foreground region segmentation with object-wise supervision. Next, we construct a Semantic-Aware Transformer (SAT) with two parallel encoders and a Semantic-Aware decoder to integrate global visual and semantic features. To provide a prediction with a precise chromosome number and category distribution, a Category Distribution Reasoning Module (CDRM) is built for foreground-background objects and chromosome class distribution reasoning. We evaluate ChromTR on 1404 newly collected R-band metaphase images and the public G-band dataset AutoKary2022. Our proposed ChromTR outperforms all previous chromosome detection methods with an average precision of 92.56% in R-band chromosome detection, surpassing the baseline method by 3.02%. In a clinical test, ChromTR is also confident in tackling normal and numerically abnormal karyotypes. When extended to the chromosome enumeration task, ChromTR also demonstrates state-of-the-art performances on R-band and G-band two metaphase image datasets. Given these superior performances to other methods, our proposed method has been applied to assist clinical karyotype diagnosis.

Understanding the molecular pathogenesis of acute myeloid leukemia (AML) with well-defined genomic abnormalities has facilitated the development of targeted therapeutics. Patients with t(8;21) AML frequently harbor a fusion gene RUNX1-RUNX1T1 and KIT mutations as “secondary hit”, making the disease one of the ideal models for exploring targeted treatment options in AML. In this study we investigated the combination therapy of agents targeting RUNX1-RUNX1T1 and KIT in the treatment of t(8;21) AML with KIT mutations. We showed that the combination of eriocalyxin B (EriB) and homoharringtonine (HHT) exerted synergistic therapeutic effects by dual inhibition of RUNX1-RUNX1T1 and KIT proteins in Kasumi-1 and SKNO-1 cells in vitro. In Kasumi-1 cells, the combination of EriB and HHT could perturb the RUNX1-RUNX1T1-responsible transcriptional network by destabilizing RUNX1-RUNX1T1 transcription factor complex (AETFC), forcing RUNX1-RUNX1T1 leaving from the chromatin, triggering cell cycle arrest and apoptosis. Meanwhile, EriB combined with HHT activated JNK signaling, resulting in the eventual degradation of RUNX1-RUNX1T1 by caspase-3. In addition, HHT and EriB inhibited NF-κB pathway through blocking p65 nuclear translocation in two different manners, to synergistically interfere with the transcription of KIT . In mice co-expressing RUNX1-RUNX1T1 and KIT N822K , co-administration of EriB and HHT significantly prolonged survival of the mice by targeting CD34 + CD38 − leukemic cells. The synergistic effects of the two drugs were also observed in bone marrow mononuclear cells (BMMCs) of t(8;21) AML patients. Collectively, this study reveals the synergistic mechanism of the combination regimen of EriB and HHT in t(8;21) AML, providing new insight into optimizing targeted treatment of AML.

Chromosome recognition is a critical and time-consuming process in karyotyping, especially for R-band chromosomes with poor visualization quality. In this paper, we propose an end-to-end grouping guided R-band chromosome recognition method GRC-Net. GRC-Net serves the chromosome recognition task as the main task and takes the chromosome length grouping task and centromere position grouping task as auxiliary tasks. Two auxiliary modules, Chromosome Length Grouping Module (CLGM) and Centromere Position Grouping Model (CPGM), are designed to extract the task-specific feature and refine the feature map of the main task. A large-scale R-band chromosome dataset with 1735 cases was collected. Experiment results on the 423 testing cases show that the proposed GRC-Net gets the highest accuracy of 96.87%, outperforming the baseline by 2.24%. With grouping task-guided feature extraction, GRC-Net reduces approximately 50% of the inter-group misclassification. The proposed GRC-Net can serve as a general framework for incorporating the domain knowledge into the process of feature learning, meanwhile, a powerful tool to assist clinical chromosome karyotyping.

