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  • 1
    Online Resource
    Online Resource
    Unsupervised meta-clustering identifies risk clusters in acute myeloid leukemia based on clinical and genetic profiles
    Springer Science and Business Media LLC ; 2023
    In:  Communications Medicine Vol. 3, No. 1 ( 2023-05-17)
    Details
    In: Communications Medicine, Springer Science and Business Media LLC, Vol. 3, No. 1 ( 2023-05-17)
    Abstract: Increasingly large and complex biomedical data sets challenge conventional hypothesis-driven analytical approaches, however, data-driven unsupervised learning can detect inherent patterns in such data sets. Methods While unsupervised analysis in the medical literature commonly only utilizes a single clustering algorithm for a given data set, we developed a large-scale model with 605 different combinations of target dimensionalities as well as transformation and clustering algorithms and subsequent meta-clustering of individual results. With this model, we investigated a large cohort of 1383 patients from 59 centers in Germany with newly diagnosed acute myeloid leukemia for whom 212 clinical, laboratory, cytogenetic and molecular genetic parameters were available. Results Unsupervised learning identifies four distinct patient clusters, and statistical analysis shows significant differences in rate of complete remissions, event-free, relapse-free and overall survival between the four clusters. In comparison to the standard-of-care hypothesis-driven European Leukemia Net (ELN2017) risk stratification model, we find all three ELN2017 risk categories being represented in all four clusters in varying proportions indicating unappreciated complexity of AML biology in current established risk stratification models. Further, by using assigned clusters as labels we subsequently train a supervised model to validate cluster assignments on a large external multicenter cohort of 664 intensively treated AML patients. Conclusions Dynamic data-driven models are likely more suitable for risk stratification in the context of increasingly complex medical data than rigid hypothesis-driven models to allow for a more personalized treatment allocation and gain novel insights into disease biology.
    Type of Medium: Online Resource
    ISSN: 2730-664X
    URL: Article
    DOI: 10.1038/s43856-023-00298-6
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2023
    detail.hit.zdb_id: 3096949-9
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  • 2
    Online Resource
    Online Resource
    Prediction of complete remission and survival in acute myeloid leukemia using supervised machine learning
    Ferrata Storti Foundation (Haematologica) ; 2022
    In:  Haematologica Vol. 108, No. 3 ( 2022-06-16), p. 690-704
    Details
    In: Haematologica, Ferrata Storti Foundation (Haematologica), Vol. 108, No. 3 ( 2022-06-16), p. 690-704
    Abstract: Achievement of complete remission signifies a crucial milestone in the therapy of acute myeloid leukemia (AML) while refractory disease is associated with dismal outcomes. Hence, accurately identifying patients at risk is essential to tailor treatment concepts individually to disease biology. We used nine machine learning (ML) models to predict complete remission and 2-year overall survival in a large multicenter cohort of 1,383 AML patients who received intensive induction therapy. Clinical, laboratory, cytogenetic and molecular genetic data were incorporated and our results were validated on an external multicenter cohort. Our ML models autonomously selected predictive features including established markers of favorable or adverse risk as well as identifying markers of so-far controversial relevance. De novo AML, extramedullary AML, double-mutated CEBPA, mutations of CEBPA-bZIP, NPM1, FLT3-ITD, ASXL1, RUNX1, SF3B1, IKZF1, TP53, and U2AF1, t(8;21), inv(16)/t(16;16), del(5)/del(5q), del(17)/del(17p), normal or complex karyotypes, age and hemoglobin concentration at initial diagnosis were statistically significant markers predictive of complete remission, while t(8;21), del(5)/del(5q), inv(16)/t(16;16), del(17)/del(17p), double-mutated CEBPA, CEBPA-bZIP, NPM1, FLT3-ITD, DNMT3A, SF3B1, U2AF1, and TP53 mutations, age, white blood cell count, peripheral blast count, serum lactate dehydrogenase level and hemoglobin concentration at initial diagnosis as well as extramedullary manifestations were predictive for 2-year overall survival. For prediction of complete remission and 2-year overall survival areas under the receiver operating characteristic curves ranged between 0.77–0.86 and between 0.63–0.74, respectively in our test set, and between 0.71–0.80 and 0.65–0.75 in the external validation cohort. We demonstrated the feasibility of ML for risk stratification in AML as a model disease for hematologic neoplasms, using a scalable and reusable ML framework. Our study illustrates the clinical applicability of ML as a decision support system in hematology.
