The dataset comprises bone marrow aspirate smear WSI for 257 pediatric cases of leukemia, including acute lymphoid leukemia (ALL), acute myeloid leukemia (AML), and chronic myeloid leukemia (CML). The smears were prepared for the initial diagnosis (i.e., without prior treatment), stained in accordance with the Pappenheim method, and scanned at 40x magnification.

The images have been annotated with rectangular regions of interest (ROI) within the evaluable monolayer area, and a total of 47176 cell bounding box annotations have been placed within the regions of interest. Cells have been annotated by multiple experts in a consensus labeling approach with 49 distinct cell type classes. This consensus approach entailed that each cell was sequentially annotated by multiple individuals until each cell had been labeled by at least two individuals, and the majority class was assigned in at least half of all annotations for that image. The labels from all annotation sessions, as well as the final consensus class for each cell, are made available.

Additionally, clinical information (age group, sex, diagnosis) and laboratory data (blasts, white blood cell count, thrombocytes, LDH, uric acid, hemoglobin) are available for each case.

Files included

Pending peer review of an accompanying manuscript, currently, this dataset contains a sample of 2 bone marrow aspirate smear whole slide images (WSIs) with their cell annotations as a first sample of the dataset described above.

The entire dataset will be available in National Cancer Institute Imaging Data Commons (https://imaging.datacommons.cancer.gov). If you have any questions about the dataset please contact IDC support at support@canceridc.dev.

Both images and annotations are in DICOM format. All DICOM objects relating to the same smear are contained in the same folder. Clinical data are contained in the DICOM metadata.

In addition lab_values_sample.csv contains the collected lab values for those two smears.

The attached files are named using the following convention using the corresponding DICOM tags: %PatientID-%Modality-%SeriesDescription-%SOPInstanceUID.dcm.

For example, files corresponding to the patient A6BBC91AE73DD21C0533F735470A9CD0 contains the following 6 DICOM Slide Microscopy (SM) modality files each representing one level of the WSI pyramid.

1. A6BBC91AE73DD21C0533F735470A9CD0-SM-Bone marrow aspirate smear, May-Gruenwald-Giemsa stain-1.2.826.0.1.3680043.8.498.26060080718466278522952527845683544045.dcm
2. A6BBC91AE73DD21C0533F735470A9CD0-SM-Bone marrow aspirate smear, May-Gruenwald-Giemsa stain-1.2.826.0.1.3680043.8.498.62030007770863397357636084490828160953.dcm
3. A6BBC91AE73DD21C0533F735470A9CD0-SM-Bone marrow aspirate smear, May-Gruenwald-Giemsa stain-1.2.826.0.1.3680043.8.498.7859053050060184362011899525686475413.dcm
4. A6BBC91AE73DD21C0533F735470A9CD0-SM-Bone marrow aspirate smear, May-Gruenwald-Giemsa stain-1.2.826.0.1.3680043.8.498.85089919641169806925347867181900526802.dcm
5. A6BBC91AE73DD21C0533F735470A9CD0-SM-Bone marrow aspirate smear, May-Gruenwald-Giemsa stain-1.2.826.0.1.3680043.8.498.88236263312726593722497600529137414206.dcm
6. A6BBC91AE73DD21C0533F735470A9CD0-SM-Bone marrow aspirate smear, May-Gruenwald-Giemsa stain-1.2.826.0.1.3680043.8.498.95738688685525699076567938918194597802.dcm

Cell annotations with labels from each annotation session in the labeling process are stored in DICOM Bulk Annotations (ANN modality) objects:

1. A6BBC91AE73DD21C0533F735470A9CD0-ANN-Cell bounding boxes with cell type labels; annotation session: 0-1.2.826.0.1.3680043.10.511.3.12557519480564734942303269163896694.dcm
2. A6BBC91AE73DD21C0533F735470A9CD0-ANN-Cell bounding boxes with cell type labels; annotation session: 1-1.2.826.0.1.3680043.10.511.3.6987603211883801558525207593845155.dcm
3. A6BBC91AE73DD21C0533F735470A9CD0-ANN-Cell bounding boxes with cell type labels; annotation session: 2-1.2.826.0.1.3680043.10.511.3.1120336965786739278582883135803528.dcm
4. A6BBC91AE73DD21C0533F735470A9CD0-ANN-Cell bounding boxes with cell type labels; annotation session: 3-1.2.826.0.1.3680043.10.511.3.6408695988615311222439105226576101.dcm
5. A6BBC91AE73DD21C0533F735470A9CD0-ANN-Cell bounding boxes with cell type labels; annotation session: 4-1.2.826.0.1.3680043.10.511.3.52627683252818668316930590707706798.dcm
6. A6BBC91AE73DD21C0533F735470A9CD0-ANN-Cell bounding boxes with consensus cell type labels-1.2.826.0.1.3680043.10.511.3.1666264618985716614248499039136585.dcm
7. A6BBC91AE73DD21C0533F735470A9CD0-ANN-Monolayer regions of interest for cell classification-1.2.826.0.1.3680043.10.511.3.6350792333250462425535421489809492.dcm

Acknowledgments

The authors thank Stefanie Barnickel, Nathalie Dollmann, Tatjana Flamann, Meinolf Suttorp, and Perdita Weller for the labelling of the cells.

The authors thank the following institutions for supplying BMA smears: University Hospital Augsburg (Univ.-Prof. Dr. Dr. med. Michael Frühwald), Charité Berlin - ALL-REZ BFM Study Group (PD Dr. med. Arend von Stackelberg), University Hospital at the TU Dresden (Prof. Dr. med. Meinolf Suttorp), University Hospital Essen - AML-BFM Study Group (Prof. Dr. Dirk Reinhardt), Technical University of Munich (Prof. Dr. med. Irene Teichert-von Lüttichau), University Hospital Würzburg (Prof. Dr. med. Matthias Eyrich).

This study was supported by a grant from the German Federal Ministry of Education and Research (FKZ: 031L0262A; BMDeep)

Preparation of the Dataset for publication was partly supported by Federal funds from the National Cancer Institute, National Institutes of Health (Task Order No. HHSN26110071 under Contract HHSN261201500003l).

The Synthetic Leukemia Risk Dataset is designed for educational and research purposes to analyze health-related factors contributing to leukemia risk. It provides anonymized, synthetic data on individuals’ medical history, lifestyle, and blood test results.

Dataset Features

• Age: Age of the individual.
• Gender: Gender of the individual (Male/Female).
• Country: Country of residence.
• WBC_Count: White Blood Cell count.
• RBC_Count: Red Blood Cell count.
• Platelet_Count: Platelet count per unit volume.
• Hemoglobin_Level: Level of hemoglobin in the blood.
• Bone_Marrow_Blasts: Percentage of blasts in the bone marrow.
• Genetic_Mutation: Presence of genetic mutations linked to leukemia (Yes/No).
• Family_History: Family history of leukemia (Yes/No).
• Smoking_Status: History of smoking (Yes/No).
• Alcohol_Consumption: History of alcohol consumption (Yes/No).
• Radiation_Exposure: Prior exposure to radiation (Yes/No).
• Infection_History: History of infections (Yes/No).
• BMI: Body Mass Index.
• Chronic_Illness: Presence of chronic diseases (Yes/No).
• Immune_Disorders: Presence of immune system disorders (Yes/No).
• Ethnicity: Ethnic group of the individual.
• Socioeconomic_Status: Economic status of the individual (Low/Medium/High).
• Urban_Rural: Whether the individual resides in an urban or rural area.
• Leukemia_Status: Leukemia diagnosis prediction (Positive/Negative).

Distribution

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Usage

This dataset can be used for the following applications:

• Leukemia Risk Prediction: Build models to predict leukemia status based on medical and lifestyle factors.
• Health Behavior Analysis: Study correlations between lifestyle factors and leukemia risk.
• Medical Research: Explore associations between genetic, environmental, and clinical factors with leukemia.
• Preventative Healthcare: Identify key predictors for early detection and risk assessment.
• Policy and Decision Making: Provide insights into population health trends and guide public health initiatives.

Coverage

This synthetic dataset is anonymized and adheres to data privacy standards. It represents diverse demographics and health profiles, enabling broad applications in healthcare research and analysis.

License

CC0 (Public Domain)

Who Can Use It

• Data Scientists and Machine Learning Practitioners: For classification tasks related to leukemia risk prediction.
• Healthcare Researchers: To study risk factors and the prevalence of leukemia.
• Public Health Professionals: For insights into population health trends and preventative care.
• Educators and Students: As a teaching resource for analyzing health-related datasets and building predictive models.