Figure S17. External validation of the GSE6891 cohort. (A) The upper panel shows the expression patterns of 9 APL9-related genes from eight GSE6891 patients with APL. The bottom panel shows the bar plot of the APL9 score in ascending order. (B) Kaplan-Meier estimates of overall survival according to the APL9 score groups in GSE6891 cohort. P-value is calculated using the log-rank test.

Figure S10. The outcomes of different APL9 score groups. (A) Sankey plot for reclassification from the Sanz risk to the APL9 score groups in the training cohort. (B) Kaplan-Meier estimates of OS, EFS, and DFS in accordance with the APL9 score groups of the training cohort. (C) Sankey plot for reclassification from the Sanz risk to the APL9 score groups in the validation cohort. (D) Kaplan-Meier estimates of OS, EFS, and DFS in accordance with the APL9 score groups of the validation cohort. (E) Area under the curve (AUC) for APL9 score to assess patients' molecular risk status as subcohorts (SC) 1 and 2 in the training (n = 158) and validation (n = 104) cohort. P-value is calculated using the log-rank test. OS, overall survival; EFS, event-free survival; DFS, disease-free survival; CI, confidence interval.

Figure S1. Kaplan-Meier plot of clinical outcomes in consolidation arms with or without ATO. Kaplan-Meier estimates of overall survival (A), event-free survival (B), and disease-free survival (C) in accordance with the consolidation arms with or without ATO. P-value is calculated using the log-rank test.

Chromosome recognition is a critical way to diagnose various hematological malignancies and genetic diseases, which is however a repetitive and time-consuming process in karyotyping. To explore the relative relation between chromosomes, in this work, we start from a global perspective and learn the contextual interactions and class distribution features between chromosomes within a karyotype. We propose an end-to-end differentiable combinatorial optimization method, KaryoNet, which captures long-range interactions between chromosomes with the proposed Masked Feature Interaction Module (MFIM) and conducts label assignment in a flexible and differentiable way with Deep Assignment Module (DAM). Specially, a Feature Matching Sub-Network is built to predict the mask array for attention computation in MFIM. Lastly, Type and Polarity Prediction Head can predict chromosome type and polarity simultaneously. Extensive experiments on R-band and G-band two clinical datasets demonstrate the merits of the proposed method. For normal karyotypes, the proposed KaryoNet achieves the accuracy of 98.41% on R-band chromosome and 99.58% on G-band chromosome. Owing to the extracted internal relation and class distribution features, KaryoNet can also achieve state-of-the-art performances on karyotypes of patients with different types of numerical abnormalities. The proposed method has been applied to assist clinical karyotype diagnosis. Our code is available at: https://github.com/xiabc612/KaryoNet.

Figure S9. The ROC curves for the establishing process of the APL9 score. The ROCs of the randomized 10 models in each training set (A) and validation set (B). The shadow indicates the 500-bootstrap confidence intervals of APL9 score. ROC, receiver operating characteristic.

Figure S4. Functional gene categories among different Sanz risk groups and pairwise co-occurrence and mutual exclusivity analysis. (A) Different distributions of mutations of known functional gene groups among the Sanz risk groups. (B) Co-occuring and mutually exclusive mutations in genes with a mutational frequency > 2%. P-value is calculated using the Fisher's exact test. *P < 0.05, **P < 0.01, ***P < 0.001.

Figure S8. Spearman's correlation between the gene expression (x axis) and methylation level (y axis) at different regions of GATA1 in patients with APL from the The Cancer Genome Atlas (TCGA) dataset (n = 15). P-values are indicated on each panel. The line shown is a y~x regression line, and the shaded regions are the 95% confidence intervals for the slope of the line.