    Type of Medium: Online Resource
    ISSN: 1592-8721 , 0390-6078
    URL: Article
    DOI: 10.3324/haematol.2021.280027
    Language: Unknown
    Publisher: Ferrata Storti Foundation (Haematologica)
    Publication Date: 2022
    detail.hit.zdb_id: 2186022-1
    detail.hit.zdb_id: 2030158-3
    detail.hit.zdb_id: 2805244-4
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  • 3
    Online Resource
    Online Resource
    Alterations of cohesin complex genes in acute myeloid leukemia: differential co-mutations, clinical presentation and impact on outcome
    Springer Science and Business Media LLC ; 2023
    In:  Blood Cancer Journal Vol. 13, No. 1 ( 2023-01-24)
    Details
    In: Blood Cancer Journal, Springer Science and Business Media LLC, Vol. 13, No. 1 ( 2023-01-24)
    Abstract: Functional perturbations of the cohesin complex with subsequent changes in chromatin structure and replication are reported in a multitude of cancers including acute myeloid leukemia (AML). Mutations of its STAG2 subunit may predict unfavorable risk as recognized by the 2022 European Leukemia Net recommendations, but the underlying evidence is limited by small sample sizes and conflicting observations regarding clinical outcomes, as well as scarce information on other cohesion complex subunits. We retrospectively analyzed data from a multi-center cohort of 1615 intensively treated AML patients and identified distinct co-mutational patters for mutations of STAG2 , which were associated with normal karyotypes (NK) and concomitant mutations in IDH2 , RUNX1, BCOR, ASXL1 , and SRSF2 . Mutated RAD21 was associated with NK, mutated EZH2, KRAS, CBL , and NPM1 . Patients harboring mutated STAG2 were older and presented with decreased white blood cell, bone marrow and peripheral blood blast counts. Overall, neither mutated STAG2, RAD21, SMC1A nor SMC3 displayed any significant, independent effect on clinical outcomes defined as complete remission, event-free, relapse-free or overall survival. However, we found almost complete mutual exclusivity of genetic alterations of individual cohesin subunits. This mutual exclusivity may be the basis for therapeutic strategies via synthetic lethality in cohesin mutated AML.
    Type of Medium: Online Resource
    ISSN: 2044-5385
    URL: Article
    DOI: 10.1038/s41408-023-00790-1
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2023
    detail.hit.zdb_id: 2600560-8
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  • 4
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    Online Resource
    UBTF tandem duplications are rare but recurrent alterations in adult AML and associated with younger age, myelodysplasia, and inferior outcome
    Springer Science and Business Media LLC ; 2023
    In:  Blood Cancer Journal Vol. 13, No. 1 ( 2023-05-26)
    Details
    In: Blood Cancer Journal, Springer Science and Business Media LLC, Vol. 13, No. 1 ( 2023-05-26)
    Abstract: Tandem-duplication mutations of the UBTF gene ( UBTF -TDs) coding for the upstream binding transcription factor have recently been described in pediatric patients with acute myeloid leukemia (AML) and were found to be associated with particular genetics (trisomy 8 (+8), FLT3 -internal tandem duplications ( FLT3 -ITD), WT1 -mutations) and inferior outcome. Due to limited knowledge on UBTF- TDs in adult AML, we screened 4247 newly diagnosed adult AML and higher-risk myelodysplastic syndrome (MDS) patients using high-resolution fragment analysis. UBTF -TDs were overall rare ( n  = 52/4247; 1.2%), but significantly enriched in younger patients (median age 41 years) and associated with MDS-related morphology as well as significantly lower hemoglobin and platelet levels. Patients with UBTF -TDs had significantly higher rates of +8 (34% vs. 9%), WT1 (52% vs. 7%) and FLT3 -ITD (50% vs. 20.8%) co-mutations, whereas UBTF -TDs were mutually exclusive with several class-defining lesions such as mutant NPM1 , in-frame CEBPA bZIP mutations as well as t(8;21). Based on the high-variant allele frequency found and the fact that all relapsed patients analyzed ( n  = 5) retained the UBTF -TD mutation, UBTF -TDs represent early clonal events and are stable over the disease course. In univariate analysis, UBTF -TDs did not represent a significant factor for overall or relapse-free survival in the entire cohort. However, in patients under 50 years of age, who represent the majority of UBTF -mutant patients, UBTF -TDs were an independent prognostic factor for inferior event-free (EFS), relapse-free (RFS) and overall survival (OS), which was confirmed by multivariable analyses including established risk factors such as age and ELN2022 genetic risk groups (EFS [HR: 2.20; 95% CI 1.52–3.17, p   〈  0.001], RFS [HR: 1.59; 95% CI 1.02–2.46, p  = 0.039] and OS [HR: 1.64; 95% CI 1.08–2.49, p  = 0.020]). In summary, UBTF -TDs appear to represent a novel class-defining lesion not only in pediatric AML but also younger adults and are associated with myelodysplasia and inferior outcome in these patients.