Data Set DescriptionThis repository contains data from a study of three families in which two or more siblings developed chronic lymphocytic leukemia (CLL). Informed consent was provided in accordance with the declaration of Helsinki and the study was approved by the hospital medical ethics committee (METC2015-741).The data consists of BAM-files from whole-genome sequencing (WGS) of nine individuals with data from tumor and matched normal tissue. WGS libraries where prepared using the TruSeq Nano Kit (Illumina Inc.) and sequenced in paired-end mode (2x150bp) on the Illumina HiSeqX Ten system (Illumina Inc.) with 30x target coverage. Reads from each library were aligned to the Grch37 reference genome using BWA mem and merged and de-duplicated using Picard. Re-alignment around known and novel indel-sites was performed with GATK. All SAM/BAM-conversion steps were completed using SAMtools.The repository also contains results from Sanger sequencing of the immunoglobulin rearrangements from the tumor samples.The data is under restricted access and can be accessed upon request through the email-adress below.Terms for accessThe WGS datasets are only to be used for research aimed at advancing the understanding of genetic factors in the development of familial chronic lymphocytic leukemia. Applications aimed at method development including bioinformatics would not be considered as acceptable for use of this dataset.

Abstract Cancer is the second leading cause of death in the world with great impact on public health and leukemia is a hematological cancer directly related to different exposures at work. This study aimed to describe the occupational profile of individuals diagnosed with leukemia. This is a cross-sectional study of cases registered between 2007 and 2011 in the Integrador RHC database. Individuals from 26 Brazilian states, aged 20 years or older, were included. Of the 7,807 cases of leukemia, Minas Gerais recorded the highest occurrence (1,351). Only 52% of the cases had information on occupation. Occupations with the greatest number of cases of leukemia were agricultural, forestry and fishing workers; services, stores and markets vendors; and workers in the production of industrial goods and services. These occupations are exposed to substances considered by literature as carcinogenic agents to humans. There was a high underreporting of occupational data, compromising the quality of information and, therefore, the effectiveness of the Brazilian health surveillance system. The RHC also does not provide information about the agent used during the working day, the exposure time during working life and data from previous occupations.

Data Set Description

These data are collected from a total of 70 participants (47 adult; 23 pediatric), all of which had relapsed or primary resistant acute myeloid leukemia. The data, which here are separated into an adult and a pediatric dataset, were generated as part of a study by Stratmann et. al. (https://doi.org/10.1182/bloodadvances.2021004962). The Stratmann et. al. study is currently pre-published here: https://ashpublications.org/bloodadvances/article/doi/10.1182/bloodadvances.2021004962/477210/Transcriptomic-analysis-reveals-pro-inflammatory Please note that separate applications are necessary for the adult and pediatric dataset, respectively. When applying for access, please indicate which of the datasets that the application applies for. The adult dataset contains transcriptome sequencing (RNA-seq) data from 25 diagnosis (D), 45 relapse (R1/R2/R3) and five (5) primary resistant (PR) leukemic samples from 47 patients, as well as five (5) normal CD34+ bone marrow control samples. The pediatric dataset contains RNA-seq data from 18 diagnosis (D), 22 relapse (R1/R2), six (6) persistent relapse (R1/2-P) and one (1) primary resistant (PR) leukemic samples from 23 patients, as well as five (5) normal CD34+ bone marrow control samples. The leukemic samples originate from bone marrow or peripheral blood. The normal RNA samples originate from purified CD34+ bone marrow cells from five different healthy individuals. Further details regarding the samples are available in the Supplemental Information part of Stratmann et. al. (https://doi.org/10.1182/bloodadvances.2021004962). RNA-seq libraries and associated next-generation sequencing were carried out by the SNP&SEQ Technology platform, SciLifeLab, National Genomics Infrastructure Uppsala, Sweden. Libraries were prepared using the TruSeq stranded total RNA library preparation kit with ribosomal depletion by RiboZero Gold (Illumina). Sequencing of adult samples was carried out on the Illumina HiSeq2500 platform, generating paired-end 125bp reads using v4 sequencing chemistry. Sequencing of pediatric samples was carried out on the Illumina NovaSeq6000 platform (S2 flowcell), generating paired-end 100bp reads using the v1 sequencing chemistry. The CD34+ bone marrow control samples were sequenced using both platforms (Illumina HiSeq2500 and NovaSeq6000). Further, all of these acute myeloid leukemia samples have also been characterized by whole genome sequencing or whole exome sequencing, with the datasets available under controlled access through doi.org/10.17044/scilifelab.12292778. Terms for accessThe adult and pediatric datasets are only to be used for research that is seeking to advance the understanding of the influence of genetic and transcriptomic factors on human acute myeloid leukemia etiology and biology. Use of the protected pediatric dataset is only for research projects that can merely be conducted using pediatric acute myeloid leukemia data, and for which the research objectives cannot be accomplished using data from adults. Applications intending various method development would thus not be considered as acceptable for use of the pediatric dataset. Further, the pediatric dataset may not be used for research investigating predisposition for acute myeloid leukemia based on germline variants.

