AML diagnostics, initially based solely on morphological evaluation, now relies on multiple disciplines to reach its full potential. Only by integrating the results of cytomorphology, cytochemistry, ...immunophenotyping, cytogenetics and molecular genetics it is possible to fulfil WHO classification and ELN prognostication systems. Especially molecular genetics has gained a lot of interest over the last decade, mainly through the introduction of next generation sequencing (NGS). NGS application ranges from the investigation of single genes and panels to even whole exomes, transcriptomes and genomes. In routine AML diagnostics panels are the preferred NGS methodology. Here, we will review the power and limitations of NGS in the context of diagnosis, prognosis and precision medicine. Due to high dimensionality, NGS data interpretation is challenging but it also offers a unique investigatory chance and the opportunity to apply data mining techniques such as artificial intelligence. We will also reflect on how the incorporation of the improved knowledge base into routine diagnostics can pave the way for better treatment and more cure in AML.
Molecular analyses of leukemic blasts from patients with acute myeloid leukemia (AML) have revealed a striking heterogeneity with regard to the presence of acquired gene mutations and changes in gene ...and microRNA expression. Multiple submicroscopic genetic alterations with prognostic significance have been discovered. Application of gene- and microRNA profiling has identified genome-wide expression signatures that separate cytogenetic and molecular subsets of patients with AML into previously unrecognized biologic and/or prognostic subgroups. These and similar future findings are likely to have a major impact on the clinical management of AML because many of the identified genetic alterations not only represent independent prognosticators, but also may constitute targets for specific therapeutic intervention. In this report, we review genetic findings in AML and discuss their clinical implications.
Biomedical applications of deep learning algorithms rely on large expert annotated data sets. The classification of bone marrow (BM) cell cytomorphology, an important cornerstone of hematological ...diagnosis, is still done manually thousands of times every day because of a lack of data sets and trained models. We applied convolutional neural networks (CNNs) to a large data set of 171 374 microscopic cytological images taken from BM smears from 945 patients diagnosed with a variety of hematological diseases. The data set is the largest expert-annotated pool of BM cytology images available in the literature. It allows us to train high-quality classifiers of leukocyte cytomorphology that identify a wide range of diagnostically relevant cell species with high precision and recall. Our CNNs outcompete previous feature-based approaches and provide a proof-of-concept for the classification problem of single BM cells. This study is a step toward automated evaluation of BM cell morphology using state-of-the-art image-classification algorithms. The underlying data set represents an educational resource, as well as a reference for future artificial intelligence-based approaches to BM cytomorphology.
Artificial intelligence (AI) is about to make itself indispensable in the health care sector. Examples of successful applications or promising approaches range from the application of pattern ...recognition software to pre-process and analyze digital medical images, to deep learning algorithms for subtype or disease classification, and digital twin technology and in silico clinical trials. Moreover, machine-learning techniques are used to identify patterns and anomalies in electronic health records and to perform ad-hoc evaluations of gathered data from wearable health tracking devices for deep longitudinal phenotyping. In the last years, substantial progress has been made in automated image classification, reaching even superhuman level in some instances. Despite the increasing awareness of the importance of the genetic context, the diagnosis in hematology is still mainly based on the evaluation of the phenotype. Either by the analysis of microscopic images of cells in cytomorphology or by the analysis of cell populations in bidimensional plots obtained by flow cytometry. Here, AI algorithms not only spot details that might escape the human eye, but might also identify entirely new ways of interpreting these images. With the introduction of high-throughput next-generation sequencing in molecular genetics, the amount of available information is increasing exponentially, priming the field for the application of machine learning approaches. The goal of all the approaches is to allow personalized and informed interventions, to enhance treatment success, to improve the timeliness and accuracy of diagnoses, and to minimize technically induced misclassifications. The potential of AI-based applications is virtually endless but where do we stand in hematology and how far can we go?
Chromosomal rearrangements are a frequent cause of oncogene deregulation in human malignancies. Overexpression of EVI1 is found in a subgroup of acute myeloid leukemia (AML) with 3q26 chromosomal ...rearrangements, which is often therapy resistant. In AMLs harboring a t(3;8)(q26;q24), we observed the translocation of a MYC super-enhancer (MYC SE) to the EVI1 locus. We generated an in vitro model mimicking a patient-based t(3;8)(q26;q24) using CRISPR-Cas9 technology and demonstrated hyperactivation of EVI1 by the hijacked MYC SE. This MYC SE contains multiple enhancer modules, of which only one recruits transcription factors active in early hematopoiesis. This enhancer module is critical for EVI1 overexpression as well as enhancer-promoter interaction. Multiple CTCF binding regions in the MYC SE facilitate this enhancer-promoter interaction, which also involves a CTCF binding site upstream of the EVI1 promoter. We hypothesize that this CTCF site acts as an enhancer-docking site in t(3;8) AML. Genomic analyses of other 3q26-rearranged AML patient cells point to a common mechanism by which EVI1 uses this docking site to hijack enhancers active in early hematopoiesis.
The only cytogenetic aberration defining a myelodysplastic syndrome subtype is the deletion of the long arm of chromosome 5, which, along with morphological features, leads to the diagnosis of ...myelodysplastic syndrome with isolated deletion of the long arm of chromosome 5. These patients show a good prognosis and respond to treatment such as lenalidomide, but some cases progress to acute myeloid leukemia; however, the molecular mutation pattern is rarely characterized. Therefore, we investigated a large cohort of 123 myelodysplastic syndrome patients with isolated deletion of the long arm of chromosome 5, diagnosed following the World Health Organization classifications 2008 and 2016, by sequencing 27 genes. A great proportion of patients showed no or only one mutation. Only seven genes showed mutation frequencies >5% (
). However, the pattern of recurrently mutated genes was comparable to other myelodysplastic syndrome subtypes by comparison to a reference cohort, except that of
which was significantly more often mutated in myelodysplastic syndrome with isolated deletion of the long arm of chromosome 5. As expected,
was frequently mutated and correlated with ring sider-oblasts, while
mutations correlated with elevated platelet counts. Surprisingly,
mutations led to significantly worse prognosis within cases with isolated deletion of the long arm of chromosome 5, but showed a comparable outcome to other myelodysplastic syndrome subtypes with
mutation. However, addressing genetic stability in follow-up cases might suggest different genetic mechanisms for progression to secondary acute myeloid leukemia compared to overall myelodysplastic syndrome patients.
Myeloid neoplasms are characterized by frequent mutations in at least seven components of the spliceosome that have distinct roles in the process of pre-mRNA splicing. Hotspot mutations in SF3B1, ...SRSF2, U2AF1 and loss of function mutations in ZRSR2 have revealed widely different aberrant splicing signatures with little overlap. However, previous studies lacked the power necessary to identify commonly mis-spliced transcripts in heterogeneous patient cohorts. By performing RNA-Seq on bone marrow samples from 1258 myeloid neoplasm patients and 63 healthy bone marrow donors, we identified transcripts frequently mis-spliced by mutated splicing factors (SF), rare SF mutations with common alternative splicing (AS) signatures, and SF-dependent neojunctions. We characterized 17,300 dysregulated AS events using a pipeline designed to predict the impact of mis-splicing on protein function. Meta-splicing analysis revealed a pattern of reduced levels of retained introns among disease samples that was exacerbated in patients with splicing factor mutations. These introns share characteristics with "detained introns," a class of introns that have been shown to promote differentiation by detaining pro-proliferative transcripts in the nucleus. In this study, we have functionally characterized 17,300 targets of mis-splicing by the SF mutations, identifying a common pathway by which AS may promote maintenance of a proliferative state.
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