    Type of Medium: Online Resource
    ISSN: 2044-5385
    URL: Article
    DOI: 10.1038/s41408-023-00858-y
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2023
    detail.hit.zdb_id: 2600560-8
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  • 5
    Online Resource
    Online Resource
    Loss-of-Function Mutations of BCOR Are an Independent Marker of Adverse Outcomes in Intensively Treated Patients with Acute Myeloid Leukemia
    MDPI AG ; 2021
    In:  Cancers Vol. 13, No. 9 ( 2021-04-26), p. 2095-
    Details
    In: Cancers, MDPI AG, Vol. 13, No. 9 ( 2021-04-26), p. 2095-
    Abstract: Acute myeloid leukemia (AML) is characterized by recurrent genetic events. The BCL6 corepressor (BCOR) and its homolog, the BCL6 corepressor-like 1 (BCORL1), have been reported to be rare but recurrent mutations in AML. Previously, smaller studies have reported conflicting results regarding impacts on outcomes. Here, we retrospectively analyzed a large cohort of 1529 patients with newly diagnosed and intensively treated AML. BCOR and BCORL1 mutations were found in 71 (4.6%) and 53 patients (3.5%), respectively. Frequently co-mutated genes were DNTM3A, TET2 and RUNX1. Mutated BCORL1 and loss-of-function mutations of BCOR were significantly more common in the ELN2017 intermediate-risk group. Patients harboring loss-of-function mutations of BCOR had a significantly reduced median event-free survival (HR = 1.464 (95%-Confidence Interval (CI): 1.005–2.134), p = 0.047), relapse-free survival (HR = 1.904 (95%-CI: 1.163–3.117), p = 0.01), and trend for reduced overall survival (HR = 1.495 (95%-CI: 0.990–2.258), p = 0.056) in multivariable analysis. Our study establishes a novel role for loss-of-function mutations of BCOR regarding risk stratification in AML, which may influence treatment allocation.
    Type of Medium: Online Resource
    ISSN: 2072-6694
    URL: Article
    DOI: 10.3390/cancers13092095
    Language: English
    Publisher: MDPI AG
    Publication Date: 2021
    detail.hit.zdb_id: 2527080-1
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  • 6
    Online Resource
    Online Resource
    Deep learning detects acute myeloid leukemia and predicts NPM1 mutation status from bone marrow smears
    Springer Science and Business Media LLC ; 2022
    In:  Leukemia Vol. 36, No. 1 ( 2022-01), p. 111-118
    Details
    In: Leukemia, Springer Science and Business Media LLC, Vol. 36, No. 1 ( 2022-01), p. 111-118
    Abstract: The evaluation of bone marrow morphology by experienced hematopathologists is essential in the diagnosis of acute myeloid leukemia (AML); however, it suffers from a lack of standardization and inter-observer variability. Deep learning (DL) can process medical image data and provides data-driven class predictions. Here, we apply a multi-step DL approach to automatically segment cells from bone marrow images, distinguish between AML samples and healthy controls with an area under the receiver operating characteristic (AUROC) of 0.9699, and predict the mutation status of Nucleophosmin 1 ( NPM1 )—one of the most common mutations in AML—with an AUROC of 0.92 using only image data from bone marrow smears. Utilizing occlusion sensitivity maps, we observed so far unreported morphologic cell features such as a pattern of condensed chromatin and perinuclear lightening zones in myeloblasts of NPM1 -mutated AML and prominent nucleoli in wild-type NPM1 AML enabling the DL model to provide accurate class predictions.