For conditional access to the adult and/or pediatric dataset in this publication, please contact datacentre@scilifelab.se

Acute myeloid leukemia (AML) is the most common type of acute leukemia in adults, characterized by the malignant transformation and uncontrolled proliferation of abnormal myeloid hematopoietic progenitor cells in the bone marrow. This study aims to analyze the pathogenesis and prognosis of AML by performing RNA-seq on bone marrow samples from both AML patients and healthy individuals. We collected a total of 20 bone marrow samples, consisting of 10 samples from AML patients and 10 from healthy controls, all of which were stored in liquid nitrogen. The study received ethical approval (Ethical Approval Number: KY2024070), and all participants signed informed consent forms. This dataset includes the RNA-seq data from these samples. By analyzing the gene expression characteristics of myeloid progenitor cells, we attempt to construct a prognostic model to guide clinical treatment. These data not only aid in further exploring the molecular mechanisms of AML but also serve to identify new biom..., A total of 20 bone marrow samples were collected for this study, consisting of 10 bone marrow samples from patients with acute myeloid leukemia (AML) (AML group), and 10 bone marrow samples from healthy individuals (control group). All AML patients were diagnosed by bone marrow smear morphology and cytogenetic testing, and individuals in the healthy control group underwent a thorough physical examination to exclude any history of blood disorders and tumors. The samples were stored in liquid nitrogen for further processing. All subjects signed an informed consent form, and the study was approved by the Ethics Committee of the Affiliated Hospital of Southwest Medical University (Ethics Approval No. KY2024070).Total RNA was extracted from frozen bone marrow samples using TRIzol reagent (Thermo Fisher Scientific, USA) for RNA extraction according to the manufacturer's instructions. The quality of extracted RNA was assessed by Agilent 2100 Bioanalyzer to ensure that the RNA Integrity Index (..., , # RNA-seq of bone marrow samples from acute myeloid leukemia patients and healthy individuals

https://doi.org/10.5061/dryad.h9w0vt4t2

Description of the data and file structure

Files and variables

File: AML.txt

Description:Â RNA-seq expression matrix, rows are sample names, columns are gene names.

Variables

• Gene_Name: gene name
• Patient1:Â Bone marrow sample from patient 1 with acute myeloid leukemia
• Patient2:Â Bone marrow sample from patient 2 with acute myeloid leukemia
• Patient3:Â Bone marrow sample from patient 3 with acute myeloid leukemia
• Patient4:Â Bone marrow sample from patient 4 with acute myeloid leukemia
• Patient5: Bone marrow sample from patient 5 with acute myeloid leukemia
• Patient6:Â Bone marrow sample from patient 6 with acute myeloid leukemia
• Patient7:Â Bone marrow sample from patient 7 with acute myeloid leukemia
• Patient8:Â Bone marrow sample from patient 8 with acute myeloid leukemia
• Pati...

The study aimed to determine the impact of interleukin 27 on the expression of immune checkpoint molecules and their ligands in/on peripheral blood lymphocytes subjected to cell culture. Such lymphocytes came from people from a healthy population and from people suffering from chronic lymphocytic leukemia. During the study, the expression of the following antigens was determined: LAG3, HLA-DR, CTLA4, CD80, IFN-g, IL-4, TIM-3, GAL-9, PDL-1, PD-1, IDO-1, GITR in the CD4+, CD8+ and CD19+ cell populations.