Figure S3. Genes newly found in APL and pairwise comparison among Sanz risk groups. (A) Schematic representation of three genes newly found in APL. (B) Genes with significant differences in the pairwise comparison among Sanz risk groups. P-value is calculated using the Fisher's exact test

A morphologic examination is essential for the diagnosis of hematological diseases. However, its conventional manual operation is time-consuming and laborious. Herein, we attempt to establish an artificial intelligence (AI)-aided diagnostic framework integrating medical expertise. This framework acts as a virtual hematological morphologist (VHM) for diagnosing hematological neoplasms. Two datasets were established as follows: An image dataset was used to train the Faster Region-based Convolutional Neural Network to develop an image-based morphologic feature extraction model. A case dataset containing retrospective morphologic diagnostic data was used to train a support vector machine algorithm to develop a feature-based case identification model based on diagnostic criteria. Integrating these 2 models established a whole-process AI-aided diagnostic framework, namely, VHM, and a 2-stage strategy was applied to practice case diagnosis. The recall and precision of VHM in bone marrow cell classification were 94.65% and 93.95%, respectively. The balanced accuracy, sensitivity, and specificity of VHM were 97.16%, 99.09%, and 92%, respectively, in the differential diagnosis of normal and abnormal cases, and 99.23%, 97.96%, and 100%, respectively, in the precise diagnosis of chronic myelogenous leukemia in chronic phase. This work represents the first attempt, to our knowledge, to extract multimodal morphologic features and to integrate a feature-based case diagnosis model for designing a comprehensive AI-aided morphologic diagnostic framework. The performance of our knowledge-based framework was superior to that of the widely used end-to-end AI-based diagnostic framework in terms of testing accuracy (96.88% vs 68.75%) or generalization ability (97.11% vs 68.75%) in differentiating normal and abnormal cases. The remarkable advantage of VHM is that it follows the logic of clinical diagnostic procedures, making it a reliable and interpretable hematological diagnostic tool.

Figure S14. The revised risk groups are associated with survivals in patients with APL. (A) Kaplan-Meier estimates of OS, EFS, and DFS of the patients from Sanz low-to-intermediate risk crossed to the revised HR group (n = 9 with both NRAS mutation and high APL9 score group) and the patients from Sanz low-to-intermediate risk remaining in the revised SR group (n = 210). (B) Kaplan-Meier estimates of OS, EFS, and DFS of the patients from Sanz high-risk crossed to the revised SR group (n = 18 with neither NRAS mutation nor high APL9 score group) and the patients from Sanz high-risk remaining in the revised HR group (n = 86). P-value is calculated using the log-rank test. OS, overall survival; EFS, event-free survival; DFS, disease-free survival; HR, high-risk; SR, standard-risk

Figure S7. The correlation of GATA1 expression with clinical and morphological characteristics. (A-B) The spearman's correlation between the BM blast percentages and GATA1 expression. (C-D) The spearman's correlation between the median number of megakaryocytes per smear and GATA1 expression. The pairwise P-value is calculated using the Wilcoxon test, and Kruskal-Wallis test is used for intergroup multiple comparisons. BM, bone marrow.

免疫检查点抑制剂( ICIs)被广泛批准用于恶性肿瘤.该类药物通过启动免疫系统而对肿瘤细胞发挥作用,并改变了现有癌症治疗的前景.然而,与其他抗肿瘤药物一样,使用ICIs调控免疫系统同样会导致各种不良事件的发生,但由于免疫相关性不良反应的表现缺乏特异性、与其他心血管和普通医学疾病相重叠、临床工作者认识的缺乏等因素的综合作用,其真实发生率存在被低估的可能.同时,免疫相关性不良反应存在潜在的致命性,早期识别和及时治疗是必要的.本文对1例使用帕博利珠单抗出现心肌炎的黑色素瘤病例进行分析讨论,以期为临床处理免疫相关性不良反应提供参考.现报道如下.

Figure S5. Unsupervised hierarchical clustering of the global gene expression profile from 323 patients with APL. Genes and samples are clustered using the ward.D method, and distances are calculated on the basis of the Pearson's correlation coefficient. Columns indicate patients with APL and rows indicate genes.

Figure S11. Relative mRNA expression levels of the 9 genes. The relative mRNA expression levels of the GATA1, CLCN4, CACNA2D2, OPTN, KRT1, LTF, S100A12, LEF1, and LGALS1 between different APL9 score groups. Values are the means {plus minus} s.e. .