    Type of Medium: Online Resource
    ISSN: 0887-6924 , 1476-5551
    URL: Article
    DOI: 10.1038/s41375-021-01408-w
    RVK:
    WA 15000
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2022
    detail.hit.zdb_id: 2008023-2
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  • 7
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    Online Resource
    Molecular profiling and clinical implications of patients with acute myeloid leukemia and extramedullary manifestations
    Springer Science and Business Media LLC ; 2022
    In:  Journal of Hematology & Oncology Vol. 15, No. 1 ( 2022-12)
    Details
    In: Journal of Hematology & Oncology, Springer Science and Business Media LLC, Vol. 15, No. 1 ( 2022-12)
    Abstract: Extramedullary manifestations (EM) are rare in acute myeloid leukemia (AML) and their impact on clinical outcomes is controversially discussed. Methods We retrospectively analyzed a large multi-center cohort of 1583 newly diagnosed AML patients, of whom 225 (14.21%) had EM. Results AML patients with EM presented with significantly higher counts of white blood cells ( p   〈  0.0001), peripheral blood blasts ( p   〈  0.0001), bone marrow blasts ( p  = 0.019), and LDH ( p   〈  0.0001). Regarding molecular genetics, EM AML was associated with mutations of NPM1 (OR: 1.66, p   〈  0.001), FLT3 -ITD (OR: 1.72, p   〈  0.001) and PTPN11 (OR: 2.46, p   〈  0.001). With regard to clinical outcomes, EM AML patients were less likely to achieve complete remissions (OR: 0.62, p  = 0.004), and had a higher early death rate (OR: 2.23, p  = 0.003). Multivariable analysis revealed EM as an independent risk factor for reduced overall survival (hazard ratio [HR]: 1.43, p   〈  0.001), however, for patients who received allogeneic hematopoietic cell transplantation (HCT) survival did not differ. For patients bearing EM AML, multivariable analysis unveiled mutated TP53 and IKZF1 as independent risk factors for reduced event-free (HR: 4.45, p   〈  0.001, and HR: 2.05, p  = 0.044, respectively) and overall survival (HR: 2.48, p  = 0.026, and HR: 2.63, p  = 0.008, respectively). Conclusion Our analysis represents one of the largest cohorts of EM AML and establishes key molecular markers linked to EM, providing new evidence that EM is associated with adverse risk in AML and may warrant allogeneic HCT in eligible patients with EM.
    Type of Medium: Online Resource
    ISSN: 1756-8722
    URL: Article
    DOI: 10.1186/s13045-022-01267-7
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2022
    detail.hit.zdb_id: 2429631-4
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  • 8
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    Deep learning identifies Acute Promyelocytic Leukemia in bone marrow smears
    Springer Science and Business Media LLC ; 2022
    In:  BMC Cancer Vol. 22, No. 1 ( 2022-12)
    Details
    In: BMC Cancer, Springer Science and Business Media LLC, Vol. 22, No. 1 ( 2022-12)
    Abstract: Acute promyelocytic leukemia (APL) is considered a hematologic emergency due to high risk of bleeding and fatal hemorrhages being a major cause of death. Despite lower death rates reported from clinical trials, patient registry data suggest an early death rate of 20%, especially for elderly and frail patients. Therefore, reliable diagnosis is required as treatment with differentiation-inducing agents leads to cure in the majority of patients. However, diagnosis commonly relies on cytomorphology and genetic confirmation of the pathognomonic t(15;17). Yet, the latter is more time consuming and in some regions unavailable. Methods In recent years, deep learning (DL) has been evaluated for medical image recognition showing outstanding capabilities in analyzing large amounts of image data and provides reliable classification results. We developed a multi-stage DL platform that automatically reads images of bone marrow smears, accurately segments cells, and subsequently predicts APL using image data only. We retrospectively identified 51 APL patients from previous multicenter trials and compared them to 1048 non-APL acute myeloid leukemia (AML) patients and 236 healthy bone marrow donor samples, respectively. Results Our DL platform segments bone marrow cells with a mean average precision and a mean average recall of both 0.97. Further, it achieves high accuracy in detecting APL by distinguishing between APL and non-APL AML as well as APL and healthy donors with an area under the receiver operating characteristic of 0.8575 and 0.9585, respectively, using visual image data only. Conclusions Our study underlines not only the feasibility of DL to detect distinct morphologies that accompany a cytogenetic aberration like t(15;17) in APL, but also shows the capability of DL to abstract information from a small medical data set, i. e. 51 APL patients, and infer correct predictions. This demonstrates the suitability of DL to assist in the diagnosis of rare cancer entities. As our DL platform predicts APL from bone marrow smear images alone, this may be used to diagnose APL in regions were molecular or cytogenetic subtyping is not routinely available and raise attention to suspected cases of APL for expert evaluation.
    Type of Medium: Online Resource
    ISSN: 1471-2407
    URL: Article
    DOI: 10.1186/s12885-022-09307-8
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2022
    detail.hit.zdb_id: 2041352-X
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