Accurate prognostic stratification of patients can provide guidance for personalized therapy. Many prognostic models for acute myeloid leukemia (AML) have been reported, but most have considerable inaccuracies due to contained variables with insufficient capacity of predicting survival and lack of adequate verification. Here, 235 genes strongly related to survival in AML were systematically identified through univariate Cox regression analysis of eight independent AML datasets. Pathway enrichment analysis of these 235 genes revealed that the IL-2/STAT5 signaling pathway was the most highly enriched. Through Cox proportional-hazards regression model and stepwise algorithm, we constructed a six-gene STAT5-associated signature based on the most robustly survival-related genes related to the IL-2/STAT5 signaling pathway. Good prognostic performance was observed in the training cohort (GSE37642-GPL96), and the signature was validated in seven other validation cohorts. As an independent prognostic factor, the STAT5-associated signature was positively correlated with patient age and ELN2017 risk levels. An integrated score based on these three prognostic factors had higher prognostic accuracy than the ELN2017 risk category. Characterization of immune cell infiltration indicated that impaired B-cell adaptive immunity, immunosuppressive effects, serious infection, and weakened anti-inflammatory function tended to accompany high-risk patients. Analysis of in-house clinical samples revealed that the STAT5-assocaited signature risk scores of AML patients were significantly higher than those of healthy people. Five chemotherapeutic drugs that were effective in these high-risk patients were screened in silico. Among the five drugs, MS.275, a known HDAC inhibitor, selectively suppressed the proliferation of cancer cells with high STAT5 phosphorylation levels in vitro. Taken together, the data indicate that the STAT5-associated signature is a reliable prognostic model that can be used to optimize prognostic stratification and guide personalized AML treatments.

Chronic lymphocytic leukemia (CLL) is characterized by substantial clinical heterogeneity, despite relatively few genetic alterations. To provide a basis for studying epigenome deregulation in CLL, we established genome-wide chromatin accessibility maps for 88 CLL samples from 55 patients using the ATAC-seq assay, and we also performed ChIPmentation and RNA-seq profiling for ten representative samples. Based on the resulting dataset, we devised and applied a bioinformatic method that links chromatin profiles to clinical annotations. Our analysis identified sample-specific variation on top of a shared core of CLL regulatory regions. IGHV mutation status – which distinguishes the two major subtypes of CLL – was accurately predicted by the chromatin profiles, and gene regulatory networks inferred for IGHV-mutated vs. IGHV-unmutated samples identified characteristic differences between these two disease subtypes. In summary, we found widespread heterogeneity in the CLL chromatin landscape, established a community resource for studying epigenome deregulation in leukemia, and demonstrated the feasibility of chromatin accessibility mapping in cancer cohorts and clinical research. Genome-wide profiling of chromatin states and gene expression levels in 88 CLL samples from 55 individuals gave rise to 88 ATAC-seq profiles, 40 ChIPmentation profiles (10 samples, each with 3 different antibodies and matched immunoglobulin control), and 10 RNA-seq profiles.Raw sequence data has been deposited at the EBI's European Genome-phenome Archive (EGA) under the accession number EGAS00001001821 (controlled access to protect patient privacy).

This observational study is collecting information from patients to form the Myeloma and Related Diseases Registry (MRDR)Who is it for?You may be eligible for this study if you have a diagnosis of multiple myeloma, plasmacytoma, plasma cell leukaemia or monoclonal gammopathy of undetermined significance (MGUS).Study detailsMedical information including diagnostic tests, therapy and demographics will be provided by medical records. Participants can also provide information regarding their quality of life using a questionnaire.It is hoped this registry will enable clinicians and hospitals to provide the best possible care to people with the included conditions and allow the evaluations of new therapies.

BackgroundLeukemia caused by occupational risk is a problem that needs more attention and remains to be solved urgently, especially for acute lymphoid leukemia (ALL), acute myeloid leukemia (AML), and chronic lymphoid leukemia (CLL). However, there is a paucity of literature on this issue. We aimed to assess the global burden and trends of leukemia attributable to occupational risk from 1990 to 2019.MethodsThis observational trend study was based on the Global Burden of Disease (GBD) 2019 database, the global deaths, and disability-adjusted life years (DALYs), which were calculated to quantify the changing trend of leukemia attributable to occupational risk, were analyzed by age, year, geographical location, and socio-demographic index (SDI), and the corresponding estimated annual percentage change (EAPC) values were calculated.ResultsGlobal age-standardized DALYs and death rates of leukemia attributable to occupational risk presented significantly decline trends with EAPC [−0.38% (95% CI: −0.58 to −0.18%) for DALYs and −0.30% (95% CI: −0.45 to −0.146%) for death]. However, it was significantly increased in people aged 65–69 years [0.42% (95% CI: 0.30–0.55%) for DALYs and 0.38% (95% CI: 0.26–0.51%) for death]. At the same time, the age-standardized DALYs and death rates of ALL, AML, and CLL were presented a significantly increased trend with EAPCs [0.78% (95% CI: 0.65–0.91%), 0.87% (95% CI: 0.81–0.93%), and 0.66% (95% CI: 0.51–0.81%) for DALYs, respectively, and 0.75% (95% CI: 0.68–0.82%), 0.96% (95% CI: 0.91–1.01%), and 0.55% (95% CI: 0.43–0.68%) for death], respectively. The ALL, AML, and CLL were shown an upward trend in almost all age groups.ConclusionWe observed a substantial reduction in leukemia due to occupational risks between 1990 and 2019. However, the people aged 65–69 years and burdens of ALL, AML, and CLL had a significantly increased trend in almost all age groups. Thus, there remains an urgent need to accelerate efforts to reduce leukemia attributable to occupational risk-related death burden in this population and specific causes.

Objective: The objective of this study was to estimate resource utilization and expenditures for patients with acute myeloid leukemia (AML) in a real-world claims database. Research design and methods: AML patients were identified in MarketScan claims databases between 1 January 2009 and 31 January 2015. Patients had a minimum of two AML diagnosis codes, hospitalization within 14 days after initial diagnosis, and ≥6 months of enrollment before initial diagnosis. Patients were monitored from first-line induction to a record of remission. A subset had a record of a second treatment period, defined as time from relapse to remission. Patient demographics, AML risk factors, and comorbidities were recorded. Descriptive analysis of utilization and expenditures (in 2014 $US) were reported for each cohort. Results: The inclusion criteria were met in 1597 patients (mean age, 58.4 years; 51.0% male). Ninety percent of patients had ≥1 risk factor for AML. Mean (SD) healthcare expenditures for patients from first-line induction to remission (n = 681) were $208,857 ($152,090). Of the 157 who had a record of relapse, 70 had a record of a second remission. Expenditures for these patients (n = 70) from relapse to remission were $142,569 ($208,307); 60% were admitted to a hospital for a mean of 18.5 hospital days, and 20% had ≥1 emergency room visit. Conclusions: Although this claims-based analysis is limited by a lack of generalizability to noninsured populations and potential underreporting of certain events and diagnoses, we found that treating AML patients poses a significant healthcare burden, during both first-line induction and relapse. With people living longer, the number of cases of AML is expected to increase in the future.

Sequencing of B-cell receptor repertoires in healthy individuals and patients with chronic lymphocytic leukemia.

1) This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute please see http://www.sanger.ac.uk/datasharing/

This dataset contains all the data available for this study on 2017-09-13.

The incidence of acute myeloid leukemia (AML) increases with age and mortality exceeds 90% when diagnosed after age 60. Only 10-15% of cases evolve from a pre-existing myeloproliferative or myelodysplastic disorder; the remaining cases arise de novo without a detectable prodrome and are diagnosed upon development of bone marrow failure. Analysis of diagnostic blood samples has demonstrated that de novo AML is preceded by the accumulation of somatic mutations in pre-leukemic hematopoietic stem and progenitor cells (preL-HSPCs) that subsequently undergo clonal expansion. If individuals in this pre-leukemic phase could be identified, methods for determination of risk and monitoring for progression to overt AML could be developed. However recurrent AML mutations also accumulate during aging in healthy individuals who never develop AML, referred to as age related clonal hematopoiesis (ARCH). To distinguish individuals with preL-HSPCs at high risk of developing AML from those with ARCH, we undertook deep targeted sequencing of genes recurrently mutated in AML in blood samples from 133 individuals in the European Prospective Investigation into Cancer and Nutrition (EPIC) study taken on average 6 years before they developed AML (pre-AML group), together with 683 matched healthy individuals (Control group). Pre-AML cases displayed accelerated age-correlated accumulation of somatic mutations.The identity, number and variant allele frequency (VAF) of mutations differed between the two groups, and were incorporated into a computational model of AML risk prediction that accurately distinguished pre-AML cases from controls on average 7 years prior to AML development. Our findings provide proof of concept that early prediction of AML development is feasible in high-risk populations, paving the way for early disease detection, monitoring, and potentially prevention.

Nucleophosmin 1 (NPM1) mutations are frequently found in patients with acute myeloid leukemia (AML) and the newly generated sequences were suggested to induce immune response contributing to the relatively favorable outcome of patients in this AML subset. We hypothesized that if an efficient immune response against mutated nucleophosmin can be induced in vivo, the individuals expressing HLA alleles suitable for presenting NPM-derived peptides should be less prone to developing AML associated with NPM1 mutation. We thus compared HLA class I frequencies in a cohort of patients with mutated NPM1 (63 patients, NPMc+), a cohort of patients with wild-type NPM1 (94 patients, NPMwt) and in normal individuals (large datasets available from Allele Frequency Net Database). Several HLA allelic groups were found to be depleted in NPMc+ patients, but not in NPMwt compared to the normal distribution. The decrease was statistically significant for HLA B*07, B*18, and B*40. Furthermore, statistically significant advantage in the overall survival was found for patients with mutated NPM1 expressing at least one of the depleted allelic groups. The majority of the depleted alleles were predicted to bind potent NPM-derived immunopeptides and, importantly, these peptides were often located in the unmutated part of the protein. Our analysis suggests that individuals expressing specific HLA allelic groups are disposed to develop an efficient anti-AML immune response thanks to aberrant cytoplasmic localization of the mutated NPM protein.

Cardinal features of CDK13-related disorders are characterized by intellectual disability, developmental delay, dysmorphic facial features, structural heart defect and structural brain abnormality. A 9-year-old boy presented with intellectual disability, development delay, characteristic craniofacial features, brain malformation, cryptorchidism, autism spectrum disorder, and recently, recurrent hemophagocytic lymphohistiocytosis (HLH) in a half year period. Further investigation revealed the diagnosis of CDK13-related disorder. Finally, we found the underlying cause of HLH is acute lymphoblastic leukemia. Probably leukemia was a coincidental finding in this boy with CDK13-related disorder, but the case herein suggests that individuals with CDK13-related disorder also face risk of developing cancers. Further detailed information could enable us to clarify this presentation because of only limited investigation in affected cases.

Infections of the prostate by bacteria, human papillomaviruses, polyomaviruses, xenotropic murine leukemia virus (MLV)-related gammaretroviruses, human cytomegaloviruses and other members of the herpesvirus family have been widely researched. However, many studies have yielded conflicting and controversial results. In this study, we systematically investigated the transcriptomes of human prostate samples for the unique genomic signatures of these pathogens using RNA-seq data from both western and Chinese patients. Human and nonhuman RNA-seq reads were mapped onto human and pathogen reference genomes respectively using alignment tools Bowtie and BLAT. Pathogen infections and integrations were analyzed in adherence with the standards from published studies. Among the nine pathogens (Propionibacterium acnes, HPV, HCMV, XMRV, BKV, JCV, SV40, EBV, and HBV) we analyzed, Propionibacterium acnes genes were detected in all prostate tumor samples and all adjacent samples, but not in prostate samples from healthy individuals. SV40, HCMV, EBV and low-risk HPVs transcripts were detected in one tumor sample and two adjacent samples from Chinese prostate cancer patients, but not in any samples of western prostate cancer patients; XMRV, BKV and JCV sequences were not identified in our work; HBV, as a negative control, was absent from any samples. Moreover, no pathogen integration was identified in our study. While further validation is required, our analysis provides evidence of Propionibacterium acnes infections in human prostate tumors. Noted differences in viral infections across ethnicity remain to be confirmed with other large prostate cancer data sets. The effects of bacterial and viral infections and their contributions to prostate cancer pathogenesis will require continuous research on associated pathogens.